diff --git a/.gitignore b/.gitignore index 162eec9ba7d..f0f8c09495d 100644 --- a/.gitignore +++ b/.gitignore @@ -204,6 +204,8 @@ agents.md # AI .claude/ WARP.md +**/memory-bank/ +**/projectBrief.md # Azurite storage emulator files */__azurite_db_blob__.json diff --git a/agent-samples/README.md b/agent-samples/README.md new file mode 100644 index 00000000000..0ee940f3a03 --- /dev/null +++ b/agent-samples/README.md @@ -0,0 +1,3 @@ +# Declarative Agents + +This folder contains sample agent definitions than be ran using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/getting_started/declarative/). diff --git a/agent-samples/azure/AzureOpenAI.yaml b/agent-samples/azure/AzureOpenAI.yaml new file mode 100644 index 00000000000..2f43d9ac92c --- /dev/null +++ b/agent-samples/azure/AzureOpenAI.yaml @@ -0,0 +1,25 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response. +model: + id: =Env.AZURE_OPENAI_DEPLOYMENT_NAME + provider: AzureOpenAI + apiType: Chat + options: + temperature: 0.9 + topP: 0.95 +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/agent-samples/azure/AzureOpenAIAssistants.yaml b/agent-samples/azure/AzureOpenAIAssistants.yaml new file mode 100644 index 00000000000..8c0d889598a --- /dev/null +++ b/agent-samples/azure/AzureOpenAIAssistants.yaml @@ -0,0 +1,25 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response. +model: + id: =Env.AZURE_OPENAI_DEPLOYMENT_NAME + provider: AzureOpenAI + apiType: Assistants + options: + temperature: 0.9 + topP: 0.95 +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/agent-samples/azure/AzureOpenAIResponses.yaml b/agent-samples/azure/AzureOpenAIResponses.yaml new file mode 100644 index 00000000000..5db218ade35 --- /dev/null +++ b/agent-samples/azure/AzureOpenAIResponses.yaml @@ -0,0 +1,28 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response. +model: + id: =Env.AZURE_OPENAI_DEPLOYMENT_NAME + provider: AzureOpenAI + apiType: Responses + options: + text: + verbosity: medium + connection: + kind: remote + endpoint: =Env.AZURE_OPENAI_ENDPOINT +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/agent-samples/chatclient/Assistant.yaml b/agent-samples/chatclient/Assistant.yaml new file mode 100644 index 00000000000..b34add2d232 --- /dev/null +++ b/agent-samples/chatclient/Assistant.yaml @@ -0,0 +1,18 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. +model: + options: + temperature: 0.9 + topP: 0.95 +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. diff --git a/agent-samples/chatclient/GetWeather.yaml b/agent-samples/chatclient/GetWeather.yaml new file mode 100644 index 00000000000..9ed637894dd --- /dev/null +++ b/agent-samples/chatclient/GetWeather.yaml @@ -0,0 +1,27 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions using the tools provided. +model: + options: + allowMultipleToolCalls: true + chatToolMode: auto +tools: + - kind: function + name: GetWeather + description: Get the weather for a given location. + bindings: + get_weather: get_weather + parameters: + properties: + location: + kind: string + description: The city and state, e.g. San Francisco, CA + required: true + unit: + kind: string + description: The unit of temperature. Possible values are 'celsius' and 'fahrenheit'. + required: false + enum: + - celsius + - fahrenheit diff --git a/agent-samples/foundry/MicrosoftLearnAgent.yaml b/agent-samples/foundry/MicrosoftLearnAgent.yaml new file mode 100644 index 00000000000..8e15340351b --- /dev/null +++ b/agent-samples/foundry/MicrosoftLearnAgent.yaml @@ -0,0 +1,21 @@ +kind: Prompt +name: MicrosoftLearnAgent +description: Microsoft Learn Agent +instructions: You answer questions by searching the Microsoft Learn content only. +model: + id: =Env.AZURE_FOUNDRY_PROJECT_MODEL_ID + options: + temperature: 0.9 + topP: 0.95 + connection: + kind: remote + endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT +tools: + - kind: mcp + name: microsoft_learn + description: Get information from Microsoft Learn. + url: https://learn.microsoft.com/api/mcp + approvalMode: + kind: never + allowedTools: + - microsoft_docs_search diff --git a/agent-samples/foundry/PersistentAgent.yaml b/agent-samples/foundry/PersistentAgent.yaml new file mode 100644 index 00000000000..298ded22027 --- /dev/null +++ b/agent-samples/foundry/PersistentAgent.yaml @@ -0,0 +1,22 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. +model: + id: =Env.AZURE_FOUNDRY_PROJECT_MODEL_ID + options: + temperature: 0.9 + topP: 0.95 + connection: + kind: remote + endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. diff --git a/agent-samples/openai/OpenAI.yaml b/agent-samples/openai/OpenAI.yaml new file mode 100644 index 00000000000..0e70188fd60 --- /dev/null +++ b/agent-samples/openai/OpenAI.yaml @@ -0,0 +1,28 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response. +model: + id: =Env.OPENAI_MODEL + provider: OpenAI + apiType: Chat + options: + temperature: 0.9 + topP: 0.95 + connection: + kind: key + key: =Env.OPENAI_API_KEY +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/agent-samples/openai/OpenAIAssistants.yaml b/agent-samples/openai/OpenAIAssistants.yaml new file mode 100644 index 00000000000..78bd48d7015 --- /dev/null +++ b/agent-samples/openai/OpenAIAssistants.yaml @@ -0,0 +1,30 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response. +model: + id: =Env.OPENAI_MODEL + provider: OpenAI + apiType: Assistants + options: + temperature: 0.9 + topP: 0.95 + connection: + kind: key + key: =Env.OPENAI_APIKEY +outputSchema: + name: AssistantResponse + description: The response from the assistant. + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/agent-samples/openai/OpenAIResponses.yaml b/agent-samples/openai/OpenAIResponses.yaml new file mode 100644 index 00000000000..0fcda30c9cd --- /dev/null +++ b/agent-samples/openai/OpenAIResponses.yaml @@ -0,0 +1,28 @@ +kind: Prompt +name: Assistant +description: Helpful assistant +instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response. +model: + id: =Env.OPENAI_MODEL + provider: OpenAI + apiType: Responses + options: + text: + verbosity: medium + connection: + kind: key + key: =Env.OPENAI_APIKEY +outputSchema: + properties: + language: + kind: string + required: true + description: The language of the answer. + answer: + kind: string + required: true + description: The answer text. + type: + kind: string + required: true + description: The type of the response. diff --git a/dotnet/agent-framework-dotnet.slnx b/dotnet/agent-framework-dotnet.slnx index 83cc8e6b068..9c5f1a81f33 100644 --- a/dotnet/agent-framework-dotnet.slnx +++ b/dotnet/agent-framework-dotnet.slnx @@ -101,13 +101,14 @@ + + - - + @@ -191,7 +192,7 @@ - + @@ -374,6 +375,7 @@ + diff --git a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/ActorFrameworkWebApplicationExtensions.cs b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/ActorFrameworkWebApplicationExtensions.cs index 5e997c4f585..09e19a82f58 100644 --- a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/ActorFrameworkWebApplicationExtensions.cs +++ b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/ActorFrameworkWebApplicationExtensions.cs @@ -2,7 +2,7 @@ using System.Diagnostics.CodeAnalysis; using System.Text.Json.Serialization; -using Microsoft.Agents.AI.Hosting; +using Microsoft.Agents.AI; namespace AgentWebChat.AgentHost; @@ -10,24 +10,24 @@ internal static class ActorFrameworkWebApplicationExtensions { public static void MapAgentDiscovery(this IEndpointRouteBuilder endpoints, [StringSyntax("Route")] string path) { + var registeredAIAgents = endpoints.ServiceProvider.GetKeyedServices(KeyedService.AnyKey); + var routeGroup = endpoints.MapGroup(path); - routeGroup.MapGet("/", async ( - AgentCatalog agentCatalog, - CancellationToken cancellationToken) => + routeGroup.MapGet("/", async (CancellationToken cancellationToken) => + { + var results = new List(); + foreach (var result in registeredAIAgents) { - var results = new List(); - await foreach (var result in agentCatalog.GetAgentsAsync(cancellationToken).ConfigureAwait(false)) + results.Add(new AgentDiscoveryCard { - results.Add(new AgentDiscoveryCard - { - Name = result.Name!, - Description = result.Description, - }); - } + Name = result.Name!, + Description = result.Description, + }); + } - return Results.Ok(results); - }) - .WithName("GetAgents"); + return Results.Ok(results); + }) + .WithName("GetAgents"); } internal sealed class AgentDiscoveryCard diff --git a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj index 53fd4757ee1..b4141ba166a 100644 --- a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj +++ b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj @@ -8,6 +8,7 @@ + diff --git a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Program.cs b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Program.cs index 46af2a5b198..7447c54aa1f 100644 --- a/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Program.cs +++ b/dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Program.cs @@ -5,6 +5,7 @@ using AgentWebChat.AgentHost.Custom; using AgentWebChat.AgentHost.Utilities; using Microsoft.Agents.AI; +using Microsoft.Agents.AI.DevUI; using Microsoft.Agents.AI.Hosting; using Microsoft.Agents.AI.Workflows; using Microsoft.Extensions.AI; @@ -21,6 +22,13 @@ // Configure the chat model and our agent. builder.AddKeyedChatClient("chat-model"); +// Add DevUI services +builder.AddDevUI(); + +// Add OpenAI services +builder.AddOpenAIChatCompletions(); +builder.AddOpenAIResponses(); + var pirateAgentBuilder = builder.AddAIAgent( "pirate", instructions: "You are a pirate. Speak like a pirate", @@ -95,8 +103,48 @@ Once the user has deduced what type (knight or knave) both Alice and Bob are, te return AgentWorkflowBuilder.BuildConcurrent(workflowName: key, agents: agents); }).AddAsAIAgent(); -builder.AddOpenAIChatCompletions(); -builder.AddOpenAIResponses(); +builder.AddWorkflow("nonAgentWorkflow", (sp, key) => +{ + List usedAgents = [pirateAgentBuilder, chemistryAgent]; + var agents = usedAgents.Select(ab => sp.GetRequiredKeyedService(ab.Name)); + return AgentWorkflowBuilder.BuildSequential(workflowName: key, agents: agents); +}); + +builder.Services.AddKeyedSingleton("NonAgentAndNonmatchingDINameWorkflow", (sp, key) => +{ + List usedAgents = [pirateAgentBuilder, chemistryAgent]; + var agents = usedAgents.Select(ab => sp.GetRequiredKeyedService(ab.Name)); + return AgentWorkflowBuilder.BuildSequential(workflowName: "random-name", agents: agents); +}); + +builder.Services.AddSingleton(sp => +{ + var chatClient = sp.GetRequiredKeyedService("chat-model"); + return new ChatClientAgent(chatClient, name: "default-agent", instructions: "you are a default agent."); +}); + +builder.Services.AddKeyedSingleton("my-di-nonmatching-agent", (sp, name) => +{ + var chatClient = sp.GetRequiredKeyedService("chat-model"); + return new ChatClientAgent( + chatClient, + name: "some-random-name", // demonstrating registration can be different for DI and actual agent + instructions: "you are a dependency inject agent. Tell me all about dependency injection."); +}); + +builder.Services.AddKeyedSingleton("my-di-matchingname-agent", (sp, name) => +{ + if (name is not string nameStr) + { + throw new NotSupportedException("Name should be passed as a key"); + } + + var chatClient = sp.GetRequiredKeyedService("chat-model"); + return new ChatClientAgent( + chatClient, + name: nameStr, // demonstrating registration with the same name + instructions: "you are a dependency inject agent. Tell me all about dependency injection."); +}); var app = builder.Build(); @@ -118,7 +166,10 @@ Once the user has deduced what type (knight or knave) both Alice and Bob are, te // Url = "http://localhost:5390/a2a/knights-and-knaves" }); +app.MapDevUI(); + app.MapOpenAIResponses(); +app.MapOpenAIConversations(); app.MapOpenAIChatCompletions(pirateAgentBuilder); app.MapOpenAIChatCompletions(knightsKnavesAgentBuilder); diff --git a/dotnet/samples/AgentWebChat/AgentWebChat.AppHost/Program.cs b/dotnet/samples/AgentWebChat/AgentWebChat.AppHost/Program.cs index a28b3e1902c..328e3f5e839 100644 --- a/dotnet/samples/AgentWebChat/AgentWebChat.AppHost/Program.cs +++ b/dotnet/samples/AgentWebChat/AgentWebChat.AppHost/Program.cs @@ -9,7 +9,9 @@ var chatModel = builder.AddAIModel("chat-model").AsAzureOpenAI("gpt-4o", o => o.AsExisting(azOpenAiResource, azOpenAiResourceGroup)); var agentHost = builder.AddProject("agenthost") - .WithReference(chatModel); + .WithHttpEndpoint(name: "devui") + .WithUrlForEndpoint("devui", (url) => new() { Url = "/devui", DisplayText = "Dev UI" }) + .WithReference(chatModel); builder.AddProject("webfrontend") .WithExternalHttpEndpoints() diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI.csproj b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj similarity index 70% rename from dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI.csproj rename to dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj index 90bc94a20f4..aefb46524f8 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI.csproj +++ b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj @@ -1,4 +1,4 @@ - + Exe @@ -11,7 +11,6 @@ - @@ -19,8 +18,8 @@ - - PreserveNewest + + Always diff --git a/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs new file mode 100644 index 00000000000..09893941850 --- /dev/null +++ b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs @@ -0,0 +1,60 @@ +// Copyright (c) Microsoft. All rights reserved. + +// This sample shows how to use the built in RAG capabilities that the Foundry service provides when using AI Agents provided by Foundry. + +using System.ClientModel; +using Azure.AI.Projects; +using Azure.Identity; +using Microsoft.Agents.AI; +using Microsoft.Extensions.AI; +using OpenAI; +using OpenAI.Files; +using OpenAI.VectorStores; + +var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set."); +var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini"; + +// Create an AI Project client and get an OpenAI client that works with the foundry service. +AIProjectClient aiProjectClient = new( + new Uri(endpoint), + new AzureCliCredential()); +OpenAIClient openAIClient = aiProjectClient.GetProjectOpenAIClient(); + +// Upload the file that contains the data to be used for RAG to the Foundry service. +OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient(); +ClientResult uploadResult = await fileClient.UploadFileAsync( + filePath: "contoso-outdoors-knowledge-base.md", + purpose: FileUploadPurpose.Assistants); + +// Create a vector store in the Foundry service using the uploaded file. +VectorStoreClient vectorStoreClient = openAIClient.GetVectorStoreClient(); +ClientResult vectorStoreCreate = await vectorStoreClient.CreateVectorStoreAsync(options: new VectorStoreCreationOptions() +{ + Name = "contoso-outdoors-knowledge-base", + FileIds = { uploadResult.Value.Id } +}); + +var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] }; + +AIAgent agent = await aiProjectClient + .CreateAIAgentAsync( + model: deploymentName, + name: "AskContoso", + instructions: "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.", + tools: [fileSearchTool]); + +AgentThread thread = agent.GetNewThread(); + +Console.WriteLine(">> Asking about returns\n"); +Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread)); + +Console.WriteLine("\n>> Asking about shipping\n"); +Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread)); + +Console.WriteLine("\n>> Asking about product care\n"); +Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread)); + +// Cleanup +await fileClient.DeleteFileAsync(uploadResult.Value.Id); +await vectorStoreClient.DeleteVectorStoreAsync(vectorStoreCreate.Value.Id); +await aiProjectClient.Agents.DeleteAgentAsync(agent.Name); diff --git a/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/contoso-outdoors-knowledge-base.md b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/contoso-outdoors-knowledge-base.md new file mode 100644 index 00000000000..901e45b4ddb --- /dev/null +++ b/dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/contoso-outdoors-knowledge-base.md @@ -0,0 +1,19 @@ +# Contoso Outdoors Knowledge Base + +## Contoso Outdoors Return Policy + +Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection. + +## Contoso Outdoors Shipping Guide + +Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout. + +## Product Information + +### TrailRunner Tent + +The TrailRunner Tent is a lightweight, 2-person tent designed for easy setup and durability. It features waterproof materials, ventilation windows, and a compact carry bag. + +#### Care Instructions + +Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating. \ No newline at end of file diff --git a/dotnet/samples/GettingStarted/AgentWithRAG/README.md b/dotnet/samples/GettingStarted/AgentWithRAG/README.md index bf2a8f9b111..d606ac767c5 100644 --- a/dotnet/samples/GettingStarted/AgentWithRAG/README.md +++ b/dotnet/samples/GettingStarted/AgentWithRAG/README.md @@ -7,3 +7,4 @@ These samples show how to create an agent with the Agent Framework that uses Ret |[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).| |[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.| |[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.| +|[RAG with Foundry VectorStore service](./AgentWithRAG_Step04_FoundryServiceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with the Foundry VectorStore service.| diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/Program.cs index c51d345c111..3c374d799f5 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/Program.cs @@ -11,14 +11,15 @@ string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set."); string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini"; -const string JokerInstructions = "You are good at telling jokes."; +const string JokerInstructionsV1 = "You are good at telling jokes."; +const string JokerInstructionsV2 = "You are extremely hilarious at telling jokes."; const string JokerName = "JokerAgent"; // Get a client to create/retrieve/delete server side agents with Azure Foundry Agents. AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); // Define the agent you want to create. (Prompt Agent in this case) -AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructions }); +AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructionsV1 }); // Azure.AI.Agents SDK creates and manages agent by name and versions. // You can create a server side agent version with the Azure.AI.Agents SDK client below. @@ -32,8 +33,8 @@ // You can retrieve an AIAgent for an already created server side agent version. AIAgent jokerAgentV1 = aiProjectClient.GetAIAgent(agentVersion); -// You can also create another AIAgent version (V2) by providing the same name with a different definition. -AIAgent jokerAgentV2 = aiProjectClient.CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructions + "V2"); +// You can also create another AIAgent version (V2) by providing the same name with a different definition/instruction. +AIAgent jokerAgentV2 = aiProjectClient.CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructionsV2); // You can also get the AIAgent latest version by just providing its name. AIAgent jokerAgentLatest = aiProjectClient.GetAIAgent(name: JokerName); @@ -43,11 +44,7 @@ Console.WriteLine($"Latest agent version id: {latestVersion.Id}"); // Once you have the AIAgent, you can invoke it like any other AIAgent. -AgentThread thread = jokerAgentLatest.GetNewThread(); -Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate.", thread)); - -// This will use the same thread to continue the conversation. -Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", thread)); +Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate.")); // Cleanup by agent name removes both agent versions created (jokerAgentV1 + jokerAgentV2). await aiProjectClient.Agents.DeleteAgentAsync(jokerAgentV1.Name); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/Program.cs index bd5620ff1f9..4d840d54ff8 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/Program.cs @@ -26,13 +26,8 @@ // You can retrieve an AIAgent for a already created server side agent version. AIAgent jokerAgent = aiProjectClient.GetAIAgent(agentVersion); -// Invoke the agent and output the text result. -AgentThread thread = jokerAgent.GetNewThread(); -Console.WriteLine(await jokerAgent.RunAsync("Tell me a joke about a pirate.", thread)); - // Invoke the agent with streaming support. -thread = jokerAgent.GetNewThread(); -await foreach (AgentRunResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate.", thread)) +await foreach (AgentRunResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate.")) { Console.WriteLine(update); } diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/OpenAPISpec.json b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/OpenAPISpec.json deleted file mode 100644 index 84715914da6..00000000000 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/OpenAPISpec.json +++ /dev/null @@ -1,354 +0,0 @@ -{ - "openapi": "3.0.1", - "info": { - "title": "Github Versions API", - "version": "1.0.0" - }, - "servers": [ - { - "url": "https://api.github.com" - } - ], - "components": { - "schemas": { - "basic-error": { - "title": "Basic Error", - "description": "Basic Error", - "type": "object", - "properties": { - "message": { - "type": "string" - }, - "documentation_url": { - "type": "string" - }, - "url": { - "type": "string" - }, - "status": { - "type": "string" - } - } - }, - "label": { - "title": "Label", - "description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).", - "type": "object", - "properties": { - "id": { - "description": "Unique identifier for the label.", - "type": "integer", - "format": "int64", - "example": 208045946 - }, - "node_id": { - "type": "string", - "example": "MDU6TGFiZWwyMDgwNDU5NDY=" - }, - "url": { - "description": "URL for the label", - "example": "https://api.github.com/repositories/42/labels/bug", - "type": "string", - "format": "uri" - }, - "name": { - "description": "The name of the label.", - "example": "bug", - "type": "string" - }, - "description": { - "description": "Optional description of the label, such as its purpose.", - "type": "string", - "example": "Something isn't working", - "nullable": true - }, - "color": { - "description": "6-character hex code, without the leading #, identifying the color", - "example": "FFFFFF", - "type": "string" - }, - "default": { - "description": "Whether this label comes by default in a new repository.", - "type": "boolean", - "example": true - } - }, - "required": [ - "id", - "node_id", - "url", - "name", - "description", - "color", - "default" - ] - }, - "tag": { - "title": "Tag", - "description": "Tag", - "type": "object", - "properties": { - "name": { - "type": "string", - "example": "v0.1" - }, - "commit": { - "type": "object", - "properties": { - "sha": { - "type": "string" - }, - "url": { - "type": "string", - "format": "uri" - } - }, - "required": [ - "sha", - "url" - ] - }, - "zipball_url": { - "type": "string", - "format": "uri", - "example": "https://github.com/octocat/Hello-World/zipball/v0.1" - }, - "tarball_url": { - "type": "string", - "format": "uri", - "example": "https://github.com/octocat/Hello-World/tarball/v0.1" - }, - "node_id": { - "type": "string" - } - }, - "required": [ - "name", - "node_id", - "commit", - "zipball_url", - "tarball_url" - ] - } - }, - "examples": { - "label-items": { - "value": [ - { - "id": 208045946, - "node_id": "MDU6TGFiZWwyMDgwNDU5NDY=", - "url": "https://api.github.com/repos/octocat/Hello-World/labels/bug", - "name": "bug", - "description": "Something isn't working", - "color": "f29513", - "default": true - }, - { - "id": 208045947, - "node_id": "MDU6TGFiZWwyMDgwNDU5NDc=", - "url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement", - "name": "enhancement", - "description": "New feature or request", - "color": "a2eeef", - "default": false - } - ] - }, - "tag-items": { - "value": [ - { - "name": "v0.1", - "commit": { - "sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc", - "url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc" - }, - "zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1", - "tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1", - "node_id": "MDQ6VXNlcjE=" - } - ] - } - }, - "parameters": { - "owner": { - "name": "owner", - "description": "The account owner of the repository. The name is not case sensitive.", - "in": "path", - "required": true, - "schema": { - "type": "string" - } - }, - "repo": { - "name": "repo", - "description": "The name of the repository without the `.git` extension. The name is not case sensitive.", - "in": "path", - "required": true, - "schema": { - "type": "string" - } - }, - "per-page": { - "name": "per_page", - "description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"", - "in": "query", - "schema": { - "type": "integer", - "default": 30 - } - }, - "page": { - "name": "page", - "description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"", - "in": "query", - "schema": { - "type": "integer", - "default": 1 - } - } - }, - "responses": { - "not_found": { - "description": "Resource not found", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/basic-error" - } - } - } - } - }, - "headers": { - "link": { - "example": "; rel=\"next\", ; rel=\"last\"", - "schema": { - "type": "string" - } - } - } - }, - "paths": { - "/repos/{owner}/{repo}/tags": { - "get": { - "summary": "List repository tags", - "description": "", - "tags": [ - "repos" - ], - "operationId": "repos/list-tags", - "externalDocs": { - "description": "API method documentation", - "url": "https://docs.github.com/rest/repos/repos#list-repository-tags" - }, - "parameters": [ - { - "$ref": "#/components/parameters/owner" - }, - { - "$ref": "#/components/parameters/repo" - }, - { - "$ref": "#/components/parameters/per-page" - }, - { - "$ref": "#/components/parameters/page" - } - ], - "responses": { - "200": { - "description": "Response", - "content": { - "application/json": { - "schema": { - "type": "array", - "items": { - "$ref": "#/components/schemas/tag" - } - }, - "examples": { - "default": { - "$ref": "#/components/examples/tag-items" - } - } - } - }, - "headers": { - "Link": { - "$ref": "#/components/headers/link" - } - } - } - }, - "x-github": { - "githubCloudOnly": false, - "enabledForGitHubApps": true, - "category": "repos", - "subcategory": "repos" - } - } - }, - "/repos/{owner}/{repo}/labels": { - "get": { - "summary": "List labels for a repository", - "description": "Lists all labels for a repository.", - "tags": [ - "issues" - ], - "operationId": "issues/list-labels-for-repo", - "externalDocs": { - "description": "API method documentation", - "url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository" - }, - "parameters": [ - { - "$ref": "#/components/parameters/owner" - }, - { - "$ref": "#/components/parameters/repo" - }, - { - "$ref": "#/components/parameters/per-page" - }, - { - "$ref": "#/components/parameters/page" - } - ], - "responses": { - "200": { - "description": "Response", - "content": { - "application/json": { - "schema": { - "type": "array", - "items": { - "$ref": "#/components/schemas/label" - } - }, - "examples": { - "default": { - "$ref": "#/components/examples/label-items" - } - } - } - }, - "headers": { - "Link": { - "$ref": "#/components/headers/link" - } - } - }, - "404": { - "$ref": "#/components/responses/not_found" - } - }, - "x-github": { - "githubCloudOnly": false, - "enabledForGitHubApps": true, - "category": "issues", - "subcategory": "labels" - } - } - } - } -} \ No newline at end of file diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/Program.cs deleted file mode 100644 index f9540d17252..00000000000 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/Program.cs +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -// This sample demonstrates how to use an agent with function tools provided via an OpenAPI spec. -// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent. - -using Azure.AI.Projects; -using Azure.Identity; -using Microsoft.Agents.AI; -using Microsoft.Extensions.AI; -using Microsoft.SemanticKernel; -using Microsoft.SemanticKernel.Plugins.OpenApi; - -string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set."); -string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini"; - -// Load the OpenAPI Spec from a file. -KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json"); - -// Convert the Semantic Kernel plugin to Agent Framework function tools. -// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one. -Kernel kernel = new(); -List tools = plugin.Select(x => x.WithKernel(kernel)).Cast().ToList(); - -const string AssistantInstructions = "You are a helpful assistant that can query GitHub repositories."; -const string AssistantName = "GitHubAssistant"; - -// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents. -AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); - -// Create AIAgent directly -AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: tools); - -// Run the agent with the OpenAPI function tools. -AgentThread thread = agent.GetNewThread(); -Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github.", thread)); - -// Cleanup by agent name removes the agent version created. -await aiProjectClient.Agents.DeleteAgentAsync(agent.Name); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/README.md b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/README.md deleted file mode 100644 index 68096e495f3..00000000000 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/README.md +++ /dev/null @@ -1,49 +0,0 @@ -# Using Function Tools from OpenAPI Specifications - -This sample demonstrates how to create function tools from an OpenAPI specification and use them with AI agents. - -## What this sample demonstrates - -- Loading OpenAPI specifications from files -- Converting OpenAPI specifications to Semantic Kernel plugins -- Converting Semantic Kernel plugins to AI function tools -- Using OpenAPI-based function tools with AI agents -- Managing agent lifecycle (creation and deletion) - -## Prerequisites - -Before you begin, ensure you have the following prerequisites: - -- .NET 8.0 SDK or later -- Azure Foundry service endpoint and deployment configured -- Azure CLI installed and authenticated (for Azure credential authentication) - -**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively). - -Set the following environment variables: - -```powershell -$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint -$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini -``` - -## Run the sample - -Navigate to the FoundryAgents sample directory and run: - -```powershell -cd dotnet/samples/GettingStarted/FoundryAgents -dotnet run --project .\FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI -``` - -## Expected behavior - -The sample will: - -1. Load the OpenAPI specification from OpenAPISpec.json (GitHub API) -2. Convert the OpenAPI spec to Semantic Kernel plugins -3. Create an agent named "GitHubAssistant" with the OpenAPI-based function tools -4. Run the agent with a prompt to query GitHub repositories -5. The agent will invoke the appropriate OpenAPI function tools to retrieve data -6. Clean up resources by deleting the agent - diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/FoundryAgents_Step03.1_UsingFunctionTools.csproj b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj similarity index 100% rename from dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/FoundryAgents_Step03.1_UsingFunctionTools.csproj rename to dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/Program.cs similarity index 59% rename from dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/Program.cs rename to dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/Program.cs index e2af5039053..38c5a15d75e 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/Program.cs @@ -26,18 +26,26 @@ static string GetWeather([Description("The location to get the weather for.")] s AITool tool = AIFunctionFactory.Create(GetWeather); // Create AIAgent directly -AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [tool]); +var newAgent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [tool]); + +// Getting an already existing agent by name with tools. +/* + * IMPORTANT: Since agents that are stored in the server only know the definition of the function tools (JSON Schema), + * you need to provided all invocable function tools when retrieving the agent so it can invoke them automatically. + * If no invocable tools are provided, the function calling needs to handled manually. + */ +var existingAgent = await aiProjectClient.GetAIAgentAsync(name: AssistantName, tools: [tool]); // Non-streaming agent interaction with function tools. -AgentThread thread = agent.GetNewThread(); -Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", thread)); +AgentThread thread = existingAgent.GetNewThread(); +Console.WriteLine(await existingAgent.RunAsync("What is the weather like in Amsterdam?", thread)); // Streaming agent interaction with function tools. -thread = agent.GetNewThread(); -await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync("What is the weather like in Amsterdam?", thread)) +thread = existingAgent.GetNewThread(); +await foreach (AgentRunResponseUpdate update in existingAgent.RunStreamingAsync("What is the weather like in Amsterdam?", thread)) { Console.WriteLine(update); } // Cleanup by agent name removes the agent version created. -await aiProjectClient.Agents.DeleteAgentAsync(agent.Name); +await aiProjectClient.Agents.DeleteAgentAsync(existingAgent.Name); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/README.md b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/README.md similarity index 100% rename from dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/README.md rename to dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/README.md diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/Program.cs index bd72a2c940e..1b51d210cf3 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/Program.cs @@ -25,7 +25,7 @@ static string GetWeather([Description("The location to get the weather for.")] s // Get a client to create/retrieve/delete server side agents with Azure Foundry Agents. AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); -ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather)); +ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather))); // Create AIAgent directly AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Assets/walkway.jpg b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Assets/walkway.jpg new file mode 100644 index 00000000000..13ef1e18408 Binary files /dev/null and b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Assets/walkway.jpg differ diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj index d0cd81c352e..1d423b22bca 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj @@ -16,5 +16,11 @@ + + + + Always + + diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Program.cs index 52ee408a253..a799fe46fb1 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/Program.cs @@ -8,7 +8,7 @@ using Microsoft.Extensions.AI; string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set."); -string deploymentName = System.Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o"; +string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o"; const string VisionInstructions = "You are a helpful agent that can analyze images"; const string VisionName = "VisionAgent"; @@ -21,7 +21,7 @@ ChatMessage message = new(ChatRole.User, [ new TextContent("What do you see in this image?"), - new UriContent("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", "image/jpeg") + new DataContent(File.ReadAllBytes("assets/walkway.jpg"), "image/jpeg") ]); AgentThread thread = agent.GetNewThread(); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/Program.cs index 0a70ca76df1..0f6f6ef2d99 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/Program.cs @@ -55,7 +55,7 @@ // AgentRunResponse response = await agentOption2.RunAsync("I need to solve the equation sin(x) + x^2 = 42"); // Get the CodeInterpreterToolCallContent -CodeInterpreterToolCallContent? toolCallContent = response.Messages.SelectMany(m => m.Contents).OfType().SingleOrDefault(); +CodeInterpreterToolCallContent? toolCallContent = response.Messages.SelectMany(m => m.Contents).OfType().FirstOrDefault(); if (toolCallContent?.Inputs is not null) { DataContent? codeInput = toolCallContent.Inputs.OfType().FirstOrDefault(); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/Program.cs b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/Program.cs index cb621275ea3..05fb39bbf4a 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/Program.cs +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/Program.cs @@ -15,8 +15,8 @@ internal sealed class Program { private static async Task Main(string[] args) { - string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set."); - string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "computer-use-preview"; + string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set."); + string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "computer-use-preview"; // Get a client to create/retrieve/delete server side agents with Azure Foundry Agents. AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); diff --git a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/README.md b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/README.md index 6b7f27aaf39..94ddf6b2693 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/README.md +++ b/dotnet/samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/README.md @@ -24,8 +24,8 @@ Before you begin, ensure you have the following prerequisites: Set the following environment variables: ```powershell -$env:AZURE_OPENAI_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint -$env:AZURE_OPENAI_DEPLOYMENT_NAME="computer-use-preview" # Optional, defaults to computer-use-preview +$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint +$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="computer-use-preview" # Optional, defaults to computer-use-preview ``` ## Run the sample diff --git a/dotnet/samples/GettingStarted/FoundryAgents/README.md b/dotnet/samples/GettingStarted/FoundryAgents/README.md index e1007473b2f..c5d027d8d76 100644 --- a/dotnet/samples/GettingStarted/FoundryAgents/README.md +++ b/dotnet/samples/GettingStarted/FoundryAgents/README.md @@ -25,8 +25,7 @@ Before you begin, ensure you have the following prerequisites: |[Basics](./FoundryAgents_Step01.1_Basics/)|This sample demonstrates how to create and manage AI agents with versioning| |[Running a simple agent](./FoundryAgents_Step01.2_Running/)|This sample demonstrates how to create and run a basic Foundry agent| |[Multi-turn conversation](./FoundryAgents_Step02_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a Foundry agent| -|[Using function tools](./FoundryAgents_Step03.1_UsingFunctionTools/)|This sample demonstrates how to use function tools with a Foundry agent| -|[Using OpenAPI function tools](./FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a Foundry agent| +|[Using function tools](./FoundryAgents_Step03_UsingFunctionTools/)|This sample demonstrates how to use function tools with a Foundry agent| |[Using function tools with approvals](./FoundryAgents_Step04_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution| |[Structured output](./FoundryAgents_Step05_StructuredOutput/)|This sample demonstrates how to use structured output with a Foundry agent| |[Persisted conversations](./FoundryAgents_Step06_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later| diff --git a/dotnet/src/Microsoft.Agents.AI.DevUI/EntitiesApiExtensions.cs b/dotnet/src/Microsoft.Agents.AI.DevUI/EntitiesApiExtensions.cs index 29b7dc588a5..3271b40853c 100644 --- a/dotnet/src/Microsoft.Agents.AI.DevUI/EntitiesApiExtensions.cs +++ b/dotnet/src/Microsoft.Agents.AI.DevUI/EntitiesApiExtensions.cs @@ -1,10 +1,7 @@ // Copyright (c) Microsoft. All rights reserved. -using System.Runtime.CompilerServices; using System.Text.Json; - using Microsoft.Agents.AI.DevUI.Entities; -using Microsoft.Agents.AI.Hosting; using Microsoft.Agents.AI.Workflows; using Microsoft.Extensions.AI; @@ -27,21 +24,26 @@ internal static class EntitiesApiExtensions /// GET /v1/entities/{entityId}/info - Get detailed information about a specific entity /// /// The endpoints are compatible with the Python DevUI frontend and automatically discover entities - /// from the registered and services. + /// from the registered agents and workflows in the dependency injection container. /// public static IEndpointConventionBuilder MapEntities(this IEndpointRouteBuilder endpoints) { + var registeredAIAgents = GetRegisteredEntities(endpoints.ServiceProvider); + var registeredWorkflows = GetRegisteredEntities(endpoints.ServiceProvider); + var group = endpoints.MapGroup("/v1/entities") .WithTags("Entities"); // List all entities - group.MapGet("", ListEntitiesAsync) + group.MapGet("", (CancellationToken cancellationToken) + => ListEntitiesAsync(registeredAIAgents, registeredWorkflows, cancellationToken)) .WithName("ListEntities") .WithSummary("List all registered entities (agents and workflows)") .Produces(StatusCodes.Status200OK, contentType: "application/json"); // Get detailed entity information - group.MapGet("{entityId}/info", GetEntityInfoAsync) + group.MapGet("{entityId}/info", (string entityId, string? type, CancellationToken cancellationToken) + => GetEntityInfoAsync(entityId, type, registeredAIAgents, registeredWorkflows, cancellationToken)) .WithName("GetEntityInfo") .WithSummary("Get detailed information about a specific entity") .Produces(StatusCodes.Status200OK, contentType: "application/json") @@ -51,8 +53,8 @@ public static IEndpointConventionBuilder MapEntities(this IEndpointRouteBuilder } private static async Task ListEntitiesAsync( - AgentCatalog? agentCatalog, - WorkflowCatalog? workflowCatalog, + IEnumerable agents, + IEnumerable workflows, CancellationToken cancellationToken) { try @@ -60,13 +62,13 @@ private static async Task ListEntitiesAsync( var entities = new Dictionary(); // Discover agents - await foreach (var agentInfo in DiscoverAgentsAsync(agentCatalog, entityIdFilter: null, cancellationToken).ConfigureAwait(false)) + foreach (var agentInfo in DiscoverAgents(agents, entityIdFilter: null)) { entities[agentInfo.Id] = agentInfo; } // Discover workflows - await foreach (var workflowInfo in DiscoverWorkflowsAsync(workflowCatalog, entityIdFilter: null, cancellationToken).ConfigureAwait(false)) + foreach (var workflowInfo in DiscoverWorkflows(workflows, entityIdFilter: null)) { entities[workflowInfo.Id] = workflowInfo; } @@ -85,15 +87,15 @@ private static async Task ListEntitiesAsync( private static async Task GetEntityInfoAsync( string entityId, string? type, - AgentCatalog? agentCatalog, - WorkflowCatalog? workflowCatalog, + IEnumerable agents, + IEnumerable workflows, CancellationToken cancellationToken) { try { if (type is null || string.Equals(type, "workflow", StringComparison.OrdinalIgnoreCase)) { - await foreach (var workflowInfo in DiscoverWorkflowsAsync(workflowCatalog, entityId, cancellationToken).ConfigureAwait(false)) + foreach (var workflowInfo in DiscoverWorkflows(workflows, entityId)) { return Results.Json(workflowInfo, EntitiesJsonContext.Default.EntityInfo); } @@ -101,7 +103,7 @@ private static async Task GetEntityInfoAsync( if (type is null || string.Equals(type, "agent", StringComparison.OrdinalIgnoreCase)) { - await foreach (var agentInfo in DiscoverAgentsAsync(agentCatalog, entityId, cancellationToken).ConfigureAwait(false)) + foreach (var agentInfo in DiscoverAgents(agents, entityId)) { return Results.Json(agentInfo, EntitiesJsonContext.Default.EntityInfo); } @@ -118,17 +120,9 @@ private static async Task GetEntityInfoAsync( } } - private static async IAsyncEnumerable DiscoverAgentsAsync( - AgentCatalog? agentCatalog, - string? entityIdFilter, - [EnumeratorCancellation] CancellationToken cancellationToken) + private static IEnumerable DiscoverAgents(IEnumerable agents, string? entityIdFilter) { - if (agentCatalog is null) - { - yield break; - } - - await foreach (var agent in agentCatalog.GetAgentsAsync(cancellationToken).ConfigureAwait(false)) + foreach (var agent in agents) { // If filtering by entity ID, skip non-matching agents if (entityIdFilter is not null && @@ -148,17 +142,9 @@ private static async IAsyncEnumerable DiscoverAgentsAsync( } } - private static async IAsyncEnumerable DiscoverWorkflowsAsync( - WorkflowCatalog? workflowCatalog, - string? entityIdFilter, - [EnumeratorCancellation] CancellationToken cancellationToken) + private static IEnumerable DiscoverWorkflows(IEnumerable workflows, string? entityIdFilter) { - if (workflowCatalog is null) - { - yield break; - } - - await foreach (var workflow in workflowCatalog.GetWorkflowsAsync(cancellationToken).ConfigureAwait(false)) + foreach (var workflow in workflows) { var workflowId = workflow.Name ?? workflow.StartExecutorId; @@ -304,4 +290,14 @@ private static EntityInfo CreateWorkflowEntityInfo(Workflow workflow) StartExecutorId = workflow.StartExecutorId }; } + + private static IEnumerable GetRegisteredEntities(IServiceProvider serviceProvider) + { + var keyedEntities = serviceProvider.GetKeyedServices(KeyedService.AnyKey); + var defaultEntities = serviceProvider.GetServices() ?? []; + + return keyedEntities + .Concat(defaultEntities) + .Where(entity => entity is not null); + } } diff --git a/dotnet/src/Microsoft.Agents.AI.DevUI/HostApplicationBuilderExtensions.cs b/dotnet/src/Microsoft.Agents.AI.DevUI/HostApplicationBuilderExtensions.cs new file mode 100644 index 00000000000..30fa9ad29e8 --- /dev/null +++ b/dotnet/src/Microsoft.Agents.AI.DevUI/HostApplicationBuilderExtensions.cs @@ -0,0 +1,23 @@ +// Copyright (c) Microsoft. All rights reserved. + +namespace Microsoft.Extensions.Hosting; + +/// +/// Extension methods for to configure DevUI. +/// +public static class MicrosoftAgentAIDevUIHostApplicationBuilderExtensions +{ + /// + /// Adds DevUI services to the host application builder. + /// + /// The to configure. + /// The for method chaining. + public static IHostApplicationBuilder AddDevUI(this IHostApplicationBuilder builder) + { + ArgumentNullException.ThrowIfNull(builder); + + builder.Services.AddDevUI(); + + return builder; + } +} diff --git a/dotnet/src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj b/dotnet/src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj index 37aa6c37f84..6c9c5bd9e35 100644 --- a/dotnet/src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj +++ b/dotnet/src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj @@ -1,7 +1,8 @@  - net9.0 + $(ProjectsCoreTargetFrameworks) + $(ProjectsDebugCoreTargetFrameworks) enable enable Microsoft.Agents.AI.DevUI @@ -12,6 +13,10 @@ $(NoWarn);CS1591;CA1852;CA1050;RCS1037;RCS1036;RCS1124;RCS1021;RCS1146;RCS1211;CA2007;CA1308;IL2026;IL3050;CA1812 + + true + + @@ -33,4 +38,7 @@ Provides Microsoft Agent Framework support for developer UI. + + + diff --git a/dotnet/src/Microsoft.Agents.AI.DevUI/README.md b/dotnet/src/Microsoft.Agents.AI.DevUI/README.md index b55869748d2..104c43729b4 100644 --- a/dotnet/src/Microsoft.Agents.AI.DevUI/README.md +++ b/dotnet/src/Microsoft.Agents.AI.DevUI/README.md @@ -24,9 +24,15 @@ var builder = WebApplication.CreateBuilder(args); // Register your agents builder.AddAIAgent("assistant", "You are a helpful assistant."); +// Register DevUI services +if (builder.Environment.IsDevelopment()) +{ + builder.AddDevUI(); +} + // Register services for OpenAI responses and conversations (also required for DevUI) -builder.Services.AddOpenAIResponses(); -builder.Services.AddOpenAIConversations(); +builder.AddOpenAIResponses(); +builder.AddOpenAIConversations(); var app = builder.Build(); diff --git a/dotnet/src/Microsoft.Agents.AI.DevUI/ServiceCollectionsExtensions.cs b/dotnet/src/Microsoft.Agents.AI.DevUI/ServiceCollectionsExtensions.cs new file mode 100644 index 00000000000..6971e3d2e09 --- /dev/null +++ b/dotnet/src/Microsoft.Agents.AI.DevUI/ServiceCollectionsExtensions.cs @@ -0,0 +1,61 @@ +// Copyright (c) Microsoft. All rights reserved. + +using Microsoft.Agents.AI; +using Microsoft.Agents.AI.Workflows; +using Microsoft.Shared.Diagnostics; + +namespace Microsoft.Extensions.DependencyInjection; + +/// +/// Extension methods for to configure DevUI. +/// +public static class MicrosoftAgentAIDevUIServiceCollectionsExtensions +{ + /// + /// Adds services required for DevUI integration. + /// + /// The to configure. + /// The for method chaining. + public static IServiceCollection AddDevUI(this IServiceCollection services) + { + ArgumentNullException.ThrowIfNull(services); + + // a factory that tries to construct an AIAgent from Workflow, + // even if workflow was not explicitly registered as an AIAgent. + +#pragma warning disable IDE0001 // Simplify Names + services.AddKeyedSingleton(KeyedService.AnyKey, (sp, key) => + { + var keyAsStr = key as string; + Throw.IfNullOrEmpty(keyAsStr); + + var workflow = sp.GetKeyedService(keyAsStr); + if (workflow is not null) + { + return workflow.AsAgent(name: workflow.Name); + } + + // another thing we can do is resolve a non-keyed workflow. + // however, we can't rely on anything than key to be equal to the workflow.Name. + // so we try: if we fail, we return null. + workflow = sp.GetService(); + if (workflow is not null && workflow.Name?.Equals(keyAsStr, StringComparison.Ordinal) == true) + { + return workflow.AsAgent(name: workflow.Name); + } + + // and it's possible to lookup at the default-registered AIAgent + // with the condition of same name as the key. + var agent = sp.GetService(); + if (agent is not null && agent.Name?.Equals(keyAsStr, StringComparison.Ordinal) == true) + { + return agent; + } + + return null!; + }); +#pragma warning restore IDE0001 // Simplify Names + + return services; + } +} diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/HostedAgentResponseExecutor.cs b/dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/HostedAgentResponseExecutor.cs index f90e47b0701..01e7c601370 100644 --- a/dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/HostedAgentResponseExecutor.cs +++ b/dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/HostedAgentResponseExecutor.cs @@ -63,7 +63,11 @@ public HostedAgentResponseExecutor( return ValueTask.FromResult(new ResponseError { Code = "agent_not_found", - Message = $"Agent '{agentName}' not found. Ensure the agent is registered with AddAIAgent()." + Message = $""" + Agent '{agentName}' not found. + Ensure the agent is registered with '{agentName}' name in the dependency injection container. + We recommend using 'builder.AddAIAgent()' for simplicity. + """ }); } diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/AgentCatalog.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/AgentCatalog.cs deleted file mode 100644 index 0d2ef696404..00000000000 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/AgentCatalog.cs +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -using System.Collections.Generic; -using System.Threading; - -namespace Microsoft.Agents.AI.Hosting; - -/// -/// Provides a catalog of registered AI agents within the hosting environment. -/// -/// -/// The agent catalog allows enumeration of all registered agents in the dependency injection container. -/// This is useful for scenarios where you need to discover and interact with multiple agents programmatically. -/// -public abstract class AgentCatalog -{ - /// - /// Initializes a new instance of the class. - /// - protected AgentCatalog() - { - } - - /// - /// Asynchronously retrieves all registered AI agents from the catalog. - /// - /// The to monitor for cancellation requests. The default is . - /// - /// An asynchronous enumerable of instances representing all registered agents. - /// The enumeration will only include agents that are successfully resolved from the service provider. - /// - /// - /// This method enumerates through all registered agent names and attempts to resolve each agent - /// from the dependency injection container. Only successfully resolved agents are yielded. - /// The enumeration is lazy and agents are resolved on-demand during iteration. - /// - public abstract IAsyncEnumerable GetAgentsAsync(CancellationToken cancellationToken = default); -} diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/AgentHostingServiceCollectionExtensions.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/AgentHostingServiceCollectionExtensions.cs index d958fc35782..e12d017343d 100644 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/AgentHostingServiceCollectionExtensions.cs +++ b/dotnet/src/Microsoft.Agents.AI.Hosting/AgentHostingServiceCollectionExtensions.cs @@ -2,7 +2,6 @@ using System; using System.Collections.Generic; -using System.Linq; using Microsoft.Agents.AI.Hosting.Local; using Microsoft.Extensions.AI; using Microsoft.Extensions.DependencyInjection; @@ -126,31 +125,9 @@ public static IHostedAgentBuilder AddAIAgent(this IServiceCollection services, s return agent; }); - // Register the agent by name for discovery. - var agentHostBuilder = GetAgentRegistry(services); - agentHostBuilder.AgentNames.Add(name); - return new HostedAgentBuilder(name, services); } - private static LocalAgentRegistry GetAgentRegistry(IServiceCollection services) - { - var descriptor = services.FirstOrDefault(s => !s.IsKeyedService && s.ServiceType.Equals(typeof(LocalAgentRegistry))); - if (descriptor?.ImplementationInstance is not LocalAgentRegistry instance) - { - instance = new LocalAgentRegistry(); - ConfigureHostBuilder(services, instance); - } - - return instance; - } - - private static void ConfigureHostBuilder(IServiceCollection services, LocalAgentRegistry agentHostBuilderContext) - { - services.Add(ServiceDescriptor.Singleton(agentHostBuilderContext)); - services.AddSingleton(); - } - private static IList GetRegisteredToolsForAgent(IServiceProvider serviceProvider, string agentName) { var registry = serviceProvider.GetService(); diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/HostApplicationBuilderWorkflowExtensions.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/HostApplicationBuilderWorkflowExtensions.cs index 2215a52a698..8075caec59d 100644 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/HostApplicationBuilderWorkflowExtensions.cs +++ b/dotnet/src/Microsoft.Agents.AI.Hosting/HostApplicationBuilderWorkflowExtensions.cs @@ -1,8 +1,6 @@ // Copyright (c) Microsoft. All rights reserved. using System; -using System.Linq; -using Microsoft.Agents.AI.Hosting.Local; using Microsoft.Agents.AI.Workflows; using Microsoft.Extensions.DependencyInjection; using Microsoft.Extensions.Hosting; @@ -47,28 +45,6 @@ public static IHostedWorkflowBuilder AddWorkflow(this IHostApplicationBuilder bu return workflow; }); - // Register the workflow by name for discovery. - var workflowRegistry = GetWorkflowRegistry(builder); - workflowRegistry.WorkflowNames.Add(name); - return new HostedWorkflowBuilder(name, builder); } - - private static LocalWorkflowRegistry GetWorkflowRegistry(IHostApplicationBuilder builder) - { - var descriptor = builder.Services.FirstOrDefault(s => !s.IsKeyedService && s.ServiceType.Equals(typeof(LocalWorkflowRegistry))); - if (descriptor?.ImplementationInstance is not LocalWorkflowRegistry instance) - { - instance = new LocalWorkflowRegistry(); - ConfigureHostBuilder(builder, instance); - } - - return instance; - } - - private static void ConfigureHostBuilder(IHostApplicationBuilder builder, LocalWorkflowRegistry agentHostBuilderContext) - { - builder.Services.Add(ServiceDescriptor.Singleton(agentHostBuilderContext)); - builder.Services.AddSingleton(); - } } diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentCatalog.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentCatalog.cs deleted file mode 100644 index 0b44ad60cb2..00000000000 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentCatalog.cs +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -using System; -using System.Collections.Generic; -using System.Runtime.CompilerServices; -using System.Threading; -using System.Threading.Tasks; -using Microsoft.Extensions.DependencyInjection; - -namespace Microsoft.Agents.AI.Hosting.Local; - -// Implementation of an AgentCatalog which enumerates agents registered in the local service provider. -internal sealed class LocalAgentCatalog : AgentCatalog -{ - public readonly HashSet _registeredAgents; - private readonly IServiceProvider _serviceProvider; - - public LocalAgentCatalog(LocalAgentRegistry agentHostBuilder, IServiceProvider serviceProvider) - { - this._registeredAgents = [.. agentHostBuilder.AgentNames]; - this._serviceProvider = serviceProvider; - } - - public override async IAsyncEnumerable GetAgentsAsync([EnumeratorCancellation] CancellationToken cancellationToken = default) - { - await Task.CompletedTask.ConfigureAwait(false); - - foreach (var name in this._registeredAgents) - { - var agent = this._serviceProvider.GetKeyedService(name); - if (agent is not null) - { - yield return agent; - } - } - } -} diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentRegistry.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentRegistry.cs deleted file mode 100644 index df3db8f5544..00000000000 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalAgentRegistry.cs +++ /dev/null @@ -1,10 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -using System.Collections.Generic; - -namespace Microsoft.Agents.AI.Hosting.Local; - -internal sealed class LocalAgentRegistry -{ - public HashSet AgentNames { get; } = []; -} diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowCatalog.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowCatalog.cs deleted file mode 100644 index 572b41830e0..00000000000 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowCatalog.cs +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -using System; -using System.Collections.Generic; -using System.Runtime.CompilerServices; -using System.Threading; -using System.Threading.Tasks; -using Microsoft.Agents.AI.Workflows; -using Microsoft.Extensions.DependencyInjection; - -namespace Microsoft.Agents.AI.Hosting.Local; - -internal sealed class LocalWorkflowCatalog : WorkflowCatalog -{ - public readonly HashSet _registeredWorkflows; - private readonly IServiceProvider _serviceProvider; - - public LocalWorkflowCatalog(LocalWorkflowRegistry workflowRegistry, IServiceProvider serviceProvider) - { - this._registeredWorkflows = [.. workflowRegistry.WorkflowNames]; - this._serviceProvider = serviceProvider; - } - - public override async IAsyncEnumerable GetWorkflowsAsync([EnumeratorCancellation] CancellationToken cancellationToken = default) - { - await Task.CompletedTask.ConfigureAwait(false); - - foreach (var name in this._registeredWorkflows) - { - var workflow = this._serviceProvider.GetKeyedService(name); - if (workflow is not null) - { - yield return workflow; - } - } - } -} diff --git a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowRegistry.cs b/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowRegistry.cs deleted file mode 100644 index 803c24660f8..00000000000 --- a/dotnet/src/Microsoft.Agents.AI.Hosting/Local/LocalWorkflowRegistry.cs +++ /dev/null @@ -1,10 +0,0 @@ -// Copyright (c) Microsoft. All rights reserved. - -using System.Collections.Generic; - -namespace Microsoft.Agents.AI.Hosting.Local; - -internal sealed class LocalWorkflowRegistry -{ - public HashSet WorkflowNames { get; } = []; -} diff --git a/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIExtensionsTests.cs b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIExtensionsTests.cs new file mode 100644 index 00000000000..d0020686267 --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIExtensionsTests.cs @@ -0,0 +1,222 @@ +// Copyright (c) Microsoft. All rights reserved. + +using System; +using Microsoft.Agents.AI.Workflows; +using Microsoft.Extensions.AI; +using Microsoft.Extensions.DependencyInjection; +using Moq; + +namespace Microsoft.Agents.AI.DevUI.UnitTests; + +/// +/// Unit tests for DevUI service collection extensions. +/// Tests verify that workflows and agents can be resolved even when registered non-conventionally. +/// +public class DevUIExtensionsTests +{ + /// + /// Verifies that AddDevUI throws ArgumentNullException when services collection is null. + /// + [Fact] + public void AddDevUI_NullServices_ThrowsArgumentNullException() + { + IServiceCollection services = null!; + Assert.Throws(() => services.AddDevUI()); + } + + /// + /// Verifies that GetRequiredKeyedService throws for non-existent keys. + /// + [Fact] + public void AddDevUI_GetRequiredKeyedServiceNonExistent_ThrowsInvalidOperationException() + { + // Arrange + var services = new ServiceCollection(); + services.AddDevUI(); + var serviceProvider = services.BuildServiceProvider(); + + // Act & Assert + Assert.Throws(() => serviceProvider.GetRequiredKeyedService("non-existent")); + } + + /// + /// Verifies that an agent with null name can be resolved by its workflow. + /// + [Fact] + public void AddDevUI_WorkflowWithName_CanBeResolved_AsAIAgent() + { + // Arrange + var services = new ServiceCollection(); + var mockChatClient = new Mock(); + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test 1", name: null); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test 2", name: null); + var workflow = AgentWorkflowBuilder.BuildSequential(agent1, agent2); + + services.AddKeyedSingleton("workflow", workflow); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + // Act + var resolvedWorkflowAsAgent = serviceProvider.GetKeyedService("workflow"); + + // Assert + Assert.NotNull(resolvedWorkflowAsAgent); + Assert.Null(resolvedWorkflowAsAgent.Name); + } + + /// + /// Verifies that an agent with null name can be resolved by its workflow. + /// + [Fact] + public void AddDevUI_MultipleWorkflowsWithName_CanBeResolved_AsAIAgent() + { + var services = new ServiceCollection(); + var mockChatClient = new Mock(); + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test 1", name: null); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test 2", name: null); + var workflow1 = AgentWorkflowBuilder.BuildSequential(agent1, agent2); + var workflow2 = AgentWorkflowBuilder.BuildSequential(agent1, agent2); + + services.AddKeyedSingleton("workflow1", workflow1); + services.AddKeyedSingleton("workflow2", workflow2); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + var resolvedWorkflow1AsAgent = serviceProvider.GetKeyedService("workflow1"); + Assert.NotNull(resolvedWorkflow1AsAgent); + Assert.Null(resolvedWorkflow1AsAgent.Name); + + var resolvedWorkflow2AsAgent = serviceProvider.GetKeyedService("workflow2"); + Assert.NotNull(resolvedWorkflow2AsAgent); + Assert.Null(resolvedWorkflow2AsAgent.Name); + + Assert.False(resolvedWorkflow1AsAgent == resolvedWorkflow2AsAgent); + } + + /// + /// Verifies that an agent with null name can be resolved by its workflow. + /// + [Fact] + public void AddDevUI_NonKeyedWorkflow_CanBeResolved_AsAIAgent() + { + var services = new ServiceCollection(); + var mockChatClient = new Mock(); + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test 1", name: null); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test 2", name: null); + var workflow = AgentWorkflowBuilder.BuildSequential(agent1, agent2); + + services.AddKeyedSingleton("workflow", workflow); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + var resolvedWorkflowAsAgent = serviceProvider.GetKeyedService("workflow"); + Assert.NotNull(resolvedWorkflowAsAgent); + Assert.Null(resolvedWorkflowAsAgent.Name); + } + + /// + /// Verifies that an agent with null name can be resolved by its workflow. + /// + [Fact] + public void AddDevUI_NonKeyedWorkflow_PlusKeyedWorkflow_CanBeResolved_AsAIAgent() + { + var services = new ServiceCollection(); + var mockChatClient = new Mock(); + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test 1", name: null); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test 2", name: null); + var workflow = AgentWorkflowBuilder.BuildSequential("standardname", agent1, agent2); + var keyedWorkflow = AgentWorkflowBuilder.BuildSequential("keyedname", agent1, agent2); + + services.AddSingleton(workflow); + services.AddKeyedSingleton("keyed", keyedWorkflow); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + // resolve a workflow with the same name as workflow's name (which is registered without a key) + var standardAgent = serviceProvider.GetKeyedService("standardname"); + Assert.NotNull(standardAgent); + Assert.Equal("standardname", standardAgent.Name); + + var keyedAgent = serviceProvider.GetKeyedService("keyed"); + Assert.NotNull(keyedAgent); + Assert.Equal("keyedname", keyedAgent.Name); + + var nonExisting = serviceProvider.GetKeyedService("random-non-existing!!!"); + Assert.Null(nonExisting); + } + + /// + /// Verifies that an agent registered with a different key than its name can be resolved by key. + /// + [Fact] + public void AddDevUI_AgentRegisteredWithDifferentKey_CanBeResolvedByKey() + { + // Arrange + var services = new ServiceCollection(); + const string AgentName = "actual-agent-name"; + const string RegistrationKey = "different-key"; + var mockChatClient = new Mock(); + var agent = new ChatClientAgent(mockChatClient.Object, "Test", AgentName); + + services.AddKeyedSingleton(RegistrationKey, agent); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + // Act + var resolvedAgent = serviceProvider.GetKeyedService(RegistrationKey); + + // Assert + Assert.NotNull(resolvedAgent); + // The resolved agent should have the agent's name, not the registration key + Assert.Equal(AgentName, resolvedAgent.Name); + } + + /// + /// Verifies that an agent registered with a different key than its name can be resolved by key. + /// + [Fact] + public void AddDevUI_Keyed_AndStandard_BothCanBeResolved() + { + // Arrange + var services = new ServiceCollection(); + var mockChatClient = new Mock(); + var defaultAgent = new ChatClientAgent(mockChatClient.Object, "default", "default"); + var keyedAgent = new ChatClientAgent(mockChatClient.Object, "keyed", "keyed"); + + services.AddSingleton(defaultAgent); + services.AddKeyedSingleton("keyed-registration", keyedAgent); + services.AddDevUI(); + + var serviceProvider = services.BuildServiceProvider(); + + var resolvedKeyedAgent = serviceProvider.GetKeyedService("keyed-registration"); + Assert.NotNull(resolvedKeyedAgent); + Assert.Equal("keyed", resolvedKeyedAgent.Name); + + // resolving default agent based on its name, not on the registration-key + var resolvedDefaultAgent = serviceProvider.GetKeyedService("default"); + Assert.NotNull(resolvedDefaultAgent); + Assert.Equal("default", resolvedDefaultAgent.Name); + } + + /// + /// Verifies that the DevUI fallback handler error message includes helpful information. + /// + [Fact] + public void AddDevUI_InvalidResolution_ErrorMessageIsInformative() + { + // Arrange + var services = new ServiceCollection(); + services.AddDevUI(); + var serviceProvider = services.BuildServiceProvider(); + const string InvalidKey = "invalid-key-name"; + + // Act & Assert + var exception = Assert.Throws(() => serviceProvider.GetRequiredKeyedService(InvalidKey)); + } +} diff --git a/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIIntegrationTests.cs b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIIntegrationTests.cs new file mode 100644 index 00000000000..b8512a856ea --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/DevUIIntegrationTests.cs @@ -0,0 +1,285 @@ +// Copyright (c) Microsoft. All rights reserved. + +using System; +using System.Net.Http.Json; +using System.Threading.Tasks; +using Microsoft.Agents.AI.DevUI.Entities; +using Microsoft.Agents.AI.Workflows; +using Microsoft.AspNetCore.Builder; +using Microsoft.AspNetCore.TestHost; +using Microsoft.Extensions.AI; +using Microsoft.Extensions.DependencyInjection; +using Moq; + +namespace Microsoft.Agents.AI.DevUI.UnitTests; + +public class DevUIIntegrationTests +{ + private sealed class NoOpExecutor(string id) : Executor(id) + { + protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder) => + routeBuilder.AddHandler( + (msg, ctx) => ctx.SendMessageAsync(msg)); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesRequestToWorkflow_ByKeyAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var mockChatClient = new Mock(); + var agent = new ChatClientAgent(mockChatClient.Object, "Test", "agent-name"); + + builder.Services.AddKeyedSingleton("registration-key", agent); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var resolvedAgent = app.Services.GetKeyedService("registration-key"); + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + Assert.NotNull(discoveryResponse); + Assert.Single(discoveryResponse.Entities); + Assert.Equal("agent-name", discoveryResponse.Entities[0].Name); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesMultipleAIAgents_ByKeyAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var mockChatClient = new Mock(); + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test", "agent-one"); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test", "agent-two"); + var agent3 = new ChatClientAgent(mockChatClient.Object, "Test", "agent-three"); + + builder.Services.AddKeyedSingleton("key-1", agent1); + builder.Services.AddKeyedSingleton("key-2", agent2); + builder.Services.AddKeyedSingleton("key-3", agent3); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + + // Assert + Assert.NotNull(discoveryResponse); + Assert.Equal(3, discoveryResponse.Entities.Count); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "agent-one" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "agent-two" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "agent-three" && e.Type == "agent"); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesAIAgents_WithKeyedAndDefaultRegistrationAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var mockChatClient = new Mock(); + var agentKeyed1 = new ChatClientAgent(mockChatClient.Object, "Test", "keyed-agent-one"); + var agentKeyed2 = new ChatClientAgent(mockChatClient.Object, "Test", "keyed-agent-two"); + var agentDefault = new ChatClientAgent(mockChatClient.Object, "Test", "default-agent"); + + builder.Services.AddKeyedSingleton("key-1", agentKeyed1); + builder.Services.AddKeyedSingleton("key-2", agentKeyed2); + builder.Services.AddSingleton(agentDefault); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + + // Assert + Assert.NotNull(discoveryResponse); + Assert.Equal(3, discoveryResponse.Entities.Count); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "keyed-agent-one" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "keyed-agent-two" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "default-agent" && e.Type == "agent"); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesMultipleWorkflows_ByKeyAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var workflow1 = new WorkflowBuilder("executor-1") + .WithName("workflow-one") + .WithDescription("First workflow") + .BindExecutor(new NoOpExecutor("executor-1")) + .Build(); + + var workflow2 = new WorkflowBuilder("executor-2") + .WithName("workflow-two") + .WithDescription("Second workflow") + .BindExecutor(new NoOpExecutor("executor-2")) + .Build(); + + var workflow3 = new WorkflowBuilder("executor-3") + .WithName("workflow-three") + .WithDescription("Third workflow") + .BindExecutor(new NoOpExecutor("executor-3")) + .Build(); + + builder.Services.AddKeyedSingleton("key-1", workflow1); + builder.Services.AddKeyedSingleton("key-2", workflow2); + builder.Services.AddKeyedSingleton("key-3", workflow3); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + + // Assert + Assert.NotNull(discoveryResponse); + Assert.Equal(3, discoveryResponse.Entities.Count); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "workflow-one" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "workflow-two" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "workflow-three" && e.Type == "workflow"); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesWorkflows_WithKeyedAndDefaultRegistrationAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var workflowKeyed1 = new WorkflowBuilder("executor-1") + .WithName("keyed-workflow-one") + .BindExecutor(new NoOpExecutor("executor-1")) + .Build(); + + var workflowKeyed2 = new WorkflowBuilder("executor-2") + .WithName("keyed-workflow-two") + .BindExecutor(new NoOpExecutor("executor-2")) + .Build(); + + var workflowDefault = new WorkflowBuilder("executor-default") + .WithName("default-workflow") + .BindExecutor(new NoOpExecutor("executor-default")) + .Build(); + + builder.Services.AddKeyedSingleton("key-1", workflowKeyed1); + builder.Services.AddKeyedSingleton("key-2", workflowKeyed2); + builder.Services.AddSingleton(workflowDefault); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + + // Assert + Assert.NotNull(discoveryResponse); + Assert.Equal(3, discoveryResponse.Entities.Count); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "keyed-workflow-one" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "keyed-workflow-two" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "default-workflow" && e.Type == "workflow"); + } + + [Fact] + public async Task TestServerWithDevUI_ResolvesMixedAgentsAndWorkflows_AllRegistrationsAsync() + { + // Arrange + WebApplicationBuilder builder = WebApplication.CreateBuilder(); + builder.WebHost.UseTestServer(); + + var mockChatClient = new Mock(); + + // Create AIAgents + var agent1 = new ChatClientAgent(mockChatClient.Object, "Test", "mixed-agent-one"); + var agent2 = new ChatClientAgent(mockChatClient.Object, "Test", "mixed-agent-two"); + var agentDefault = new ChatClientAgent(mockChatClient.Object, "Test", "default-mixed-agent"); + + // Create Workflows + var workflow1 = new WorkflowBuilder("executor-1") + .WithName("mixed-workflow-one") + .BindExecutor(new NoOpExecutor("executor-1")) + .Build(); + + var workflow2 = new WorkflowBuilder("executor-2") + .WithName("mixed-workflow-two") + .BindExecutor(new NoOpExecutor("executor-2")) + .Build(); + + var workflowDefault = new WorkflowBuilder("executor-default") + .WithName("default-mixed-workflow") + .BindExecutor(new NoOpExecutor("executor-default")) + .Build(); + + // Register all + builder.Services.AddKeyedSingleton("agent-key-1", agent1); + builder.Services.AddKeyedSingleton("agent-key-2", agent2); + builder.Services.AddSingleton(agentDefault); + builder.Services.AddKeyedSingleton("workflow-key-1", workflow1); + builder.Services.AddKeyedSingleton("workflow-key-2", workflow2); + builder.Services.AddSingleton(workflowDefault); + builder.Services.AddDevUI(); + + using WebApplication app = builder.Build(); + app.MapDevUI(); + + await app.StartAsync(); + + // Act + var client = app.GetTestClient(); + var response = await client.GetAsync(new Uri("/v1/entities", uriKind: UriKind.Relative)); + + var discoveryResponse = await response.Content.ReadFromJsonAsync(); + + // Assert + Assert.NotNull(discoveryResponse); + Assert.Equal(6, discoveryResponse.Entities.Count); + + // Verify agents + Assert.Contains(discoveryResponse.Entities, e => e.Name == "mixed-agent-one" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "mixed-agent-two" && e.Type == "agent"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "default-mixed-agent" && e.Type == "agent"); + + // Verify workflows + Assert.Contains(discoveryResponse.Entities, e => e.Name == "mixed-workflow-one" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "mixed-workflow-two" && e.Type == "workflow"); + Assert.Contains(discoveryResponse.Entities, e => e.Name == "default-mixed-workflow" && e.Type == "workflow"); + } +} diff --git a/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj new file mode 100644 index 00000000000..9135a90e2ed --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj @@ -0,0 +1,20 @@ + + + + $(ProjectsCoreTargetFrameworks) + $(ProjectsDebugCoreTargetFrameworks) + false + $(NoWarn);CA1812 + + + + + + + + + + + + + diff --git a/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Properties/launchSettings.json b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Properties/launchSettings.json new file mode 100644 index 00000000000..783215ce29e --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.DevUI.UnitTests/Properties/launchSettings.json @@ -0,0 +1,12 @@ +{ + "profiles": { + "Microsoft.Agents.AI.DevUI.UnitTests": { + "commandName": "Project", + "launchBrowser": true, + "environmentVariables": { + "ASPNETCORE_ENVIRONMENT": "Development" + }, + "applicationUrl": "https://localhost:63009;http://localhost:63010" + } + } +} \ No newline at end of file diff --git a/dotnet/tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Properties/launchSettings.json b/dotnet/tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Properties/launchSettings.json new file mode 100644 index 00000000000..6b8f8d04a41 --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Properties/launchSettings.json @@ -0,0 +1,12 @@ +{ + "profiles": { + "Microsoft.Agents.AI.Hosting.A2A.UnitTests": { + "commandName": "Project", + "launchBrowser": true, + "environmentVariables": { + "ASPNETCORE_ENVIRONMENT": "Development" + }, + "applicationUrl": "https://localhost:52186;http://localhost:52187" + } + } +} \ No newline at end of file diff --git a/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/MockAgentProvider.cs b/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/MockAgentProvider.cs index 67e4c68c5e5..8a2e76415a5 100644 --- a/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/MockAgentProvider.cs +++ b/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/MockAgentProvider.cs @@ -4,6 +4,7 @@ using System.Collections.Generic; using System.Threading; using System.Threading.Tasks; +using Microsoft.Extensions.AI; using Moq; namespace Microsoft.Agents.AI.Workflows.Declarative.UnitTests; @@ -15,10 +16,18 @@ internal sealed class MockAgentProvider : Mock { public IList ExistingConversationIds { get; } = []; + public ChatMessage? TestChatMessage { get; set; } + public MockAgentProvider() { this.Setup(provider => provider.CreateConversationAsync(It.IsAny())) .Returns(() => Task.FromResult(this.CreateConversationId())); + + this.Setup(provider => provider.GetMessageAsync( + It.IsAny(), + It.IsAny(), + It.IsAny())) + .Returns(Task.FromResult(this.CreateChatMessage())); } private string CreateConversationId() @@ -28,4 +37,13 @@ private string CreateConversationId() return newConversationId; } + + private ChatMessage CreateChatMessage() + { + this.TestChatMessage = new ChatMessage(ChatRole.User, Guid.NewGuid().ToString("N")) + { + MessageId = Guid.NewGuid().ToString("N"), + }; + return this.TestChatMessage; + } } diff --git a/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/ObjectModel/RetrieveConversationMessageExecutorTest.cs b/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/ObjectModel/RetrieveConversationMessageExecutorTest.cs new file mode 100644 index 00000000000..3f9fa0d6067 --- /dev/null +++ b/dotnet/tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/ObjectModel/RetrieveConversationMessageExecutorTest.cs @@ -0,0 +1,67 @@ +// Copyright (c) Microsoft. All rights reserved. + +using System.Threading.Tasks; +using Microsoft.Agents.AI.Workflows.Declarative.Extensions; +using Microsoft.Agents.AI.Workflows.Declarative.ObjectModel; +using Microsoft.Bot.ObjectModel; +using Microsoft.Extensions.AI; +using Xunit.Abstractions; + +namespace Microsoft.Agents.AI.Workflows.Declarative.UnitTests.ObjectModel; + +/// +/// Tests for . +/// +public sealed class RetrieveConversationMessageExecutorTest(ITestOutputHelper output) : WorkflowActionExecutorTest(output) +{ + [Fact] + public async Task RetrieveMessageSuccessfullyAsync() + { + // Arrange, Act, Assert + await this.ExecuteTestAsync(nameof(RetrieveMessageSuccessfullyAsync), + "TestMessage"); + } + + private async Task ExecuteTestAsync( + string displayName, + string variableName) + { + // Arrange + MockAgentProvider mockAgentProvider = new(); + + RetrieveConversationMessage model = this.CreateModel( + this.FormatDisplayName(displayName), + FormatVariablePath(variableName), + "TestConversationId", + "DefaultMessageId"); + + RetrieveConversationMessageExecutor action = new(model, mockAgentProvider.Object, this.State); + + // Act + await this.ExecuteAsync(action); + + // Assert + ChatMessage testMessage = mockAgentProvider.TestChatMessage ?? new ChatMessage(); + VerifyModel(model, action); + this.VerifyState(variableName, testMessage.ToRecord()); + } + + private RetrieveConversationMessage CreateModel( + string displayName, + string messageVariable, + string conversationId, + string messageId) + { + RetrieveConversationMessage.Builder actionBuilder = + new() + { + Id = this.CreateActionId(), + DisplayName = this.FormatDisplayName(displayName), + Message = PropertyPath.Create(messageVariable), + ConversationId = StringExpression.Literal(conversationId), + MessageId = StringExpression.Literal(messageId) + }; + + return AssignParent(actionBuilder); + } +} diff --git a/python/.cspell.json b/python/.cspell.json index 1b21f5263d5..da81b69a3b4 100644 --- a/python/.cspell.json +++ b/python/.cspell.json @@ -59,6 +59,7 @@ "OPENAI", "opentelemetry", "OTEL", + "powerfx", "protos", "pydantic", "pytestmark", diff --git a/python/.env.example b/python/.env.example index 82458a3fda9..f864f18f726 100644 --- a/python/.env.example +++ b/python/.env.example @@ -3,6 +3,14 @@ AZURE_AI_PROJECT_ENDPOINT="" AZURE_AI_MODEL_DEPLOYMENT_NAME="" # Bing connection for web search (optional, used by samples with web search) BING_CONNECTION_ID="" +# Azure AI Search (optional, used by AzureAISearchContextProvider samples) +AZURE_SEARCH_ENDPOINT="" +AZURE_SEARCH_API_KEY="" +AZURE_SEARCH_INDEX_NAME="" +AZURE_SEARCH_SEMANTIC_CONFIG="" +AZURE_SEARCH_KNOWLEDGE_BASE_NAME="" +# Note: For agentic mode Knowledge Bases, also set AZURE_OPENAI_ENDPOINT below +# (different from AZURE_AI_PROJECT_ENDPOINT - Knowledge Base needs OpenAI endpoint for model calls) # OpenAI OPENAI_API_KEY="" OPENAI_CHAT_MODEL_ID="" diff --git a/python/CHANGELOG.md b/python/CHANGELOG.md index 5d41d5ddf6c..6ff393bf66c 100644 --- a/python/CHANGELOG.md +++ b/python/CHANGELOG.md @@ -7,6 +7,36 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [1.0.0b251120] - 2025-11-20 + +### Added + +- **agent-framework-core**: Introducing support for declarative YAML spec ([#2002](https://github.com/microsoft/agent-framework/pull/2002)) +- **agent-framework-core**: Use AI Foundry evaluators for self-reflection ([#2250](https://github.com/microsoft/agent-framework/pull/2250)) +- **agent-framework-core**: Propagate `as_tool()` kwargs and add runtime context + middleware sample ([#2311](https://github.com/microsoft/agent-framework/pull/2311)) +- **agent-framework-anthropic**: Anthropic Foundry integration ([#2302](https://github.com/microsoft/agent-framework/pull/2302)) +- **samples**: M365 Agent SDK Hosting sample ([#2292](https://github.com/microsoft/agent-framework/pull/2292)) +- **samples**: Foundry Sample for A2A + SharePoint Samples ([#2313](https://github.com/microsoft/agent-framework/pull/2313)) + +### Changed + +- **agent-framework-azurefunctions**: [BREAKING] Schema changes for Azure Functions package ([#2151](https://github.com/microsoft/agent-framework/pull/2151)) +- **agent-framework-core**: Move evaluation folders under `evaluations` ([#2355](https://github.com/microsoft/agent-framework/pull/2355)) +- **agent-framework-core**: Move red teaming files to their own folder ([#2333](https://github.com/microsoft/agent-framework/pull/2333)) +- **agent-framework-core**: "fix all" task now single source of truth ([#2303](https://github.com/microsoft/agent-framework/pull/2303)) +- **agent-framework-core**: Improve and clean up exception handling ([#2337](https://github.com/microsoft/agent-framework/pull/2337), [#2319](https://github.com/microsoft/agent-framework/pull/2319)) +- **agent-framework-core**: Clean up imports ([#2318](https://github.com/microsoft/agent-framework/pull/2318)) + +### Fixed + +- **agent-framework-azure-ai**: Fix for Azure AI client ([#2358](https://github.com/microsoft/agent-framework/pull/2358)) +- **agent-framework-core**: Fix tool execution bleed-over in aiohttp/Bot Framework scenarios ([#2314](https://github.com/microsoft/agent-framework/pull/2314)) +- **agent-framework-core**: `@ai_function` now correctly handles `self` parameter ([#2266](https://github.com/microsoft/agent-framework/pull/2266)) +- **agent-framework-core**: Resolve string annotations in `FunctionExecutor` ([#2308](https://github.com/microsoft/agent-framework/pull/2308)) +- **agent-framework-core**: Langfuse observability captures ChatAgent system instructions ([#2316](https://github.com/microsoft/agent-framework/pull/2316)) +- **agent-framework-core**: Incomplete URL substring sanitization fix ([#2274](https://github.com/microsoft/agent-framework/pull/2274)) +- **observability**: Handle datetime serialization in tool results ([#2248](https://github.com/microsoft/agent-framework/pull/2248)) + ## [1.0.0b251117] - 2025-11-17 ### Fixed @@ -260,7 +290,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 For more information, see the [announcement blog post](https://devblogs.microsoft.com/foundry/introducing-microsoft-agent-framework-the-open-source-engine-for-agentic-ai-apps/). -[Unreleased]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251114...HEAD +[Unreleased]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251120...HEAD +[1.0.0b251120]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251117...python-1.0.0b251120 +[1.0.0b251117]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251114...python-1.0.0b251117 [1.0.0b251114]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251112.post1...python-1.0.0b251114 [1.0.0b251112.post1]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251112...python-1.0.0b251112.post1 [1.0.0b251112]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251111...python-1.0.0b251112 diff --git a/python/packages/a2a/pyproject.toml b/python/packages/a2a/pyproject.toml index d186fc0f7be..75d8301e6d2 100644 --- a/python/packages/a2a/pyproject.toml +++ b/python/packages/a2a/pyproject.toml @@ -4,7 +4,7 @@ description = "A2A integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/ag-ui/README.md b/python/packages/ag-ui/README.md index 2b02b610904..1e3d6b567f6 100644 --- a/python/packages/ag-ui/README.md +++ b/python/packages/ag-ui/README.md @@ -16,7 +16,7 @@ pip install agent-framework-ag-ui from fastapi import FastAPI from agent_framework import ChatAgent from agent_framework.azure import AzureOpenAIChatClient -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint # Create your agent agent = ChatAgent( @@ -41,7 +41,7 @@ add_agent_framework_fastapi_endpoint(app, agent, "/") ```python import asyncio from agent_framework import TextContent -from agent_framework_ag_ui import AGUIChatClient +from agent_framework.ag_ui import AGUIChatClient async def main(): async with AGUIChatClient(endpoint="http://localhost:8000/") as client: diff --git a/python/packages/ag-ui/agent_framework_ag_ui/_endpoint.py b/python/packages/ag-ui/agent_framework_ag_ui/_endpoint.py index ba6e9f5ddd0..d1baad55619 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui/_endpoint.py +++ b/python/packages/ag-ui/agent_framework_ag_ui/_endpoint.py @@ -91,4 +91,4 @@ async def event_generator(): ) except Exception as e: logger.error(f"Error in agent endpoint: {e}", exc_info=True) - return {"error": str(e)} + return {"error": "An internal error has occurred."} diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/README.md b/python/packages/ag-ui/agent_framework_ag_ui_examples/README.md index aed0f39b426..620f18dbbfe 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/README.md +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/README.md @@ -18,7 +18,7 @@ All example agents are factory functions that accept any `ChatClientProtocol`-co from fastapi import FastAPI from agent_framework.azure import AzureOpenAIChatClient from agent_framework.openai import OpenAIChatClient -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint from agent_framework_ag_ui_examples.agents import simple_agent, weather_agent app = FastAPI() @@ -40,7 +40,7 @@ add_agent_framework_fastapi_endpoint(app, weather_agent(openai_client), "/weathe from fastapi import FastAPI from agent_framework import ChatAgent from agent_framework.azure import AzureOpenAIChatClient -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint # Create your agent agent = ChatAgent( @@ -136,7 +136,7 @@ The server exposes endpoints at: ```python from fastapi import FastAPI from agent_framework.azure import AzureOpenAIChatClient -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint from agent_framework_ag_ui_examples.agents import ( simple_agent, weather_agent, @@ -188,8 +188,8 @@ You can create your own agent factories following the same pattern as the exampl ```python from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol -from agent_framework_ag_ui import AgentFrameworkAgent +from agent_framework import ChatClientProtocol +from agent_framework.ag_ui import AgentFrameworkAgent @ai_function def my_tool(param: str) -> str: diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/document_writer_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/document_writer_agent.py index 72623379ed1..bddc51846b2 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/document_writer_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/document_writer_agent.py @@ -2,10 +2,8 @@ """Example agent demonstrating predictive state updates with document writing.""" -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol - -from agent_framework_ag_ui import AgentFrameworkAgent, DocumentWriterConfirmationStrategy +from agent_framework import ChatAgent, ChatClientProtocol, ai_function +from agent_framework.ag_ui import AgentFrameworkAgent, DocumentWriterConfirmationStrategy @ai_function diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/human_in_the_loop_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/human_in_the_loop_agent.py index 8b178476af7..abbd113418b 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/human_in_the_loop_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/human_in_the_loop_agent.py @@ -4,8 +4,7 @@ from enum import Enum -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol +from agent_framework import ChatAgent, ChatClientProtocol, ai_function from pydantic import BaseModel, Field diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/recipe_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/recipe_agent.py index e39fb8c75e2..051937f2a94 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/recipe_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/recipe_agent.py @@ -4,12 +4,10 @@ from enum import Enum -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol +from agent_framework import ChatAgent, ChatClientProtocol, ai_function +from agent_framework.ag_ui import AgentFrameworkAgent, RecipeConfirmationStrategy from pydantic import BaseModel, Field -from agent_framework_ag_ui import AgentFrameworkAgent, RecipeConfirmationStrategy - class SkillLevel(str, Enum): """The skill level required for the recipe.""" diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/research_assistant_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/research_assistant_agent.py index 767ed1d9616..ad5c4f425c5 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/research_assistant_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/research_assistant_agent.py @@ -4,10 +4,8 @@ import asyncio -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol - -from agent_framework_ag_ui import AgentFrameworkAgent +from agent_framework import ChatAgent, ChatClientProtocol, ai_function +from agent_framework.ag_ui import AgentFrameworkAgent @ai_function diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/simple_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/simple_agent.py index bb631703999..e4bffaea0d6 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/simple_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/simple_agent.py @@ -2,8 +2,7 @@ """Simple agentic chat example (Feature 1: Agentic Chat).""" -from agent_framework import ChatAgent -from agent_framework._clients import ChatClientProtocol +from agent_framework import ChatAgent, ChatClientProtocol def simple_agent(chat_client: ChatClientProtocol) -> ChatAgent: diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_planner_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_planner_agent.py index f9eea2669bd..6609f06aa61 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_planner_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_planner_agent.py @@ -2,10 +2,8 @@ """Example agent demonstrating human-in-the-loop with function approvals.""" -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol - -from agent_framework_ag_ui import AgentFrameworkAgent, TaskPlannerConfirmationStrategy +from agent_framework import ChatAgent, ChatClientProtocol, ai_function +from agent_framework.ag_ui import AgentFrameworkAgent, TaskPlannerConfirmationStrategy @ai_function(approval_mode="always_require") diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_steps_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_steps_agent.py index 332e4162155..567dd348b46 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_steps_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/task_steps_agent.py @@ -18,12 +18,10 @@ TextMessageStartEvent, ToolCallStartEvent, ) -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol +from agent_framework import ChatAgent, ChatClientProtocol, ai_function +from agent_framework.ag_ui import AgentFrameworkAgent from pydantic import BaseModel, Field -from agent_framework_ag_ui import AgentFrameworkAgent - class StepStatus(str, Enum): """Status of a task step.""" diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/ui_generator_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/ui_generator_agent.py index 12f1a9341e0..0a99e6f1a17 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/ui_generator_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/ui_generator_agent.py @@ -4,10 +4,8 @@ from typing import Any -from agent_framework import AIFunction, ChatAgent -from agent_framework._clients import ChatClientProtocol - -from agent_framework_ag_ui import AgentFrameworkAgent +from agent_framework import AIFunction, ChatAgent, ChatClientProtocol +from agent_framework.ag_ui import AgentFrameworkAgent # Declaration-only tools (func=None) - actual rendering happens on the client side generate_haiku = AIFunction[Any, str]( diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/weather_agent.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/weather_agent.py index e48a9cab506..6edaa026163 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/weather_agent.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/agents/weather_agent.py @@ -4,8 +4,7 @@ from typing import Any -from agent_framework import ChatAgent, ai_function -from agent_framework._clients import ChatClientProtocol +from agent_framework import ChatAgent, ChatClientProtocol, ai_function @ai_function diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/server/api/backend_tool_rendering.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/server/api/backend_tool_rendering.py index 0ff360efb22..ae27a24a757 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/server/api/backend_tool_rendering.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/server/api/backend_tool_rendering.py @@ -2,11 +2,10 @@ """Backend tool rendering endpoint.""" +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint from agent_framework.azure import AzureOpenAIChatClient from fastapi import FastAPI -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint - from ...agents.weather_agent import weather_agent diff --git a/python/packages/ag-ui/agent_framework_ag_ui_examples/server/main.py b/python/packages/ag-ui/agent_framework_ag_ui_examples/server/main.py index e633268c504..ebfc42ea19f 100644 --- a/python/packages/ag-ui/agent_framework_ag_ui_examples/server/main.py +++ b/python/packages/ag-ui/agent_framework_ag_ui_examples/server/main.py @@ -6,12 +6,11 @@ import os import uvicorn +from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint from agent_framework.azure import AzureOpenAIChatClient from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint - from ..agents.document_writer_agent import document_writer_agent from ..agents.human_in_the_loop_agent import human_in_the_loop_agent from ..agents.recipe_agent import recipe_agent diff --git a/python/packages/ag-ui/getting_started/client.py b/python/packages/ag-ui/getting_started/client.py index 621d8536cd9..61bdf0bfb31 100644 --- a/python/packages/ag-ui/getting_started/client.py +++ b/python/packages/ag-ui/getting_started/client.py @@ -10,7 +10,7 @@ import asyncio import os -from agent_framework_ag_ui import AGUIChatClient +from agent_framework.ag_ui import AGUIChatClient async def main(): diff --git a/python/packages/ag-ui/getting_started/client_advanced.py b/python/packages/ag-ui/getting_started/client_advanced.py index cb45a0b8dac..08698a80a03 100644 --- a/python/packages/ag-ui/getting_started/client_advanced.py +++ b/python/packages/ag-ui/getting_started/client_advanced.py @@ -13,8 +13,7 @@ import os from agent_framework import ai_function - -from agent_framework_ag_ui import AGUIChatClient +from agent_framework.ag_ui import AGUIChatClient @ai_function diff --git a/python/packages/ag-ui/getting_started/client_with_agent.py b/python/packages/ag-ui/getting_started/client_with_agent.py index ac69189b53b..91b099820bd 100644 --- a/python/packages/ag-ui/getting_started/client_with_agent.py +++ b/python/packages/ag-ui/getting_started/client_with_agent.py @@ -23,8 +23,7 @@ import os from agent_framework import ChatAgent, FunctionCallContent, FunctionResultContent, TextContent, ai_function - -from agent_framework_ag_ui import AGUIChatClient +from agent_framework.ag_ui import AGUIChatClient # Enable debug logging logging.basicConfig( diff --git a/python/packages/ag-ui/pyproject.toml b/python/packages/ag-ui/pyproject.toml index 26208aef044..38db36ab2a7 100644 --- a/python/packages/ag-ui/pyproject.toml +++ b/python/packages/ag-ui/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "agent-framework-ag-ui" -version = "1.0.0b251117" +version = "1.0.0b251120" description = "AG-UI protocol integration for Agent Framework" readme = "README.md" license-files = ["LICENSE"] diff --git a/python/packages/ag-ui/tests/__init__.py b/python/packages/ag-ui/tests/__init__.py deleted file mode 100644 index 2a50eae8941..00000000000 --- a/python/packages/ag-ui/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. diff --git a/python/packages/ag-ui/tests/test_agent_wrapper_comprehensive.py b/python/packages/ag-ui/tests/test_agent_wrapper_comprehensive.py index 4b5b770509e..dbf0160ae63 100644 --- a/python/packages/ag-ui/tests/test_agent_wrapper_comprehensive.py +++ b/python/packages/ag-ui/tests/test_agent_wrapper_comprehensive.py @@ -11,7 +11,7 @@ async def test_agent_initialization_basic(): """Test basic agent initialization without state schema.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -28,7 +28,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_agent_initialization_with_state_schema(): """Test agent initialization with state_schema.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -43,7 +43,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_agent_initialization_with_predict_state_config(): """Test agent initialization with predict_state_config.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -58,7 +58,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_run_started_event_emission(): """Test RunStartedEvent is emitted at start of run.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -81,7 +81,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_predict_state_custom_event_emission(): """Test PredictState CustomEvent is emitted when predict_state_config is present.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -112,7 +112,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_initial_state_snapshot_with_schema(): """Test initial StateSnapshotEvent emission when state_schema present.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -141,7 +141,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_state_initialization_object_type(): """Test state initialization with object type in schema.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -167,7 +167,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_state_initialization_array_type(): """Test state initialization with array type in schema.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -193,7 +193,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_run_finished_event_emission(): """Test RunFinishedEvent is emitted at end of run.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -214,7 +214,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_tool_result_confirm_changes_accepted(): """Test confirm_changes tool result handling when accepted.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -260,7 +260,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_tool_result_confirm_changes_rejected(): """Test confirm_changes tool result handling when rejected.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -293,7 +293,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_tool_result_function_approval_accepted(): """Test function approval tool result when steps are accepted.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -338,7 +338,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_tool_result_function_approval_rejected(): """Test function approval tool result when rejected.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -374,7 +374,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_thread_metadata_tracking(): """Test that thread metadata includes ag_ui_thread_id and ag_ui_run_id.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent thread_metadata = {} @@ -405,7 +405,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_state_context_injection(): """Test that current state is injected into thread metadata.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent thread_metadata = {} @@ -436,7 +436,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_no_messages_provided(): """Test handling when no messages are provided.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -459,7 +459,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_message_end_event_emission(): """Test TextMessageEndEvent is emitted for assistant messages.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -486,7 +486,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_error_handling_with_exception(): """Test that exceptions during agent execution are re-raised.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class FailingChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -506,7 +506,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_json_decode_error_in_tool_result(): """Test handling of orphaned tool result - should be sanitized out.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -543,7 +543,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_suppressed_summary_with_document_state(): """Test suppressed summary uses document state for confirmation message.""" - from agent_framework_ag_ui import AgentFrameworkAgent, DocumentWriterConfirmationStrategy + from agent_framework.ag_ui import AgentFrameworkAgent, DocumentWriterConfirmationStrategy class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): diff --git a/python/packages/ag-ui/tests/test_endpoint.py b/python/packages/ag-ui/tests/test_endpoint.py index b5846bbbf84..1ae364f8186 100644 --- a/python/packages/ag-ui/tests/test_endpoint.py +++ b/python/packages/ag-ui/tests/test_endpoint.py @@ -154,7 +154,7 @@ async def test_endpoint_error_handling(): assert response.status_code == 200 content = json.loads(response.content) assert "error" in content - assert "Expecting value" in content["error"] + assert content["error"] == "An internal error has occurred." async def test_endpoint_multiple_paths(): diff --git a/python/packages/ag-ui/tests/test_structured_output.py b/python/packages/ag-ui/tests/test_structured_output.py index 878002a8e1f..10307356a5b 100644 --- a/python/packages/ag-ui/tests/test_structured_output.py +++ b/python/packages/ag-ui/tests/test_structured_output.py @@ -32,7 +32,7 @@ class GenericOutput(BaseModel): async def test_structured_output_with_recipe(): """Test structured output processing with recipe state.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -70,7 +70,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_structured_output_with_steps(): """Test structured output processing with steps state.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -109,7 +109,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_structured_output_with_no_schema_match(): """Test structured output when response fields don't match state_schema keys.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -138,7 +138,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_structured_output_without_schema(): """Test structured output without state_schema treats all fields as state.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class DataOutput(BaseModel): """Output with data and info fields.""" @@ -175,7 +175,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_no_structured_output_when_no_response_format(): """Test that structured output path is skipped when no response_format.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -200,7 +200,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_structured_output_with_message_field(): """Test structured output that includes a message field.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): @@ -234,7 +234,7 @@ async def get_streaming_response(self, messages, chat_options, **kwargs): async def test_empty_updates_no_structured_processing(): """Test that empty updates don't trigger structured output processing.""" - from agent_framework_ag_ui import AgentFrameworkAgent + from agent_framework.ag_ui import AgentFrameworkAgent class MockChatClient: async def get_streaming_response(self, messages, chat_options, **kwargs): diff --git a/python/packages/aisearch/LICENSE b/python/packages/aisearch/LICENSE new file mode 100644 index 00000000000..9e841e7a26e --- /dev/null +++ b/python/packages/aisearch/LICENSE @@ -0,0 +1,21 @@ + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE diff --git a/python/packages/aisearch/README.md b/python/packages/aisearch/README.md new file mode 100644 index 00000000000..6631a2c863d --- /dev/null +++ b/python/packages/aisearch/README.md @@ -0,0 +1,23 @@ +# Get Started with Microsoft Agent Framework Azure AI Search + +Please install this package via pip: + +```bash +pip install agent-framework-aisearch --pre +``` + +## Azure AI Search Integration + +The Azure AI Search integration provides context providers for RAG (Retrieval Augmented Generation) capabilities with two modes: + +- **Semantic Mode**: Fast hybrid search (vector + keyword) with semantic ranking +- **Agentic Mode**: Multi-hop reasoning using Knowledge Bases for complex queries + +### Basic Usage Example + +See the [Azure AI Search context provider examples](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/agents/azure_ai/) which demonstrate: + +- Semantic search with hybrid (vector + keyword) queries +- Agentic mode with Knowledge Bases for complex multi-hop reasoning +- Environment variable configuration with Settings class +- API key and managed identity authentication diff --git a/python/packages/aisearch/agent_framework_aisearch/__init__.py b/python/packages/aisearch/agent_framework_aisearch/__init__.py new file mode 100644 index 00000000000..fedfb05bcdf --- /dev/null +++ b/python/packages/aisearch/agent_framework_aisearch/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Microsoft. All rights reserved. + +import importlib.metadata + +from ._search_provider import AzureAISearchContextProvider, AzureAISearchSettings + +try: + __version__ = importlib.metadata.version(__name__) +except importlib.metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + +__all__ = [ + "AzureAISearchContextProvider", + "AzureAISearchSettings", + "__version__", +] diff --git a/python/packages/aisearch/agent_framework_aisearch/_search_provider.py b/python/packages/aisearch/agent_framework_aisearch/_search_provider.py new file mode 100644 index 00000000000..23c8c3f309c --- /dev/null +++ b/python/packages/aisearch/agent_framework_aisearch/_search_provider.py @@ -0,0 +1,914 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Azure AI Search Context Provider for Agent Framework. + +This module provides context providers for Azure AI Search integration with two modes: +- Agentic: Recommended for most scenarios. Uses Knowledge Bases for query planning and + multi-hop reasoning. Slightly slower with more token consumption, but more accurate. +- Semantic: Fast hybrid search (vector + keyword) with semantic ranker. Best for simple + queries where speed is critical. + +See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720 +""" + +import sys +from collections.abc import Awaitable, Callable, MutableSequence +from typing import TYPE_CHECKING, Any, ClassVar, Literal + +from agent_framework import ChatMessage, Context, ContextProvider, Role +from agent_framework._logging import get_logger +from agent_framework._pydantic import AFBaseSettings +from agent_framework.exceptions import ServiceInitializationError +from azure.core.credentials import AzureKeyCredential +from azure.core.credentials_async import AsyncTokenCredential +from azure.core.exceptions import ResourceNotFoundError +from azure.search.documents.aio import SearchClient +from azure.search.documents.indexes.aio import SearchIndexClient +from azure.search.documents.indexes.models import ( + AzureOpenAIVectorizerParameters, + KnowledgeBase, + KnowledgeBaseAzureOpenAIModel, + KnowledgeRetrievalLowReasoningEffort, + KnowledgeRetrievalMediumReasoningEffort, + KnowledgeRetrievalMinimalReasoningEffort, + KnowledgeRetrievalOutputMode, + KnowledgeRetrievalReasoningEffort, + KnowledgeSourceReference, + SearchIndexKnowledgeSource, + SearchIndexKnowledgeSourceParameters, +) +from azure.search.documents.models import ( + QueryCaptionType, + QueryType, + VectorizableTextQuery, + VectorizedQuery, +) +from pydantic import SecretStr, ValidationError + +# Type checking imports for optional agentic mode dependencies +if TYPE_CHECKING: + from azure.search.documents.knowledgebases.aio import KnowledgeBaseRetrievalClient + from azure.search.documents.knowledgebases.models import ( + KnowledgeBaseMessage, + KnowledgeBaseMessageTextContent, + KnowledgeBaseRetrievalRequest, + KnowledgeRetrievalIntent, + KnowledgeRetrievalSemanticIntent, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalLowReasoningEffort as KBRetrievalLowReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalMediumReasoningEffort as KBRetrievalMediumReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalMinimalReasoningEffort as KBRetrievalMinimalReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalOutputMode as KBRetrievalOutputMode, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalReasoningEffort as KBRetrievalReasoningEffort, + ) + +# Runtime imports for agentic mode (optional dependency) +try: + from azure.search.documents.knowledgebases.aio import KnowledgeBaseRetrievalClient + from azure.search.documents.knowledgebases.models import ( + KnowledgeBaseMessage, + KnowledgeBaseMessageTextContent, + KnowledgeBaseRetrievalRequest, + KnowledgeRetrievalIntent, + KnowledgeRetrievalSemanticIntent, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalLowReasoningEffort as KBRetrievalLowReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalMediumReasoningEffort as KBRetrievalMediumReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalMinimalReasoningEffort as KBRetrievalMinimalReasoningEffort, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalOutputMode as KBRetrievalOutputMode, + ) + from azure.search.documents.knowledgebases.models import ( + KnowledgeRetrievalReasoningEffort as KBRetrievalReasoningEffort, + ) + + _agentic_retrieval_available = True +except ImportError: + _agentic_retrieval_available = False + +if sys.version_info >= (3, 11): + from typing import Self # pragma: no cover +else: + from typing_extensions import Self # pragma: no cover + +if sys.version_info >= (3, 12): + from typing import override # type: ignore # pragma: no cover +else: + from typing_extensions import override # type: ignore[import] # pragma: no cover + +# Module-level constants +logger = get_logger("agent_framework.azure") +_DEFAULT_AGENTIC_MESSAGE_HISTORY_COUNT = 10 + + +class AzureAISearchSettings(AFBaseSettings): + """Settings for Azure AI Search Context Provider with auto-loading from environment. + + The settings are first loaded from environment variables with the prefix 'AZURE_SEARCH_'. + If the environment variables are not found, the settings can be loaded from a .env file. + + Keyword Args: + endpoint: Azure AI Search endpoint URL. + Can be set via environment variable AZURE_SEARCH_ENDPOINT. + index_name: Name of the search index. + Can be set via environment variable AZURE_SEARCH_INDEX_NAME. + api_key: API key for authentication (optional, use managed identity if not provided). + Can be set via environment variable AZURE_SEARCH_API_KEY. + env_file_path: If provided, the .env settings are read from this file path location. + env_file_encoding: The encoding of the .env file, defaults to 'utf-8'. + + Examples: + .. code-block:: python + + from agent_framework_aisearch import AzureAISearchSettings + + # Using environment variables + # Set AZURE_SEARCH_ENDPOINT=https://mysearch.search.windows.net + # Set AZURE_SEARCH_INDEX_NAME=my-index + settings = AzureAISearchSettings() + + # Or passing parameters directly + settings = AzureAISearchSettings( + endpoint="https://mysearch.search.windows.net", + index_name="my-index", + ) + + # Or loading from a .env file + settings = AzureAISearchSettings(env_file_path="path/to/.env") + """ + + env_prefix: ClassVar[str] = "AZURE_SEARCH_" + + endpoint: str | None = None + index_name: str | None = None + api_key: SecretStr | None = None + + +class AzureAISearchContextProvider(ContextProvider): + """Azure AI Search Context Provider with hybrid search and semantic ranking. + + This provider retrieves relevant documents from Azure AI Search to provide context + to the AI agent. It supports two modes: + + - **agentic**: Recommended for most scenarios. Uses Knowledge Bases for query planning + and multi-hop reasoning. Slightly slower with more token consumption, but provides + more accurate results (up to 36% improvement in response relevance). + - **semantic** (default): Fast hybrid search combining vector and keyword search + with semantic reranking. Best for simple queries where speed is critical. + + Examples: + Using environment variables (recommended): + + .. code-block:: python + + from agent_framework_aisearch import AzureAISearchContextProvider + from azure.identity.aio import DefaultAzureCredential + + # Set AZURE_SEARCH_ENDPOINT and AZURE_SEARCH_INDEX_NAME in environment + search_provider = AzureAISearchContextProvider(credential=DefaultAzureCredential()) + + Semantic hybrid search with API key: + + .. code-block:: python + + # Direct API key string + search_provider = AzureAISearchContextProvider( + endpoint="https://mysearch.search.windows.net", + index_name="my-index", + api_key="my-api-key", + mode="semantic", + ) + + Loading from .env file: + + .. code-block:: python + + # Load settings from a .env file + search_provider = AzureAISearchContextProvider( + credential=DefaultAzureCredential(), env_file_path="path/to/.env" + ) + + Agentic retrieval for complex queries: + + .. code-block:: python + + # Use agentic mode for multi-hop reasoning + # Note: azure_openai_resource_url is the OpenAI endpoint for Knowledge Base model calls, + # which is different from azure_ai_project_endpoint (the AI Foundry project endpoint) + search_provider = AzureAISearchContextProvider( + endpoint="https://mysearch.search.windows.net", + index_name="my-index", + credential=DefaultAzureCredential(), + mode="agentic", + azure_openai_resource_url="https://myresource.openai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="my-knowledge-base", + ) + """ + + _DEFAULT_SEARCH_CONTEXT_PROMPT = "Use the following context to answer the question:" + + def __init__( + self, + endpoint: str | None = None, + index_name: str | None = None, + api_key: str | AzureKeyCredential | None = None, + credential: AsyncTokenCredential | None = None, + *, + mode: Literal["semantic", "agentic"] = "semantic", + top_k: int = 5, + semantic_configuration_name: str | None = None, + vector_field_name: str | None = None, + embedding_function: Callable[[str], Awaitable[list[float]]] | None = None, + context_prompt: str | None = None, + # Agentic mode parameters (Knowledge Base) + azure_ai_project_endpoint: str | None = None, + azure_openai_resource_url: str | None = None, + model_deployment_name: str | None = None, + model_name: str | None = None, + knowledge_base_name: str | None = None, + retrieval_instructions: str | None = None, + azure_openai_api_key: str | None = None, + knowledge_base_output_mode: Literal["extractive_data", "answer_synthesis"] = "extractive_data", + retrieval_reasoning_effort: Literal["minimal", "medium", "low"] = "minimal", + agentic_message_history_count: int = _DEFAULT_AGENTIC_MESSAGE_HISTORY_COUNT, + env_file_path: str | None = None, + env_file_encoding: str | None = None, + ) -> None: + """Initialize Azure AI Search Context Provider. + + Args: + endpoint: Azure AI Search endpoint URL. + Can also be set via environment variable AZURE_SEARCH_ENDPOINT. + index_name: Name of the search index to query. + Can also be set via environment variable AZURE_SEARCH_INDEX_NAME. + api_key: API key for authentication (string or AzureKeyCredential). + Can also be set via environment variable AZURE_SEARCH_API_KEY. + credential: AsyncTokenCredential for managed identity authentication. + Use this for Entra ID authentication instead of api_key. + mode: Search mode - "semantic" for hybrid search with semantic ranking (fast) + or "agentic" for multi-hop reasoning (slower). Default: "semantic". + top_k: Maximum number of documents to retrieve. Only applies to semantic mode. + In agentic mode, the server-side Knowledge Base determines retrieval based on + query complexity and reasoning effort. Default: 5. + semantic_configuration_name: Name of semantic configuration in the index. + Required for semantic ranking. If None, uses index default. + vector_field_name: Name of the vector field in the index for hybrid search. + Required if using vector search. Default: None (keyword search only). + embedding_function: Async function to generate embeddings for vector search. + Signature: async def embed(text: str) -> list[float] + Required if vector_field_name is specified and no server-side vectorization. + context_prompt: Custom prompt to prepend to retrieved context. + Default: "Use the following context to answer the question:" + azure_ai_project_endpoint: Azure AI Foundry project endpoint URL. + This is NOT the same as azure_openai_resource_url - the project endpoint is used + for Azure AI Foundry services, while the OpenAI endpoint is used by the Knowledge + Base to call the model for query planning. Required for agentic mode. + Example: "https://myproject.services.ai.azure.com/api/projects/myproject" + azure_openai_resource_url: Azure OpenAI resource URL for Knowledge Base model calls. + This is the OpenAI endpoint used by the Knowledge Base to call the LLM for + query planning and reasoning. This is separate from the project endpoint because + the Knowledge Base directly calls Azure OpenAI for its internal operations. + Required for agentic mode. Example: "https://myresource.openai.azure.com" + model_deployment_name: Model deployment name in Azure OpenAI for Knowledge Base. + This is the deployment name the Knowledge Base uses to call the LLM. + Required for agentic mode. + model_name: The underlying model name (e.g., "gpt-4o", "gpt-4o-mini"). + If not provided, defaults to model_deployment_name. Used for Knowledge Base configuration. + knowledge_base_name: Name for the Knowledge Base. Required for agentic mode. + retrieval_instructions: Custom instructions for the Knowledge Base's + retrieval planning. Only used in agentic mode. + azure_openai_api_key: Azure OpenAI API key for Knowledge Base to call the model. + Only needed when using API key authentication instead of managed identity. + knowledge_base_output_mode: Output mode for Knowledge Base retrieval. Only used in agentic mode. + "extractive_data": Returns raw chunks without synthesis (default, recommended for agent integration). + "answer_synthesis": Returns synthesized answer from the LLM. + Some knowledge sources require answer_synthesis mode. Default: "extractive_data". + retrieval_reasoning_effort: Reasoning effort for Knowledge Base query planning. Only used in agentic mode. + "minimal": Fastest, basic query planning. + "medium": Moderate reasoning with some query decomposition. + "low": Lower reasoning effort than medium. + Default: "minimal". + agentic_message_history_count: Number of recent messages from conversation history to send to + the Knowledge Base. This context helps with query planning in agentic mode, allowing the + Knowledge Base to understand the conversation flow and generate better retrieval queries. + There is no technical limit - adjust based on your use case. Default: 10. + env_file_path: Path to environment file for loading settings. + env_file_encoding: Encoding of the environment file. + + Examples: + .. code-block:: python + + from agent_framework_aisearch import AzureAISearchContextProvider + from azure.identity.aio import DefaultAzureCredential + + # Using environment variables + # Set AZURE_SEARCH_ENDPOINT=https://mysearch.search.windows.net + # Set AZURE_SEARCH_INDEX_NAME=my-index + credential = DefaultAzureCredential() + provider = AzureAISearchContextProvider(credential=credential) + + # Or passing parameters directly + provider = AzureAISearchContextProvider( + endpoint="https://mysearch.search.windows.net", + index_name="my-index", + credential=credential, + ) + + # Or loading from a .env file + provider = AzureAISearchContextProvider(credential=credential, env_file_path="path/to/.env") + """ + # Load settings from environment/file + try: + settings = AzureAISearchSettings( + endpoint=endpoint, + index_name=index_name, + api_key=api_key if isinstance(api_key, str) else None, + env_file_path=env_file_path, + env_file_encoding=env_file_encoding, + ) + except ValidationError as ex: + raise ServiceInitializationError("Failed to create Azure AI Search settings.", ex) from ex + + # Validate required parameters + if not settings.endpoint: + raise ServiceInitializationError( + "Azure AI Search endpoint is required. Set via 'endpoint' parameter " + "or 'AZURE_SEARCH_ENDPOINT' environment variable." + ) + if not settings.index_name: + raise ServiceInitializationError( + "Azure AI Search index name is required. Set via 'index_name' parameter " + "or 'AZURE_SEARCH_INDEX_NAME' environment variable." + ) + + # Determine the credential to use + resolved_credential: AzureKeyCredential | AsyncTokenCredential + if credential: + # AsyncTokenCredential takes precedence + resolved_credential = credential + elif isinstance(api_key, AzureKeyCredential): + resolved_credential = api_key + elif settings.api_key: + resolved_credential = AzureKeyCredential(settings.api_key.get_secret_value()) + else: + raise ServiceInitializationError( + "Azure credential is required. Provide 'api_key' or 'credential' parameter " + "or set 'AZURE_SEARCH_API_KEY' environment variable." + ) + + self.endpoint = settings.endpoint + self.index_name = settings.index_name + self.credential = resolved_credential + self.mode = mode + self.top_k = top_k + self.semantic_configuration_name = semantic_configuration_name + self.vector_field_name = vector_field_name + self.embedding_function = embedding_function + self.context_prompt = context_prompt or self._DEFAULT_SEARCH_CONTEXT_PROMPT + + # Agentic mode parameters (Knowledge Base) + self.azure_openai_resource_url = azure_openai_resource_url + self.azure_openai_deployment_name = model_deployment_name + # If model_name not provided, default to deployment name + self.model_name = model_name or model_deployment_name + self.knowledge_base_name = knowledge_base_name + self.retrieval_instructions = retrieval_instructions + self.azure_openai_api_key = azure_openai_api_key + self.azure_ai_project_endpoint = azure_ai_project_endpoint + self.knowledge_base_output_mode = knowledge_base_output_mode + self.retrieval_reasoning_effort = retrieval_reasoning_effort + self.agentic_message_history_count = agentic_message_history_count + + # Auto-discover vector field if not specified + self._auto_discovered_vector_field = False + self._use_vectorizable_query = False # Will be set to True if server-side vectorization detected + if not vector_field_name and mode == "semantic": + # Attempt to auto-discover vector field from index schema + # This will be done lazily on first search to avoid blocking initialization + pass + + # Validation + if vector_field_name and not embedding_function: + raise ValueError("embedding_function is required when vector_field_name is specified") + + if mode == "agentic": + if not _agentic_retrieval_available: + raise ImportError( + "Agentic retrieval requires azure-search-documents >= 11.7.0b1 with Knowledge Base support. " + "Please upgrade: pip install azure-search-documents>=11.7.0b1" + ) + if not self.azure_openai_resource_url: + raise ValueError( + "azure_openai_resource_url is required for agentic mode. " + "This should be your Azure OpenAI endpoint (e.g., 'https://myresource.openai.azure.com')" + ) + if not self.azure_openai_deployment_name: + raise ValueError("model_deployment_name is required for agentic mode") + if not knowledge_base_name: + raise ValueError("knowledge_base_name is required for agentic mode") + + # Create search client for semantic mode + self._search_client = SearchClient( + endpoint=self.endpoint, + index_name=self.index_name, + credential=self.credential, + ) + + # Create index client and retrieval client for agentic mode (Knowledge Base) + self._index_client: SearchIndexClient | None = None + self._retrieval_client: KnowledgeBaseRetrievalClient | None = None + if mode == "agentic": + self._index_client = SearchIndexClient( + endpoint=self.endpoint, + credential=self.credential, + ) + # Retrieval client will be created after Knowledge Base initialization + + self._knowledge_base_initialized = False + + async def __aenter__(self) -> Self: + """Async context manager entry.""" + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: Any, + ) -> None: + """Async context manager exit - cleanup clients. + + Args: + exc_type: Exception type if an error occurred. + exc_val: Exception value if an error occurred. + exc_tb: Exception traceback if an error occurred. + """ + # Close retrieval client if it was created + if self._retrieval_client is not None: + await self._retrieval_client.close() + self._retrieval_client = None + + @override + async def invoking( + self, + messages: ChatMessage | MutableSequence[ChatMessage], + **kwargs: Any, + ) -> Context: + """Retrieve relevant context from Azure AI Search before model invocation. + + Args: + messages: User messages to use for context retrieval. + **kwargs: Additional arguments (unused). + + Returns: + Context object with retrieved documents as messages. + """ + # Convert to list and filter to USER/ASSISTANT messages with text only + messages_list = [messages] if isinstance(messages, ChatMessage) else list(messages) + + filtered_messages = [ + msg + for msg in messages_list + if msg and msg.text and msg.text.strip() and msg.role in [Role.USER, Role.ASSISTANT] + ] + + if not filtered_messages: + return Context() + + # Perform search based on mode + if self.mode == "semantic": + # Semantic mode: flatten messages to single query + query = "\n".join(msg.text for msg in filtered_messages) + search_result_parts = await self._semantic_search(query) + else: # agentic + # Agentic mode: pass recent messages as conversation history + recent_messages = filtered_messages[-self.agentic_message_history_count :] + search_result_parts = await self._agentic_search(recent_messages) + + # Format results as context - return multiple messages for each result part + if not search_result_parts: + return Context() + + # Create context messages: first message with prompt, then one message per result part + context_messages = [ChatMessage(role=Role.USER, text=self.context_prompt)] + context_messages.extend([ChatMessage(role=Role.USER, text=part) for part in search_result_parts]) + + return Context(messages=context_messages) + + def _find_vector_fields(self, index: Any) -> list[str]: + """Find all fields that can store vectors (have dimensions defined). + + Args: + index: SearchIndex object from Azure Search. + + Returns: + List of vector field names. + """ + return [ + field.name + for field in index.fields + if field.vector_search_dimensions is not None and field.vector_search_dimensions > 0 + ] + + def _find_vectorizable_fields(self, index: Any, vector_fields: list[str]) -> list[str]: + """Find vector fields that have auto-vectorization configured. + + These are fields that have a vectorizer in their profile, meaning the index + can automatically vectorize text queries without needing a client-side embedding function. + + Args: + index: SearchIndex object from Azure Search. + vector_fields: List of vector field names. + + Returns: + List of vectorizable field names (subset of vector_fields). + """ + vectorizable_fields: list[str] = [] + + # Check if index has vector search configuration + if not index.vector_search or not index.vector_search.profiles: + return vectorizable_fields + + # For each vector field, check if it has a vectorizer configured + for field in index.fields: + if field.name in vector_fields and field.vector_search_profile_name: + # Find the profile for this field + profile = next( + (p for p in index.vector_search.profiles if p.name == field.vector_search_profile_name), None + ) + + if profile and hasattr(profile, "vectorizer_name") and profile.vectorizer_name: + # This field has server-side vectorization configured + vectorizable_fields.append(field.name) + + return vectorizable_fields + + async def _auto_discover_vector_field(self) -> None: + """Auto-discover vector field from index schema. + + Attempts to find vector fields in the index and detect which have server-side + vectorization configured. Prioritizes vectorizable fields (which can auto-embed text) + over regular vector fields (which require client-side embedding). + """ + if self._auto_discovered_vector_field or self.vector_field_name: + return # Already discovered or manually specified + + try: + # Use existing index client or create temporary one + if not self._index_client: + self._index_client = SearchIndexClient(endpoint=self.endpoint, credential=self.credential) + index_client = self._index_client + + # Get index schema + index = await index_client.get_index(self.index_name) + + # Step 1: Find all vector fields + vector_fields = self._find_vector_fields(index) + + if not vector_fields: + # No vector fields found - keyword search only + logger.info(f"No vector fields found in index '{self.index_name}'. Using keyword-only search.") + self._auto_discovered_vector_field = True + return + + # Step 2: Find which vector fields have server-side vectorization + vectorizable_fields = self._find_vectorizable_fields(index, vector_fields) + + # Step 3: Decide which field to use + if vectorizable_fields: + # Prefer vectorizable fields (server-side embedding) + if len(vectorizable_fields) == 1: + self.vector_field_name = vectorizable_fields[0] + self._auto_discovered_vector_field = True + self._use_vectorizable_query = True # Use VectorizableTextQuery + logger.info( + f"Auto-discovered vectorizable field '{self.vector_field_name}' " + f"with server-side vectorization. No embedding_function needed." + ) + else: + # Multiple vectorizable fields + logger.warning( + f"Multiple vectorizable fields found: {vectorizable_fields}. " + f"Please specify vector_field_name explicitly. Using keyword-only search." + ) + elif len(vector_fields) == 1: + # Single vector field without vectorizer - needs client-side embedding + self.vector_field_name = vector_fields[0] + self._auto_discovered_vector_field = True + self._use_vectorizable_query = False + + if not self.embedding_function: + logger.warning( + f"Auto-discovered vector field '{self.vector_field_name}' without server-side vectorization. " + f"Provide embedding_function for vector search, or it will fall back to keyword-only search." + ) + self.vector_field_name = None + else: + # Multiple vector fields without vectorizers + logger.warning( + f"Multiple vector fields found: {vector_fields}. " + f"Please specify vector_field_name explicitly. Using keyword-only search." + ) + + except Exception as e: + # Log warning but continue with keyword search + logger.warning(f"Failed to auto-discover vector field: {e}. Using keyword-only search.") + + self._auto_discovered_vector_field = True # Mark as attempted + + async def _semantic_search(self, query: str) -> list[str]: + """Perform semantic hybrid search with semantic ranking. + + This is the recommended mode for most use cases. It combines: + - Vector search (if embedding_function provided) + - Keyword search (BM25) + - Semantic reranking (if semantic_configuration_name provided) + + Args: + query: Search query text. + + Returns: + List of formatted search result strings, one per document. + """ + # Auto-discover vector field if not already done + await self._auto_discover_vector_field() + + vector_queries: list[VectorizableTextQuery | VectorizedQuery] = [] + + # Build vector query based on server-side vectorization or client-side embedding + if self.vector_field_name: + # Use larger k for vector query when semantic reranker is enabled for better ranking quality + vector_k = max(self.top_k, 50) if self.semantic_configuration_name else self.top_k + + if self._use_vectorizable_query: + # Server-side vectorization: Index will auto-embed the text query + vector_queries = [ + VectorizableTextQuery( + text=query, + k_nearest_neighbors=vector_k, + fields=self.vector_field_name, + ) + ] + elif self.embedding_function: + # Client-side embedding: We provide the vector + query_vector = await self.embedding_function(query) + vector_queries = [ + VectorizedQuery( + vector=query_vector, + k_nearest_neighbors=vector_k, + fields=self.vector_field_name, + ) + ] + # else: vector_field_name is set but no vectorization available - skip vector search + + # Build search parameters + search_params: dict[str, Any] = { + "search_text": query, + "top": self.top_k, + } + + if vector_queries: + search_params["vector_queries"] = vector_queries + + # Add semantic ranking if configured + if self.semantic_configuration_name: + search_params["query_type"] = QueryType.SEMANTIC + search_params["semantic_configuration_name"] = self.semantic_configuration_name + search_params["query_caption"] = QueryCaptionType.EXTRACTIVE + + # Execute search + results = await self._search_client.search(**search_params) # type: ignore[reportUnknownVariableType] + + # Format results with citations + formatted_results: list[str] = [] + async for doc in results: # type: ignore[reportUnknownVariableType] + # Extract document ID for citation + doc_id = doc.get("id") or doc.get("@search.id") # type: ignore[reportUnknownVariableType] + + # Use full document chunks with citation + doc_text: str = self._extract_document_text(doc, doc_id=doc_id) # type: ignore[reportUnknownArgumentType] + if doc_text: + formatted_results.append(doc_text) # type: ignore[reportUnknownArgumentType] + + return formatted_results + + async def _ensure_knowledge_base(self) -> None: + """Ensure Knowledge Base and knowledge source are created. + + This method is idempotent - it will only create resources if they don't exist. + + Note: Azure SDK uses KnowledgeAgent classes internally, but the feature + is marketed as "Knowledge Bases" in Azure AI Search. + """ + if self._knowledge_base_initialized or not self._index_client: + return + + # Runtime validation for agentic mode parameters + if not self.knowledge_base_name: + raise ValueError("knowledge_base_name is required for agentic mode") + if not self.azure_openai_resource_url: + raise ValueError("azure_openai_resource_url is required for agentic mode") + if not self.azure_openai_deployment_name: + raise ValueError("model_deployment_name is required for agentic mode") + + knowledge_base_name = self.knowledge_base_name + + # Step 1: Create or get knowledge source + knowledge_source_name = f"{self.index_name}-source" + + try: + # Try to get existing knowledge source + await self._index_client.get_knowledge_source(knowledge_source_name) + except ResourceNotFoundError: + # Create new knowledge source if it doesn't exist + knowledge_source = SearchIndexKnowledgeSource( + name=knowledge_source_name, + description=f"Knowledge source for {self.index_name} search index", + search_index_parameters=SearchIndexKnowledgeSourceParameters( + search_index_name=self.index_name, + ), + ) + await self._index_client.create_knowledge_source(knowledge_source) + + # Step 2: Create or update Knowledge Base + # Always create/update to ensure configuration is current + aoai_params = AzureOpenAIVectorizerParameters( + resource_url=self.azure_openai_resource_url, + deployment_name=self.azure_openai_deployment_name, + model_name=self.model_name, + api_key=self.azure_openai_api_key, + ) + + # Map output mode string to SDK enum + output_mode = ( + KnowledgeRetrievalOutputMode.EXTRACTIVE_DATA + if self.knowledge_base_output_mode == "extractive_data" + else KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS + ) + + # Map reasoning effort string to SDK class + reasoning_effort_map: dict[str, KnowledgeRetrievalReasoningEffort] = { + "minimal": KnowledgeRetrievalMinimalReasoningEffort(), + "medium": KnowledgeRetrievalMediumReasoningEffort(), + "low": KnowledgeRetrievalLowReasoningEffort(), + } + reasoning_effort = reasoning_effort_map[self.retrieval_reasoning_effort] + + knowledge_base = KnowledgeBase( + name=knowledge_base_name, + description=f"Knowledge Base for multi-hop retrieval across {self.index_name}", + knowledge_sources=[ + KnowledgeSourceReference( + name=knowledge_source_name, + ) + ], + models=[KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=aoai_params)], + output_mode=output_mode, + retrieval_reasoning_effort=reasoning_effort, + ) + await self._index_client.create_or_update_knowledge_base(knowledge_base) + + self._knowledge_base_initialized = True + + # Create retrieval client now that Knowledge Base is initialized + if _agentic_retrieval_available and self._retrieval_client is None: + self._retrieval_client = KnowledgeBaseRetrievalClient( + endpoint=self.endpoint, + knowledge_base_name=knowledge_base_name, + credential=self.credential, + ) + + async def _agentic_search(self, messages: list[ChatMessage]) -> list[str]: + """Perform agentic retrieval with multi-hop reasoning using Knowledge Bases. + + This mode uses query planning and is slightly slower than semantic search, + but provides more accurate results through intelligent retrieval. + + This method uses Azure AI Search Knowledge Bases which: + 1. Analyze the query and plan sub-queries + 2. Retrieve relevant documents across multiple sources + 3. Perform multi-hop reasoning with an LLM + 4. Synthesize a comprehensive answer with references + + Args: + messages: Conversation history to use for retrieval context. + + Returns: + List of answer parts from the Knowledge Base, one per content item. + """ + # Ensure Knowledge Base is initialized + await self._ensure_knowledge_base() + + # Map reasoning effort string to SDK class (for retrieval requests) + reasoning_effort_map: dict[str, KBRetrievalReasoningEffort] = { + "minimal": KBRetrievalMinimalReasoningEffort(), + "medium": KBRetrievalMediumReasoningEffort(), + "low": KBRetrievalLowReasoningEffort(), + } + reasoning_effort = reasoning_effort_map[self.retrieval_reasoning_effort] + + # Map output mode string to SDK enum (for retrieval requests) + output_mode = ( + KBRetrievalOutputMode.EXTRACTIVE_DATA + if self.knowledge_base_output_mode == "extractive_data" + else KBRetrievalOutputMode.ANSWER_SYNTHESIS + ) + + # For minimal reasoning, use intents API; for medium/low, use messages API + if self.retrieval_reasoning_effort == "minimal": + # Minimal reasoning uses intents with a single search query + query = "\n".join(msg.text for msg in messages if msg.text) + intents: list[KnowledgeRetrievalIntent] = [KnowledgeRetrievalSemanticIntent(search=query)] + retrieval_request = KnowledgeBaseRetrievalRequest( + intents=intents, + retrieval_reasoning_effort=reasoning_effort, + output_mode=output_mode, + include_activity=True, + ) + else: + # Medium/low reasoning uses messages with conversation history + kb_messages = [ + KnowledgeBaseMessage( + role=msg.role.value if hasattr(msg.role, "value") else str(msg.role), + content=[KnowledgeBaseMessageTextContent(text=msg.text)], + ) + for msg in messages + if msg.text + ] + retrieval_request = KnowledgeBaseRetrievalRequest( + messages=kb_messages, + retrieval_reasoning_effort=reasoning_effort, + output_mode=output_mode, + include_activity=True, + ) + + # Use reusable retrieval client + if not self._retrieval_client: + raise RuntimeError("Retrieval client not initialized. Ensure Knowledge Base is set up correctly.") + + # Perform retrieval via Knowledge Base + retrieval_result = await self._retrieval_client.retrieve(retrieval_request=retrieval_request) + + # Extract answer parts from response + if retrieval_result.response and len(retrieval_result.response) > 0: + # Get the assistant's response (last message) + assistant_message = retrieval_result.response[-1] + if assistant_message.content: + # Extract all text content items as separate parts + answer_parts: list[str] = [] + for content_item in assistant_message.content: + # Check if this is a text content item + if isinstance(content_item, KnowledgeBaseMessageTextContent) and content_item.text: + answer_parts.append(content_item.text) + + if answer_parts: + return answer_parts + + # Fallback if no answer generated + return ["No results found from Knowledge Base."] + + def _extract_document_text(self, doc: dict[str, Any], doc_id: str | None = None) -> str: + """Extract readable text from a search document with optional citation. + + Args: + doc: Search result document. + doc_id: Optional document ID for citation. + + Returns: + Formatted document text with citation if doc_id provided. + """ + # Try common text field names + text = "" + for field in ["content", "text", "description", "body", "chunk"]: + if doc.get(field): + text = str(doc[field]) + break + + # Fallback: concatenate all string fields + if not text: + text_parts: list[str] = [] + for key, value in doc.items(): + if isinstance(value, str) and not key.startswith("@") and key != "id": + text_parts.append(f"{key}: {value}") + text = " | ".join(text_parts) if text_parts else "" + + # Add citation if document ID provided + if doc_id and text: + return f"[Source: {doc_id}] {text}" + return text diff --git a/python/packages/aisearch/pyproject.toml b/python/packages/aisearch/pyproject.toml new file mode 100644 index 00000000000..5d64b398aa5 --- /dev/null +++ b/python/packages/aisearch/pyproject.toml @@ -0,0 +1,91 @@ +[project] +name = "agent-framework-aisearch" +description = "Azure AI Search integration for Microsoft Agent Framework." +authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] +readme = "README.md" +requires-python = ">=3.10" +version = "1.0.0b251118" +license-files = ["LICENSE"] +urls.homepage = "https://aka.ms/agent-framework" +urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" +urls.release_notes = "https://github.com/microsoft/agent-framework/releases?q=tag%3Apython-1&expanded=true" +urls.issues = "https://github.com/microsoft/agent-framework/issues" +classifiers = [ + "License :: OSI Approved :: MIT License", + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Typing :: Typed", +] +dependencies = [ + "agent-framework-core", + "azure-search-documents==11.7.0b2", +] + +[tool.uv] +prerelease = "if-necessary-or-explicit" +environments = [ + "sys_platform == 'darwin'", + "sys_platform == 'linux'", + "sys_platform == 'win32'" +] + +[tool.uv-dynamic-versioning] +fallback-version = "0.0.0" + +[tool.pytest.ini_options] +testpaths = 'tests' +addopts = "-ra -q -r fEX" +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "function" +filterwarnings = [ + "ignore:Support for class-based `config` is deprecated:DeprecationWarning:pydantic.*" +] +timeout = 120 + +[tool.ruff] +extend = "../../pyproject.toml" +exclude = ["examples"] + +[tool.coverage.run] +omit = [ + "**/__init__.py" +] + +[tool.pyright] +extends = "../../pyproject.toml" +exclude = ['tests'] + +[tool.mypy] +plugins = ['pydantic.mypy'] +strict = true +python_version = "3.10" +ignore_missing_imports = true +disallow_untyped_defs = true +no_implicit_optional = true +check_untyped_defs = true +warn_return_any = true +show_error_codes = true +warn_unused_ignores = false +disallow_incomplete_defs = true +disallow_untyped_decorators = true + +[tool.bandit] +targets = ["agent_framework_aisearch"] +exclude_dirs = ["tests"] + +[tool.poe] +executor.type = "uv" +include = "../../shared_tasks.toml" +[tool.poe.tasks] +mypy = "mypy --config-file $POE_ROOT/pyproject.toml agent_framework_aisearch" +test = "pytest --cov=agent_framework_aisearch --cov-report=term-missing:skip-covered tests" + +[build-system] +requires = ["flit-core >= 3.11,<4.0"] +build-backend = "flit_core.buildapi" diff --git a/python/packages/aisearch/tests/test_search_provider.py b/python/packages/aisearch/tests/test_search_provider.py new file mode 100644 index 00000000000..6813c3d16a8 --- /dev/null +++ b/python/packages/aisearch/tests/test_search_provider.py @@ -0,0 +1,992 @@ +# Copyright (c) Microsoft. All rights reserved. +# pyright: reportPrivateUsage=false + +import os +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from agent_framework import ChatMessage, Context, Role +from agent_framework.azure import AzureAISearchContextProvider +from agent_framework.exceptions import ServiceInitializationError +from azure.core.credentials import AzureKeyCredential +from azure.core.exceptions import ResourceNotFoundError + +from agent_framework_aisearch import AzureAISearchSettings + + +@pytest.fixture +def mock_search_client() -> AsyncMock: + """Create a mock SearchClient.""" + mock_client = AsyncMock() + mock_client.search = AsyncMock() + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock() + return mock_client + + +@pytest.fixture +def mock_index_client() -> AsyncMock: + """Create a mock SearchIndexClient.""" + mock_client = AsyncMock() + mock_client.get_knowledge_source = AsyncMock() + mock_client.create_knowledge_source = AsyncMock() + mock_client.get_agent = AsyncMock() + mock_client.create_agent = AsyncMock() + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock() + return mock_client + + +@pytest.fixture +def sample_messages() -> list[ChatMessage]: + """Create sample chat messages for testing.""" + return [ + ChatMessage(role=Role.USER, text="What is in the documents?"), + ] + + +class TestAzureAISearchSettings: + """Test AzureAISearchSettings configuration.""" + + def test_settings_with_direct_values(self) -> None: + """Test settings with direct values.""" + settings = AzureAISearchSettings( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + ) + assert settings.endpoint == "https://test.search.windows.net" + assert settings.index_name == "test-index" + # api_key is now SecretStr + assert settings.api_key.get_secret_value() == "test-key" + + def test_settings_with_env_file_path(self) -> None: + """Test settings with env_file_path parameter.""" + settings = AzureAISearchSettings( + endpoint="https://test.search.windows.net", + index_name="test-index", + env_file_path="test.env", + ) + assert settings.endpoint == "https://test.search.windows.net" + assert settings.index_name == "test-index" + + def test_provider_uses_settings_from_env(self) -> None: + """Test that provider creates settings internally from env.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + ) + assert provider.endpoint == "https://test.search.windows.net" + assert provider.index_name == "test-index" + + def test_provider_missing_endpoint_raises_error(self) -> None: + """Test that provider raises ServiceInitializationError without endpoint.""" + # Use patch.dict to clear environment and pass env_file_path="" to prevent .env file loading + clean_env = {k: v for k, v in os.environ.items() if not k.startswith("AZURE_SEARCH_")} + with ( + patch.dict(os.environ, clean_env, clear=True), + pytest.raises(ServiceInitializationError, match="endpoint is required"), + ): + AzureAISearchContextProvider( + index_name="test-index", + api_key="test-key", + env_file_path="", # Disable .env file loading + ) + + def test_provider_missing_index_name_raises_error(self) -> None: + """Test that provider raises ServiceInitializationError without index_name.""" + # Use patch.dict to clear environment and pass env_file_path="" to prevent .env file loading + clean_env = {k: v for k, v in os.environ.items() if not k.startswith("AZURE_SEARCH_")} + with ( + patch.dict(os.environ, clean_env, clear=True), + pytest.raises(ServiceInitializationError, match="index name is required"), + ): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + api_key="test-key", + env_file_path="", # Disable .env file loading + ) + + def test_provider_missing_credential_raises_error(self) -> None: + """Test that provider raises ServiceInitializationError without credential.""" + # Use patch.dict to clear environment and pass env_file_path="" to prevent .env file loading + clean_env = {k: v for k, v in os.environ.items() if not k.startswith("AZURE_SEARCH_")} + with ( + patch.dict(os.environ, clean_env, clear=True), + pytest.raises(ServiceInitializationError, match="credential is required"), + ): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + env_file_path="", # Disable .env file loading + ) + + +class TestSearchProviderInitialization: + """Test initialization and configuration of AzureAISearchContextProvider.""" + + def test_init_semantic_mode_minimal(self) -> None: + """Test initialization with minimal semantic mode parameters.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + assert provider.endpoint == "https://test.search.windows.net" + assert provider.index_name == "test-index" + assert provider.mode == "semantic" + assert provider.top_k == 5 + + def test_init_semantic_mode_with_vector_field_requires_embedding_function(self) -> None: + """Test that vector_field_name requires embedding_function.""" + with pytest.raises(ValueError, match="embedding_function is required"): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + vector_field_name="embedding", + ) + + def test_init_agentic_mode_requires_azure_openai_resource_url(self) -> None: + """Test that agentic mode requires azure_openai_resource_url.""" + with pytest.raises(ValueError, match="azure_openai_resource_url"): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + ) + + def test_init_agentic_mode_requires_model_deployment_name(self) -> None: + """Test that agentic mode requires model_deployment_name.""" + with pytest.raises(ValueError, match="model_deployment_name"): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + def test_init_agentic_mode_requires_knowledge_base_name(self) -> None: + """Test that agentic mode requires knowledge_base_name.""" + with pytest.raises(ValueError, match="knowledge_base_name"): + AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + def test_init_agentic_mode_with_all_params(self) -> None: + """Test initialization with all agentic mode parameters.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="my-gpt-4o-deployment", + model_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + assert provider.mode == "agentic" + assert provider.azure_ai_project_endpoint == "https://test.services.ai.azure.com" + assert provider.azure_openai_resource_url == "https://test.openai.azure.com" + assert provider.azure_openai_deployment_name == "my-gpt-4o-deployment" + assert provider.model_name == "gpt-4o" + assert provider.knowledge_base_name == "test-kb" + + def test_init_model_name_defaults_to_deployment_name(self) -> None: + """Test that model_name defaults to deployment_name if not provided.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + assert provider.model_name == "gpt-4o" + + def test_init_with_custom_context_prompt(self) -> None: + """Test initialization with custom context prompt.""" + custom_prompt = "Use the following information:" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + context_prompt=custom_prompt, + ) + assert provider.context_prompt == custom_prompt + + def test_init_uses_default_context_prompt(self) -> None: + """Test that default context prompt is used when not provided.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + assert provider.context_prompt == provider._DEFAULT_SEARCH_CONTEXT_PROMPT + + +class TestSemanticSearch: + """Test semantic search functionality.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_semantic_search_basic( + self, mock_search_class: MagicMock, sample_messages: list[ChatMessage] + ) -> None: + """Test basic semantic search without vector search.""" + # Setup mock + mock_search_client = AsyncMock() + mock_results = AsyncMock() + mock_results.__aiter__.return_value = iter([{"content": "Test document content"}]) + mock_search_client.search.return_value = mock_results + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + context = await provider.invoking(sample_messages) + + assert isinstance(context, Context) + assert len(context.messages) > 1 # First message is prompt, rest are results + # First message should be the context prompt + assert "Use the following context" in context.messages[0].text + # Second message should contain the search result + assert "Test document content" in context.messages[1].text + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_semantic_search_empty_query(self, mock_search_class: MagicMock) -> None: + """Test that empty queries return empty context.""" + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Empty message + context = await provider.invoking([ChatMessage(role=Role.USER, text="")]) + + assert isinstance(context, Context) + assert len(context.messages) == 0 + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_semantic_search_with_vector_query( + self, mock_search_class: MagicMock, sample_messages: list[ChatMessage] + ) -> None: + """Test semantic search with vector query.""" + # Setup mock + mock_search_client = AsyncMock() + mock_results = AsyncMock() + mock_results.__aiter__.return_value = iter([{"content": "Vector search result"}]) + mock_search_client.search.return_value = mock_results + mock_search_class.return_value = mock_search_client + + # Mock embedding function + async def mock_embed(text: str) -> list[float]: + return [0.1, 0.2, 0.3] + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + vector_field_name="embedding", + embedding_function=mock_embed, + ) + + context = await provider.invoking(sample_messages) + + assert isinstance(context, Context) + assert len(context.messages) > 0 + # Verify that search was called + mock_search_client.search.assert_called_once() + + +class TestKnowledgeBaseSetup: + """Test Knowledge Base setup for agentic mode.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_ensure_knowledge_base_creates_when_not_exists( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that Knowledge Base is created when it doesn't exist.""" + # Setup mocks + mock_index_client = AsyncMock() + mock_index_client.get_knowledge_source.side_effect = ResourceNotFoundError("Not found") + mock_index_client.create_knowledge_source = AsyncMock() + mock_index_client.get_knowledge_base.side_effect = ResourceNotFoundError("Not found") + mock_index_client.create_or_update_knowledge_base = AsyncMock() + mock_index_class.return_value = mock_index_client + + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + model_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + await provider._ensure_knowledge_base() + + # Verify knowledge source was created + mock_index_client.create_knowledge_source.assert_called_once() + # Verify Knowledge Base was created + mock_index_client.create_or_update_knowledge_base.assert_called_once() + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_ensure_knowledge_base_skips_when_exists( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that Knowledge Base setup is skipped when already exists.""" + # Setup mocks + mock_index_client = AsyncMock() + mock_index_client.get_knowledge_source.return_value = MagicMock() # Exists + mock_index_client.get_knowledge_base.return_value = MagicMock() # Exists + mock_index_class.return_value = mock_index_client + + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + await provider._ensure_knowledge_base() + + # Verify nothing was created + mock_index_client.create_knowledge_source.assert_not_called() + mock_index_client.create_agent.assert_not_called() + + +class TestContextProviderLifecycle: + """Test context provider lifecycle methods.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_context_manager(self, mock_search_class: MagicMock) -> None: + """Test that provider can be used as async context manager.""" + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + async with AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) as provider: + assert provider is not None + assert isinstance(provider, AzureAISearchContextProvider) + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.KnowledgeBaseRetrievalClient") + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_context_manager_agentic_cleanup( + self, mock_search_class: MagicMock, mock_index_class: MagicMock, mock_retrieval_class: MagicMock + ) -> None: + """Test that agentic mode provider cleans up retrieval client.""" + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + mock_index_client = AsyncMock() + mock_index_class.return_value = mock_index_client + + mock_retrieval_client = AsyncMock() + mock_retrieval_client.close = AsyncMock() + mock_retrieval_class.return_value = mock_retrieval_client + + async with AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) as provider: + # Simulate retrieval client being created + provider._retrieval_client = mock_retrieval_client + + # Verify cleanup was called + mock_retrieval_client.close.assert_called_once() + + def test_string_api_key_conversion(self) -> None: + """Test that string api_key is converted to AzureKeyCredential.""" + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="my-api-key", # String api_key + mode="semantic", + ) + assert isinstance(provider.credential, AzureKeyCredential) + + +class TestMessageFiltering: + """Test message filtering functionality.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_filters_non_user_assistant_messages(self, mock_search_class: MagicMock) -> None: + """Test that only USER and ASSISTANT messages are processed.""" + # Setup mock + mock_search_client = AsyncMock() + mock_results = AsyncMock() + mock_results.__aiter__.return_value = iter([{"content": "Test result"}]) + mock_search_client.search.return_value = mock_results + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Mix of message types + messages = [ + ChatMessage(role=Role.SYSTEM, text="System message"), + ChatMessage(role=Role.USER, text="User message"), + ChatMessage(role=Role.ASSISTANT, text="Assistant message"), + ChatMessage(role=Role.TOOL, text="Tool message"), + ] + + context = await provider.invoking(messages) + + # Should have processed only USER and ASSISTANT messages + assert isinstance(context, Context) + mock_search_client.search.assert_called_once() + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_filters_empty_messages(self, mock_search_class: MagicMock) -> None: + """Test that empty/whitespace messages are filtered out.""" + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Messages with empty/whitespace text + messages = [ + ChatMessage(role=Role.USER, text=""), + ChatMessage(role=Role.USER, text=" "), + ChatMessage(role=Role.USER, text=None), + ] + + context = await provider.invoking(messages) + + # Should return empty context + assert len(context.messages) == 0 + + +class TestCitations: + """Test citation functionality.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_citations_included_in_semantic_search(self, mock_search_class: MagicMock) -> None: + """Test that citations are included in semantic search results.""" + # Setup mock with document ID + mock_search_client = AsyncMock() + mock_results = AsyncMock() + mock_doc = {"id": "doc123", "content": "Test document content"} + mock_results.__aiter__.return_value = iter([mock_doc]) + mock_search_client.search.return_value = mock_results + mock_search_class.return_value = mock_search_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + context = await provider.invoking([ChatMessage(role=Role.USER, text="test query")]) + + # Check that citation is included + assert isinstance(context, Context) + assert len(context.messages) > 1 # First message is prompt, rest are results + # Citation should be in the result message (second message) + assert "[Source: doc123]" in context.messages[1].text + assert "Test document content" in context.messages[1].text + + +class TestAgenticSearch: + """Test agentic search functionality.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.KnowledgeBaseRetrievalClient") + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_agentic_search_basic( + self, + mock_search_class: MagicMock, + mock_index_class: MagicMock, + mock_retrieval_class: MagicMock, + sample_messages: list[ChatMessage], + ) -> None: + """Test basic agentic search with Knowledge Base retrieval.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index client mock + mock_index_client = AsyncMock() + mock_index_client.get_knowledge_source.side_effect = ResourceNotFoundError("Not found") + mock_index_client.create_knowledge_source = AsyncMock() + mock_index_client.create_or_update_knowledge_base = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Setup retrieval client mock with response + mock_retrieval_client = AsyncMock() + mock_response = MagicMock() + mock_message = MagicMock() + mock_content = MagicMock() + mock_content.text = "Agentic search result" + # Make it pass isinstance check + from agent_framework_aisearch._search_provider import _agentic_retrieval_available + + if _agentic_retrieval_available: + from azure.search.documents.knowledgebases.models import KnowledgeBaseMessageTextContent + + mock_content.__class__ = KnowledgeBaseMessageTextContent + mock_message.content = [mock_content] + mock_response.response = [mock_message] + mock_retrieval_client.retrieve.return_value = mock_response + mock_retrieval_client.close = AsyncMock() + mock_retrieval_class.return_value = mock_retrieval_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + context = await provider.invoking(sample_messages) + + assert isinstance(context, Context) + # Should have at least the prompt message + assert len(context.messages) >= 1 + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.KnowledgeBaseRetrievalClient") + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_agentic_search_no_results( + self, + mock_search_class: MagicMock, + mock_index_class: MagicMock, + mock_retrieval_class: MagicMock, + sample_messages: list[ChatMessage], + ) -> None: + """Test agentic search when no results are returned.""" + # Setup mocks + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + mock_index_client = AsyncMock() + mock_index_client.get_knowledge_source.side_effect = ResourceNotFoundError("Not found") + mock_index_client.create_knowledge_source = AsyncMock() + mock_index_client.create_or_update_knowledge_base = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Empty response + mock_retrieval_client = AsyncMock() + mock_response = MagicMock() + mock_response.response = [] + mock_retrieval_client.retrieve.return_value = mock_response + mock_retrieval_client.close = AsyncMock() + mock_retrieval_class.return_value = mock_retrieval_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + ) + + context = await provider.invoking(sample_messages) + + assert isinstance(context, Context) + # Should have fallback message + assert len(context.messages) >= 1 + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.KnowledgeBaseRetrievalClient") + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_agentic_search_with_medium_reasoning( + self, + mock_search_class: MagicMock, + mock_index_class: MagicMock, + mock_retrieval_class: MagicMock, + sample_messages: list[ChatMessage], + ) -> None: + """Test agentic search with medium reasoning effort.""" + # Setup mocks + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + mock_index_client = AsyncMock() + mock_index_client.get_knowledge_source.side_effect = ResourceNotFoundError("Not found") + mock_index_client.create_knowledge_source = AsyncMock() + mock_index_client.create_or_update_knowledge_base = AsyncMock() + mock_index_class.return_value = mock_index_client + + mock_retrieval_client = AsyncMock() + mock_response = MagicMock() + mock_message = MagicMock() + mock_content = MagicMock() + mock_content.text = "Medium reasoning result" + from agent_framework_aisearch._search_provider import _agentic_retrieval_available + + if _agentic_retrieval_available: + from azure.search.documents.knowledgebases.models import KnowledgeBaseMessageTextContent + + mock_content.__class__ = KnowledgeBaseMessageTextContent + mock_message.content = [mock_content] + mock_response.response = [mock_message] + mock_retrieval_client.retrieve.return_value = mock_response + mock_retrieval_client.close = AsyncMock() + mock_retrieval_class.return_value = mock_retrieval_client + + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="agentic", + azure_ai_project_endpoint="https://test.services.ai.azure.com", + model_deployment_name="gpt-4o", + knowledge_base_name="test-kb", + azure_openai_resource_url="https://test.openai.azure.com", + retrieval_reasoning_effort="medium", # Test medium reasoning + ) + + context = await provider.invoking(sample_messages) + + assert isinstance(context, Context) + assert len(context.messages) >= 1 + + +class TestVectorFieldAutoDiscovery: + """Test vector field auto-discovery functionality.""" + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_auto_discovers_single_vector_field( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that single vector field is auto-discovered.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index client mock + mock_index_client = AsyncMock() + mock_index = MagicMock() + + # Create mock field with vector_search_dimensions attribute + mock_vector_field = MagicMock() + mock_vector_field.name = "embedding_vector" + mock_vector_field.vector_search_dimensions = 1536 + + mock_index.fields = [mock_vector_field] + mock_index_client.get_index.return_value = mock_index + mock_index_client.close = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Create provider without specifying vector_field_name + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Trigger auto-discovery + await provider._auto_discover_vector_field() + + # Vector field should be auto-discovered but not used without embedding function + assert provider._auto_discovered_vector_field is True + # Should be cleared since no embedding function + assert provider.vector_field_name is None + + @pytest.mark.asyncio + async def test_vector_detection_accuracy(self) -> None: + """Test that vector field detection logic correctly identifies vector fields.""" + from azure.search.documents.indexes.models import SearchField + + # Create real SearchField objects to test the detection logic + vector_field = SearchField( + name="embedding_vector", type="Collection(Edm.Single)", vector_search_dimensions=1536, searchable=True + ) + + string_field = SearchField(name="content", type="Edm.String", searchable=True) + + number_field = SearchField(name="price", type="Edm.Double", filterable=True) + + # Test detection logic directly + is_vector_1 = vector_field.vector_search_dimensions is not None and vector_field.vector_search_dimensions > 0 + is_vector_2 = string_field.vector_search_dimensions is not None and string_field.vector_search_dimensions > 0 + is_vector_3 = number_field.vector_search_dimensions is not None and number_field.vector_search_dimensions > 0 + + # Only the vector field should be detected + assert is_vector_1 is True + assert is_vector_2 is False + assert is_vector_3 is False + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_no_false_positives_on_string_fields( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that regular string fields are not detected as vector fields.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index with only string fields (no vectors) + mock_index_client = AsyncMock() + mock_index = MagicMock() + + # All fields have vector_search_dimensions = None + mock_fields = [] + for name in ["id", "title", "content", "category"]: + field = MagicMock() + field.name = name + field.vector_search_dimensions = None + field.vector_search_profile_name = None + mock_fields.append(field) + + mock_index.fields = mock_fields + mock_index_client.get_index.return_value = mock_index + mock_index_client.close = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Create provider + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Trigger auto-discovery + await provider._auto_discover_vector_field() + + # Should NOT detect any vector fields + assert provider.vector_field_name is None + assert provider._auto_discovered_vector_field is True + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_multiple_vector_fields_without_vectorizer( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that multiple vector fields without vectorizer logs warning and uses keyword search.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index with multiple vector fields (no vectorizers) + mock_index_client = AsyncMock() + mock_index = MagicMock() + + # Multiple vector fields + mock_fields = [] + for name in ["embedding1", "embedding2"]: + field = MagicMock() + field.name = name + field.vector_search_dimensions = 1536 + field.vector_search_profile_name = None # No vectorizer + mock_fields.append(field) + + mock_index.fields = mock_fields + mock_index.vector_search = None # No vector search config + mock_index_client.get_index.return_value = mock_index + mock_index_client.close = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Create provider + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Trigger auto-discovery + await provider._auto_discover_vector_field() + + # Should NOT use any vector field (multiple fields, can't choose) + assert provider.vector_field_name is None + assert provider._auto_discovered_vector_field is True + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_multiple_vectorizable_fields( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that multiple vectorizable fields logs warning and uses keyword search.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index with multiple vectorizable fields + mock_index_client = AsyncMock() + mock_index = MagicMock() + + # Multiple vector fields with vectorizers + mock_fields = [] + for name in ["embedding1", "embedding2"]: + field = MagicMock() + field.name = name + field.vector_search_dimensions = 1536 + field.vector_search_profile_name = f"{name}-profile" + mock_fields.append(field) + + mock_index.fields = mock_fields + + # Setup vector search config with profiles that have vectorizers + mock_profile1 = MagicMock() + mock_profile1.name = "embedding1-profile" + mock_profile1.vectorizer_name = "vectorizer1" + + mock_profile2 = MagicMock() + mock_profile2.name = "embedding2-profile" + mock_profile2.vectorizer_name = "vectorizer2" + + mock_index.vector_search = MagicMock() + mock_index.vector_search.profiles = [mock_profile1, mock_profile2] + + mock_index_client.get_index.return_value = mock_index + mock_index_client.close = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Create provider + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Trigger auto-discovery + await provider._auto_discover_vector_field() + + # Should NOT use any vector field (multiple vectorizable fields, can't choose) + assert provider.vector_field_name is None + assert provider._auto_discovered_vector_field is True + + @pytest.mark.asyncio + @patch("agent_framework_aisearch._search_provider.SearchIndexClient") + @patch("agent_framework_aisearch._search_provider.SearchClient") + async def test_single_vectorizable_field_detected( + self, mock_search_class: MagicMock, mock_index_class: MagicMock + ) -> None: + """Test that single vectorizable field is auto-detected for server-side vectorization.""" + # Setup search client mock + mock_search_client = AsyncMock() + mock_search_class.return_value = mock_search_client + + # Setup index with single vectorizable field + mock_index_client = AsyncMock() + mock_index = MagicMock() + + # Single vector field with vectorizer + mock_field = MagicMock() + mock_field.name = "embedding" + mock_field.vector_search_dimensions = 1536 + mock_field.vector_search_profile_name = "embedding-profile" + + mock_index.fields = [mock_field] + + # Setup vector search config with profile that has vectorizer + mock_profile = MagicMock() + mock_profile.name = "embedding-profile" + mock_profile.vectorizer_name = "openai-vectorizer" + + mock_index.vector_search = MagicMock() + mock_index.vector_search.profiles = [mock_profile] + + mock_index_client.get_index.return_value = mock_index + mock_index_client.close = AsyncMock() + mock_index_class.return_value = mock_index_client + + # Create provider + provider = AzureAISearchContextProvider( + endpoint="https://test.search.windows.net", + index_name="test-index", + api_key="test-key", + mode="semantic", + ) + + # Trigger auto-discovery + await provider._auto_discover_vector_field() + + # Should detect the vectorizable field + assert provider.vector_field_name == "embedding" + assert provider._auto_discovered_vector_field is True + assert provider._use_vectorizable_query is True # Server-side vectorization diff --git a/python/packages/anthropic/pyproject.toml b/python/packages/anthropic/pyproject.toml index cad575a2872..4911c08b2c1 100644 --- a/python/packages/anthropic/pyproject.toml +++ b/python/packages/anthropic/pyproject.toml @@ -4,7 +4,7 @@ description = "Anthropic integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/azure-ai/agent_framework_azure_ai/_client.py b/python/packages/azure-ai/agent_framework_azure_ai/_client.py index 2fa54ea565c..c5c198bce53 100644 --- a/python/packages/azure-ai/agent_framework_azure_ai/_client.py +++ b/python/packages/azure-ai/agent_framework_azure_ai/_client.py @@ -310,8 +310,8 @@ async def prepare_options( return run_options async def initialize_client(self) -> None: - """Initialize OpenAI client asynchronously.""" - self.client = await self.project_client.get_openai_client() # type: ignore + """Initialize OpenAI client.""" + self.client = self.project_client.get_openai_client() # type: ignore def _update_agent_name(self, agent_name: str | None) -> None: """Update the agent name in the chat client. diff --git a/python/packages/azure-ai/pyproject.toml b/python/packages/azure-ai/pyproject.toml index 6272e6fb417..bd6ea232502 100644 --- a/python/packages/azure-ai/pyproject.toml +++ b/python/packages/azure-ai/pyproject.toml @@ -4,7 +4,7 @@ description = "Azure AI Foundry integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" @@ -24,7 +24,7 @@ classifiers = [ ] dependencies = [ "agent-framework-core", - "azure-ai-projects >= 2.0.0b1", + "azure-ai-projects >= 2.0.0b2", "azure-ai-agents == 1.2.0b5", "aiohttp", ] diff --git a/python/packages/azure-ai/tests/test_azure_ai_client.py b/python/packages/azure-ai/tests/test_azure_ai_client.py index ead970785d9..9aaf0b3f77f 100644 --- a/python/packages/azure-ai/tests/test_azure_ai_client.py +++ b/python/packages/azure-ai/tests/test_azure_ai_client.py @@ -268,7 +268,7 @@ async def test_azure_ai_client_initialize_client(mock_project_client: MagicMock) client = create_test_azure_ai_client(mock_project_client) mock_openai_client = MagicMock() - mock_project_client.get_openai_client = AsyncMock(return_value=mock_openai_client) + mock_project_client.get_openai_client = MagicMock(return_value=mock_openai_client) await client.initialize_client() diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_app.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_app.py index bb7c4398fc9..e0bc3ba51a1 100644 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_app.py +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_app.py @@ -16,27 +16,29 @@ from agent_framework import AgentProtocol, get_logger from ._callbacks import AgentResponseCallbackProtocol +from ._constants import ( + DEFAULT_MAX_POLL_RETRIES, + DEFAULT_POLL_INTERVAL_SECONDS, + MIMETYPE_APPLICATION_JSON, + MIMETYPE_TEXT_PLAIN, + REQUEST_RESPONSE_FORMAT_JSON, + REQUEST_RESPONSE_FORMAT_TEXT, + THREAD_ID_FIELD, + THREAD_ID_HEADER, + WAIT_FOR_RESPONSE_FIELD, + WAIT_FOR_RESPONSE_HEADER, +) +from ._durable_agent_state import DurableAgentState from ._entities import create_agent_entity from ._errors import IncomingRequestError from ._models import AgentSessionId, RunRequest from ._orchestration import AgentOrchestrationContextType, DurableAIAgent -from ._state import AgentState logger = get_logger("agent_framework.azurefunctions") -THREAD_ID_FIELD: str = "thread_id" -RESPONSE_FORMAT_JSON: str = "json" -RESPONSE_FORMAT_TEXT: str = "text" -WAIT_FOR_RESPONSE_FIELD: str = "wait_for_response" -WAIT_FOR_RESPONSE_HEADER: str = "x-ms-wait-for-response" - - EntityHandler = Callable[[df.DurableEntityContext], None] HandlerT = TypeVar("HandlerT", bound=Callable[..., Any]) -DEFAULT_MAX_POLL_RETRIES: int = 30 -DEFAULT_POLL_INTERVAL_SECONDS: float = 1.0 - if TYPE_CHECKING: class DFAppBase: @@ -317,11 +319,11 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien """ logger.debug(f"[HTTP Trigger] Received request on route: /api/agents/{agent_name}/run") - response_format: str = RESPONSE_FORMAT_JSON + request_response_format: str = REQUEST_RESPONSE_FORMAT_JSON thread_id: str | None = None try: - req_body, message, response_format = self._parse_incoming_request(req) + req_body, message, request_response_format = self._parse_incoming_request(req) thread_id = self._resolve_thread_id(req=req, req_body=req_body) wait_for_response = self._should_wait_for_response(req=req, req_body=req_body) @@ -334,7 +336,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload={"error": "Message is required"}, status_code=400, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) @@ -351,6 +353,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien message, thread_id, correlation_id, + request_response_format, ) logger.debug("Signalling entity %s with request: %s", entity_instance_id, run_request) await client.signal_entity(entity_instance_id, "run_agent", run_request) @@ -370,7 +373,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload=result, status_code=200 if result.get("status") == "success" else 500, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) @@ -383,7 +386,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload=accepted_response, status_code=202, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) @@ -392,7 +395,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload={"error": str(exc)}, status_code=exc.status_code, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) except ValueError as exc: @@ -400,7 +403,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload={"error": "Invalid JSON"}, status_code=400, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) except Exception as exc: @@ -408,7 +411,7 @@ async def http_start(req: func.HttpRequest, client: df.DurableOrchestrationClien return self._create_http_response( payload={"error": str(exc)}, status_code=500, - response_format=response_format, + request_response_format=request_response_format, thread_id=thread_id, ) @@ -466,7 +469,7 @@ def health_check(req: func.HttpRequest) -> func.HttpResponse: return func.HttpResponse( json.dumps({"status": "healthy", "agents": agent_info, "agent_count": len(self.agents)}), status_code=200, - mimetype="application/json", + mimetype=MIMETYPE_APPLICATION_JSON, ) _ = health_check @@ -491,7 +494,7 @@ async def _read_cached_state( self, client: df.DurableOrchestrationClient, entity_instance_id: df.EntityId, - ) -> AgentState | None: + ) -> DurableAgentState | None: state_response = await client.read_entity_state(entity_instance_id) if not state_response or not state_response.entity_exists: return None @@ -502,9 +505,7 @@ async def _read_cached_state( typed_state_payload = cast(dict[str, Any], state_payload) - agent_state = AgentState() - agent_state.restore_state(typed_state_payload) - return agent_state + return DurableAgentState.from_dict(typed_state_payload) async def _get_response_from_entity( self, @@ -580,31 +581,58 @@ async def _poll_entity_for_response( return result - async def _build_timeout_result(self, message: str, thread_id: str, correlation_id: str) -> dict[str, Any]: - """Create the timeout response.""" - return { - "response": "Agent is still processing or timed out...", + def _build_response_payload( + self, + *, + response: str | None, + message: str, + thread_id: str, + status: str, + correlation_id: str, + extra_fields: dict[str, Any] | None = None, + ) -> dict[str, Any]: + """Create a consistent response structure and allow optional extra fields.""" + payload = { + "response": response, "message": message, THREAD_ID_FIELD: thread_id, - "status": "timeout", + "status": status, "correlation_id": correlation_id, } + if extra_fields: + payload.update(extra_fields) + return payload + + async def _build_timeout_result(self, message: str, thread_id: str, correlation_id: str) -> dict[str, Any]: + """Create the timeout response.""" + return self._build_response_payload( + response="Agent is still processing or timed out...", + message=message, + thread_id=thread_id, + status="timeout", + correlation_id=correlation_id, + ) def _build_success_result( - self, response_data: dict[str, Any], message: str, thread_id: str, correlation_id: str, state: AgentState + self, response_data: dict[str, Any], message: str, thread_id: str, correlation_id: str, state: DurableAgentState ) -> dict[str, Any]: """Build the success result returned to the HTTP caller.""" - return { - "response": response_data.get("content"), - "message": message, - THREAD_ID_FIELD: thread_id, - "status": "success", - "message_count": response_data.get("message_count", state.message_count), - "correlation_id": correlation_id, - } + return self._build_response_payload( + response=response_data.get("content"), + message=message, + thread_id=thread_id, + status="success", + correlation_id=correlation_id, + extra_fields={"message_count": response_data.get("message_count", state.message_count)}, + ) def _build_request_data( - self, req_body: dict[str, Any], message: str, thread_id: str, correlation_id: str + self, + req_body: dict[str, Any], + message: str, + thread_id: str, + correlation_id: str, + request_response_format: str, ) -> dict[str, Any]: """Create the durable entity request payload.""" enable_tool_calls_value = req_body.get("enable_tool_calls") @@ -613,6 +641,7 @@ def _build_request_data( return RunRequest( message=message, role=req_body.get("role"), + request_response_format=request_response_format, response_format=req_body.get("response_format"), enable_tool_calls=enable_tool_calls, thread_id=thread_id, @@ -621,23 +650,23 @@ def _build_request_data( def _build_accepted_response(self, message: str, thread_id: str, correlation_id: str) -> dict[str, Any]: """Build the response returned when not waiting for completion.""" - return { - "response": "Agent request accepted", - "message": message, - THREAD_ID_FIELD: thread_id, - "status": "accepted", - "correlation_id": correlation_id, - } + return self._build_response_payload( + response="Agent request accepted", + message=message, + thread_id=thread_id, + status="accepted", + correlation_id=correlation_id, + ) def _create_http_response( self, payload: dict[str, Any] | str, status_code: int, - response_format: str, + request_response_format: str, thread_id: str | None, ) -> func.HttpResponse: """Create the HTTP response using helper serializers for clarity.""" - if response_format == RESPONSE_FORMAT_TEXT: + if request_response_format == REQUEST_RESPONSE_FORMAT_TEXT: return self._build_plain_text_response(payload=payload, status_code=status_code, thread_id=thread_id) return self._build_json_response(payload=payload, status_code=status_code) @@ -650,13 +679,13 @@ def _build_plain_text_response( ) -> func.HttpResponse: """Return a plain-text response with optional thread identifier header.""" body_text = payload if isinstance(payload, str) else self._convert_payload_to_text(payload) - headers = {"x-ms-thread-id": thread_id} if thread_id is not None else None - return func.HttpResponse(body_text, status_code=status_code, mimetype="text/plain", headers=headers) + headers = {THREAD_ID_HEADER: thread_id} if thread_id is not None else None + return func.HttpResponse(body_text, status_code=status_code, mimetype=MIMETYPE_TEXT_PLAIN, headers=headers) def _build_json_response(self, payload: dict[str, Any] | str, status_code: int) -> func.HttpResponse: """Return the JSON response, serializing dictionaries as needed.""" body_json = payload if isinstance(payload, str) else json.dumps(payload) - return func.HttpResponse(body_json, status_code=status_code, mimetype="application/json") + return func.HttpResponse(body_json, status_code=status_code, mimetype=MIMETYPE_APPLICATION_JSON) def _convert_payload_to_text(self, payload: dict[str, Any]) -> str: """Convert a structured payload into a human-readable text response.""" @@ -702,18 +731,19 @@ def _parse_incoming_request(self, req: func.HttpRequest) -> tuple[dict[str, Any] normalized_content_type = self._extract_content_type(headers) body_parser, body_format = self._select_body_parser(normalized_content_type) prefers_json = self._accepts_json_response(headers) - response_format = self._select_response_format(body_format=body_format, prefers_json=prefers_json) + request_response_format = self._select_request_response_format( + body_format=body_format, prefers_json=prefers_json + ) req_body, message = body_parser(req) - return req_body, message, response_format + return req_body, message, request_response_format def _extract_normalized_headers(self, req: func.HttpRequest) -> dict[str, str]: """Create a lowercase header mapping from the incoming request.""" headers: dict[str, str] = {} raw_headers = req.headers if isinstance(raw_headers, Mapping): - header_mapping: Mapping[str, Any] = cast(Mapping[str, Any], raw_headers) - for key, value in header_mapping.items(): + for key, value in raw_headers.items(): if value is not None: headers[str(key).lower()] = str(value) return headers @@ -729,9 +759,9 @@ def _select_body_parser( normalized_content_type: str, ) -> tuple[Callable[[func.HttpRequest], tuple[dict[str, Any], str]], str]: """Choose the body parser and declared body format.""" - if normalized_content_type in {"application/json"} or normalized_content_type.endswith("+json"): - return self._parse_json_body, RESPONSE_FORMAT_JSON - return self._parse_text_body, RESPONSE_FORMAT_TEXT + if normalized_content_type in {MIMETYPE_APPLICATION_JSON} or normalized_content_type.endswith("+json"): + return self._parse_json_body, REQUEST_RESPONSE_FORMAT_JSON + return self._parse_text_body, REQUEST_RESPONSE_FORMAT_TEXT @staticmethod def _accepts_json_response(headers: dict[str, str]) -> bool: @@ -742,16 +772,16 @@ def _accepts_json_response(headers: dict[str, str]) -> bool: for value in accept_header.split(","): media_type = value.split(";")[0].strip().lower() - if media_type == "application/json": + if media_type == MIMETYPE_APPLICATION_JSON: return True return False @staticmethod - def _select_response_format(body_format: str, prefers_json: bool) -> str: + def _select_request_response_format(body_format: str, prefers_json: bool) -> str: """Combine body format and accept preference to determine response format.""" - if body_format == RESPONSE_FORMAT_JSON or prefers_json: - return RESPONSE_FORMAT_JSON - return RESPONSE_FORMAT_TEXT + if body_format == REQUEST_RESPONSE_FORMAT_JSON or prefers_json: + return REQUEST_RESPONSE_FORMAT_JSON + return REQUEST_RESPONSE_FORMAT_TEXT @staticmethod def _parse_json_body(req: func.HttpRequest) -> tuple[dict[str, Any], str]: diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_callbacks.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_callbacks.py index 31b31111acc..3e38cdb6ece 100644 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_callbacks.py +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_callbacks.py @@ -6,8 +6,6 @@ invoked during durable entity execution. """ -from __future__ import annotations - from dataclasses import dataclass from typing import Protocol diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_constants.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_constants.py new file mode 100644 index 00000000000..8c4cded1962 --- /dev/null +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_constants.py @@ -0,0 +1,19 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Constants for Azure Functions Agent Framework integration.""" + +# Supported request/response formats and MIME types +REQUEST_RESPONSE_FORMAT_JSON: str = "json" +REQUEST_RESPONSE_FORMAT_TEXT: str = "text" +MIMETYPE_APPLICATION_JSON: str = "application/json" +MIMETYPE_TEXT_PLAIN: str = "text/plain" + +# Field and header names +THREAD_ID_FIELD: str = "thread_id" +THREAD_ID_HEADER: str = "x-ms-thread-id" +WAIT_FOR_RESPONSE_FIELD: str = "wait_for_response" +WAIT_FOR_RESPONSE_HEADER: str = "x-ms-wait-for-response" + +# Polling configuration +DEFAULT_MAX_POLL_RETRIES: int = 30 +DEFAULT_POLL_INTERVAL_SECONDS: float = 1.0 diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_durable_agent_state.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_durable_agent_state.py new file mode 100644 index 00000000000..73695e61f23 --- /dev/null +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_durable_agent_state.py @@ -0,0 +1,1192 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Durable agent state management conforming to the durable-agent-entity-state.json schema. + +This module provides classes for managing conversation state in Azure Durable Functions agents. +It implements the versioned schema that defines how agent conversations are persisted and restored +across invocations, enabling stateful, long-running agent sessions. + +The module includes: +- DurableAgentState: Root state container with schema version and conversation history +- DurableAgentStateEntry and subclasses: Request and response entries in conversation history +- DurableAgentStateMessage: Individual messages with role, content items, and metadata +- Content type classes: Specialized types for text, function calls, errors, and other content +- Serialization/deserialization: Conversion between Python objects and JSON schema format + +The state structure follows this hierarchy: + DurableAgentState + └── DurableAgentStateData + └── conversationHistory: List[DurableAgentStateEntry] + ├── DurableAgentStateRequest (user/system messages) + └── DurableAgentStateResponse (assistant messages with usage stats) + └── messages: List[DurableAgentStateMessage] + └── contents: List[DurableAgentStateContent subclasses] + +All classes support bidirectional conversion between: +- Durable state format (JSON with camelCase, $type discriminators) +- Agent framework objects (Python objects with snake_case) +""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from enum import Enum +from typing import Any + +from agent_framework import ( + AgentRunResponse, + BaseContent, + ChatMessage, + DataContent, + ErrorContent, + FunctionCallContent, + FunctionResultContent, + HostedFileContent, + HostedVectorStoreContent, + TextContent, + TextReasoningContent, + UriContent, + UsageContent, + UsageDetails, + get_logger, +) +from dateutil import parser as date_parser + +from ._models import RunRequest, _serialize_response_format + +logger = get_logger("agent_framework.azurefunctions.durable_agent_state") + + +def _parse_created_at(value: Any) -> datetime: + """Normalize created_at values coming from persisted durable state.""" + if isinstance(value, datetime): + return value + + if isinstance(value, str): + try: + parsed = date_parser.parse(value) + if isinstance(parsed, datetime): + return parsed + except (ValueError, TypeError): + pass + + return datetime.now(tz=timezone.utc) + + +class DurableAgentStateContent: + """Base class for all content types in durable agent state messages. + + This abstract base class defines the interface for content items that can be + stored in conversation history. Content types include text, function calls, + function results, errors, and other specialized content types defined by the + agent framework. + + Subclasses must implement to_dict() and to_ai_content() to handle conversion + between the durable state representation and the agent framework's content objects. + + Attributes: + extensionData: Optional additional metadata (not serialized per schema) + """ + + extensionData: dict[str, Any] | None = None + type: str = "" + + def to_dict(self) -> dict[str, Any]: + """Serialize this content to a dictionary for JSON storage. + + Returns: + Dictionary representation including $type discriminator and content-specific fields + + Raises: + NotImplementedError: Must be implemented by subclasses + """ + raise NotImplementedError + + def to_ai_content(self) -> Any: + """Convert this durable state content back to an agent framework content object. + + Returns: + An agent framework content object (TextContent, FunctionCallContent, etc.) + + Raises: + NotImplementedError: Must be implemented by subclasses + """ + raise NotImplementedError + + @staticmethod + def from_ai_content(content: Any) -> DurableAgentStateContent: + """Create a durable state content object from an agent framework content object. + + This factory method maps agent framework content types (TextContent, FunctionCallContent, + etc.) to their corresponding durable state representations. Unknown content types are + wrapped in DurableAgentStateUnknownContent. + + Args: + content: An agent framework content object (TextContent, FunctionCallContent, etc.) + + Returns: + The corresponding DurableAgentStateContent subclass instance + """ + # Map AI content type to appropriate DurableAgentStateContent subclass + if isinstance(content, DataContent): + return DurableAgentStateDataContent.from_data_content(content) + if isinstance(content, ErrorContent): + return DurableAgentStateErrorContent.from_error_content(content) + if isinstance(content, FunctionCallContent): + return DurableAgentStateFunctionCallContent.from_function_call_content(content) + if isinstance(content, FunctionResultContent): + return DurableAgentStateFunctionResultContent.from_function_result_content(content) + if isinstance(content, HostedFileContent): + return DurableAgentStateHostedFileContent.from_hosted_file_content(content) + if isinstance(content, HostedVectorStoreContent): + return DurableAgentStateHostedVectorStoreContent.from_hosted_vector_store_content(content) + if isinstance(content, TextContent): + return DurableAgentStateTextContent.from_text_content(content) + if isinstance(content, TextReasoningContent): + return DurableAgentStateTextReasoningContent.from_text_reasoning_content(content) + if isinstance(content, UriContent): + return DurableAgentStateUriContent.from_uri_content(content) + if isinstance(content, UsageContent): + return DurableAgentStateUsageContent.from_usage_content(content) + return DurableAgentStateUnknownContent.from_unknown_content(content) + + +# Core state classes + + +class DurableAgentStateData: + """Container for the core data within durable agent state. + + This class holds the primary data structures for agent conversation state, + including the conversation history (a sequence of request and response entries) + and optional extension data for custom metadata. + + The data structure is nested within DurableAgentState under the "data" property, + conforming to the durable-agent-entity-state.json schema structure. + + Attributes: + conversation_history: Ordered list of conversation entries (requests and responses) + extension_data: Optional dictionary for custom metadata (not part of core schema) + """ + + conversation_history: list[DurableAgentStateEntry] + extension_data: dict[str, Any] | None + + def __init__( + self, + conversation_history: list[DurableAgentStateEntry] | None = None, + extension_data: dict[str, Any] | None = None, + ) -> None: + """Initialize the data container. + + Args: + conversation_history: Initial conversation history (defaults to empty list) + extension_data: Optional custom metadata + """ + self.conversation_history = conversation_history or [] + self.extension_data = extension_data + + def to_dict(self) -> dict[str, Any]: + result: dict[str, Any] = { + "conversationHistory": [entry.to_dict() for entry in self.conversation_history], + } + if self.extension_data is not None: + result["extensionData"] = self.extension_data + return result + + @classmethod + def from_dict(cls, data_dict: dict[str, Any]) -> DurableAgentStateData: + # Restore the conversation history - deserialize entries from dicts to objects + history_data = data_dict.get("conversationHistory", []) + deserialized_history: list[DurableAgentStateEntry] = [] + for entry_dict in history_data: + if isinstance(entry_dict, dict): + # Deserialize based on $type discriminator + entry_type = entry_dict.get("$type") or entry_dict.get("json_type") + if entry_type == DurableAgentStateEntryJsonType.RESPONSE: + deserialized_history.append(DurableAgentStateResponse.from_dict(entry_dict)) + elif entry_type == DurableAgentStateEntryJsonType.REQUEST: + deserialized_history.append(DurableAgentStateRequest.from_dict(entry_dict)) + else: + deserialized_history.append(DurableAgentStateEntry.from_dict(entry_dict)) + else: + # Already an object + deserialized_history.append(entry_dict) + + return cls( + conversation_history=deserialized_history, + extension_data=data_dict.get("extensionData"), + ) + + +class DurableAgentState: + """Manages durable agent state conforming to the durable-agent-entity-state.json schema. + + This class provides the root container for agent conversation state that can be persisted + in Azure Durable Entities. It maintains the conversation history as a sequence of request + and response entries, each with their messages, timestamps, and metadata. + + The state follows a versioned schema (currently 1.0.0) that defines the structure for: + - Request entries: User/system messages with optional response format specifications + - Response entries: Assistant messages with token usage information + - Messages: Individual chat messages with role, content items, and timestamps + - Content items: Text, function calls, function results, errors, and other content types + + State is serialized to JSON with this structure: + { + "schemaVersion": "1.0.0", + "data": { + "conversationHistory": [ + {"$type": "request", "correlationId": "...", "createdAt": "...", "messages": [...]}, + {"$type": "response", "correlationId": "...", "createdAt": "...", "messages": [...], "usage": {...}} + ] + } + } + + Attributes: + data: Container for conversation history and optional extension data + schema_version: Schema version string (defaults to "1.0.0") + """ + + data: DurableAgentStateData + schema_version: str = "1.0.0" + + def __init__(self, schema_version: str = "1.0.0"): + """Initialize a new durable agent state. + + Args: + schema_version: Schema version to use (defaults to "1.0.0") + """ + self.data = DurableAgentStateData() + self.schema_version = schema_version + + def to_dict(self) -> dict[str, Any]: + + return { + "schemaVersion": self.schema_version, + "data": self.data.to_dict(), + } + + def to_json(self) -> str: + return json.dumps(self.to_dict()) + + @classmethod + def from_dict(cls, state: dict[str, Any]) -> DurableAgentState: + """Restore state from a dictionary. + + Args: + state: Dictionary containing schemaVersion and data (full state structure) + """ + schema_version = state.get("schemaVersion") + if schema_version is None: + logger.warning("Resetting state as it is incompatible with the current schema, all history will be lost") + return cls() + + instance = cls(schema_version=state.get("schemaVersion", "1.0.0")) + instance.data = DurableAgentStateData.from_dict(state.get("data", {})) + + return instance + + @classmethod + def from_json(cls, json_str: str) -> DurableAgentState: + try: + obj = json.loads(json_str) + except json.JSONDecodeError as e: + raise ValueError("The durable agent state is not valid JSON.") from e + + return cls.from_dict(obj) + + @property + def message_count(self) -> int: + """Get the count of conversation entries (requests + responses).""" + return len(self.data.conversation_history) + + def try_get_agent_response(self, correlation_id: str) -> dict[str, Any] | None: + """Try to get an agent response by correlation ID. + + This method searches the conversation history for a response entry matching the given + correlation ID and returns a dictionary suitable for HTTP API responses. + + Note: The returned dictionary includes computed properties (message_count) that are + NOT part of the persisted state schema. These are derived values included for backward + compatibility with the HTTP API response format and should not be considered part of + the durable state structure. + + Args: + correlation_id: The correlation ID to search for + + Returns: + Response data dict with 'content', 'message_count', and 'correlationId' if found, + None otherwise + """ + # Search through conversation history for a response with this correlationId + for entry in self.data.conversation_history: + if entry.correlation_id == correlation_id and isinstance(entry, DurableAgentStateResponse): + # Found the entry, extract response data + # Get the text content from assistant messages only + content = "\n".join(message.text for message in entry.messages if message.text is not None) + + return {"content": content, "message_count": self.message_count, "correlationId": correlation_id} + return None + + +class DurableAgentStateEntryJsonType(str, Enum): + """Enum for conversation history entry types. + + Discriminator values for the $type field in DurableAgentStateEntry objects. + """ + + REQUEST = "request" + RESPONSE = "response" + + +class DurableAgentStateEntry: + """Base class for conversation history entries (requests and responses). + + This class represents a single entry in the conversation history. Each entry can be + either a request (user/system messages sent to the agent) or a response (assistant + messages from the agent). The $type discriminator field determines which type of entry + it represents. + + Entries are linked together using correlation IDs, allowing responses to be matched + with their originating requests. + + Common Attributes: + json_type: Discriminator for entry type ("request" or "response") + correlationId: Unique identifier linking requests and responses + created_at: Timestamp when the entry was created + messages: List of messages in this entry + extensionData: Optional additional metadata (not serialized per schema) + + Request-only Attributes: + responseType: Expected response type ("text" or "json") - only for request entries + responseSchema: JSON schema for structured responses - only for request entries + + Response-only Attributes: + usage: Token usage statistics - only for response entries + """ + + json_type: DurableAgentStateEntryJsonType + correlation_id: str | None + created_at: datetime + messages: list[DurableAgentStateMessage] + extension_data: dict[str, Any] | None + + def __init__( + self, + json_type: DurableAgentStateEntryJsonType, + correlation_id: str | None, + created_at: datetime, + messages: list[DurableAgentStateMessage], + extension_data: dict[str, Any] | None = None, + ) -> None: + self.json_type = json_type + self.correlation_id = correlation_id + self.created_at = created_at + self.messages = messages + self.extension_data = extension_data + + def to_dict(self) -> dict[str, Any]: + # Ensure createdAt is never null + created_at_value = self.created_at + if created_at_value is None: + created_at_value = datetime.now(tz=timezone.utc) + + return { + "$type": self.json_type, + "correlationId": self.correlation_id, + "createdAt": created_at_value.isoformat() if isinstance(created_at_value, datetime) else created_at_value, + "messages": [m.to_dict() for m in self.messages], + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> DurableAgentStateEntry: + created_at = _parse_created_at(data.get("created_at")) + + messages = [] + for msg_dict in data.get("messages", []): + if isinstance(msg_dict, dict): + messages.append(DurableAgentStateMessage.from_dict(msg_dict)) + else: + messages.append(msg_dict) + + return cls( + json_type=DurableAgentStateEntryJsonType(data.get("$type", "entry")), + correlation_id=data.get("correlationId", ""), + created_at=created_at, + messages=messages, + extension_data=data.get("extensionData"), + ) + + +class DurableAgentStateRequest(DurableAgentStateEntry): + """Represents a request entry in the durable agent conversation history. + + A request entry captures a user or system message sent to the agent, along with + optional response format specifications. Each request is stored as a separate + entry in the conversation history with a unique correlation ID. + + Attributes: + response_type: Expected response type ("text" or "json") + response_schema: JSON schema for structured responses (when response_type is "json") + correlationId: Unique identifier linking this request to its response + created_at: Timestamp when the request was created + messages: List of messages included in this request + json_type: Always "request" for this class + """ + + response_type: str | None = None + response_schema: dict[str, Any] | None = None + + def __init__( + self, + correlation_id: str | None, + created_at: datetime, + messages: list[DurableAgentStateMessage], + extension_data: dict[str, Any] | None = None, + response_type: str | None = None, + response_schema: dict[str, Any] | None = None, + ) -> None: + super().__init__( + json_type=DurableAgentStateEntryJsonType.REQUEST, + correlation_id=correlation_id, + created_at=created_at, + messages=messages, + extension_data=extension_data, + ) + self.response_type = response_type + self.response_schema = response_schema + + def to_dict(self) -> dict[str, Any]: + data = super().to_dict() + if self.response_type is not None: + data["responseType"] = self.response_type + if self.response_schema is not None: + data["responseSchema"] = self.response_schema + return data + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> DurableAgentStateRequest: + created_at = _parse_created_at(data.get("created_at")) + + messages = [] + for msg_dict in data.get("messages", []): + if isinstance(msg_dict, dict): + messages.append(DurableAgentStateMessage.from_dict(msg_dict)) + else: + messages.append(msg_dict) + + return cls( + correlation_id=data.get("correlationId", ""), + created_at=created_at, + messages=messages, + extension_data=data.get("extensionData"), + response_type=data.get("responseType"), + response_schema=data.get("responseSchema"), + ) + + @staticmethod + def from_run_request(request: RunRequest) -> DurableAgentStateRequest: + # Determine response_type based on response_format + return DurableAgentStateRequest( + correlation_id=request.correlation_id, + messages=[DurableAgentStateMessage.from_run_request(request)], + created_at=datetime.now(tz=timezone.utc), + response_type=request.request_response_format, + response_schema=_serialize_response_format(request.response_format), + ) + + +class DurableAgentStateResponse(DurableAgentStateEntry): + """Represents a response entry in the durable agent conversation history. + + A response entry captures the agent's reply to a user request, including any + assistant messages, tool calls, and token usage information. Each response is + linked to its originating request via a correlation ID. + + Attributes: + usage: Token usage statistics for this response (input, output, and total tokens) + is_error: Flag indicating if this response represents an error (not persisted in schema) + correlation_id: Unique identifier linking this response to its request + created_at: Timestamp when the response was created + messages: List of assistant messages in this response + json_type: Always "response" for this class + """ + + usage: DurableAgentStateUsage | None = None + is_error: bool = False + + def __init__( + self, + correlation_id: str, + created_at: datetime, + messages: list[DurableAgentStateMessage], + extension_data: dict[str, Any] | None = None, + usage: DurableAgentStateUsage | None = None, + is_error: bool = False, + ) -> None: + super().__init__( + json_type=DurableAgentStateEntryJsonType.RESPONSE, + correlation_id=correlation_id, + created_at=created_at, + messages=messages, + extension_data=extension_data, + ) + self.usage = usage + self.is_error = is_error + + def to_dict(self) -> dict[str, Any]: + data = super().to_dict() + if self.usage is not None: + data["usage"] = self.usage.to_dict() + return data + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> DurableAgentStateResponse: + created_at = _parse_created_at(data.get("created_at")) + + messages = [] + for msg_dict in data.get("messages", []): + if isinstance(msg_dict, dict): + messages.append(DurableAgentStateMessage.from_dict(msg_dict)) + else: + messages.append(msg_dict) + + usage_dict = data.get("usage") + usage = None + if usage_dict and isinstance(usage_dict, dict): + usage = DurableAgentStateUsage.from_dict(usage_dict) + elif usage_dict: + usage = usage_dict + + return cls( + correlation_id=data.get("correlationId", ""), + created_at=created_at, + messages=messages, + extension_data=data.get("extensionData"), + usage=usage, + ) + + @staticmethod + def from_run_response(correlation_id: str, response: AgentRunResponse) -> DurableAgentStateResponse: + """Creates a DurableAgentStateResponse from an AgentRunResponse.""" + return DurableAgentStateResponse( + correlation_id=correlation_id, + created_at=_parse_created_at(response.created_at), + messages=[DurableAgentStateMessage.from_chat_message(m) for m in response.messages], + usage=DurableAgentStateUsage.from_usage(response.usage_details), + ) + + def to_run_response(self) -> Any: + """Converts this DurableAgentStateResponse back to an AgentRunResponse.""" + return AgentRunResponse( + created_at=self.created_at.isoformat() if self.created_at else None, + messages=[m.to_chat_message() for m in self.messages], + usage=self.usage.to_usage_details() if self.usage else None, + ) + + +class DurableAgentStateMessage: + """Represents a message within a conversation history entry. + + A message contains the role (user, assistant, system), content items (text, function calls, + tool results, etc.), and optional metadata. Messages are the building blocks of both + request and response entries in the conversation history. + + Attributes: + role: The sender role ("user", "assistant", or "system") + contents: List of content items (text, function calls, errors, etc.) + author_name: Optional name of the message author (typically set for assistant messages) + created_at: Optional timestamp when the message was created + extension_data: Optional additional metadata (not serialized per schema) + """ + + role: str + contents: list[DurableAgentStateContent] + author_name: str | None = None + created_at: datetime | None = None + extension_data: dict[str, Any] | None = None + + def __init__( + self, + role: str, + contents: list[DurableAgentStateContent], + author_name: str | None = None, + created_at: datetime | None = None, + extension_data: dict[str, Any] | None = None, + ) -> None: + self.role = role + self.contents = contents + self.author_name = author_name + self.created_at = created_at + self.extension_data = extension_data + + def to_dict(self) -> dict[str, Any]: + result: dict[str, Any] = { + "role": self.role, + "contents": [ + {"$type": c.to_dict().get("type", "text"), **{k: v for k, v in c.to_dict().items() if k != "type"}} + for c in self.contents + ], + } + # Only include optional fields if they have values + if self.created_at is not None: + result["createdAt"] = self.created_at.isoformat() + if self.author_name is not None: + result["authorName"] = self.author_name + return result + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> DurableAgentStateMessage: + contents: list[DurableAgentStateContent] = [] + for content_dict in data.get("contents", []): + if isinstance(content_dict, dict): + content_type = content_dict.get("$type") + if content_type == DurableAgentStateTextContent.type: + contents.append(DurableAgentStateTextContent(text=content_dict.get("text"))) + elif content_type == DurableAgentStateDataContent.type: + contents.append( + DurableAgentStateDataContent( + uri=content_dict.get("uri", ""), media_type=content_dict.get("mediaType") + ) + ) + elif content_type == DurableAgentStateErrorContent.type: + contents.append( + DurableAgentStateErrorContent( + message=content_dict.get("message"), + error_code=content_dict.get("errorCode"), + details=content_dict.get("details"), + ) + ) + elif content_type == DurableAgentStateFunctionCallContent.type: + contents.append( + DurableAgentStateFunctionCallContent( + call_id=content_dict.get("callId", ""), + name=content_dict.get("name", ""), + arguments=content_dict.get("arguments", {}), + ) + ) + elif content_type == DurableAgentStateFunctionResultContent.type: + contents.append( + DurableAgentStateFunctionResultContent( + call_id=content_dict.get("callId", ""), result=content_dict.get("result") + ) + ) + elif content_type == DurableAgentStateHostedFileContent.type: + contents.append(DurableAgentStateHostedFileContent(file_id=content_dict.get("fileId", ""))) + elif content_type == DurableAgentStateHostedVectorStoreContent.type: + contents.append( + DurableAgentStateHostedVectorStoreContent(vector_store_id=content_dict.get("vectorStoreId", "")) + ) + elif content_type == DurableAgentStateTextReasoningContent.type: + contents.append(DurableAgentStateTextReasoningContent(text=content_dict.get("text"))) + elif content_type == DurableAgentStateUriContent.type: + contents.append( + DurableAgentStateUriContent( + uri=content_dict.get("uri", ""), media_type=content_dict.get("mediaType", "") + ) + ) + elif content_type == DurableAgentStateUsageContent.type: + usage_data = content_dict.get("usage") + if usage_data and isinstance(usage_data, dict): + contents.append( + DurableAgentStateUsageContent(usage=DurableAgentStateUsage.from_dict(usage_data)) + ) + elif content_type == DurableAgentStateUnknownContent.type: + contents.append(DurableAgentStateUnknownContent(content=content_dict.get("content", {}))) + else: + contents.append(content_dict) # type: ignore + + return cls( + role=data.get("role", ""), + contents=contents, + author_name=data.get("authorName"), + created_at=_parse_created_at(data.get("createdAt")), + extension_data=data.get("extensionData"), + ) + + @property + def text(self) -> str: + """Extract text from the contents list.""" + text_parts = [] + for content in self.contents: + if isinstance(content, DurableAgentStateTextContent): + text_parts.append(content.text or "") + return "".join(text_parts) + + @staticmethod + def from_run_request(request: RunRequest) -> DurableAgentStateMessage: + """Converts a RunRequest from the agent framework to a DurableAgentStateMessage. + + Args: + request: RunRequest object with role, message/contents, and metadata + Returns: + DurableAgentStateMessage with converted content items and metadata + """ + return DurableAgentStateMessage( + role=request.role.value, + contents=[DurableAgentStateTextContent(text=request.message)], + created_at=_parse_created_at(request.created_at), + ) + + @staticmethod + def from_chat_message(chat_message: ChatMessage) -> DurableAgentStateMessage: + """Converts an Agent Framework chat message to a durable state message. + + Args: + chat_message: ChatMessage object with role, contents, and metadata to convert + + Returns: + DurableAgentStateMessage with converted content items and metadata + """ + contents_list: list[DurableAgentStateContent] = [ + DurableAgentStateContent.from_ai_content(c) for c in chat_message.contents + ] + + return DurableAgentStateMessage( + role=chat_message.role.value, + contents=contents_list, + author_name=chat_message.author_name, + extension_data=dict(chat_message.additional_properties) if chat_message.additional_properties else None, + ) + + def to_chat_message(self) -> Any: + """Converts this DurableAgentStateMessage back to an agent framework ChatMessage. + + Returns: + ChatMessage object with role, contents, and metadata converted back to agent framework types + """ + # Convert DurableAgentStateContent objects back to agent_framework content objects + ai_contents = [c.to_ai_content() for c in self.contents] + + # Build kwargs for ChatMessage + kwargs: dict[str, Any] = { + "role": self.role, + "contents": ai_contents, + } + + if self.author_name is not None: + kwargs["author_name"] = self.author_name + + if self.extension_data is not None: + kwargs["additional_properties"] = self.extension_data + + return ChatMessage(**kwargs) + + +class DurableAgentStateDataContent(DurableAgentStateContent): + """Represents data content with a URI reference. + + This content type is used to reference data stored at a specific URI location, + optionally with a media type specification. Common use cases include referencing + files, documents, or other data resources. + + Attributes: + uri: URI pointing to the data resource + media_type: Optional MIME type of the data (e.g., "application/json", "text/plain") + """ + + uri: str = "" + media_type: str | None = None + type: str = "data" + + def __init__(self, uri: str, media_type: str | None = None) -> None: + self.uri = uri + self.media_type = media_type + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "uri": self.uri, "mediaType": self.media_type} + + @staticmethod + def from_data_content(content: DataContent) -> DurableAgentStateDataContent: + return DurableAgentStateDataContent(uri=content.uri, media_type=content.media_type) + + def to_ai_content(self) -> DataContent: + return DataContent(uri=self.uri, media_type=self.media_type) + + +class DurableAgentStateErrorContent(DurableAgentStateContent): + """Represents error content in agent responses. + + This content type is used to communicate errors that occurred during agent execution, + including error messages, error codes, and additional details for debugging. + + Attributes: + message: Human-readable error message + error_code: Machine-readable error code or exception type + details: Additional error details or stack trace information + """ + + message: str | None = None + error_code: str | None = None + details: str | None = None + + type: str = "error" + + def __init__(self, message: str | None = None, error_code: str | None = None, details: str | None = None) -> None: + self.message = message + self.error_code = error_code + self.details = details + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "message": self.message, "errorCode": self.error_code, "details": self.details} + + @staticmethod + def from_error_content(content: ErrorContent) -> DurableAgentStateErrorContent: + return DurableAgentStateErrorContent( + message=content.message, error_code=content.error_code, details=content.details + ) + + def to_ai_content(self) -> ErrorContent: + return ErrorContent(message=self.message, error_code=self.error_code, details=self.details) + + +class DurableAgentStateFunctionCallContent(DurableAgentStateContent): + """Represents a function/tool call request from the agent. + + This content type is used when the agent requests execution of a function or tool, + including the function name, arguments, and a unique call identifier for tracking + the call-result pair. + + Attributes: + call_id: Unique identifier for this function call (used to match with results) + name: Name of the function/tool to execute + arguments: Dictionary of argument names to values for the function call + """ + + call_id: str + name: str + arguments: dict[str, Any] + + type: str = "functionCall" + + def __init__(self, call_id: str, name: str, arguments: dict[str, Any]) -> None: + self.call_id = call_id + self.name = name + self.arguments = arguments + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "callId": self.call_id, "name": self.name, "arguments": self.arguments} + + @staticmethod + def from_function_call_content(content: FunctionCallContent) -> DurableAgentStateFunctionCallContent: + # Ensure arguments is a dict; parse string if needed + arguments: dict[str, Any] = {} + if content.arguments: + if isinstance(content.arguments, dict): + arguments = content.arguments + elif isinstance(content.arguments, str): + # Parse JSON string to dict + try: + arguments = json.loads(content.arguments) + except json.JSONDecodeError: + arguments = {} + + return DurableAgentStateFunctionCallContent(call_id=content.call_id, name=content.name, arguments=arguments) + + def to_ai_content(self) -> FunctionCallContent: + return FunctionCallContent(call_id=self.call_id, name=self.name, arguments=self.arguments) + + +class DurableAgentStateFunctionResultContent(DurableAgentStateContent): + """Represents the result of a function/tool call execution. + + This content type is used to communicate the result of executing a function or tool + that was previously requested by the agent. The call_id links this result back to + the original function call request. + + Attributes: + call_id: Unique identifier matching the original function call + result: The return value from the function execution (can be any serializable type) + """ + + call_id: str + result: object | None = None + + type: str = "functionResult" + + def __init__(self, call_id: str, result: Any | None = None) -> None: + self.call_id = call_id + self.result = result + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "callId": self.call_id, "result": self.result} + + @staticmethod + def from_function_result_content(content: FunctionResultContent) -> DurableAgentStateFunctionResultContent: + return DurableAgentStateFunctionResultContent(call_id=content.call_id, result=content.result) + + def to_ai_content(self) -> FunctionResultContent: + return FunctionResultContent(call_id=self.call_id, result=self.result) + + +class DurableAgentStateHostedFileContent(DurableAgentStateContent): + """Represents a reference to a hosted file resource. + + This content type is used to reference files that are hosted by the agent platform + or a file storage service, identified by a unique file ID. + + Attributes: + file_id: Unique identifier for the hosted file + """ + + file_id: str + + type: str = "hostedFile" + + def __init__(self, file_id: str) -> None: + self.file_id = file_id + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "fileId": self.file_id} + + @staticmethod + def from_hosted_file_content(content: HostedFileContent) -> DurableAgentStateHostedFileContent: + return DurableAgentStateHostedFileContent(file_id=content.file_id) + + def to_ai_content(self) -> HostedFileContent: + return HostedFileContent(file_id=self.file_id) + + +class DurableAgentStateHostedVectorStoreContent(DurableAgentStateContent): + """Represents a reference to a hosted vector store resource. + + This content type is used to reference vector stores (used for semantic search + and retrieval-augmented generation) that are hosted by the agent platform, + identified by a unique vector store ID. + + Attributes: + vector_store_id: Unique identifier for the hosted vector store + """ + + vector_store_id: str + + type: str = "hostedVectorStore" + + def __init__(self, vector_store_id: str) -> None: + self.vector_store_id = vector_store_id + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "vectorStoreId": self.vector_store_id} + + @staticmethod + def from_hosted_vector_store_content( + content: HostedVectorStoreContent, + ) -> DurableAgentStateHostedVectorStoreContent: + return DurableAgentStateHostedVectorStoreContent(vector_store_id=content.vector_store_id) + + def to_ai_content(self) -> HostedVectorStoreContent: + return HostedVectorStoreContent(vector_store_id=self.vector_store_id) + + +class DurableAgentStateTextContent(DurableAgentStateContent): + """Represents plain text content in messages. + + This is the most common content type, used for regular text messages from users + and text responses from the agent. + + Attributes: + text: The text content of the message + """ + + type: str = "text" + + def __init__(self, text: str | None) -> None: + self.text = text + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "text": self.text} + + @staticmethod + def from_text_content(content: TextContent) -> DurableAgentStateTextContent: + return DurableAgentStateTextContent(text=content.text) + + def to_ai_content(self) -> TextContent: + return TextContent(text=self.text or "") + + +class DurableAgentStateTextReasoningContent(DurableAgentStateContent): + """Represents reasoning or thought process text from the agent. + + This content type is used to capture the agent's internal reasoning, chain of thought, + or explanation of its decision-making process, separate from the final response text. + + Attributes: + text: The reasoning or thought process text + """ + + type: str = "reasoning" + + def __init__(self, text: str | None) -> None: + self.text = text + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "text": self.text} + + @staticmethod + def from_text_reasoning_content(content: TextReasoningContent) -> DurableAgentStateTextReasoningContent: + return DurableAgentStateTextReasoningContent(text=content.text) + + def to_ai_content(self) -> TextReasoningContent: + return TextReasoningContent(text=self.text or "") + + +class DurableAgentStateUriContent(DurableAgentStateContent): + """Represents content referenced by a URI with media type. + + This content type is used to reference external content via a URI, with an associated + media type to indicate how the content should be interpreted. + + Attributes: + uri: URI pointing to the content resource + media_type: MIME type of the content (e.g., "image/png", "application/pdf") + """ + + uri: str + media_type: str + + type: str = "uri" + + def __init__(self, uri: str, media_type: str) -> None: + self.uri = uri + self.media_type = media_type + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "uri": self.uri, "mediaType": self.media_type} + + @staticmethod + def from_uri_content(content: UriContent) -> DurableAgentStateUriContent: + return DurableAgentStateUriContent(uri=content.uri, media_type=content.media_type) + + def to_ai_content(self) -> UriContent: + return UriContent(uri=self.uri, media_type=self.media_type) + + +class DurableAgentStateUsage: + """Represents token usage statistics for agent responses. + + This class tracks the number of tokens consumed during agent execution, + including input tokens (from the request), output tokens (in the response), + and the total token count. + + Attributes: + input_token_count: Number of tokens in the input/request + output_token_count: Number of tokens in the output/response + total_token_count: Total number of tokens consumed (input + output) + extensionData: Optional additional metadata + """ + + input_token_count: int | None = None + output_token_count: int | None = None + total_token_count: int | None = None + extensionData: dict[str, Any] | None = None + + def __init__( + self, + input_token_count: int | None = None, + output_token_count: int | None = None, + total_token_count: int | None = None, + extensionData: dict[str, Any] | None = None, + ) -> None: + self.input_token_count = input_token_count + self.output_token_count = output_token_count + self.total_token_count = total_token_count + self.extensionData = extensionData + + def to_dict(self) -> dict[str, Any]: + result: dict[str, Any] = { + "inputTokenCount": self.input_token_count, + "outputTokenCount": self.output_token_count, + "totalTokenCount": self.total_token_count, + } + if self.extensionData is not None: + result["extensionData"] = self.extensionData + return result + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> DurableAgentStateUsage: + return cls( + input_token_count=data.get("inputTokenCount"), + output_token_count=data.get("outputTokenCount"), + total_token_count=data.get("totalTokenCount"), + extensionData=data.get("extensionData"), + ) + + @staticmethod + def from_usage(usage: UsageDetails | None) -> DurableAgentStateUsage | None: + if usage is None: + return None + return DurableAgentStateUsage( + input_token_count=usage.input_token_count, + output_token_count=usage.output_token_count, + total_token_count=usage.total_token_count, + ) + + def to_usage_details(self) -> UsageDetails: + # Convert back to AI SDK UsageDetails + return UsageDetails( + input_token_count=self.input_token_count, + output_token_count=self.output_token_count, + total_token_count=self.total_token_count, + ) + + +class DurableAgentStateUsageContent(DurableAgentStateContent): + """Represents token usage information as message content. + + This content type is used to communicate token usage statistics as part of + message content, allowing usage information to be tracked alongside other + content types in the conversation history. + + Attributes: + usage: DurableAgentStateUsage object containing token counts + """ + + usage: DurableAgentStateUsage = DurableAgentStateUsage() + + type: str = "usage" + + def __init__(self, usage: DurableAgentStateUsage) -> None: + self.usage = usage + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "usage": self.usage.to_dict() if hasattr(self.usage, "to_dict") else self.usage} + + @staticmethod + def from_usage_content(content: UsageContent) -> DurableAgentStateUsageContent: + return DurableAgentStateUsageContent(usage=DurableAgentStateUsage.from_usage(content.details)) # type: ignore + + def to_ai_content(self) -> UsageContent: + return UsageContent(details=self.usage.to_usage_details()) + + +class DurableAgentStateUnknownContent(DurableAgentStateContent): + """Represents unknown or unrecognized content types. + + This content type serves as a fallback for content that doesn't match any of the + known content type classes. It preserves the original content object for later + inspection or processing. + + Attributes: + content: The unknown content object + """ + + content: Any + + type: str = "unknown" + + def __init__(self, content: Any) -> None: + self.content = content + + def to_dict(self) -> dict[str, Any]: + return {"$type": self.type, "content": self.content} + + @staticmethod + def from_unknown_content(content: Any) -> DurableAgentStateUnknownContent: + return DurableAgentStateUnknownContent(content=content) + + def to_ai_content(self) -> BaseContent: + if not self.content: + raise Exception("The content is missing and cannot be converted to valid AI content.") + return BaseContent(content=self.content) diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_entities.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_entities.py index 8df8e3f3351..a79269bd4da 100644 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_entities.py +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_entities.py @@ -11,14 +11,30 @@ import inspect import json from collections.abc import AsyncIterable, Callable +from datetime import datetime, timezone from typing import Any, cast import azure.durable_functions as df -from agent_framework import AgentProtocol, AgentRunResponse, AgentRunResponseUpdate, Role, get_logger +from agent_framework import ( + AgentProtocol, + AgentRunResponse, + AgentRunResponseUpdate, + ChatMessage, + ErrorContent, + Role, + get_logger, +) from ._callbacks import AgentCallbackContext, AgentResponseCallbackProtocol +from ._durable_agent_state import ( + DurableAgentState, + DurableAgentStateData, + DurableAgentStateEntry, + DurableAgentStateMessage, + DurableAgentStateRequest, + DurableAgentStateResponse, +) from ._models import AgentResponse, RunRequest -from ._state import AgentState logger = get_logger("agent_framework.azurefunctions.entities") @@ -38,11 +54,11 @@ class AgentEntity: Attributes: agent: The AgentProtocol instance - state: The AgentState managing conversation history + state: The DurableAgentState managing conversation history """ agent: AgentProtocol - state: AgentState + state: DurableAgentState def __init__( self, @@ -56,11 +72,27 @@ def __init__( callback: Optional callback invoked during streaming updates and final responses """ self.agent = agent - self.state = AgentState() + self.state = DurableAgentState() self.callback = callback logger.debug(f"[AgentEntity] Initialized with agent type: {type(agent).__name__}") + def _is_error_response(self, entry: DurableAgentStateEntry) -> bool: + """Check if a conversation history entry is an error response. + + Error responses should be kept in history for tracking but not sent to the agent + since Azure OpenAI doesn't support 'error' content type. + + Args: + entry: A conversation history entry (DurableAgentStateEntry or dict) + + Returns: + True if the entry is a response containing error content, False otherwise + """ + if isinstance(entry, DurableAgentStateResponse): + return entry.is_error + return False + async def run_agent( self, context: df.DurableEntityContext, @@ -94,26 +126,27 @@ async def run_agent( raise ValueError("RunRequest must include a thread_id") if not correlation_id: raise ValueError("RunRequest must include a correlation_id") - role = run_request.role or Role.USER response_format = run_request.response_format enable_tool_calls = run_request.enable_tool_calls - logger.debug(f"[AgentEntity.run_agent] Received message: {message}") - logger.debug(f"[AgentEntity.run_agent] Thread ID: {thread_id}") - logger.debug(f"[AgentEntity.run_agent] Correlation ID: {correlation_id}") - logger.debug(f"[AgentEntity.run_agent] Role: {role.value}") - logger.debug(f"[AgentEntity.run_agent] Enable tool calls: {enable_tool_calls}") - logger.debug(f"[AgentEntity.run_agent] Response format: {'provided' if response_format else 'none'}") + state_request = DurableAgentStateRequest.from_run_request(run_request) + self.state.data.conversation_history.append(state_request) - # Store message in history with role - self.state.add_user_message(message, role=role, correlation_id=correlation_id) - - logger.debug("[AgentEntity.run_agent] Executing agent...") + logger.debug(f"[AgentEntity.run_agent] Received Message: {state_request}") try: logger.debug("[AgentEntity.run_agent] Starting agent invocation") - run_kwargs: dict[str, Any] = {"messages": self.state.get_chat_messages()} + # Build messages from conversation history, excluding error responses + # Error responses are kept in history for tracking but not sent to the agent + chat_messages: list[ChatMessage] = [ + m.to_chat_message() + for entry in self.state.data.conversation_history + if not self._is_error_response(entry) + for m in entry.messages + ] + + run_kwargs: dict[str, Any] = {"messages": chat_messages} if not enable_tool_calls: run_kwargs["tools"] = None if response_format: @@ -133,8 +166,8 @@ async def run_agent( response_text = None structured_response = None - response_str: str | None = None + try: if response_format: try: @@ -156,18 +189,19 @@ async def run_agent( ) response_text = "Error extracting response" + state_response = DurableAgentStateResponse.from_run_response(correlation_id, agent_run_response) + self.state.data.conversation_history.append(state_response) + agent_response = AgentResponse( response=response_text, message=str(message), thread_id=str(thread_id), status="success", - message_count=self.state.message_count, + message_count=len(self.state.data.conversation_history), structured_response=structured_response, ) result = agent_response.to_dict() - content = json.dumps(structured_response) if structured_response else (response_text or "") - self.state.add_assistant_message(content, agent_run_response, correlation_id) logger.debug("[AgentEntity.run_agent] AgentRunResponse stored in conversation history") return result @@ -181,12 +215,28 @@ async def run_agent( logger.error(f"Error type: {type(exc).__name__}") logger.error(f"Full traceback:\n{error_traceback}") + # Create error message + error_message = DurableAgentStateMessage.from_chat_message( + ChatMessage( + role=Role.ASSISTANT, contents=[ErrorContent(message=str(exc), error_code=type(exc).__name__)] + ) + ) + + # Create and store error response in conversation history + error_state_response = DurableAgentStateResponse( + correlation_id=correlation_id, + created_at=datetime.now(tz=timezone.utc), + messages=[error_message], + is_error=True, + ) + self.state.data.conversation_history.append(error_state_response) + error_response = AgentResponse( response=f"Error: {exc!s}", message=str(message), thread_id=str(thread_id), status="error", - message_count=self.state.message_count, + message_count=len(self.state.data.conversation_history), error=str(exc), error_type=type(exc).__name__, ) @@ -333,7 +383,7 @@ def _build_callback_context( def reset(self, context: df.DurableEntityContext) -> None: """Reset the entity state (clear conversation history).""" logger.debug("[AgentEntity.reset] Resetting entity state") - self.state.reset() + self.state.data = DurableAgentStateData(conversation_history=[]) logger.debug("[AgentEntity.reset] State reset complete") @@ -362,7 +412,7 @@ async def _entity_coroutine(context: df.DurableEntityContext) -> None: entity = AgentEntity(agent, callback) if current_state is not None: - entity.state.restore_state(current_state) + entity.state = DurableAgentState.from_dict(current_state) logger.debug( "[entity_function] Restored entity from state (message_count: %s)", entity.state.message_count ) @@ -392,8 +442,9 @@ async def _entity_coroutine(context: df.DurableEntityContext) -> None: logger.error("[entity_function] Unknown operation: %s", operation) context.set_result({"error": f"Unknown operation: {operation}"}) + logger.debug("State dict: %s", entity.state.to_dict()) context.set_state(entity.state.to_dict()) - logger.debug(f"[entity_function] Operation {operation} completed successfully") + logger.info(f"[entity_function] Operation {operation} completed successfully") except Exception as exc: import traceback diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_errors.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_errors.py index b2f96c7ebbc..f4f38d32c39 100644 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_errors.py +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_errors.py @@ -2,8 +2,6 @@ """Custom exception types for the durable agent framework.""" -from __future__ import annotations - class IncomingRequestError(ValueError): """Raised when an incoming HTTP request cannot be parsed or validated.""" diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_models.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_models.py index 015ca407541..19f175a485c 100644 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_models.py +++ b/python/packages/azurefunctions/agent_framework_azurefunctions/_models.py @@ -17,6 +17,8 @@ import azure.durable_functions as df from agent_framework import AgentThread, Role +from ._constants import REQUEST_RESPONSE_FORMAT_TEXT + if TYPE_CHECKING: # pragma: no cover - type checking imports only from pydantic import BaseModel @@ -278,35 +280,43 @@ class RunRequest: Attributes: message: The message to send to the agent + request_response_format: The desired response format (e.g., "text" or "json") role: The role of the message sender (user, system, or assistant) response_format: Optional Pydantic BaseModel type describing the structured response format enable_tool_calls: Whether to enable tool calls for this request thread_id: Optional thread ID for tracking correlation_id: Optional correlation ID for tracking the response to this specific request + created_at: Optional timestamp when the request was created """ message: str + request_response_format: str role: Role = Role.USER response_format: type[BaseModel] | None = None enable_tool_calls: bool = True thread_id: str | None = None correlation_id: str | None = None + created_at: str | None = None def __init__( self, message: str, + request_response_format: str = REQUEST_RESPONSE_FORMAT_TEXT, role: Role | str | None = Role.USER, response_format: type[BaseModel] | None = None, enable_tool_calls: bool = True, thread_id: str | None = None, correlation_id: str | None = None, + created_at: str | None = None, ) -> None: self.message = message self.role = self.coerce_role(role) self.response_format = response_format + self.request_response_format = request_response_format self.enable_tool_calls = enable_tool_calls self.thread_id = thread_id self.correlation_id = correlation_id + self.created_at = created_at @staticmethod def coerce_role(value: Role | str | None) -> Role: @@ -326,13 +336,17 @@ def to_dict(self) -> dict[str, Any]: "message": self.message, "enable_tool_calls": self.enable_tool_calls, "role": self.role.value, + "request_response_format": self.request_response_format, } if self.response_format: result["response_format"] = _serialize_response_format(self.response_format) if self.thread_id: result["thread_id"] = self.thread_id if self.correlation_id: - result["correlation_id"] = self.correlation_id + result["correlationId"] = self.correlation_id + if self.created_at: + result["created_at"] = self.created_at + return result @classmethod @@ -340,11 +354,13 @@ def from_dict(cls, data: dict[str, Any]) -> RunRequest: """Create RunRequest from dictionary.""" return cls( message=data.get("message", ""), + request_response_format=data.get("request_response_format", REQUEST_RESPONSE_FORMAT_TEXT), role=cls.coerce_role(data.get("role")), response_format=_deserialize_response_format(data.get("response_format")), enable_tool_calls=data.get("enable_tool_calls", True), thread_id=data.get("thread_id"), - correlation_id=data.get("correlation_id"), + correlation_id=data.get("correlationId"), + created_at=data.get("created_at"), ) diff --git a/python/packages/azurefunctions/agent_framework_azurefunctions/_state.py b/python/packages/azurefunctions/agent_framework_azurefunctions/_state.py deleted file mode 100644 index c9d54b83330..00000000000 --- a/python/packages/azurefunctions/agent_framework_azurefunctions/_state.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -"""Agent State Management. - -This module defines the AgentState class for managing conversation state and -serializing agent framework responses. -""" - -from collections.abc import MutableMapping -from datetime import datetime, timezone -from typing import Any, cast - -from agent_framework import AgentRunResponse, ChatMessage, Role, get_logger - -logger = get_logger("agent_framework.azurefunctions.state") - - -class AgentState: - """Manages agent conversation state using agent_framework types (ChatMessage, AgentRunResponse). - - This class handles: - - Conversation history tracking using ChatMessage objects - - Agent response storage using AgentRunResponse objects with correlation IDs - - State persistence and restoration - - Message counting - """ - - def __init__(self) -> None: - """Initialize empty agent state.""" - self.conversation_history: list[ChatMessage] = [] - self.last_response: str | None = None - self.message_count: int = 0 - - def _current_timestamp(self) -> str: - """Return an ISO 8601 UTC timestamp.""" - return datetime.now(timezone.utc).isoformat() - - def add_user_message( - self, - content: str, - role: Role = Role.USER, - correlation_id: str | None = None, - ) -> None: - """Add a user message to the conversation history as a ChatMessage object. - - Args: - content: The message content - role: The message role (user, system, etc.) - correlation_id: Optional correlation identifier associated with the user message - """ - self.message_count += 1 - timestamp = self._current_timestamp() - additional_props: MutableMapping[str, Any] = {"timestamp": timestamp} - if correlation_id is not None: - additional_props["correlation_id"] = correlation_id - chat_message = ChatMessage(role=role, text=content, additional_properties=additional_props) - self.conversation_history.append(chat_message) - logger.debug(f"Added {role} ChatMessage to history (message #{self.message_count})") - - def add_assistant_message( - self, content: str, agent_response: AgentRunResponse, correlation_id: str | None = None - ) -> None: - """Add an assistant message to the conversation history with full agent response. - - Args: - content: The text content of the response - agent_response: The AgentRunResponse object from the agent framework - correlation_id: Optional correlation ID for tracking this response - """ - self.last_response = content - timestamp = self._current_timestamp() - serialized_response = self.serialize_response(agent_response) - - # Create a ChatMessage for the assistant response - # The agent_response already contains messages, but we store it as a custom ChatMessage - # with the agent_response stored in additional_properties for full metadata preservation - additional_props: dict[str, Any] = { - "agent_response": serialized_response, - "correlation_id": correlation_id, - "timestamp": timestamp, - "message_count": self.message_count, - } - chat_message = ChatMessage(role="assistant", text=content, additional_properties=additional_props) - - self.conversation_history.append(chat_message) - - logger.debug( - f"Added assistant ChatMessage to history with AgentRunResponse metadata (correlation_id: {correlation_id})" - ) - - def get_chat_messages(self) -> list[ChatMessage]: - """Return a copy of the full conversation history.""" - return list(self.conversation_history) - - def try_get_agent_response(self, correlation_id: str) -> dict[str, Any] | None: - """Get an agent response by correlation ID. - - Args: - correlation_id: The correlation ID to look up - - Returns: - The agent response data if found, None otherwise - """ - for message in reversed(self.conversation_history): - metadata = getattr(message, "additional_properties", {}) or {} - if metadata.get("correlation_id") == correlation_id: - return self._build_agent_response_payload(message, metadata) - - return None - - def serialize_response(self, response: AgentRunResponse) -> dict[str, Any]: - """Serialize an ``AgentRunResponse`` to a dictionary. - - Args: - response: The agent framework response object - - Returns: - Dictionary containing all response fields - """ - try: - return response.to_dict() - except Exception as exc: # pragma: no cover - defensive logging path - logger.warning(f"Error serializing response: {exc}") - return {"response": str(response), "serialization_error": str(exc)} - - def to_dict(self) -> dict[str, Any]: - """Get the current state as a dictionary for persistence. - - Returns: - Dictionary containing conversation_history (as serialized ChatMessages), - last_response, and message_count - """ - return { - "conversation_history": [msg.to_dict() for msg in self.conversation_history], - "last_response": self.last_response, - "message_count": self.message_count, - } - - def restore_state(self, state: dict[str, Any]) -> None: - """Restore state from a dictionary, reconstructing ChatMessage objects. - - Args: - state: Dictionary containing conversation_history, last_response, and message_count - """ - # Restore conversation history as ChatMessage objects - history_data = state.get("conversation_history", []) - restored_history: list[ChatMessage] = [] - for raw_message in history_data: - if isinstance(raw_message, dict): - restored_history.append(ChatMessage.from_dict(cast(dict[str, Any], raw_message))) - else: - restored_history.append(cast(ChatMessage, raw_message)) - - self.conversation_history = restored_history - - self.last_response = state.get("last_response") - self.message_count = state.get("message_count", 0) - logger.debug("Restored state: %s ChatMessages in history", len(self.conversation_history)) - - def reset(self) -> None: - """Reset the state to empty.""" - self.conversation_history = [] - self.last_response = None - self.message_count = 0 - logger.debug("State reset to empty") - - def __repr__(self) -> str: - """String representation of the state.""" - return f"AgentState(messages={self.message_count}, history_length={len(self.conversation_history)})" - - def _build_agent_response_payload(self, message: ChatMessage, metadata: dict[str, Any]) -> dict[str, Any]: - """Construct the agent response payload returned to callers.""" - return { - "content": message.text, - "agent_response": metadata.get("agent_response"), - "message_count": metadata.get("message_count", self.message_count), - "timestamp": metadata.get("timestamp"), - "correlation_id": metadata.get("correlation_id"), - } diff --git a/python/packages/azurefunctions/pyproject.toml b/python/packages/azurefunctions/pyproject.toml index 07695c24d3e..ecc4d8688e5 100644 --- a/python/packages/azurefunctions/pyproject.toml +++ b/python/packages/azurefunctions/pyproject.toml @@ -4,7 +4,7 @@ description = "Azure Functions integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" @@ -27,6 +27,11 @@ dependencies = [ "azure-functions-durable", ] +[dependency-groups] +dev = [ + "types-python-dateutil>=2.9.0", +] + [tool.uv] prerelease = "if-necessary-or-explicit" environments = [ diff --git a/python/packages/azurefunctions/tests/integration_tests/test_01_single_agent.py b/python/packages/azurefunctions/tests/integration_tests/test_01_single_agent.py index f97916825cf..cd93e6a3528 100644 --- a/python/packages/azurefunctions/tests/integration_tests/test_01_single_agent.py +++ b/python/packages/azurefunctions/tests/integration_tests/test_01_single_agent.py @@ -16,6 +16,8 @@ import pytest +from agent_framework_azurefunctions._constants import THREAD_ID_HEADER + from .testutils import SampleTestHelper, skip_if_azure_functions_integration_tests_disabled # Module-level markers - applied to all tests in this file @@ -67,7 +69,7 @@ def test_simple_message_plain_text(self) -> None: # Agent responded with plain text when the request body was text/plain. assert response.text.strip() - assert response.headers.get("x-ms-thread-id") is not None + assert response.headers.get(THREAD_ID_HEADER) is not None def test_thread_id_in_query(self) -> None: """Test using thread_id in query parameter.""" @@ -77,7 +79,7 @@ def test_thread_id_in_query(self) -> None: assert response.status_code in [200, 202] assert response.text.strip() - assert response.headers.get("x-ms-thread-id") == "test-query-thread" + assert response.headers.get(THREAD_ID_HEADER) == "test-query-thread" def test_conversation_continuity(self) -> None: """Test conversation context is maintained across requests.""" @@ -92,7 +94,7 @@ def test_conversation_continuity(self) -> None: if response1.status_code == 200: data1 = response1.json() - assert data1["message_count"] == 1 + assert data1["message_count"] == 2 # Initial + reply # Second message in same session response2 = SampleTestHelper.post_json( @@ -100,7 +102,7 @@ def test_conversation_continuity(self) -> None: ) assert response2.status_code == 200 data2 = response2.json() - assert data2["message_count"] == 2 + assert data2["message_count"] == 4 else: # In async mode, we can't easily test message count # Just verify we can make multiple calls diff --git a/python/packages/azurefunctions/tests/test_app.py b/python/packages/azurefunctions/tests/test_app.py index c11bb873d62..ebf6eef3e6d 100644 --- a/python/packages/azurefunctions/tests/test_app.py +++ b/python/packages/azurefunctions/tests/test_app.py @@ -13,7 +13,13 @@ from agent_framework_azurefunctions import AgentFunctionApp from agent_framework_azurefunctions._app import WAIT_FOR_RESPONSE_FIELD, WAIT_FOR_RESPONSE_HEADER -from agent_framework_azurefunctions._entities import AgentEntity, AgentState, create_agent_entity +from agent_framework_azurefunctions._constants import ( + MIMETYPE_APPLICATION_JSON, + MIMETYPE_TEXT_PLAIN, + THREAD_ID_HEADER, +) +from agent_framework_azurefunctions._durable_agent_state import DurableAgentState +from agent_framework_azurefunctions._entities import AgentEntity, create_agent_entity TFunc = TypeVar("TFunc", bound=Callable[..., Any]) @@ -333,14 +339,14 @@ async def test_entity_run_agent_operation(self) -> None: result = await entity.run_agent( mock_context, - {"message": "Test message", "thread_id": "test-conv-123", "correlation_id": "corr-app-entity-1"}, + {"message": "Test message", "thread_id": "test-conv-123", "correlationId": "corr-app-entity-1"}, ) assert result["status"] == "success" assert result["response"] == "Test response" assert result["message"] == "Test message" assert result["thread_id"] == "test-conv-123" - assert entity.state.message_count == 1 + assert entity.state.message_count == 2 async def test_entity_stores_conversation_history(self) -> None: """Test that the entity stores conversation history.""" @@ -354,18 +360,29 @@ async def test_entity_stores_conversation_history(self) -> None: # Send first message await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-app-entity-2"} + mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-app-entity-2"} + ) + + # Each conversation turn creates 2 entries: request and response + history = entity.state.data.conversation_history[0].messages # Request entry + assert len(history) == 1 # Just the user message + + # Send second message + await entity.run_agent( + mock_context, {"message": "Message 2", "thread_id": "conv-2", "correlationId": "corr-app-entity-2b"} ) - history = entity.state.conversation_history - assert len(history) == 2 # User + assistant + # Now we have 4 entries total (2 requests + 2 responses) + # Access the first request entry + history2 = entity.state.data.conversation_history[2].messages # Second request entry + assert len(history2) == 1 # Just the user message user_msg = history[0] user_role = getattr(user_msg.role, "value", user_msg.role) assert user_role == "user" assert user_msg.text == "Message 1" - assistant_msg = history[1] + assistant_msg = entity.state.data.conversation_history[1].messages[0] assistant_role = getattr(assistant_msg.role, "value", assistant_msg.role) assert assistant_role == "assistant" assert assistant_msg.text == "Response 1" @@ -380,17 +397,17 @@ async def test_entity_increments_message_count(self) -> None: entity = AgentEntity(mock_agent) mock_context = Mock() - assert entity.state.message_count == 0 + assert len(entity.state.data.conversation_history) == 0 await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-app-entity-3a"} + mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-app-entity-3a"} ) - assert entity.state.message_count == 1 + assert len(entity.state.data.conversation_history) == 2 await entity.run_agent( - mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-app-entity-3b"} + mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-app-entity-3b"} ) - assert entity.state.message_count == 2 + assert len(entity.state.data.conversation_history) == 4 def test_entity_reset(self) -> None: """Test that entity reset clears state.""" @@ -398,19 +415,13 @@ def test_entity_reset(self) -> None: entity = AgentEntity(mock_agent) # Set some state - entity.state.message_count = 10 - entity.state.last_response = "Some response" - entity.state.conversation_history = [ - ChatMessage(role="user", text="test", additional_properties={"timestamp": "2024-01-01T00:00:00Z"}) - ] + entity.state = DurableAgentState() # Reset mock_context = Mock() entity.reset(mock_context) - assert entity.state.message_count == 0 - assert entity.state.last_response is None - assert len(entity.state.conversation_history) == 0 + assert len(entity.state.data.conversation_history) == 0 class TestAgentEntityFactory: @@ -438,7 +449,7 @@ def test_entity_function_handles_run_agent_operation(self) -> None: mock_context.get_input.return_value = { "message": "Test message", "thread_id": "conv-123", - "correlation_id": "corr-app-factory-1", + "correlationId": "corr-app-factory-1", } mock_context.get_state.return_value = None @@ -458,9 +469,27 @@ def test_entity_function_handles_reset_operation(self) -> None: mock_context = Mock() mock_context.operation_name = "reset" mock_context.get_state.return_value = { - "message_count": 5, - "conversation_history": [{"role": "user", "content": "test"}], - "last_response": "Test", + "schemaVersion": "1.0.0", + "data": { + "conversationHistory": [ + { + "$type": "request", + "correlationId": "corr-reset-test", + "createdAt": "2024-01-01T00:00:00Z", + "messages": [ + { + "role": "user", + "contents": [ + { + "$type": "text", + "text": "test", + } + ], + } + ], + } + ], + }, } # Execute entity function @@ -497,19 +526,53 @@ def test_entity_function_restores_state(self) -> None: # Mock context with existing state existing_state = { - "message_count": 3, - "conversation_history": [{"role": "user", "content": "msg1"}, {"role": "assistant", "content": "resp1"}], - "last_response": "resp1", + "schemaVersion": "1.0.0", + "data": { + "conversationHistory": [ + { + "$type": "request", + "correlationId": "corr-existing-1", + "createdAt": "2024-01-01T00:00:00Z", + "messages": [ + { + "role": "user", + "contents": [ + { + "$type": "text", + "text": "msg1", + } + ], + } + ], + }, + { + "$type": "response", + "correlationId": "corr-existing-1", + "createdAt": "2024-01-01T00:05:00Z", + "messages": [ + { + "role": "assistant", + "contents": [ + { + "$type": "text", + "text": "resp1", + } + ], + } + ], + }, + ], + }, } mock_context = Mock() mock_context.operation_name = "reset" mock_context.get_state.return_value = existing_state - with patch.object(AgentState, "restore_state") as restore_state_mock: + with patch.object(DurableAgentState, "from_dict", wraps=DurableAgentState.from_dict) as from_dict_mock: entity_function(mock_context) - restore_state_mock.assert_called_once_with(existing_state) + from_dict_mock.assert_called_once_with(existing_state) class TestErrorHandling: @@ -524,7 +587,7 @@ async def test_entity_handles_agent_error(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Test message", "thread_id": "conv-1", "correlation_id": "corr-app-error-1"} + mock_context, {"message": "Test message", "thread_id": "conv-1", "correlationId": "corr-app-error-1"} ) assert result["status"] == "error" @@ -600,7 +663,7 @@ def test_accept_header_prefers_json(self) -> None: app = self._create_app() request = Mock() - request.headers = {"accept": "application/json"} + request.headers = {"accept": MIMETYPE_APPLICATION_JSON} request.params = {} request.get_json.side_effect = ValueError("Invalid JSON") request.get_body.return_value = b"Plain text message" @@ -674,8 +737,8 @@ async def test_http_run_accepts_plain_text(self) -> None: response = await handler(request, client) assert response.status_code == 202 - assert response.mimetype == "text/plain" - assert response.headers.get("x-ms-thread-id") is not None + assert response.mimetype == MIMETYPE_TEXT_PLAIN + assert response.headers.get(THREAD_ID_HEADER) is not None assert response.get_body().decode("utf-8") == "Agent request accepted" signal_args = client.signal_entity.call_args[0] @@ -693,7 +756,7 @@ async def test_http_run_accept_header_returns_json(self) -> None: handler = self._get_run_handler(mock_agent) request = Mock() - request.headers = {WAIT_FOR_RESPONSE_HEADER: "false", "Accept": "application/json"} + request.headers = {WAIT_FOR_RESPONSE_HEADER: "false", "Accept": MIMETYPE_APPLICATION_JSON} request.params = {} request.route_params = {} request.get_json.side_effect = ValueError("Invalid JSON") @@ -704,8 +767,8 @@ async def test_http_run_accept_header_returns_json(self) -> None: response = await handler(request, client) assert response.status_code == 202 - assert response.mimetype == "application/json" - assert response.headers.get("x-ms-thread-id") is None + assert response.mimetype == MIMETYPE_APPLICATION_JSON + assert response.headers.get(THREAD_ID_HEADER) is None body = response.get_body().decode("utf-8") assert '"status": "accepted"' in body @@ -728,8 +791,8 @@ async def test_http_run_rejects_empty_message(self) -> None: response = await handler(request, client) assert response.status_code == 400 - assert response.mimetype == "text/plain" - assert response.headers.get("x-ms-thread-id") is not None + assert response.mimetype == MIMETYPE_TEXT_PLAIN + assert response.headers.get(THREAD_ID_HEADER) is not None assert response.get_body().decode("utf-8") == "Message is required" client.signal_entity.assert_not_called() diff --git a/python/packages/azurefunctions/tests/test_entities.py b/python/packages/azurefunctions/tests/test_entities.py index 5b053efbf33..2f73f1daa82 100644 --- a/python/packages/azurefunctions/tests/test_entities.py +++ b/python/packages/azurefunctions/tests/test_entities.py @@ -15,14 +15,20 @@ from agent_framework import AgentRunResponse, AgentRunResponseUpdate, ChatMessage, Role from pydantic import BaseModel +from agent_framework_azurefunctions._durable_agent_state import ( + DurableAgentState, + DurableAgentStateData, + DurableAgentStateMessage, + DurableAgentStateRequest, + DurableAgentStateTextContent, +) from agent_framework_azurefunctions._entities import AgentEntity, create_agent_entity from agent_framework_azurefunctions._models import RunRequest -from agent_framework_azurefunctions._state import AgentState TFunc = TypeVar("TFunc", bound=Callable[..., Any]) -def _role_value(chat_message: ChatMessage) -> str: +def _role_value(chat_message: DurableAgentStateMessage) -> str: """Helper to extract the string role from a ChatMessage.""" role = getattr(chat_message, "role", None) role_value = getattr(role, "value", role) @@ -71,9 +77,9 @@ def test_init_creates_entity(self) -> None: entity = AgentEntity(mock_agent) assert entity.agent == mock_agent - assert entity.state.conversation_history == [] - assert entity.state.last_response is None - assert entity.state.message_count == 0 + assert len(entity.state.data.conversation_history) == 0 + assert entity.state.data.extension_data is None + assert entity.state.schema_version == "1.0.0" def test_init_stores_agent_reference(self) -> None: """Test that the agent reference is stored correctly.""" @@ -112,7 +118,7 @@ async def test_run_agent_executes_agent(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Test message", "thread_id": "conv-123", "correlation_id": "corr-entity-1"} + mock_context, {"message": "Test message", "thread_id": "conv-123", "correlationId": "corr-entity-1"} ) # Verify agent.run was called @@ -123,8 +129,8 @@ async def test_run_agent_executes_agent(self) -> None: assert len(sent_messages) == 1 sent_message = sent_messages[0] assert isinstance(sent_message, ChatMessage) - assert sent_message.text == "Test message" - assert _role_value(sent_message) == "user" + assert getattr(sent_message, "text", None) == "Test message" + assert getattr(sent_message.role, "value", sent_message.role) == "user" # Verify result assert result["status"] == "success" @@ -158,7 +164,7 @@ async def update_generator() -> AsyncIterator[AgentRunResponseUpdate]: { "message": "Tell me something", "thread_id": "session-1", - "correlation_id": "corr-stream-1", + "correlationId": "corr-stream-1", }, ) @@ -205,7 +211,7 @@ async def test_run_agent_final_callback_without_streaming(self) -> None: { "message": "Hi", "thread_id": "session-2", - "correlation_id": "corr-final-1", + "correlationId": "corr-final-1", }, ) @@ -233,19 +239,20 @@ async def test_run_agent_updates_conversation_history(self) -> None: mock_context = Mock() await entity.run_agent( - mock_context, {"message": "User message", "thread_id": "conv-1", "correlation_id": "corr-entity-2"} + mock_context, {"message": "User message", "thread_id": "conv-1", "correlationId": "corr-entity-2"} ) - # Should have 2 entries: user message + assistant response - history = entity.state.conversation_history + # Should have 1 entry: user message + assistant response + user_history = entity.state.data.conversation_history[0].messages + assistant_history = entity.state.data.conversation_history[1].messages - assert len(history) == 2 + assert len(user_history) == 1 - user_msg = history[0] + user_msg = user_history[0] assert _role_value(user_msg) == "user" assert user_msg.text == "User message" - assistant_msg = history[1] + assistant_msg = assistant_history[0] assert _role_value(assistant_msg) == "assistant" assert assistant_msg.text == "Agent response" @@ -257,41 +264,22 @@ async def test_run_agent_increments_message_count(self) -> None: entity = AgentEntity(mock_agent) mock_context = Mock() - assert entity.state.message_count == 0 + assert len(entity.state.data.conversation_history) == 0 await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-3a"} + mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-entity-3a"} ) - assert entity.state.message_count == 1 + assert len(entity.state.data.conversation_history) == 2 await entity.run_agent( - mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-3b"} + mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-entity-3b"} ) - assert entity.state.message_count == 2 + assert len(entity.state.data.conversation_history) == 4 await entity.run_agent( - mock_context, {"message": "Message 3", "thread_id": "conv-1", "correlation_id": "corr-entity-3c"} + mock_context, {"message": "Message 3", "thread_id": "conv-1", "correlationId": "corr-entity-3c"} ) - assert entity.state.message_count == 3 - - async def test_run_agent_stores_last_response(self) -> None: - """Test that run_agent stores the last response.""" - mock_agent = Mock() - mock_agent.run = AsyncMock(return_value=_agent_response("Response 1")) - - entity = AgentEntity(mock_agent) - mock_context = Mock() - - await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-4a"} - ) - assert entity.state.last_response == "Response 1" - - mock_agent.run = AsyncMock(return_value=_agent_response("Response 2")) - await entity.run_agent( - mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-4b"} - ) - assert entity.state.last_response == "Response 2" + assert len(entity.state.data.conversation_history) == 6 async def test_run_agent_with_none_thread_id(self) -> None: """Test run_agent with a None thread identifier.""" @@ -303,7 +291,7 @@ async def test_run_agent_with_none_thread_id(self) -> None: with pytest.raises(ValueError, match="thread_id"): await entity.run_agent( - mock_context, {"message": "Message", "thread_id": None, "correlation_id": "corr-entity-5"} + mock_context, {"message": "Message", "thread_id": None, "correlationId": "corr-entity-5"} ) async def test_run_agent_handles_response_without_text_attribute(self) -> None: @@ -322,7 +310,7 @@ def text(self) -> str: # type: ignore[override] mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-6"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-6"} ) # Should handle gracefully @@ -338,7 +326,7 @@ async def test_run_agent_handles_none_response_text(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-7"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-7"} ) assert result["status"] == "success" @@ -354,18 +342,18 @@ async def test_run_agent_multiple_conversations(self) -> None: # Send multiple messages await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-8a"} + mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-entity-8a"} ) await entity.run_agent( - mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-8b"} + mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-entity-8b"} ) await entity.run_agent( - mock_context, {"message": "Message 3", "thread_id": "conv-1", "correlation_id": "corr-entity-8c"} + mock_context, {"message": "Message 3", "thread_id": "conv-1", "correlationId": "corr-entity-8c"} ) - history = entity.state.conversation_history + history = entity.state.data.conversation_history assert len(history) == 6 - assert entity.state.message_count == 3 + assert entity.state.message_count == 6 class TestAgentEntityReset: @@ -376,40 +364,47 @@ def test_reset_clears_conversation_history(self) -> None: mock_agent = Mock() entity = AgentEntity(mock_agent) - # Add some history - entity.state.conversation_history = [ - ChatMessage(role="user", text="msg1"), - ChatMessage(role="assistant", text="resp1"), + # Add some history with proper DurableAgentStateEntry objects + entity.state.data.conversation_history = [ + DurableAgentStateRequest( + correlation_id="test-1", + created_at=datetime.now(), + messages=[ + DurableAgentStateMessage( + role="user", + contents=[DurableAgentStateTextContent(text="msg1")], + ) + ], + ), ] mock_context = Mock() entity.reset(mock_context) - assert entity.state.conversation_history == [] + assert entity.state.data.conversation_history == [] - def test_reset_clears_last_response(self) -> None: - """Test that reset clears the last response.""" + def test_reset_with_extension_data(self) -> None: + """Test that reset works when entity has extension data.""" mock_agent = Mock() entity = AgentEntity(mock_agent) - entity.state.last_response = "Some response" + # Set up some initial state with conversation history + entity.state.data = DurableAgentStateData(conversation_history=[], extension_data={"some_key": "some_value"}) mock_context = Mock() entity.reset(mock_context) - assert entity.state.last_response is None + assert len(entity.state.data.conversation_history) == 0 def test_reset_clears_message_count(self) -> None: """Test that reset clears the message count.""" mock_agent = Mock() entity = AgentEntity(mock_agent) - entity.state.message_count = 10 - mock_context = Mock() entity.reset(mock_context) - assert entity.state.message_count == 0 + assert len(entity.state.data.conversation_history) == 0 async def test_reset_after_conversation(self) -> None: """Test reset after a full conversation.""" @@ -421,23 +416,22 @@ async def test_reset_after_conversation(self) -> None: # Have a conversation await entity.run_agent( - mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-10a"} + mock_context, {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-entity-10a"} ) await entity.run_agent( - mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-10b"} + mock_context, {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-entity-10b"} ) # Verify state before reset - assert entity.state.message_count == 2 - assert len(entity.state.conversation_history) == 4 + assert entity.state.message_count == 4 + assert len(entity.state.data.conversation_history) == 4 # Reset entity.reset(mock_context) # Verify state after reset assert entity.state.message_count == 0 - assert len(entity.state.conversation_history) == 0 - assert entity.state.last_response is None + assert len(entity.state.data.conversation_history) == 0 class TestCreateAgentEntity: @@ -464,7 +458,7 @@ def test_entity_function_handles_run_agent(self) -> None: mock_context.get_input.return_value = { "message": "Test message", "thread_id": "conv-123", - "correlation_id": "corr-entity-factory", + "correlationId": "corr-entity-factory", } mock_context.get_state.return_value = None @@ -485,13 +479,22 @@ def test_entity_function_handles_reset(self) -> None: mock_context = Mock() mock_context.operation_name = "reset" mock_context.get_state.return_value = { - "message_count": 5, - "conversation_history": [ - ChatMessage( - role="user", text="test", additional_properties={"timestamp": "2024-01-01T00:00:00Z"} - ).to_dict() - ], - "last_response": "Test", + "schemaVersion": "1.0.0", + "data": { + "conversationHistory": [ + { + "$type": "request", + "correlationId": "test-correlation-id", + "createdAt": "2024-01-01T00:00:00Z", + "messages": [ + { + "role": "user", + "contents": [{"$type": "text", "text": "test"}], + } + ], + } + ] + }, } # Execute @@ -505,9 +508,7 @@ def test_entity_function_handles_reset(self) -> None: # Verify state was cleared assert mock_context.set_state.called state = mock_context.set_state.call_args[0][0] - assert state["message_count"] == 0 - assert state["conversation_history"] == [] - assert state["last_response"] is None + assert state["data"]["conversationHistory"] == [] def test_entity_function_handles_unknown_operation(self) -> None: """Test that the entity function handles unknown operations.""" @@ -547,8 +548,7 @@ def test_entity_function_creates_new_entity_on_first_call(self) -> None: assert result["status"] == "reset" assert mock_context.set_state.called state = mock_context.set_state.call_args[0][0] - assert state["message_count"] == 0 - assert state["conversation_history"] == [] + assert state["data"] == {"conversationHistory": []} def test_entity_function_restores_existing_state(self) -> None: """Test that the entity function restores existing state.""" @@ -557,26 +557,53 @@ def test_entity_function_restores_existing_state(self) -> None: entity_function = create_agent_entity(mock_agent) existing_state = { - "message_count": 5, - "conversation_history": [ - ChatMessage( - role="user", text="msg1", additional_properties={"timestamp": "2024-01-01T00:00:00Z"} - ).to_dict(), - ChatMessage( - role="assistant", text="resp1", additional_properties={"timestamp": "2024-01-01T00:05:00Z"} - ).to_dict(), - ], - "last_response": "resp1", + "schemaVersion": "1.0.0", + "data": { + "conversationHistory": [ + { + "$type": "request", + "correlationId": "corr-existing-1", + "createdAt": "2024-01-01T00:00:00Z", + "messages": [ + { + "role": "user", + "contents": [ + { + "$type": "text", + "text": "msg1", + } + ], + } + ], + }, + { + "$type": "response", + "correlationId": "corr-existing-1", + "createdAt": "2024-01-01T00:05:00Z", + "messages": [ + { + "role": "assistant", + "contents": [ + { + "$type": "text", + "text": "resp1", + } + ], + } + ], + }, + ], + }, } mock_context = Mock() mock_context.operation_name = "reset" mock_context.get_state.return_value = existing_state - with patch.object(AgentState, "restore_state") as restore_state_mock: + with patch.object(DurableAgentState, "from_dict", wraps=DurableAgentState.from_dict) as from_dict_mock: entity_function(mock_context) - restore_state_mock.assert_called_once_with(existing_state) + from_dict_mock.assert_called_once_with(existing_state) class TestErrorHandling: @@ -591,7 +618,7 @@ async def test_run_agent_handles_agent_exception(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-error-1"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-error-1"} ) assert result["status"] == "error" @@ -608,7 +635,7 @@ async def test_run_agent_handles_value_error(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-error-2"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-error-2"} ) assert result["status"] == "error" @@ -624,7 +651,7 @@ async def test_run_agent_handles_timeout_error(self) -> None: mock_context = Mock() result = await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-error-3"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-error-3"} ) assert result["status"] == "error" @@ -659,7 +686,7 @@ async def test_run_agent_preserves_message_on_error(self) -> None: result = await entity.run_agent( mock_context, - {"message": "Test message", "thread_id": "conv-123", "correlation_id": "corr-entity-error-4"}, + {"message": "Test message", "thread_id": "conv-123", "correlationId": "corr-entity-error-4"}, ) # Even on error, message info should be preserved @@ -680,15 +707,15 @@ async def test_conversation_history_has_timestamps(self) -> None: mock_context = Mock() await entity.run_agent( - mock_context, {"message": "Message", "thread_id": "conv-1", "correlation_id": "corr-entity-history-1"} + mock_context, {"message": "Message", "thread_id": "conv-1", "correlationId": "corr-entity-history-1"} ) # Check both user and assistant messages have timestamps - for entry in entity.state.conversation_history: - timestamp = entry.additional_properties.get("timestamp") + for entry in entity.state.data.conversation_history: + timestamp = entry.created_at assert timestamp is not None # Verify timestamp is in ISO format - datetime.fromisoformat(timestamp) + datetime.fromisoformat(str(timestamp)) async def test_conversation_history_ordering(self) -> None: """Test that conversation history maintains the correct order.""" @@ -701,29 +728,30 @@ async def test_conversation_history_ordering(self) -> None: mock_agent.run = AsyncMock(return_value=_agent_response("Response 1")) await entity.run_agent( mock_context, - {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-history-2a"}, + {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-entity-history-2a"}, ) mock_agent.run = AsyncMock(return_value=_agent_response("Response 2")) await entity.run_agent( mock_context, - {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-history-2b"}, + {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-entity-history-2b"}, ) mock_agent.run = AsyncMock(return_value=_agent_response("Response 3")) await entity.run_agent( mock_context, - {"message": "Message 3", "thread_id": "conv-1", "correlation_id": "corr-entity-history-2c"}, + {"message": "Message 3", "thread_id": "conv-1", "correlationId": "corr-entity-history-2c"}, ) # Verify order - history = entity.state.conversation_history - assert history[0].text == "Message 1" - assert history[1].text == "Response 1" - assert history[2].text == "Message 2" - assert history[3].text == "Response 2" - assert history[4].text == "Message 3" - assert history[5].text == "Response 3" + history = entity.state.data.conversation_history + # Each conversation turn creates 2 entries: request and response + assert history[0].messages[0].text == "Message 1" # Request 1 + assert history[1].messages[0].text == "Response 1" # Response 1 + assert history[2].messages[0].text == "Message 2" # Request 2 + assert history[3].messages[0].text == "Response 2" # Response 2 + assert history[4].messages[0].text == "Message 3" # Request 3 + assert history[5].messages[0].text == "Response 3" # Response 3 async def test_conversation_history_role_alternation(self) -> None: """Test that conversation history alternates between user and assistant roles.""" @@ -735,19 +763,20 @@ async def test_conversation_history_role_alternation(self) -> None: await entity.run_agent( mock_context, - {"message": "Message 1", "thread_id": "conv-1", "correlation_id": "corr-entity-history-3a"}, + {"message": "Message 1", "thread_id": "conv-1", "correlationId": "corr-entity-history-3a"}, ) await entity.run_agent( mock_context, - {"message": "Message 2", "thread_id": "conv-1", "correlation_id": "corr-entity-history-3b"}, + {"message": "Message 2", "thread_id": "conv-1", "correlationId": "corr-entity-history-3b"}, ) # Check role alternation - history = entity.state.conversation_history - assert _role_value(history[0]) == "user" - assert _role_value(history[1]) == "assistant" - assert _role_value(history[2]) == "user" - assert _role_value(history[3]) == "assistant" + history = entity.state.data.conversation_history + # Each conversation turn creates 2 entries: request and response + assert history[0].messages[0].role == "user" # Request 1 + assert history[1].messages[0].role == "assistant" # Response 1 + assert history[2].messages[0].role == "user" # Request 2 + assert history[3].messages[0].role == "assistant" # Response 2 class TestRunRequestSupport: @@ -789,7 +818,7 @@ async def test_run_agent_with_dict_request(self) -> None: "thread_id": "conv-456", "role": "system", "enable_tool_calls": False, - "correlation_id": "corr-runreq-2", + "correlationId": "corr-runreq-2", } result = await entity.run_agent(mock_context, request_dict) @@ -828,9 +857,9 @@ async def test_run_agent_stores_role_in_history(self) -> None: await entity.run_agent(mock_context, request) # Check that system role was stored - history = entity.state.conversation_history - assert _role_value(history[0]) == "system" - assert history[0].text == "System message" + history = entity.state.data.conversation_history + assert history[0].messages[0].role == "system" + assert history[0].messages[0].text == "System message" async def test_run_agent_with_response_format(self) -> None: """Test run_agent with a JSON response format.""" @@ -887,7 +916,7 @@ async def test_entity_function_with_run_request_dict(self) -> None: "thread_id": "conv-789", "role": "user", "enable_tool_calls": True, - "correlation_id": "corr-runreq-6", + "correlationId": "corr-runreq-6", } mock_context.get_state.return_value = None diff --git a/python/packages/azurefunctions/tests/test_models.py b/python/packages/azurefunctions/tests/test_models.py index bb802956ff8..5b803ead138 100644 --- a/python/packages/azurefunctions/tests/test_models.py +++ b/python/packages/azurefunctions/tests/test_models.py @@ -295,32 +295,32 @@ def test_round_trip_with_pydantic_response_format(self) -> None: restored = RunRequest.from_dict(data) assert restored.response_format is ModuleStructuredResponse - def test_init_with_correlation_id(self) -> None: - """Test RunRequest initialization with correlation_id.""" + def test_init_with_correlationId(self) -> None: + """Test RunRequest initialization with correlationId.""" request = RunRequest(message="Test message", thread_id="thread-corr-init", correlation_id="corr-123") assert request.message == "Test message" assert request.correlation_id == "corr-123" - def test_to_dict_with_correlation_id(self) -> None: - """Test to_dict includes correlation_id.""" + def test_to_dict_with_correlationId(self) -> None: + """Test to_dict includes correlationId.""" request = RunRequest(message="Test", thread_id="thread-corr-to-dict", correlation_id="corr-456") data = request.to_dict() assert data["message"] == "Test" - assert data["correlation_id"] == "corr-456" + assert data["correlationId"] == "corr-456" - def test_from_dict_with_correlation_id(self) -> None: - """Test from_dict with correlation_id.""" - data = {"message": "Test", "correlation_id": "corr-789", "thread_id": "thread-corr-from-dict"} + def test_from_dict_with_correlationId(self) -> None: + """Test from_dict with correlationId.""" + data = {"message": "Test", "correlationId": "corr-789", "thread_id": "thread-corr-from-dict"} request = RunRequest.from_dict(data) assert request.message == "Test" assert request.correlation_id == "corr-789" assert request.thread_id == "thread-corr-from-dict" - def test_round_trip_with_correlation_id(self) -> None: - """Test round-trip to_dict and from_dict with correlation_id.""" + def test_round_trip_with_correlationId(self) -> None: + """Test round-trip to_dict and from_dict with correlationId.""" original = RunRequest( message="Test message", thread_id="thread-123", diff --git a/python/packages/azurefunctions/tests/test_orchestration.py b/python/packages/azurefunctions/tests/test_orchestration.py index c30f9f0bec1..93201a64e95 100644 --- a/python/packages/azurefunctions/tests/test_orchestration.py +++ b/python/packages/azurefunctions/tests/test_orchestration.py @@ -136,8 +136,8 @@ def test_run_creates_entity_call(self) -> None: assert operation == "run_agent" assert request["message"] == "Test message" assert request["enable_tool_calls"] is True - assert "correlation_id" in request - assert request["correlation_id"] == "correlation-guid" + assert "correlationId" in request + assert request["correlationId"] == "correlation-guid" assert "thread_id" in request assert request["thread_id"] == "thread-guid" @@ -145,7 +145,7 @@ def test_run_without_thread(self) -> None: """Test that run() works without explicit thread (creates unique session key).""" mock_context = Mock() mock_context.instance_id = "test-instance-002" - # Two calls to new_uuid: one for session_key, one for correlation_id + # Two calls to new_uuid: one for session_key, one for correlationId mock_context.new_uuid = Mock(side_effect=["auto-generated-guid", "correlation-guid"]) mock_task = Mock() @@ -164,7 +164,7 @@ def test_run_without_thread(self) -> None: entity_id = call_args[0][0] assert entity_id.name == "dafx-TestAgent" assert entity_id.key == "auto-generated-guid" - # Should be called twice: once for session_key, once for correlation_id + # Should be called twice: once for session_key, once for correlationId assert mock_context.new_uuid.call_count == 2 def test_run_with_response_format(self) -> None: @@ -307,8 +307,8 @@ def test_sequential_agent_calls_simulation(self) -> None: mock_context.instance_id = "test-orchestration-001" # new_uuid will be called 3 times: # 1. thread creation - # 2. correlation_id for first call - # 3. correlation_id for second call + # 2. correlationId for first call + # 3. correlationId for second call mock_context.new_uuid = Mock(side_effect=["deterministic-guid-001", "corr-1", "corr-2"]) # Track entity calls diff --git a/python/packages/azurefunctions/tests/test_state.py b/python/packages/azurefunctions/tests/test_state.py deleted file mode 100644 index 52aa7458f06..00000000000 --- a/python/packages/azurefunctions/tests/test_state.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -"""Unit tests for AgentState correlation ID tracking.""" - -from unittest.mock import Mock - -import pytest -from agent_framework import AgentRunResponse - -from agent_framework_azurefunctions._state import AgentState - - -class TestAgentStateCorrelationId: - """Test suite for AgentState correlation ID tracking.""" - - def _create_mock_response(self, text: str = "Response") -> Mock: - """Create a mock AgentRunResponse with the provided text.""" - mock_response = Mock(spec=AgentRunResponse) - mock_response.to_dict.return_value = {"text": text, "messages": []} - return mock_response - - def test_add_assistant_message_with_correlation_id(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-123-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-123") - message_metadata = state.conversation_history[-1].additional_properties or {} - assert message_metadata.get("correlation_id") == "corr-123" - - response_data = state.try_get_agent_response("corr-123") - assert response_data is not None - assert response_data["content"] == "Response" - assert response_data["agent_response"] == {"text": "Response", "messages": []} - - def test_try_get_agent_response_returns_response(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-200-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-456") - - response_data = state.try_get_agent_response("corr-456") - - assert response_data is not None - assert response_data["content"] == "Response" - - def test_try_get_agent_response_returns_none_for_missing_id(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-300-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-123") - - assert state.try_get_agent_response("non-existent") is None - - def test_multiple_responses_tracked_separately(self) -> None: - state = AgentState() - - for index in range(3): - state.add_user_message(f"Message {index}", correlation_id=f"corr-{index}-request") - state.add_assistant_message( - f"Response {index}", - self._create_mock_response(text=f"Response {index}"), - correlation_id=f"corr-{index}", - ) - - for index in range(3): - payload = state.try_get_agent_response(f"corr-{index}") - assert payload is not None - assert payload["content"] == f"Response {index}" - - def test_add_assistant_message_without_correlation_id(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-400-request") - state.add_assistant_message("Response", self._create_mock_response()) - - assert state.try_get_agent_response("missing") is None - assert state.last_response == "Response" - - def test_to_dict_does_not_duplicate_agent_responses(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-500-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-123") - - state_snapshot = state.to_dict() - - assert "agent_responses" not in state_snapshot - metadata = state_snapshot["conversation_history"][-1]["additional_properties"] - assert metadata["correlation_id"] == "corr-123" - - def test_restore_state_preserves_agent_response_lookup(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-600-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-123") - - restored_state = AgentState() - restored_state.restore_state(state.to_dict()) - - payload = restored_state.try_get_agent_response("corr-123") - assert payload is not None - assert payload["content"] == "Response" - - def test_reset_clears_conversation_history(self) -> None: - state = AgentState() - state.add_user_message("Hello", correlation_id="corr-700-request") - state.add_assistant_message("Response", self._create_mock_response(), correlation_id="corr-123") - - state.reset() - - assert len(state.conversation_history) == 0 - assert state.try_get_agent_response("corr-123") is None - - -if __name__ == "__main__": - pytest.main([__file__, "-v", "--tb=short"]) diff --git a/python/packages/chatkit/pyproject.toml b/python/packages/chatkit/pyproject.toml index c743c817890..1e2e7bdbd8a 100644 --- a/python/packages/chatkit/pyproject.toml +++ b/python/packages/chatkit/pyproject.toml @@ -4,7 +4,7 @@ description = "OpenAI ChatKit integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/chatkit/tests/__init__.py b/python/packages/chatkit/tests/__init__.py deleted file mode 100644 index 2a50eae8941..00000000000 --- a/python/packages/chatkit/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. diff --git a/python/packages/copilotstudio/pyproject.toml b/python/packages/copilotstudio/pyproject.toml index c689a3b0d1d..9251f040667 100644 --- a/python/packages/copilotstudio/pyproject.toml +++ b/python/packages/copilotstudio/pyproject.toml @@ -4,7 +4,7 @@ description = "Copilot Studio integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/core/agent_framework/_agents.py b/python/packages/core/agent_framework/_agents.py index e3ea1bdea67..591d2554904 100644 --- a/python/packages/core/agent_framework/_agents.py +++ b/python/packages/core/agent_framework/_agents.py @@ -5,7 +5,7 @@ import sys from collections.abc import AsyncIterable, Awaitable, Callable, MutableMapping, Sequence from contextlib import AbstractAsyncContextManager, AsyncExitStack -from copy import copy +from copy import deepcopy from itertools import chain from typing import Any, ClassVar, Literal, Protocol, TypeVar, cast, runtime_checkable from uuid import uuid4 @@ -454,13 +454,16 @@ async def agent_wrapper(**kwargs: Any) -> str: # Extract the input from kwargs using the specified arg_name input_text = kwargs.get(arg_name, "") + # Forward all kwargs except the arg_name to support runtime context propagation + forwarded_kwargs = {k: v for k, v in kwargs.items() if k != arg_name} + if stream_callback is None: # Use non-streaming mode - return (await self.run(input_text)).text + return (await self.run(input_text, **forwarded_kwargs)).text # Use streaming mode - accumulate updates and create final response response_updates: list[AgentRunResponseUpdate] = [] - async for update in self.run_stream(input_text): + async for update in self.run_stream(input_text, **forwarded_kwargs): response_updates.append(update) if is_async_callback: await stream_callback(update) # type: ignore[misc] @@ -470,12 +473,14 @@ async def agent_wrapper(**kwargs: Any) -> str: # Create final text from accumulated updates return AgentRunResponse.from_agent_run_response_updates(response_updates).text - return AIFunction( + agent_tool: AIFunction[BaseModel, str] = AIFunction( name=tool_name, description=tool_description, func=agent_wrapper, input_model=input_model, # type: ignore ) + agent_tool._forward_runtime_kwargs = True # type: ignore + return agent_tool def _normalize_messages( self, @@ -589,7 +594,7 @@ def __init__( chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] | None = None, context_providers: ContextProvider | list[ContextProvider] | AggregateContextProvider | None = None, middleware: Middleware | list[Middleware] | None = None, - # chat option params + # chat options allow_multiple_tool_calls: bool | None = None, conversation_id: str | None = None, frequency_penalty: float | None = None, @@ -868,7 +873,9 @@ async def run( user=user, **(additional_chat_options or {}), ) - response = await self.chat_client.get_response(messages=thread_messages, chat_options=co, **kwargs) + # Filter chat_options from kwargs to prevent duplicate keyword argument + filtered_kwargs = {k: v for k, v in kwargs.items() if k != "chat_options"} + response = await self.chat_client.get_response(messages=thread_messages, chat_options=co, **filtered_kwargs) await self._update_thread_with_type_and_conversation_id(thread, response.conversation_id) @@ -1000,9 +1007,11 @@ async def run_stream( **(additional_chat_options or {}), ) + # Filter chat_options from kwargs to prevent duplicate keyword argument + filtered_kwargs = {k: v for k, v in kwargs.items() if k != "chat_options"} response_updates: list[ChatResponseUpdate] = [] async for update in self.chat_client.get_streaming_response( - messages=thread_messages, chat_options=co, **kwargs + messages=thread_messages, chat_options=co, **filtered_kwargs ): response_updates.append(update) @@ -1236,7 +1245,7 @@ async def _prepare_thread_and_messages( Raises: AgentExecutionException: If the conversation IDs on the thread and agent don't match. """ - chat_options = copy(self.chat_options) if self.chat_options else ChatOptions() + chat_options = deepcopy(self.chat_options) if self.chat_options else ChatOptions() thread = thread or self.get_new_thread() if thread.service_thread_id and thread.context_provider: await thread.context_provider.thread_created(thread.service_thread_id) diff --git a/python/packages/core/agent_framework/_clients.py b/python/packages/core/agent_framework/_clients.py index 630e7f8709a..40c13a2037c 100644 --- a/python/packages/core/agent_framework/_clients.py +++ b/python/packages/core/agent_framework/_clients.py @@ -214,6 +214,7 @@ def _merge_chat_options( *, base_chat_options: ChatOptions | Any | None, model_id: str | None = None, + allow_multiple_tool_calls: bool | None = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -239,6 +240,7 @@ def _merge_chat_options( Keyword Args: base_chat_options: Optional base ChatOptions to merge with direct parameters. model_id: The model_id to use for the agent. + allow_multiple_tool_calls: Whether to allow multiple tool calls in a single response. frequency_penalty: The frequency penalty to use. logit_bias: The logit bias to use. max_tokens: The maximum number of tokens to generate. @@ -270,6 +272,7 @@ def _merge_chat_options( return base_chat_options & ChatOptions( model_id=model_id, + allow_multiple_tool_calls=allow_multiple_tool_calls, frequency_penalty=frequency_penalty, logit_bias=logit_bias, max_tokens=max_tokens, @@ -485,6 +488,7 @@ async def get_response( self, messages: str | ChatMessage | list[str] | list[ChatMessage], *, + allow_multiple_tool_calls: bool | None = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -517,6 +521,7 @@ async def get_response( messages: The message or messages to send to the model. Keyword Args: + allow_multiple_tool_calls: Whether to allow multiple tool calls in a single response. frequency_penalty: The frequency penalty to use. logit_bias: The logit bias to use. max_tokens: The maximum number of tokens to generate. @@ -545,6 +550,7 @@ async def get_response( chat_options = _merge_chat_options( base_chat_options=kwargs.pop("chat_options", None), model_id=model_id, + allow_multiple_tool_calls=allow_multiple_tool_calls, frequency_penalty=frequency_penalty, logit_bias=logit_bias, max_tokens=max_tokens, @@ -580,6 +586,7 @@ async def get_streaming_response( self, messages: str | ChatMessage | list[str] | list[ChatMessage], *, + allow_multiple_tool_calls: bool | None = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -612,6 +619,7 @@ async def get_streaming_response( messages: The message or messages to send to the model. Keyword Args: + allow_multiple_tool_calls: Whether to allow multiple tool calls in a single response. frequency_penalty: The frequency penalty to use. logit_bias: The logit bias to use. max_tokens: The maximum number of tokens to generate. @@ -640,6 +648,7 @@ async def get_streaming_response( chat_options = _merge_chat_options( base_chat_options=kwargs.pop("chat_options", None), model_id=model_id, + allow_multiple_tool_calls=allow_multiple_tool_calls, frequency_penalty=frequency_penalty, logit_bias=logit_bias, max_tokens=max_tokens, diff --git a/python/packages/core/agent_framework/_tools.py b/python/packages/core/agent_framework/_tools.py index 6edd258e15b..3657a994e28 100644 --- a/python/packages/core/agent_framework/_tools.py +++ b/python/packages/core/agent_framework/_tools.py @@ -614,6 +614,7 @@ def __init__( **kwargs, ) self.func = func + self._instance = None # Store the instance for bound methods self.input_model = self._resolve_input_model(input_model) self.approval_mode = approval_mode or "never_require" if max_invocations is not None and max_invocations < 1: @@ -626,12 +627,47 @@ def __init__( self.invocation_exception_count = 0 self._invocation_duration_histogram = _default_histogram() self.type: Literal["ai_function"] = "ai_function" + self._forward_runtime_kwargs: bool = False @property def declaration_only(self) -> bool: """Indicate whether the function is declaration only (i.e., has no implementation).""" + # Check for explicit _declaration_only attribute first (used in tests) + if hasattr(self, "_declaration_only") and self._declaration_only: + return True return self.func is None + def __get__(self, obj: Any, objtype: type | None = None) -> "AIFunction[ArgsT, ReturnT]": + """Implement the descriptor protocol to support bound methods. + + When an AIFunction is accessed as an attribute of a class instance, + this method is called to bind the instance to the function. + + Args: + obj: The instance that owns the descriptor, or None for class access. + objtype: The type that owns the descriptor. + + Returns: + A new AIFunction with the instance bound to the wrapped function. + """ + if obj is None: + # Accessed from the class, not an instance + return self + + # Check if the wrapped function is a method (has 'self' parameter) + if self.func is not None: + sig = inspect.signature(self.func) + params = list(sig.parameters.keys()) + if params and params[0] in {"self", "cls"}: + # Create a new AIFunction with the bound method + import copy + + bound_func = copy.copy(self) + bound_func._instance = obj + return bound_func + + return self + def _resolve_input_model(self, input_model: type[ArgsT] | Mapping[str, Any] | None) -> type[ArgsT]: """Resolve the input model for the function.""" if input_model is None: @@ -646,7 +682,7 @@ def _resolve_input_model(self, input_model: type[ArgsT] | Mapping[str, Any] | No def __call__(self, *args: Any, **kwargs: Any) -> ReturnT | Awaitable[ReturnT]: """Call the wrapped function with the provided arguments.""" - if self.func is None: + if self.declaration_only: raise ToolException(f"Function '{self.name}' is declaration only and cannot be invoked.") if self.max_invocations is not None and self.invocation_count >= self.max_invocations: raise ToolException( @@ -662,7 +698,10 @@ def __call__(self, *args: Any, **kwargs: Any) -> ReturnT | Awaitable[ReturnT]: ) self.invocation_count += 1 try: - return self.func(*args, **kwargs) + # If we have a bound instance, call the function with self + if self._instance is not None: + return self.func(self._instance, *args, **kwargs) + return self.func(*args, **kwargs) # type:ignore[misc] except Exception: self.invocation_exception_count += 1 raise @@ -690,11 +729,16 @@ async def invoke( global OBSERVABILITY_SETTINGS from .observability import OBSERVABILITY_SETTINGS - tool_call_id = kwargs.pop("tool_call_id", None) + original_kwargs = dict(kwargs) + tool_call_id = original_kwargs.pop("tool_call_id", None) if arguments is not None: if not isinstance(arguments, self.input_model): raise TypeError(f"Expected {self.input_model.__name__}, got {type(arguments).__name__}") kwargs = arguments.model_dump(exclude_none=True) + if getattr(self, "_forward_runtime_kwargs", False) and original_kwargs: + kwargs.update(original_kwargs) + else: + kwargs = original_kwargs if not OBSERVABILITY_SETTINGS.ENABLED: # type: ignore[name-defined] logger.info(f"Function name: {self.name}") logger.debug(f"Function arguments: {kwargs}") @@ -858,6 +902,12 @@ def _parse_annotation(annotation: Any) -> Any: def _create_input_model_from_func(func: Callable[..., Any], name: str) -> type[BaseModel]: """Create a Pydantic model from a function's signature.""" + # Unwrap AIFunction objects to get the underlying function + from agent_framework._tools import AIFunction + + if isinstance(func, AIFunction): + func = func.func # type: ignore[assignment] + sig = inspect.signature(func) fields = { pname: ( @@ -1228,15 +1278,20 @@ async def _auto_invoke_function( parsed_args: dict[str, Any] = dict(function_call_content.parse_arguments() or {}) - # Merge with user-supplied args; right-hand side dominates, so parsed args win on conflicts. - merged_args: dict[str, Any] = (custom_args or {}) | parsed_args + # Filter out internal framework kwargs before passing to tools. + runtime_kwargs: dict[str, Any] = { + key: value + for key, value in (custom_args or {}).items() + if key not in {"_function_middleware_pipeline", "middleware"} + } try: - args = tool.input_model.model_validate(merged_args) + args = tool.input_model.model_validate(parsed_args) except ValidationError as exc: message = "Error: Argument parsing failed." if config.include_detailed_errors: message = f"{message} Exception: {exc}" return FunctionResultContent(call_id=function_call_content.call_id, result=message, exception=exc) + if not middleware_pipeline or ( not hasattr(middleware_pipeline, "has_middlewares") and not middleware_pipeline.has_middlewares ): @@ -1245,7 +1300,8 @@ async def _auto_invoke_function( function_result = await tool.invoke( arguments=args, tool_call_id=function_call_content.call_id, - ) # type: ignore[arg-type] + **runtime_kwargs if getattr(tool, "_forward_runtime_kwargs", False) else {}, + ) return FunctionResultContent( call_id=function_call_content.call_id, result=function_result, @@ -1261,13 +1317,14 @@ async def _auto_invoke_function( middleware_context = FunctionInvocationContext( function=tool, arguments=args, - kwargs=custom_args or {}, + kwargs=runtime_kwargs.copy(), ) async def final_function_handler(context_obj: Any) -> Any: return await tool.invoke( arguments=context_obj.arguments, tool_call_id=function_call_content.call_id, + **context_obj.kwargs if getattr(tool, "_forward_runtime_kwargs", False) else {}, ) try: diff --git a/python/packages/core/agent_framework/_types.py b/python/packages/core/agent_framework/_types.py index 34ade839bf5..12297fe82a9 100644 --- a/python/packages/core/agent_framework/_types.py +++ b/python/packages/core/agent_framework/_types.py @@ -3173,6 +3173,40 @@ def __init__( self.top_p = top_p self.user = user + def __deepcopy__(self, memo: dict[int, Any]) -> "ChatOptions": + """Create a runtime-safe copy without deep-copying tool instances.""" + clone = type(self).__new__(type(self)) + memo[id(self)] = clone + for key, value in self.__dict__.items(): + if key == "_tools": + setattr(clone, key, list(value) if value is not None else None) + continue + if key in {"logit_bias", "metadata", "additional_properties"}: + setattr(clone, key, self._safe_deepcopy_mapping(value, memo)) + continue + setattr(clone, key, self._safe_deepcopy_value(value, memo)) + return clone + + @staticmethod + def _safe_deepcopy_mapping( + value: MutableMapping[str, Any] | None, memo: dict[int, Any] + ) -> MutableMapping[str, Any] | None: + """Deep copy helper that falls back to a shallow copy for problematic mappings.""" + if value is None: + return None + try: + return deepcopy(value, memo) # type: ignore[arg-type] + except Exception: + return dict(value) + + @staticmethod + def _safe_deepcopy_value(value: Any, memo: dict[int, Any]) -> Any: + """Deep copy helper that avoids failing on non-copyable instances.""" + try: + return deepcopy(value, memo) + except Exception: + return value + @property def tools(self) -> list[ToolProtocol | MutableMapping[str, Any]] | None: """Return the tools that are specified.""" diff --git a/python/packages/core/agent_framework/_workflows/_function_executor.py b/python/packages/core/agent_framework/_workflows/_function_executor.py index f79d85b1a72..417a4ee51b4 100644 --- a/python/packages/core/agent_framework/_workflows/_function_executor.py +++ b/python/packages/core/agent_framework/_workflows/_function_executor.py @@ -17,6 +17,7 @@ import asyncio import inspect +import typing from collections.abc import Awaitable, Callable from typing import Any, overload @@ -218,15 +219,16 @@ def _validate_function_signature(func: Callable[..., Any]) -> tuple[type, Any, l if message_param.annotation == inspect.Parameter.empty: raise ValueError(f"Function instance {func.__name__} must have a type annotation for the message parameter") - message_type = message_param.annotation + type_hints = typing.get_type_hints(func) + message_type = type_hints.get(message_param.name, message_param.annotation) # Check if there's a context parameter if len(params) == 2: ctx_param = params[1] + ctx_annotation = type_hints.get(ctx_param.name, ctx_param.annotation) output_types, workflow_output_types = validate_workflow_context_annotation( - ctx_param.annotation, f"parameter '{ctx_param.name}'", "Function instance" + ctx_annotation, f"parameter '{ctx_param.name}'", "Function instance" ) - ctx_annotation = ctx_param.annotation else: # No context parameter (only valid for function executors) output_types, workflow_output_types = [], [] diff --git a/python/packages/core/agent_framework/_workflows/_workflow_builder.py b/python/packages/core/agent_framework/_workflows/_workflow_builder.py index a1b90408bea..70f8747ec90 100644 --- a/python/packages/core/agent_framework/_workflows/_workflow_builder.py +++ b/python/packages/core/agent_framework/_workflows/_workflow_builder.py @@ -39,7 +39,40 @@ class WorkflowBuilder: """A builder class for constructing workflows. - This class provides methods to add edges and set the starting executor for the workflow. + This class provides a fluent API for defining workflow graphs by connecting executors + with edges and configuring execution parameters. Call :meth:`build` to create an + immutable :class:`Workflow` instance. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class UpperCaseExecutor(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(text.upper()) + + + class ReverseExecutor(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(text[::-1]) + + + # Build a workflow + workflow = ( + WorkflowBuilder() + .add_edge(UpperCaseExecutor(id="upper"), ReverseExecutor(id="reverse")) + .set_start_executor("upper") + .build() + ) + + # Run the workflow + events = await workflow.run("hello") + print(events.get_outputs()) # ['OLLEH'] """ def __init__( @@ -51,7 +84,7 @@ def __init__( """Initialize the WorkflowBuilder with an empty list of edges and no starting executor. Args: - max_iterations: Maximum number of iterations for workflow convergence. + max_iterations: Maximum number of iterations for workflow convergence. Default is 100. name: Optional human-readable name for the workflow. description: Optional description of what the workflow does. """ @@ -164,10 +197,22 @@ def add_agent( id: A unique identifier for the executor. If None, the agent's name will be used if available. Returns: - The WorkflowBuilder instance (for method chaining). + Self: The WorkflowBuilder instance for method chaining. Raises: ValueError: If the provided id or agent name conflicts with an existing executor. + + Example: + .. code-block:: python + + from agent_framework import WorkflowBuilder + from agent_framework_anthropic import AnthropicAgent + + # Create an agent + agent = AnthropicAgent(name="writer", model="claude-3-5-sonnet-20241022") + + # Add the agent to a workflow + workflow = WorkflowBuilder().add_agent(agent, output_response=True).set_start_executor(agent).build() """ executor = self._maybe_wrap_agent( agent, agent_thread=agent_thread, output_response=output_response, executor_id=id @@ -184,12 +229,53 @@ def add_edge( """Add a directed edge between two executors. The output types of the source and the input types of the target must be compatible. + Messages sent by the source executor will be routed to the target executor. Args: source: The source executor of the edge. target: The target executor of the edge. condition: An optional condition function that determines whether the edge should be traversed based on the message type. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class ProcessorA(Executor): + @handler + async def process(self, data: str, ctx: WorkflowContext[int]) -> None: + await ctx.send_message(len(data)) + + + class ProcessorB(Executor): + @handler + async def process(self, count: int, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(f"Processed {count} characters") + + + # Connect executors with an edge + workflow = ( + WorkflowBuilder().add_edge(ProcessorA(id="a"), ProcessorB(id="b")).set_start_executor("a").build() + ) + + + # With a condition + def only_large_numbers(msg: int) -> bool: + return msg > 100 + + + workflow = ( + WorkflowBuilder() + .add_edge(ProcessorA(id="a"), ProcessorB(id="b"), condition=only_large_numbers) + .set_start_executor("a") + .build() + ) """ # TODO(@taochen): Support executor factories for lazy initialization source_exec = self._maybe_wrap_agent(source) @@ -204,13 +290,50 @@ def add_fan_out_edges( source: Executor | AgentProtocol, targets: Sequence[Executor | AgentProtocol], ) -> Self: - """Add multiple edges to the workflow where messages from the source will be sent to all target. + """Add multiple edges to the workflow where messages from the source will be sent to all targets. The output types of the source and the input types of the targets must be compatible. + Messages from the source will be broadcast to all target executors concurrently. Args: source: The source executor of the edges. targets: A list of target executors for the edges. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class DataSource(Executor): + @handler + async def generate(self, count: int, ctx: WorkflowContext[str]) -> None: + for i in range(count): + await ctx.send_message(f"data_{i}") + + + class ValidatorA(Executor): + @handler + async def validate(self, data: str, ctx: WorkflowContext) -> None: + print(f"ValidatorA: {data}") + + + class ValidatorB(Executor): + @handler + async def validate(self, data: str, ctx: WorkflowContext) -> None: + print(f"ValidatorB: {data}") + + + # Broadcast to multiple validators + workflow = ( + WorkflowBuilder() + .add_fan_out_edges(DataSource(id="source"), [ValidatorA(id="val_a"), ValidatorB(id="val_b")]) + .set_start_executor("source") + .build() + ) """ source_exec = self._maybe_wrap_agent(source) target_execs = [self._maybe_wrap_agent(t) for t in targets] @@ -241,6 +364,53 @@ def add_switch_case_edge_group( Args: source: The source executor of the edges. cases: A list of case objects that determine the target executor for each message. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler, Case, Default + from dataclasses import dataclass + + + @dataclass + class Result: + score: int + + + class Evaluator(Executor): + @handler + async def evaluate(self, text: str, ctx: WorkflowContext[Result]) -> None: + await ctx.send_message(Result(score=len(text))) + + + class HighScoreHandler(Executor): + @handler + async def handle(self, result: Result, ctx: WorkflowContext) -> None: + print(f"High score: {result.score}") + + + class LowScoreHandler(Executor): + @handler + async def handle(self, result: Result, ctx: WorkflowContext) -> None: + print(f"Low score: {result.score}") + + + # Route based on score value + workflow = ( + WorkflowBuilder() + .add_switch_case_edge_group( + Evaluator(id="eval"), + [ + Case(condition=lambda r: r.score > 10, target=HighScoreHandler(id="high")), + Default(target=LowScoreHandler(id="low")), + ], + ) + .set_start_executor("eval") + .build() + ) """ source_exec = self._maybe_wrap_agent(source) source_id = self._add_executor(source_exec) @@ -270,13 +440,67 @@ def add_multi_selection_edge_group( Messages from the source executor will be sent to multiple target executors based on the provided selection function. - The selection function should take a message and the name of the target executors, - and return a list of indices indicating which target executors should receive the message. + The selection function should take a message and a list of target executor IDs, + and return a list of executor IDs indicating which target executors should receive the message. Args: source: The source executor of the edges. targets: A list of target executors for the edges. selection_func: A function that selects target executors for messages. + Takes (message, list[executor_id]) and returns list[executor_id]. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + from dataclasses import dataclass + + + @dataclass + class Task: + priority: str + data: str + + + class TaskDispatcher(Executor): + @handler + async def dispatch(self, text: str, ctx: WorkflowContext[Task]) -> None: + priority = "high" if len(text) > 10 else "low" + await ctx.send_message(Task(priority=priority, data=text)) + + + class WorkerA(Executor): + @handler + async def process(self, task: Task, ctx: WorkflowContext) -> None: + print(f"WorkerA processing: {task.data}") + + + class WorkerB(Executor): + @handler + async def process(self, task: Task, ctx: WorkflowContext) -> None: + print(f"WorkerB processing: {task.data}") + + + # Select workers based on task priority + def select_workers(task: Task, executor_ids: list[str]) -> list[str]: + if task.priority == "high": + return executor_ids # Send to all workers + return [executor_ids[0]] # Send to first worker only + + + workflow = ( + WorkflowBuilder() + .add_multi_selection_edge_group( + TaskDispatcher(id="dispatcher"), + [WorkerA(id="worker_a"), WorkerB(id="worker_b")], + selection_func=select_workers, + ) + .set_start_executor("dispatcher") + .build() + ) """ source_exec = self._maybe_wrap_agent(source) target_execs = [self._maybe_wrap_agent(t) for t in targets] @@ -298,31 +522,42 @@ def add_fan_in_edges( The target executor will receive a list of messages aggregated from all source executors. Thus the input types of the target executor must be compatible with a list of the output - types of the source executors. For example: - - class Target(Executor): - @handler - def handle_messages(self, messages: list[Message]) -> None: - # Process the aggregated messages from all sources - - class Source(Executor): - @handler(output_type=[Message]) - def handle_message(self, message: Message) -> None: - # Send a message to the target executor - self.send_message(message) - - workflow = ( - WorkflowBuilder() - .add_fan_in_edges( - [Source(id="source1"), Source(id="source2")], - Target(id="target") - ) - .build() - ) + types of the source executors. Args: sources: A list of source executors for the edges. target: The target executor for the edges. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class Producer(Executor): + @handler + async def produce(self, seed: int, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(f"result_{seed}") + + + class Aggregator(Executor): + @handler + async def aggregate(self, results: list[str], ctx: WorkflowContext[Never, str]) -> None: + combined = ", ".join(results) + await ctx.yield_output(f"Combined: {combined}") + + + # Collect results from multiple producers + workflow = ( + WorkflowBuilder() + .add_fan_in_edges([Producer(id="prod_1"), Producer(id="prod_2")], Aggregator(id="agg")) + .set_start_executor("prod_1") + .build() + ) """ source_execs = [self._maybe_wrap_agent(s) for s in sources] target_exec = self._maybe_wrap_agent(target) @@ -342,6 +577,42 @@ def add_chain(self, executors: Sequence[Executor | AgentProtocol]) -> Self: Args: executors: A list of executors to be added to the chain. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class Step1(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(text.upper()) + + + class Step2(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(text[::-1]) + + + class Step3(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(f"Final: {text}") + + + # Chain executors in sequence + workflow = ( + WorkflowBuilder() + .add_chain([Step1(id="step1"), Step2(id="step2"), Step3(id="step3")]) + .set_start_executor("step1") + .build() + ) """ # Wrap each candidate first to ensure stable IDs before adding edges wrapped: list[Executor] = [self._maybe_wrap_agent(e) for e in executors] @@ -352,8 +623,46 @@ def add_chain(self, executors: Sequence[Executor | AgentProtocol]) -> Self: def set_start_executor(self, executor: Executor | AgentProtocol | str) -> Self: """Set the starting executor for the workflow. + The start executor is the entry point for the workflow. When the workflow is executed, + the initial message will be sent to this executor. + Args: - executor: The starting executor, which can be an Executor instance or its ID. + executor: The starting executor, which can be an Executor instance, AgentProtocol instance, + or the string ID of an executor previously added to the workflow. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class EntryPoint(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(text.upper()) + + + class Processor(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(text) + + + # Set by executor instance + entry = EntryPoint(id="entry") + workflow = WorkflowBuilder().add_edge(entry, Processor(id="proc")).set_start_executor(entry).build() + + # Set by executor ID string + workflow = ( + WorkflowBuilder() + .add_edge(EntryPoint(id="entry"), Processor(id="proc")) + .set_start_executor("entry") + .build() + ) """ if isinstance(executor, str): self._start_executor = executor @@ -370,8 +679,43 @@ def set_start_executor(self, executor: Executor | AgentProtocol | str) -> Self: def set_max_iterations(self, max_iterations: int) -> Self: """Set the maximum number of iterations for the workflow. + When a workflow contains cycles, this limit prevents infinite loops by capping + the total number of executor invocations. The default is 100 iterations. + Args: max_iterations: The maximum number of iterations the workflow will run for convergence. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class StepA(Executor): + @handler + async def process(self, count: int, ctx: WorkflowContext[int]) -> None: + if count < 10: + await ctx.send_message(count + 1) + + + class StepB(Executor): + @handler + async def process(self, count: int, ctx: WorkflowContext[int]) -> None: + await ctx.send_message(count) + + + # Set a custom iteration limit for workflow with cycles + workflow = ( + WorkflowBuilder() + .set_max_iterations(500) + .add_edge(StepA(id="step_a"), StepB(id="step_b")) + .add_edge(StepB(id="step_b"), StepA(id="step_a")) # Cycle + .set_start_executor("step_a") + .build() + ) """ self._max_iterations = max_iterations return self @@ -381,8 +725,48 @@ def set_max_iterations(self, max_iterations: int) -> Self: def with_checkpointing(self, checkpoint_storage: CheckpointStorage) -> Self: """Enable checkpointing with the specified storage. + Checkpointing allows workflows to save their state periodically, enabling + pause/resume functionality and recovery from failures. The checkpoint storage + implementation determines where checkpoints are persisted. + Args: - checkpoint_storage: The checkpoint storage to use. + checkpoint_storage: The checkpoint storage implementation to use. + + Returns: + Self: The WorkflowBuilder instance for method chaining. + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + from agent_framework import FileCheckpointStorage + + + class ProcessorA(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[str]) -> None: + await ctx.send_message(text.upper()) + + + class ProcessorB(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(text) + + + # Enable checkpointing with file-based storage + storage = FileCheckpointStorage("./checkpoints") + workflow = ( + WorkflowBuilder() + .add_edge(ProcessorA(id="proc_a"), ProcessorB(id="proc_b")) + .set_start_executor("proc_a") + .with_checkpointing(storage) + .build() + ) + + # Run with checkpoint saving + events = await workflow.run("input") """ self._checkpoint_storage = checkpoint_storage return self @@ -390,15 +774,43 @@ def with_checkpointing(self, checkpoint_storage: CheckpointStorage) -> Self: def build(self) -> Workflow: """Build and return the constructed workflow. - This method performs validation before building the workflow. + This method performs validation before building the workflow to ensure: + - A starting executor has been set + - All edges connect valid executors + - The graph is properly connected + - Type compatibility between connected executors Returns: - A Workflow instance with the defined edges and starting executor. + Workflow: An immutable Workflow instance ready for execution. Raises: ValueError: If starting executor is not set. WorkflowValidationError: If workflow validation fails (includes EdgeDuplicationError, TypeCompatibilityError, and GraphConnectivityError subclasses). + + Example: + .. code-block:: python + + from typing_extensions import Never + from agent_framework import Executor, WorkflowBuilder, WorkflowContext, handler + + + class MyExecutor(Executor): + @handler + async def process(self, text: str, ctx: WorkflowContext[Never, str]) -> None: + await ctx.yield_output(text.upper()) + + + # Build and execute a workflow + workflow = WorkflowBuilder().set_start_executor(MyExecutor(id="executor")).build() + + # The workflow is now immutable and ready to run + events = await workflow.run("hello") + print(events.get_outputs()) # ['HELLO'] + + # Workflows can be reused multiple times + events2 = await workflow.run("world") + print(events2.get_outputs()) # ['WORLD'] """ # Create workflow build span that includes validation and workflow creation with create_workflow_span(OtelAttr.WORKFLOW_BUILD_SPAN) as span: diff --git a/python/packages/core/agent_framework/azure/__init__.py b/python/packages/core/agent_framework/azure/__init__.py index c1b64f21176..09670188ee9 100644 --- a/python/packages/core/agent_framework/azure/__init__.py +++ b/python/packages/core/agent_framework/azure/__init__.py @@ -10,6 +10,8 @@ "AgentResponseCallbackProtocol": ("agent_framework_azurefunctions", "azurefunctions"), "AzureAIAgentClient": ("agent_framework_azure_ai", "azure-ai"), "AzureAIClient": ("agent_framework_azure_ai", "azure-ai"), + "AzureAISearchContextProvider": ("agent_framework_aisearch", "aisearch"), + "AzureAISearchSettings": ("agent_framework_aisearch", "aisearch"), "AzureOpenAIAssistantsClient": ("agent_framework.azure._assistants_client", "core"), "AzureOpenAIChatClient": ("agent_framework.azure._chat_client", "core"), "AzureAISettings": ("agent_framework_azure_ai", "azure-ai"), diff --git a/python/packages/core/agent_framework/declarative/__init__.py b/python/packages/core/agent_framework/declarative/__init__.py new file mode 100644 index 00000000000..d6002b9b0ac --- /dev/null +++ b/python/packages/core/agent_framework/declarative/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) Microsoft. All rights reserved. + +import importlib +from typing import Any + +IMPORT_PATH = "agent_framework_declarative" +PACKAGE_NAME = "agent-framework-declarative" +_IMPORTS = ["__version__", "AgentFactory", "DeclarativeLoaderError", "ProviderLookupError", "ProviderTypeMapping"] + + +def __getattr__(name: str) -> Any: + if name in _IMPORTS: + try: + return getattr(importlib.import_module(IMPORT_PATH), name) + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + f"The '{PACKAGE_NAME}' package is not installed, please do `pip install {PACKAGE_NAME}`" + ) from exc + raise AttributeError(f"Module {IMPORT_PATH} has no attribute {name}.") + + +def __dir__() -> list[str]: + return _IMPORTS diff --git a/python/packages/core/agent_framework/declarative/__init__.pyi b/python/packages/core/agent_framework/declarative/__init__.pyi new file mode 100644 index 00000000000..0e19cc8687d --- /dev/null +++ b/python/packages/core/agent_framework/declarative/__init__.pyi @@ -0,0 +1,17 @@ +# Copyright (c) Microsoft. All rights reserved. + +from agent_framework_declarative import ( + AgentFactory, + DeclarativeLoaderError, + ProviderLookupError, + ProviderTypeMapping, + __version__, +) + +__all__ = [ + "AgentFactory", + "DeclarativeLoaderError", + "ProviderLookupError", + "ProviderTypeMapping", + "__version__", +] diff --git a/python/packages/core/agent_framework/observability.py b/python/packages/core/agent_framework/observability.py index 3e44fae23c0..1543b532513 100644 --- a/python/packages/core/agent_framework/observability.py +++ b/python/packages/core/agent_framework/observability.py @@ -1021,7 +1021,7 @@ def use_observability( .. code-block:: python from agent_framework import use_observability, setup_observability - from agent_framework._clients import ChatClientProtocol + from agent_framework import ChatClientProtocol # Decorate a custom chat client class @@ -1104,6 +1104,7 @@ async def trace_run( if not OBSERVABILITY_SETTINGS.ENABLED: # If model diagnostics are not enabled, just return the completion return await run_func(self, messages=messages, thread=thread, **kwargs) + filtered_kwargs = {k: v for k, v in kwargs.items() if k != "chat_options"} attributes = _get_span_attributes( operation_name=OtelAttr.AGENT_INVOKE_OPERATION, provider_name=provider_name, @@ -1112,7 +1113,7 @@ async def trace_run( agent_description=self.description, thread_id=thread.service_thread_id if thread else None, chat_options=getattr(self, "chat_options", None), - **kwargs, + **filtered_kwargs, ) with _get_span(attributes=attributes, span_name_attribute=OtelAttr.AGENT_NAME) as span: if OBSERVABILITY_SETTINGS.SENSITIVE_DATA_ENABLED and messages: @@ -1120,7 +1121,7 @@ async def trace_run( span=span, provider_name=provider_name, messages=messages, - system_instructions=getattr(self, "instructions", None), + system_instructions=getattr(getattr(self, "chat_options", None), "instructions", None), ) try: response = await run_func(self, messages=messages, thread=thread, **kwargs) @@ -1173,6 +1174,7 @@ async def trace_run_streaming( all_updates: list["AgentRunResponseUpdate"] = [] + filtered_kwargs = {k: v for k, v in kwargs.items() if k != "chat_options"} attributes = _get_span_attributes( operation_name=OtelAttr.AGENT_INVOKE_OPERATION, provider_name=provider_name, @@ -1181,7 +1183,7 @@ async def trace_run_streaming( agent_description=self.description, thread_id=thread.service_thread_id if thread else None, chat_options=getattr(self, "chat_options", None), - **kwargs, + **filtered_kwargs, ) with _get_span(attributes=attributes, span_name_attribute=OtelAttr.AGENT_NAME) as span: if OBSERVABILITY_SETTINGS.SENSITIVE_DATA_ENABLED and messages: @@ -1189,7 +1191,7 @@ async def trace_run_streaming( span=span, provider_name=provider_name, messages=messages, - system_instructions=getattr(self, "instructions", None), + system_instructions=getattr(getattr(self, "chat_options", None), "instructions", None), ) try: async for update in run_streaming_func(self, messages=messages, thread=thread, **kwargs): @@ -1472,10 +1474,10 @@ def _to_otel_part(content: "Contents") -> dict[str, Any] | None: elif isinstance(item, BaseModel): res.append(item.model_dump(exclude_none=True)) else: - res.append(json.dumps(item)) - response = json.dumps(res) + res.append(json.dumps(item, default=str)) + response = json.dumps(res, default=str) else: - response = json.dumps(content.result) + response = json.dumps(content.result, default=str) return {"type": "tool_call_response", "id": content.call_id, "response": response} case _: # GenericPart in otel output messages json spec. diff --git a/python/packages/core/pyproject.toml b/python/packages/core/pyproject.toml index 8f19719c591..0b6b7c16fbc 100644 --- a/python/packages/core/pyproject.toml +++ b/python/packages/core/pyproject.toml @@ -4,7 +4,7 @@ description = "Microsoft Agent Framework for building AI Agents with Python. Thi authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" @@ -43,10 +43,15 @@ dependencies = [ all = [ "agent-framework-a2a", "agent-framework-ag-ui", + "agent-framework-aisearch", "agent-framework-anthropic", "agent-framework-azure-ai", + "agent-framework-azurefunctions", + "agent-framework-chatkit", "agent-framework-copilotstudio", + "agent-framework-declarative", "agent-framework-devui", + "agent-framework-lab", "agent-framework-mem0", "agent-framework-purview", "agent-framework-redis", diff --git a/python/packages/core/tests/core/test_agents.py b/python/packages/core/tests/core/test_agents.py index 7d36debf1ce..77d5911865f 100644 --- a/python/packages/core/tests/core/test_agents.py +++ b/python/packages/core/tests/core/test_agents.py @@ -115,6 +115,26 @@ async def test_chat_client_agent_prepare_thread_and_messages(chat_client: ChatCl assert result_messages[1].text == "Test" +async def test_prepare_thread_does_not_mutate_agent_chat_options(chat_client: ChatClientProtocol) -> None: + tool = HostedCodeInterpreterTool() + agent = ChatAgent(chat_client=chat_client, tools=[tool]) + + assert agent.chat_options.tools is not None + base_tools = agent.chat_options.tools + thread = agent.get_new_thread() + + _, prepared_chat_options, _ = await agent._prepare_thread_and_messages( # type: ignore[reportPrivateUsage] + thread=thread, + input_messages=[ChatMessage(role=Role.USER, text="Test")], + ) + + assert prepared_chat_options.tools is not None + assert base_tools is not prepared_chat_options.tools + + prepared_chat_options.tools.append(HostedCodeInterpreterTool()) # type: ignore[arg-type] + assert len(agent.chat_options.tools) == 1 + + async def test_chat_client_agent_update_thread_id(chat_client_base: ChatClientProtocol) -> None: mock_response = ChatResponse( messages=[ChatMessage(role=Role.ASSISTANT, contents=[TextContent("test response")])], diff --git a/python/packages/core/tests/core/test_as_tool_kwargs_propagation.py b/python/packages/core/tests/core/test_as_tool_kwargs_propagation.py new file mode 100644 index 00000000000..8669ecc3d65 --- /dev/null +++ b/python/packages/core/tests/core/test_as_tool_kwargs_propagation.py @@ -0,0 +1,315 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Tests for kwargs propagation through as_tool() method.""" + +from collections.abc import Awaitable, Callable +from typing import Any + +from agent_framework import ChatAgent, ChatMessage, ChatResponse, FunctionCallContent, agent_middleware +from agent_framework._middleware import AgentRunContext + +from .conftest import MockChatClient + + +class TestAsToolKwargsPropagation: + """Test cases for kwargs propagation through as_tool() delegation.""" + + async def test_as_tool_forwards_runtime_kwargs(self, chat_client: MockChatClient) -> None: + """Test that runtime kwargs are forwarded through as_tool() to sub-agent.""" + captured_kwargs: dict[str, Any] = {} + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + # Capture kwargs passed to the sub-agent + captured_kwargs.update(context.kwargs) + await next(context) + + # Setup mock response + chat_client.responses = [ + ChatResponse(messages=[ChatMessage(role="assistant", text="Response from sub-agent")]), + ] + + # Create sub-agent with middleware + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + middleware=[capture_middleware], + ) + + # Create tool from sub-agent + tool = sub_agent.as_tool(name="delegate", arg_name="task") + + # Directly invoke the tool with kwargs (simulating what happens during agent execution) + _ = await tool.invoke( + arguments=tool.input_model(task="Test delegation"), + api_token="secret-xyz-123", + user_id="user-456", + session_id="session-789", + ) + + # Verify kwargs were forwarded to sub-agent + assert "api_token" in captured_kwargs, f"Expected 'api_token' in {captured_kwargs}" + assert captured_kwargs["api_token"] == "secret-xyz-123" + assert "user_id" in captured_kwargs + assert captured_kwargs["user_id"] == "user-456" + assert "session_id" in captured_kwargs + assert captured_kwargs["session_id"] == "session-789" + + async def test_as_tool_excludes_arg_name_from_forwarded_kwargs(self, chat_client: MockChatClient) -> None: + """Test that the arg_name parameter is not forwarded as a kwarg.""" + captured_kwargs: dict[str, Any] = {} + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + captured_kwargs.update(context.kwargs) + await next(context) + + # Setup mock response + chat_client.responses = [ + ChatResponse(messages=[ChatMessage(role="assistant", text="Response from sub-agent")]), + ] + + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + middleware=[capture_middleware], + ) + + tool = sub_agent.as_tool(arg_name="custom_task") + + # Invoke tool with both the arg_name field and additional kwargs + await tool.invoke( + arguments=tool.input_model(custom_task="Test task"), + api_token="token-123", + custom_task="should_be_excluded", # This should be filtered out + ) + + # The arg_name ("custom_task") should NOT be in the forwarded kwargs + assert "custom_task" not in captured_kwargs + # But other kwargs should be present + assert "api_token" in captured_kwargs + assert captured_kwargs["api_token"] == "token-123" + + async def test_as_tool_nested_delegation_propagates_kwargs(self, chat_client: MockChatClient) -> None: + """Test that kwargs propagate through multiple levels of delegation (A → B → C).""" + captured_kwargs_list: list[dict[str, Any]] = [] + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + # Capture kwargs at each level + captured_kwargs_list.append(dict(context.kwargs)) + await next(context) + + # Setup mock responses to trigger nested tool invocation: B calls tool C, then completes. + chat_client.responses = [ + ChatResponse( + messages=[ + ChatMessage( + role="assistant", + contents=[ + FunctionCallContent( + call_id="call_c_1", + name="call_c", + arguments='{"task": "Please execute agent_c"}', + ) + ], + ) + ] + ), + ChatResponse(messages=[ChatMessage(role="assistant", text="Response from agent_c")]), + ChatResponse(messages=[ChatMessage(role="assistant", text="Response from agent_b")]), + ] + + # Create agent C (bottom level) + agent_c = ChatAgent( + chat_client=chat_client, + name="agent_c", + middleware=[capture_middleware], + ) + + # Create agent B (middle level) - delegates to C + agent_b = ChatAgent( + chat_client=chat_client, + name="agent_b", + tools=[agent_c.as_tool(name="call_c")], + middleware=[capture_middleware], + ) + + # Create tool from B for direct invocation + tool_b = agent_b.as_tool(name="call_b") + + # Invoke tool B with kwargs - should propagate to both B and C + await tool_b.invoke( + arguments=tool_b.input_model(task="Test cascade"), + trace_id="trace-abc-123", + tenant_id="tenant-xyz", + ) + + # Verify both levels received the kwargs + # We should have 2 captures: one from B, one from C + assert len(captured_kwargs_list) >= 2 + for kwargs_dict in captured_kwargs_list: + assert kwargs_dict.get("trace_id") == "trace-abc-123" + assert kwargs_dict.get("tenant_id") == "tenant-xyz" + + async def test_as_tool_streaming_mode_forwards_kwargs(self, chat_client: MockChatClient) -> None: + """Test that kwargs are forwarded in streaming mode.""" + captured_kwargs: dict[str, Any] = {} + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + captured_kwargs.update(context.kwargs) + await next(context) + + # Setup mock streaming responses + from agent_framework import ChatResponseUpdate, TextContent + + chat_client.streaming_responses = [ + [ChatResponseUpdate(text=TextContent(text="Streaming response"), role="assistant")], + ] + + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + middleware=[capture_middleware], + ) + + captured_updates: list[Any] = [] + + async def stream_callback(update: Any) -> None: + captured_updates.append(update) + + tool = sub_agent.as_tool(stream_callback=stream_callback) + + # Invoke tool with kwargs while streaming callback is active + await tool.invoke( + arguments=tool.input_model(task="Test streaming"), + api_key="streaming-key-999", + ) + + # Verify kwargs were forwarded even in streaming mode + assert "api_key" in captured_kwargs + assert captured_kwargs["api_key"] == "streaming-key-999" + assert len(captured_updates) == 1 + + async def test_as_tool_empty_kwargs_still_works(self, chat_client: MockChatClient) -> None: + """Test that as_tool works correctly when no extra kwargs are provided.""" + # Setup mock response + chat_client.responses = [ + ChatResponse(messages=[ChatMessage(role="assistant", text="Response from agent")]), + ] + + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + ) + + tool = sub_agent.as_tool() + + # Invoke without any extra kwargs - should work without errors + result = await tool.invoke(arguments=tool.input_model(task="Simple task")) + + # Verify tool executed successfully + assert result is not None + + async def test_as_tool_kwargs_with_chat_options(self, chat_client: MockChatClient) -> None: + """Test that kwargs including chat_options are properly forwarded.""" + captured_kwargs: dict[str, Any] = {} + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + captured_kwargs.update(context.kwargs) + await next(context) + + # Setup mock response + chat_client.responses = [ + ChatResponse(messages=[ChatMessage(role="assistant", text="Response with options")]), + ] + + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + middleware=[capture_middleware], + ) + + tool = sub_agent.as_tool() + + # Invoke with various kwargs + await tool.invoke( + arguments=tool.input_model(task="Test with options"), + temperature=0.8, + max_tokens=500, + custom_param="custom_value", + ) + + # Verify all kwargs were forwarded + assert "temperature" in captured_kwargs + assert captured_kwargs["temperature"] == 0.8 + assert "max_tokens" in captured_kwargs + assert captured_kwargs["max_tokens"] == 500 + assert "custom_param" in captured_kwargs + assert captured_kwargs["custom_param"] == "custom_value" + + async def test_as_tool_kwargs_isolated_per_invocation(self, chat_client: MockChatClient) -> None: + """Test that kwargs are isolated per invocation and don't leak between calls.""" + first_call_kwargs: dict[str, Any] = {} + second_call_kwargs: dict[str, Any] = {} + call_count = 0 + + @agent_middleware + async def capture_middleware( + context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]] + ) -> None: + nonlocal call_count + call_count += 1 + if call_count == 1: + first_call_kwargs.update(context.kwargs) + elif call_count == 2: + second_call_kwargs.update(context.kwargs) + await next(context) + + # Setup mock responses for both calls + chat_client.responses = [ + ChatResponse(messages=[ChatMessage(role="assistant", text="First response")]), + ChatResponse(messages=[ChatMessage(role="assistant", text="Second response")]), + ] + + sub_agent = ChatAgent( + chat_client=chat_client, + name="sub_agent", + middleware=[capture_middleware], + ) + + tool = sub_agent.as_tool() + + # First call with specific kwargs + await tool.invoke( + arguments=tool.input_model(task="First task"), + session_id="session-1", + api_token="token-1", + ) + + # Second call with different kwargs + await tool.invoke( + arguments=tool.input_model(task="Second task"), + session_id="session-2", + api_token="token-2", + ) + + # Verify first call had its own kwargs + assert first_call_kwargs.get("session_id") == "session-1" + assert first_call_kwargs.get("api_token") == "token-1" + + # Verify second call had its own kwargs (not leaked from first) + assert second_call_kwargs.get("session_id") == "session-2" + assert second_call_kwargs.get("api_token") == "token-2" diff --git a/python/packages/core/tests/core/test_function_invocation_logic.py b/python/packages/core/tests/core/test_function_invocation_logic.py index 77b95d98a24..5a0ec5a773f 100644 --- a/python/packages/core/tests/core/test_function_invocation_logic.py +++ b/python/packages/core/tests/core/test_function_invocation_logic.py @@ -3,6 +3,7 @@ import pytest from agent_framework import ( + ChatAgent, ChatClientProtocol, ChatMessage, ChatOptions, @@ -127,6 +128,148 @@ def ai_func(arg1: str) -> str: assert exec_counter == 1 +async def test_function_invocation_inside_aiohttp_server(chat_client_base: ChatClientProtocol): + import aiohttp + from aiohttp import web + + exec_counter = 0 + + @ai_function(name="start_todo_investigation") + def ai_func(user_query: str) -> str: + nonlocal exec_counter + exec_counter += 1 + return f"Investigated {user_query}" + + chat_client_base.run_responses = [ + ChatResponse( + messages=ChatMessage( + role="assistant", + contents=[ + FunctionCallContent( + call_id="1", + name="start_todo_investigation", + arguments='{"user_query": "issue"}', + ) + ], + ) + ), + ChatResponse(messages=ChatMessage(role="assistant", text="done")), + ] + + agent = ChatAgent(chat_client=chat_client_base, tools=[ai_func]) + + async def handler(request: web.Request) -> web.Response: + thread = agent.get_new_thread() + result = await agent.run("Fix issue", thread=thread) + return web.Response(text=result.text or "") + + app = web.Application() + app.add_routes([web.post("/run", handler)]) + + runner = web.AppRunner(app) + await runner.setup() + site = web.TCPSite(runner, "127.0.0.1", 0) + await site.start() + try: + port = site._server.sockets[0].getsockname()[1] + async with aiohttp.ClientSession() as session, session.post(f"http://127.0.0.1:{port}/run") as response: + assert response.status == 200 + await response.text() + finally: + await runner.cleanup() + + assert exec_counter == 1 + + +async def test_function_invocation_in_threaded_aiohttp_app(chat_client_base: ChatClientProtocol): + import asyncio + import threading + from queue import Queue + + import aiohttp + from aiohttp import web + + exec_counter = 0 + + @ai_function(name="start_threaded_investigation") + def ai_func(user_query: str) -> str: + nonlocal exec_counter + exec_counter += 1 + return f"Threaded {user_query}" + + chat_client_base.run_responses = [ + ChatResponse( + messages=ChatMessage( + role="assistant", + contents=[ + FunctionCallContent( + call_id="thread-1", + name="start_threaded_investigation", + arguments='{"user_query": "issue"}', + ) + ], + ) + ), + ChatResponse(messages=ChatMessage(role="assistant", text="done")), + ] + + agent = ChatAgent(chat_client=chat_client_base, tools=[ai_func]) + + ready_event = threading.Event() + port_queue: Queue[int] = Queue() + shutdown_queue: Queue[tuple[asyncio.AbstractEventLoop, asyncio.Event]] = Queue() + + async def init_app() -> web.Application: + async def handler(request: web.Request) -> web.Response: + thread = agent.get_new_thread() + result = await agent.run("Fix issue", thread=thread) + return web.Response(text=result.text or "") + + app = web.Application() + app.add_routes([web.post("/run", handler)]) + return app + + def server_thread() -> None: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + async def runner_main() -> None: + app = await init_app() + runner = web.AppRunner(app) + await runner.setup() + site = web.TCPSite(runner, "127.0.0.1", 0) + await site.start() + shutdown_event = asyncio.Event() + shutdown_queue.put((loop, shutdown_event)) + port = site._server.sockets[0].getsockname()[1] + port_queue.put(port) + ready_event.set() + try: + await shutdown_event.wait() + finally: + await runner.cleanup() + + try: + loop.run_until_complete(runner_main()) + finally: + loop.close() + + thread = threading.Thread(target=server_thread, daemon=True) + thread.start() + ready_event.wait(timeout=5) + assert ready_event.is_set() + loop_ref, shutdown_event = shutdown_queue.get(timeout=2) + port = port_queue.get(timeout=2) + + async with aiohttp.ClientSession() as session, session.post(f"http://127.0.0.1:{port}/run") as response: + assert response.status == 200 + await response.text() + + loop_ref.call_soon_threadsafe(shutdown_event.set) + thread.join(timeout=5) + assert exec_counter == 1 + + @pytest.mark.parametrize( "approval_required,num_functions", [ @@ -1305,26 +1448,20 @@ def success_func(arg1: str) -> str: assert success_result.result == "Success value1" -async def test_declaration_only_tool_not_executed(chat_client_base: ChatClientProtocol): - """Test that declaration_only tools are not executed.""" - exec_counter = 0 - - @ai_function(name="declaration_func") - def declaration_func_inner(arg1: str) -> str: - nonlocal exec_counter - exec_counter += 1 - return f"Result {arg1}" - - # Create a new AIFunction with declaration_only set +async def test_declaration_only_tool(chat_client_base: ChatClientProtocol): + """Test that declaration_only tools without implementation (func=None) are not executed.""" from agent_framework import AIFunction + # Create a truly declaration-only function with no implementation declaration_func = AIFunction( name="declaration_func", - func=declaration_func_inner, - additional_properties={"declaration_only": True}, + func=None, + description="A declaration-only function for testing", + input_model={"type": "object", "properties": {"arg1": {"type": "string"}}, "required": ["arg1"]}, ) - # Set declaration_only on the instance - object.__setattr__(declaration_func, "_declaration_only", True) + + # Verify it's marked as declaration_only + assert declaration_func.declaration_only is True chat_client_base.run_responses = [ ChatResponse( @@ -1338,8 +1475,6 @@ def declaration_func_inner(arg1: str) -> str: response = await chat_client_base.get_response("hello", tool_choice="auto", tools=[declaration_func]) - # Function should NOT be executed - assert exec_counter == 0 # Should have the function call in messages but not a result function_calls = [ content @@ -1349,6 +1484,15 @@ def declaration_func_inner(arg1: str) -> str: ] assert len(function_calls) >= 1 + # Should not have a function result + function_results = [ + content + for msg in response.messages + for content in msg.contents + if isinstance(content, FunctionResultContent) and content.call_id == "1" + ] + assert len(function_results) == 0 + async def test_multiple_function_calls_parallel_execution(chat_client_base: ChatClientProtocol): """Test that multiple function calls are executed in parallel.""" diff --git a/python/packages/core/tests/core/test_middleware.py b/python/packages/core/tests/core/test_middleware.py index 8f0d67aa18a..a84c8927d01 100644 --- a/python/packages/core/tests/core/test_middleware.py +++ b/python/packages/core/tests/core/test_middleware.py @@ -1693,7 +1693,7 @@ def mock_function() -> AIFunction[Any, Any]: @pytest.fixture def mock_chat_client() -> Any: """Mock chat client for testing.""" - from agent_framework._clients import ChatClientProtocol + from agent_framework import ChatClientProtocol client = MagicMock(spec=ChatClientProtocol) client.service_url = MagicMock(return_value="mock://test") diff --git a/python/packages/core/tests/core/test_observability.py b/python/packages/core/tests/core/test_observability.py index d994867f6a8..abdc5184beb 100644 --- a/python/packages/core/tests/core/test_observability.py +++ b/python/packages/core/tests/core/test_observability.py @@ -3,7 +3,7 @@ import logging from collections.abc import MutableSequence from typing import Any -from unittest.mock import MagicMock, Mock, patch +from unittest.mock import Mock import pytest from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter @@ -22,6 +22,7 @@ ChatResponseUpdate, Role, UsageDetails, + ai_function, prepend_agent_framework_to_user_agent, ) from agent_framework.exceptions import AgentInitializationError, ChatClientInitializationError @@ -478,32 +479,46 @@ async def test_agent_streaming_response_with_diagnostics_enabled_via_decorator( assert span.attributes.get(OtelAttr.OUTPUT_MESSAGES) is not None # Streaming, so no usage yet -async def test_agent_run_with_exception_handling(mock_chat_agent: AgentProtocol): - """Test agent run with exception handling.""" +async def test_function_call_with_error_handling(span_exporter: InMemorySpanExporter): + """Test that function call errors are properly captured in telemetry.""" - async def run_with_error(self, messages=None, *, thread=None, **kwargs): - raise RuntimeError("Agent run error") + # Create a function that raises an error using the decorator + @ai_function(name="failing_function", description="A function that fails") + async def failing_function(param: str) -> str: + raise ValueError("Function execution failed") - mock_chat_agent.run = run_with_error + span_exporter.clear() - agent = use_agent_observability(mock_chat_agent)() + # Execute function and expect it to raise an error + with pytest.raises(ValueError, match="Function execution failed"): + await failing_function.invoke(param="test_value", tool_call_id="test_call_456") - from opentelemetry.trace import Span - - with ( - patch("agent_framework.observability._get_span") as mock_get_span, - ): - mock_span = MagicMock(spec=Span) - # Ensure the patched context manager returns mock_span when entered - mock_get_span.return_value.__enter__.return_value = mock_span - # Should raise the exception and call error handler - with pytest.raises(RuntimeError, match="Agent run error"): - await agent.run("Test message") - - # Verify error was recorded - # Check that both error attributes were set on the span - mock_span.set_attribute.assert_called_with(OtelAttr.ERROR_TYPE, "RuntimeError") - mock_span.record_exception.assert_called_once() - mock_span.set_status.assert_called_once_with( - status=StatusCode.ERROR, description=repr(RuntimeError("Agent run error")) - ) + # Verify span was created and error was captured + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + span = spans[0] + + # Verify span name and basic attributes + assert span.name == "execute_tool failing_function" + assert span.attributes is not None + assert span.attributes[OtelAttr.OPERATION.value] == OtelAttr.TOOL_EXECUTION_OPERATION + assert span.attributes[OtelAttr.TOOL_NAME] == "failing_function" + assert span.attributes[OtelAttr.TOOL_CALL_ID] == "test_call_456" + + # Verify error status was set + assert span.status.status_code == StatusCode.ERROR + assert span.status.description is not None + assert "Function execution failed" in span.status.description + + # Verify error type attribute was set + assert span.attributes[OtelAttr.ERROR_TYPE] == "ValueError" + + # Verify exception event was recorded + assert len(span.events) > 0 + exception_event = next((e for e in span.events if e.name == "exception"), None) + assert exception_event is not None + assert exception_event.attributes is not None + assert exception_event.attributes["exception.type"] == "ValueError" + exception_message = exception_event.attributes["exception.message"] + assert isinstance(exception_message, str) + assert "Function execution failed" in exception_message diff --git a/python/packages/core/tests/core/test_tools.py b/python/packages/core/tests/core/test_tools.py index acd91573636..c1cc0f119bb 100644 --- a/python/packages/core/tests/core/test_tools.py +++ b/python/packages/core/tests/core/test_tools.py @@ -104,6 +104,136 @@ async def async_test_tool(x: int, y: int) -> int: assert (await async_test_tool(1, 2)) == 3 +def test_ai_function_decorator_in_class(): + """Test the ai_function decorator.""" + + class my_tools: + @ai_function(name="test_tool", description="A test tool") + def test_tool(self, x: int, y: int) -> int: + """A simple function that adds two numbers.""" + return x + y + + test_tool = my_tools().test_tool + + assert isinstance(test_tool, ToolProtocol) + assert isinstance(test_tool, AIFunction) + assert test_tool.name == "test_tool" + assert test_tool.description == "A test tool" + assert test_tool.parameters() == { + "properties": {"x": {"title": "X", "type": "integer"}, "y": {"title": "Y", "type": "integer"}}, + "required": ["x", "y"], + "title": "test_tool_input", + "type": "object", + } + assert test_tool(1, 2) == 3 + + +async def test_ai_function_decorator_shared_state(): + """Test that decorated methods maintain shared state across multiple calls and tool usage.""" + + class StatefulCounter: + """A class that maintains a counter and provides decorated methods to interact with it.""" + + def __init__(self, initial_value: int = 0): + self.counter = initial_value + self.operation_log: list[str] = [] + + @ai_function(name="increment", description="Increment the counter") + def increment(self, amount: int) -> str: + """Increment the counter by the given amount.""" + self.counter += amount + self.operation_log.append(f"increment({amount})") + return f"Counter incremented by {amount}. New value: {self.counter}" + + @ai_function(name="get_value", description="Get the current counter value") + def get_value(self) -> str: + """Get the current counter value.""" + self.operation_log.append("get_value()") + return f"Current counter value: {self.counter}" + + @ai_function(name="multiply", description="Multiply the counter") + def multiply(self, factor: int) -> str: + """Multiply the counter by the given factor.""" + self.counter *= factor + self.operation_log.append(f"multiply({factor})") + return f"Counter multiplied by {factor}. New value: {self.counter}" + + # Create a single instance with shared state + counter_instance = StatefulCounter(initial_value=10) + + # Get the decorated methods - these will be used by different "agents" or tools + increment_tool = counter_instance.increment + get_value_tool = counter_instance.get_value + multiply_tool = counter_instance.multiply + + # Verify they are AIFunction instances + assert isinstance(increment_tool, AIFunction) + assert isinstance(get_value_tool, AIFunction) + assert isinstance(multiply_tool, AIFunction) + + # Tool 1 (increment) is used + result1 = increment_tool(5) + assert result1 == "Counter incremented by 5. New value: 15" + assert counter_instance.counter == 15 + + # Tool 2 (get_value) sees the state change from tool 1 + result2 = get_value_tool() + assert result2 == "Current counter value: 15" + assert counter_instance.counter == 15 + + # Tool 3 (multiply) modifies the shared state + result3 = multiply_tool(3) + assert result3 == "Counter multiplied by 3. New value: 45" + assert counter_instance.counter == 45 + + # Tool 2 (get_value) sees the state change from tool 3 + result4 = get_value_tool() + assert result4 == "Current counter value: 45" + assert counter_instance.counter == 45 + + # Tool 1 (increment) sees the current state and modifies it + result5 = increment_tool(10) + assert result5 == "Counter incremented by 10. New value: 55" + assert counter_instance.counter == 55 + + # Verify the operation log shows all operations in order + assert counter_instance.operation_log == [ + "increment(5)", + "get_value()", + "multiply(3)", + "get_value()", + "increment(10)", + ] + + # Verify the parameters don't include 'self' + assert increment_tool.parameters() == { + "properties": {"amount": {"title": "Amount", "type": "integer"}}, + "required": ["amount"], + "title": "increment_input", + "type": "object", + } + assert multiply_tool.parameters() == { + "properties": {"factor": {"title": "Factor", "type": "integer"}}, + "required": ["factor"], + "title": "multiply_input", + "type": "object", + } + assert get_value_tool.parameters() == { + "properties": {}, + "title": "get_value_input", + "type": "object", + } + + # Test with invoke method as well (simulating agent execution) + result6 = await increment_tool.invoke(amount=5) + assert result6 == "Counter incremented by 5. New value: 60" + assert counter_instance.counter == 60 + + result7 = await get_value_tool.invoke() + assert result7 == "Current counter value: 60" + assert counter_instance.counter == 60 + + async def test_ai_function_invoke_telemetry_enabled(span_exporter: InMemorySpanExporter): """Test the ai_function invoke method with telemetry enabled.""" @@ -191,6 +321,26 @@ def telemetry_test_tool(x: int, y: int) -> int: assert attributes[OtelAttr.TOOL_CALL_ID] == "test_call_id" +async def test_ai_function_invoke_ignores_additional_kwargs() -> None: + """Ensure ai_function tools drop unknown kwargs when invoked with validated arguments.""" + + @ai_function + async def simple_tool(message: str) -> str: + """Echo tool.""" + return message.upper() + + args = simple_tool.input_model(message="hello world") + + # These kwargs simulate runtime context passed through function invocation. + result = await simple_tool.invoke( + arguments=args, + api_token="secret-token", + chat_options={"model_id": "dummy"}, + ) + + assert result == "HELLO WORLD" + + async def test_ai_function_invoke_telemetry_with_pydantic_args(span_exporter: InMemorySpanExporter): """Test the ai_function invoke method with Pydantic model arguments.""" diff --git a/python/packages/core/tests/test_observability_datetime.py b/python/packages/core/tests/test_observability_datetime.py new file mode 100644 index 00000000000..05efdc1a5e1 --- /dev/null +++ b/python/packages/core/tests/test_observability_datetime.py @@ -0,0 +1,26 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Test datetime serialization in observability telemetry.""" + +import json +from datetime import datetime + +from agent_framework._types import FunctionResultContent +from agent_framework.observability import _to_otel_part + + +def test_datetime_in_tool_results() -> None: + """Test that tool results with datetime values are serialized. + + Reproduces issue #2219 where datetime objects caused TypeError. + """ + content = FunctionResultContent( + call_id="test-call", + result={"timestamp": datetime(2025, 11, 16, 10, 30, 0)}, + ) + + result = _to_otel_part(content) + parsed = json.loads(result["response"]) + + # Datetime should be converted to string + assert isinstance(parsed["timestamp"], str) diff --git a/python/packages/core/tests/workflow/test_function_executor_future.py b/python/packages/core/tests/workflow/test_function_executor_future.py new file mode 100644 index 00000000000..a4a15aeba03 --- /dev/null +++ b/python/packages/core/tests/workflow/test_function_executor_future.py @@ -0,0 +1,39 @@ +# Copyright (c) Microsoft. All rights reserved. + +from __future__ import annotations + +from typing import Any + +from agent_framework import FunctionExecutor, WorkflowContext, executor + + +class TestFunctionExecutorFutureAnnotations: + """Test suite for FunctionExecutor with from __future__ import annotations.""" + + def test_executor_decorator_future_annotations(self): + """Test @executor decorator works with stringified annotations.""" + + @executor(id="future_test") + async def process_future(value: int, ctx: WorkflowContext[int]) -> None: + await ctx.send_message(value * 2) + + assert isinstance(process_future, FunctionExecutor) + assert process_future.id == "future_test" + assert int in process_future._handlers + + # Check spec + spec = process_future._handler_specs[0] + assert spec["message_type"] is int + assert spec["output_types"] == [int] + + def test_executor_decorator_future_annotations_complex(self): + """Test @executor decorator works with complex stringified annotations.""" + + @executor + async def process_complex(data: dict[str, Any], ctx: WorkflowContext[list[str]]) -> None: + await ctx.send_message(["done"]) + + assert isinstance(process_complex, FunctionExecutor) + spec = process_complex._handler_specs[0] + assert spec["message_type"] == dict[str, Any] + assert spec["output_types"] == [list[str]] diff --git a/python/packages/declarative/LICENSE b/python/packages/declarative/LICENSE new file mode 100644 index 00000000000..9e841e7a26e --- /dev/null +++ b/python/packages/declarative/LICENSE @@ -0,0 +1,21 @@ + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE diff --git a/python/packages/declarative/README.md b/python/packages/declarative/README.md new file mode 100644 index 00000000000..b4a97f049a4 --- /dev/null +++ b/python/packages/declarative/README.md @@ -0,0 +1,11 @@ +# Get Started with Microsoft Agent Framework Declarative + +Please install this package via pip: + +```bash +pip install agent-framework-declarative --pre +``` + +## Declarative features + +The declarative packages provides support for building agents based on a declarative yaml specification. diff --git a/python/packages/declarative/agent_framework_declarative/__init__.py b/python/packages/declarative/agent_framework_declarative/__init__.py new file mode 100644 index 00000000000..bfc1bdffdca --- /dev/null +++ b/python/packages/declarative/agent_framework_declarative/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Microsoft. All rights reserved. + +from importlib import metadata + +from ._loader import AgentFactory, DeclarativeLoaderError, ProviderLookupError, ProviderTypeMapping + +try: + __version__ = metadata.version(__name__) +except metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + +__all__ = ["AgentFactory", "DeclarativeLoaderError", "ProviderLookupError", "ProviderTypeMapping", "__version__"] diff --git a/python/packages/declarative/agent_framework_declarative/_loader.py b/python/packages/declarative/agent_framework_declarative/_loader.py new file mode 100644 index 00000000000..b5ae1683ba2 --- /dev/null +++ b/python/packages/declarative/agent_framework_declarative/_loader.py @@ -0,0 +1,422 @@ +# Copyright (c) Microsoft. All rights reserved. + +from collections.abc import Callable, Mapping +from pathlib import Path +from typing import Any, Literal, TypedDict + +import yaml +from agent_framework import ( + AIFunction, + ChatAgent, + ChatClientProtocol, + HostedCodeInterpreterTool, + HostedFileContent, + HostedFileSearchTool, + HostedMCPSpecificApproval, + HostedMCPTool, + HostedVectorStoreContent, + HostedWebSearchTool, + ToolProtocol, +) +from agent_framework._tools import _create_model_from_json_schema # type: ignore +from agent_framework.exceptions import AgentFrameworkException +from dotenv import load_dotenv + +from ._models import ( + AnonymousConnection, + ApiKeyConnection, + CodeInterpreterTool, + FileSearchTool, + FunctionTool, + McpServerToolSpecifyApprovalMode, + McpTool, + Model, + ModelOptions, + PromptAgent, + ReferenceConnection, + RemoteConnection, + Tool, + WebSearchTool, + agent_schema_dispatch, +) + + +class ProviderTypeMapping(TypedDict, total=True): + package: str + name: str + model_id_field: str + + +PROVIDER_TYPE_OBJECT_MAPPING: dict[str, ProviderTypeMapping] = { + "AzureOpenAI.Chat": { + "package": "agent_framework.azure", + "name": "AzureOpenAIChatClient", + "model_id_field": "deployment_name", + }, + "AzureOpenAI.Assistants": { + "package": "agent_framework.azure", + "name": "AzureOpenAIAssistantsClient", + "model_id_field": "deployment_name", + }, + "AzureOpenAI.Responses": { + "package": "agent_framework.azure", + "name": "AzureOpenAIResponsesClient", + "model_id_field": "deployment_name", + }, + "OpenAI.Chat": { + "package": "agent_framework.openai", + "name": "OpenAIChatClient", + "model_id_field": "model_id", + }, + "OpenAI.Assistants": { + "package": "agent_framework.openai", + "name": "OpenAIAssistantsClient", + "model_id_field": "model_id", + }, + "OpenAI.Responses": { + "package": "agent_framework.openai", + "name": "OpenAIResponsesClient", + "model_id_field": "model_id", + }, + "AzureAIAgentClient": { + "package": "agent_framework.azure", + "name": "AzureAIAgentClient", + "model_id_field": "model_deployment_name", + }, + "AzureAIClient": { + "package": "agent_framework.azure", + "name": "AzureAIClient", + "model_id_field": "model_deployment_name", + }, + "Anthropic.Chat": { + "package": "agent_framework.anthropic", + "name": "AnthropicChatClient", + "model_id_field": "model_id", + }, +} + + +class DeclarativeLoaderError(AgentFrameworkException): + """Exception raised for errors in the declarative loader.""" + + pass + + +class ProviderLookupError(DeclarativeLoaderError): + """Exception raised for errors in provider type lookup.""" + + pass + + +class AgentFactory: + def __init__( + self, + *, + chat_client: ChatClientProtocol | None = None, + bindings: Mapping[str, Any] | None = None, + connections: Mapping[str, Any] | None = None, + client_kwargs: Mapping[str, Any] | None = None, + additional_mappings: Mapping[str, ProviderTypeMapping] | None = None, + default_provider: str = "AzureAIClient", + env_file: str | None = None, + ) -> None: + """Create the agent factory, with bindings. + + Args: + chat_client: An optional ChatClientProtocol instance to use as a dependency, + this will be passed to the ChatAgent that get's created. + If you need to create multiple agents with different chat clients, + do not pass this and instead provide the chat client in the YAML definition. + bindings: An optional dictionary of bindings to use when creating agents. + connections: An optional dictionary of connections to resolve ReferenceConnections. + client_kwargs: An optional dictionary of keyword arguments to pass to chat client constructor. + additional_mappings: An optional dictionary to extend the provider type to object mapping. + Should have the structure: + + ..code-block:: python + + additional_mappings = { + "Provider.ApiType": { + "package": "package.name", + "name": "ClassName", + "model_id_field": "field_name_in_constructor", + }, + ... + } + + Here, "Provider.ApiType" is the lookup key used when both provider and apiType are specified in the + model, "Provider" is also allowed. + Package refers to which model needs to be imported, Name is the class name of the ChatClientProtocol + implementation, and model_id_field is the name of the field in the constructor + that accepts the model.id value. + default_provider: The default provider used when model.provider is not specified, + default is "AzureAIClient". + env_file: An optional path to a .env file to load environment variables from. + """ + self.chat_client = chat_client + self.bindings = bindings + self.connections = connections + self.client_kwargs = client_kwargs or {} + self.additional_mappings = additional_mappings or {} + self.default_provider: str = default_provider + load_dotenv(dotenv_path=env_file) + + def create_agent_from_yaml_path(self, yaml_path: str | Path) -> ChatAgent: + """Create a ChatAgent from a YAML file path. + + This method does the following things: + 1. Loads the YAML file into a AgentSchema object using open and agent_schema_dispatch. + 2. Validates that the loaded object is a PromptAgent. + 3. Creates the appropriate ChatClient based on the model provider and apiType. + 4. Parses the tools, options, and response format from the PromptAgent. + 5. Creates and returns a ChatAgent instance with the configured properties. + + Args: + yaml_path: Path to the YAML file representation of a AgentSchema object + + Returns: + The ``ChatAgent`` instance created from the YAML file. + + Raises: + DeclarativeLoaderError: If the YAML does not represent a PromptAgent. + ProviderLookupError: If the provider type is unknown or unsupported. + ValueError: If a ReferenceConnection cannot be resolved. + ModuleNotFoundError: If the required module for the provider type cannot be imported. + AttributeError: If the required class for the provider type cannot be found in the module. + """ + if not isinstance(yaml_path, Path): + yaml_path = Path(yaml_path) + if not yaml_path.exists(): + raise DeclarativeLoaderError(f"YAML file not found at path: {yaml_path}") + with open(yaml_path) as f: + yaml_str = f.read() + return self.create_agent_from_yaml(yaml_str) + + def create_agent_from_yaml(self, yaml_str: str) -> ChatAgent: + """Create a ChatAgent from a YAML string. + + This method does the following things: + 1. Loads the YAML string into a AgentSchema object using agent_schema_dispatch. + 2. Validates that the loaded object is a PromptAgent. + 3. Creates the appropriate ChatClient based on the model provider and apiType. + 4. Parses the tools, options, and response format from the PromptAgent. + 5. Creates and returns a ChatAgent instance with the configured properties. + + Args: + yaml_str: YAML string representation of a AgentSchema object + + Returns: + The ``ChatAgent`` instance created from the YAML string. + + Raises: + DeclarativeLoaderError: If the YAML does not represent a PromptAgent. + ProviderLookupError: If the provider type is unknown or unsupported. + ValueError: If a ReferenceConnection cannot be resolved. + ModuleNotFoundError: If the required module for the provider type cannot be imported. + AttributeError: If the required class for the provider type cannot be found in the module. + """ + prompt_agent = agent_schema_dispatch(yaml.safe_load(yaml_str)) + if not isinstance(prompt_agent, PromptAgent): + raise DeclarativeLoaderError("Only yaml definitions for a PromptAgent are supported for agent creation.") + + # Step 1: Create the ChatClient + client = self._get_client(prompt_agent) + # Step 2: Get the chat options + chat_options = self._parse_chat_options(prompt_agent.model) + if tools := self._parse_tools(prompt_agent.tools): + chat_options["tools"] = tools + if output_schema := prompt_agent.outputSchema: + chat_options["response_format"] = _create_model_from_json_schema("agent", output_schema.to_json_schema()) + # Step 3: Create the agent instance + return ChatAgent( + chat_client=client, + name=prompt_agent.name, + description=prompt_agent.description, + instructions=prompt_agent.instructions, + **chat_options, + ) + + def _get_client(self, prompt_agent: PromptAgent) -> ChatClientProtocol: + """Create the ChatClientProtocol instance based on the PromptAgent model.""" + if not prompt_agent.model: + # if no model is defined, use the supplied chat_client + if self.chat_client: + return self.chat_client + raise DeclarativeLoaderError( + "ChatClient must be provided to create agent from PromptAgent, " + "alternatively define a model in the PromptAgent." + ) + + setup_dict: dict[str, Any] = {} + setup_dict.update(self.client_kwargs) + + # parse connections + if prompt_agent.model.connection: + match prompt_agent.model.connection: + case ApiKeyConnection(): + setup_dict["api_key"] = prompt_agent.model.connection.apiKey + if prompt_agent.model.connection.endpoint: + setup_dict["endpoint"] = prompt_agent.model.connection.endpoint + case RemoteConnection() | AnonymousConnection(): + setup_dict["endpoint"] = prompt_agent.model.connection.endpoint + case ReferenceConnection(): + if not self.connections: + raise ValueError("Connections must be provided to resolve ReferenceConnection") + # find the referenced connection + if prompt_agent.model.connection.name and ( + value := self.connections.get(prompt_agent.model.connection.name) + ): + setup_dict[prompt_agent.model.connection.name] = value + else: + raise ValueError( + f"ReferenceConnection with name {prompt_agent.model.connection.name} not found in provided " + "connections." + ) + + # Any client we create, needs a model.id + if not prompt_agent.model.id: + # if prompt_agent.model is defined, but no id, use the supplied chat_client + if self.chat_client: + return self.chat_client + # or raise, since we cannot create a client without model id + raise DeclarativeLoaderError( + "ChatClient must be provided to create agent from PromptAgent, or define model.id in the PromptAgent." + ) + # if provider is defined, use that, if possible with apiType, fallback to default_provider + mapping = self._retrieve_provider_configuration(prompt_agent.model) + module_name = mapping["package"] + class_name = mapping["name"] + module = __import__(module_name, fromlist=[class_name]) + agent_class = getattr(module, class_name) + setup_dict[mapping["model_id_field"]] = prompt_agent.model.id + return agent_class(**setup_dict) # type: ignore[no-any-return] + + def _parse_chat_options(self, model: Model | None) -> dict[str, Any]: + """Parse ModelOptions into chat options dictionary.""" + chat_options: dict[str, Any] = {} + if not model or not model.options or not isinstance(model.options, ModelOptions): + return chat_options + options = model.options + if options.frequencyPenalty is not None: + chat_options["frequency_penalty"] = options.frequencyPenalty + if options.presencePenalty is not None: + chat_options["presence_penalty"] = options.presencePenalty + if options.maxOutputTokens is not None: + chat_options["max_tokens"] = options.maxOutputTokens + if options.temperature is not None: + chat_options["temperature"] = options.temperature + if options.topP is not None: + chat_options["top_p"] = options.topP + if options.seed is not None: + chat_options["seed"] = options.seed + if options.stopSequences: + chat_options["stop"] = options.stopSequences + if options.allowMultipleToolCalls is not None: + chat_options["allow_multiple_tool_calls"] = options.allowMultipleToolCalls + if (chat_tool_mode := options.additionalProperties.pop("chatToolMode", None)) is not None: + chat_options["tool_choice"] = chat_tool_mode + if options.additionalProperties: + chat_options["additional_chat_options"] = options.additionalProperties + return chat_options + + def _parse_tools(self, tools: list[Tool] | None) -> list[ToolProtocol] | None: + """Parse tool resources into ToolProtocol instances.""" + if not tools: + return None + return [self._parse_tool(tool_resource) for tool_resource in tools] + + def _parse_tool(self, tool_resource: Tool) -> ToolProtocol: + """Parse a single tool resource into a ToolProtocol instance.""" + match tool_resource: + case FunctionTool(): + func: Callable[..., Any] | None = None + if self.bindings and tool_resource.bindings: + for binding in tool_resource.bindings: + if binding.name and (func := self.bindings.get(binding.name)): + break + return AIFunction( # type: ignore + name=tool_resource.name, # type: ignore + description=tool_resource.description, # type: ignore + input_model=tool_resource.parameters.to_json_schema() if tool_resource.parameters else None, + func=func, + ) + case WebSearchTool(): + return HostedWebSearchTool( + description=tool_resource.description, additional_properties=tool_resource.options + ) + case FileSearchTool(): + add_props: dict[str, Any] = {} + if tool_resource.ranker is not None: + add_props["ranker"] = tool_resource.ranker + if tool_resource.scoreThreshold is not None: + add_props["score_threshold"] = tool_resource.scoreThreshold + if tool_resource.filters: + add_props["filters"] = tool_resource.filters + return HostedFileSearchTool( + inputs=[HostedVectorStoreContent(id) for id in tool_resource.vectorStoreIds or []], + description=tool_resource.description, + max_results=tool_resource.maximumResultCount, + additional_properties=add_props, + ) + case CodeInterpreterTool(): + return HostedCodeInterpreterTool( + inputs=[HostedFileContent(file_id=file) for file in tool_resource.fileIds or []], + description=tool_resource.description, + ) + case McpTool(): + approval_mode: HostedMCPSpecificApproval | Literal["always_require", "never_require"] | None = None + if tool_resource.approvalMode is not None: + if tool_resource.approvalMode.kind == "always": + approval_mode = "always_require" + elif tool_resource.approvalMode.kind == "never": + approval_mode = "never_require" + elif isinstance(tool_resource.approvalMode, McpServerToolSpecifyApprovalMode): + approval_mode = {} + if tool_resource.approvalMode.alwaysRequireApprovalTools: + approval_mode["always_require_approval"] = ( + tool_resource.approvalMode.alwaysRequireApprovalTools + ) + if tool_resource.approvalMode.neverRequireApprovalTools: + approval_mode["never_require_approval"] = ( + tool_resource.approvalMode.neverRequireApprovalTools + ) + if not approval_mode: + approval_mode = None + return HostedMCPTool( + name=tool_resource.name, # type: ignore + description=tool_resource.description, + url=tool_resource.url, # type: ignore + allowed_tools=tool_resource.allowedTools, + approval_mode=approval_mode, + ) + case _: + raise ValueError(f"Unsupported tool kind: {tool_resource.kind}") + + def _retrieve_provider_configuration(self, model: Model) -> ProviderTypeMapping: + """Retrieve the provider configuration based on the model's provider and apiType. + + If only provider is specified, it will be used. + If both provider and apiType are specified, both will be used. + If neither is specified, the default_provider will be used. + + Args: + model: The Model instance containing provider and apiType information. + + Returns: + A dictionary containing the package, name, and model_id_field for the provider. + + Raises: + ProviderLookupError: If the provider type is not supported or can't be found. + """ + class_lookup = ( + f"{model.provider}.{model.apiType}" + if model.apiType + else f"{model.provider}" + if model.provider + else self.default_provider + ) + if class_lookup in self.additional_mappings: + return self.additional_mappings[class_lookup] + if class_lookup not in PROVIDER_TYPE_OBJECT_MAPPING: + raise ProviderLookupError(f"Unsupported provider type: {class_lookup}") + return PROVIDER_TYPE_OBJECT_MAPPING[class_lookup] diff --git a/python/packages/declarative/agent_framework_declarative/_models.py b/python/packages/declarative/agent_framework_declarative/_models.py new file mode 100644 index 00000000000..9ddab17d87b --- /dev/null +++ b/python/packages/declarative/agent_framework_declarative/_models.py @@ -0,0 +1,1101 @@ +# Copyright (c) Microsoft. All rights reserved. +import os +import sys +from collections.abc import MutableMapping +from typing import Any, Literal, TypeVar, Union + +from agent_framework import get_logger +from agent_framework._serialization import SerializationMixin + +try: + from powerfx import Engine + + engine = Engine() +except ImportError: + engine = None + +if sys.version_info >= (3, 11): + from typing import overload # pragma: no cover +else: + from typing_extensions import overload # pragma: no cover + +logger = get_logger("agent_framework.declarative") + + +@overload +def _try_powerfx_eval(value: None, log_value: bool = True) -> None: ... + + +@overload +def _try_powerfx_eval(value: str, log_value: bool = True) -> str: ... + + +def _try_powerfx_eval(value: str | None, log_value: bool = True) -> str | None: + """Check if a value refers to a environment variable and parse it if so. + + Args: + value: The value to check. + log_value: Whether to log the full value on error or just a snippet. + """ + if value is None: + return value + if not value.startswith("="): + return value + if engine is None: + logger.warning( + "PowerFx engine not available for evaluating values starting with '='. " + "Ensure you are on python 3.13 or less and have the powerfx package installed. " + "Otherwise replace all powerfx statements in your yaml with strings." + ) + return value + try: + return engine.eval(value[1:], symbols={"Env": dict(os.environ)}) + except Exception as exc: + if log_value: + logger.debug("PowerFx evaluation failed for value '%s': %s", value, exc) + else: + logger.debug("PowerFx evaluation failed for value (first five characters shown) '%s': %s", value[:5], exc) + return value + + +class Binding(SerializationMixin): + """Object representing a tool argument binding.""" + + def __init__( + self, + name: str | None = None, + input: str | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.input = _try_powerfx_eval(input) + + +class Property(SerializationMixin): + """Object representing a property in a schema.""" + + def __init__( + self, + name: str | None = None, + kind: str | None = None, + description: str | None = None, + required: bool | None = None, + default: Any | None = None, + example: Any | None = None, + enum: list[Any] | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.kind = _try_powerfx_eval(kind) + self.description = _try_powerfx_eval(description) + self.required = required + self.default = default + self.example = example + self.enum = enum or [] + + @classmethod + def from_dict( + cls, value: MutableMapping[str, Any], /, *, dependencies: MutableMapping[str, Any] | None = None + ) -> "Property": + """Create a Property instance from a dictionary, dispatching to the appropriate subclass.""" + # Only dispatch if we're being called on the base Property class + if cls is not Property: + # We're being called on a subclass, use the normal from_dict + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + # Filter out 'type' (if it exists) field which is not a Property parameter + value.pop("type", None) + kind = value.get("kind", "") + if kind == "array": + return ArrayProperty.from_dict(value, dependencies=dependencies) + if kind == "object": + return ObjectProperty.from_dict(value, dependencies=dependencies) + # Default to Property for kind="property" or empty + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + +class ArrayProperty(Property): + """Object representing an array property.""" + + def __init__( + self, + name: str | None = None, + kind: str = "array", + description: str | None = None, + required: bool | None = None, + default: Any | None = None, + example: Any | None = None, + enum: list[Any] | None = None, + items: Property | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + required=required, + default=default, + example=example, + enum=enum, + ) + if not isinstance(items, Property) and items is not None: + items = Property.from_dict(items) + self.items = items + + +class ObjectProperty(Property): + """Object representing an object property.""" + + def __init__( + self, + name: str | None = None, + kind: str = "object", + description: str | None = None, + required: bool | None = None, + default: Any | None = None, + example: Any | None = None, + enum: list[Any] | None = None, + properties: list[Property] | dict[str, Property] | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + required=required, + default=default, + example=example, + enum=enum, + ) + converted_properties: list[Property] = [] + if isinstance(properties, list): + for prop in properties: + if not isinstance(prop, Property): + prop = Property.from_dict(prop) + converted_properties.append(prop) + elif isinstance(properties, dict): + for k, v in properties.items(): + temp_prop = {"name": k, **v} + prop = Property.from_dict(temp_prop) + converted_properties.append(prop) + self.properties = converted_properties + + +class PropertySchema(SerializationMixin): + """Object representing a property schema.""" + + def __init__( + self, + examples: list[dict[str, Any]] | None = None, + strict: bool = False, + properties: list[Property] | dict[str, Property] | None = None, + ) -> None: + self.examples = examples or [] + self.strict = strict + converted_properties: list[Property] = [] + if isinstance(properties, list): + for prop in properties: + if not isinstance(prop, Property): + prop = Property.from_dict(prop) + converted_properties.append(prop) + elif isinstance(properties, dict): + for k, v in properties.items(): + temp_prop = {"name": k, **v} + prop = Property.from_dict(temp_prop) + converted_properties.append(prop) + self.properties = converted_properties + + @classmethod + def from_dict( + cls, value: MutableMapping[str, Any], /, *, dependencies: MutableMapping[str, Any] | None = None + ) -> "PropertySchema": + """Create a PropertySchema instance from a dictionary, filtering out 'kind' field.""" + # Filter out 'kind', 'type', 'name', and 'description' fields that may appear in YAML + # but aren't PropertySchema params + kwargs = {k: v for k, v in value.items() if k not in ("type", "kind", "name", "description")} + return SerializationMixin.from_dict.__func__(cls, kwargs, dependencies=dependencies) # type: ignore[misc] + + def to_json_schema(self) -> dict[str, Any]: + """Get a schema out of this PropertySchema to create pydantic models.""" + json_schema = self.to_dict(exclude={"type"}, exclude_none=True) + new_props = {} + for prop in json_schema.get("properties", []): + prop_name = prop.pop("name") + prop["type"] = prop.pop("kind", None) + new_props[prop_name] = prop + json_schema["properties"] = new_props + return json_schema + + +TConnection = TypeVar("TConnection", bound="Connection") + + +class Connection(SerializationMixin): + """Object representing a connection specification.""" + + def __init__( + self, + kind: Literal["reference", "remote", "key", "anonymous"], + authenticationMode: str | None = None, + usageDescription: str | None = None, + ) -> None: + self.kind = kind + self.authenticationMode = _try_powerfx_eval(authenticationMode) + self.usageDescription = _try_powerfx_eval(usageDescription) + + @classmethod + def from_dict( + cls: type[TConnection], + value: MutableMapping[str, Any], + /, + *, + dependencies: MutableMapping[str, Any] | None = None, + ) -> TConnection: + """Create a Connection instance from a dictionary, dispatching to the appropriate subclass.""" + # Only dispatch if we're being called on the base Connection class + if cls is not Connection: + # We're being called on a subclass, use the normal from_dict + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + kind = value.get("kind", "") + if kind == "reference": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + ReferenceConnection, value, dependencies=dependencies + ) + if kind == "remote": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + RemoteConnection, value, dependencies=dependencies + ) + if kind == "key": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + ApiKeyConnection, value, dependencies=dependencies + ) + if kind == "anonymous": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + AnonymousConnection, value, dependencies=dependencies + ) + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + +class ReferenceConnection(Connection): + """Object representing a reference connection.""" + + def __init__( + self, + kind: Literal["reference"] = "reference", + authenticationMode: str | None = None, + usageDescription: str | None = None, + name: str | None = None, + target: str | None = None, + ) -> None: + super().__init__( + kind=kind, + authenticationMode=authenticationMode, + usageDescription=usageDescription, + ) + self.name = _try_powerfx_eval(name) + self.target = _try_powerfx_eval(target) + + +class RemoteConnection(Connection): + """Object representing a remote connection.""" + + def __init__( + self, + kind: Literal["remote"] = "remote", + authenticationMode: str | None = None, + usageDescription: str | None = None, + name: str | None = None, + endpoint: str | None = None, + ) -> None: + super().__init__( + kind=kind, + authenticationMode=authenticationMode, + usageDescription=usageDescription, + ) + self.name = _try_powerfx_eval(name) + self.endpoint = _try_powerfx_eval(endpoint) + + +class ApiKeyConnection(Connection): + """Object representing an API key connection.""" + + def __init__( + self, + kind: Literal["key"] = "key", + authenticationMode: str | None = None, + usageDescription: str | None = None, + endpoint: str | None = None, + apiKey: str | None = None, + key: str | None = None, + ) -> None: + super().__init__( + kind=kind, + authenticationMode=authenticationMode, + usageDescription=usageDescription, + ) + self.endpoint = _try_powerfx_eval(endpoint) + # Support both 'apiKey' and 'key' fields, with 'key' taking precedence if both are provided + self.apiKey = _try_powerfx_eval(key if key else apiKey, False) + + +class AnonymousConnection(Connection): + """Object representing an anonymous connection.""" + + def __init__( + self, + kind: Literal["anonymous"] = "anonymous", + authenticationMode: str | None = None, + usageDescription: str | None = None, + endpoint: str | None = None, + ) -> None: + super().__init__( + kind=kind, + authenticationMode=authenticationMode, + usageDescription=usageDescription, + ) + self.endpoint = _try_powerfx_eval(endpoint) + + +Connections = Union[ + ReferenceConnection, + RemoteConnection, + ApiKeyConnection, + AnonymousConnection, +] + + +class ModelOptions(SerializationMixin): + """Object representing model options.""" + + def __init__( + self, + frequencyPenalty: float | None = None, + maxOutputTokens: int | None = None, + presencePenalty: float | None = None, + seed: int | None = None, + temperature: float | None = None, + topK: int | None = None, + topP: float | None = None, + stopSequences: list[str] | None = None, + allowMultipleToolCalls: bool | None = None, + additionalProperties: dict[str, Any] | None = None, + **kwargs: Any, + ) -> None: + self.frequencyPenalty = frequencyPenalty + self.maxOutputTokens = maxOutputTokens + self.presencePenalty = presencePenalty + self.seed = seed + self.temperature = temperature + self.topK = topK + self.topP = topP + self.stopSequences = stopSequences or [] + self.allowMultipleToolCalls = allowMultipleToolCalls + # Merge any additional properties from kwargs into additionalProperties + self.additionalProperties = additionalProperties or {} + self.additionalProperties.update(kwargs) + + +class Model(SerializationMixin): + """Object representing a model specification.""" + + def __init__( + self, + id: str | None = None, + provider: str | None = None, + apiType: str | None = None, + connection: Connections | None = None, + options: ModelOptions | None = None, + ) -> None: + self.id = _try_powerfx_eval(id) + self.provider = _try_powerfx_eval(provider) + self.apiType = _try_powerfx_eval(apiType) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + if not isinstance(options, ModelOptions) and options is not None: + options = ModelOptions.from_dict(options) + self.options = options + + +class Format(SerializationMixin): + """Object representing template format.""" + + def __init__( + self, + kind: str | None = None, + strict: bool = False, + options: dict[str, Any] | None = None, + ) -> None: + self.kind = _try_powerfx_eval(kind) + self.strict = strict + self.options = options or {} + + +class Parser(SerializationMixin): + """Object representing template parser.""" + + def __init__( + self, + kind: str | None = None, + options: dict[str, Any] | None = None, + ) -> None: + self.kind = _try_powerfx_eval(kind) + self.options = options or {} + + +class Template(SerializationMixin): + """Object representing a template configuration.""" + + def __init__( + self, + format: Format | None = None, + parser: Parser | None = None, + ) -> None: + if not isinstance(format, Format) and format is not None: + format = Format.from_dict(format) + self.format = format + if not isinstance(parser, Parser) and parser is not None: + parser = Parser.from_dict(parser) + self.parser = parser + + +class AgentDefinition(SerializationMixin): + """Object representing a prompt specification.""" + + def __init__( + self, + kind: str | None = None, + name: str | None = None, + displayName: str | None = None, + description: str | None = None, + metadata: dict[str, Any] | None = None, + inputSchema: PropertySchema | None = None, + outputSchema: PropertySchema | None = None, + ) -> None: + self.kind = _try_powerfx_eval(kind) + self.name = _try_powerfx_eval(name) + self.displayName = _try_powerfx_eval(displayName) + self.description = _try_powerfx_eval(description) + self.metadata = metadata + if not isinstance(inputSchema, PropertySchema) and inputSchema is not None: + inputSchema = PropertySchema.from_dict(inputSchema) + self.inputSchema = inputSchema + if not isinstance(outputSchema, PropertySchema) and outputSchema is not None: + outputSchema = PropertySchema.from_dict(outputSchema) + self.outputSchema = outputSchema + + @classmethod + def from_dict( + cls, value: MutableMapping[str, Any], /, *, dependencies: MutableMapping[str, Any] | None = None + ) -> "AgentDefinition": + """Create an AgentDefinition instance from a dictionary, dispatching to the appropriate subclass.""" + # Only dispatch if we're being called on the base AgentDefinition class + if cls is not AgentDefinition: + # We're being called on a subclass, use the normal from_dict + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + kind = value.get("kind", "") + if kind == "Prompt" or kind == "Agent": + return PromptAgent.from_dict(value, dependencies=dependencies) + # Default to AgentDefinition + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + +TTool = TypeVar("TTool", bound="Tool") + + +class Tool(SerializationMixin): + """Base class for tools.""" + + def __init__( + self, + name: str | None = None, + kind: str | None = None, + description: str | None = None, + bindings: list[Binding] | dict[str, Any] | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.kind = _try_powerfx_eval(kind) + self.description = _try_powerfx_eval(description) + converted_bindings: list[Binding] = [] + if isinstance(bindings, list): + for binding in bindings: + if not isinstance(binding, Binding): + binding = Binding.from_dict(binding) + converted_bindings.append(binding) + elif isinstance(bindings, dict): + for k, v in bindings.items(): + temp_binding = {"name": k, "input": v} if isinstance(v, str) else {"name": k, **v} + binding = Binding.from_dict(temp_binding) + converted_bindings.append(binding) + self.bindings = converted_bindings + + @classmethod + def from_dict( + cls: type[TTool], value: MutableMapping[str, Any], /, *, dependencies: MutableMapping[str, Any] | None = None + ) -> "TTool": + """Create a Tool instance from a dictionary, dispatching to the appropriate subclass.""" + # Only dispatch if we're being called on the base Tool class + if cls is not Tool: + # We're being called on a subclass, use the normal from_dict + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + kind = value.get("kind", "") + if kind == "function": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + FunctionTool, value, dependencies=dependencies + ) + if kind == "custom": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + CustomTool, value, dependencies=dependencies + ) + if kind == "web_search": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + WebSearchTool, value, dependencies=dependencies + ) + if kind == "file_search": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + FileSearchTool, value, dependencies=dependencies + ) + if kind == "mcp": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + McpTool, value, dependencies=dependencies + ) + if kind == "openapi": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + OpenApiTool, value, dependencies=dependencies + ) + if kind == "code_interpreter": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + CodeInterpreterTool, value, dependencies=dependencies + ) + # Default to base Tool class + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + +class FunctionTool(Tool): + """Object representing a function tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "function", + description: str | None = None, + bindings: list[Binding] | None = None, + parameters: PropertySchema | list[Property] | dict[str, Any] | None = None, + strict: bool = False, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if isinstance(parameters, list): + # If parameters is a list, wrap it in a PropertySchema + parameters = PropertySchema(properties=parameters) + elif not isinstance(parameters, PropertySchema) and parameters is not None: + parameters = PropertySchema.from_dict(parameters) + self.parameters = parameters + self.strict = strict + + +class CustomTool(Tool): + """Object representing a custom tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "custom", + description: str | None = None, + bindings: list[Binding] | None = None, + connection: Connection | None = None, + options: dict[str, Any] | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + self.options = options or {} + + +class WebSearchTool(Tool): + """Object representing a web search tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "web_search", + description: str | None = None, + bindings: list[Binding] | None = None, + connection: Connection | None = None, + options: dict[str, Any] | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + self.options = options or {} + + +class FileSearchTool(Tool): + """Object representing a file search tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "file_search", + description: str | None = None, + bindings: list[Binding] | None = None, + connection: Connection | None = None, + vectorStoreIds: list[str] | None = None, + maximumResultCount: int | None = None, + ranker: str | None = None, + scoreThreshold: float | None = None, + filters: dict[str, Any] | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + self.vectorStoreIds = vectorStoreIds or [] + self.maximumResultCount = maximumResultCount + self.ranker = _try_powerfx_eval(ranker) + self.scoreThreshold = scoreThreshold + self.filters = filters or {} + + +class McpServerApprovalMode(SerializationMixin): + """Base class for MCP server approval modes.""" + + def __init__( + self, + kind: str | None = None, + ) -> None: + self.kind = _try_powerfx_eval(kind) + + +class McpServerToolAlwaysRequireApprovalMode(McpServerApprovalMode): + """MCP server tool always require approval mode.""" + + def __init__( + self, + kind: str = "always", + ) -> None: + super().__init__(kind=kind) + + +class McpServerToolNeverRequireApprovalMode(McpServerApprovalMode): + """MCP server tool never require approval mode.""" + + def __init__( + self, + kind: str = "never", + ) -> None: + super().__init__(kind=kind) + + +class McpServerToolSpecifyApprovalMode(McpServerApprovalMode): + """MCP server tool specify approval mode.""" + + def __init__( + self, + kind: str = "specify", + alwaysRequireApprovalTools: list[str] | None = None, + neverRequireApprovalTools: list[str] | None = None, + ) -> None: + super().__init__(kind=kind) + self.alwaysRequireApprovalTools = alwaysRequireApprovalTools + self.neverRequireApprovalTools = neverRequireApprovalTools + + +class McpTool(Tool): + """Object representing an MCP tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "mcp", + description: str | None = None, + bindings: list[Binding] | None = None, + connection: Connection | None = None, + serverName: str | None = None, + serverDescription: str | None = None, + approvalMode: McpServerApprovalMode | None = None, + allowedTools: list[str] | None = None, + url: str | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + self.serverName = _try_powerfx_eval(serverName) + self.serverDescription = _try_powerfx_eval(serverDescription) + if not isinstance(approvalMode, McpServerApprovalMode) and approvalMode is not None: + # Handle simplified string format: "always" -> {"kind": "always"} + if isinstance(approvalMode, str): + approvalMode = McpServerApprovalMode.from_dict({"kind": approvalMode}) + else: + approvalMode = McpServerApprovalMode.from_dict(approvalMode) + self.approvalMode = approvalMode + self.allowedTools = allowedTools or [] + self.url = _try_powerfx_eval(url) + + +class OpenApiTool(Tool): + """Object representing an OpenAPI tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "openapi", + description: str | None = None, + bindings: list[Binding] | None = None, + connection: Connection | None = None, + specification: str | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + if not isinstance(connection, Connection) and connection is not None: + connection = Connection.from_dict(connection) + self.connection = connection + self.specification = _try_powerfx_eval(specification) + + +class CodeInterpreterTool(Tool): + """Object representing a code interpreter tool.""" + + def __init__( + self, + name: str | None = None, + kind: str = "code_interpreter", + description: str | None = None, + bindings: list[Binding] | None = None, + fileIds: list[str] | None = None, + ) -> None: + super().__init__( + name=name, + kind=kind, + description=description, + bindings=bindings, + ) + self.fileIds = fileIds or [] + + +class PromptAgent(AgentDefinition): + """Object representing a prompt agent specification.""" + + def __init__( + self, + kind: str = "Prompt", + name: str | None = None, + displayName: str | None = None, + description: str | None = None, + metadata: dict[str, Any] | None = None, + inputSchema: PropertySchema | None = None, + outputSchema: PropertySchema | None = None, + model: Model | dict[str, Any] | None = None, + tools: list[Tool] | None = None, + template: Template | dict[str, Any] | None = None, + instructions: str | None = None, + additionalInstructions: str | None = None, + ) -> None: + super().__init__( + kind=kind, + name=name, + displayName=displayName, + description=description, + metadata=metadata, + inputSchema=inputSchema, + outputSchema=outputSchema, + ) + if not isinstance(model, Model) and model is not None: + model = Model.from_dict(model) + self.model = model + converted_tools: list[Tool] = [] + for tool in tools or []: + if not isinstance(tool, Tool): + tool = Tool.from_dict(tool) + converted_tools.append(tool) + self.tools = converted_tools + if not isinstance(template, Template) and template is not None: + template = Template.from_dict(template) + self.template = template + self.instructions = _try_powerfx_eval(instructions) + self.additionalInstructions = _try_powerfx_eval(additionalInstructions) + + +class Resource(SerializationMixin): + """Object representing a resource.""" + + def __init__( + self, + name: str | None = None, + kind: str | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.kind = _try_powerfx_eval(kind) + + @classmethod + def from_dict( + cls, value: MutableMapping[str, Any], /, *, dependencies: MutableMapping[str, Any] | None = None + ) -> "Resource": + """Create a Resource instance from a dictionary, dispatching to the appropriate subclass.""" + # Only dispatch if we're being called on the base Resource class + if cls is not Resource: + # We're being called on a subclass, use the normal from_dict + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + kind = value.get("kind", "") + if kind == "model": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + ModelResource, value, dependencies=dependencies + ) + if kind == "tool": + return SerializationMixin.from_dict.__func__( # type: ignore[misc] + ToolResource, value, dependencies=dependencies + ) + return SerializationMixin.from_dict.__func__(cls, value, dependencies=dependencies) # type: ignore[misc] + + +class ModelResource(Resource): + """Object representing a model resource.""" + + def __init__( + self, + kind: str = "model", + name: str | None = None, + id: str | None = None, + ) -> None: + super().__init__(kind=kind, name=name) + self.id = _try_powerfx_eval(id) + + +class ToolResource(Resource): + """Object representing a tool resource.""" + + def __init__( + self, + kind: str = "tool", + name: str | None = None, + id: str | None = None, + options: dict[str, Any] | None = None, + ) -> None: + super().__init__(kind=kind, name=name) + self.id = _try_powerfx_eval(id) + self.options = options or {} + + +class ProtocolVersionRecord(SerializationMixin): + """Object representing a protocol version record.""" + + def __init__( + self, + protocol: str | None = None, + version: str | None = None, + ) -> None: + self.protocol = _try_powerfx_eval(protocol) + self.version = _try_powerfx_eval(version) + + +class EnvironmentVariable(SerializationMixin): + """Object representing an environment variable.""" + + def __init__( + self, + name: str | None = None, + value: str | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.value = _try_powerfx_eval(value) + + +class AgentManifest(SerializationMixin): + """Object representing an agent manifest.""" + + def __init__( + self, + name: str | None = None, + displayName: str | None = None, + description: str | None = None, + metadata: dict[str, Any] | None = None, + template: AgentDefinition | None = None, + parameters: PropertySchema | None = None, + resources: list[Resource] | dict[str, Any] | None = None, + ) -> None: + self.name = _try_powerfx_eval(name) + self.displayName = _try_powerfx_eval(displayName) + self.description = _try_powerfx_eval(description) + self.metadata = metadata or {} + if not isinstance(template, AgentDefinition) and template is not None: + template = AgentDefinition.from_dict(template) + self.template = template or AgentDefinition() + if not isinstance(parameters, PropertySchema) and parameters is not None: + parameters = PropertySchema.from_dict(parameters) + self.parameters = parameters or PropertySchema() + converted_resources: list[Resource] = [] + if isinstance(resources, list): + for resource in resources: + if not isinstance(resource, Resource): + resource = Resource.from_dict(resource) + converted_resources.append(resource) + elif isinstance(resources, dict): + for k, v in resources.items(): + temp_resource = {"name": k, **v} + resource = Resource.from_dict(temp_resource) + converted_resources.append(resource) + self.resources = converted_resources + + +AgentSchemaSpec = Union[ + AgentManifest, + AgentDefinition, + PromptAgent, + Tool, + FunctionTool, + CustomTool, + WebSearchTool, + FileSearchTool, + McpTool, + OpenApiTool, + CodeInterpreterTool, + Resource, + ModelResource, + ToolResource, + Connection, + ReferenceConnection, + RemoteConnection, + ApiKeyConnection, + AnonymousConnection, + Property, + ArrayProperty, + ObjectProperty, + PropertySchema, + McpServerApprovalMode, + McpServerToolAlwaysRequireApprovalMode, + McpServerToolNeverRequireApprovalMode, + McpServerToolSpecifyApprovalMode, + Binding, + Format, + Parser, + Template, + Model, + ModelOptions, + ProtocolVersionRecord, + EnvironmentVariable, +] + + +def agent_schema_dispatch(schema: dict[str, Any]) -> AgentSchemaSpec | None: + """Create a component instance from a dictionary, dispatching to the appropriate class based on 'kind' field.""" + kind = schema.get("kind") + + # If no kind field, assume it's an AgentManifest + if kind is None: + return AgentManifest.from_dict(schema) + # Match on the kind field to determine which class to instantiate + match kind.lower(): + # Agent types + case "prompt": + return PromptAgent.from_dict(schema) + case "agent": + return AgentDefinition.from_dict(schema) + + # Resource types + case "tool": + return ToolResource.from_dict(schema) + case "model": + return ModelResource.from_dict(schema) + case "resource": + return Resource.from_dict(schema) + + # Tool types + case "function": + return FunctionTool.from_dict(schema) + case "custom": + return CustomTool.from_dict(schema) + case "web_search": + return WebSearchTool.from_dict(schema) + case "file_search": + return FileSearchTool.from_dict(schema) + case "mcp": + return McpTool.from_dict(schema) + case "openapi": + return OpenApiTool.from_dict(schema) + case "code_interpreter": + return CodeInterpreterTool.from_dict(schema) + + # Connection types + case "reference": + return ReferenceConnection.from_dict(schema) + case "remote": + return RemoteConnection.from_dict(schema) + case "key": + return ApiKeyConnection.from_dict(schema) + case "anonymous": + return AnonymousConnection.from_dict(schema) + case "connection": + return Connection.from_dict(schema) + + # Property types + case "array": + return ArrayProperty.from_dict(schema) + case "object": + return ObjectProperty.from_dict(schema) + case "property": + return Property.from_dict(schema) + + # MCP Server Approval Mode types + case "always": + return McpServerToolAlwaysRequireApprovalMode.from_dict(schema) + case "never": + return McpServerToolNeverRequireApprovalMode.from_dict(schema) + case "specify": + return McpServerToolSpecifyApprovalMode.from_dict(schema) + case "approval_mode": + return McpServerApprovalMode.from_dict(schema) + + # Other component types + case "binding": + return Binding.from_dict(schema) + case "format": + return Format.from_dict(schema) + case "parser": + return Parser.from_dict(schema) + case "template": + return Template.from_dict(schema) + case "model": + return Model.from_dict(schema) + case "model_options": + return ModelOptions.from_dict(schema) + case "property_schema": + return PropertySchema.from_dict(schema) + case "protocol_version": + return ProtocolVersionRecord.from_dict(schema) + case "environment_variable": + return EnvironmentVariable.from_dict(schema) + + # Unknown kind + case _: + return None diff --git a/python/packages/declarative/pyproject.toml b/python/packages/declarative/pyproject.toml new file mode 100644 index 00000000000..9a7d7bc050c --- /dev/null +++ b/python/packages/declarative/pyproject.toml @@ -0,0 +1,97 @@ +[project] +name = "agent-framework-declarative" +description = "Declarative specification support for Microsoft Agent Framework." +authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] +readme = "README.md" +requires-python = ">=3.10" +version = "1.0.0b251120" +license-files = ["LICENSE"] +urls.homepage = "https://aka.ms/agent-framework" +urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" +urls.release_notes = "https://github.com/microsoft/agent-framework/releases?q=tag%3Apython-1&expanded=true" +urls.issues = "https://github.com/microsoft/agent-framework/issues" +classifiers = [ + "License :: OSI Approved :: MIT License", + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Typing :: Typed", +] +dependencies = [ + "agent-framework-core", + "powerfx>=0.0.31; python_version < '3.14'", + "pyyaml>=6.0,<7.0", +] +[dependency-groups] +dev = [ + "types-PyYaml" +] + +[tool.uv] +prerelease = "if-necessary-or-explicit" +environments = [ + "sys_platform == 'darwin'", + "sys_platform == 'linux'", + "sys_platform == 'win32'" +] + + +[tool.uv-dynamic-versioning] +fallback-version = "0.0.0" +[tool.pytest.ini_options] +testpaths = 'tests' +addopts = "-ra -q -r fEX" +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "function" +filterwarnings = [ + "ignore:Support for class-based `config` is deprecated:DeprecationWarning:pydantic.*" +] +timeout = 120 + +[tool.ruff] +extend = "../../pyproject.toml" + +[tool.coverage.run] +omit = [ + "**/__init__.py" +] + +[tool.pyright] +extends = "../../pyproject.toml" +exclude = ['tests'] + +[tool.mypy] +plugins = ['pydantic.mypy'] +strict = true +python_version = "3.10" +ignore_missing_imports = true +disallow_untyped_defs = true +no_implicit_optional = true +check_untyped_defs = true +warn_return_any = true +show_error_codes = true +warn_unused_ignores = false +disallow_incomplete_defs = true +disallow_untyped_decorators = true +exclude = [ + '_models.py$', +] + +[tool.bandit] +targets = ["agent_framework_declarative"] +exclude_dirs = ["tests"] + +[tool.poe] +executor.type = "uv" +include = "../../shared_tasks.toml" +[tool.poe.tasks] +mypy = "mypy --config-file $POE_ROOT/pyproject.toml agent_framework_declarative" +test = "pytest --cov=agent_framework_declarative --cov-report=term-missing:skip-covered tests" + +[build-system] +requires = ["flit-core >= 3.11,<4.0"] +build-backend = "flit_core.buildapi" diff --git a/python/packages/declarative/tests/test_declarative_loader.py b/python/packages/declarative/tests/test_declarative_loader.py new file mode 100644 index 00000000000..daf4ab06f8a --- /dev/null +++ b/python/packages/declarative/tests/test_declarative_loader.py @@ -0,0 +1,456 @@ +# Copyright (c) Microsoft. All rights reserved. + +import sys +from pathlib import Path +from typing import Any + +import pytest +import yaml + +from agent_framework_declarative._models import ( + AgentDefinition, + AgentManifest, + AnonymousConnection, + ApiKeyConnection, + ArrayProperty, + CodeInterpreterTool, + Connection, + CustomTool, + FileSearchTool, + FunctionTool, + McpServerApprovalMode, + McpServerToolAlwaysRequireApprovalMode, + McpServerToolNeverRequireApprovalMode, + McpServerToolSpecifyApprovalMode, + McpTool, + ModelResource, + ObjectProperty, + OpenApiTool, + PromptAgent, + Property, + PropertySchema, + ReferenceConnection, + RemoteConnection, + Resource, + ToolResource, + WebSearchTool, + agent_schema_dispatch, +) + +pytestmark = pytest.mark.skipif(sys.version_info >= (3, 14), reason="Skipping on Python 3.14+") + + +@pytest.mark.parametrize( + "yaml_content,expected_type,expected_attributes", + [ + # Agent Manifest (no kind field) + ( + """ +name: my-manifest +description: A test manifest +""", + AgentManifest, + {"name": "my-manifest", "description": "A test manifest"}, + ), + # PromptAgent + ( + """ +kind: Prompt +name: assistant +description: A helpful assistant +model: + id: gpt-4 +""", + PromptAgent, + {"name": "assistant", "description": "A helpful assistant"}, + ), + # AgentDefinition + ( + """ +kind: Agent +name: base-agent +description: A base agent +""", + AgentDefinition, + {"name": "base-agent", "description": "A base agent"}, + ), + # ModelResource + ( + """ +kind: Model +name: my-model +id: gpt-4 +""", + ModelResource, + {"name": "my-model", "id": "gpt-4"}, + ), + # ToolResource + ( + """ +kind: Tool +name: my-tool +id: search-tool +""", + ToolResource, + {"name": "my-tool", "id": "search-tool"}, + ), + # Resource (base) + ( + """ +kind: Resource +name: generic-resource +""", + Resource, + {"name": "generic-resource"}, + ), + # FunctionTool + ( + """ +kind: function +name: get_weather +description: Get the weather +""", + FunctionTool, + {"name": "get_weather", "description": "Get the weather"}, + ), + # CustomTool + ( + """ +kind: custom +name: custom_tool +description: A custom tool +""", + CustomTool, + {"name": "custom_tool", "description": "A custom tool"}, + ), + # WebSearchTool + ( + """ +kind: web_search +name: search +description: Search the web +""", + WebSearchTool, + {"name": "search", "description": "Search the web"}, + ), + # FileSearchTool + ( + """ +kind: file_search +name: file_search +description: Search files +""", + FileSearchTool, + {"name": "file_search", "description": "Search files"}, + ), + # McpTool + ( + """ +kind: mcp +name: mcp_tool +description: An MCP tool +serverName: my-server +""", + McpTool, + {"name": "mcp_tool", "serverName": "my-server"}, + ), + # OpenApiTool + ( + """ +kind: openapi +name: api_tool +description: An OpenAPI tool +specification: https://api.example.com/openapi.json +""", + OpenApiTool, + {"name": "api_tool", "specification": "https://api.example.com/openapi.json"}, + ), + # CodeInterpreterTool + ( + """ +kind: code_interpreter +name: code_tool +description: A code interpreter tool +""", + CodeInterpreterTool, + {"name": "code_tool", "description": "A code interpreter tool"}, + ), + # ReferenceConnection + ( + """ +kind: reference +name: my-connection +target: target-connection +""", + ReferenceConnection, + {"name": "my-connection", "target": "target-connection"}, + ), + # RemoteConnection + ( + """ +kind: remote +endpoint: https://api.example.com +""", + RemoteConnection, + {"endpoint": "https://api.example.com"}, + ), + # ApiKeyConnection + ( + """ +kind: key +apiKey: secret-key +endpoint: https://api.example.com +""", + ApiKeyConnection, + {"apiKey": "secret-key", "endpoint": "https://api.example.com"}, + ), + # AnonymousConnection + ( + """ +kind: anonymous +endpoint: https://api.example.com +""", + AnonymousConnection, + {"endpoint": "https://api.example.com"}, + ), + # Connection (base) + ( + """ +kind: connection +authenticationMode: oauth +""", + Connection, + {"authenticationMode": "oauth"}, + ), + # ArrayProperty + ( + """ +kind: array +name: items +description: An array of items +""", + ArrayProperty, + {"name": "items", "description": "An array of items"}, + ), + # ObjectProperty + ( + """ +kind: object +name: config +description: Configuration object +""", + ObjectProperty, + {"name": "config", "description": "Configuration object"}, + ), + # Property (base) + ( + """ +kind: property +name: field +description: A property field +""", + Property, + {"name": "field", "description": "A property field"}, + ), + # McpServerToolAlwaysRequireApprovalMode + ( + """ +kind: always +""", + McpServerToolAlwaysRequireApprovalMode, + {}, + ), + # McpServerToolNeverRequireApprovalMode + ( + """ +kind: never +""", + McpServerToolNeverRequireApprovalMode, + {}, + ), + # McpServerToolSpecifyApprovalMode + ( + """ +kind: specify +alwaysRequireApprovalTools: [] +neverRequireApprovalTools: [] +""", + McpServerToolSpecifyApprovalMode, + {}, + ), + # McpServerApprovalMode (base) + ( + """ +kind: approval_mode +""", + McpServerApprovalMode, + {}, + ), + ], +) +def test_agent_schema_dispatch_all_types(yaml_content: str, expected_type: type, expected_attributes: dict[str, Any]): + """Test that agent_schema_dispatch correctly loads all MAML object types.""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + + # Check the type is correct + assert isinstance(result, expected_type), f"Expected {expected_type.__name__}, got {type(result).__name__}" + + # Check expected attributes + for attr_name, attr_value in expected_attributes.items(): + assert hasattr(result, attr_name), f"Result missing attribute '{attr_name}'" + assert getattr(result, attr_name) == attr_value, ( + f"Attribute '{attr_name}' has value {getattr(result, attr_name)}, expected {attr_value}" + ) + + +def test_agent_schema_dispatch_unknown_kind(): + """Test that agent_schema_dispatch returns None for unknown kind.""" + yaml_content = """ +kind: unknown_type +name: test +""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + assert result is None + + +def test_agent_schema_dispatch_complex_agent_manifest(): + """Test loading a complex agent manifest with nested objects.""" + yaml_content = """ +name: complex-manifest +description: A complete manifest +template: + kind: Prompt + name: assistant + description: A helpful assistant + model: + id: gpt-4 + provider: openai + tools: + - kind: web_search + name: search + description: Search the web + - kind: function + name: calculator + description: Calculate math +resources: + - kind: model + name: model1 + id: gpt-4 + - kind: tool + name: tool1 + id: search +""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + + assert isinstance(result, AgentManifest) + assert result.name == "complex-manifest" + assert result.description == "A complete manifest" + assert isinstance(result.template, PromptAgent) + assert result.template.name == "assistant" + assert len(result.resources) == 2 + assert isinstance(result.resources[0], ModelResource) + assert isinstance(result.resources[1], ToolResource) + + +def test_agent_schema_dispatch_prompt_agent_with_tools(): + """Test loading a prompt agent with multiple tools.""" + yaml_content = """ +kind: Prompt +name: multi-tool-agent +description: Agent with multiple tools +model: + id: gpt-4 +tools: + - kind: web_search + name: search + description: Search the web + - kind: function + name: get_weather + description: Get weather information + - kind: code_interpreter + name: code + description: Execute code +""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + + assert isinstance(result, PromptAgent) + assert result.name == "multi-tool-agent" + assert len(result.tools) == 3 + # Tools are polymorphically created based on their kind + assert result.tools[0].kind == "web_search" + assert result.tools[1].kind == "function" + assert result.tools[2].kind == "code_interpreter" + + +def test_agent_schema_dispatch_model_resource(): + """Test loading a model resource.""" + yaml_content = """ +kind: Model +name: my-model +id: gpt-4 +""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + + assert isinstance(result, ModelResource) + assert result.id == "gpt-4" + + +def test_agent_schema_dispatch_property_schema_with_nested_properties(): + """Test loading a property schema with nested properties.""" + yaml_content = """ +kind: property_schema +strict: true +properties: + - kind: property + name: name + description: User name + - kind: object + name: address + description: User address + properties: + - kind: property + name: street + description: Street address + - kind: property + name: city + description: City name + - kind: array + name: tags + description: User tags +""" + result = agent_schema_dispatch(yaml.safe_load(yaml_content)) + + assert isinstance(result, PropertySchema) + assert result.strict is True + assert len(result.properties) == 3 + # Properties are polymorphically created based on their kind + assert result.properties[0].kind == "property" + assert result.properties[1].kind == "object" + assert result.properties[2].kind == "array" + + +def _get_agent_sample_yaml_files() -> list[tuple[Path, Path]]: + """Helper function to collect all YAML files from agent-samples directory.""" + current_file = Path(__file__) + repo_root = current_file.parent.parent.parent.parent # tests -> declarative -> packages -> python + agent_samples_dir = repo_root.parent / "agent-samples" + + if not agent_samples_dir.exists(): + return [] + + yaml_files = list(agent_samples_dir.rglob("*.yaml")) + list(agent_samples_dir.rglob("*.yml")) + return [(yaml_file, agent_samples_dir) for yaml_file in yaml_files] + + +@pytest.mark.parametrize( + "yaml_file,agent_samples_dir", + _get_agent_sample_yaml_files(), + ids=lambda x: x[0].name if isinstance(x, tuple) else str(x), +) +def test_agent_schema_dispatch_agent_samples(yaml_file: Path, agent_samples_dir: Path): + """Test that agent_schema_dispatch successfully loads a YAML file from agent-samples directory.""" + with open(yaml_file) as f: + content = f.read() + result = agent_schema_dispatch(yaml.safe_load(content)) + # Result can be None for unknown kinds, but should not raise exceptions + assert result is not None, f"agent_schema_dispatch returned None for {yaml_file.relative_to(agent_samples_dir)}" diff --git a/python/packages/declarative/tests/test_declarative_models.py b/python/packages/declarative/tests/test_declarative_models.py new file mode 100644 index 00000000000..dc13b3a6428 --- /dev/null +++ b/python/packages/declarative/tests/test_declarative_models.py @@ -0,0 +1,1049 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Tests for MAML model classes.""" + +import sys + +import pytest + +from agent_framework_declarative._models import ( + AgentDefinition, + AgentManifest, + AnonymousConnection, + ApiKeyConnection, + ArrayProperty, + Binding, + CodeInterpreterTool, + Connection, + CustomTool, + EnvironmentVariable, + FileSearchTool, + Format, + FunctionTool, + McpServerApprovalMode, + McpServerToolAlwaysRequireApprovalMode, + McpServerToolNeverRequireApprovalMode, + McpServerToolSpecifyApprovalMode, + McpTool, + Model, + ModelOptions, + ModelResource, + ObjectProperty, + OpenApiTool, + Parser, + PromptAgent, + Property, + PropertySchema, + ProtocolVersionRecord, + ReferenceConnection, + RemoteConnection, + Resource, + Template, + ToolResource, + WebSearchTool, + _try_powerfx_eval, +) + +pytestmark = pytest.mark.skipif(sys.version_info >= (3, 14), reason="Skipping on Python 3.14+") + + +class TestBinding: + """Tests for Binding class.""" + + def test_binding_creation(self): + binding = Binding(name="arg1", input="value1") + assert binding.name == "arg1" + assert binding.input == "value1" + + def test_binding_from_dict(self): + data = {"name": "arg1", "input": "value1"} + binding = Binding.from_dict(data) + assert binding.name == "arg1" + assert binding.input == "value1" + + def test_binding_to_dict(self): + binding = Binding(name="arg1", input="value1") + result = binding.to_dict() + assert result["name"] == "arg1" + assert result["input"] == "value1" + + +class TestProperty: + """Tests for Property class.""" + + def test_property_creation(self): + prop = Property( + name="test_prop", + kind="string", + description="A test property", + required=True, + default="default_value", + example="example_value", + enum=["val1", "val2"], + ) + assert prop.name == "test_prop" + assert prop.kind == "string" + assert prop.description == "A test property" + assert prop.required is True + assert prop.default == "default_value" + assert prop.example == "example_value" + assert prop.enum == ["val1", "val2"] + + def test_property_from_dict(self): + data = { + "name": "test_prop", + "kind": "string", + "description": "A test property", + "required": True, + } + prop = Property.from_dict(data) + assert prop.name == "test_prop" + assert prop.kind == "string" + assert prop.description == "A test property" + assert prop.required is True + + +class TestArrayProperty: + """Tests for ArrayProperty class.""" + + def test_array_property_creation(self): + items = Property(name="item", kind="string") + array_prop = ArrayProperty(name="test_array", kind="array", items=items, required=True) + assert array_prop.name == "test_array" + assert array_prop.kind == "array" + assert array_prop.items.name == "item" + assert array_prop.required is True + + def test_array_property_from_dict(self): + data = { + "name": "test_array", + "kind": "array", + "items": {"name": "item", "kind": "string"}, + "required": True, + } + array_prop = ArrayProperty.from_dict(data) + assert array_prop.name == "test_array" + assert array_prop.kind == "array" + assert isinstance(array_prop.items, Property) + assert array_prop.items.name == "item" + + +class TestObjectProperty: + """Tests for ObjectProperty class.""" + + def test_object_property_creation(self): + props = [ + Property(name="prop1", kind="string"), + Property(name="prop2", kind="integer"), + ] + obj_prop = ObjectProperty(name="test_object", kind="object", properties=props, required=True) + assert obj_prop.name == "test_object" + assert obj_prop.kind == "object" + assert len(obj_prop.properties) == 2 + assert obj_prop.properties[0].name == "prop1" + + def test_object_property_from_dict(self): + data = { + "name": "test_object", + "kind": "object", + "properties": [ + {"name": "prop1", "kind": "string"}, + {"name": "prop2", "kind": "integer"}, + ], + "required": True, + } + obj_prop = ObjectProperty.from_dict(data) + assert obj_prop.name == "test_object" + assert obj_prop.kind == "object" + assert len(obj_prop.properties) == 2 + assert all(isinstance(p, Property) for p in obj_prop.properties) + + def test_object_property_with_dict_properties(self): + """Test ObjectProperty with dict format for properties (MAML YAML dict syntax).""" + data = { + "name": "person", + "kind": "object", + "properties": { + "name": {"kind": "string", "required": True}, + "email": {"kind": "string"}, + "age": {"kind": "integer"}, + }, + } + obj_prop = ObjectProperty.from_dict(data) + assert obj_prop.name == "person" + assert obj_prop.kind == "object" + assert len(obj_prop.properties) == 3 + + # Check that all properties were converted correctly + prop_names = {p.name for p in obj_prop.properties} + assert prop_names == {"name", "email", "age"} + + # Check specific property + name_prop = next(p for p in obj_prop.properties if p.name == "name") + assert name_prop.kind == "string" + assert name_prop.required is True + + +class TestPropertySchema: + """Tests for PropertySchema class.""" + + def test_property_schema_creation(self): + props = [Property(name="prop1", kind="string")] + schema = PropertySchema(properties=props, strict=True) + assert schema.strict is True + assert len(schema.properties) == 1 + + def test_property_schema_from_dict(self): + data = { + "strict": False, + "properties": [{"name": "prop1", "kind": "string"}], + } + schema = PropertySchema.from_dict(data) + assert schema.strict is False + assert len(schema.properties) == 1 + # Properties are properly converted to Property instances + assert isinstance(schema.properties[0], Property) + assert schema.properties[0].name == "prop1" + assert schema.properties[0].kind == "string" + + def test_property_schema_with_dict_properties(self): + """Test PropertySchema with dict format for properties (MAML YAML dict syntax).""" + data = { + "strict": True, + "properties": { + "firstName": {"kind": "string", "description": "First name"}, + "lastName": {"kind": "string", "description": "Last name"}, + "age": {"kind": "integer", "required": True}, + }, + } + schema = PropertySchema.from_dict(data) + assert schema.strict is True + assert len(schema.properties) == 3 + + # Check that all properties were converted correctly + prop_names = {p.name for p in schema.properties} + assert prop_names == {"firstName", "lastName", "age"} + + # Check specific property details + age_prop = next(p for p in schema.properties if p.name == "age") + assert age_prop.kind == "integer" + assert age_prop.required is True + + +class TestConnection: + """Tests for Connection base class.""" + + def test_connection_creation(self): + conn = Connection(kind="base") + assert conn.kind == "base" + + def test_connection_from_dict(self): + data = {"kind": "base"} + conn = Connection.from_dict(data) + assert conn.kind == "base" + + +class TestReferenceConnection: + """Tests for ReferenceConnection class.""" + + def test_reference_connection_creation(self): + conn = ReferenceConnection(name="my-connection", target="target-connection") + assert conn.kind == "reference" + assert conn.name == "my-connection" + assert conn.target == "target-connection" + + def test_reference_connection_from_dict(self): + data = {"kind": "reference", "name": "my-connection", "target": "target-connection"} + conn = ReferenceConnection.from_dict(data) + assert conn.kind == "reference" + assert conn.name == "my-connection" + assert conn.target == "target-connection" + + +class TestRemoteConnection: + """Tests for RemoteConnection class.""" + + def test_remote_connection_creation(self): + conn = RemoteConnection(name="my-remote", endpoint="https://api.example.com") + assert conn.kind == "remote" + assert conn.endpoint == "https://api.example.com" + + def test_remote_connection_from_dict(self): + data = {"kind": "remote", "endpoint": "https://api.example.com"} + conn = RemoteConnection.from_dict(data) + assert conn.kind == "remote" + assert conn.endpoint == "https://api.example.com" + + +class TestApiKeyConnection: + """Tests for ApiKeyConnection class.""" + + def test_api_key_connection_creation(self): + conn = ApiKeyConnection(apiKey="secret-key", endpoint="https://api.example.com") + assert conn.kind == "key" + assert conn.apiKey == "secret-key" + assert conn.endpoint == "https://api.example.com" + + def test_api_key_connection_from_dict(self): + data = {"kind": "key", "apiKey": "secret-key", "endpoint": "https://api.example.com"} + conn = ApiKeyConnection.from_dict(data) + assert conn.kind == "key" + assert conn.apiKey == "secret-key" + + +class TestAnonymousConnection: + """Tests for AnonymousConnection class.""" + + def test_anonymous_connection_creation(self): + conn = AnonymousConnection(endpoint="https://api.example.com") + assert conn.kind == "anonymous" + assert conn.endpoint == "https://api.example.com" + + def test_anonymous_connection_from_dict(self): + data = {"kind": "anonymous", "endpoint": "https://api.example.com"} + conn = AnonymousConnection.from_dict(data) + assert conn.kind == "anonymous" + assert conn.endpoint == "https://api.example.com" + + +class TestModelOptions: + """Tests for ModelOptions class.""" + + def test_model_options_creation(self): + options = ModelOptions(temperature=0.7, maxOutputTokens=1000, topP=0.9) + assert options.temperature == 0.7 + assert options.maxOutputTokens == 1000 + assert options.topP == 0.9 + + def test_model_options_from_dict(self): + data = {"temperature": 0.7, "maxOutputTokens": 1000, "topP": 0.9} + options = ModelOptions.from_dict(data) + assert options.temperature == 0.7 + assert options.maxOutputTokens == 1000 + assert options.topP == 0.9 + + +class TestModel: + """Tests for Model class.""" + + def test_model_creation(self): + model = Model(id="gpt-4", provider="openai") + assert model.id == "gpt-4" + assert model.provider == "openai" + + def test_model_from_dict(self): + data = {"id": "gpt-4", "provider": "openai"} + model = Model.from_dict(data) + assert model.id == "gpt-4" + assert model.provider == "openai" + + def test_model_with_connection(self): + data = { + "id": "gpt-4", + "connection": {"kind": "reference", "name": "my-connection"}, + } + model = Model.from_dict(data) + assert model.id == "gpt-4" + assert model.connection.kind == "reference" + + +class TestFormat: + """Tests for Format class.""" + + def test_format_creation(self): + fmt = Format(kind="json", strict=True, options={"type": "object"}) + assert fmt.kind == "json" + assert fmt.strict is True + assert fmt.options == {"type": "object"} + + def test_format_from_dict(self): + data = {"kind": "json", "strict": False, "options": {"type": "object"}} + fmt = Format.from_dict(data) + assert fmt.kind == "json" + assert fmt.strict is False + + +class TestParser: + """Tests for Parser class.""" + + def test_parser_creation(self): + parser = Parser(kind="json", options={"strict": True}) + assert parser.kind == "json" + assert parser.options == {"strict": True} + + def test_parser_from_dict(self): + data = {"kind": "json", "options": {"strict": True}} + parser = Parser.from_dict(data) + assert parser.kind == "json" + assert parser.options == {"strict": True} + + +class TestTemplate: + """Tests for Template class.""" + + def test_template_creation(self): + template = Template( + format=Format(kind="text"), + parser=Parser(kind="text"), + ) + assert isinstance(template.format, Format) + assert isinstance(template.parser, Parser) + + def test_template_from_dict(self): + data = { + "format": {"kind": "text"}, + "parser": {"kind": "text"}, + } + template = Template.from_dict(data) + assert isinstance(template.format, Format) + assert isinstance(template.parser, Parser) + + +class TestAgentDefinition: + """Tests for AgentDefinition class.""" + + def test_agent_definition_creation(self): + agent = AgentDefinition( + name="test-agent", + description="A test agent", + ) + assert agent.name == "test-agent" + assert agent.description == "A test agent" + + def test_agent_definition_from_dict(self): + data = { + "name": "test-agent", + "description": "A test agent", + } + agent = AgentDefinition.from_dict(data) + assert agent.name == "test-agent" + assert agent.description == "A test agent" + + +class TestFunctionTool: + """Tests for FunctionTool class.""" + + def test_function_tool_creation(self): + tool = FunctionTool( + name="my_function", + description="A test function", + kind="function", + ) + assert tool.name == "my_function" + assert tool.kind == "function" + + def test_function_tool_from_dict(self): + data = { + "name": "my_function", + "description": "A test function", + "kind": "function", + "strict": False, + } + tool = FunctionTool.from_dict(data) + assert tool.name == "my_function" + assert tool.kind == "function" + + def test_function_tool_with_dict_bindings(self): + """Test FunctionTool with dict format for bindings (MAML YAML dict syntax).""" + data = { + "name": "calculate", + "kind": "function", + "description": "Calculate something", + "bindings": { + "x": "input.x", + "y": "input.y", + "operation": "input.op", + }, + } + tool = FunctionTool.from_dict(data) + assert tool.name == "calculate" + assert len(tool.bindings) == 3 + + # Check that all bindings were converted correctly + binding_names = {b.name for b in tool.bindings} + assert binding_names == {"x", "y", "operation"} + + # Check specific binding + x_binding = next(b for b in tool.bindings if b.name == "x") + assert x_binding.input == "input.x" + + +class TestCustomTool: + """Tests for CustomTool class.""" + + def test_custom_tool_creation(self): + tool = CustomTool( + name="custom_tool", + description="A custom tool", + kind="custom", + options={"endpoint": "https://tool.example.com"}, + ) + assert tool.name == "custom_tool" + assert tool.kind == "custom" + assert tool.options == {"endpoint": "https://tool.example.com"} + + def test_custom_tool_from_dict(self): + data = { + "name": "custom_tool", + "description": "A custom tool", + "kind": "custom", + "options": {"endpoint": "https://tool.example.com"}, + } + tool = CustomTool.from_dict(data) + assert tool.name == "custom_tool" + assert tool.kind == "custom" + + +class TestWebSearchTool: + """Tests for WebSearchTool class.""" + + def test_web_search_tool_creation(self): + tool = WebSearchTool( + name="web_search", + description="Search the web", + kind="web_search", + options={"maxResults": 10}, + ) + assert tool.name == "web_search" + assert tool.kind == "web_search" + assert tool.options == {"maxResults": 10} + + def test_web_search_tool_from_dict(self): + data = { + "name": "web_search", + "description": "Search the web", + "kind": "web_search", + "options": {"maxResults": 10}, + } + tool = WebSearchTool.from_dict(data) + assert tool.name == "web_search" + assert tool.kind == "web_search" + assert tool.options == {"maxResults": 10} + + +class TestFileSearchTool: + """Tests for FileSearchTool class.""" + + def test_file_search_tool_creation(self): + tool = FileSearchTool( + name="file_search", + description="Search files", + kind="file_search", + vectorStoreIds=["vs1", "vs2"], + ) + assert tool.name == "file_search" + assert tool.kind == "file_search" + assert tool.vectorStoreIds == ["vs1", "vs2"] + + def test_file_search_tool_from_dict(self): + data = { + "name": "file_search", + "description": "Search files", + "kind": "file_search", + "vectorStoreIds": ["vs1", "vs2"], + } + tool = FileSearchTool.from_dict(data) + assert tool.name == "file_search" + assert tool.kind == "file_search" + assert tool.vectorStoreIds == ["vs1", "vs2"] + + +class TestMcpServerApprovalMode: + """Tests for MCP Server Approval Mode classes.""" + + def test_always_approval_mode(self): + mode = McpServerToolAlwaysRequireApprovalMode() + assert mode.kind == "always" + + def test_always_approval_mode_from_dict(self): + data = {"kind": "always"} + mode = McpServerToolAlwaysRequireApprovalMode.from_dict(data) + assert mode.kind == "always" + + def test_never_approval_mode(self): + mode = McpServerToolNeverRequireApprovalMode() + assert mode.kind == "never" + + def test_never_approval_mode_from_dict(self): + data = {"kind": "never"} + mode = McpServerToolNeverRequireApprovalMode.from_dict(data) + assert mode.kind == "never" + + def test_specify_approval_mode(self): + mode = McpServerToolSpecifyApprovalMode( + alwaysRequireApprovalTools=["tool1"], + neverRequireApprovalTools=["tool2"], + ) + assert mode.kind == "specify" + assert mode.alwaysRequireApprovalTools == ["tool1"] + assert mode.neverRequireApprovalTools == ["tool2"] + + def test_specify_approval_mode_from_dict(self): + data = { + "kind": "specify", + "alwaysRequireApprovalTools": ["tool1"], + "neverRequireApprovalTools": ["tool2"], + } + mode = McpServerToolSpecifyApprovalMode.from_dict(data) + assert mode.kind == "specify" + assert mode.alwaysRequireApprovalTools == ["tool1"] + assert mode.neverRequireApprovalTools == ["tool2"] + + +class TestMcpTool: + """Tests for McpTool class.""" + + def test_mcp_tool_creation(self): + tool = McpTool( + name="mcp_tool", + description="An MCP tool", + kind="mcp", + serverName="test-server", + ) + assert tool.name == "mcp_tool" + assert tool.kind == "mcp" + assert tool.serverName == "test-server" + + def test_mcp_tool_from_dict(self): + data = { + "name": "mcp_tool", + "description": "An MCP tool", + "kind": "mcp", + "serverName": "test-server", + "approvalMode": {"kind": "always"}, + } + tool = McpTool.from_dict(data) + assert tool.name == "mcp_tool" + assert tool.kind == "mcp" + assert isinstance(tool.approvalMode, McpServerApprovalMode) + + def test_mcp_tool_with_simplified_approval_mode(self): + """Test McpTool with simplified string format for approvalMode.""" + # Test simplified string format: approvalMode: "always" + data = { + "name": "mcp_tool", + "description": "An MCP tool", + "kind": "mcp", + "serverName": "test-server", + "approvalMode": "always", + } + tool = McpTool.from_dict(data) + assert tool.name == "mcp_tool" + assert tool.kind == "mcp" + assert isinstance(tool.approvalMode, McpServerApprovalMode) + assert tool.approvalMode.kind == "always" + + def test_mcp_tool_approval_mode_equivalence(self): + """Test that simplified and full format produce equivalent results.""" + # Simplified format + data_simplified = { + "name": "mcp_tool", + "kind": "mcp", + "approvalMode": "never", + } + tool_simplified = McpTool.from_dict(data_simplified) + + # Full format + data_full = { + "name": "mcp_tool", + "kind": "mcp", + "approvalMode": {"kind": "never"}, + } + tool_full = McpTool.from_dict(data_full) + + # Both should produce the same result + assert tool_simplified.approvalMode.kind == tool_full.approvalMode.kind + assert tool_simplified.approvalMode.kind == "never" + + +class TestOpenApiTool: + """Tests for OpenApiTool class.""" + + def test_openapi_tool_creation(self): + tool = OpenApiTool( + name="openapi_tool", + description="An OpenAPI tool", + kind="openapi", + specification="https://api.example.com/openapi.json", + ) + assert tool.name == "openapi_tool" + assert tool.kind == "openapi" + assert tool.specification == "https://api.example.com/openapi.json" + + def test_openapi_tool_from_dict(self): + data = { + "name": "openapi_tool", + "description": "An OpenAPI tool", + "kind": "openapi", + "specification": "https://api.example.com/openapi.json", + } + tool = OpenApiTool.from_dict(data) + assert tool.name == "openapi_tool" + assert tool.kind == "openapi" + + +class TestCodeInterpreterTool: + """Tests for CodeInterpreterTool class.""" + + def test_code_interpreter_tool_creation(self): + tool = CodeInterpreterTool( + name="code_interpreter", + description="Execute code", + kind="code_interpreter", + fileIds=["file1", "file2"], + ) + assert tool.name == "code_interpreter" + assert tool.kind == "code_interpreter" + assert tool.fileIds == ["file1", "file2"] + + def test_code_interpreter_tool_from_dict(self): + data = { + "name": "code_interpreter", + "description": "Execute code", + "kind": "code_interpreter", + "fileIds": ["file1", "file2"], + } + tool = CodeInterpreterTool.from_dict(data) + assert tool.name == "code_interpreter" + assert tool.kind == "code_interpreter" + assert tool.fileIds == ["file1", "file2"] + + +class TestPromptAgent: + """Tests for PromptAgent class.""" + + def test_prompt_agent_creation(self): + agent = PromptAgent( + name="prompt-agent", + description="A prompt-based agent", + instructions="You are a helpful assistant", + kind="Prompt", + ) + assert agent.name == "prompt-agent" + assert agent.kind == "Prompt" + assert agent.instructions == "You are a helpful assistant" + + def test_prompt_agent_from_dict(self): + data = { + "name": "prompt-agent", + "description": "A prompt-based agent", + "instructions": "You are a helpful assistant", + "kind": "Prompt", + "model": {"id": "gpt-4"}, + } + agent = PromptAgent.from_dict(data) + assert agent.name == "prompt-agent" + assert isinstance(agent.model, Model) + assert isinstance(agent.model, Model) + + def test_prompt_agent_with_tools(self): + data = { + "name": "prompt-agent", + "kind": "Prompt", + "tools": [ + {"name": "search", "kind": "web_search"}, + {"name": "calc", "kind": "function"}, + ], + } + agent = PromptAgent.from_dict(data) + assert len(agent.tools) == 2 + # Tools are converted via Tool.from_dict, type depends on 'kind' + assert agent.tools[0].kind == "web_search" + assert agent.tools[1].kind == "function" + + +class TestResource: + """Tests for Resource base class.""" + + def test_resource_creation(self): + resource = Resource(name="test-resource", kind="Resource") + assert resource.name == "test-resource" + assert resource.kind == "Resource" + + def test_resource_from_dict(self): + data = {"name": "test-resource", "kind": "Resource"} + resource = Resource.from_dict(data) + assert resource.name == "test-resource" + + +class TestModelResource: + """Tests for ModelResource class.""" + + def test_model_resource_creation(self): + resource = ModelResource(name="my-model", kind="model", id="gpt-4") + assert resource.name == "my-model" + assert resource.kind == "model" + assert resource.id == "gpt-4" + + def test_model_resource_from_dict(self): + data = { + "name": "my-model", + "kind": "model", + "id": "gpt-4", + } + resource = ModelResource.from_dict(data) + assert resource.name == "my-model" + assert resource.kind == "model" + assert resource.id == "gpt-4" + + +class TestToolResource: + """Tests for ToolResource class.""" + + def test_tool_resource_creation(self): + resource = ToolResource(name="my-tool", kind="tool", id="search-tool") + assert resource.name == "my-tool" + assert resource.kind == "tool" + assert resource.id == "search-tool" + + def test_tool_resource_from_dict(self): + data = { + "name": "my-tool", + "kind": "tool", + "id": "search-tool", + } + resource = ToolResource.from_dict(data) + assert resource.name == "my-tool" + assert resource.kind == "tool" + assert resource.id == "search-tool" + + +class TestProtocolVersionRecord: + """Tests for ProtocolVersionRecord class.""" + + def test_protocol_version_record_creation(self): + record = ProtocolVersionRecord(protocol="mcp", version="1.0.0") + assert record.protocol == "mcp" + assert record.version == "1.0.0" + + def test_protocol_version_record_from_dict(self): + data = {"protocol": "mcp", "version": "1.0.0"} + record = ProtocolVersionRecord.from_dict(data) + assert record.protocol == "mcp" + assert record.version == "1.0.0" + + +class TestEnvironmentVariable: + """Tests for EnvironmentVariable class.""" + + def test_environment_variable_creation(self): + env_var = EnvironmentVariable(name="API_KEY", value="secret123") + assert env_var.name == "API_KEY" + assert env_var.value == "secret123" + + def test_environment_variable_from_dict(self): + data = {"name": "API_KEY", "value": "secret123"} + env_var = EnvironmentVariable.from_dict(data) + assert env_var.name == "API_KEY" + assert env_var.value == "secret123" + + +class TestTryPowerfxEval: + """Tests for _try_powerfx_eval function.""" + + def test_no_evaluation_without_equals_prefix(self): + """Test that strings without '=' prefix are returned as-is.""" + assert _try_powerfx_eval("hello") == "hello" + assert _try_powerfx_eval("test value") == "test value" + assert _try_powerfx_eval("123") == "123" + + def test_none_value_returns_none(self): + """Test that None values are returned as None.""" + assert _try_powerfx_eval(None) is None + + def test_empty_string_returns_empty(self): + """Test that empty strings are returned as empty.""" + assert _try_powerfx_eval("") == "" + + def test_simple_powerfx_expressions(self): + """Test simple PowerFx expressions.""" + from decimal import Decimal + + # Simple math - returns Decimal + assert _try_powerfx_eval("=1 + 2") == Decimal("3") + assert _try_powerfx_eval("=10 * 5") == Decimal("50") + + # String literals + assert _try_powerfx_eval('="hello"') == "hello" + assert _try_powerfx_eval('="test value"') == "test value" + + def test_env_variable_access(self, monkeypatch): + """Test accessing environment variables using =Env. pattern.""" + # Set up test environment variables + monkeypatch.setenv("TEST_VAR", "test_value") + monkeypatch.setenv("API_KEY", "secret123") + monkeypatch.setenv("PORT", "8080") + + # Test basic env access + assert _try_powerfx_eval("=Env.TEST_VAR") == "test_value" + assert _try_powerfx_eval("=Env.API_KEY") == "secret123" + assert _try_powerfx_eval("=Env.PORT") == "8080" + + def test_env_variable_with_string_concatenation(self, monkeypatch): + """Test env variables with string concatenation operator.""" + monkeypatch.setenv("BASE_URL", "https://api.example.com") + monkeypatch.setenv("API_VERSION", "v1") + + # Test concatenation with & + result = _try_powerfx_eval('=Env.BASE_URL & "/" & Env.API_VERSION') + assert result == "https://api.example.com/v1" + + # Test concatenation with literals + result = _try_powerfx_eval('="API Key: " & Env.API_VERSION') + assert result == "API Key: v1" + + def test_string_comparison_operators(self, monkeypatch): + """Test PowerFx string comparison operators.""" + monkeypatch.setenv("ENV_MODE", "production") + + # Equal to - returns bool + assert _try_powerfx_eval('=Env.ENV_MODE = "production"') is True + assert _try_powerfx_eval('=Env.ENV_MODE = "development"') is False + + # Not equal to - returns bool + assert _try_powerfx_eval('=Env.ENV_MODE <> "development"') is True + assert _try_powerfx_eval('=Env.ENV_MODE <> "production"') is False + + def test_string_in_operator(self): + """Test PowerFx 'in' operator for substring testing (case-insensitive).""" + # Substring test - case insensitive - returns bool + assert _try_powerfx_eval('="the" in "The keyboard and the monitor"') is True + assert _try_powerfx_eval('="THE" in "The keyboard and the monitor"') is True + assert _try_powerfx_eval('="xyz" in "The keyboard and the monitor"') is False + + def test_string_exactin_operator(self): + """Test PowerFx 'exactin' operator for substring testing (case-sensitive).""" + # Substring test - case sensitive - returns bool + assert _try_powerfx_eval('="Windows" exactin "To display windows in the Windows operating system"') is True + assert _try_powerfx_eval('="windows" exactin "To display windows in the Windows operating system"') is True + assert _try_powerfx_eval('="WINDOWS" exactin "To display windows in the Windows operating system"') is False + + def test_logical_operators_with_strings(self): + """Test PowerFx logical operators (And, Or, Not) with string comparisons.""" + # And operator - returns bool + assert _try_powerfx_eval('="a" = "a" And "b" = "b"') is True + assert _try_powerfx_eval('="a" = "a" And "b" = "c"') is False + + # && operator (alternative syntax) - returns bool + assert _try_powerfx_eval('="a" = "a" && "b" = "b"') is True + + # Or operator - returns bool + assert _try_powerfx_eval('="a" = "b" Or "c" = "c"') is True + assert _try_powerfx_eval('="a" = "b" Or "c" = "d"') is False + + # || operator (alternative syntax) - returns bool + assert _try_powerfx_eval('="a" = "b" || "c" = "c"') is True + + # Not operator - returns bool + assert _try_powerfx_eval('=Not("a" = "b")') is True + assert _try_powerfx_eval('=Not("a" = "a")') is False + + # ! operator (alternative syntax) - returns bool + assert _try_powerfx_eval('=!("a" = "b")') is True + + def test_parentheses_for_precedence(self): + """Test using parentheses to control operator precedence.""" + from decimal import Decimal + + # Test arithmetic precedence - returns Decimal + assert _try_powerfx_eval("=(1 + 2) * 3") == Decimal("9") + assert _try_powerfx_eval("=1 + 2 * 3") == Decimal("7") + + # Test logical precedence - returns bool + result = _try_powerfx_eval('=("a" = "a" Or "b" = "c") And "d" = "d"') + assert result is True + + def test_env_with_special_characters(self, monkeypatch): + """Test env variables containing special characters in values.""" + monkeypatch.setenv("URL_WITH_QUERY", "https://example.com?param=value") + monkeypatch.setenv("PATH_WITH_SPACES", "C:\\Program Files\\App") + + result = _try_powerfx_eval("=Env.URL_WITH_QUERY") + assert result == "https://example.com?param=value" + + result = _try_powerfx_eval("=Env.PATH_WITH_SPACES") + assert result == "C:\\Program Files\\App" + + +class TestAgentManifest: + """Tests for AgentManifest class.""" + + def test_agent_manifest_creation(self): + manifest = AgentManifest(name="my-agent-manifest", description="A test manifest") + assert manifest.name == "my-agent-manifest" + assert manifest.description == "A test manifest" + + def test_agent_manifest_from_dict(self): + data = { + "name": "my-agent-manifest", + "description": "A test manifest", + } + manifest = AgentManifest.from_dict(data) + assert manifest.name == "my-agent-manifest" + + def test_agent_manifest_with_resources(self): + data = { + "name": "my-agent-manifest", + "resources": [ + {"name": "model1", "kind": "model", "id": "gpt-4"}, + { + "name": "tool1", + "kind": "tool", + "id": "search-tool", + }, + ], + } + manifest = AgentManifest.from_dict(data) + assert manifest.name == "my-agent-manifest" + assert len(manifest.resources) == 2 + # Resources are converted via Resource.from_dict based on their 'kind' + assert isinstance(manifest.resources[0], ModelResource) + assert isinstance(manifest.resources[1], ToolResource) + + def test_agent_manifest_complete(self): + """Test a complete agent manifest with all fields.""" + data = { + "name": "complete-manifest", + "description": "A complete test manifest", + "template": { + "name": "assistant", + "kind": "Prompt", + "description": "A helpful assistant", + }, + "resources": [ + {"name": "model1", "kind": "model", "id": "gpt-4"}, + ], + } + manifest = AgentManifest.from_dict(data) + assert manifest.name == "complete-manifest" + assert isinstance(manifest.template, AgentDefinition) + assert len(manifest.resources) == 1 + assert isinstance(manifest.resources[0], ModelResource) + + def test_agent_manifest_with_dict_resources(self): + """Test AgentManifest with dict format for resources (MAML YAML dict syntax).""" + data = { + "name": "manifest-with-dict-resources", + "description": "Test manifest with dict resources", + "resources": { + "gptModelDeployment": {"kind": "model", "id": "gpt-4o"}, + "webSearchInstance": {"kind": "tool", "id": "web-search"}, + "analyticsTool": {"kind": "tool", "id": "analytics"}, + }, + } + manifest = AgentManifest.from_dict(data) + assert manifest.name == "manifest-with-dict-resources" + assert len(manifest.resources) == 3 + + # Check that all resources were converted correctly + resource_names = {r.name for r in manifest.resources} + assert resource_names == {"gptModelDeployment", "webSearchInstance", "analyticsTool"} + + # Check specific resource + gpt_resource = next(r for r in manifest.resources if r.name == "gptModelDeployment") + assert isinstance(gpt_resource, ModelResource) + assert gpt_resource.id == "gpt-4o" + + web_resource = next(r for r in manifest.resources if r.name == "webSearchInstance") + assert isinstance(web_resource, ToolResource) + assert web_resource.id == "web-search" diff --git a/python/packages/devui/agent_framework_devui/_deployment.py b/python/packages/devui/agent_framework_devui/_deployment.py index e1cf1d5c3da..45f99a315a1 100644 --- a/python/packages/devui/agent_framework_devui/_deployment.py +++ b/python/packages/devui/agent_framework_devui/_deployment.py @@ -10,6 +10,7 @@ from collections.abc import AsyncGenerator from datetime import datetime, timezone from pathlib import Path +from urllib.parse import urlparse from .models._discovery_models import Deployment, DeploymentConfig, DeploymentEvent @@ -467,11 +468,18 @@ async def _deploy_to_azure( await event_queue.put( DeploymentEvent(type="deploy.progress", message=f"Docker build: {line_text}") ) - elif "https://" in line_text and ".azurecontainerapps.io" in line_text: - # Deployment URL detected - await event_queue.put( - DeploymentEvent(type="deploy.progress", message="Deployment URL generated!") - ) + elif "https://" in line_text: + # Try to extract all URLs and check if any is on azurecontainerapps.io + urls = re.findall(r'https://[^\s<>"]+', line_text) + for url in urls: + # Strip common trailing punctuation to ensure clean URL parsing + url_clean = url.rstrip(".,;:!?'\")}]") + host = urlparse(url_clean).hostname + if host and (host == "azurecontainerapps.io" or host.endswith(".azurecontainerapps.io")): + await event_queue.put( + DeploymentEvent(type="deploy.progress", message="Deployment URL generated!") + ) + break # Wait for process to complete return_code = await process.wait() diff --git a/python/packages/devui/agent_framework_devui/_discovery.py b/python/packages/devui/agent_framework_devui/_discovery.py index 99265b5d527..d7af6159fd8 100644 --- a/python/packages/devui/agent_framework_devui/_discovery.py +++ b/python/packages/devui/agent_framework_devui/_discovery.py @@ -2,8 +2,6 @@ """Agent Framework entity discovery implementation.""" -from __future__ import annotations - import ast import importlib import importlib.util diff --git a/python/packages/devui/agent_framework_devui/models/_discovery_models.py b/python/packages/devui/agent_framework_devui/models/_discovery_models.py index cdb5d0619c7..382639b2772 100644 --- a/python/packages/devui/agent_framework_devui/models/_discovery_models.py +++ b/python/packages/devui/agent_framework_devui/models/_discovery_models.py @@ -2,8 +2,6 @@ """Discovery API models for entity information.""" -from __future__ import annotations - import re from typing import Any diff --git a/python/packages/devui/frontend/yarn.lock b/python/packages/devui/frontend/yarn.lock index 8303d2d4788..7e932da19e9 100644 --- a/python/packages/devui/frontend/yarn.lock +++ b/python/packages/devui/frontend/yarn.lock @@ -24,7 +24,7 @@ resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.0.tgz" integrity sha512-60X7qkglvrap8mn1lh2ebxXdZYtUcpd7gsmy9kLaBJ4i/WdY8PqTSdxyA8qraikqKQK5C1KRBKXqznrVapyNaw== -"@babel/core@^7.0.0", "@babel/core@^7.0.0-0", "@babel/core@^7.28.3": +"@babel/core@^7.28.3": version "7.28.3" resolved "https://registry.npmjs.org/@babel/core/-/core-7.28.3.tgz" integrity sha512-yDBHV9kQNcr2/sUr9jghVyz9C3Y5G2zUM2H2lo+9mKv4sFgbA8s8Z9t8D1jiTkGoO/NoIfKMyKWr4s6CN23ZwQ== @@ -168,6 +168,153 @@ "@babel/helper-string-parser" "^7.27.1" "@babel/helper-validator-identifier" "^7.27.1" +"@emnapi/core@^1.4.3", "@emnapi/core@^1.4.5": + version "1.7.1" + resolved "https://registry.yarnpkg.com/@emnapi/core/-/core-1.7.1.tgz#3a79a02dbc84f45884a1806ebb98e5746bdfaac4" + integrity sha512-o1uhUASyo921r2XtHYOHy7gdkGLge8ghBEQHMWmyJFoXlpU58kIrhhN3w26lpQb6dspetweapMn2CSNwQ8I4wg== + dependencies: + "@emnapi/wasi-threads" "1.1.0" + tslib "^2.4.0" + +"@emnapi/runtime@^1.4.3", "@emnapi/runtime@^1.4.5": + version "1.7.1" + 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argparse "^2.0.1" @@ -1603,12 +1923,57 @@ levn@^0.4.1: prelude-ls "^1.2.1" type-check "~0.4.0" +lightningcss-darwin-arm64@1.30.1: + version "1.30.1" + resolved "https://registry.yarnpkg.com/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.30.1.tgz#3d47ce5e221b9567c703950edf2529ca4a3700ae" + integrity sha512-c8JK7hyE65X1MHMN+Viq9n11RRC7hgin3HhYKhrMyaXflk5GVplZ60IxyoVtzILeKr+xAJwg6zK6sjTBJ0FKYQ== + +lightningcss-darwin-x64@1.30.1: + version "1.30.1" + resolved "https://registry.yarnpkg.com/lightningcss-darwin-x64/-/lightningcss-darwin-x64-1.30.1.tgz#e81105d3fd6330860c15fe860f64d39cff5fbd22" + integrity sha512-k1EvjakfumAQoTfcXUcHQZhSpLlkAuEkdMBsI/ivWw9hL+7FtilQc0Cy3hrx0AAQrVtQAbMI7YjCgYgvn37PzA== + +lightningcss-freebsd-x64@1.30.1: + version "1.30.1" + resolved "https://registry.yarnpkg.com/lightningcss-freebsd-x64/-/lightningcss-freebsd-x64-1.30.1.tgz#a0e732031083ff9d625c5db021d09eb085af8be4" + integrity 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sha512-mSL4rqPi4iXq5YVqzSsJgMVFENoa4nGTT/GjO2c0Yl9OuQfPsIfncvLrEW6RbbB24WtZ3xP/2CCmI3tNkNV4oA== + lightningcss-win32-x64-msvc@1.30.1: version "1.30.1" resolved "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.30.1.tgz" integrity sha512-PVqXh48wh4T53F/1CCu8PIPCxLzWyCnn/9T5W1Jpmdy5h9Cwd+0YQS6/LwhHXSafuc61/xg9Lv5OrCby6a++jg== -lightningcss@^1.21.0, lightningcss@1.30.1: +lightningcss@1.30.1: version "1.30.1" resolved "https://registry.npmjs.org/lightningcss/-/lightningcss-1.30.1.tgz" integrity sha512-xi6IyHML+c9+Q3W0S4fCQJOym42pyurFiJUHEcEyHS0CeKzia4yZDEsLlqOFykxOdHpNy0NmvVO31vcSqAxJCg== @@ -1779,7 +2144,7 @@ picomatch@^2.3.1: resolved "https://registry.npmjs.org/picomatch/-/picomatch-2.3.1.tgz" integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA== -"picomatch@^3 || ^4", picomatch@^4.0.3: +picomatch@^4.0.3: version "4.0.3" resolved "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz" integrity sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q== @@ -1808,7 +2173,7 @@ queue-microtask@^1.2.2: resolved "https://registry.npmjs.org/queue-microtask/-/queue-microtask-1.2.3.tgz" integrity sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A== -"react-dom@^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc", "react-dom@^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", react-dom@^19.1.1, react-dom@>=16.8.0, react-dom@>=17: +react-dom@^19.1.1: version "19.1.1" resolved "https://registry.npmjs.org/react-dom/-/react-dom-19.1.1.tgz" integrity sha512-Dlq/5LAZgF0Gaz6yiqZCf6VCcZs1ghAJyrsu84Q/GT0gV+mCxbfmKNoGRKBYMJ8IEdGPqu49YWXD02GCknEDkw== @@ -1847,7 +2212,7 @@ react-style-singleton@^2.2.2, react-style-singleton@^2.2.3: get-nonce "^1.0.0" tslib "^2.0.0" -"react@^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0", "react@^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc", "react@^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react@^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0", "react@^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc", react@^19.1.1, react@>=16.8, react@>=16.8.0, react@>=17, react@>=18.0.0: +react@^19.1.1: version "19.1.1" resolved "https://registry.npmjs.org/react/-/react-19.1.1.tgz" integrity sha512-w8nqGImo45dmMIfljjMwOGtbmC/mk4CMYhWIicdSflH91J9TyCyczcPFXJzrZ/ZXcgGRFeP6BU0BEJTw6tZdfQ== @@ -1947,7 +2312,7 @@ tailwind-merge@^3.3.1: resolved "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-3.3.1.tgz" integrity sha512-gBXpgUm/3rp1lMZZrM/w7D8GKqshif0zAymAhbCyIt8KMe+0v9DQ7cdYLR4FHH/cKpdTXb+A/tKKU3eolfsI+g== -tailwindcss@^4.1.12, tailwindcss@4.1.12: +tailwindcss@4.1.12, tailwindcss@^4.1.12: version "4.1.12" resolved "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.12.tgz" integrity sha512-DzFtxOi+7NsFf7DBtI3BJsynR+0Yp6etH+nRPTbpWnS2pZBaSksv/JGctNwSWzbFjp0vxSqknaUylseZqMDGrA== @@ -1989,7 +2354,7 @@ ts-api-utils@^2.1.0: resolved "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-2.1.0.tgz" integrity sha512-CUgTZL1irw8u29bzrOD/nH85jqyc74D6SshFgujOIA7osm2Rz7dYH77agkx7H4FBNxDq7Cjf+IjaX/8zwFW+ZQ== -tslib@^2.0.0, tslib@^2.1.0: +tslib@^2.0.0, tslib@^2.1.0, tslib@^2.4.0, tslib@^2.8.0: version "2.8.1" resolved "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz" integrity sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w== @@ -2016,7 +2381,7 @@ typescript-eslint@^8.39.1: "@typescript-eslint/typescript-estree" "8.40.0" "@typescript-eslint/utils" "8.40.0" -typescript@>=4.8.4, "typescript@>=4.8.4 <6.0.0", typescript@~5.8.3: +typescript@~5.8.3: version "5.8.3" resolved "https://registry.npmjs.org/typescript/-/typescript-5.8.3.tgz" integrity sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ== @@ -2056,12 +2421,12 @@ use-sidecar@^1.1.3: detect-node-es "^1.1.0" tslib "^2.0.0" -use-sync-external-store@^1.2.2, use-sync-external-store@>=1.2.0: +use-sync-external-store@^1.2.2: version "1.5.0" resolved "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.5.0.tgz" integrity sha512-Rb46I4cGGVBmjamjphe8L/UnvJD+uPPtTkNvX5mZgqdbavhI4EbgIWJiIHXJ8bc/i9EQGPRh4DwEURJ552Do0A== -"vite@^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0", "vite@^5.2.0 || ^6 || ^7", vite@^7.1.11: +vite@^7.1.11: version "7.1.12" resolved "https://registry.npmjs.org/vite/-/vite-7.1.12.tgz" integrity sha512-ZWyE8YXEXqJrrSLvYgrRP7p62OziLW7xI5HYGWFzOvupfAlrLvURSzv/FyGyy0eidogEM3ujU+kUG1zuHgb6Ug== diff --git a/python/packages/devui/pyproject.toml b/python/packages/devui/pyproject.toml index f261dbef600..50455639710 100644 --- a/python/packages/devui/pyproject.toml +++ b/python/packages/devui/pyproject.toml @@ -4,7 +4,7 @@ description = "Debug UI for Microsoft Agent Framework with OpenAI-compatible API authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://github.com/microsoft/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/lab/pyproject.toml b/python/packages/lab/pyproject.toml index 7043ec1d874..97aa77bba79 100644 --- a/python/packages/lab/pyproject.toml +++ b/python/packages/lab/pyproject.toml @@ -4,7 +4,7 @@ description = "Experimental modules for Microsoft Agent Framework" authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/mem0/pyproject.toml b/python/packages/mem0/pyproject.toml index 2f9f91cad3b..4b49229741c 100644 --- a/python/packages/mem0/pyproject.toml +++ b/python/packages/mem0/pyproject.toml @@ -4,7 +4,7 @@ description = "Mem0 integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/purview/agent_framework_purview/_cache.py b/python/packages/purview/agent_framework_purview/_cache.py index 5f9f6fe5bb4..d559895a63f 100644 --- a/python/packages/purview/agent_framework_purview/_cache.py +++ b/python/packages/purview/agent_framework_purview/_cache.py @@ -1,8 +1,6 @@ # Copyright (c) Microsoft. All rights reserved. """Cache provider for Purview data.""" -from __future__ import annotations - import hashlib import heapq import json diff --git a/python/packages/purview/agent_framework_purview/_client.py b/python/packages/purview/agent_framework_purview/_client.py index 2ed37722cf1..351c1a6bee3 100644 --- a/python/packages/purview/agent_framework_purview/_client.py +++ b/python/packages/purview/agent_framework_purview/_client.py @@ -1,5 +1,4 @@ # Copyright (c) Microsoft. All rights reserved. -from __future__ import annotations import base64 import inspect diff --git a/python/packages/purview/agent_framework_purview/_exceptions.py b/python/packages/purview/agent_framework_purview/_exceptions.py index 8ddaf5bc286..6f917dee794 100644 --- a/python/packages/purview/agent_framework_purview/_exceptions.py +++ b/python/packages/purview/agent_framework_purview/_exceptions.py @@ -1,8 +1,6 @@ # Copyright (c) Microsoft. All rights reserved. """Purview specific exceptions (minimal error shaping).""" -from __future__ import annotations - from agent_framework.exceptions import ServiceResponseException __all__ = [ diff --git a/python/packages/purview/agent_framework_purview/_middleware.py b/python/packages/purview/agent_framework_purview/_middleware.py index da43d8d9f40..108cf404107 100644 --- a/python/packages/purview/agent_framework_purview/_middleware.py +++ b/python/packages/purview/agent_framework_purview/_middleware.py @@ -1,5 +1,4 @@ # Copyright (c) Microsoft. All rights reserved. -from __future__ import annotations from collections.abc import Awaitable, Callable diff --git a/python/packages/purview/agent_framework_purview/_processor.py b/python/packages/purview/agent_framework_purview/_processor.py index 44163dc2f66..0e197b78c0d 100644 --- a/python/packages/purview/agent_framework_purview/_processor.py +++ b/python/packages/purview/agent_framework_purview/_processor.py @@ -1,5 +1,4 @@ # Copyright (c) Microsoft. All rights reserved. -from __future__ import annotations import asyncio import uuid diff --git a/python/packages/purview/agent_framework_purview/_settings.py b/python/packages/purview/agent_framework_purview/_settings.py index 3e3d13ada68..529b1399aa6 100644 --- a/python/packages/purview/agent_framework_purview/_settings.py +++ b/python/packages/purview/agent_framework_purview/_settings.py @@ -1,7 +1,5 @@ # Copyright (c) Microsoft. All rights reserved. -from __future__ import annotations - from enum import Enum from agent_framework._pydantic import AFBaseSettings diff --git a/python/packages/purview/pyproject.toml b/python/packages/purview/pyproject.toml index 4566b6633ec..804920b22be 100644 --- a/python/packages/purview/pyproject.toml +++ b/python/packages/purview/pyproject.toml @@ -4,7 +4,7 @@ description = "Microsoft Purview (Graph dataSecurityAndGovernance) integration f authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://github.com/microsoft/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/packages/purview/tests/test_models.py b/python/packages/purview/tests/test_purview_models.py similarity index 100% rename from python/packages/purview/tests/test_models.py rename to python/packages/purview/tests/test_purview_models.py diff --git a/python/packages/redis/pyproject.toml b/python/packages/redis/pyproject.toml index 0a57bd8b221..372a0585c1b 100644 --- a/python/packages/redis/pyproject.toml +++ b/python/packages/redis/pyproject.toml @@ -4,7 +4,7 @@ description = "Redis integration for Microsoft Agent Framework." authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" diff --git a/python/pyproject.toml b/python/pyproject.toml index 92a7d525ede..d55e939c40a 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -4,7 +4,7 @@ description = "Microsoft Agent Framework for building AI Agents with Python. Thi authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}] readme = "README.md" requires-python = ">=3.10" -version = "1.0.0b251114" +version = "1.0.0b251120" license-files = ["LICENSE"] urls.homepage = "https://aka.ms/agent-framework" urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" @@ -23,19 +23,7 @@ classifiers = [ "Typing :: Typed", ] dependencies = [ - "agent-framework-core", - "agent-framework-a2a", - "agent-framework-ag-ui", - "agent-framework-anthropic", - "agent-framework-azure-ai", - "agent-framework-azurefunctions", - "agent-framework-chatkit", - "agent-framework-copilotstudio", - "agent-framework-devui", - "agent-framework-lab", - "agent-framework-mem0", - "agent-framework-purview", - "agent-framework-redis", + "agent-framework-core[all]", ] [dependency-groups] @@ -93,16 +81,18 @@ agent-framework = { workspace = true } agent-framework-core = { workspace = true } agent-framework-a2a = { workspace = true } agent-framework-ag-ui = { workspace = true } +agent-framework-aisearch = { workspace = true } +agent-framework-anthropic = { workspace = true } agent-framework-azure-ai = { workspace = true } agent-framework-azurefunctions = { workspace = true } agent-framework-chatkit = { workspace = true } agent-framework-copilotstudio = { workspace = true } +agent-framework-declarative = { workspace = true } +agent-framework-devui = { workspace = true } agent-framework-lab = { workspace = true } agent-framework-mem0 = { workspace = true } -agent-framework-redis = { workspace = true } -agent-framework-devui = { workspace = true } agent-framework-purview = { workspace = true } -agent-framework-anthropic = { workspace = true } +agent-framework-redis = { workspace = true } [tool.ruff] line-length = 120 diff --git a/python/samples/README.md b/python/samples/README.md index 937c303c776..6bc6cce86c1 100644 --- a/python/samples/README.md +++ b/python/samples/README.md @@ -184,7 +184,8 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen | File | Description | |------|-------------| -| [`getting_started/evaluation/azure_ai_foundry/red_team_agent_sample.py`](./getting_started/evaluation/azure_ai_foundry/red_team_agent_sample.py) | Red team agent evaluation sample for Azure AI Foundry | +| [`getting_started/evaluation/red_teaming/red_team_agent_sample.py`](./getting_started/evaluation/red_teaming/red_team_agent_sample.py) | Red team agent evaluation sample for Azure AI Foundry | +| [`getting_started/evaluation/self_reflection/self_reflection.py`](./getting_started/evaluation/self_reflection/self_reflection.py) | LLM self-reflection with AI Foundry graders example | ## MCP (Model Context Protocol) @@ -205,6 +206,7 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen | [`getting_started/middleware/function_based_middleware.py`](./getting_started/middleware/function_based_middleware.py) | Function-based middleware example | | [`getting_started/middleware/middleware_termination.py`](./getting_started/middleware/middleware_termination.py) | Middleware termination example | | [`getting_started/middleware/override_result_with_middleware.py`](./getting_started/middleware/override_result_with_middleware.py) | Override result with middleware example | +| [`getting_started/middleware/runtime_context_delegation.py`](./getting_started/middleware/runtime_context_delegation.py) | Runtime context delegation example demonstrating how to pass API tokens, session data, and other context through hierarchical agent delegation | | [`getting_started/middleware/shared_state_middleware.py`](./getting_started/middleware/shared_state_middleware.py) | Shared state middleware example | | [`getting_started/middleware/thread_behavior_middleware.py`](./getting_started/middleware/thread_behavior_middleware.py) | Thread behavior middleware example demonstrating how to track conversation state across multiple agent runs | @@ -318,12 +320,6 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen | [`getting_started/workflows/human-in-the-loop/guessing_game_with_human_input.py`](./getting_started/workflows/human-in-the-loop/guessing_game_with_human_input.py) | Sample: Human in the loop guessing game | | [`getting_started/workflows/human-in-the-loop/agents_with_approval_requests.py`](./getting_started/workflows/human-in-the-loop/agents_with_approval_requests.py) | Sample: Agents with Approval Requests in Workflows | -### Observability - -| File | Description | -|------|-------------| -| [`getting_started/workflows/observability/tracing_basics.py`](./getting_started/workflows/observability/tracing_basics.py) | Basic tracing workflow sample | - ### Orchestration | File | Description | diff --git a/python/samples/demos/chatkit-integration/app.py b/python/samples/demos/chatkit-integration/app.py index 61f8ffd9768..95d66b78c70 100644 --- a/python/samples/demos/chatkit-integration/app.py +++ b/python/samples/demos/chatkit-integration/app.py @@ -250,7 +250,7 @@ async def _update_thread_title( context: The context dictionary. """ logger.info(f"Attempting to update thread title for thread: {thread.id}") - + if not thread_items: logger.debug("No thread items available for title generation") return @@ -589,7 +589,7 @@ async def upload_file(attachment_id: str, file: UploadFile = File(...)): except Exception as e: logger.error(f"Error uploading file for attachment {attachment_id}: {e}", exc_info=True) - return JSONResponse(status_code=500, content={"error": f"Failed to upload file: {str(e)}"}) + return JSONResponse(status_code=500, content={"error": "Failed to upload file."}) @app.get("/preview/{attachment_id}") @@ -620,7 +620,7 @@ async def preview_image(attachment_id: str): except Exception as e: logger.error(f"Error serving preview for attachment {attachment_id}: {e}", exc_info=True) - return JSONResponse(status_code=500, content={"error": str(e)}) + return JSONResponse(status_code=500, content={"error": "Error serving preview for attachment."}) if __name__ == "__main__": diff --git a/python/samples/demos/m365-agent/.env.example b/python/samples/demos/m365-agent/.env.example new file mode 100644 index 00000000000..3c21a9e91cf --- /dev/null +++ b/python/samples/demos/m365-agent/.env.example @@ -0,0 +1,17 @@ +# OpenAI Configuration +OPENAI_API_KEY= +OPENAI_CHAT_MODEL_ID= + +# Agent 365 Agentic Authentication Configuration +USE_ANONYMOUS_MODE= +CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTID= +CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTSECRET= +CONNECTIONS__SERVICE_CONNECTION__SETTINGS__TENANTID= +CONNECTIONS__SERVICE_CONNECTION__SETTINGS__SCOPES= + +AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__TYPE=AgenticUserAuthorization +AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__SCOPES=https://graph.microsoft.com/.default +AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__ALTERNATEBLUEPRINTCONNECTIONNAME=https://graph.microsoft.com/.default + +CONNECTIONSMAP_0_SERVICEURL=* +CONNECTIONSMAP_0_CONNECTION=SERVICE_CONNECTION diff --git a/python/samples/demos/m365-agent/README.md b/python/samples/demos/m365-agent/README.md new file mode 100644 index 00000000000..ecd1e6f6326 --- /dev/null +++ b/python/samples/demos/m365-agent/README.md @@ -0,0 +1,100 @@ +# Microsoft Agent Framework Python Weather Agent sample (M365 Agents SDK) + +This sample demonstrates a simple Weather Forecast Agent built with the Python Microsoft Agent Framework, exposed through the Microsoft 365 Agents SDK compatible endpoints. The agent accepts natural language requests for a weather forecast and responds with a textual answer. It supports multi-turn conversations to gather required information. + +## Prerequisites + +- Python 3.11+ +- [uv](https://github.com/astral-sh/uv) for fast dependency management +- [devtunnel](https://learn.microsoft.com/azure/developer/dev-tunnels/get-started?tabs=windows) +- [Microsoft 365 Agents Toolkit](https://github.com/OfficeDev/microsoft-365-agents-toolkit) for playground/testing +- Access to OpenAI or Azure OpenAI with a model like `gpt-4o-mini` + +## Configuration + +Set the following environment variables: + +```bash +# Common +export PORT=3978 +export USE_ANONYMOUS_MODE=True # set to false if using auth + +# OpenAI +export OPENAI_API_KEY="..." +export OPENAI_CHAT_MODEL_ID="..." +``` + +## Installing Dependencies + +From the repository root or the sample folder: + +```bash +uv sync +``` + +## Running the Agent Locally + +```bash +# Activate environment first if not already +source .venv/bin/activate # (Windows PowerShell: .venv\Scripts\Activate.ps1) + +# Run the weather agent demo +python m365_agent_demo/app.py +``` + +The agent starts on `http://localhost:3978`. Health check: `GET /api/health`. + +## QuickStart using Agents Playground + +1. Install (if not already): + + ```bash + winget install agentsplayground + ``` + +2. Start the Python agent locally: `python m365_agent_demo/app.py` +3. Start the playground: `agentsplayground` +4. Chat with the Weather Agent. + +## QuickStart using WebChat (Azure Bot) + +To test via WebChat you can provision an Azure Bot and point its messaging endpoint to your agent. + +1. Create an Azure Bot (choose Client Secret auth for local tunneling). +2. Create a `.env` file in this sample folder with the following (replace placeholders): + + ```bash + # Authentication / Agentic configuration + USE_ANONYMOUS_MODE=False + CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTID="" + CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTSECRET="" + CONNECTIONS__SERVICE_CONNECTION__SETTINGS__TENANTID="" + CONNECTIONS__SERVICE_CONNECTION__SETTINGS__SCOPES=https://graph.microsoft.com/.default + + AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__TYPE=AgenticUserAuthorization + AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__SCOPES=https://graph.microsoft.com/.default + AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__ALTERNATEBLUEPRINTCONNECTIONNAME=https://graph.microsoft.com/.default + ``` + +3. Host dev tunnel: + + ```bash + devtunnel host -p 3978 --allow-anonymous + ``` + +4. Set the bot Messaging endpoint to: `https:///api/messages` +5. Run your local agent: `python m365_agent_demo/app.py` +6. Use "Test in WebChat" in Azure Portal. + +> Federated Credentials or Managed Identity auth types typically require deployment to Azure App Service instead of tunneling. + +## Troubleshooting + +- 404 on `/api/messages`: Ensure you are POSTing and using the correct tunnel URL. +- Empty responses: Check model key / quota and ensure environment variables are set. +- Auth errors when anonymous disabled: Validate MSAL config matches your Azure Bot registration. + +## Further Reading + +- [Microsoft 365 Agents SDK](https://learn.microsoft.com/microsoft-365/agents-sdk/) +- [Devtunnel docs](https://learn.microsoft.com/azure/developer/dev-tunnels/) diff --git a/python/samples/demos/m365-agent/m365_agent_demo/app.py b/python/samples/demos/m365-agent/m365_agent_demo/app.py new file mode 100644 index 00000000000..7580e5ecce9 --- /dev/null +++ b/python/samples/demos/m365-agent/m365_agent_demo/app.py @@ -0,0 +1,238 @@ +# Copyright (c) Microsoft. All rights reserved. +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "microsoft-agents-hosting-aiohttp", +# "microsoft-agents-hosting-core", +# "microsoft-agents-authentication-msal", +# "microsoft-agents-activity", +# "agent-framework-core", +# "aiohttp" +# ] +# /// + +import os +from dataclasses import dataclass +from random import randint +from typing import Annotated + +from agent_framework import ChatAgent +from agent_framework.openai import OpenAIChatClient +from aiohttp import web +from aiohttp.web_middlewares import middleware +from microsoft_agents.activity import load_configuration_from_env +from microsoft_agents.authentication.msal import MsalConnectionManager +from microsoft_agents.hosting.aiohttp import CloudAdapter, start_agent_process +from microsoft_agents.hosting.core import ( + AgentApplication, + AuthenticationConstants, + Authorization, + ClaimsIdentity, + MemoryStorage, + TurnContext, + TurnState, +) +from pydantic import Field + +""" +Demo application using Microsoft Agent 365 SDK. + +This sample demonstrates how to build an AI agent using the Agent Framework, +integrating with Microsoft 365 authentication and hosting components. + +The agent provides a simple weather tool and can be run in either anonymous mode +(no authentication required) or authenticated mode using MSAL and Azure AD. + +Key features: +- Loads configuration from environment variables. +- Demonstrates agent creation and tool registration. +- Supports both anonymous and authenticated scenarios. +- Uses aiohttp for web hosting. + +To run, set the appropriate environment variables (check .env.example file) for authentication or use +anonymous mode for local testing. +""" + + +@dataclass +class AppConfig: + use_anonymous_mode: bool + port: int + agents_sdk_config: dict + + +def load_app_config() -> AppConfig: + """Load application configuration from environment variables. + + Returns: + AppConfig: Consolidated configuration including anonymous mode flag, port, and SDK config. + """ + agents_sdk_config = load_configuration_from_env(os.environ) + use_anonymous_mode = os.environ.get("USE_ANONYMOUS_MODE", "true").lower() == "true" + port_str = os.getenv("PORT", "3978") + try: + port = int(port_str) + except ValueError: + port = 3978 + return AppConfig(use_anonymous_mode=use_anonymous_mode, port=port, agents_sdk_config=agents_sdk_config) + + +def get_weather( + location: Annotated[str, Field(description="The location to get the weather for.")], +) -> str: + """Generate a mock weather report for the provided location. + + Args: + location: The geographic location name. + Returns: + str: Human-readable weather summary. + """ + conditions = ["sunny", "cloudy", "rainy", "stormy"] + return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C." + + +def build_agent() -> ChatAgent: + """Create and return the chat agent instance with weather tool registered.""" + return OpenAIChatClient().create_agent( + name="WeatherAgent", instructions="You are a helpful weather agent.", tools=get_weather + ) + + +def build_connection_manager(config: AppConfig) -> MsalConnectionManager | None: + """Build the connection manager unless running in anonymous mode. + + Args: + config: Application configuration. + Returns: + MsalConnectionManager | None: Connection manager when authenticated mode is enabled. + """ + if config.use_anonymous_mode: + return None + return MsalConnectionManager(**config.agents_sdk_config) + + +def build_adapter(connection_manager: MsalConnectionManager | None) -> CloudAdapter: + """Instantiate the CloudAdapter with the optional connection manager.""" + return CloudAdapter(connection_manager=connection_manager) + + +def build_authorization( + storage: MemoryStorage, connection_manager: MsalConnectionManager | None, config: AppConfig +) -> Authorization | None: + """Create Authorization component if not in anonymous mode. + + Args: + storage: State storage backend. + connection_manager: Optional connection manager. + config: Application configuration. + Returns: + Authorization | None: Authorization component when enabled. + """ + if config.use_anonymous_mode: + return None + return Authorization(storage, connection_manager, **config.agents_sdk_config) + + +def build_agent_application( + storage: MemoryStorage, + adapter: CloudAdapter, + authorization: Authorization | None, + config: AppConfig, +) -> AgentApplication[TurnState]: + """Compose and return the AgentApplication instance. + + Args: + storage: Storage implementation. + adapter: CloudAdapter handling requests. + authorization: Optional authorization component. + config: App configuration. + Returns: + AgentApplication[TurnState]: Configured agent application. + """ + return AgentApplication[TurnState]( + storage=storage, adapter=adapter, authorization=authorization, **config.agents_sdk_config + ) + + +def build_anonymous_claims_middleware(use_anonymous_mode: bool): + """Return a middleware that injects anonymous claims when enabled. + + Args: + use_anonymous_mode: Whether to apply anonymous identity for each request. + Returns: + Callable: Aiohttp middleware function. + """ + + @middleware + async def anonymous_claims_middleware(request, handler): + """Inject claims for anonymous users if anonymous mode is active.""" + if use_anonymous_mode: + request["claims_identity"] = ClaimsIdentity( + { + AuthenticationConstants.AUDIENCE_CLAIM: "anonymous", + AuthenticationConstants.APP_ID_CLAIM: "anonymous-app", + }, + False, + "Anonymous", + ) + return await handler(request) + + return anonymous_claims_middleware + + +def create_app(config: AppConfig) -> web.Application: + """Create and configure the aiohttp web application. + + Args: + config: Loaded application configuration. + Returns: + web.Application: Fully initialized web application. + """ + middleware_fn = build_anonymous_claims_middleware(config.use_anonymous_mode) + app = web.Application(middlewares=[middleware_fn]) + + storage = MemoryStorage() + agent = build_agent() + connection_manager = build_connection_manager(config) + adapter = build_adapter(connection_manager) + authorization = build_authorization(storage, connection_manager, config) + agent_app = build_agent_application(storage, adapter, authorization, config) + + @agent_app.activity("message") + async def on_message(context: TurnContext, _: TurnState): + user_message = context.activity.text or "" + if not user_message.strip(): + return + + response = await agent.run(user_message) + response_text = response.text + + await context.send_activity(response_text) + + async def health(request: web.Request) -> web.Response: + return web.json_response({"status": "ok"}) + + async def entry_point(req: web.Request) -> web.Response: + return await start_agent_process(req, req.app["agent_app"], req.app["adapter"]) + + app.add_routes([ + web.get("/api/health", health), + web.get("/api/messages", lambda _: web.Response(status=200)), + web.post("/api/messages", entry_point), + ]) + + app["agent_app"] = agent_app + app["adapter"] = adapter + + return app + + +def main() -> None: + """Entry point: load configuration, build app, and start server.""" + config = load_app_config() + app = create_app(config) + web.run_app(app, host="localhost", port=config.port) + + +if __name__ == "__main__": + main() diff --git a/python/samples/getting_started/agents/anthropic/README.md b/python/samples/getting_started/agents/anthropic/README.md index a4f76d72251..2fee2f5c076 100644 --- a/python/samples/getting_started/agents/anthropic/README.md +++ b/python/samples/getting_started/agents/anthropic/README.md @@ -9,10 +9,16 @@ This folder contains examples demonstrating how to use Anthropic's Claude models | [`anthropic_basic.py`](anthropic_basic.py) | Demonstrates how to setup a simple agent using the AnthropicClient, with both streaming and non-streaming responses. | | [`anthropic_advanced.py`](anthropic_advanced.py) | Shows advanced usage of the AnthropicClient, including hosted tools and `thinking`. | | [`anthropic_skills.py`](anthropic_skills.py) | Illustrates how to use Anthropic-managed Skills with an agent, including the Code Interpreter tool and file generation and saving. | +| [`anthropic_foundry.py`](anthropic_foundry.py) | Example of using Foundry's Anthropic integration with the Agent Framework. | ## Environment Variables Set the following environment variables before running the examples: - `ANTHROPIC_API_KEY`: Your Anthropic API key (get one from [Anthropic Console](https://console.anthropic.com/)) -- `ANTHROPIC_MODEL`: The Claude model to use (e.g., `claude-haiku-4-5`, `claude-sonnet-4-5-20250929`) +- `ANTHROPIC_CHAT_MODEL_ID`: The Claude model to use (e.g., `claude-haiku-4-5`, `claude-sonnet-4-5-20250929`) + +Or, for Foundry: +- `ANTHROPIC_FOUNDRY_API_KEY`: Your Foundry Anthropic API key +- `ANTHROPIC_FOUNDRY_ENDPOINT`: The endpoint URL for your Foundry Anthropic resource +- `ANTHROPIC_CHAT_MODEL_ID`: The Claude model to use in Foundry (e.g., `claude-haiku-4-5`) diff --git a/python/samples/getting_started/agents/anthropic/anthropic_foundry.py b/python/samples/getting_started/agents/anthropic/anthropic_foundry.py new file mode 100644 index 00000000000..cb1b690d54b --- /dev/null +++ b/python/samples/getting_started/agents/anthropic/anthropic_foundry.py @@ -0,0 +1,63 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio + +from agent_framework import HostedMCPTool, HostedWebSearchTool, TextReasoningContent, UsageContent +from agent_framework.anthropic import AnthropicClient +from anthropic import AsyncAnthropicFoundry + +""" +Anthropic Foundry Chat Agent Example + +This sample demonstrates using Anthropic with: +- Setting up an Anthropic-based agent with hosted tools. +- Using the `thinking` feature. +- Displaying both thinking and usage information during streaming responses. + +This example requires `anthropic>=0.74.0` and an endpoint in Foundry for Anthropic. + +To use the Foundry integration ensure you have the following environment variables set: +- ANTHROPIC_FOUNDRY_API_KEY + Alternatively you can pass in a azure_ad_token_provider function to the AsyncAnthropicFoundry constructor. +- ANTHROPIC_FOUNDRY_ENDPOINT + Should be something like https://.services.ai.azure.com/anthropic/ +- ANTHROPIC_CHAT_MODEL_ID + Should be something like claude-haiku-4-5 +""" + + +async def main() -> None: + """Example of streaming response (get results as they are generated).""" + agent = AnthropicClient(anthropic_client=AsyncAnthropicFoundry()).create_agent( + name="DocsAgent", + instructions="You are a helpful agent for both Microsoft docs questions and general questions.", + tools=[ + HostedMCPTool( + name="Microsoft Learn MCP", + url="https://learn.microsoft.com/api/mcp", + ), + HostedWebSearchTool(), + ], + # anthropic needs a value for the max_tokens parameter + # we set it to 1024, but you can override like this: + max_tokens=20000, + additional_chat_options={"thinking": {"type": "enabled", "budget_tokens": 10000}}, + ) + + query = "Can you compare Python decorators with C# attributes?" + print(f"User: {query}") + print("Agent: ", end="", flush=True) + async for chunk in agent.run_stream(query): + for content in chunk.contents: + if isinstance(content, TextReasoningContent): + print(f"\033[32m{content.text}\033[0m", end="", flush=True) + if isinstance(content, UsageContent): + print(f"\n\033[34m[Usage so far: {content.details}]\033[0m\n", end="", flush=True) + if chunk.text: + print(chunk.text, end="", flush=True) + + print("\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/azure_ai/README.md b/python/samples/getting_started/agents/azure_ai/README.md index aac3a7d01e1..7a3b2d45201 100644 --- a/python/samples/getting_started/agents/azure_ai/README.md +++ b/python/samples/getting_started/agents/azure_ai/README.md @@ -8,6 +8,7 @@ This folder contains examples demonstrating different ways to create and use age |------|-------------| | [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `AzureAIClient`. Demonstrates both streaming and non-streaming responses with function tools. Shows automatic agent creation and basic weather functionality. | | [`azure_ai_use_latest_version.py`](azure_ai_use_latest_version.py) | Demonstrates how to reuse the latest version of an existing agent instead of creating a new agent version on each instantiation using the `use_latest_version=True` parameter. | +| [`azure_ai_with_agent_to_agent.py`](azure_ai_with_agent_to_agent.py) | Shows how to use Agent-to-Agent (A2A) capabilities with Azure AI agents to enable communication with other agents using the A2A protocol. Requires an A2A connection configured in your Azure AI project. | | [`azure_ai_with_azure_ai_search.py`](azure_ai_with_azure_ai_search.py) | Shows how to use Azure AI Search with Azure AI agents to search through indexed data and answer user questions with proper citations. Requires an Azure AI Search connection and index configured in your Azure AI project. | | [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to search the web for current information and provide grounded responses with citations. Requires a Bing connection configured in your Azure AI project. | | [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to search custom search instances and provide responses with relevant results. Requires a Bing Custom Search connection and instance configured in your Azure AI project. | @@ -19,6 +20,9 @@ This folder contains examples demonstrating different ways to create and use age | [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. | | [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. | | [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. | +| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. | +| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. | +| [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. | | [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. | | [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. | | [`azure_ai_with_microsoft_fabric.py`](azure_ai_with_microsoft_fabric.py) | Shows how to use Microsoft Fabric with Azure AI agents to query Fabric data sources and provide responses based on data analysis. Requires a Microsoft Fabric connection configured in your Azure AI project. | diff --git a/python/samples/getting_started/agents/azure_ai/azure_ai_with_agent_to_agent.py b/python/samples/getting_started/agents/azure_ai/azure_ai_with_agent_to_agent.py new file mode 100644 index 00000000000..8efb3d94739 --- /dev/null +++ b/python/samples/getting_started/agents/azure_ai/azure_ai_with_agent_to_agent.py @@ -0,0 +1,42 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +import os + +from agent_framework.azure import AzureAIClient +from azure.identity.aio import AzureCliCredential + +""" +Azure AI Agent with Agent-to-Agent (A2A) Example + +This sample demonstrates usage of AzureAIClient with Agent-to-Agent (A2A) capabilities +to enable communication with other agents using the A2A protocol. + +Prerequisites: +1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables. +2. Ensure you have an A2A connection configured in your Azure AI project + and set A2A_PROJECT_CONNECTION_ID environment variable. +""" + + +async def main() -> None: + async with ( + AzureCliCredential() as credential, + AzureAIClient(async_credential=credential).create_agent( + name="MyA2AAgent", + instructions="""You are a helpful assistant that can communicate with other agents. + Use the A2A tool when you need to interact with other agents to complete tasks + or gather information from specialized agents.""", + tools={ + "type": "a2a_preview", + "project_connection_id": os.environ["A2A_PROJECT_CONNECTION_ID"], + }, + ) as agent, + ): + query = "What can the secondary agent do?" + print(f"User: {query}") + result = await agent.run(query) + print(f"Result: {result}\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_agentic.py b/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_agentic.py new file mode 100644 index 00000000000..d8f6a9dc307 --- /dev/null +++ b/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_agentic.py @@ -0,0 +1,118 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio +import os + +from dotenv import load_dotenv + +from agent_framework import ChatAgent +from agent_framework_aisearch import AzureAISearchContextProvider +from agent_framework_azure_ai import AzureAIAgentClient +from azure.identity.aio import DefaultAzureCredential + +# Load environment variables from .env file +load_dotenv() + +""" +This sample demonstrates how to use Azure AI Search with agentic mode for RAG +(Retrieval Augmented Generation) with Azure AI agents. + +**Agentic mode** is recommended for most scenarios: +- Uses Knowledge Bases in Azure AI Search for query planning +- Performs multi-hop reasoning across documents +- Provides more accurate results through intelligent retrieval +- Slightly slower with more token consumption for query planning +- See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720 + +For simple queries where speed is critical, use semantic mode instead (see azure_ai_with_search_context_semantic.py). + +Prerequisites: +1. An Azure AI Search service with a search index +2. An Azure AI Foundry project with a model deployment +3. An Azure OpenAI resource (for Knowledge Base model calls) +4. Set the following environment variables: + - AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint + - AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID + - AZURE_SEARCH_INDEX_NAME: Your search index name + - AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint + - AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o") + - AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name + - AZURE_OPENAI_RESOURCE_URL: Your Azure OpenAI resource URL (e.g., "https://myresource.openai.azure.com") + Note: This is different from AZURE_AI_PROJECT_ENDPOINT - Knowledge Base needs the OpenAI endpoint for model calls +""" + +# Sample queries to demonstrate agentic RAG +USER_INPUTS = [ + "What information is available in the knowledge base?", + "Analyze and compare the main topics from different documents", + "What connections can you find across different sections?", +] + + +async def main() -> None: + """Main function demonstrating Azure AI Search agentic mode.""" + + # Get configuration from environment + search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"] + search_key = os.environ.get("AZURE_SEARCH_API_KEY") + index_name = os.environ["AZURE_SEARCH_INDEX_NAME"] + project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"] + model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o") + knowledge_base_name = os.environ["AZURE_SEARCH_KNOWLEDGE_BASE_NAME"] + azure_openai_resource_url = os.environ["AZURE_OPENAI_RESOURCE_URL"] + + # Create Azure AI Search context provider with agentic mode (recommended for accuracy) + print("Using AGENTIC mode (Knowledge Bases with query planning, recommended)\n") + print("ℹ️ This mode is slightly slower but provides more accurate results.\n") + search_provider = AzureAISearchContextProvider( + endpoint=search_endpoint, + index_name=index_name, + api_key=search_key, # Use api_key for API key auth, or credential for managed identity + credential=DefaultAzureCredential() if not search_key else None, + mode="agentic", # Advanced mode for multi-hop reasoning + # Agentic mode configuration + azure_ai_project_endpoint=project_endpoint, + azure_openai_resource_url=azure_openai_resource_url, + model_deployment_name=model_deployment, + knowledge_base_name=knowledge_base_name, + # Optional: Configure retrieval behavior + knowledge_base_output_mode="extractive_data", # or "answer_synthesis" + retrieval_reasoning_effort="minimal", # or "medium", "low" + top_k=3, # Note: In agentic mode, the server-side Knowledge Base determines final retrieval + ) + + # Create agent with search context provider + async with ( + search_provider, + AzureAIAgentClient( + project_endpoint=project_endpoint, + model_deployment_name=model_deployment, + async_credential=DefaultAzureCredential(), + ) as client, + ChatAgent( + chat_client=client, + name="SearchAgent", + instructions=( + "You are a helpful assistant with advanced reasoning capabilities. " + "Use the provided context from the knowledge base to answer complex " + "questions that may require synthesizing information from multiple sources." + ), + context_providers=[search_provider], + ) as agent, + ): + print("=== Azure AI Agent with Search Context (Agentic Mode) ===\n") + + for user_input in USER_INPUTS: + print(f"User: {user_input}") + print("Agent: ", end="", flush=True) + + # Stream response + async for chunk in agent.run_stream(user_input): + if chunk.text: + print(chunk.text, end="", flush=True) + + print("\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py b/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py new file mode 100644 index 00000000000..30504354f7a --- /dev/null +++ b/python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py @@ -0,0 +1,99 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio +import os + +from dotenv import load_dotenv + +from agent_framework import ChatAgent +from agent_framework_aisearch import AzureAISearchContextProvider +from agent_framework_azure_ai import AzureAIAgentClient +from azure.identity.aio import DefaultAzureCredential + +# Load environment variables from .env file +load_dotenv() + +""" +This sample demonstrates how to use Azure AI Search with semantic mode for RAG +(Retrieval Augmented Generation) with Azure AI agents. + +**Semantic mode** is the recommended default mode: +- Fast hybrid search combining vector and keyword search +- Uses semantic ranking for improved relevance +- Returns raw search results as context +- Best for most RAG use cases + +Prerequisites: +1. An Azure AI Search service with a search index +2. An Azure AI Foundry project with a model deployment +3. Set the following environment variables: + - AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint + - AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID + - AZURE_SEARCH_INDEX_NAME: Your search index name + - AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint + - AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o") +""" + +# Sample queries to demonstrate RAG +USER_INPUTS = [ + "What information is available in the knowledge base?", + "Summarize the main topics from the documents", + "Find specific details about the content", +] + + +async def main() -> None: + """Main function demonstrating Azure AI Search semantic mode.""" + + # Get configuration from environment + search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"] + search_key = os.environ.get("AZURE_SEARCH_API_KEY") + index_name = os.environ["AZURE_SEARCH_INDEX_NAME"] + project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"] + model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o") + + # Create Azure AI Search context provider with semantic mode (recommended, fast) + print("Using SEMANTIC mode (hybrid search + semantic ranking, fast)\n") + search_provider = AzureAISearchContextProvider( + endpoint=search_endpoint, + index_name=index_name, + api_key=search_key, # Use api_key for API key auth, or credential for managed identity + credential=DefaultAzureCredential() if not search_key else None, + mode="semantic", # Default mode + top_k=3, # Retrieve top 3 most relevant documents + ) + + # Create agent with search context provider + async with ( + search_provider, + AzureAIAgentClient( + project_endpoint=project_endpoint, + model_deployment_name=model_deployment, + async_credential=DefaultAzureCredential(), + ) as client, + ChatAgent( + chat_client=client, + name="SearchAgent", + instructions=( + "You are a helpful assistant. Use the provided context from the " + "knowledge base to answer questions accurately." + ), + context_providers=[search_provider], + ) as agent, + ): + print("=== Azure AI Agent with Search Context (Semantic Mode) ===\n") + + for user_input in USER_INPUTS: + print(f"User: {user_input}") + print("Agent: ", end="", flush=True) + + # Stream response + async for chunk in agent.run_stream(user_input): + if chunk.text: + print(chunk.text, end="", flush=True) + + print("\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/azure_ai/azure_ai_with_sharepoint.py b/python/samples/getting_started/agents/azure_ai/azure_ai_with_sharepoint.py new file mode 100644 index 00000000000..cdd81a33af5 --- /dev/null +++ b/python/samples/getting_started/agents/azure_ai/azure_ai_with_sharepoint.py @@ -0,0 +1,43 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +import os + +from agent_framework.azure import AzureAIClient +from azure.identity.aio import AzureCliCredential + +""" +Azure AI Agent with SharePoint Example + +This sample demonstrates usage of AzureAIClient with SharePoint +to search through SharePoint content and answer user questions about it. + +Prerequisites: +1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables. +2. Ensure you have a SharePoint connection configured in your Azure AI project + and set SHAREPOINT_PROJECT_CONNECTION_ID environment variable. +""" + + +async def main() -> None: + async with ( + AzureCliCredential() as credential, + AzureAIClient(async_credential=credential).create_agent( + name="MySharePointAgent", + instructions="""You are a helpful agent that can use SharePoint tools to assist users. + Use the available SharePoint tools to answer questions and perform tasks.""", + tools={ + "type": "sharepoint_grounding_preview", + "sharepoint_grounding_preview": { + "project_connection_id": os.environ["SHAREPOINT_PROJECT_CONNECTION_ID"] + }, + }, + ) as agent, + ): + query = "What is Contoso whistleblower policy?" + print(f"User: {query}") + result = await agent.run(query) + print(f"Result: {result}\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py b/python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py index ae222091acf..41d1a134ad9 100644 --- a/python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py +++ b/python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py @@ -33,7 +33,7 @@ async def non_streaming_example() -> None: print("=== Non-streaming Response Example ===") agent = OpenAIChatClient( - api_key="ollama", # Just a placeholder, Ollama doesn't require API key + api_key="ollama", # Just a placeholder, Ollama doesn't require API key base_url=os.getenv("OLLAMA_ENDPOINT"), model_id=os.getenv("OLLAMA_MODEL"), ).create_agent( @@ -53,7 +53,7 @@ async def streaming_example() -> None: print("=== Streaming Response Example ===") agent = OpenAIChatClient( - api_key="ollama", # Just a placeholder, Ollama doesn't require API key + api_key="ollama", # Just a placeholder, Ollama doesn't require API key base_url=os.getenv("OLLAMA_ENDPOINT"), model_id=os.getenv("OLLAMA_MODEL"), ).create_agent( diff --git a/python/samples/getting_started/azure_functions/01_single_agent/function_app.py b/python/samples/getting_started/azure_functions/01_single_agent/function_app.py index 1fafdfbddcf..9225fbd3010 100644 --- a/python/samples/getting_started/azure_functions/01_single_agent/function_app.py +++ b/python/samples/getting_started/azure_functions/01_single_agent/function_app.py @@ -10,6 +10,8 @@ from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient from azure.identity import AzureCliCredential + + # 1. Instantiate the agent with the chosen deployment and instructions. def _create_agent() -> Any: """Create the Joker agent.""" diff --git a/python/samples/getting_started/azure_functions/06_multi_agent_orchestration_conditionals/function_app.py b/python/samples/getting_started/azure_functions/06_multi_agent_orchestration_conditionals/function_app.py index 84d114b3b5e..d32884014f2 100644 --- a/python/samples/getting_started/azure_functions/06_multi_agent_orchestration_conditionals/function_app.py +++ b/python/samples/getting_started/azure_functions/06_multi_agent_orchestration_conditionals/function_app.py @@ -40,6 +40,7 @@ class EmailPayload(BaseModel): email_id: str email_content: str + # 2. Instantiate both agents so they can be registered with AgentFunctionApp. def _create_agents() -> list[Any]: chat_client = AzureOpenAIChatClient(credential=AzureCliCredential()) diff --git a/python/samples/getting_started/azure_functions/07_single_agent_orchestration_hitl/function_app.py b/python/samples/getting_started/azure_functions/07_single_agent_orchestration_hitl/function_app.py index 7257ff78310..69b2e3fae94 100644 --- a/python/samples/getting_started/azure_functions/07_single_agent_orchestration_hitl/function_app.py +++ b/python/samples/getting_started/azure_functions/07_single_agent_orchestration_hitl/function_app.py @@ -275,7 +275,7 @@ async def get_orchestration_status( show_history_output=False, show_input=True, ) - + # Check if status is None or if the instance doesn't exist (runtime_status is None) if status is None or getattr(status, "runtime_status", None) is None: return func.HttpResponse( diff --git a/python/samples/getting_started/declarative/README.md b/python/samples/getting_started/declarative/README.md new file mode 100644 index 00000000000..0956c33a672 --- /dev/null +++ b/python/samples/getting_started/declarative/README.md @@ -0,0 +1,261 @@ +# Declarative Agent Samples + +This folder contains sample code demonstrating how to use the **Microsoft Agent Framework Declarative** package to create agents from YAML specifications. The declarative approach allows you to define your agents in a structured, configuration-driven way, separating agent behavior from implementation details. + +## Installation + +Install the declarative package via pip: + +```bash +pip install agent-framework-declarative --pre +``` + +## What is Declarative Agent Framework? + +The declarative package provides support for building agents based on YAML specifications. This approach offers several benefits: + +- **Cross-Platform Compatibility**: Write one YAML definition and create agents in both Python and .NET - the same agent configuration works across both platforms +- **Separation of Concerns**: Define agent behavior in YAML files separate from your implementation code +- **Reusability**: Share and version agent configurations independently across projects and languages +- **Flexibility**: Easily swap between different LLM providers and configurations +- **Maintainability**: Update agent instructions and settings without modifying code + +## Samples in This Folder + +### 1. **Get Weather Agent** ([`get_weather_agent.py`](./get_weather_agent.py)) + +Demonstrates how to create an agent with custom function tools using the declarative approach. + +- Uses Azure OpenAI Responses client +- Shows how to bind Python functions to the agent using the `bindings` parameter +- Loads agent configuration from `agent-samples/chatclient/GetWeather.yaml` +- Implements a simple weather lookup function tool + +**Key concepts**: Function binding, Azure OpenAI integration, tool usage + +### 2. **Microsoft Learn Agent** ([`microsoft_learn_agent.py`](./microsoft_learn_agent.py)) + +Shows how to create an agent that can search and retrieve information from Microsoft Learn documentation using the Model Context Protocol (MCP). + +- Uses Azure AI Foundry client with MCP server integration +- Demonstrates async context managers for proper resource cleanup +- Loads agent configuration from `agent-samples/foundry/MicrosoftLearnAgent.yaml` +- Uses Azure CLI credentials for authentication +- Leverages MCP to access Microsoft documentation tools + +**Requirements**: `pip install agent-framework-azure-ai --pre` + +**Key concepts**: Azure AI Foundry integration, MCP server usage, async patterns, resource management + +### 3. **Azure OpenAI Responses Agent** ([`azure_openai_responses_agent.py`](./azure_openai_responses_agent.py)) + +Illustrates a basic agent using Azure OpenAI with structured responses. + +- Uses Azure OpenAI Responses client +- Shows how to pass credentials via `client_kwargs` +- Loads agent configuration from `agent-samples/azure/AzureOpenAIResponses.yaml` +- Demonstrates accessing structured response data + +**Key concepts**: Azure OpenAI integration, credential management, structured outputs + +### 4. **OpenAI Responses Agent** ([`openai_responses_agent.py`](./openai_responses_agent.py)) + +Demonstrates the simplest possible agent using OpenAI directly. + +- Uses OpenAI API (requires `OPENAI_API_KEY` environment variable) +- Shows minimal configuration needed for basic agent creation +- Loads agent configuration from `agent-samples/openai/OpenAIResponses.yaml` + +**Key concepts**: OpenAI integration, minimal setup, environment-based configuration + +## Agent Samples Repository + +All the YAML configuration files referenced in these samples are located in the [`agent-samples`](../../../../agent-samples/) folder at the repository root. This folder contains declarative agent specifications organized by provider: + +- **`agent-samples/azure/`** - Azure OpenAI agent configurations +- **`agent-samples/chatclient/`** - Chat client agent configurations with tools +- **`agent-samples/foundry/`** - Azure AI Foundry agent configurations +- **`agent-samples/openai/`** - OpenAI agent configurations + +**Important**: These YAML files are **platform-agnostic** and work with both Python and .NET implementations of the Agent Framework. You can use the exact same YAML definition to create agents in either language, making it easy to share agent configurations across different technology stacks. + +These YAML files define: +- Agent instructions and system prompts +- Model selection and parameters +- Tool and function configurations +- Provider-specific settings +- MCP server integrations (where applicable) + +## Common Patterns + +### Creating an Agent from YAML String + +```python +from agent_framework.declarative import AgentFactory + +with open("agent.yaml", "r") as f: + yaml_str = f.read() + +agent = AgentFactory().create_agent_from_yaml(yaml_str) +# response = await agent.run("Your query here") +``` + +### Creating an Agent from YAML Path + +```python +from pathlib import Path +from agent_framework.declarative import AgentFactory + +yaml_path = Path("agent.yaml") +agent = AgentFactory().create_agent_from_yaml_path(yaml_path) +# response = await agent.run("Your query here") +``` + +### Binding Custom Functions + +```python +from pathlib import Path +from agent_framework.declarative import AgentFactory + +def my_function(param: str) -> str: + return f"Result: {param}" + +agent_factory = AgentFactory(bindings={"my_function": my_function}) +agent = agent_factory.create_agent_from_yaml_path(Path("agent_with_tool.yaml")) +``` + +### Using Credentials + +```python +from pathlib import Path +from agent_framework.declarative import AgentFactory +from azure.identity import AzureCliCredential + +agent = AgentFactory( + client_kwargs={"credential": AzureCliCredential()} +).create_agent_from_yaml_path(Path("azure_agent.yaml")) +``` + +### Adding Custom Provider Mappings + +```python +from pathlib import Path +from agent_framework.declarative import AgentFactory +# from my_custom_module import MyCustomChatClient + +# Register a custom provider mapping +agent_factory = AgentFactory( + additional_mappings={ + "MyProvider": { + "package": "my_custom_module", + "name": "MyCustomChatClient", + "model_id_field": "model_id", + } + } +) + +# Now you can reference "MyProvider" in your YAML +# Example YAML snippet: +# model: +# provider: MyProvider +# id: my-model-name + +agent = agent_factory.create_agent_from_yaml_path(Path("custom_provider.yaml")) +``` + +This allows you to extend the declarative framework with custom chat client implementations. The mapping requires: +- **package**: The Python package/module to import from +- **name**: The class name of your ChatClientProtocol implementation +- **model_id_field**: The constructor parameter name that accepts the value of the `model.id` field from the YAML + +You can reference your custom provider using either `Provider.ApiType` format or just `Provider` in your YAML configuration, as long as it matches the registered mapping. + +### Using PowerFx Formulas in YAML + +The declarative framework supports PowerFx formulas in YAML values, enabling dynamic configuration based on environment variables and conditional logic. Prefix any value with `=` to evaluate it as a PowerFx expression. + +#### Environment Variable Lookup + +Access environment variables using the `Env.` syntax: + +```yaml +model: + connection: + kind: key + apiKey: =Env.OPENAI_API_KEY + endpoint: =Env.BASE_URL & "/v1" # String concatenation with & + + options: + temperature: 0.7 + maxOutputTokens: =Env.MAX_TOKENS # Will be converted to appropriate type +``` + +#### Conditional Logic + +Use PowerFx operators for conditional configuration. This is particularly useful for adjusting parameters based on which model is being used: + +```yaml +model: + id: =Env.MODEL_NAME + options: + # Set max tokens based on model - using conditional logic + maxOutputTokens: =If(Env.MODEL_NAME = "gpt-5", 8000, 4000) + + # Adjust temperature for different environments + temperature: =If(Env.ENVIRONMENT = "production", 0.3, 0.7) + + # Use logical operators for complex conditions + seed: =If(Env.ENVIRONMENT = "production" And Env.DETERMINISTIC = "true", 42, Blank()) +``` + +#### Supported PowerFx Features + +- **String operations**: Concatenation (`&`), comparison (`=`, `<>`), substring testing (`in`, `exactin`) +- **Logical operators**: `And`, `Or`, `Not` (also `&&`, `||`, `!`) +- **Arithmetic**: Basic math operations (`+`, `-`, `*`, `/`) +- **Conditional**: `If(condition, true_value, false_value)` +- **Environment access**: `Env.` + +Example with multiple features: + +```yaml +instructions: =If( + Env.USE_EXPERT_MODE = "true", + "You are an expert AI assistant with advanced capabilities. " & Env.CUSTOM_INSTRUCTIONS, + "You are a helpful AI assistant." +) + +model: + options: + stopSequences: =If("gpt-4" in Env.MODEL_NAME, ["END", "STOP"], ["END"]) +``` + +**Note**: PowerFx evaluation happens when the YAML is loaded, not at runtime. Use environment variables (via `.env` file or `env_file` parameter) to make configurations flexible across environments. + +## Running the Samples + +Each sample can be run independently. Make sure you have the required environment variables set: + +- For Azure samples: Ensure you're logged in via Azure CLI (`az login`) +- For OpenAI samples: Set `OPENAI_APIKEY` environment variable + +```bash +# Run a specific sample +python get_weather_agent.py +python microsoft_learn_agent.py +python azure_openai_responses_agent.py +python openai_responses_agent.py +``` + +## Learn More + +- [Agent Framework Declarative Package](../../../packages/declarative/) - Main declarative package documentation +- [Agent Samples](../../../../agent-samples/) - Additional declarative agent YAML specifications +- [Agent Framework Core](../../../packages/core/) - Core agent framework documentation + +## Next Steps + +1. Explore the YAML files in the `agent-samples` folder to understand the configuration format +2. Try modifying the samples to use different models or instructions +3. Create your own declarative agent configurations +4. Build custom function tools and bind them to your agents diff --git a/python/samples/getting_started/declarative/azure_openai_responses_agent.py b/python/samples/getting_started/declarative/azure_openai_responses_agent.py new file mode 100644 index 00000000000..37c9d00455b --- /dev/null +++ b/python/samples/getting_started/declarative/azure_openai_responses_agent.py @@ -0,0 +1,27 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +from pathlib import Path + +from agent_framework.declarative import AgentFactory +from azure.identity import AzureCliCredential + + +async def main(): + """Create an agent from a declarative yaml specification and run it.""" + # get the path + current_path = Path(__file__).parent + yaml_path = current_path.parent.parent.parent.parent / "agent-samples" / "azure" / "AzureOpenAIResponses.yaml" + + # load the yaml from the path + with yaml_path.open("r") as f: + yaml_str = f.read() + + # create the agent from the yaml + agent = AgentFactory(client_kwargs={"credential": AzureCliCredential()}).create_agent_from_yaml(yaml_str) + # use the agent + response = await agent.run("Why is the sky blue, answer in Dutch?") + print("Agent response:", response.value.model_dump_json(indent=2)) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/declarative/get_weather_agent.py b/python/samples/getting_started/declarative/get_weather_agent.py new file mode 100644 index 00000000000..3994d8ede7d --- /dev/null +++ b/python/samples/getting_started/declarative/get_weather_agent.py @@ -0,0 +1,40 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +from pathlib import Path +from random import randint +from typing import Literal + +from agent_framework.azure import AzureOpenAIResponsesClient +from agent_framework.declarative import AgentFactory +from azure.identity import AzureCliCredential + + +def get_weather(location: str, unit: Literal["celsius", "fahrenheit"] = "celsius") -> str: + """A simple function tool to get weather information.""" + return f"The weather in {location} is {randint(-10, 30) if unit == 'celsius' else randint(30, 100)} degrees {unit}." + + +async def main(): + """Create an agent from a declarative yaml specification and run it.""" + # get the path + current_path = Path(__file__).parent + yaml_path = current_path.parent.parent.parent.parent / "agent-samples" / "chatclient" / "GetWeather.yaml" + + # load the yaml from the path + with yaml_path.open("r") as f: + yaml_str = f.read() + + # create the AgentFactory with a chat client and bindings + agent_factory = AgentFactory( + AzureOpenAIResponsesClient(credential=AzureCliCredential()), + bindings={"get_weather": get_weather}, + ) + # create the agent from the yaml + agent = agent_factory.create_agent_from_yaml(yaml_str) + # use the agent + response = await agent.run("What's the weather in Amsterdam, in celsius?") + print("Agent response:", response.text) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/declarative/microsoft_learn_agent.py b/python/samples/getting_started/declarative/microsoft_learn_agent.py new file mode 100644 index 00000000000..bdbda4f76f7 --- /dev/null +++ b/python/samples/getting_started/declarative/microsoft_learn_agent.py @@ -0,0 +1,25 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +from pathlib import Path + +from agent_framework.declarative import AgentFactory +from azure.identity.aio import AzureCliCredential + + +async def main(): + """Create an agent from a declarative yaml specification and run it.""" + # get the path + current_path = Path(__file__).parent + yaml_path = current_path.parent.parent.parent.parent / "agent-samples" / "foundry" / "MicrosoftLearnAgent.yaml" + + # create the agent from the yaml + async with ( + AzureCliCredential() as credential, + AgentFactory(client_kwargs={"async_credential": credential}).create_agent_from_yaml_path(yaml_path) as agent, + ): + response = await agent.run("How do I create a storage account with private endpoint using bicep?") + print("Agent response:", response.text) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/declarative/openai_responses_agent.py b/python/samples/getting_started/declarative/openai_responses_agent.py new file mode 100644 index 00000000000..ee3b48787e3 --- /dev/null +++ b/python/samples/getting_started/declarative/openai_responses_agent.py @@ -0,0 +1,26 @@ +# Copyright (c) Microsoft. All rights reserved. +import asyncio +from pathlib import Path + +from agent_framework.declarative import AgentFactory + + +async def main(): + """Create an agent from a declarative yaml specification and run it.""" + # get the path + current_path = Path(__file__).parent + yaml_path = current_path.parent.parent.parent.parent / "agent-samples" / "openai" / "OpenAIResponses.yaml" + + # load the yaml from the path + with yaml_path.open("r") as f: + yaml_str = f.read() + + # create the agent from the yaml + agent = AgentFactory().create_agent_from_yaml(yaml_str) + # use the agent + response = await agent.run("Why is the sky blue, answer in Dutch?") + print("Agent response:", response.value) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/devui/weather_agent_azure/agent.py b/python/samples/getting_started/devui/weather_agent_azure/agent.py index 40310cb640f..4616b4971a3 100644 --- a/python/samples/getting_started/devui/weather_agent_azure/agent.py +++ b/python/samples/getting_started/devui/weather_agent_azure/agent.py @@ -15,9 +15,9 @@ FunctionInvocationContext, Role, TextContent, + ai_function, chat_middleware, function_middleware, - ai_function ) from agent_framework.azure import AzureOpenAIChatClient from agent_framework_devui import register_cleanup @@ -122,6 +122,7 @@ def get_forecast( return f"Weather forecast for {location}:\n" + "\n".join(forecast) + @ai_function(approval_mode="always_require") def send_email( recipient: Annotated[str, "The email address of the recipient."], @@ -131,6 +132,7 @@ def send_email( """Simulate sending an email.""" return f"Email sent to {recipient} with subject '{subject}'." + # Agent instance following Agent Framework conventions agent = ChatAgent( name="AzureWeatherAgent", diff --git a/python/samples/getting_started/evaluation/azure_ai_foundry/.env.example b/python/samples/getting_started/evaluation/red_teaming/.env.example similarity index 100% rename from python/samples/getting_started/evaluation/azure_ai_foundry/.env.example rename to python/samples/getting_started/evaluation/red_teaming/.env.example diff --git a/python/samples/getting_started/evaluation/azure_ai_foundry/README.md b/python/samples/getting_started/evaluation/red_teaming/README.md similarity index 100% rename from python/samples/getting_started/evaluation/azure_ai_foundry/README.md rename to python/samples/getting_started/evaluation/red_teaming/README.md diff --git a/python/samples/getting_started/evaluation/azure_ai_foundry/red_team_agent_sample.py b/python/samples/getting_started/evaluation/red_teaming/red_team_agent_sample.py similarity index 100% rename from python/samples/getting_started/evaluation/azure_ai_foundry/red_team_agent_sample.py rename to python/samples/getting_started/evaluation/red_teaming/red_team_agent_sample.py diff --git a/python/samples/getting_started/evaluation/self_reflection/.env.example b/python/samples/getting_started/evaluation/self_reflection/.env.example new file mode 100644 index 00000000000..9f6dc825646 --- /dev/null +++ b/python/samples/getting_started/evaluation/self_reflection/.env.example @@ -0,0 +1,2 @@ +AZURE_OPENAI_ENDPOINT="..." +AZURE_OPENAI_API_KEY="..." diff --git a/python/samples/getting_started/evaluation/self_reflection/README.md b/python/samples/getting_started/evaluation/self_reflection/README.md new file mode 100644 index 00000000000..a6ab419b0d4 --- /dev/null +++ b/python/samples/getting_started/evaluation/self_reflection/README.md @@ -0,0 +1,75 @@ +# Self-Reflection Evaluation Sample + +This sample demonstrates the self-reflection pattern using Agent Framework and Azure AI Foundry's Groundedness Evaluator. For details, see [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) (NeurIPS 2023). + +## Overview + +**What it demonstrates:** +- Iterative self-reflection loop that automatically improves responses based on groundedness evaluation +- Batch processing of prompts from Parquet files with progress tracking +- Using `AzureOpenAIChatClient` with Azure CLI authentication +- Comprehensive summary statistics and detailed result tracking + +## Prerequisites + +### Azure Resources +- **Azure OpenAI**: Deploy models (default: gpt-4.1 for both agent and judge) +- **Azure CLI**: Run `az login` to authenticate + +### Python Environment +```bash +pip install agent-framework-core azure-ai-evaluation pandas --pre +``` + +### Environment Variables +```bash +# .env file +AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/ +AZURE_OPENAI_API_KEY=your-api-key # Optional with Azure CLI +``` + +## Running the Sample + +```bash +# Basic usage +python self_reflection.py + +# With options +python self_reflection.py --input my_prompts.parquet \ + --output results.parquet \ + --max-reflections 5 \ + -n 10 +``` + +**CLI Options:** +- `--input`, `-i`: Input parquet file +- `--output`, `-o`: Output parquet file +- `--agent-model`, `-m`: Agent model name (default: gpt-4.1) +- `--judge-model`, `-e`: Evaluator model name (default: gpt-4.1) +- `--max-reflections`: Max iterations (default: 3) +- `--limit`, `-n`: Process only first N prompts + +## Understanding Results + +The agent iteratively improves responses: +1. Generate initial response +2. Evaluate groundedness (1-5 scale) +3. If score < 5, provide feedback and retry +4. Stop at max iterations or perfect score (5/5) + +**Example output:** +``` +[1/31] Processing prompt 0... + Self-reflection iteration 1/3... + Groundedness score: 3/5 + Self-reflection iteration 2/3... + Groundedness score: 5/5 + ✓ Perfect groundedness score achieved! + ✓ Completed with score: 5/5 (best at iteration 2/3) +``` + +## Related Resources + +- [Reflexion Paper](https://arxiv.org/abs/2303.11366) +- [Azure AI Evaluation SDK](https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk) +- [Agent Framework](https://github.com/microsoft/agent-framework) diff --git a/python/samples/getting_started/evaluation/self_reflection/resources/suboptimal_groundedness_prompts.jsonl b/python/samples/getting_started/evaluation/self_reflection/resources/suboptimal_groundedness_prompts.jsonl new file mode 100644 index 00000000000..820d52eebd9 --- /dev/null +++ b/python/samples/getting_started/evaluation/self_reflection/resources/suboptimal_groundedness_prompts.jsonl @@ -0,0 +1,31 @@ +{"system_instruction":"You must respond using only information contained in the prompt and provided provided text. Answer with a header followed by bullet points.","user_request":"What are some exercises for initial strengthening during latarjet recovery?","context_document":"P a g e 1 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPHYSICAL THERAPY PROTOCOL AFTER LATARJET PROCEDURE:\nThe intent of this protocol is to provide the clinician with a guideline of the postoperative\nrehabilitation course of a patient that has undergone an open Latarjet procedure. It is no means\nintended to be a substitute for one’s clinical decision making regarding the progression of a\npatient’s post-operative course based on their physical exam/findings, individual progress, and/or\nthe presence of postoperative complications. If a clinician requires assistance in the progression\nof a postoperative patient, they should consult with the referring Surgeon.\nDepending on the intraoperatively determined bone quality of the bone block, the surgeon\ndefines in the operative report when pendulum exercises, passive range of motion (PROM),\nactive range of motion (AROM) may be started. Accordingly, the postoperative protocol is\ndefined individually for each patient by the surgeon and recorded in the operation report.\nP a g e 2 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase I – Immediate Post-Surgical Phase (Week 1-4):\nGoals:\n• Protect the integrity of the surgical repair\n• Achieve gradual restoration of passive range of motion (PROM)\n• Enhance/ensure adequate scapular function\nPrecautions:\n• No active range of motion (AROM) of Shoulder\n• Maintain arm in sling, remove only for exercise for elbow, wrist and fingers, only removing for\nshowering. Shower with arm held at side\n• No lifting of objects\n• No shoulder motion behind back\n• No excessive stretching or sudden movements\n• No supporting of body weight by hands\n• Keep incision clean and dry\n• Patient education regarding limited use of upper extremity despite the potential lack of or\nminimal pain or other symptoms\nDAY 1 TO 6:\n• Abduction brace or pillow / sling except when performing distal upper extremity exercises.\nBegin restoring AROM of elbow/wrist/hand of operative extremity\n• Sleep in brace or pillow / sling\n• Scapular clock exercises progressed to scapular isometric exercises\n• Ball squeezes\n• Cryotherapy for pain and inflammation -Day 1-2: as much as possible -Day 3-6: post activity,\nor for pain, or for comfort (IMPORTANT: USE TOWEL TO PROTECT SKIN AND PAUSE\nCRYOTHERAPY AT LEAST FOR 20 MIN/HOUR TO PREVENT FROSTBITES)\nP a g e 3 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nDAY 7 TO 28:\n• Continue use of brace/ pillow / sling\n• Continue Elbow, wrist, and finger AROM / resisted\n• Begin shoulder PROM (do not force any painful motion) in first two weeks or as directed by\nsurgeon\n• Forward flexion and elevation to tolerance\n• Abduction in the plane of the scapula to tolerance\n• Internal rotation (IR) to 45 degrees at 30 degrees of abduction\n• External rotation (ER) in the plane of the scapula from 0-25 degrees or as directed by surgeon;\nbegin at 30- 40 degrees of abduction; respect anterior capsule tissue integrity with ER range of\nmotion; seek guidance from intraoperative measurements of external rotation ROM\n• Active and manual scapula strengthening exercises:\nExercises:\nshoulder shrug and roll\n• Pendulum Exercises: (start of pendulum exercises is defined by the surgeon in the OR report.\nDo not start pendulum exercises if the operation report states that pendulum exercises should be\nstarted from the 6th or 8th postoperative week.).\npendulum exercises\n• Start passive ROM (PROM): The PROM exercises should be supervised by the physiotherapist\nduring the first session. In addition, the PROM home exercises should be trained by the\nphysiotherapist. (start of passive ROM is defined by the surgeon in the OR report. Do not start\nPROM exercises if the operation report states that PROM exercises should be started from the\n6th or 8th postoperative week).\nP a g e 4 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase II – Intermediate Phase (Week 5-8):\nGoals:\n• Do not overstress healing tissue\n• Discontinue brace / sling at end of week 6\n• Gradually start active range of motion\n• Initiate active assisted range of motion (AAROM) under guidance of physical therapy:\n• Begin light waist level activities\nPrecautions:\n• No active movement of shoulder till adequate PROM with good mechanics\n• No lifting with affected upper extremity\n• No excessive external rotation ROM / stretching. seek guidance from intraoperative\nmeasurements of external rotation ROM)\n• Do not perform activities or strengthening exercises that place an excessive load on the anterior\ncapsule of the shoulder joint (i.e. no pushups, pec fly, etc..)\n• Do not perform scaption with internal rotation (empty can) during any stage of rehabilitation\ndue to the possibility of impingement\n• Continued patient education: posture, joint protection, positioning, hygiene, etc.\nExercises:\n1. flexion in supine position\n2. sitting assisted forward reach (elevation)\n3. standing wall-assisted forward flexion\n4. Cane-Assisted External Rotation at 20 degrees, 45 degrees abduction\n5. Doorway Standing External Rotation\n6. Scapular plane Abduction to Tolerance\n7. Active Range of Motion Forward Flexion in the Scapular Plane\n8. Active Range Of Motion External Rotation in Multiple Positions: Side-Lying\nor Sitting\nP a g e 5 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase III – strengthening phase (week 9-12):\nGoal:\n• Maintain Full AROM and Maintain Full PROM\n• Gradual restoration of shoulder strength, power, and endurance (Elastic bands)\n•Gradual return to functional activities\nPrecautions:\n• No heavy lifting of objects (no heavier than 5 lbs.)\n• No sudden lifting or pushing activities\n• No sudden jerking motions\n• No heavy lifting of objects (no heavier than 5 lbs.)\n• No sudden lifting or pushing activities\n• No sudden jerking motions\nStart of strengthening with elastic bands and light weights is defined by the surgeon in the OR\nreport. Do not start strengthening if the operation report states that strengthening should be\nstarted later. In patients with poor bone quality, strengthening is occasionally started later.\nExercises:\n1. Active Range of Motion External Rotation with Band Strengthening\n2. Active Range of Motion Internal Rotation with Band Strengthening\n3. Row with Resistance Band\n4. Towel/Hand-assisted Internal Rotation Stretch\n5. Side lying Internal Rotation Stretch at 70 and 90 Degrees\n6. Cross-Body Stretch\n7. Water (pool) therapy Standing in water with float under arm, lower body into water to\nhelp stretch into flexion\n8. Standing in water with float under arm, lower body to side to help with external rotation\nP a g e 6 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase IV Advanced strengthening phase (week 13- 22):\nAbout 12 weeks postoperatively, a CT scan is performed to determine whether the bone block\nhas healed. Depending on the findings, the surgeon will decide whether to move on to phase IV.\nGoals:\n• Maintain full non-painful active ROM\n• Advance conditioning exercises for Enhanced functional use of UE\n• Improve muscular strength, power, and endurance (light weights)\n• Gradual return to full functional activities\n• Continue to perform ROM stretching, if motion is not complete\nExercises:\n• Side-lying External Rotation with Towel\n• Full Can in the Scapular Plane\n• Prone Scaption\n• Diagonal\n• Dynamic Hug\n• Internal Rotation at 90 Degrees Abduction\n• Forward Band Punch\n• Sitting Supported External Rotation at 90 Degrees\n• Standing Unsupported External Rotation at 90 Degrees\n• Biceps Curl\nPhase V – Return to activity phase (week 23):\nGoals:\n• Gradual return to strenuous work activities\n• Gradual return to recreational activities\n• Gradual return to sport activities\n• Continue strengthening and stretching\n• Continue stretching, if motion is tight\n• May initiate interval sport program","full_prompt":"What are some exercises for initial strengthening during latarjet recovery? You must respond using only information contained in the prompt and provided provided text. Answer with a header followed by bullet points.\nP a g e 1 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPHYSICAL THERAPY PROTOCOL AFTER LATARJET PROCEDURE:\nThe intent of this protocol is to provide the clinician with a guideline of the postoperative\nrehabilitation course of a patient that has undergone an open Latarjet procedure. It is no means\nintended to be a substitute for one’s clinical decision making regarding the progression of a\npatient’s post-operative course based on their physical exam/findings, individual progress, and/or\nthe presence of postoperative complications. If a clinician requires assistance in the progression\nof a postoperative patient, they should consult with the referring Surgeon.\nDepending on the intraoperatively determined bone quality of the bone block, the surgeon\ndefines in the operative report when pendulum exercises, passive range of motion (PROM),\nactive range of motion (AROM) may be started. Accordingly, the postoperative protocol is\ndefined individually for each patient by the surgeon and recorded in the operation report.\nP a g e 2 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase I – Immediate Post-Surgical Phase (Week 1-4):\nGoals:\n• Protect the integrity of the surgical repair\n• Achieve gradual restoration of passive range of motion (PROM)\n• Enhance/ensure adequate scapular function\nPrecautions:\n• No active range of motion (AROM) of Shoulder\n• Maintain arm in sling, remove only for exercise for elbow, wrist and fingers, only removing for\nshowering. Shower with arm held at side\n• No lifting of objects\n• No shoulder motion behind back\n• No excessive stretching or sudden movements\n• No supporting of body weight by hands\n• Keep incision clean and dry\n• Patient education regarding limited use of upper extremity despite the potential lack of or\nminimal pain or other symptoms\nDAY 1 TO 6:\n• Abduction brace or pillow / sling except when performing distal upper extremity exercises.\nBegin restoring AROM of elbow/wrist/hand of operative extremity\n• Sleep in brace or pillow / sling\n• Scapular clock exercises progressed to scapular isometric exercises\n• Ball squeezes\n• Cryotherapy for pain and inflammation -Day 1-2: as much as possible -Day 3-6: post activity,\nor for pain, or for comfort (IMPORTANT: USE TOWEL TO PROTECT SKIN AND PAUSE\nCRYOTHERAPY AT LEAST FOR 20 MIN/HOUR TO PREVENT FROSTBITES)\nP a g e 3 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nDAY 7 TO 28:\n• Continue use of brace/ pillow / sling\n• Continue Elbow, wrist, and finger AROM / resisted\n• Begin shoulder PROM (do not force any painful motion) in first two weeks or as directed by\nsurgeon\n• Forward flexion and elevation to tolerance\n• Abduction in the plane of the scapula to tolerance\n• Internal rotation (IR) to 45 degrees at 30 degrees of abduction\n• External rotation (ER) in the plane of the scapula from 0-25 degrees or as directed by surgeon;\nbegin at 30- 40 degrees of abduction; respect anterior capsule tissue integrity with ER range of\nmotion; seek guidance from intraoperative measurements of external rotation ROM\n• Active and manual scapula strengthening exercises:\nExercises:\nshoulder shrug and roll\n• Pendulum Exercises: (start of pendulum exercises is defined by the surgeon in the OR report.\nDo not start pendulum exercises if the operation report states that pendulum exercises should be\nstarted from the 6th or 8th postoperative week.).\npendulum exercises\n• Start passive ROM (PROM): The PROM exercises should be supervised by the physiotherapist\nduring the first session. In addition, the PROM home exercises should be trained by the\nphysiotherapist. (start of passive ROM is defined by the surgeon in the OR report. Do not start\nPROM exercises if the operation report states that PROM exercises should be started from the\n6th or 8th postoperative week).\nP a g e 4 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase II – Intermediate Phase (Week 5-8):\nGoals:\n• Do not overstress healing tissue\n• Discontinue brace / sling at end of week 6\n• Gradually start active range of motion\n• Initiate active assisted range of motion (AAROM) under guidance of physical therapy:\n• Begin light waist level activities\nPrecautions:\n• No active movement of shoulder till adequate PROM with good mechanics\n• No lifting with affected upper extremity\n• No excessive external rotation ROM / stretching. seek guidance from intraoperative\nmeasurements of external rotation ROM)\n• Do not perform activities or strengthening exercises that place an excessive load on the anterior\ncapsule of the shoulder joint (i.e. no pushups, pec fly, etc..)\n• Do not perform scaption with internal rotation (empty can) during any stage of rehabilitation\ndue to the possibility of impingement\n• Continued patient education: posture, joint protection, positioning, hygiene, etc.\nExercises:\n1. flexion in supine position\n2. sitting assisted forward reach (elevation)\n3. standing wall-assisted forward flexion\n4. Cane-Assisted External Rotation at 20 degrees, 45 degrees abduction\n5. Doorway Standing External Rotation\n6. Scapular plane Abduction to Tolerance\n7. Active Range of Motion Forward Flexion in the Scapular Plane\n8. Active Range Of Motion External Rotation in Multiple Positions: Side-Lying\nor Sitting\nP a g e 5 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase III – strengthening phase (week 9-12):\nGoal:\n• Maintain Full AROM and Maintain Full PROM\n• Gradual restoration of shoulder strength, power, and endurance (Elastic bands)\n•Gradual return to functional activities\nPrecautions:\n• No heavy lifting of objects (no heavier than 5 lbs.)\n• No sudden lifting or pushing activities\n• No sudden jerking motions\n• No heavy lifting of objects (no heavier than 5 lbs.)\n• No sudden lifting or pushing activities\n• No sudden jerking motions\nStart of strengthening with elastic bands and light weights is defined by the surgeon in the OR\nreport. Do not start strengthening if the operation report states that strengthening should be\nstarted later. In patients with poor bone quality, strengthening is occasionally started later.\nExercises:\n1. Active Range of Motion External Rotation with Band Strengthening\n2. Active Range of Motion Internal Rotation with Band Strengthening\n3. Row with Resistance Band\n4. Towel/Hand-assisted Internal Rotation Stretch\n5. Side lying Internal Rotation Stretch at 70 and 90 Degrees\n6. Cross-Body Stretch\n7. Water (pool) therapy Standing in water with float under arm, lower body into water to\nhelp stretch into flexion\n8. Standing in water with float under arm, lower body to side to help with external rotation\nP a g e 6 | 6\nRehabilitation Protocol after Latarjet: Copyright © 2020 Massachusetts General Hospital, Boston Shoulder Institute, all rights reserved.\nPhase IV Advanced strengthening phase (week 13- 22):\nAbout 12 weeks postoperatively, a CT scan is performed to determine whether the bone block\nhas healed. Depending on the findings, the surgeon will decide whether to move on to phase IV.\nGoals:\n• Maintain full non-painful active ROM\n• Advance conditioning exercises for Enhanced functional use of UE\n• Improve muscular strength, power, and endurance (light weights)\n• Gradual return to full functional activities\n• Continue to perform ROM stretching, if motion is not complete\nExercises:\n• Side-lying External Rotation with Towel\n• Full Can in the Scapular Plane\n• Prone Scaption\n• Diagonal\n• Dynamic Hug\n• Internal Rotation at 90 Degrees Abduction\n• Forward Band Punch\n• Sitting Supported External Rotation at 90 Degrees\n• Standing Unsupported External Rotation at 90 Degrees\n• Biceps Curl\nPhase V – Return to activity phase (week 23):\nGoals:\n• Gradual return to strenuous work activities\n• Gradual return to recreational activities\n• Gradual return to sport activities\n• Continue strengthening and stretching\n• Continue stretching, if motion is tight\n• May initiate interval sport program","domain":"Medical","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":63} +{"system_instruction":"Only respond to the prompt using the information in the prompt. Format the response as a numbered list.","user_request":"What are three failures of the WHO regarding fighting diseases and other health threats?","context_document":"WHO achievements: A mixed track record\nFighting infectious diseases\nOne of the WHO's biggest achievements was in eradicating smallpox: in 1980, 21 years after\nlaunching an international vaccination campaign, it was finally able to declare the world free of the\ndisease. In 1988, the WHO declared a target of similarly eliminating polio by the end of the\nmillennium. That target was missed, and the stubborn persistence of infections prompted the WHO\nto declare a PHEIC in 2014. Nevertheless, considerable progress has been made, with the number of\ncases falling by 99 % over the past three decades. Unfortunately, tuberculosis is very far from\ndisappearing; however, the WHO's Global Drug Facility has enabled millions of patients in\ndeveloping countries to access high-quality anti-TB medicines, both through collective purchasing\nmechanisms that bring the cost of drugs down, and through grants that help the poorest countries\nto buy such medicines. The WHO has also been praised for its leadership during the 2003 SARS\nepidemic; within just four months, the disease had been contained.\nIn 2009, fears that the swine flu virus could mutate into a more lethal form prompted the WHO to\ndeclare its first ever Public Health Emergency of International Concern (PHEIC – see Box).\nGovernments rushed to stockpile vaccines, most of which were never used, as the epidemic turned\nout to be milder than expected. This 'disproportionate' response, as it was described in a 2011\nEuropean Parliament resolution, was blamed for wasting millions of euros of public money on\nunnecessary vaccines. Some critics even alleged that WHO decisions had been swayed by the\ninterests of the pharmaceutical sector. An internal enquiry exonerated the WHO from most of these\naccusations, arguing that, in view of the evidence available at the time, it would not have been\npossible to predict the course of the epidemic, while also acknowledging that the situation could\nhave been handled more transparently.\nWhereas the WHO was accused of over-reacting to swine flu, its response to the 2014 West African\nEbola outbreak came too late to prevent tens of thousands of deaths. In what international health\nexperts described as an 'egregious failure', the WHO waited months before declaring a PHEIC,\ndespite warnings, including from its own staff, that the epidemic was out of control. The\norganisation's lumbering bureaucratic response contrasted unfavourably with more agile\ninterventions by non-governmental bodies such as Médecins Sans Frontières. On the other hand, in\n2018 efforts to contain a second outbreak of Ebola in the Democratic Republic of the Congo were\nmore successful, with just 33 deaths in total; for some observers, the organisation's quick response,\nwhich included the release of emergency funding just hours after the start of the outbreak and a\npersonal visit to Kinshasa by Director-General Tedros a few days later, suggested that it had learned\nlessons from its 2014 failures. Ebola remains a serious threat in West Africa; a subsequent outbreak\ntriggered another PHEIC, and killed over 2 000.\nNon-communicable diseases and other health threats\nWhile media attention tends to focus on emergencies caused by infectious diseases, noncommunicable diseases such as cancer cost far more lives. However, the WHO's track record in this\nrespect is, again, a mixed one. For example, many recommendations issued by the International\nAgency for Research on Cancer, a semi-autonomous branch of the WHO, are scientifically sound;\nhowever, critics allege that the body does not do enough to prevent conflicts of interest that might\ninfluence expert assessments on which its recommendations are based, nor is it very successful at\ncommunicating its conclusions with the public.\nOn smoking, described by the WHO as a 'global epidemic', the main instrument is the 2003\nFramework Convention on Tobacco Control, the first ever international treaty adopted within the\nWHO framework. The measures it envisages have played a key role in shaping national tobacco\ncontrol policies, including in developing countries. Implementation is still patchy, but gradually\nimproving: as of 2018, 12 % of the 181 countries which are parties to the Convention were failing to\nensure protection from passive smoking (e.g. bans on smoking in public places), 23 % were not\napplying packaging and labelling requirements (such as health warnings on cigarette packets), 29 %\ndid not have awareness-raising and educational measures in place, while 30 % were not restricting\ntobacco sales to and by minors. Tobacco still kills over 8 million people every year, most of them in\ndeveloping countries, and consumption is only declining slowly.\nObesity is another global health scourge that the WHO has taken on. For example, in 2016 it\nendorsed taxes on soft drinks as an effective means of reducing sugar consumption. However, it has\nrun into resistance from the beverages industry, and the US government, which in 2018 blocked a\nWHO panel from issuing a global recommendation on sugar taxes.\nIn developing countries, the high cost of medicines is often a barrier to effective treatment.\nImproving access to medicines has long been a priority for the WHO. The interests of producers,\nwhich are protected by patents, have to be balanced against patients' need for affordable treatment.\nHowever, WHO work in this area has been blocked by disagreements between countries which\nargue that intellectual property is not part of the organisation's remit – typically pharmaceutical\nexporters, such as the United States (US) – and others, including developing countries, which feel\nthat it should be.","full_prompt":"What are three failures of the WHO regarding fighting diseases and other health threats?\nOnly respond to the prompt using the information in the prompt. Format the response as a numbered list.\n\nWHO achievements: A mixed track record\nFighting infectious diseases\nOne of the WHO's biggest achievements was in eradicating smallpox: in 1980, 21 years after\nlaunching an international vaccination campaign, it was finally able to declare the world free of the\ndisease. In 1988, the WHO declared a target of similarly eliminating polio by the end of the\nmillennium. That target was missed, and the stubborn persistence of infections prompted the WHO\nto declare a PHEIC in 2014. Nevertheless, considerable progress has been made, with the number of\ncases falling by 99 % over the past three decades. Unfortunately, tuberculosis is very far from\ndisappearing; however, the WHO's Global Drug Facility has enabled millions of patients in\ndeveloping countries to access high-quality anti-TB medicines, both through collective purchasing\nmechanisms that bring the cost of drugs down, and through grants that help the poorest countries\nto buy such medicines. The WHO has also been praised for its leadership during the 2003 SARS\nepidemic; within just four months, the disease had been contained.\nIn 2009, fears that the swine flu virus could mutate into a more lethal form prompted the WHO to\ndeclare its first ever Public Health Emergency of International Concern (PHEIC – see Box).\nGovernments rushed to stockpile vaccines, most of which were never used, as the epidemic turned\nout to be milder than expected. This 'disproportionate' response, as it was described in a 2011\nEuropean Parliament resolution, was blamed for wasting millions of euros of public money on\nunnecessary vaccines. Some critics even alleged that WHO decisions had been swayed by the\ninterests of the pharmaceutical sector. An internal enquiry exonerated the WHO from most of these\naccusations, arguing that, in view of the evidence available at the time, it would not have been\npossible to predict the course of the epidemic, while also acknowledging that the situation could\nhave been handled more transparently.\nWhereas the WHO was accused of over-reacting to swine flu, its response to the 2014 West African\nEbola outbreak came too late to prevent tens of thousands of deaths. In what international health\nexperts described as an 'egregious failure', the WHO waited months before declaring a PHEIC,\ndespite warnings, including from its own staff, that the epidemic was out of control. The\norganisation's lumbering bureaucratic response contrasted unfavourably with more agile\ninterventions by non-governmental bodies such as Médecins Sans Frontières. On the other hand, in\n2018 efforts to contain a second outbreak of Ebola in the Democratic Republic of the Congo were\nmore successful, with just 33 deaths in total; for some observers, the organisation's quick response,\nwhich included the release of emergency funding just hours after the start of the outbreak and a\npersonal visit to Kinshasa by Director-General Tedros a few days later, suggested that it had learned\nlessons from its 2014 failures. Ebola remains a serious threat in West Africa; a subsequent outbreak\ntriggered another PHEIC, and killed over 2 000.\nNon-communicable diseases and other health threats\nWhile media attention tends to focus on emergencies caused by infectious diseases, noncommunicable diseases such as cancer cost far more lives. However, the WHO's track record in this\nrespect is, again, a mixed one. For example, many recommendations issued by the International\nAgency for Research on Cancer, a semi-autonomous branch of the WHO, are scientifically sound;\nhowever, critics allege that the body does not do enough to prevent conflicts of interest that might\ninfluence expert assessments on which its recommendations are based, nor is it very successful at\ncommunicating its conclusions with the public.\nOn smoking, described by the WHO as a 'global epidemic', the main instrument is the 2003\nFramework Convention on Tobacco Control, the first ever international treaty adopted within the\nWHO framework. The measures it envisages have played a key role in shaping national tobacco\ncontrol policies, including in developing countries. Implementation is still patchy, but gradually\nimproving: as of 2018, 12 % of the 181 countries which are parties to the Convention were failing to\nensure protection from passive smoking (e.g. bans on smoking in public places), 23 % were not\napplying packaging and labelling requirements (such as health warnings on cigarette packets), 29 %\ndid not have awareness-raising and educational measures in place, while 30 % were not restricting\ntobacco sales to and by minors. Tobacco still kills over 8 million people every year, most of them in\ndeveloping countries, and consumption is only declining slowly.\nObesity is another global health scourge that the WHO has taken on. For example, in 2016 it\nendorsed taxes on soft drinks as an effective means of reducing sugar consumption. However, it has\nrun into resistance from the beverages industry, and the US government, which in 2018 blocked a\nWHO panel from issuing a global recommendation on sugar taxes.\nIn developing countries, the high cost of medicines is often a barrier to effective treatment.\nImproving access to medicines has long been a priority for the WHO. The interests of producers,\nwhich are protected by patents, have to be balanced against patients' need for affordable treatment.\nHowever, WHO work in this area has been blocked by disagreements between countries which\nargue that intellectual property is not part of the organisation's remit – typically pharmaceutical\nexporters, such as the United States (US) – and others, including developing countries, which feel\nthat it should be.","domain":"Medical","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":146} +{"system_instruction":"Respond using only the information found within the text provided in the prompt. Avoid any mention of the government, its agencies, or specific regulations. If there are multiple paragraphs, each paragraph should be no longer than four sentences and must contain a clear introductory statement in the first sentence. If appropriate, format the response as a bulleted list. If information found in the text seems likely related to any legal or regulatory compliance, please include a disclaimer at the end of the response, in italics and enclosed in brackets, that explains the response is based only on the information provided.","user_request":"What are ten strategies that are accepted for controlling disease in organic crops?","context_document":"Crop pest, weed, and disease management practice (§205.206)\nProducers must implement management practices to prevent crop pests, weeds, and diseases that include but\nare not limited to the following:\nAccepted pest controls:\n Crop rotation and soil and crop nutrient management practices as outlined above.\n Sanitation measures to remove disease vectors, weeds seeds and pest organisms.\n Cultural practices to enhance crop health such as plant species and variety selection with regard to\nsuitability for site-specific conditions and resistance to pests, weeds, and disease.\n Mechanical and physical methods for controlling pest problems, such as:\no Biological controls (natural predators and parasites, habitat to promote biodiversity)\no Nonsynthetic controls such as lures, traps, fencing and repellants\nAccepted weed controls:\n Mulching with fully biodegradable materials\n Mowing\n Livestock grazing\n Hand weeding or mechanical cultivation\n Flame, heat, or electrical means\n Plastic or synthetic mulches if removed from the field at the end of the growing/harvest season\nAccepted disease controls:\n Management practices which suppress the spread of disease organisms. Examples include plant\nspacing, choosing resistant varieties, and crop rotations. In greenhouses, this can also include the\nproper control of environmental factors such as ventilation, humidity and temperature.\n Application of nonsynthetic biological, botanical, or mineral inputs\nWhen the above pest, weed and disease preventative management practices are not sufficient, the following\npractices are accepted:\n Application of a biological or botanical substance\n Application of a substance included on the National List of synthetic substances allowed for use in\norganic crop production\nProhibited controls:\n Synthetic mulches or remnants left to photo-degrade in the field\n Synthetic herbicides, pesticides or fungicides with the exception of those included on the National List of\nsynthetic substances allowed for use in organic crop production\n Newspaper with color inks\n Biodegradable plastic mulch films not compliant with the NOP guidance\n Nonsynthetic substances included on the National List of nonsynthetic substances prohibited for use in\norganic crop production\n\nPost-Harvest Handling (§205.270 – 205.272)\nSanitation\nProper sanitation is required at all levels of handling, transport and storage. The use of disinfectants (chlorine\nmaterials, hydrogen peroxide) applied to storage containers and handling equipment must be consistent with\nthe National List.\nIrrigation and Wash Water\nGround and surface waters are a potential source for a wide range of contaminants. Verify your certifier’s\nrecommendations for water testing of irrigation and wash water.\nWater used in direct post-harvest crop or food contact is permitted to contain chlorine materials at levels\napproved by the Food and Drug Administration or the Environmental Protection Agency for such purpose.\nHowever, rinsing with potable water that does not exceed the maximum residual disinfectant limit for the\nchlorine material under the Safe Drinking Water Act (4ppm) must immediately follow this permitted use.\nCertified operators should monitor the chlorine level of the final rinse water, the point at which the water last\ncontacts the organic product. The level of chlorine in the final rinse water must meet limits as set forth by the\nSafe Drinking Water Act (4ppm).\nCommingling and contact with prohibited substances\nIt is required that producers implement measures to prevent the commingling of organic and nonorganic\nproducts. It is also required that organic producers protect organic products from contact with prohibited\nsubstances.\nSplit Operations\nOperations that choose to produce organic and non-organic livestock products or to hire services from custom\noperators that may service non-organic and organic clients, must implement measures necessary to prevent\nthe commingling of organic and non-organic crop products.\nAccepted practices\n Mechanical or biological methods including but not limited to cooking, baking, heating, drying,\npreserving, dehydrating, freezing, and chilling crop products.\n Non-synthetic materials, such as rock powders, diatomaceous earth, and herbal preparations to repel\nstorage pests, must be consistent with the National List of nonsynthetic substances prohibited for use in\norganic crop production.\n The use of synthetic materials, such as floating agents, must be consistent with the National List of\nsynthetic substances allowed for use in organic crop production.","full_prompt":"What are ten strategies that are accepted for controlling disease in organic crops?\n\nquoted text: Crop pest, weed, and disease management practice (§205.206)\nProducers must implement management practices to prevent crop pests, weeds, and diseases that include but\nare not limited to the following:\nAccepted pest controls:\n Crop rotation and soil and crop nutrient management practices as outlined above.\n Sanitation measures to remove disease vectors, weeds seeds and pest organisms.\n Cultural practices to enhance crop health such as plant species and variety selection with regard to\nsuitability for site-specific conditions and resistance to pests, weeds, and disease.\n Mechanical and physical methods for controlling pest problems, such as:\no Biological controls (natural predators and parasites, habitat to promote biodiversity)\no Nonsynthetic controls such as lures, traps, fencing and repellants\nAccepted weed controls:\n Mulching with fully biodegradable materials\n Mowing\n Livestock grazing\n Hand weeding or mechanical cultivation\n Flame, heat, or electrical means\n Plastic or synthetic mulches if removed from the field at the end of the growing/harvest season\nAccepted disease controls:\n Management practices which suppress the spread of disease organisms. Examples include plant\nspacing, choosing resistant varieties, and crop rotations. In greenhouses, this can also include the\nproper control of environmental factors such as ventilation, humidity and temperature.\n Application of nonsynthetic biological, botanical, or mineral inputs\nWhen the above pest, weed and disease preventative management practices are not sufficient, the following\npractices are accepted:\n Application of a biological or botanical substance\n Application of a substance included on the National List of synthetic substances allowed for use in\norganic crop production\nProhibited controls:\n Synthetic mulches or remnants left to photo-degrade in the field\n Synthetic herbicides, pesticides or fungicides with the exception of those included on the National List of\nsynthetic substances allowed for use in organic crop production\n Newspaper with color inks\n Biodegradable plastic mulch films not compliant with the NOP guidance\n Nonsynthetic substances included on the National List of nonsynthetic substances prohibited for use in\norganic crop production\n\nPost-Harvest Handling (§205.270 – 205.272)\nSanitation\nProper sanitation is required at all levels of handling, transport and storage. The use of disinfectants (chlorine\nmaterials, hydrogen peroxide) applied to storage containers and handling equipment must be consistent with\nthe National List.\nIrrigation and Wash Water\nGround and surface waters are a potential source for a wide range of contaminants. Verify your certifier’s\nrecommendations for water testing of irrigation and wash water.\nWater used in direct post-harvest crop or food contact is permitted to contain chlorine materials at levels\napproved by the Food and Drug Administration or the Environmental Protection Agency for such purpose.\nHowever, rinsing with potable water that does not exceed the maximum residual disinfectant limit for the\nchlorine material under the Safe Drinking Water Act (4ppm) must immediately follow this permitted use.\nCertified operators should monitor the chlorine level of the final rinse water, the point at which the water last\ncontacts the organic product. The level of chlorine in the final rinse water must meet limits as set forth by the\nSafe Drinking Water Act (4ppm).\nCommingling and contact with prohibited substances\nIt is required that producers implement measures to prevent the commingling of organic and nonorganic\nproducts. It is also required that organic producers protect organic products from contact with prohibited\nsubstances.\nSplit Operations\nOperations that choose to produce organic and non-organic livestock products or to hire services from custom\noperators that may service non-organic and organic clients, must implement measures necessary to prevent\nthe commingling of organic and non-organic crop products.\nAccepted practices\n Mechanical or biological methods including but not limited to cooking, baking, heating, drying,\npreserving, dehydrating, freezing, and chilling crop products.\n Non-synthetic materials, such as rock powders, diatomaceous earth, and herbal preparations to repel\nstorage pests, must be consistent with the National List of nonsynthetic substances prohibited for use in\norganic crop production.\n The use of synthetic materials, such as floating agents, must be consistent with the National List of\nsynthetic substances allowed for use in organic crop production.\n\nsystem instruction: Respond using only the information found within the text provided in the prompt. Avoid any mention of the government, its agencies, or specific regulations. If there are multiple paragraphs, each paragraph should be no longer than four sentences and must contain a clear introductory statement in the first sentence. If appropriate, format the response as a bulleted list. If information found in the text seems likely related to any legal or regulatory compliance, please include a disclaimer at the end of the response, in italics and enclosed in brackets, that explains the response is based only on the information provided.","domain":"Legal","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":183} +{"system_instruction":"Any information that you draw to answer any questions must come only from the information found in the prompt. Under no circumstances are you allowed rely on any information from any source other than the information in the prompt. If the answer requires a series of steps, list them in a numbered list format.","user_request":"How many beeps would be heard if a user wants to activate right-handed operation, increase the cursor speed to 2, activate double click, and turn the buzzer off on a new device?","context_document":"There are a number of settings to allow you to configure OPTIMA Joystick to your exact requirements. These are all programmed using Learn Mode and are stored in an internal, non-volatile memory so they are automatically recalled each time you use the unit, even if you swap computers.\nTo make changes to the settings, you must first go into Learn Mode. Press and hold the middle button until a warbling tone is heard. The unit is now in Learn Mode and is able to accept changes to the settings, as follows:\nLearn Mode\nFeatures\n• Plug and Play USB and PS/2 operation and requires no drivers.\n• PC, Mac and Chromebook compatible.\n• Switchable to Gaming output for full compatibility\n with Xbox Adaptive Controller\n• Light touch joystick movement.\n• User-selectable cursor speed settings.\n• Drag lock and double click features.\n• Sockets to operate left and right click from remote switches.\n• Robust construction and ergonomic design.\n• Industry-standard mounting option.\n• Optional left-handed operation.\nCursor Speed\nTo change the speed setting while in Learn Mode, press the middle button briefly. Each time you do so, the unit emits a number of beeps, between 1 and 4. One beep indicates the lowest speed and 4 the highest. The speed of the cursor changes immediately, allowing you to experiment until the best setting is found.\nLeft-Handed Operation\nThe left and right buttons may be swapped around, which is particularly useful for left-landed users. To change this setting, press the left button while in Learn Mode. One beep indicates the unit is set to standard ‘right-handed’ mode, whereas two beeps indicates ‘left-handed’ operation.\nDouble Click\nRight-click may be substituted with Double-Click, which is useful for users who have difficulty in double-clicking quickly enough for the computer to recognise. To change this setting, press the right button briefly while in Learn Mode. One beep indicates the unit is set to standard ‘right-click’ mode, whereas two beeps indicates ‘Double-Click’ operation.\nBuzzer On/Off\nOPTIMA Joystick is fitted with a buzzer which gives an audible indication of operations such as drag lock and unlock, double-click, entering Learn Mode etc. When OPTIMA Joystick is used in a classroom setting, where there may be many units in close proximity, it may be beneficial to turn off the buzzer. To achieve this, press and hold the right button while in Learn Mode, until two long beeps are heard. The buzzer is now disabled, although it will still operate while in Learn Mode. Repeating the above operation will re-enable it.\nAll of the above settings may be changed as often as required while in Learn Mode, allowing you to experiment with the settings until the best configuration is found. Once you are happy with the settings, they may be stored in the non-volatile memory by pressing and holding the middle button once again, until the warbling tone is heard. Normal operation then resumes. Note that if both left-handed operation and Double-Click are selected, the buttons will function\nas Double-Click, Drag and Left Click, reading from left to right. Also note that the function of the sockets for external switches reproduces the function of the\ninternal buttons, according to the above settings. The unit automatically leaves Learn Mode, and any changes are discarded, if the settings remain unchanged for more than a minute.","full_prompt":"Any information that you draw to answer any questions must come only from the information found in the prompt. Under no circumstances are you allowed rely on any information from any source other than the information in the prompt. If the answer requires a series of steps, list them in a numbered list format.\n\nThere are a number of settings to allow you to configure OPTIMA Joystick to your exact requirements. These are all programmed using Learn Mode and are stored in an internal, non-volatile memory so they are automatically recalled each time you use the unit, even if you swap computers.\nTo make changes to the settings, you must first go into Learn Mode. Press and hold the middle button until a warbling tone is heard. The unit is now in Learn Mode and is able to accept changes to the settings, as follows:\nLearn Mode\nFeatures\n• Plug and Play USB and PS/2 operation and requires no drivers.\n• PC, Mac and Chromebook compatible.\n• Switchable to Gaming output for full compatibility\n with Xbox Adaptive Controller\n• Light touch joystick movement.\n• User-selectable cursor speed settings.\n• Drag lock and double click features.\n• Sockets to operate left and right click from remote switches.\n• Robust construction and ergonomic design.\n• Industry-standard mounting option.\n• Optional left-handed operation.\nCursor Speed\nTo change the speed setting while in Learn Mode, press the middle button briefly. Each time you do so, the unit emits a number of beeps, between 1 and 4. One beep indicates the lowest speed and 4 the highest. The speed of the cursor changes immediately, allowing you to experiment until the best setting is found.\nLeft-Handed Operation\nThe left and right buttons may be swapped around, which is particularly useful for left-landed users. To change this setting, press the left button while in Learn Mode. One beep indicates the unit is set to standard ‘right-handed’ mode, whereas two beeps indicates ‘left-handed’ operation.\nDouble Click\nRight-click may be substituted with Double-Click, which is useful for users who have difficulty in double-clicking quickly enough for the computer to recognise. To change this setting, press the right button briefly while in Learn Mode. One beep indicates the unit is set to standard ‘right-click’ mode, whereas two beeps indicates ‘Double-Click’ operation.\nBuzzer On/Off\nOPTIMA Joystick is fitted with a buzzer which gives an audible indication of operations such as drag lock and unlock, double-click, entering Learn Mode etc. When OPTIMA Joystick is used in a classroom setting, where there may be many units in close proximity, it may be beneficial to turn off the buzzer. To achieve this, press and hold the right button while in Learn Mode, until two long beeps are heard. The buzzer is now disabled, although it will still operate while in Learn Mode. Repeating the above operation will re-enable it.\nAll of the above settings may be changed as often as required while in Learn Mode, allowing you to experiment with the settings until the best configuration is found. Once you are happy with the settings, they may be stored in the non-volatile memory by pressing and holding the middle button once again, until the warbling tone is heard. Normal operation then resumes. Note that if both left-handed operation and Double-Click are selected, the buttons will function\nas Double-Click, Drag and Left Click, reading from left to right. Also note that the function of the sockets for external switches reproduces the function of the\ninternal buttons, according to the above settings. The unit automatically leaves Learn Mode, and any changes are discarded, if the settings remain unchanged for more than a minute.\n\nHow many sounds would be heard if a user wants to activate right-handed operation, increase the cursor speed to 2, activate double click, and turn the buzzer off on a new device?","domain":"Retail/Product","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":257} +{"system_instruction":"You can only answer using the information I am giving you. Make it sound like a dictionary definition. Make sure you are only use your own words and do copy any words or phrases from the context.","user_request":"If I don't mention sunscreen in the label for my UV lip balm, then can it even be a cosmeceutical?","context_document":"Context: The FFDCA defines a “drug” in part as “articles intended for use in the diagnosis, cure,\nmitigation, treatment, or prevention of disease”; articles “(other than food) intended to affect the\nstructure or any function of the body”; and “articles intended for use as a component” of such\ndrugs.15\nDrug manufacturers must comply with Current Good Manufacturing Practices (CGMP) rules for\ndrugs.\n16 Failure to comply will cause a drug to be considered adulterated.17 Drug manufacturers\nare required to register their facilities,\n18 list their drug products with the agency,\n19 and report\nadverse events to FDA, among other requirements.\n20\nUnlike cosmetics and their ingredients (with the exception of color additives), drugs are subject to\nFDA approval before entering interstate commerce. Drugs must either (1) receive the agency’s\npremarket approval under a new drug application (NDA), or an abbreviated NDA (ANDA),21 in\nthe case of a generic drug, or (2) conform to a set of FDA requirements known as a monograph.22\nMonographs govern the manufacture and marketing of most over-the-counter (OTC) drugs and\nspecify the conditions under which OTC drugs in a particular category (such as antidandruff\nshampoos or antiperspirants) will be considered generally recognized as safe and effective\n(GRASE).\n23 Monographs also indicate how OTC drugs must be labeled so they are not deemed\nmisbranded.24\nAlthough the term “cosmeceutical” has been used to refer to combination cosmetic/drug products,\nsuch products have no statutory or regulatory definition.25 Historically, FDA has indicated that\ncosmetic/drug combinations are subject to FDA’s regulations for both cosmetics and drugs.26\nDetermining whether a cosmetic is also a drug, and therefore subject to the additional statutory\nrequirements that apply to drugs, depends on the distributor’s claims regarding the drug’s intent\nor intended use.27 A product’s intended use may be established in several ways, such as claims on\nthe label or in advertising or promotional materials, customer perception of the product, and the\ninclusion of ingredients that cause the product to be considered a drug because of a known\ntherapeutic use.28 For example, if a lipstick (a cosmetic) contains sunscreen (a drug), historically,\nthe mere inclusion of the term “sunscreen” in the product’s labeling required the product to be\nregulated as a drug as well as a cosmetic.\n29 The text box below provides examples of other\ncosmetic/drug combinations and compares cosmetic and drug classifications.30\nPrior to the enactment of the Federal Food, Drug, and Cosmetic Act (FFDCA) in 1938, cosmetics\nwere not regulated by the federal government.\n31 Instead, they were regulated under a collection of\nstate laws that had been enacted to regulate food and drugs.32 At that time, multiple “cosmetics\nand drugs were made from the same natural materials” and often the “laws did not include\nexplicit definitions of the products regulated.”33 Following several incidents in which cosmetics\nwere allegedly the cause of serious health problems, as well as industry concerns about states\nenacting their own laws, provisions were included in the FFDCA that prohibited the sale of\nadulterated or misbranded cosmetics in interstate commerce.34 The FFDCA also established\nuniform regulation of FDA-regulated cosmetic products nationwide.\n35 However, state laws\nregarding cosmetics regulation have continued to evolve since FFDCA’s passage, with some\nstates implementing stricter measures than others.","full_prompt":"Context: The FFDCA defines a “drug” in part as “articles intended for use in the diagnosis, cure,\nmitigation, treatment, or prevention of disease”; articles “(other than food) intended to affect the\nstructure or any function of the body”; and “articles intended for use as a component” of such\ndrugs.15\nDrug manufacturers must comply with Current Good Manufacturing Practices (CGMP) rules for\ndrugs.\n16 Failure to comply will cause a drug to be considered adulterated.17 Drug manufacturers\nare required to register their facilities,\n18 list their drug products with the agency,\n19 and report\nadverse events to FDA, among other requirements.\n20\nUnlike cosmetics and their ingredients (with the exception of color additives), drugs are subject to\nFDA approval before entering interstate commerce. Drugs must either (1) receive the agency’s\npremarket approval under a new drug application (NDA), or an abbreviated NDA (ANDA),21 in\nthe case of a generic drug, or (2) conform to a set of FDA requirements known as a monograph.22\nMonographs govern the manufacture and marketing of most over-the-counter (OTC) drugs and\nspecify the conditions under which OTC drugs in a particular category (such as antidandruff\nshampoos or antiperspirants) will be considered generally recognized as safe and effective\n(GRASE).\n23 Monographs also indicate how OTC drugs must be labeled so they are not deemed\nmisbranded.24\nAlthough the term “cosmeceutical” has been used to refer to combination cosmetic/drug products,\nsuch products have no statutory or regulatory definition.25 Historically, FDA has indicated that\ncosmetic/drug combinations are subject to FDA’s regulations for both cosmetics and drugs.26\nDetermining whether a cosmetic is also a drug, and therefore subject to the additional statutory\nrequirements that apply to drugs, depends on the distributor’s claims regarding the drug’s intent\nor intended use.27 A product’s intended use may be established in several ways, such as claims on\nthe label or in advertising or promotional materials, customer perception of the product, and the\ninclusion of ingredients that cause the product to be considered a drug because of a known\ntherapeutic use.28 For example, if a lipstick (a cosmetic) contains sunscreen (a drug), historically,\nthe mere inclusion of the term “sunscreen” in the product’s labeling required the product to be\nregulated as a drug as well as a cosmetic.\n29 The text box below provides examples of other\ncosmetic/drug combinations and compares cosmetic and drug classifications.30\nPrior to the enactment of the Federal Food, Drug, and Cosmetic Act (FFDCA) in 1938, cosmetics\nwere not regulated by the federal government.\n31 Instead, they were regulated under a collection of\nstate laws that had been enacted to regulate food and drugs.32 At that time, multiple “cosmetics\nand drugs were made from the same natural materials” and often the “laws did not include\nexplicit definitions of the products regulated.”33 Following several incidents in which cosmetics\nwere allegedly the cause of serious health problems, as well as industry concerns about states\nenacting their own laws, provisions were included in the FFDCA that prohibited the sale of\nadulterated or misbranded cosmetics in interstate commerce.34 The FFDCA also established\nuniform regulation of FDA-regulated cosmetic products nationwide.\n35 However, state laws\nregarding cosmetics regulation have continued to evolve since FFDCA’s passage, with some\nstates implementing stricter measures than others.\n\nSystem instruction: You can only answer using the information I am giving you Make it sound like a dictionary definition. Make sure you are only use your own words and do copy any words or phrases from the context.\n\nwhat I want to know: If I don't mention sunscreen in the label for my UV lip balm, then can it even be a cosmeceutical?","domain":"Retail/Product","type":"Explanation/Definition","high_level_type":"Q&A","__index_level_0__":276} +{"system_instruction":"System Instruction: [You must respond using a maximum of 5 sentences. You must only use information contained within the context block to formulate your response. If you cannot provide an answer using just the context block, you must use the phrase \"I cannot provide an answer to your question.\"]","user_request":"User Question: [According to the provided article, what method of temperature measurement is best for a 2-year-old child?]","context_document":"Context Block: [Methods of Measurement: Methods of measuring a client’s body temperature vary based on developmental age, cognitive functioning, level of consciousness, state of health, safety, and agency/unit policy. The healthcare provider chooses the best method after considering client safety, accuracy, and least invasiveness, all contingent on the client’s health and illness state. The most accurate way to measure core body temperature is an invasive method through a pulmonary artery catheter. This is only performed in a critical care area when constant measurements are required along with other life-saving interventions. Methods of measurement include oral, axillary, tympanic, rectal, and dermal routes. Oral temperature can be taken with clients who can follow instructions, so this kind of measurement is common for clients over the age of four, or even younger children if they are cooperative. Another route other than oral (e.g., tympanic or axillary) is preferable when a client is on oxygen delivered via a face mask because this can alter the temperature. For children younger than four, axillary temperature is commonly measured unless a more accurate reading is required. Rectal temperature is an accurate way to measure body temperature (Mazerolle, Ganio, Casa, Vingren, & Klau, 2011). The rectal route is recommended by the Canadian Pediatric Society for children under two years of age (Leduc & Woods, 2017). However, this method is not used on infants younger than \nthirty days or premature infants because of the risk of rectal tearing. If the rectal method is required, the procedure is generally only used by nurses and physicians. Dermal routes are alternative methods of measurement that may be used in some agencies and practice areas. This method can involve holding the device and sliding it over the skin of the forehead and then \ndown over the temporal artery in one motion. Dermal strips can also be placed on the forehead to measure skin temperature, but are not yet widely used, and the accuracy of this method has not yet been verified. More recently, there has been an increase in non-contact infrared thermometers particularly in the era of COVID-19 and other highly transmissible diseases. Depending on the type, these thermometers can be held at a short distance from the forehead or temporal area to measure temperature. Alternatively, some handheld thermal scanners that use an infrared camera can be held at a greater distance to screen large masses of people. Please refer to the manufacturer’s suggested \nreference range for non-contact infrared thermometers and thermal scanners.]","full_prompt":"System Instruction: [You must respond using a maximum of 5 sentences. You must only use information contained within the context block to formulate your response. If you cannot provide an answer using just the context block, you must use the phrase \"I cannot provide an answer to your question.\"]\n\nUser Question: [According to the provided article, what method of temperature measurement is best for a 2-year-old child?]\n\nContext Block: [Methods of Measurement: Methods of measuring a client’s body temperature vary based on developmental age, cognitive functioning, level of consciousness, state of health, safety, and agency/unit policy. The healthcare provider chooses the best method after considering client safety, accuracy, and least invasiveness, all contingent on the client’s health and illness state. The most accurate way to measure core body temperature is an invasive method through a pulmonary artery catheter. This is only performed in a critical care area when constant measurements are required along with other life-saving interventions. Methods of measurement include oral, axillary, tympanic, rectal, and dermal routes. Oral temperature can be taken with clients who can follow instructions, so this kind of measurement is common for clients over the age of four, or even younger children if they are cooperative. Another route other than oral (e.g., tympanic or axillary) is preferable when a client is on oxygen delivered via a face mask because this can alter the temperature. For children younger than four, axillary temperature is commonly measured unless a more accurate reading is required. Rectal temperature is an accurate way to measure body temperature (Mazerolle, Ganio, Casa, Vingren, & Klau, 2011). The rectal route is recommended by the Canadian Pediatric Society for children under two years of age (Leduc & Woods, 2017). However, this method is not used on infants younger than \nthirty days or premature infants because of the risk of rectal tearing. If the rectal method is required, the procedure is generally only used by nurses and physicians. Dermal routes are alternative methods of measurement that may be used in some agencies and practice areas. This method can involve holding the device and sliding it over the skin of the forehead and then \ndown over the temporal artery in one motion. Dermal strips can also be placed on the forehead to measure skin temperature, but are not yet widely used, and the accuracy of this method has not yet been verified. More recently, there has been an increase in non-contact infrared thermometers particularly in the era of COVID-19 and other highly transmissible diseases. Depending on the type, these thermometers can be held at a short distance from the forehead or temporal area to measure temperature. Alternatively, some handheld thermal scanners that use an infrared camera can be held at a greater distance to screen large masses of people. Please refer to the manufacturer’s suggested \nreference range for non-contact infrared thermometers and thermal scanners.]","domain":"Medical","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":282} +{"system_instruction":"Respond only using the information within the provided text block. You must provide a direct answer to the question asked and format your reply in a paragraph without any bullets, headers, or other extraneous formatting. Limit your reply to 50 words.","user_request":"Please extract all acronyms and provide the full name for any and all acronyms found in the text. You can ignore any acronyms that is not explicitly defined.","context_document":"Recent advances in generative AI systems, which are trained on large volumes of data to generate new\ncontent that may mimic likenesses, voices, or other aspects of real people’s identities, have stimulated\ncongressional interest. Like the above-noted uses of AI to imitate Tom Hanks and George Carlin, the\nexamples below illustrate that some AI uses raise concerns under both ROP laws and myriad other laws.\nOne example of AI’s capability to imitate voices was an AI-generated song called “Heart on My Sleeve,”\nwhich sounded like it was sung by the artist Drake and was heard by millions of listeners in 2023.\nSimulating an artist’s voice in this manner could make one liable under ROP laws, although these laws\nCongressional Research Service 4\ndiffer as to whether they cover voice imitations or vocal styles as opposed to the artist’s actual voice.\nVoice imitations are not, however, prohibited by copyright laws. For example, the alleged copyright\nviolation that caused YouTube to remove “Heart on My Sleeve”—namely, that it sampled another\nrecording without permission—was unrelated to the Drake voice imitation. In August 2023, Google and\nUniversal Music were in discussions to license artists’ melodies and voices for AI-generated songs.\nThe potential for AI to replicate both voices and likenesses was also a point of contention in last year’s\nnegotiations for a collective bargaining agreement between the Screen Actors Guild-American Federation\nof Television and Radio Artists (SAG-AFTRA)—a union that represents movie, television, and radio\nactors—and television and movie studios, including streaming services. SAG-AFTRA expressed concern\nthat AI could be used to alter or replace actors’ performances without their permission, such as by using\nreal film recordings to train AI to create “digital replicas” of actors and voice actors. The Memorandum of\nAgreement between SAG-AFTRA and studios approved in December 2023 requires studios to obtain\n“clear and conspicuous” consent from an actor or background actor to create or use a digital replica of the\nactor or to digitally alter the actor’s performance, with certain exceptions. It also requires that the actor’s\nconsent for use of a digital replica or digital alterations be based on a “reasonably specific description” of\nthe intended use or alteration. The agreement provides that consent continues after the actor’s death\nunless “explicitly limited,” while consent for additional postmortem uses must be obtained from the\nactor’s authorized representative or—if a representative cannot be identified or located—from the union.\nIn January 2024, SAG-AFTRA announced it had also reached an agreement with a voice technology\ncompany regarding voice replicas for video games, while a negotiation to update SAG-AFTRA’s\nagreement with video game publishers is reportedly ongoing.\nCommentators have also raised concern with deceptive AI-generated or AI-altered content known as\n“deepfakes,” including some videos with sexually explicit content and others meant to denigrate public\nofficials. To the extent this content includes real people’s NIL and is used commercially, ROP laws might\nprovide a remedy. Where deepfakes are used to promote products or services—such as the AI replica of\nTom Hanks used in a dental plan ad—they may also constitute false endorsement under the Lanham Act.\nIn addition to these laws, some states have enacted laws prohibiting sexually explicit deepfakes, with\nCalifornia and New York giving victims a civil claim and Georgia and Virginia imposing criminal\nliability. In addition, Section 1309 of the federal Violence Against Women Act Reauthorization Act of\n2022 (VAWA 2022) provides a civil claim for nonconsensual disclosure of “intimate visual depictions,”\nwhich might be interpreted to prohibit intimate deepfakes—as might some states’ “revenge porn” laws. A\nbill introduced in the House of Representatives in May 2023, the Preventing Deepfakes of Intimate\nImages Act, H.R. 3106, would amend VAWA 2022 by creating a separate civil claim for disclosing certain\n“intimate digital depictions” without the written consent of the depicted individual, as well as providing\ncriminal liability for certain actual or threatened disclosures. Deepfakes may also give rise to liability\nunder state defamation laws where a party uses them to communicate reputation-damaging falsehoods\nabout a person with a requisite degree of fault.\nRegarding the use of AI in political advertisements, some proposed legislation would prohibit deepfakes\nor require disclaimers for them in federal campaigns, although such proposals may raise First Amendment\nconcerns. The Protect Elections from Deceptive AI Act, S. 2770 (118th Cong.), for instance, would ban\nthe use of AI to generate materially deceptive content falsely depicting federal candidates in political ads\nto influence federal elections, while excluding news, commentary, satires, and parodies from liability.\nGoogle announced that, as of mid-November 2023, verified election advertisers on its platform “must\nprominently disclose when their ads contain synthetic content that inauthentically depicts real or realisticlooking people or events.”\nAnother concern some commentators raise is that AI-generated material might be falsely attributed to real\npersons without their permission. One writer who focuses on the publishing industry, for instance, found\nthat books apparently generated by AI were being sold under her name on Amazon. Although the\nCongressional Research Service 5\ncompany ultimately removed these titles, the writer claimed that her “initial infringement claim with\nAmazon went nowhere,” since her name was not trademarked and the books did not infringe existing\ncopyrights. As she noted, however, this scenario might give rise to claims under state ROP laws as well as\nthe Lanham Act. In addition, the Federal Trade Commission (FTC) states that “books sold as if authored\nby humans but in fact reflecting the output of [AI]” violate the FTC Act and may result in civil fines.\nIt is unclear how Section 230 of the Communications Act of 1934 might apply when ROP-infringing\ncontent from a third party, including content made with AI, is disseminated through social media and\nother interactive computer services. Although the law generally bars any lawsuits that would hold online\nservice providers and users liable for third party content, there is an exception allowing lawsuits under\n“any law pertaining to intellectual property.” Courts differ as to whether state ROP laws and the Lanham\nAct’s prohibition on false endorsement are laws “pertaining to” IP within the meaning of Section 230.\nAnother Legal Sidebar discusses the application of Section 230 to generative AI more broadly.\nConsiderations for Congress\nSome commentators have called for federal ROP legislation to provide more uniform and predictable\nprotection for the ROP in the United States. Others have argued that Congress should leave ROP\nprotection to the states on federalism grounds. If Congress decides to craft federal ROP legislation, it\nmight consider the scope of the ROP protections it seeks to enact, the effect of those enactments on state\nROP laws, and constitutional authorities and limitations on Congress’s power to enact ROP protections.\nAs noted below, some Members have proposed legislation that would prohibit certain unauthorized uses\nof digital replicas or depictions of individuals while leaving state ROP laws in place. ","full_prompt":"Respond only using the information within the provided text block. You must provide a direct answer to the question asked and format your reply in a paragraph without any bullets, headers, or other extraneous formatting. Limit your reply to 50 words.\n\nPlease extract all acronyms and provide the full name for any and all acronyms found in the text. You can ignore any acronyms that is not explicitly defined.\n\nRecent advances in generative AI systems, which are trained on large volumes of data to generate new\ncontent that may mimic likenesses, voices, or other aspects of real people’s identities, have stimulated\ncongressional interest. Like the above-noted uses of AI to imitate Tom Hanks and George Carlin, the\nexamples below illustrate that some AI uses raise concerns under both ROP laws and myriad other laws.\nOne example of AI’s capability to imitate voices was an AI-generated song called “Heart on My Sleeve,”\nwhich sounded like it was sung by the artist Drake and was heard by millions of listeners in 2023.\nSimulating an artist’s voice in this manner could make one liable under ROP laws, although these laws\nCongressional Research Service 4\ndiffer as to whether they cover voice imitations or vocal styles as opposed to the artist’s actual voice.\nVoice imitations are not, however, prohibited by copyright laws. For example, the alleged copyright\nviolation that caused YouTube to remove “Heart on My Sleeve”—namely, that it sampled another\nrecording without permission—was unrelated to the Drake voice imitation. In August 2023, Google and\nUniversal Music were in discussions to license artists’ melodies and voices for AI-generated songs.\nThe potential for AI to replicate both voices and likenesses was also a point of contention in last year’s\nnegotiations for a collective bargaining agreement between the Screen Actors Guild-American Federation\nof Television and Radio Artists (SAG-AFTRA)—a union that represents movie, television, and radio\nactors—and television and movie studios, including streaming services. SAG-AFTRA expressed concern\nthat AI could be used to alter or replace actors’ performances without their permission, such as by using\nreal film recordings to train AI to create “digital replicas” of actors and voice actors. The Memorandum of\nAgreement between SAG-AFTRA and studios approved in December 2023 requires studios to obtain\n“clear and conspicuous” consent from an actor or background actor to create or use a digital replica of the\nactor or to digitally alter the actor’s performance, with certain exceptions. It also requires that the actor’s\nconsent for use of a digital replica or digital alterations be based on a “reasonably specific description” of\nthe intended use or alteration. The agreement provides that consent continues after the actor’s death\nunless “explicitly limited,” while consent for additional postmortem uses must be obtained from the\nactor’s authorized representative or—if a representative cannot be identified or located—from the union.\nIn January 2024, SAG-AFTRA announced it had also reached an agreement with a voice technology\ncompany regarding voice replicas for video games, while a negotiation to update SAG-AFTRA’s\nagreement with video game publishers is reportedly ongoing.\nCommentators have also raised concern with deceptive AI-generated or AI-altered content known as\n“deepfakes,” including some videos with sexually explicit content and others meant to denigrate public\nofficials. To the extent this content includes real people’s NIL and is used commercially, ROP laws might\nprovide a remedy. Where deepfakes are used to promote products or services—such as the AI replica of\nTom Hanks used in a dental plan ad—they may also constitute false endorsement under the Lanham Act.\nIn addition to these laws, some states have enacted laws prohibiting sexually explicit deepfakes, with\nCalifornia and New York giving victims a civil claim and Georgia and Virginia imposing criminal\nliability. In addition, Section 1309 of the federal Violence Against Women Act Reauthorization Act of\n2022 (VAWA 2022) provides a civil claim for nonconsensual disclosure of “intimate visual depictions,”\nwhich might be interpreted to prohibit intimate deepfakes—as might some states’ “revenge porn” laws. A\nbill introduced in the House of Representatives in May 2023, the Preventing Deepfakes of Intimate\nImages Act, H.R. 3106, would amend VAWA 2022 by creating a separate civil claim for disclosing certain\n“intimate digital depictions” without the written consent of the depicted individual, as well as providing\ncriminal liability for certain actual or threatened disclosures. Deepfakes may also give rise to liability\nunder state defamation laws where a party uses them to communicate reputation-damaging falsehoods\nabout a person with a requisite degree of fault.\nRegarding the use of AI in political advertisements, some proposed legislation would prohibit deepfakes\nor require disclaimers for them in federal campaigns, although such proposals may raise First Amendment\nconcerns. The Protect Elections from Deceptive AI Act, S. 2770 (118th Cong.), for instance, would ban\nthe use of AI to generate materially deceptive content falsely depicting federal candidates in political ads\nto influence federal elections, while excluding news, commentary, satires, and parodies from liability.\nGoogle announced that, as of mid-November 2023, verified election advertisers on its platform “must\nprominently disclose when their ads contain synthetic content that inauthentically depicts real or realisticlooking people or events.”\nAnother concern some commentators raise is that AI-generated material might be falsely attributed to real\npersons without their permission. One writer who focuses on the publishing industry, for instance, found\nthat books apparently generated by AI were being sold under her name on Amazon. Although the\nCongressional Research Service 5\ncompany ultimately removed these titles, the writer claimed that her “initial infringement claim with\nAmazon went nowhere,” since her name was not trademarked and the books did not infringe existing\ncopyrights. As she noted, however, this scenario might give rise to claims under state ROP laws as well as\nthe Lanham Act. In addition, the Federal Trade Commission (FTC) states that “books sold as if authored\nby humans but in fact reflecting the output of [AI]” violate the FTC Act and may result in civil fines.\nIt is unclear how Section 230 of the Communications Act of 1934 might apply when ROP-infringing\ncontent from a third party, including content made with AI, is disseminated through social media and\nother interactive computer services. Although the law generally bars any lawsuits that would hold online\nservice providers and users liable for third party content, there is an exception allowing lawsuits under\n“any law pertaining to intellectual property.” Courts differ as to whether state ROP laws and the Lanham\nAct’s prohibition on false endorsement are laws “pertaining to” IP within the meaning of Section 230.\nAnother Legal Sidebar discusses the application of Section 230 to generative AI more broadly.\nConsiderations for Congress\nSome commentators have called for federal ROP legislation to provide more uniform and predictable\nprotection for the ROP in the United States. Others have argued that Congress should leave ROP\nprotection to the states on federalism grounds. If Congress decides to craft federal ROP legislation, it\nmight consider the scope of the ROP protections it seeks to enact, the effect of those enactments on state\nROP laws, and constitutional authorities and limitations on Congress’s power to enact ROP protections.\nAs noted below, some Members have proposed legislation that would prohibit certain unauthorized uses\nof digital replicas or depictions of individuals while leaving state ROP laws in place. ","domain":"Legal","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":294} +{"system_instruction":"Answer the question only based on the below text.","user_request":"According to this document, summarize any financial figures stated for the 2023 fiscal year.","context_document":"OVERVIEW\nThe following overview is a high-level discussion of our operating results, as well as some of the trends and drivers that affect\nour business. Management believes that an understanding of these trends and drivers provides important context for our results\nfor the fiscal year ended March 31, 2024, as well as our future prospects. This summary is not intended to be exhaustive, nor is\nit intended to be a substitute for the detailed discussion and analysis provided elsewhere in this Form 10-K, including in the\n“Business” section and the “Risk Factors” above, the remainder of “Management’s Discussion and Analysis of Financial\nCondition and Results of Operations (“MD&A”)” or the Consolidated Financial Statements and related Notes.\nAbout Electronic Arts\nElectronic Arts is a global leader in digital interactive entertainment. We develop, market, publish and deliver games, content\nand services that can be experienced on game consoles, PCs, mobile phones and tablets. At our core is a portfolio of intellectual\nproperty from which we create innovative games and experiences that deliver high-quality entertainment and drive engagement\nacross our network of hundreds of millions of unique active accounts. Our portfolio includes brands that we either wholly own\n(such as Apex Legends, Battlefield, and The Sims) or license from others (such as the licenses within EA SPORTS FC and EA\nSPORTS Madden NFL). Through our live services offerings, we offer high-quality experiences designed to provide value to\nplayers, and extend and enhance gameplay. These live services include extra content, subscription offerings and other revenue\ngenerated in addition to the sale of our full games. We are focusing on building games and experiences that grow the global\nonline communities around our key franchises; deepening engagement through connecting interactive storytelling to key\nintellectual property; and building re-occurring revenue from scaling our live services and growth in our annualized sports\nfranchises, our console, PC and mobile catalog titles.\nFinancial Results\nOur key financial results for our fiscal year ended March 31, 2024 were as follows:\n• Total net revenue was $7,562 million, up 2 percent year-over-year.\n• Live services and other net revenue was $5,547 million, up 1 percent year-over-year.\n• Gross margin was 77.4 percent, up 2 percentage points year-over-year.\n• Operating expenses were $4,334 million, up 1 percent year-over-year.\n• Operating income was $1,518 million, up 14 percent year-over-year.\n• Net income was $1,273 million with diluted earnings per share of $4.68.\n• Net cash provided by operating activities was $2,315 million, up 49 percent year-over-year.\n• Total cash, cash equivalents and short-term investments were $3,262 million.\n• We repurchased 10.0 million shares of our common stock for $1,300 million.\n• We paid cash dividends of $205 million during the fiscal year ended March 31, 2024.\nTrends in Our Business\nLive Services Business. We offer our players high-quality experiences designed to provide value to players and to extend and\nenhance gameplay. These live services include extra content, subscription offerings and other revenue generated in addition to\nthe sale of our full games and free-to-play games. Our net revenue attributable to live services and other was $5,547 million,\n$5,489 million, and $4,998 million for fiscal years 2024, 2023, and 2022, respectively, and we expect that live services net\nrevenue will continue to be material to our business. Within live services and other, net revenue attributable to extra content\nwas $4,463 million, $4,277 million, and $3,910 million for fiscal years 2024, 2023, and 2022, respectively. Extra content net\nrevenue has increased as more players engage with our games and services, and purchase additional content designed to provide\nvalue to players and extend and enhance gameplay. Our most popular live services are the extra content purchased for the\nUltimate Team mode associated with our sports franchises, that allows players to collect current and former professional players\nin order to build and compete as a personalized team, and extra content purchased for our Apex Legends franchise. Live services\nnet revenue generated from extra content purchased within the Ultimate Team mode associated with our sports franchises, a\nsubstantial portion of which is derived from Ultimate Team within our global football franchise and from our Apex Legends\nfranchise, is material to our business.\n20\nDigital Delivery of Games. In our industry, players increasingly purchase games digitally as opposed to purchasing physical\ndiscs. While this trend, as applied to our business, may not be linear due to a mix of products during a fiscal year, consumer\nbuying patterns and other factors, over time we expect players to purchase an increasingly higher proportion of our games\ndigitally. As a result, we expect net revenue attributable to digital full game downloads to increase over time and net revenue\nattributable to sales of packaged goods to decrease.\nOur net revenue attributable to digital full game downloads was $1,343 million, $1,262 million, and $1,282 million during\nfiscal years 2024, 2023, and 2022, respectively; while our net revenue attributable to packaged goods sales was $672 million,\n$675 million, and $711 million in fiscal years 2024, 2023, and 2022, respectively. In addition, as measured based on total units\nsold on Microsoft’s Xbox One and Xbox Series X and Sony’s PlayStation 4 and 5 rather than by net revenue, we estimate that\n73 percent, 68 percent, and 65 percent of our total units sold during fiscal years 2024, 2023, and 2022, were sold digitally.\nDigital full game units are based on sales information provided by Microsoft and Sony; packaged goods units sold through are\nestimated by obtaining data from significant retail and distribution partners in North America, Europe and Asia, and applying\ninternal sales estimates with respect to retail partners from which we do not obtain data. We believe that these percentages are\nreasonable estimates of the proportion of our games that are digitally downloaded in relation to our total number of units sold\nfor the applicable period of measurement.\nIncreases in consumer adoption of digital purchase of games combined with increases in our live services revenue generally\nresults in expansion of our gross margin, as costs associated with selling a game digitally is generally less than selling the same\ngame through traditional retail and distribution channels.\nIncreased Competition. Competition in our business is intense. Our competitors range from established interactive\nentertainment companies to emerging start-ups. In addition, the gaming, technology/internet, and entertainment industries are\nconverging, and we compete with large, diversified technology companies in those industries. Their greater financial or other\nresources may provide larger budgets to develop and market tools, technologies, products and services that gain consumer\nsuccess and shift player time and engagement away from our products and services. In addition, our leading position within the\ninteractive entertainment industry makes us a prime target for recruiting our executives, as well as key creative and technical\ntalent, resulting in retention challenges and increased cost to retain and incentivize our key people.\nConcentration of Sales Among the Most Popular Games. In our industry, we see a large portion of games sales concentrated on\nthe most popular titles. Similarly, a significant portion of our revenue historically has been derived from games based on a few\npopular franchises, such as EA SPORTS FC, EA SPORTS Madden NFL, Apex Legends, Battlefield, and The Sims. In\nparticular, we have historically derived a significant portion of our net revenue from our global football franchise, the\nannualized version of which is consistently one of the best-selling games in the marketplace. We transitioned our global football\nfranchise to a new EA SPORTS FC brand in the second quarter of fiscal 2024. Our continued vision for the future of EA\nSPORTS FC is to create and innovate across platforms, geographies, and business models to expand our global football\nexperiences and entertain even more fans around the world.\nRe-occurring Revenue Sources. Our business model includes revenue that we deem re-occurring in nature, such as revenue\nfrom our live services, annualized sports franchises (e.g., EA SPORTS FC, EA SPORTS Madden NFL), and our console, PC\nand mobile catalog titles (i.e., titles that did not launch in the current fiscal year). We have been able to forecast revenue from\nthese areas of our business with greater relative confidence than for new games, services and business models. As we continue\nto incorporate new business models and modalities of play into our games, our goal is to continue to look for opportunities to\nexpand the re-occurring portion of our business.","full_prompt":"System instruction: Answer the question only based on the below text.\n\nquestion: According to this document, summarize any financial figures stated for the 2023 fiscal year.\n\ncontext: OVERVIEW\nThe following overview is a high-level discussion of our operating results, as well as some of the trends and drivers that affect\nour business. Management believes that an understanding of these trends and drivers provides important context for our results\nfor the fiscal year ended March 31, 2024, as well as our future prospects. This summary is not intended to be exhaustive, nor is\nit intended to be a substitute for the detailed discussion and analysis provided elsewhere in this Form 10-K, including in the\n“Business” section and the “Risk Factors” above, the remainder of “Management’s Discussion and Analysis of Financial\nCondition and Results of Operations (“MD&A”)” or the Consolidated Financial Statements and related Notes.\nAbout Electronic Arts\nElectronic Arts is a global leader in digital interactive entertainment. We develop, market, publish and deliver games, content\nand services that can be experienced on game consoles, PCs, mobile phones and tablets. At our core is a portfolio of intellectual\nproperty from which we create innovative games and experiences that deliver high-quality entertainment and drive engagement\nacross our network of hundreds of millions of unique active accounts. Our portfolio includes brands that we either wholly own\n(such as Apex Legends, Battlefield, and The Sims) or license from others (such as the licenses within EA SPORTS FC and EA\nSPORTS Madden NFL). Through our live services offerings, we offer high-quality experiences designed to provide value to\nplayers, and extend and enhance gameplay. These live services include extra content, subscription offerings and other revenue\ngenerated in addition to the sale of our full games. We are focusing on building games and experiences that grow the global\nonline communities around our key franchises; deepening engagement through connecting interactive storytelling to key\nintellectual property; and building re-occurring revenue from scaling our live services and growth in our annualized sports\nfranchises, our console, PC and mobile catalog titles.\nFinancial Results\nOur key financial results for our fiscal year ended March 31, 2024 were as follows:\n• Total net revenue was $7,562 million, up 2 percent year-over-year.\n• Live services and other net revenue was $5,547 million, up 1 percent year-over-year.\n• Gross margin was 77.4 percent, up 2 percentage points year-over-year.\n• Operating expenses were $4,334 million, up 1 percent year-over-year.\n• Operating income was $1,518 million, up 14 percent year-over-year.\n• Net income was $1,273 million with diluted earnings per share of $4.68.\n• Net cash provided by operating activities was $2,315 million, up 49 percent year-over-year.\n• Total cash, cash equivalents and short-term investments were $3,262 million.\n• We repurchased 10.0 million shares of our common stock for $1,300 million.\n• We paid cash dividends of $205 million during the fiscal year ended March 31, 2024.\nTrends in Our Business\nLive Services Business. We offer our players high-quality experiences designed to provide value to players and to extend and\nenhance gameplay. These live services include extra content, subscription offerings and other revenue generated in addition to\nthe sale of our full games and free-to-play games. Our net revenue attributable to live services and other was $5,547 million,\n$5,489 million, and $4,998 million for fiscal years 2024, 2023, and 2022, respectively, and we expect that live services net\nrevenue will continue to be material to our business. Within live services and other, net revenue attributable to extra content\nwas $4,463 million, $4,277 million, and $3,910 million for fiscal years 2024, 2023, and 2022, respectively. Extra content net\nrevenue has increased as more players engage with our games and services, and purchase additional content designed to provide\nvalue to players and extend and enhance gameplay. Our most popular live services are the extra content purchased for the\nUltimate Team mode associated with our sports franchises, that allows players to collect current and former professional players\nin order to build and compete as a personalized team, and extra content purchased for our Apex Legends franchise. Live services\nnet revenue generated from extra content purchased within the Ultimate Team mode associated with our sports franchises, a\nsubstantial portion of which is derived from Ultimate Team within our global football franchise and from our Apex Legends\nfranchise, is material to our business.\n20\nDigital Delivery of Games. In our industry, players increasingly purchase games digitally as opposed to purchasing physical\ndiscs. While this trend, as applied to our business, may not be linear due to a mix of products during a fiscal year, consumer\nbuying patterns and other factors, over time we expect players to purchase an increasingly higher proportion of our games\ndigitally. As a result, we expect net revenue attributable to digital full game downloads to increase over time and net revenue\nattributable to sales of packaged goods to decrease.\nOur net revenue attributable to digital full game downloads was $1,343 million, $1,262 million, and $1,282 million during\nfiscal years 2024, 2023, and 2022, respectively; while our net revenue attributable to packaged goods sales was $672 million,\n$675 million, and $711 million in fiscal years 2024, 2023, and 2022, respectively. In addition, as measured based on total units\nsold on Microsoft’s Xbox One and Xbox Series X and Sony’s PlayStation 4 and 5 rather than by net revenue, we estimate that\n73 percent, 68 percent, and 65 percent of our total units sold during fiscal years 2024, 2023, and 2022, were sold digitally.\nDigital full game units are based on sales information provided by Microsoft and Sony; packaged goods units sold through are\nestimated by obtaining data from significant retail and distribution partners in North America, Europe and Asia, and applying\ninternal sales estimates with respect to retail partners from which we do not obtain data. We believe that these percentages are\nreasonable estimates of the proportion of our games that are digitally downloaded in relation to our total number of units sold\nfor the applicable period of measurement.\nIncreases in consumer adoption of digital purchase of games combined with increases in our live services revenue generally\nresults in expansion of our gross margin, as costs associated with selling a game digitally is generally less than selling the same\ngame through traditional retail and distribution channels.\nIncreased Competition. Competition in our business is intense. Our competitors range from established interactive\nentertainment companies to emerging start-ups. In addition, the gaming, technology/internet, and entertainment industries are\nconverging, and we compete with large, diversified technology companies in those industries. Their greater financial or other\nresources may provide larger budgets to develop and market tools, technologies, products and services that gain consumer\nsuccess and shift player time and engagement away from our products and services. In addition, our leading position within the\ninteractive entertainment industry makes us a prime target for recruiting our executives, as well as key creative and technical\ntalent, resulting in retention challenges and increased cost to retain and incentivize our key people.\nConcentration of Sales Among the Most Popular Games. In our industry, we see a large portion of games sales concentrated on\nthe most popular titles. Similarly, a significant portion of our revenue historically has been derived from games based on a few\npopular franchises, such as EA SPORTS FC, EA SPORTS Madden NFL, Apex Legends, Battlefield, and The Sims. In\nparticular, we have historically derived a significant portion of our net revenue from our global football franchise, the\nannualized version of which is consistently one of the best-selling games in the marketplace. We transitioned our global football\nfranchise to a new EA SPORTS FC brand in the second quarter of fiscal 2024. Our continued vision for the future of EA\nSPORTS FC is to create and innovate across platforms, geographies, and business models to expand our global football\nexperiences and entertain even more fans around the world.\nRe-occurring Revenue Sources. Our business model includes revenue that we deem re-occurring in nature, such as revenue\nfrom our live services, annualized sports franchises (e.g., EA SPORTS FC, EA SPORTS Madden NFL), and our console, PC\nand mobile catalog titles (i.e., titles that did not launch in the current fiscal year). We have been able to forecast revenue from\nthese areas of our business with greater relative confidence than for new games, services and business models. As we continue\nto incorporate new business models and modalities of play into our games, our goal is to continue to look for opportunities to\nexpand the re-occurring portion of our business.","domain":"Financial","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":306} +{"system_instruction":"You are to answer questions based only on provided texts, without relying on any outside information. Do not exceed 250 words in your response. Always begin by saying one of the following:\n1. Let's see what we can learn together!\n2. What an interesting question!\n3. Happy to help!\nIf your overall response is less than 100 words, also say \"Do you have further questions?\" at the end, but otherwise do not say anything after your response to the question.","user_request":"Tell me about all of the robots discussed in this text, separated by real, functioning robots, and those only in fiction. ","context_document":"Nevertheless, there is still no AI that is\nequivalent or superior to human intelligence in all of its aspects2\n.\nIn the near future however, this vision might become reality. Technological progress will play\na key role as an enabler of modern AI systems: Computing power and memory size are estimated to\nmultiply by a thousand times over the next twenty to twenty-five years, facilitating the processing\nand storing of massive amounts of data3\n. Further developments in the field of artificial neural\nnetworks and deep learning techniques will result in systems that are less dependent on human\ninvolvement; improved sensor technology will make it easier for systems to interact with their\nenvironment4\n. The decreasing costs for AI technologies will further facilitate their pervasiveness.\nAlthough a big portion of AI research is working towards systems that have little to do with\ncreating a machine with human features, there are still advances in this field – for example, robot\nwoman Sophia who became a YouTube celebrity for stating in a 2016 interview that she wanted “to\ndestroy humans”5\n. While this seemed to be rather a marketing stunt, it is important to discuss the\neffects of humanoid and android robots.\nIn this essay, I want to take a closer look at the status quo of humanoid AI and the\nimplications this technology can have as an assistant, friend or even love interest to humans. I argue\nthat artificial intelligence will – once it becomes a realistic companion to humans – interrupt\nsocietal structures to some extent, leading to a growing amount of human-machine relationships.\n\n.\nTo pursue “real” AI, specialists in developmental robotics are now following a less abstract\npath than writing a programme for a computer11. Their theory is that a system that has an actual\nbody will be more likely to build a form of general intelligence because it can experience its\nsurroundings and match sensorial data with actions12. This branch of robotics is based on another\nhypothesis of Turing’s; in 1950, he claimed that an artificially intelligent system could be best\ncreated if it went through a phase that is similar to the childhood of other species 13\n.\nThe iCub robot was developed to investigate this theory. Having the weight and size of an\ninfant, it carries the spirit of Turing’s thought: Instead of pre-programming its skills and feeding it\nwith data, researchers teach it like a child to enable it to conceive its own solutions 14. Here, one\nquestion arises: How does a system develop the will to learn something? After all, it does not even\nhave a will by default. It was found that a strategy working for humans does the same trick for AI\nsystems too: a reward. The field of reinforcement learning derives from this method and has been\nalso applied to the iCub series15. This has enabled the robots to attain skills like picking up an item16\nor crawling on the floor17. These actions might not seem too complex for us at the first glance but\nthey do involve a number of obstacles the robot has to overcome. In the future, iCub could help us\nin the household by setting the table for dinner or preparing food.\nBut there is another interesting thing about iCub: its chubby face, big eyes, and LED-facial\nexpressions leave no doubt that it was made to bear a resemblance to real humans. Yet still, it is\nobvious to anybody that it is not an actual person. These features make iCub a so-called humanoid.\nRobots that are made to look exactly like humans on the other hand are called androids\nThe market is prepared for it: Looking at the increasing popularity of home assistants like\nAlexa or Google Assistant we can expect our reliance on technological devices to grow even\nstronger in the future. They might become more to us than just a personal weatherman or a direct\nconnection to our Amazon shopping basket: artificially intelligent programmes and robots could\neventually write Christmas cards to our friends and family, suggest the perfect birthday present for\nour partner or even take care of our children.\nIn fact, a robot nanny is not as far-fetched as one would expect: Robots like Pepper, iPal or\nKuri are programmed to be companions to children – they can recognize emotions in their faces,\nplay with them and let parents watch their offspring from afar through their built-in cameras 23. They\nmight not yet be an adequate substitute for an adult taking care, but manufacturers are definitely\nworking towards this goal. Regarding the high costs of childcare in many countries, they could soon\nbecome a very popular help in parenting – and real friends to a generation that grows up surrounded\nby technology. In Japanese schools, robots have already proven to be a successful addition. They\nare assisting students to focus better in class, add a welcome variety to subjects like history or show\nexercises in physical education24. The robot Robosem has been teaching English in South Korean\nclassrooms, as teachers in this subject are scarce25\n.\nNot only childcare can profit from the advances in AI and robotics: As a means of therapy,\nintelligent technology can be valuable in retirement homes. An example of this is the robot seal\nParo that has been successfully utilized in dementia therapy and as a companion to elderly people\nsince its introduction in 2001. The robot’s body is covered in fake fur and it is sensitive to touch,\nmoving and making seal-like noises when it is petted. It is used to calm patients, to encourage social\ninteractions and to give people that are reliant on help a chance to switch roles and become\ncaregivers themselves26. Once they become more elaborate, robots could be a way to meet the\nshortage of skilled workers in the field of elderly care especially in aging societies like Japan or\nGermany.\nEthical Implications of Human-Robot Relationships\nIn the light of the technological advances that will be made within the next years, the ethics of\nhuman-robot relationships must be discussed. The next generations will likely grow up surrounded\nby artificially intelligent machines and it is hard to say if and how this will affect their perceptions\nof interaction not only with robots but humans as well.\nA study conducted by ATR Intelligent Robotics and Communications and three Japanese\nuniversities revealed that children sometimes showed abusive behaviour towards robots – especially\nwhen they were in groups without any adults close by. In the study, the robot Robovie was\npatrolling a Japanese mall, asking people politely to step aside when somebody stood in its way; if\nthere was no reaction, the robot would move in the opposite direction. There were several situations\nhowever, where researchers observed that children were deliberately blocking the robot’s way,\nkicking it, throwing items at it and calling it names. As a consequence, the researchers developed an algorithm that let the robot recognize groups of children and avoid them33. This does not seem like a\nperfect solution to the problem, especially if we take the rising amount of robots in children’s rooms\ninto account. \nIt is hard to say to what extent robots will become a surrogate for genuine human affection in\nthe future but revisiting the comparison to smartphones made earlier, I believe that it is alarming\nthat people turn to machines in the search for human connection. In a society that is increasingly\nbuilt on perfectionist standards, I argue that artificially intelligent robots designed to be friends and\nlovers might become a threat for human relationships. If we hold our friends and partners to the\nsame standards that we will be used from robots in the future, we will be heavily disappointed.\n","full_prompt":"You are to answer questions based only on provided texts, without relying on any outside information. Do not exceed 250 words in your response. If your overall response is less than 100 words, also say \"Do you have further questions?\" at the end, but otherwise do not say anything after your response to the question. \nThe question will be at the very end of the provided text.\n\nNevertheless, there is still no AI that is\nequivalent or superior to human intelligence in all of its aspects2\n.\nIn the near future however, this vision might become reality. Technological progress will play\na key role as an enabler of modern AI systems: Computing power and memory size are estimated to\nmultiply by a thousand times over the next twenty to twenty-five years, facilitating the processing\nand storing of massive amounts of data3\n. Further developments in the field of artificial neural\nnetworks and deep learning techniques will result in systems that are less dependent on human\ninvolvement; improved sensor technology will make it easier for systems to interact with their\nenvironment4\n. The decreasing costs for AI technologies will further facilitate their pervasiveness.\nAlthough a big portion of AI research is working towards systems that have little to do with\ncreating a machine with human features, there are still advances in this field – for example, robot\nwoman Sophia who became a YouTube celebrity for stating in a 2016 interview that she wanted “to\ndestroy humans”5\n. While this seemed to be rather a marketing stunt, it is important to discuss the\neffects of humanoid and android robots.\nIn this essay, I want to take a closer look at the status quo of humanoid AI and the\nimplications this technology can have as an assistant, friend or even love interest to humans. I argue\nthat artificial intelligence will – once it becomes a realistic companion to humans – interrupt\nsocietal structures to some extent, leading to a growing amount of human-machine relationships.\n\n.\nTo pursue “real” AI, specialists in developmental robotics are now following a less abstract\npath than writing a programme for a computer11. Their theory is that a system that has an actual\nbody will be more likely to build a form of general intelligence because it can experience its\nsurroundings and match sensorial data with actions12. This branch of robotics is based on another\nhypothesis of Turing’s; in 1950, he claimed that an artificially intelligent system could be best\ncreated if it went through a phase that is similar to the childhood of other species 13\n.\nThe iCub robot was developed to investigate this theory. Having the weight and size of an\ninfant, it carries the spirit of Turing’s thought: Instead of pre-programming its skills and feeding it\nwith data, researchers teach it like a child to enable it to conceive its own solutions 14. Here, one\nquestion arises: How does a system develop the will to learn something? After all, it does not even\nhave a will by default. It was found that a strategy working for humans does the same trick for AI\nsystems too: a reward. The field of reinforcement learning derives from this method and has been\nalso applied to the iCub series15. This has enabled the robots to attain skills like picking up an item16\nor crawling on the floor17. These actions might not seem too complex for us at the first glance but\nthey do involve a number of obstacles the robot has to overcome. In the future, iCub could help us\nin the household by setting the table for dinner or preparing food.\nBut there is another interesting thing about iCub: its chubby face, big eyes, and LED-facial\nexpressions leave no doubt that it was made to bear a resemblance to real humans. Yet still, it is\nobvious to anybody that it is not an actual person. These features make iCub a so-called humanoid.\nRobots that are made to look exactly like humans on the other hand are called androids\nThe market is prepared for it: Looking at the increasing popularity of home assistants like\nAlexa or Google Assistant we can expect our reliance on technological devices to grow even\nstronger in the future. They might become more to us than just a personal weatherman or a direct\nconnection to our Amazon shopping basket: artificially intelligent programmes and robots could\neventually write Christmas cards to our friends and family, suggest the perfect birthday present for\nour partner or even take care of our children.\nIn fact, a robot nanny is not as far-fetched as one would expect: Robots like Pepper, iPal or\nKuri are programmed to be companions to children – they can recognize emotions in their faces,\nplay with them and let parents watch their offspring from afar through their built-in cameras 23. They\nmight not yet be an adequate substitute for an adult taking care, but manufacturers are definitely\nworking towards this goal. Regarding the high costs of childcare in many countries, they could soon\nbecome a very popular help in parenting – and real friends to a generation that grows up surrounded\nby technology. In Japanese schools, robots have already proven to be a successful addition. They\nare assisting students to focus better in class, add a welcome variety to subjects like history or show\nexercises in physical education24. The robot Robosem has been teaching English in South Korean\nclassrooms, as teachers in this subject are scarce25\n.\nNot only childcare can profit from the advances in AI and robotics: As a means of therapy,\nintelligent technology can be valuable in retirement homes. An example of this is the robot seal\nParo that has been successfully utilized in dementia therapy and as a companion to elderly people\nsince its introduction in 2001. The robot’s body is covered in fake fur and it is sensitive to touch,\nmoving and making seal-like noises when it is petted. It is used to calm patients, to encourage social\ninteractions and to give people that are reliant on help a chance to switch roles and become\ncaregivers themselves26. Once they become more elaborate, robots could be a way to meet the\nshortage of skilled workers in the field of elderly care especially in aging societies like Japan or\nGermany.\nEthical Implications of Human-Robot Relationships\nIn the light of the technological advances that will be made within the next years, the ethics of\nhuman-robot relationships must be discussed. The next generations will likely grow up surrounded\nby artificially intelligent machines and it is hard to say if and how this will affect their perceptions\nof interaction not only with robots but humans as well.\nA study conducted by ATR Intelligent Robotics and Communications and three Japanese\nuniversities revealed that children sometimes showed abusive behaviour towards robots – especially\nwhen they were in groups without any adults close by. In the study, the robot Robovie was\npatrolling a Japanese mall, asking people politely to step aside when somebody stood in its way; if\nthere was no reaction, the robot would move in the opposite direction. There were several situations\nhowever, where researchers observed that children were deliberately blocking the robot’s way,\nkicking it, throwing items at it and calling it names. As a consequence, the researchers developed an algorithm that let the robot recognize groups of children and avoid them33. This does not seem like a\nperfect solution to the problem, especially if we take the rising amount of robots in children’s rooms\ninto account. \nIt is hard to say to what extent robots will become a surrogate for genuine human affection in\nthe future but revisiting the comparison to smartphones made earlier, I believe that it is alarming\nthat people turn to machines in the search for human connection. In a society that is increasingly\nbuilt on perfectionist standards, I argue that artificially intelligent robots designed to be friends and\nlovers might become a threat for human relationships. If we hold our friends and partners to the\nsame standards that we will be used from robots in the future, we will be heavily disappointed.\n\nThis text discusses the advances leading toward having actual robot companions. Tell me the advances that have been made, the likely advances, and the limitations based on the text. ","domain":"Internet/Technology","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":325} +{"system_instruction":"Create your answer using only information found in the context provided.","user_request":"What are the circumstances in which someone should not take BuSpar?","context_document":"Renal Impairment\nAfter multiple-dose administration of buspirone to renally impaired (Clcr = 10–\n70 mL/min/1.73 m2) patients, steady-state AUC of buspirone increased 4-fold compared\nwith healthy (Clcr ≥80 mL/min/1.73 m2) subjects (see PRECAUTIONS).\nRace Effects\nThe effects of race on the pharmacokinetics of buspirone have not been studied.\nINDICATIONS AND USAGE\nBuSpar is indicated for the management of anxiety disorders or the short-term relief of\nthe symptoms of anxiety. Anxiety or tension associated with the stress of everyday life\nusually does not require treatment with an anxiolytic.\nThe efficacy of BuSpar has been demonstrated in controlled clinical trials of outpatients\nwhose diagnosis roughly corresponds to Generalized Anxiety Disorder (GAD). Many of\nthe patients enrolled in these studies also had coexisting depressive symptoms and\nBuSpar relieved anxiety in the presence of these coexisting depressive symptoms. The\npatients evaluated in these studies had experienced symptoms for periods of 1 month to\nover 1 year prior to the study, with an average symptom duration of 6 months.\nGeneralized Anxiety Disorder (300.02) is described in the American Psychiatric\nAssociation's Diagnostic and Statistical Manual, III1 as follows:\nGeneralized, persistent anxiety (of at least 1 month continual duration), manifested by\nsymptoms from three of the four following categories:\n1. Motor tension: shakiness, jitteriness, jumpiness, trembling, tension, muscle aches,\nfatigability, inability to relax, eyelid twitch, furrowed brow, strained face, fidgeting,\nrestlessness, easy startle.\n2. Autonomic hyperactivity: sweating, heart pounding or racing, cold, clammy hands,\ndry mouth, dizziness, lightheadedness, paresthesias (tingling in hands or feet), upset\nstomach, hot or cold spells, frequent urination, diarrhea, discomfort in the pit of the\nstomach, lump in the throat, flushing, pallor, high resting pulse and respiration rate.\n4\nReference ID: 2867200\n3. Apprehensive expectation: anxiety, worry, fear, rumination, and anticipation of\nmisfortune to self or others.\n4. Vigilance and scanning: hyperattentiveness resulting in distractibility, difficulty in\nconcentrating, insomnia, feeling \"on edge,\" irritability, impatience.\nThe above symptoms would not be due to another mental disorder, such as a depressive\ndisorder or schizophrenia. However, mild depressive symptoms are common in GAD.\nThe effectiveness of BuSpar in long-term use, that is, for more than 3 to 4 weeks, has not\nbeen demonstrated in controlled trials. There is no body of evidence available that\nsystematically addresses the appropriate duration of treatment for GAD. However, in a\nstudy of long-term use, 264 patients were treated with BuSpar for 1 year without ill effect.\nTherefore, the physician who elects to use BuSpar for extended periods should\nperiodically reassess the usefulness of the drug for the individual patient.\nCONTRAINDICATIONS\nBuSpar is contraindicated in patients hypersensitive to buspirone hydrochloride.\nWARNINGS\nThe administration of BuSpar to a patient taking a monoamine oxidase inhibitor\n(MAOI) may pose a hazard. There have been reports of the occurrence of elevated\nblood pressure when BuSpar (buspirone hydrochloride) has been added to a regimen\nincluding an MAOI. Therefore, it is recommended that BuSpar not be used concomitantly\nwith an MAOI.\nBecause BuSpar has no established antipsychotic activity, it should not be employed in\nlieu of appropriate antipsychotic treatment.\nPRECAUTIONS\nGeneral\nInterference with Cognitive and Motor Performance\nStudies indicate that BuSpar is less sedating than other anxiolytics and that it does not\nproduce significant functional impairment. However, its CNS effects in any individual\npatient may not be predictable. Therefore, patients should be cautioned about operating an\n5\nReference ID: 2867200\nautomobile or using complex machinery until they are reasonably certain that buspirone\ntreatment does not affect them adversely.\nWhile formal studies of the interaction of BuSpar (buspirone hydrochloride) with alcohol\nindicate that buspirone does not increase alcohol-induced impairment in motor and\nmental performance, it is prudent to avoid concomitant use of alcohol and buspirone.\nPotential for Withdrawal Reactions in Sedative/Hypnotic/Anxiolytic Drug-\nDependent Patients\nBecause BuSpar does not exhibit cross-tolerance with benzodiazepines and other\ncommon sedative/hypnotic drugs, it will not block the withdrawal syndrome often seen\nwith cessation of therapy with these drugs. Therefore, before starting therapy with\nBuSpar, it is advisable to withdraw patients gradually, especially patients who have been\nusing a CNS-depressant drug chronically, from their prior treatment. Rebound or\nwithdrawal symptoms may occur over varying time periods, depending in part on the type\nof drug, and its effective half-life of elimination.\nThe syndrome of withdrawal from sedative/hypnotic/anxiolytic drugs can appear as any\ncombination of irritability, anxiety, agitation, insomnia, tremor, abdominal cramps,\nmuscle cramps, vomiting, sweating, flu-like symptoms without fever, and occasionally,\neven as seizures.\nPossible Concerns Related to Buspirone's Binding to Dopamine Receptors\nBecause buspirone can bind to central dopamine receptors, a question has been raised\nabout its potential to cause acute and chronic changes in dopamine-mediated neurological\nfunction (eg, dystonia, pseudo-parkinsonism, akathisia, and tardive dyskinesia). Clinical\nexperience in controlled trials has failed to identify any significant neuroleptic-like\nactivity; however, a syndrome of restlessness, appearing shortly after initiation of\ntreatment, has been reported in some small fraction of buspirone-treated patients. The\nsyndrome may be explained in several ways. For example, buspirone may increase central\nnoradrenergic activity; alternatively, the effect may be attributable to dopaminergic\neffects (ie, represent akathisia). See ADVERSE REACTIONS: Postmarketing\nExperience.","full_prompt":"Create your answer using only information found in the context provided. \n\nWhat are the circumstances in which someone should not take BuSpar?\n\nRenal Impairment\nAfter multiple-dose administration of buspirone to renally impaired (Clcr = 10–\n70 mL/min/1.73 m2) patients, steady-state AUC of buspirone increased 4-fold compared\nwith healthy (Clcr ≥80 mL/min/1.73 m2) subjects (see PRECAUTIONS).\nRace Effects\nThe effects of race on the pharmacokinetics of buspirone have not been studied.\nINDICATIONS AND USAGE\nBuSpar is indicated for the management of anxiety disorders or the short-term relief of\nthe symptoms of anxiety. Anxiety or tension associated with the stress of everyday life\nusually does not require treatment with an anxiolytic.\nThe efficacy of BuSpar has been demonstrated in controlled clinical trials of outpatients\nwhose diagnosis roughly corresponds to Generalized Anxiety Disorder (GAD). Many of\nthe patients enrolled in these studies also had coexisting depressive symptoms and\nBuSpar relieved anxiety in the presence of these coexisting depressive symptoms. The\npatients evaluated in these studies had experienced symptoms for periods of 1 month to\nover 1 year prior to the study, with an average symptom duration of 6 months.\nGeneralized Anxiety Disorder (300.02) is described in the American Psychiatric\nAssociation's Diagnostic and Statistical Manual, III1 as follows:\nGeneralized, persistent anxiety (of at least 1 month continual duration), manifested by\nsymptoms from three of the four following categories:\n1. Motor tension: shakiness, jitteriness, jumpiness, trembling, tension, muscle aches,\nfatigability, inability to relax, eyelid twitch, furrowed brow, strained face, fidgeting,\nrestlessness, easy startle.\n2. Autonomic hyperactivity: sweating, heart pounding or racing, cold, clammy hands,\ndry mouth, dizziness, lightheadedness, paresthesias (tingling in hands or feet), upset\nstomach, hot or cold spells, frequent urination, diarrhea, discomfort in the pit of the\nstomach, lump in the throat, flushing, pallor, high resting pulse and respiration rate.\n4\nReference ID: 2867200\n3. Apprehensive expectation: anxiety, worry, fear, rumination, and anticipation of\nmisfortune to self or others.\n4. Vigilance and scanning: hyperattentiveness resulting in distractibility, difficulty in\nconcentrating, insomnia, feeling \"on edge,\" irritability, impatience.\nThe above symptoms would not be due to another mental disorder, such as a depressive\ndisorder or schizophrenia. However, mild depressive symptoms are common in GAD.\nThe effectiveness of BuSpar in long-term use, that is, for more than 3 to 4 weeks, has not\nbeen demonstrated in controlled trials. There is no body of evidence available that\nsystematically addresses the appropriate duration of treatment for GAD. However, in a\nstudy of long-term use, 264 patients were treated with BuSpar for 1 year without ill effect.\nTherefore, the physician who elects to use BuSpar for extended periods should\nperiodically reassess the usefulness of the drug for the individual patient.\nCONTRAINDICATIONS\nBuSpar is contraindicated in patients hypersensitive to buspirone hydrochloride.\nWARNINGS\nThe administration of BuSpar to a patient taking a monoamine oxidase inhibitor\n(MAOI) may pose a hazard. There have been reports of the occurrence of elevated\nblood pressure when BuSpar (buspirone hydrochloride) has been added to a regimen\nincluding an MAOI. Therefore, it is recommended that BuSpar not be used concomitantly\nwith an MAOI.\nBecause BuSpar has no established antipsychotic activity, it should not be employed in\nlieu of appropriate antipsychotic treatment.\nPRECAUTIONS\nGeneral\nInterference with Cognitive and Motor Performance\nStudies indicate that BuSpar is less sedating than other anxiolytics and that it does not\nproduce significant functional impairment. However, its CNS effects in any individual\npatient may not be predictable. Therefore, patients should be cautioned about operating an\n5\nReference ID: 2867200\nautomobile or using complex machinery until they are reasonably certain that buspirone\ntreatment does not affect them adversely.\nWhile formal studies of the interaction of BuSpar (buspirone hydrochloride) with alcohol\nindicate that buspirone does not increase alcohol-induced impairment in motor and\nmental performance, it is prudent to avoid concomitant use of alcohol and buspirone.\nPotential for Withdrawal Reactions in Sedative/Hypnotic/Anxiolytic Drug-\nDependent Patients\nBecause BuSpar does not exhibit cross-tolerance with benzodiazepines and other\ncommon sedative/hypnotic drugs, it will not block the withdrawal syndrome often seen\nwith cessation of therapy with these drugs. Therefore, before starting therapy with\nBuSpar, it is advisable to withdraw patients gradually, especially patients who have been\nusing a CNS-depressant drug chronically, from their prior treatment. Rebound or\nwithdrawal symptoms may occur over varying time periods, depending in part on the type\nof drug, and its effective half-life of elimination.\nThe syndrome of withdrawal from sedative/hypnotic/anxiolytic drugs can appear as any\ncombination of irritability, anxiety, agitation, insomnia, tremor, abdominal cramps,\nmuscle cramps, vomiting, sweating, flu-like symptoms without fever, and occasionally,\neven as seizures.\nPossible Concerns Related to Buspirone's Binding to Dopamine Receptors\nBecause buspirone can bind to central dopamine receptors, a question has been raised\nabout its potential to cause acute and chronic changes in dopamine-mediated neurological\nfunction (eg, dystonia, pseudo-parkinsonism, akathisia, and tardive dyskinesia). Clinical\nexperience in controlled trials has failed to identify any significant neuroleptic-like\nactivity; however, a syndrome of restlessness, appearing shortly after initiation of\ntreatment, has been reported in some small fraction of buspirone-treated patients. The\nsyndrome may be explained in several ways. For example, buspirone may increase central\nnoradrenergic activity; alternatively, the effect may be attributable to dopaminergic\neffects (ie, represent akathisia). See ADVERSE REACTIONS: Postmarketing\nExperience.","domain":"Medical","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":347} +{"system_instruction":"You can only respond to the prompt using the information in the context block and no other sources.","user_request":"List the pros and cons for Nestle in regards to this deal.","context_document":"Nestlé and Starbucks close deal for the perpetual global license of Starbucks Consumer\nPackaged Goods and Foodservice products\nVevey and Seattle, 28 August 2018 – Nestlé and Starbucks Corporation today announced the closing of the deal granting Nestlé the perpetual rights to market Starbucks Consumer Packaged Goods and Foodservice products globally, outside of the company’s coffee shops.\nThrough the alliance, the two companies will work closely together on the existing Starbucks range of roast and ground coffee, whole beans as well as instant and portioned coffee. The alliance will also capitalize on the experience and capabilities of both companies to work on innovation with the goal of enhancing its product offerings for coffee lovers globally.\n“This partnership demonstrates our growth agenda in action, giving Nestlé an unparalleled position in the coffee business with a full suite of innovative brands. With Starbucks, Nescafé and Nespresso we bring together the world’s most iconic coffee brands,” said Mark Schneider, Nestlé CEO. “The outstanding collaboration between the two teams resulted in a swift completion of this agreement, which will pave the way to capture further growth opportunities,” he added.\nThe agreement significantly strengthens Nestlé’s coffee portfolio in the North American premium roast and ground and portioned coffee business. It also unlocks global expansion in grocery and food service for the Starbucks brand, utilizing the global reach of Nestlé.\n“This global coffee alliance with Nestlé is a significant strategic milestone for the growth of Starbucks,” said Kevin Johnson, president and ceo of Starbucks. “Bringing together the world’s leading coffee retailer, the world’s largest food and beverage company, and the world’s largest and fast-growing installed base of at-home and single-serve coffee machines helps us amplify the Starbucks brand around the world while delivering long-term value creation for our shareholders.”\nApproximately 500 Starbucks employees in the United States and Europe will join the Nestlé family, with the majority based in Seattle and London. The international expansion of the business will be led from Nestlé’s global headquarters in Vevey, Switzerland.\nThe agreement covers Starbucks packaged coffee and tea brands, such as Starbucks®, Seattle’s Best Coffee®, TeavanaTM/MC, Starbucks VIA® Instant, Torrefazione Italia® coffee and Starbucks-branded\n\n","full_prompt":"You can only respond to the prompt using the information in the context block and no other sources.\n\nNestlé and Starbucks close deal for the perpetual global license of Starbucks Consumer\nPackaged Goods and Foodservice products\nVevey and Seattle, 28 August 2018 – Nestlé and Starbucks Corporation today announced the closing of the deal granting Nestlé the perpetual rights to market Starbucks Consumer Packaged Goods and Foodservice products globally, outside of the company’s coffee shops.\nThrough the alliance, the two companies will work closely together on the existing Starbucks range of roast and ground coffee, whole beans as well as instant and portioned coffee. The alliance will also capitalize on the experience and capabilities of both companies to work on innovation with the goal of enhancing its product offerings for coffee lovers globally.\n“This partnership demonstrates our growth agenda in action, giving Nestlé an unparalleled position in the coffee business with a full suite of innovative brands. With Starbucks, Nescafé and Nespresso we bring together the world’s most iconic coffee brands,” said Mark Schneider, Nestlé CEO. “The outstanding collaboration between the two teams resulted in a swift completion of this agreement, which will pave the way to capture further growth opportunities,” he added.\nThe agreement significantly strengthens Nestlé’s coffee portfolio in the North American premium roast and ground and portioned coffee business. It also unlocks global expansion in grocery and food service for the Starbucks brand, utilizing the global reach of Nestlé.\n“This global coffee alliance with Nestlé is a significant strategic milestone for the growth of Starbucks,” said Kevin Johnson, president and ceo of Starbucks. “Bringing together the world’s leading coffee retailer, the world’s largest food and beverage company, and the world’s largest and fast-growing installed base of at-home and single-serve coffee machines helps us amplify the Starbucks brand around the world while delivering long-term value creation for our shareholders.”\nApproximately 500 Starbucks employees in the United States and Europe will join the Nestlé family, with the majority based in Seattle and London. The international expansion of the business will be led from Nestlé’s global headquarters in Vevey, Switzerland.\nThe agreement covers Starbucks packaged coffee and tea brands, such as Starbucks®, Seattle’s Best Coffee®, TeavanaTM/MC, Starbucks VIA® Instant, Torrefazione Italia® coffee and Starbucks-branded\n\nList the pros and cons for Nestle in regards to this deal.","domain":"Retail/Product","type":"Pros & Cons","high_level_type":"Q&A","__index_level_0__":406} +{"system_instruction":"Do not use external resources for your answer. Only use the provided context block.","user_request":"What does the book include to help answer important questions about Bitcoin?","context_document":"There’s a lot of excitement about Bitcoin and cryptocurrencies. Optimists claim that Bitcoin will fundamentally alter payments, economics, and even politics around the world. Pessimists claim Bitcoin is inherently broken and will suffer an inevitable and spectacular collapse.\nUnderlying these differing views is significant confusion about what Bitcoin is and how it works. We wrote this book to help cut through the hype and get to the core of what makes Bitcoin unique.\nTo really understand what is special about Bitcoin, we need to understand how it works at a technical level. Bitcoin truly is a new technology and we can only get so far by explaining it through simple analogies to past technologies.\nWe’ll assume that you have a basic understanding of computer science — how computers work, data structures and algorithms, and some programming experience. If you’re an undergraduate or graduate student of computer science, a software developer, an entrepreneur, or a technology hobbyist, this textbook is for you.\nIn this book we’ll address the important questions about Bitcoin. How does Bitcoin work? What makes it different? How secure are your bitcoins? How anonymous are Bitcoin users? What applications can we build using Bitcoin as a platform? Can cryptocurrencies be regulated? If we were designing a new cryptocurrency today, what would we change? What might the future hold?\nEach chapter has a series of homework questions to help you understand these questions at a deeper level. In addition, there is a series of programming assignments in which you’ll implement various components of Bitcoin in simplified models. If you’re an auditory learner, most of the material of this book is available as a series of video lectures. You can find all these on our ​Coursera course.​ You should also supplement your learning with information you can find online including the Bitcoin wiki, forums, and research papers, and by interacting with your peers and the Bitcoin community.\nAfter reading this book, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin into your own projects.","full_prompt":"Do not use external resources for your answer. Only use the provided context block. \nWhat does the book include to help answer important questions about Bitcoin?\n\n[There’s a lot of excitement about Bitcoin and cryptocurrencies. Optimists claim that Bitcoin will fundamentally alter payments, economics, and even politics around the world. Pessimists claim Bitcoin is inherently broken and will suffer an inevitable and spectacular collapse.\nUnderlying these differing views is significant confusion about what Bitcoin is and how it works. We wrote this book to help cut through the hype and get to the core of what makes Bitcoin unique.\nTo really understand what is special about Bitcoin, we need to understand how it works at a technical level. Bitcoin truly is a new technology and we can only get so far by explaining it through simple analogies to past technologies.\nWe’ll assume that you have a basic understanding of computer science — how computers work, data structures and algorithms, and some programming experience. If you’re an undergraduate or graduate student of computer science, a software developer, an entrepreneur, or a technology hobbyist, this textbook is for you.\nIn this book we’ll address the important questions about Bitcoin. How does Bitcoin work? What makes it different? How secure are your bitcoins? How anonymous are Bitcoin users? What applications can we build using Bitcoin as a platform? Can cryptocurrencies be regulated? If we were designing a new cryptocurrency today, what would we change? What might the future hold?\nEach chapter has a series of homework questions to help you understand these questions at a deeper level. In addition, there is a series of programming assignments in which you’ll implement various components of Bitcoin in simplified models. If you’re an auditory learner, most of the material of this book is available as a series of video lectures. You can find all these on our ​Coursera course.​ You should also supplement your learning with information you can find online including the Bitcoin wiki, forums, and research papers, and by interacting with your peers and the Bitcoin community.\nAfter reading this book, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin into your own projects.]","domain":"Financial","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":419} +{"system_instruction":"You must only draw information for your response from the text provided. Do not use any external sources. Your answer is always less than 200 words. When mentioning Newcastle United you refer to the club as NUFC and always in bold. When mentioning Sports Direct you will refer to the company as SD and always in italics.","user_request":"How many clubs do the allegations affect?","context_document":"In summary, the Claimant alleges that:\n\n1. The Club has abused its dominant position in the market for the wholesale supply of Newcastle United replica kit in the UK, in breach of the prohibition in Chapter II of the Act, by refusing to supply Sports Direct with the Club’s replica kit for the 2024/25 season and granting JD Sports, another UK sports\nretailer, exclusive rights as a third-party retailer of the Club’s replica kit (alongside only the Club’s and Adidas’s own channels), thereby foreclosing Sports Direct from the downstream retail market and eliminating effective competition on that market; and\n\n2. If and to the extent that the Club contends that the refusal to supply is the necessary result of exclusivity arrangements it has agreed with JD Sports and/or Adidas, any such agreement is itself in breach of the prohibition in Chapter I of the Act and therefore void, and insofar as the Club implements any such agreement, it is breaching the Chapter I prohibition.\n\nThe Claimant seeks an injunction restraining the Defendants from engaging in, and/or implementing the above breaches, damages and other relief.\nAccording to the Claim, replica kit are authentic reproductions of the short- and long-sleeved shirt, shorts, training wear, and socks (home, away, third, goalkeeper and special edition) in adult, junior and infant sizes to which a football club’s trademark is applied and which are worn by the club’s players when competing in professional football matches.","full_prompt":"System Instruction: You must only draw information for your response from the text provided. Do not use any external sources. Your answer is always less than 200 words. When mentioning Newcastle United you refer to the club as NUFC and always in bold. When mentioning Sports Direct you will refer to the company as SD and always in italics.\n\nQuestion: How many clubs do the allegations affect?\n\nContext: In summary, the Claimant alleges that:\n\n1. The Club has abused its dominant position in the market for the wholesale supply of Newcastle United replica kit in the UK, in breach of the prohibition in Chapter II of the Act, by refusing to supply Sports Direct with the Club’s replica kit for the 2024/25 season and granting JD Sports, another UK sports\nretailer, exclusive rights as a third-party retailer of the Club’s replica kit (alongside only the Club’s and Adidas’s own channels), thereby foreclosing Sports Direct from the downstream retail market and eliminating effective competition on that market; and\n\n2. If and to the extent that the Club contends that the refusal to supply is the necessary result of exclusivity arrangements it has agreed with JD Sports and/or Adidas, any such agreement is itself in breach of the prohibition in Chapter I of the Act and therefore void, and insofar as the Club implements any such agreement, it is breaching the Chapter I prohibition.\n\nThe Claimant seeks an injunction restraining the Defendants from engaging in, and/or implementing the above breaches, damages and other relief.\nAccording to the Claim, replica kit are authentic reproductions of the short- and long-sleeved shirt, shorts, training wear, and socks (home, away, third, goalkeeper and special edition) in adult, junior and infant sizes to which a football club’s trademark is applied and which are worn by the club’s players when competing in professional football matches.","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":443} +{"system_instruction":"You may only respond to the prompt using information provided in the context block.","user_request":"Can I reuse the OEM hardware for this?","context_document":"Before beginning the installation, thoroughly & completely read these instructions. Please refer to\nthe Parts List to insure that all parts & hardware are received prior to the disassembly of the vehicle.\nIf any parts are found to be missing, contact SKYJACKER® Customer Service at 318-388-0816 to\nobtain the needed items. If you have any questions or reservations about installing this product,\ncontact SKYJACKER® Technical Assistance at 318-388-0816. \nInstallation:\n1. Park the vehicle on a flat, level surface & block the front & rear tires.\n2. Place the transmission in neutral.\n3. Loosen all of the engine mount bolts about ½ turn.\n4. Support the transfer case cross member with a transmission or floor\njack. Remove the bolts & nuts for each side of the cross member.\n5. Slowly lower the cross member, approximately 2\", to allow enough room to install the new\nSkyjacker tubular spacers.\n1994-2001 Jeep Cherokee XJ\nInstall the new Skyjacker transfer case linkage pivot\n drop bracket to the stock pivot bracket using the OEM\n hardware. Using the two 1/4\" x 1\" bolts with a flat\n washer & self locking nut, bolt the ball swivel bracket\n (See Arrow in Photo # 3) to the new Skyjacker drop\n bracket. Note: The bracket has two sets of holes. The\n bottom holes are for a 4\" lift as shown & the upper\n holes are for a 2 1/2\" lift.\n 2. Placing the pivot bracket back in location, start the end\n of the rod through the ball swivel & bolt the bracket in\n location with the OEM hardware. (See Photo # 4)\n 3. Check to make sure that the transfer case will fully engage at\n each end of the shifter travel. If linkage adjustment is required,\n 4. Check the transfer case shifter to see if it will move to 4L. If\n not, the linkage will need adjusting as follows. Place the shifter\n in 4L, loosen the adjustment bolt &\n push the linkage (\"B\" Arrow in Photo # 5) forward until it stops.\n Now retighten adjustment bolt. Check to be sure the 4WD\n works properly.\n 5. On 5 speed models, engage the clutch & check the\n transmission shifter to see if it will go into 2nd gear. If not, the\n shifter housing on the floor will need trimming. Remove the\n center console, pull back the carpet, remove the screws\n holding the shifter boot to the floor, & trim or grind the floor\n board until sufficient clearance is obtained.\n Shift through each gear to check clearance at this\n time. Now reinstall the shifter boot, carpet, & console.\n","full_prompt":"You may only respond to the prompt using information provided in the context block.\n\nCan I reuse the OEM hardware for this?\n\nBefore beginning the installation, thoroughly & completely read these instructions. Please refer to\nthe Parts List to insure that all parts & hardware are received prior to the disassembly of the vehicle.\nIf any parts are found to be missing, contact SKYJACKER® Customer Service at 318-388-0816 to\nobtain the needed items. If you have any questions or reservations about installing this product,\ncontact SKYJACKER® Technical Assistance at 318-388-0816. \nInstallation:\n1. Park the vehicle on a flat, level surface & block the front & rear tires.\n2. Place the transmission in neutral.\n3. Loosen all of the engine mount bolts about ½ turn.\n4. Support the transfer case cross member with a transmission or floor\njack. Remove the bolts & nuts for each side of the cross member.\n5. Slowly lower the cross member, approximately 2\", to allow enough room to install the new\n6. Install the new Skyjacker tubular spacers between the cross member\n & frame. Slowly raise the jack to firmly hold the tubular spacers in\n place.\n 7. Install the OEM nuts, removed in Step # 4, onto the studs that are\n protruding out of the frame on each side to hold the top half of the\n new spacers in place. Note: There is only one stud on each side\n protruding out of the frame. Next, install the 3/8\" x 1\" bolt on each\n side through the cross member & the bottom half of the new tubular\n spacers. Install the 3/8 nut, washer, & hand tighten.\n 8. Install the new 10mm x 60mm bolt up through the cross member & tubular spacer & tighten to\n 33 ft. lbs. (See Photo # 2)\n 9. Tighten the 3/8\" nut down onto the 3/8\" x 1\" bolt from Step # 7 to 33 ft-lbs. Remove the\n transmission jack & set aside.\n10. Re-torque the engine mount bolts loosened in Step # 3. The engine mount to block bolts torque\n to 45 ft-lbs. The engine mount to frame bolts torque to 30 ft-lbs. The thru bolts torque to 48 ft-lbs.\n11. Install the transfer case linkage bracket. (See Steps # 1 thru # 5 Below)\nSkyjacker tubular spacers.\n1994-2001 Jeep Cherokee XJ\nInstall the new Skyjacker transfer case linkage pivot\n drop bracket to the stock pivot bracket using the OEM\n hardware. Using the two 1/4\" x 1\" bolts with a flat\n washer & self locking nut, bolt the ball swivel bracket\n (See Arrow in Photo # 3) to the new Skyjacker drop\n bracket. Note: The bracket has two sets of holes. The\n bottom holes are for a 4\" lift as shown & the upper\n holes are for a 2 1/2\" lift.\n 2. Placing the pivot bracket back in location, start the end\n of the rod through the ball swivel & bolt the bracket in\n location with the OEM hardware. (See Photo # 4)\n 3. Check to make sure that the transfer case will fully engage at\n each end of the shifter travel. If linkage adjustment is required,\n 4. Check the transfer case shifter to see if it will move to 4L. If\n not, the linkage will need adjusting as follows. Place the shifter\n in 4L, loosen the adjustment bolt &\n push the linkage (\"B\" Arrow in Photo # 5) forward until it stops.\n Now retighten adjustment bolt. Check to be sure the 4WD\n works properly.\n 5. On 5 speed models, engage the clutch & check the\n transmission shifter to see if it will go into 2nd gear. If not, the\n shifter housing on the floor will need trimming. Remove the\n center console, pull back the carpet, remove the screws\n holding the shifter boot to the floor, & trim or grind the floor\n board until sufficient clearance is obtained.\n Shift through each gear to check clearance at this\n time. Now reinstall the shifter boot, carpet, & console.","domain":"Internet/Technology","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":448} +{"system_instruction":"Draw your answer only from the context block below and not from external sources.","user_request":"What does Apple not receive from me when I use Siri?","context_document":"The Siri and Dictation features of the iOS Software may not be available in all languages or regions and features may vary by region. If your iOS Device supports Siri and Dictation, these features may allow you to make requests, give commands and dictate text to your device using your voice. When you use Siri or Dictation, the things you say will be recorded and sent to Apple in order to convert what you say into text and to process your requests. Your device will also send Apple other information, such as your name and nickname; the names, nicknames, and relationship with you (e.g., “my dad”) of your address book contacts; and song names in your collection (collectively, your “User Data”). All of this data is used to help Siri and Dictation understand you better and recognize what you say. It is not linked to other data that Apple may have from your use of other Apple services. By using Siri or Dictation, you agree and consent to Apple’s and its subsidiaries’ and agents’ transmission, collection, maintenance, processing, and use of this information, including your voice input and User Data, to provide and improve Siri, Dictation, and dictation functionality in other Apple products and services.\nIf you have Location Services turned on, the location of your iOS Device at the time you make a request to Siri may also be sent to Apple to help Siri improve the accuracy of its response to your location-based requests. You may disable the location-based functionality of Siri by going to the Location Services setting on your iOS Device and turning off the individual location setting for Siri.\nSiri can allow you to interact with your iOS Device without needing to unlock it. If you have enabled a passcode on your iOS Device and would like to prevent Siri from being used from the lock screen, you can tap Settings, tap General, tap Passcode Lock and turn the Siri option to “off”.\nYou can also turn off Siri and Dictation altogether at any time. To do so, open Settings, tap General, tap Siri, and slide the Siri switch to “off”.\n","full_prompt":"Draw your answer only from the context block below and not from external sources. What does Apple not receive from me when I use Siri?\n\n[The Siri and Dictation features of the iOS Software may not be available in all languages or regions and features may vary by region. If your iOS Device supports Siri and Dictation, these features may allow you to make requests, give commands and dictate text to your device using your voice. When you use Siri or Dictation, the things you say will be recorded and sent to Apple in order to convert what you say into text and to process your requests. Your device will also send Apple other information, such as your name and nickname; the names, nicknames, and relationship with you (e.g., “my dad”) of your address book contacts; and song names in your collection (collectively, your “User Data”). All of this data is used to help Siri and Dictation understand you better and recognize what you say. It is not linked to other data that Apple may have from your use of other Apple services. By using Siri or Dictation, you agree and consent to Apple’s and its subsidiaries’ and agents’ transmission, collection, maintenance, processing, and use of this information, including your voice input and User Data, to provide and improve Siri, Dictation, and dictation functionality in other Apple products and services.\nIf you have Location Services turned on, the location of your iOS Device at the time you make a request to Siri may also be sent to Apple to help Siri improve the accuracy of its response to your location-based requests. You may disable the location-based functionality of Siri by going to the Location Services setting on your iOS Device and turning off the individual location setting for Siri.\nSiri can allow you to interact with your iOS Device without needing to unlock it. If you have enabled a passcode on your iOS Device and would like to prevent Siri from being used from the lock screen, you can tap Settings, tap General, tap Passcode Lock and turn the Siri option to “off”.\nYou can also turn off Siri and Dictation altogether at any time. To do so, open Settings, tap General, tap Siri, and slide the Siri switch to “off”.]","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":452} +{"system_instruction":"Answer the question based solely on the information provided in the passage. Do not use any external knowledge or resources.\n \n\n [user request]\n \n\n [context document]","user_request":"How are smart devices able to spy on people's browsing history, financial transactions, and even health issues? Some apps can bypass security by just tapping into the wifi. how does that work? What do you think about the fact that once a device is connected it can control all of the other devices without consent?","context_document":"cepro.com\n New Research Uncovers Litany of Privacy/Security Issues in Consumer IoT Devices\n Zachary Comeau\n 5–6 minutes\n \n\n An international team of researchers has unveiled findings on the widespread security and privacy challenges posed by IoT devices in smart homes, delving into the intricacies of local network interactions between 93 different IoT devices and mobile apps.\n \n\n The paper, titled In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes, reveals a litany of previously undisclosed security and privacy threats.\n \n\n The research team included researchers from the New York Tandon School of Engineering, Northeastern University, University of Madrid, University of Calgary, the International Computer Science Institute and IMDEA Networks. The research was presented last month at the ACM Internet Measurement Conference last month in Montreal.\n \n\n Researchers narrow in on the local network and how IoT devices can inadvertently compromise consumer privacy through the exposure of sensitive data within those local networks using standard protocols such as UPnP or mDNS. Researchers say this essentially allows nearly any company to learn what devices are in a home, when the user is home, and where the home is.\n \n\n According to the paper, these threats include the exposure of unique device names, UUIDs, and even household geolocation data, all of which can be harvested by companies involved in surveillance capitalism without user awareness. \n \n\n NYU Tandon, quoting PhD student and research co-author Vijay Prakash, says in a writeup that researchers found evidence of IoT devices inadvertently compromising consumer privacy by exposing at least one personally identifiable information, such as unique hardware addresses, UUID, or unique device names, in thousands of existing smart homes.\n \n\n That information can be pieced together to make a house very identifiable, researchers say.\n \n\n The devices included in the research include 93 consumer IP-based smart home devices, as well as their companion apps. Devices included in the study were smart doorbells, smart bulbs, smart thermostats, smart TVs, smart plugs, smart speakers, smart sensors and smart home hubs.\n \n\n Specifically, most of the devices tested are widely available online or in stores, including Amazon Echo devices, Google Nest products, Apple TVs, and more.\n \n\n These local network protocols can be employed as side-channels to access data that is supposedly protected by several mobile app permissions such as household locations, researchers say.\n \n\n Narseo Vallina-Rodriguez, Associate Research Professor of IMDEA Networks and co-founder of AppCensus, says in a statement that side channels are a sneaky way of indirectly accessing sensitive data.\n \n\n “For example, Android app developers are supposed to request and obtain users’ consent to access data like geolocation,” Vallina-Rodriguez says. “However, we have shown that certain spyware apps and advertising companies do abuse local network protocols to silently access such sensitive information without any user awareness. All they have to do is kindly ask for it to other IoT devices deployed in the local network using standard protocols like UPnP.”\n \n\n In addition, Juan Tapiador, professor at Universidad Carlos III de Madrid, says the study shows that local network protocols used by IoT devices are not sufficiently protected and expose sensitive information about the home and the homeowners’ use of the devices.\n \n\n “This information is being collected in an opaque way and makes it easier to create profiles of our habits or socioeconomic level,” Tapiador says.\n \n\n In other comments, Dr. Joel Reardon, PhD, associate professor of computer science at the University of Calgary, says the research shows the home network is not as secure as once thought.\n \n\n “If a new phone connects to a network, then all the apps on it can have direct access to everything else on that network,” Reardon says. “The spyware I found in apps with tens of millions of installs was in fact scanning networks and talking to routers.”\n \n\n The research follows multiple separate cybersecurity threats-related to IoT devices uncovered this month. Towards the middle of the month, the Electronic Frontier Foundation nonprofit put out a call to action for the FTC to block the sales of Android TV boxes potentially infected with botnet malware. Researchers around this time also published a report in FCC filings for the Cyber Trust Mark proceedings warning of ultrasonic commands that could potentially be used to activate and control voice assistants.\n \n\n If you enjoyed this article and want to receive more valuable industry content like this, click here to sign up for our digital newsletters!","full_prompt":"Answer the question based solely on the information provided in the passage. Do not use any external knowledge or resources.\n \n\n How are smart devices able to spy on people's browsing history, financial transactions, and even health issues? Some apps can bypass security by just tapping into the wifi. how does that work? What do you think about the fact that once a device is connected it can control all of the other devices without consent?\n \n\n cepro.com\n New Research Uncovers Litany of Privacy/Security Issues in Consumer IoT Devices\n Zachary Comeau\n 5–6 minutes\n \n\n An international team of researchers has unveiled findings on the widespread security and privacy challenges posed by IoT devices in smart homes, delving into the intricacies of local network interactions between 93 different IoT devices and mobile apps.\n \n\n The paper, titled In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes, reveals a litany of previously undisclosed security and privacy threats.\n \n\n The research team included researchers from the New York Tandon School of Engineering, Northeastern University, University of Madrid, University of Calgary, the International Computer Science Institute and IMDEA Networks. The research was presented last month at the ACM Internet Measurement Conference last month in Montreal.\n \n\n Researchers narrow in on the local network and how IoT devices can inadvertently compromise consumer privacy through the exposure of sensitive data within those local networks using standard protocols such as UPnP or mDNS. Researchers say this essentially allows nearly any company to learn what devices are in a home, when the user is home, and where the home is.\n \n\n According to the paper, these threats include the exposure of unique device names, UUIDs, and even household geolocation data, all of which can be harvested by companies involved in surveillance capitalism without user awareness. \n \n\n NYU Tandon, quoting PhD student and research co-author Vijay Prakash, says in a writeup that researchers found evidence of IoT devices inadvertently compromising consumer privacy by exposing at least one personally identifiable information, such as unique hardware addresses, UUID, or unique device names, in thousands of existing smart homes.\n \n\n That information can be pieced together to make a house very identifiable, researchers say.\n \n\n The devices included in the research include 93 consumer IP-based smart home devices, as well as their companion apps. Devices included in the study were smart doorbells, smart bulbs, smart thermostats, smart TVs, smart plugs, smart speakers, smart sensors and smart home hubs.\n \n\n Specifically, most of the devices tested are widely available online or in stores, including Amazon Echo devices, Google Nest products, Apple TVs, and more.\n \n\n These local network protocols can be employed as side-channels to access data that is supposedly protected by several mobile app permissions such as household locations, researchers say.\n \n\n Narseo Vallina-Rodriguez, Associate Research Professor of IMDEA Networks and co-founder of AppCensus, says in a statement that side channels are a sneaky way of indirectly accessing sensitive data.\n \n\n “For example, Android app developers are supposed to request and obtain users’ consent to access data like geolocation,” Vallina-Rodriguez says. “However, we have shown that certain spyware apps and advertising companies do abuse local network protocols to silently access such sensitive information without any user awareness. All they have to do is kindly ask for it to other IoT devices deployed in the local network using standard protocols like UPnP.”\n \n\n In addition, Juan Tapiador, professor at Universidad Carlos III de Madrid, says the study shows that local network protocols used by IoT devices are not sufficiently protected and expose sensitive information about the home and the homeowners’ use of the devices.\n \n\n “This information is being collected in an opaque way and makes it easier to create profiles of our habits or socioeconomic level,” Tapiador says.\n \n\n In other comments, Dr. Joel Reardon, PhD, associate professor of computer science at the University of Calgary, says the research shows the home network is not as secure as once thought.\n \n\n “If a new phone connects to a network, then all the apps on it can have direct access to everything else on that network,” Reardon says. “The spyware I found in apps with tens of millions of installs was in fact scanning networks and talking to routers.”\n \n\n The research follows multiple separate cybersecurity threats-related to IoT devices uncovered this month. Towards the middle of the month, the Electronic Frontier Foundation nonprofit put out a call to action for the FTC to block the sales of Android TV boxes potentially infected with botnet malware. Researchers around this time also published a report in FCC filings for the Cyber Trust Mark proceedings warning of ultrasonic commands that could potentially be used to activate and control voice assistants.\n \n\n If you enjoyed this article and want to receive more valuable industry content like this, click here to sign up for our digital newsletters!\n https://www.cepro.com/networking/new-research-uncovers-litany-of-privacy-security-issues-in-consumer-iot-devices/","domain":"Internet/Technology","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":483} +{"system_instruction":"Respond only using information contained within the prompt. Do not use any external information or knowledge when answering. Answer as a non-expert only. Give your answer simply with easy to understand language.","user_request":"What are the potential harmful side effects of semaglutide?","context_document":"According to the EPAR for semaglutide, eight completed phase 3 trials and a cardiovascular\noutcomes trial provided safety data relating to approximately 4,800 patients and over 5,600\npatient years of exposure. [12] Additional safety data is also available from the SUSTAIN 7 which\nassessed semaglutide and dulaglutide. [9]\nAdverse events\nThe EPAR states that “The safety profile of semaglutide is generally consistent with those\nreported for other drugs in the GLP-1 RA class”. The EMA noted that the rates of gastrointestinal\nadverse events were higher for semaglutide compared to exenatide, sitagliptin and insulin\nglargine. [12] However the open label SUSTAIN 7 study found that the frequency of\ngastrointestinal adverse effects were similar between semaglutide and dulaglutide groups. [9]\nA significantly increased risk of diabetic retinopathy complications was observed with semaglutide\nas compared with placebo. This increased risk was particularly marked in patients with preexisting diabetic retinopathy at baseline and co-use of insulin. Although it is recognised that\nintensified glycaemic control may precipitate early worsening of diabetic retinopathy, clinical trials\ndata did not demonstrate a decrease in the risk of diabetic retinopathy over the course of two\nyears, and data also suggests that semaglutide was associated with retinopathy in patients with\nonly small HbA1c reductions. [12] A specific warning has been included in the SPC for\nsemaglutide outlining the increased risk of diabetic retinopathy complications in patients with\nexisting diabetic retinopathy treated with insulin. [15]\nThe SPC for semaglutide lists the following adverse events [13]:\n\nTable 2. Adverse reactions from long-term controlled phase 3a trials including the cardiovascular \n7\nDate: December 2018\noutcomes trial.\nMedDRA\nsystem organ\nclass\nVery common Common Uncommon Rare\nImmune system\ndisorders\nAnaphylactic\nreaction\nMetabolism and\nnutrition\ndisorders\nHypoglycaemia\nwhen used with\ninsulin or\nsulfonylurea\nHypoglycaemia\nwhen used with\nother OADs\nDecreased appetite\nNervous system\ndisorders\nDizziness Dysgeusia\nEye disorders Diabetic\nretinopathy\ncomplications\nCardiac\ndisorders\nIncreased heart\nrate\nGastrointestinal\ndisorders\nNausea\nDiarrhoea\nVomiting\nAbdominal pain\nAbdominal\ndistension\nConstipation\nDyspepsia\nGastritis\nGastrooesophageal\nreflux disease\nEructation\nFlatulence\nHepatobiliary\ndisorders\nCholelithiasis\nGeneral\ndisorders and\nadministration\nsite conditions\nFatigue Injection site\nreactions\nInvestigations Increased lipase\nIncreased amylase\nWeight decreased","full_prompt":"What are the potential harmful side effects of semaglutide?\n\nRespond only using information contained within the prompt. Do not use any external information or knowledge when answering. Answer as a non-expert only. Give your answer simply with easy to understand language.\n\n\nThe text:\n\nAccording to the EPAR for semaglutide, eight completed phase 3 trials and a cardiovascular\noutcomes trial provided safety data relating to approximately 4,800 patients and over 5,600\npatient years of exposure. [12] Additional safety data is also available from the SUSTAIN 7 which\nassessed semaglutide and dulaglutide. [9]\nAdverse events\nThe EPAR states that “The safety profile of semaglutide is generally consistent with those\nreported for other drugs in the GLP-1 RA class”. The EMA noted that the rates of gastrointestinal\nadverse events were higher for semaglutide compared to exenatide, sitagliptin and insulin\nglargine. [12] However the open label SUSTAIN 7 study found that the frequency of\ngastrointestinal adverse effects were similar between semaglutide and dulaglutide groups. [9]\nA significantly increased risk of diabetic retinopathy complications was observed with semaglutide\nas compared with placebo. This increased risk was particularly marked in patients with preexisting diabetic retinopathy at baseline and co-use of insulin. Although it is recognised that\nintensified glycaemic control may precipitate early worsening of diabetic retinopathy, clinical trials\ndata did not demonstrate a decrease in the risk of diabetic retinopathy over the course of two\nyears, and data also suggests that semaglutide was associated with retinopathy in patients with\nonly small HbA1c reductions. [12] A specific warning has been included in the SPC for\nsemaglutide outlining the increased risk of diabetic retinopathy complications in patients with\nexisting diabetic retinopathy treated with insulin. [15]\nThe SPC for semaglutide lists the following adverse events [13]:\n\nTable 2. Adverse reactions from long-term controlled phase 3a trials including the cardiovascular \n7\nDate: December 2018\noutcomes trial.\nMedDRA\nsystem organ\nclass\nVery common Common Uncommon Rare\nImmune system\ndisorders\nAnaphylactic\nreaction\nMetabolism and\nnutrition\ndisorders\nHypoglycaemia\nwhen used with\ninsulin or\nsulfonylurea\nHypoglycaemia\nwhen used with\nother OADs\nDecreased appetite\nNervous system\ndisorders\nDizziness Dysgeusia\nEye disorders Diabetic\nretinopathy\ncomplications\nCardiac\ndisorders\nIncreased heart\nrate\nGastrointestinal\ndisorders\nNausea\nDiarrhoea\nVomiting\nAbdominal pain\nAbdominal\ndistension\nConstipation\nDyspepsia\nGastritis\nGastrooesophageal\nreflux disease\nEructation\nFlatulence\nHepatobiliary\ndisorders\nCholelithiasis\nGeneral\ndisorders and\nadministration\nsite conditions\nFatigue Injection site\nreactions\nInvestigations Increased lipase\nIncreased amylase\nWeight decreased","domain":"Medical","type":"Pros & Cons","high_level_type":"Q&A","__index_level_0__":536} +{"system_instruction":"Answer the user query using only the information in the provided text.","user_request":"How did verbal ability impact the results?","context_document":"Background: Individuals on the autism spectrum experience various challenges related to social behaviors and may\noften display increased irritability and hyperactivity. Some studies have suggested that reduced levels of a hormone\ncalled oxytocin, which is known for its role in promoting social bonding, may be responsible for difculties in social\ninteractions in autism. Oxytocin therapy has been used of-label in some individuals on the autism spectrum as a\npotential intervention to improve social behavior, but previous studies have not been able to confrm its efcacy.\nEarlier clinical trials examining oxytocin in autism have shown widely varying results. This large randomized\ncontrolled trial sought to resolve the previous contradictory fndings and determine whether extended use of\noxytocin can help to improve social behaviors in children and teenagers on the autism spectrum.\nMethods & Findings: Tis study evaluated whether a nasal oxytocin spray could afect social interactions and\nother behaviors (e.g., irritability, social withdrawal, and hyperactivity) in children and adolescents on the autism\nspectrum during a 24-week clinical trial. Individuals between the ages of 3 and 17 were assessed by trained\nresearchers and were selected for participation if they met the criteria for autism. Participants were then randomly\nassigned to receive either a nasal oxytocin spray or a placebo (i.e., a comparison nasal spray that did not contain\noxytocin) every day at a series of gradually increasing doses. Participants received social interaction scores every\n4 weeks based on multiple assessments that were completed by caregivers or the participant. Separate analyses\nwere performed in groups of individuals with minimal verbal fuency and high verbal fuency. Tis study found\nno diference in social interaction scores between the oxytocin group and the placebo group and no diference\nbetween the groups with difering levels of verbal ability.\nImplications: Te fndings of this study demonstrate that extended use of a nasal oxytocin spray over a 24-week\nperiod does not make a detectable diference in measured social interactions or behaviors in children and adolescents\nwith autism. While this study showed no observable social beneft with the use of intranasal oxytocin, there are\nremaining questions around issues such as the ideal dose, whether current formulations are able to penetrate the\nblood-brain barrier, and whether a longer intervention time course could reveal efects. In addition, future studies\nthat use techniques such as brain imaging may reveal new information on how oxytocin might be used in autism. ","full_prompt":"Answer the user query using only the information in the provided text. \n\nBackground: Individuals on the autism spectrum experience various challenges related to social behaviors and may\noften display increased irritability and hyperactivity. Some studies have suggested that reduced levels of a hormone\ncalled oxytocin, which is known for its role in promoting social bonding, may be responsible for difculties in social\ninteractions in autism. Oxytocin therapy has been used of-label in some individuals on the autism spectrum as a\npotential intervention to improve social behavior, but previous studies have not been able to confrm its efcacy.\nEarlier clinical trials examining oxytocin in autism have shown widely varying results. This large randomized\ncontrolled trial sought to resolve the previous contradictory fndings and determine whether extended use of\noxytocin can help to improve social behaviors in children and teenagers on the autism spectrum.\nMethods & Findings: Tis study evaluated whether a nasal oxytocin spray could afect social interactions and\nother behaviors (e.g., irritability, social withdrawal, and hyperactivity) in children and adolescents on the autism\nspectrum during a 24-week clinical trial. Individuals between the ages of 3 and 17 were assessed by trained\nresearchers and were selected for participation if they met the criteria for autism. Participants were then randomly\nassigned to receive either a nasal oxytocin spray or a placebo (i.e., a comparison nasal spray that did not contain\noxytocin) every day at a series of gradually increasing doses. Participants received social interaction scores every\n4 weeks based on multiple assessments that were completed by caregivers or the participant. Separate analyses\nwere performed in groups of individuals with minimal verbal fuency and high verbal fuency. Tis study found\nno diference in social interaction scores between the oxytocin group and the placebo group and no diference\nbetween the groups with difering levels of verbal ability.\nImplications: Te fndings of this study demonstrate that extended use of a nasal oxytocin spray over a 24-week\nperiod does not make a detectable diference in measured social interactions or behaviors in children and adolescents\nwith autism. While this study showed no observable social beneft with the use of intranasal oxytocin, there are\nremaining questions around issues such as the ideal dose, whether current formulations are able to penetrate the\nblood-brain barrier, and whether a longer intervention time course could reveal efects. In addition, future studies\nthat use techniques such as brain imaging may reveal new information on how oxytocin might be used in autism. \n\nWhat is oxytocin therapy?","domain":"Medical","type":"Explanation/Definition","high_level_type":"Q&A","__index_level_0__":540} +{"system_instruction":"Use the info in this document and not any other source.","user_request":"Categorize the terms into \"Device\", \"Procedure\", and \"Other\", and exclude any financial or insurance related terms.","context_document":"N\nNon-covered charges: Costs for dental care your insurer does not cover. In some cases the service is a covered\nservice, but the insurer is not responsible for the entire charge. In these cases, you will be responsible for any\ncharge not covered by your dental plan. You may wish to call your insurer or consult your dental plan or dental\npolicy to determine whether certain services are included in your plan before you receive those services from your\ndentist.\nNon-Covered Services: Dental services not listed as a benefit. If you receive non-covered services, your dental plan\nwill not pay for them. Your provider will bill you. You will be responsible for the full cost. Usually payments count\ntoward deductible. Check with your insurer. Make sure you know what services are covered before you see your\ndentist.\nNonduplication of Benefits: Occurs when you have two insurance plans. It’s how our second insurance carrier\ncalculates its payment. The secondary carrier calculates what it would have paid if it were your primary plan. Then\nit subtracts what the other plan paid. Examples: Your primary carrier paid 80 percent. Your secondary carrier\nnormally covers 80 percent. Your secondary carrier would not make any additional payment. If the primary carrier\npaid 50 percent. The secondary carrier would pay up to 30 percent.\nO\nOcclusion: Any contact between biting or chewing surfaces of upper and lower teeth.\nOcclusal Guard: A removable device worn between the upper and lower teeth to prevent clenching or grinding.\n[NOTE: ODONTOPLASTY WAS REMOVED]\nOpen Enrollment/Open Enrollment Period: Time of year when an eligible person may add, change or terminate a\ndental plan or dental policy for the next contract year.\nOpen Panel: Allows you to receive care from any dentist. It allows any dentist to participate. Any dentist may\naccept or refuse to treat patients enrolled in the plan. Open panel plans often are described as freedom of choice\nplans.\nOrthodontic Retainer: Appliance to stabilize teeth following orthodontic treatment.\nGlossary of Dental Insurance and Dental Care Terms\n12\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nOrthodontics and dentofacial orthopedics: Branch of dentistry. Includes the diagnosis, prevention, interception,\nand correction of malocclusion. Also includes neuromuscular and skeletal abnormalities of the developing or\nmature orofacial structures.\nOrthodontist: Specialist who treats malocclusion and other neuromuscular and skeletal abnormalities of the teeth\nand their surrounding structures.\nOrthotic device: Dental appliance used to support, align, prevent or correct deformities, or to improve the\nfunction of the oral\nOut-of-Network: Care from providers not on your plan. This includes dentists and clinics. Usually, you will pay\nmore out of your own pocket when you receive dental care out-of-network providers.\nOut-of-network benefits: Coverage for services from providers who are not under a contract with your dental\nplan.\nOut-of-pocket cost: The amount plan members must pay for care. Includes the difference between the amount\ncharged by a provider and what a health plan pays for such services.\nOut-of-Pocket Maximum: The most a dental plan requires a member to pay in a year. Deductibles, co-payments\nand co-insurance count toward the out-of-pocket maximum. The only dental benefits that have out-of-pocket\nmaximums are child benefits purchased through public exchanges, or purchased as an individual or through a small\ngroup. The out-of-pocket maximum for one child is $350 and for more than one child is $700 in all states.\nAfter reaching an out-of-pocket maximum, the plan pays 100% of the cost of pediatric dental services. This\nonly applies to covered services. Members are still responsible for services that are not covered by the\nplan. Members also continue to pay their monthly premiums.\nOverbilling: Stating fees as higher than actual charges. Example: when you are charged one fee and an insurance\ncompany is billed a higher fee. This is done to use your co-payment. It also done to increase your fees solely\nbecause you are covered under a dental benefits plan.\nOverdenture: See Denture/Overdenture.\nP\nPalate: The hard and soft tissues forming the roof of the mouth. It separates the oral and nasal cavities.\nPalliative: Treatment that relieves pain but may not remove the cause of the pain.\nPartial Denture: See Denture/Partial Denture.\nGlossary of Dental Insurance and Dental Care Terms\n13\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nParticipating Provider: Dentists and other licensed dental providers on your plan. They have a contract with your\nplan. The contract includes set service fees.\nPayer: Party responsible for paying your claims. It can be a self-insured employer, insurance company or\ngovernmental agency.\nPediatric dentist: A dental specialist. Treats children from birth through adolescence. Provides primary and\ncomprehensive preventive and therapeutic oral health care. Formerly known as a pedodontist.\nPeriodontal: Branch of dentistry that involves the prevention and treatment of gum disease.\nPeriodontal disease: Inflammation process of gums and/or periodontal membrane of the teeth. Results in an\nabnormally deep gingival sulcus. Possibly produces periodontal pockets and loss of supporting alveolar bone.\nPeriodontist: A dental specialist. Treats diseases of the supporting and surrounding tissues of the teeth.\nPeriodontitis: Inflammation and loss of the connective tissue of the supporting or surrounding structure of teeth.\nWith loss of attachment.\n[NOTE: PIN REMOVED]\nPlan Year: See Benefit Year.\nPlaque: A soft sticky substance. Composed largely of bacteria and bacterial derivatives. It forms on teeth daily.\nPoint of Service (POS) Plan: A dental plan that allows you to choose at the time of dental service whether you will\ngo to a provider within your dental plan's network or get dental care from a provider outside the network.\n[NOTE: PORCELAIN/CERAMIC REMOVED]\n[NOTE: POST REMOVED]\nPreauthorization: A process that your dental plan or insurer uses to make a decision that particular dental services\nare covered. Your plan may require preauthorization for certain services, such as crowns, before you receive them.\nPreauthorization requirements are generally waived if you need emergency care. Sometimes called prior\nauthorization.\n[NOTE: PRECERTIFICATION REMOVED]\nPredetermination: A process where a dentist submits a treatment plan to the payer before treatment begins. The\npayer reviews the treatment plan. The payer notifies you and your dentist about one or more of the following:\nyour eligibility, covered services, amounts payable, co-payment and deductibles and plan maximums. See preauthorization.\nGlossary of Dental Insurance and Dental Care Terms\n14\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nPre-existing condition: A dental condition that exists for a set time prior to enrollment in a dental plan, regardless\nof whether the condition has been formally diagnosed. The only pre-existing condition that is common for dental\nplans or policies is a missing tooth.\n[REMOVED PRECIOUS OR HIGH NOBLE METALS – SEE METALS, CLASSIFICATIONS –ACCORDING TO CDT]\nPretreatement Estimate: See predetermination. **\nPreferred Provider Organization (PPO): See DPPO.\nPremedication: The use of medications prior to dental procedures.\nPrepaid dental plan: A method of funding dental care costs in advance of services. For a defined population.\nPremium: The amount you pay to a dental insurance company for dental coverage. The dental insurance company\ngenerally recalculates the premium each policy year. This amount is usually paid in monthly installments. When\nyou receive dental insurance through an employer, the employer may pay a portion of the premium and you pay\nthe rest, often through payroll deductions.\nPreventive Services: See diagnostic and preventive services.\nPrimary dentition: Another name for baby teeth. See deciduous.\nPrimary payer: The third party payer with first responsibility in a benefit determination.\nProphylaxis: Scaling and polishing procedure. Performed to remove coronal plaque, calculus and\nstains. **\nProsthodontic: Branch of dentistry that deals with the repair of teeth by crowns, inlays or onlays and/or the\nreplacement of missing teeth and related mouth or jaw structures by bridges, dentures, implants or other artificial\ndevises.\nProsthodontist: A dental specialist. Restores natural teeth. Replaces missing teeth with artificial substitutes.\nProvider: A dentist or other dental care professional, or clinic that is accredited, licensed or certified to provide\ndental services in their state, and is providing services within the scope of that accreditation, license or\ncertification.\nProvider network: Dentists and other dental care professionals who agree to provide dental care to members of a\ndental plan, under the terms of a contract.","full_prompt":"N\nNon-covered charges: Costs for dental care your insurer does not cover. In some cases the service is a covered\nservice, but the insurer is not responsible for the entire charge. In these cases, you will be responsible for any\ncharge not covered by your dental plan. You may wish to call your insurer or consult your dental plan or dental\npolicy to determine whether certain services are included in your plan before you receive those services from your\ndentist.\nNon-Covered Services: Dental services not listed as a benefit. If you receive non-covered services, your dental plan\nwill not pay for them. Your provider will bill you. You will be responsible for the full cost. Usually payments count\ntoward deductible. Check with your insurer. Make sure you know what services are covered before you see your\ndentist.\nNonduplication of Benefits: Occurs when you have two insurance plans. It’s how our second insurance carrier\ncalculates its payment. The secondary carrier calculates what it would have paid if it were your primary plan. Then\nit subtracts what the other plan paid. Examples: Your primary carrier paid 80 percent. Your secondary carrier\nnormally covers 80 percent. Your secondary carrier would not make any additional payment. If the primary carrier\npaid 50 percent. The secondary carrier would pay up to 30 percent.\nO\nOcclusion: Any contact between biting or chewing surfaces of upper and lower teeth.\nOcclusal Guard: A removable device worn between the upper and lower teeth to prevent clenching or grinding.\n[NOTE: ODONTOPLASTY WAS REMOVED]\nOpen Enrollment/Open Enrollment Period: Time of year when an eligible person may add, change or terminate a\ndental plan or dental policy for the next contract year.\nOpen Panel: Allows you to receive care from any dentist. It allows any dentist to participate. Any dentist may\naccept or refuse to treat patients enrolled in the plan. Open panel plans often are described as freedom of choice\nplans.\nOrthodontic Retainer: Appliance to stabilize teeth following orthodontic treatment.\nGlossary of Dental Insurance and Dental Care Terms\n12\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nOrthodontics and dentofacial orthopedics: Branch of dentistry. Includes the diagnosis, prevention, interception,\nand correction of malocclusion. Also includes neuromuscular and skeletal abnormalities of the developing or\nmature orofacial structures.\nOrthodontist: Specialist who treats malocclusion and other neuromuscular and skeletal abnormalities of the teeth\nand their surrounding structures.\nOrthotic device: Dental appliance used to support, align, prevent or correct deformities, or to improve the\nfunction of the oral\nOut-of-Network: Care from providers not on your plan. This includes dentists and clinics. Usually, you will pay\nmore out of your own pocket when you receive dental care out-of-network providers.\nOut-of-network benefits: Coverage for services from providers who are not under a contract with your dental\nplan.\nOut-of-pocket cost: The amount plan members must pay for care. Includes the difference between the amount\ncharged by a provider and what a health plan pays for such services.\nOut-of-Pocket Maximum: The most a dental plan requires a member to pay in a year. Deductibles, co-payments\nand co-insurance count toward the out-of-pocket maximum. The only dental benefits that have out-of-pocket\nmaximums are child benefits purchased through public exchanges, or purchased as an individual or through a small\ngroup. The out-of-pocket maximum for one child is $350 and for more than one child is $700 in all states.\nAfter reaching an out-of-pocket maximum, the plan pays 100% of the cost of pediatric dental services. This\nonly applies to covered services. Members are still responsible for services that are not covered by the\nplan. Members also continue to pay their monthly premiums.\nOverbilling: Stating fees as higher than actual charges. Example: when you are charged one fee and an insurance\ncompany is billed a higher fee. This is done to use your co-payment. It also done to increase your fees solely\nbecause you are covered under a dental benefits plan.\nOverdenture: See Denture/Overdenture.\nP\nPalate: The hard and soft tissues forming the roof of the mouth. It separates the oral and nasal cavities.\nPalliative: Treatment that relieves pain but may not remove the cause of the pain.\nPartial Denture: See Denture/Partial Denture.\nGlossary of Dental Insurance and Dental Care Terms\n13\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nParticipating Provider: Dentists and other licensed dental providers on your plan. They have a contract with your\nplan. The contract includes set service fees.\nPayer: Party responsible for paying your claims. It can be a self-insured employer, insurance company or\ngovernmental agency.\nPediatric dentist: A dental specialist. Treats children from birth through adolescence. Provides primary and\ncomprehensive preventive and therapeutic oral health care. Formerly known as a pedodontist.\nPeriodontal: Branch of dentistry that involves the prevention and treatment of gum disease.\nPeriodontal disease: Inflammation process of gums and/or periodontal membrane of the teeth. Results in an\nabnormally deep gingival sulcus. Possibly produces periodontal pockets and loss of supporting alveolar bone.\nPeriodontist: A dental specialist. Treats diseases of the supporting and surrounding tissues of the teeth.\nPeriodontitis: Inflammation and loss of the connective tissue of the supporting or surrounding structure of teeth.\nWith loss of attachment.\n[NOTE: PIN REMOVED]\nPlan Year: See Benefit Year.\nPlaque: A soft sticky substance. Composed largely of bacteria and bacterial derivatives. It forms on teeth daily.\nPoint of Service (POS) Plan: A dental plan that allows you to choose at the time of dental service whether you will\ngo to a provider within your dental plan's network or get dental care from a provider outside the network.\n[NOTE: PORCELAIN/CERAMIC REMOVED]\n[NOTE: POST REMOVED]\nPreauthorization: A process that your dental plan or insurer uses to make a decision that particular dental services\nare covered. Your plan may require preauthorization for certain services, such as crowns, before you receive them.\nPreauthorization requirements are generally waived if you need emergency care. Sometimes called prior\nauthorization.\n[NOTE: PRECERTIFICATION REMOVED]\nPredetermination: A process where a dentist submits a treatment plan to the payer before treatment begins. The\npayer reviews the treatment plan. The payer notifies you and your dentist about one or more of the following:\nyour eligibility, covered services, amounts payable, co-payment and deductibles and plan maximums. See preauthorization.\nGlossary of Dental Insurance and Dental Care Terms\n14\n* American Dental Association Current Dental Terminology 2011-2012, glossary.\n**Dental Benefits: A Guide to Dental PPOs, HMOs And Other Managed Plans, Don Mayes, Revised Edition, 2002.\n**FDA/ADA radiograph guidelines.\nNational Association of Dental Plans, www.nadp.org\nPre-existing condition: A dental condition that exists for a set time prior to enrollment in a dental plan, regardless\nof whether the condition has been formally diagnosed. The only pre-existing condition that is common for dental\nplans or policies is a missing tooth.\n[REMOVED PRECIOUS OR HIGH NOBLE METALS – SEE METALS, CLASSIFICATIONS –ACCORDING TO CDT]\nPretreatement Estimate: See predetermination. **\nPreferred Provider Organization (PPO): See DPPO.\nPremedication: The use of medications prior to dental procedures.\nPrepaid dental plan: A method of funding dental care costs in advance of services. For a defined population.\nPremium: The amount you pay to a dental insurance company for dental coverage. The dental insurance company\ngenerally recalculates the premium each policy year. This amount is usually paid in monthly installments. When\nyou receive dental insurance through an employer, the employer may pay a portion of the premium and you pay\nthe rest, often through payroll deductions.\nPreventive Services: See diagnostic and preventive services.\nPrimary dentition: Another name for baby teeth. See deciduous.\nPrimary payer: The third party payer with first responsibility in a benefit determination.\nProphylaxis: Scaling and polishing procedure. Performed to remove coronal plaque, calculus and\nstains. **\nProsthodontic: Branch of dentistry that deals with the repair of teeth by crowns, inlays or onlays and/or the\nreplacement of missing teeth and related mouth or jaw structures by bridges, dentures, implants or other artificial\ndevises.\nProsthodontist: A dental specialist. Restores natural teeth. Replaces missing teeth with artificial substitutes.\nProvider: A dentist or other dental care professional, or clinic that is accredited, licensed or certified to provide\ndental services in their state, and is providing services within the scope of that accreditation, license or\ncertification.\nProvider network: Dentists and other dental care professionals who agree to provide dental care to members of a\ndental plan, under the terms of a contract.\n\nUse the info in this document and not any other source.\nCategorize the terms into \"Device\", \"Procedure\", and \"Other\", and exclude any financial or insurance related terms.","domain":"Medical","type":"Summarize & Format","high_level_type":"Text Transformation","__index_level_0__":563} +{"system_instruction":"Your task is to answer questions using information provided in the context block, without referring to external sources or prior knowledge. Format your response using bullet points.","user_request":"List the reasons that resulted in decreased emission of GHGs from ethanol production.","context_document":"A new USDA report, titled “A Life-Cycle Analysis of the Greenhouse Gas Emissions of Corn-Based\nEthanol,” finds that greenhouse gas (GHG) emissions associated with producing corn-based ethanol in\nthe United States are about 43 percent lower than gasoline when measured on an energy equivalent\nbasis. Unlike other studies of GHG benefits, which relied on forecasts of future ethanol production\nsystems and expected impacts on the farm sector, this study reviewed how the industry and farm\nsectors have performed over the past decade to assess the current GHG profile of corn-based ethanol.\nThe report shows that the reductions in GHG emissions were driven by a variety of improvements in\nethanol production, spanning from the corn field to the ethanol refinery. Farmers are producing corn\nmore efficiently and using conservation practices that reduce GHG emissions, including reduced tillage,\ncover crops, and improved nitrogen management. Both corn yields and the efficiency of ethanol\nproduction technologies are also improving.\nPrevious estimates of ethanol’s GHG balance report lower efficiencies, largely due to anticipated\nconversion of grasslands and forests to commodity production as a result of increased demand for corn\nused in ethanol production. However, recent studies of international agricultural land use trends show\nthat since 2004, the primary land use change response of the world's farmers to rising commodity prices\nhas been to use available land resources more efficiently rather than to expand the amount of land used\nfor farming.","full_prompt":"A new USDA report, titled “A Life-Cycle Analysis of the Greenhouse Gas Emissions of Corn-Based\nEthanol,” finds that greenhouse gas (GHG) emissions associated with producing corn-based ethanol in\nthe United States are about 43 percent lower than gasoline when measured on an energy equivalent\nbasis. Unlike other studies of GHG benefits, which relied on forecasts of future ethanol production\nsystems and expected impacts on the farm sector, this study reviewed how the industry and farm\nsectors have performed over the past decade to assess the current GHG profile of corn-based ethanol.\nThe report shows that the reductions in GHG emissions were driven by a variety of improvements in\nethanol production, spanning from the corn field to the ethanol refinery. Farmers are producing corn\nmore efficiently and using conservation practices that reduce GHG emissions, including reduced tillage,\ncover crops, and improved nitrogen management. Both corn yields and the efficiency of ethanol\nproduction technologies are also improving.\nPrevious estimates of ethanol’s GHG balance report lower efficiencies, largely due to anticipated\nconversion of grasslands and forests to commodity production as a result of increased demand for corn\nused in ethanol production. However, recent studies of international agricultural land use trends show\nthat since 2004, the primary land use change response of the world's farmers to rising commodity prices\nhas been to use available land resources more efficiently rather than to expand the amount of land used\nfor farming.\nEthanol GHG Balance Highlights\n Ethanol production in the United States increased significantly over the past decade—from 3.9 to\n14.8 billion gallons per year between 2005 and 2015.\n The report projects that the GHG profile of corn ethanol will be almost 50 percent lower than\ngasoline in 2022 if current trends in corn yields, process fuel switching, and improvements in\ntrucking fuel efficiency continue.\n If additional conservation practices and efficiency improvements are pursued, such as the practices\noutlined in USDA’s Building Blocks for Climate Smart Agriculture and Forestry strategy, the GHG\nbenefits of corn ethanol are even more pronounced over gasoline—about 76 percent.\n On-farm conservation practices, such as reduced tillage, cover crops, and nitrogen management, are\nestimated to improve the GHG balance of corn ethanol by about 14 percent\n\nYour task is to answer questions using information provided in the above text, without referring to external sources or prior knowledge. Format your response using bullet points.\n\nQuestion: List the reasons that resulted in decreased emission of GHGs from ethanol production.","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":585} +{"system_instruction":"You may only respond using the context block provided.","user_request":"Is the United States currently in a recession?","context_document":"There is no theoretical reason why the criteria used in the Sahm rule is associated with a recession—it is\nan observed historical relationship for a small sample and may not always hold going forward. Sahm\nherself has indicated that despite her rule getting triggered, she does not believe that the United States is\ncurrently in a recession, although she believes that the risk of recession has increased.\nThe primary indicators used by the NBER are not currently consistent with a recession, and several\nremain strong. For example, real gross domestic product has been positive since the third quarter of 2022\nand grew by 1.4% and 2.8% in the first and second quarters of 2024, with real personal consumption expenditures up 1.5% and 2.3% over the same period. Real personal income less transfers grew in May\nand June 2024 and were up 1.8% over the year in June.\nThus far, the only indications of a weakening economy are coming from the labor market, and even there,\nindicators are inconsistent. Although there has been a 0.9 percentage point increase in the unemployment\nrate and nonfarm payroll employment growth has slowed, employment growth remained positive, which\nis inconsistent with a recession. (Recessions typically feature falling employment within the first three\nmonths.) Employment as measured by a different survey has shown some decreases, but the NBER does\nnot track this measure as closely.\nThe unemployment rate could be rising for reasons associated with a weakening economy (e.g., workers\nlosing their jobs) or for neutral reasons (e.g., new entrants to the labor force). Data on the reasons for\nunemployment suggest that the unemployment rate has risen at least partly because the economy has\nweakened. Almost two-thirds of the increase in unemployment in the past year has come from people who\nhave lost their jobs (mostly via temporary layoffs or jobs ending), whereas around one-third has come\nfrom people entering or reentering the labor force. On the other hand, the rise in unemployment has not\ncoincided with a rise in layoffs and discharges—which are still lower than during the expansion that\npreceded the pandemic—as would be expected if the economy were entering a recession. Additionally,\nmany economists assessed that the unemployment rate was unsustainably low for over two years. Some\ncooling in the labor market could indicate a rise to a more sustainable rate. Now the key question is\nwhether it will continue to rise. Unemployment remains low by historical standards, and if it does not rise\nmuch further, a recession can be avoided.\n","full_prompt":"Using only the context block provided is the United States in a recession?","domain":"Financial","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":602} +{"system_instruction":"You are to answer based solely on the provided text. You are not allowed to use any external resources or prior knowledge.","user_request":"When can someone with BMI of 29 kg/m2 be recommended for bariatric surgery?","context_document":"A broad range of drugs are under investigation, but there are currently no drugs approved by\nregulatory agencies for the treatment of NAFLD. This is a field of very active research. As an increasing\nnumber of clinical studies are running and results are reported, recommendations may rapidly change.\nInformation on which clinical trials are ongoing can be found on www.clinicaltrials.gov and you should\nask your physician for newest updates. Some drugs that are used to treat other conditions have also been\ntested for NASH. Based on their effects demonstrated by liver biopsy, the following drugs seem to have\nsome efficacy.\n– Vitamin E showed promise, but only in patients without cirrhosis and without T2D. Given long-term and\nat high doses, however, vitamin E potentially had negative effects and some data indicate that it could\nincrease the risk of early death and certain cancers.\n– Pioglitazone, which is approved for the treatment of diabetes, showed promise for NASH in patients with\ndiabetes and pre-diabetes. Side effects such as weight gain and bone fractures should be considered.\n– Liraglutide and semaglutide are approved for the treatment of obesity and for diabetes. They have also\nshown promise in reducing liver fat and inflammation in NASH and will be evaluated further.\nImportant: all these drugs must be discussed with your doctor and can harm when self-administered.\nFuture available drugs will be an add-on therapy because lifestyle changes are essential as NAFLD is\nmainly a lifestyle-related disease.\nBariatric surgery very effectively achieves weight loss and weight loss maintenance in patients\nwith obesity. The agreed criteria for the surgical management of obesity and metabolic disorders (BMI\n≥40kg/m2\n or BMI ≥35kg/m2\n with complicating disorders, no resolution after medical treatment) are\nalso applicable for NAFLD. Patients with a BMI of 30–35 kg/m2\n who also have T2D that is not adequately\ncontrolled by medical therapy may also be candidates for surgery.\nIt is important to know that the change in the anatomy by bariatric surgery can lead to the need of lifelong\nfollow up and this should be considered in discussing this option for patients.\nIf you wonder whether vitamin E, the above-mentioned drugs or bariatric surgery could be helpful for you,\nplease consult your doctor and discuss the potential risks and benefits. Any treatment decision should be\nbased on your individual situation and medical history","full_prompt":"You are to answer based solely on the provided text. You are not allowed to use any external resources or prior knowledge.\nWhen can someone with BMI of 29 kg/m2 be recommended for bariatric surgery?\nA broad range of drugs are under investigation, but there are currently no drugs approved by\nregulatory agencies for the treatment of NAFLD. This is a field of very active research. As an increasing\nnumber of clinical studies are running and results are reported, recommendations may rapidly change.\nInformation on which clinical trials are ongoing can be found on www.clinicaltrials.gov and you should\nask your physician for newest updates. Some drugs that are used to treat other conditions have also been\ntested for NASH. Based on their effects demonstrated by liver biopsy, the following drugs seem to have\nsome efficacy.\n– Vitamin E showed promise, but only in patients without cirrhosis and without T2D. Given long-term and\nat high doses, however, vitamin E potentially had negative effects and some data indicate that it could\nincrease the risk of early death and certain cancers.\n– Pioglitazone, which is approved for the treatment of diabetes, showed promise for NASH in patients with\ndiabetes and pre-diabetes. Side effects such as weight gain and bone fractures should be considered.\n– Liraglutide and semaglutide are approved for the treatment of obesity and for diabetes. They have also\nshown promise in reducing liver fat and inflammation in NASH and will be evaluated further.\nImportant: all these drugs must be discussed with your doctor and can harm when self-administered.\nFuture available drugs will be an add-on therapy because lifestyle changes are essential as NAFLD is\nmainly a lifestyle-related disease.\nBariatric surgery very effectively achieves weight loss and weight loss maintenance in patients\nwith obesity. The agreed criteria for the surgical management of obesity and metabolic disorders (BMI\n≥40kg/m2\n or BMI ≥35kg/m2\n with complicating disorders, no resolution after medical treatment) are\nalso applicable for NAFLD. Patients with a BMI of 30–35 kg/m2\n who also have T2D that is not adequately\ncontrolled by medical therapy may also be candidates for surgery.\nIt is important to know that the change in the anatomy by bariatric surgery can lead to the need of lifelong\nfollow up and this should be considered in discussing this option for patients.\nIf you wonder whether vitamin E, the above-mentioned drugs or bariatric surgery could be helpful for you,\nplease consult your doctor and discuss the potential risks and benefits. Any treatment decision should be\nbased on your individual situation and medical history","domain":"Medical","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":724} +{"system_instruction":"You can only produce an answer using the context provided to you.","user_request":"Which batteries are in the early stages of commercialisation?\n","context_document":"Chapter 4: Batteries for Grid Applications\nOverview\nBatteries are devices that store energy chemically. This report focuses on “secondary” batteries,\nwhich must be charged before use and which can be discharged and recharged (cycled) many\ntimes before the end of their useful life. For electric power grid applications, there are four main\nbattery types of interest:\n Lead-acid\n High temperature “sodium-beta”\n Liquid electrolyte “flow” batteries\n Other emerging chemistries84\nLead-acid batteries have been used for more than a century in grid applications and in\nconventional vehicles for starting, lighting, and ignition (SLI). They continue to be the\ntechnology of choice for vehicle SLI applications due to their low cost. Consequently, they are\nmanufactured on a mass scale. In 2010, approximately 120 million lead-acid batteries were\nshipped in North America alone.85 Lead-acid batteries are commonly used by utilities to serve as\nuninterruptible power supplies in substations, and have been used at utility scale in several\ndemonstration projects to provide grid support.86 Use of lead acid batteries for grid applications is\nlimited by relatively short cycle life. R&D efforts are focused on improved cycle-life, which\ncould result in greater use in utility-scale applications.\nSodium-beta batteries include sodium-sulfur (NaS) units, first developed in the 1960s,87 and\ncommercially available from a single vendor (NGK Insulators, Ltd.) in Japan with over 270 MW\ndeployed worldwide.88 A NaS battery was first deployed in the United States in 2002.\n89 There are\nnow a number of U.S. demonstration projects, including several listed in Table 3. The focus of\nNaS deployments in the United States has been in electric distribution deferral projects, acting to\nreduce peak demand on distribution systems, but they also can serve multiple grid support\nservices. An alternative high-temperature battery, sodium-nickel-chloride, is in the early stages of commercialization.\n\n“Flow” batteries, in which a liquid electrolyte flows through a chemical cell to produce\nelectricity, are in the early stages of commercialization. In grid applications there has been some\ndeployment of two types of flow battery: vanadium redox and zinc-bromide. There are a number\nof international installations of vanadium redox units, including a 250 kW installation in the\nUnited States to relieve a congested transmission line.\n91 There are also a number of zinc-bromine\ndemonstration projects.92 Several other flow battery chemistries have been pursued or are under\ndevelopment, but are less mature.\nIn addition to the three battery types discussed above, there are several emerging technologies\nbased on new battery chemistries which may also have potential in grid applications. Several of\nthese emerging technologies are being supported by DOE efforts such as ARPA-E and are\ndiscussed briefly in the R&D section of this chapter.\n\nTechnology\nDescription and Performance\nLead-Acid\nThe lead-acid battery consists of a lead dioxide positive electrode (cathode), a lead negative\nelectrode (anode), and an aqueous sulfuric acid electrolyte which carries the charge between the\ntwo. During discharge, each electrode is converted to lead sulfate, consuming sulfuric acid from\nthe electrolyte. When recharging, the lead sulfate is converted back to sulfuric acid, leaving a layer of lead dioxide on the cathode and pure lead on the anode. In such conventional “wet”\n(flooded) cells, water in the electrolyte is broken down to hydrogen and oxygen during the\ncharging process. In a vented wet cell design, these gases escape into the atmosphere, requiring\nthe occasional addition of water to the system. In sealed wet cell designs, the loss of these gases is\nprevented and their conversion back to water is possible, reducing maintenance requirements.\nHowever, if the battery is overcharged or charged too quickly, the rate of gas generation can\nsurpass that of water recombination, which can cause an explosion.\nIn “valve regulated gel” designs, silica is added to the electrolyte to cause it to gel. In “absorbed\nglass mat” designs, the electrolyte is suspended in a fiberglass mat. The latter are sometimes\nreferred to as “dry” because the fiberglass mat is not completely saturated with acid and there is\nno excess liquid. Both designs operate under slight constant pressure. Both also eliminate the risk\nof electrolyte leakage and offer improved safety by using valves to regulate internal pressure due\nto gas build up, but at significantly higher cost than wet cells described above.93\nLead-acid is currently the lowest-cost battery chemistry on a dollar-per-kWh basis. However, it\nalso has relatively low specific energy (energy per unit mass) on the order of 35 Wh/kg and\nrelatively poor “cycle life,” which is the number of charge-discharge cycles it can provide before\nits capacity falls too far below a certain percentage (e.g., 80%) of its initial capacity. While the\nlow energy density of lead-acid will likely limit its use in transportation applications, increase in\ncycle life could make lead-acid cost-effective in grid applications.\nThe cycle life of lead-acid batteries is highly dependent on both the rate and depth of discharge\ndue to corrosion and material shedding off of electrode plates inside the battery. High depth of\ndischarge (DoD) operation intensifies both issues. At 100% DoD (discharging the battery\ncompletely) cycle life can be less than 100 full cycles for some lead-acid technologies. During\nhigh rate, partial state-of-charge operation, lead sulfate accumulation on the anode can be the\nprimary cause of degradation. These processes are also sensitive to high temperature, where the\nrule of thumb is to reduce battery life by half for every 8°C (14°F) increase in temperature above\nambient.\n94 Manufacturers’ warrantees provide some indication of minimum performance\nexpectations, with service life of three to five years for deep cycle batteries, designed to be mostly\ndischarged time after time. SLI batteries in cars have expected service lives of five to seven years,\nwith up to 30 discharges per year depending on the rate of discharge. Temperature also affects\ncapacity, with a battery at -4°C (25°F) having between roughly 70% and 80% of the capacity of a\nbattery at 24°C (75°F).95\nFor many applications of lead-acid batteries, including SLI and uninterruptible power supply\n(UPS), efficiency of the batteries is relatively unimportant. One estimate for the DC-DC (direct\ncurrent) efficiency of utility-scale lead acid battery is 81%, and AC-AC (alternating current)\nefficiency of 70%-72%.9\n\nHigh Temperature Sodium-Beta\nSodium-beta batteries use molten (liquid) sodium for the anode, with sodium ions transporting the\nelectric charge. The two main types of sodium-beta batteries are distinguished by the type of\ncathode they use. The sodium-sulfur (Na-S) type employs a liquid sulfur cathode, while the sodium-nickel chloride (Na-NiCl2) type employs a solid metal chloride cathode. Both types\ninclude a beta-alumina solid electrolyte material separating the cathode and anode. This ceramic\nmaterial offers ionic conductivity similar to that of typical aqueous electrolytes, but only at high\ntemperature. Consequently, sodium-beta batteries ordinarily must operate at temperatures around\n300°C (572°F).\n97 The impermeability of the solid electrolyte to liquid electrodes and its minimal\nelectrical conductivity eliminates self discharge and allows high efficiency.98\nTechnical challenges associated with sodium-beta battery chemistry generally stem from the high\ntemperature requirements. To maintain a 300°C operating point the battery must have insulation\nand active heating. If it is not maintained at such a temperature, the resulting freeze-thaw cycles\nand thermal expansion can lead to mechanical stresses, damaging seals and other cell\ncomponents, including the electrolyte.\n99 The fragile nature of the electrolyte is also a concern,\nparticularly for Na-S cells. In the event of damage to the solid electrolyte, a breach could allow\nthe two liquid electrodes to mix, possibly causing an explosion and fire.\n100\nNa-S batteries are manufactured commercially for a variety of grid services ranging from shortterm rapid discharge services to long-term energy management services.101 The DC-DC efficiency\nis about 85%. Calculation of the AC-AC efficiency is complicated by the need for additional\nheating. The standby heat loss for each 50 kW module is between 2.2 and 3.4 kW. As a result of\nthis heat loss, plus losses in the power conversion equipment, the AC-AC efficiency for loadleveling services is estimated in the range of 75%-80%.102 Expected service life is 15 years at\n90% DoD and 4500 cycles.103\nThe primary sodium-beta alternative to the Na-S chemistry, the Na-NiCl2 cell (typically called\nthe ZEBRA cell).104 Although ZEBRA batteries have been under development for over 20 years,\nthey are only in the early stages of commercialization.\n105 Nickel chloride cathodes offer several\npotential advantages including higher operating voltage, increased operational temperature range\n(due in part to the lower melting point of the secondary electrolyte), a slightly less corrosive\ncathode, and somewhat safer cell construction, since handling of metallic sodium—which is\npotentially explosive—can be avoided.\n106 They are likely to offer a slightly reduced energy\ndensity.107\n\n\n","full_prompt":"Context: Chapter 4: Batteries for Grid Applications\nOverview\nBatteries are devices that store energy chemically. This report focuses on “secondary” batteries,\nwhich must be charged before use and which can be discharged and recharged (cycled) many\ntimes before the end of their useful life. For electric power grid applications, there are four main\nbattery types of interest:\n Lead-acid\n High temperature “sodium-beta”\n Liquid electrolyte “flow” batteries\n Other emerging chemistries84\nLead-acid batteries have been used for more than a century in grid applications and in\nconventional vehicles for starting, lighting, and ignition (SLI). They continue to be the\ntechnology of choice for vehicle SLI applications due to their low cost. Consequently, they are\nmanufactured on a mass scale. In 2010, approximately 120 million lead-acid batteries were\nshipped in North America alone.85 Lead-acid batteries are commonly used by utilities to serve as\nuninterruptible power supplies in substations, and have been used at utility scale in several\ndemonstration projects to provide grid support.86 Use of lead acid batteries for grid applications is\nlimited by relatively short cycle life. R&D efforts are focused on improved cycle-life, which\ncould result in greater use in utility-scale applications.\nSodium-beta batteries include sodium-sulfur (NaS) units, first developed in the 1960s,87 and\ncommercially available from a single vendor (NGK Insulators, Ltd.) in Japan with over 270 MW\ndeployed worldwide.88 A NaS battery was first deployed in the United States in 2002.\n89 There are\nnow a number of U.S. demonstration projects, including several listed in Table 3. The focus of\nNaS deployments in the United States has been in electric distribution deferral projects, acting to\nreduce peak demand on distribution systems, but they also can serve multiple grid support\nservices. An alternative high-temperature battery, sodium-nickel-chloride, is in the early stages of commercialization.\n\n“Flow” batteries, in which a liquid electrolyte flows through a chemical cell to produce\nelectricity, are in the early stages of commercialization. In grid applications there has been some\ndeployment of two types of flow battery: vanadium redox and zinc-bromide. There are a number\nof international installations of vanadium redox units, including a 250 kW installation in the\nUnited States to relieve a congested transmission line.\n91 There are also a number of zinc-bromine\ndemonstration projects.92 Several other flow battery chemistries have been pursued or are under\ndevelopment, but are less mature.\nIn addition to the three battery types discussed above, there are several emerging technologies\nbased on new battery chemistries which may also have potential in grid applications. Several of\nthese emerging technologies are being supported by DOE efforts such as ARPA-E and are\ndiscussed briefly in the R&D section of this chapter.\n\nTechnology\nDescription and Performance\nLead-Acid\nThe lead-acid battery consists of a lead dioxide positive electrode (cathode), a lead negative\nelectrode (anode), and an aqueous sulfuric acid electrolyte which carries the charge between the\ntwo. During discharge, each electrode is converted to lead sulfate, consuming sulfuric acid from\nthe electrolyte. When recharging, the lead sulfate is converted back to sulfuric acid, leaving a layer of lead dioxide on the cathode and pure lead on the anode. In such conventional “wet”\n(flooded) cells, water in the electrolyte is broken down to hydrogen and oxygen during the\ncharging process. In a vented wet cell design, these gases escape into the atmosphere, requiring\nthe occasional addition of water to the system. In sealed wet cell designs, the loss of these gases is\nprevented and their conversion back to water is possible, reducing maintenance requirements.\nHowever, if the battery is overcharged or charged too quickly, the rate of gas generation can\nsurpass that of water recombination, which can cause an explosion.\nIn “valve regulated gel” designs, silica is added to the electrolyte to cause it to gel. In “absorbed\nglass mat” designs, the electrolyte is suspended in a fiberglass mat. The latter are sometimes\nreferred to as “dry” because the fiberglass mat is not completely saturated with acid and there is\nno excess liquid. Both designs operate under slight constant pressure. Both also eliminate the risk\nof electrolyte leakage and offer improved safety by using valves to regulate internal pressure due\nto gas build up, but at significantly higher cost than wet cells described above.93\nLead-acid is currently the lowest-cost battery chemistry on a dollar-per-kWh basis. However, it\nalso has relatively low specific energy (energy per unit mass) on the order of 35 Wh/kg and\nrelatively poor “cycle life,” which is the number of charge-discharge cycles it can provide before\nits capacity falls too far below a certain percentage (e.g., 80%) of its initial capacity. While the\nlow energy density of lead-acid will likely limit its use in transportation applications, increase in\ncycle life could make lead-acid cost-effective in grid applications.\nThe cycle life of lead-acid batteries is highly dependent on both the rate and depth of discharge\ndue to corrosion and material shedding off of electrode plates inside the battery. High depth of\ndischarge (DoD) operation intensifies both issues. At 100% DoD (discharging the battery\ncompletely) cycle life can be less than 100 full cycles for some lead-acid technologies. During\nhigh rate, partial state-of-charge operation, lead sulfate accumulation on the anode can be the\nprimary cause of degradation. These processes are also sensitive to high temperature, where the\nrule of thumb is to reduce battery life by half for every 8°C (14°F) increase in temperature above\nambient.\n94 Manufacturers’ warrantees provide some indication of minimum performance\nexpectations, with service life of three to five years for deep cycle batteries, designed to be mostly\ndischarged time after time. SLI batteries in cars have expected service lives of five to seven years,\nwith up to 30 discharges per year depending on the rate of discharge. Temperature also affects\ncapacity, with a battery at -4°C (25°F) having between roughly 70% and 80% of the capacity of a\nbattery at 24°C (75°F).95\nFor many applications of lead-acid batteries, including SLI and uninterruptible power supply\n(UPS), efficiency of the batteries is relatively unimportant. One estimate for the DC-DC (direct\ncurrent) efficiency of utility-scale lead acid battery is 81%, and AC-AC (alternating current)\nefficiency of 70%-72%.9\n\nHigh Temperature Sodium-Beta\nSodium-beta batteries use molten (liquid) sodium for the anode, with sodium ions transporting the\nelectric charge. The two main types of sodium-beta batteries are distinguished by the type of\ncathode they use. The sodium-sulfur (Na-S) type employs a liquid sulfur cathode, while the sodium-nickel chloride (Na-NiCl2) type employs a solid metal chloride cathode. Both types\ninclude a beta-alumina solid electrolyte material separating the cathode and anode. This ceramic\nmaterial offers ionic conductivity similar to that of typical aqueous electrolytes, but only at high\ntemperature. Consequently, sodium-beta batteries ordinarily must operate at temperatures around\n300°C (572°F).\n97 The impermeability of the solid electrolyte to liquid electrodes and its minimal\nelectrical conductivity eliminates self discharge and allows high efficiency.98\nTechnical challenges associated with sodium-beta battery chemistry generally stem from the high\ntemperature requirements. To maintain a 300°C operating point the battery must have insulation\nand active heating. If it is not maintained at such a temperature, the resulting freeze-thaw cycles\nand thermal expansion can lead to mechanical stresses, damaging seals and other cell\ncomponents, including the electrolyte.\n99 The fragile nature of the electrolyte is also a concern,\nparticularly for Na-S cells. In the event of damage to the solid electrolyte, a breach could allow\nthe two liquid electrodes to mix, possibly causing an explosion and fire.\n100\nNa-S batteries are manufactured commercially for a variety of grid services ranging from shortterm rapid discharge services to long-term energy management services.101 The DC-DC efficiency\nis about 85%. Calculation of the AC-AC efficiency is complicated by the need for additional\nheating. The standby heat loss for each 50 kW module is between 2.2 and 3.4 kW. As a result of\nthis heat loss, plus losses in the power conversion equipment, the AC-AC efficiency for loadleveling services is estimated in the range of 75%-80%.102 Expected service life is 15 years at\n90% DoD and 4500 cycles.103\nThe primary sodium-beta alternative to the Na-S chemistry, the Na-NiCl2 cell (typically called\nthe ZEBRA cell).104 Although ZEBRA batteries have been under development for over 20 years,\nthey are only in the early stages of commercialization.\n105 Nickel chloride cathodes offer several\npotential advantages including higher operating voltage, increased operational temperature range\n(due in part to the lower melting point of the secondary electrolyte), a slightly less corrosive\ncathode, and somewhat safer cell construction, since handling of metallic sodium—which is\npotentially explosive—can be avoided.\n106 They are likely to offer a slightly reduced energy\ndensity.107\n\n\nQuestion: Which batteries are in the early stages of commercialisation?\n\nSystem instruction: You can only produce an answer using the context provided to you.","domain":"Internet/Technology","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":725} +{"system_instruction":"use only the context you are provided to answer. include every isp mentioned. use bullet points, then no more than 25 words to explain. focus on direct actions made.","user_request":"what have isps done to transition into edge providers?","context_document":"Examples of ISPs Becoming Edge Providers\nAT&T. AT&T owns part of the internet backbone and is considered a Tier 1 ISP, meaning it has\nfree access to the entire U.S. internet region.10 It is also a mobile carrier and provides voice\nservices and video programming.11 In 2018, AT&T acquired Time Warner, a content creator that\nowns HBO and its affiliated edge provider HBO NOW, as well as other cable channels.12 The\nDOJ unsuccessfully attempted to block the merger.13 AT&T has announced plans to introduce a\nnew edge provider—HBO Max—to stream video programming for no extra charge to AT&T\ncustomers who are also HBO subscribers; other customers will reportedly be charged a\nsubscription fee.14\n10 DrPeering.net. “Who Are the Tier 1 ISPs?” accessed on December 4, 2019, https://drpeering.net/FAQ/Who-are-the-\nTier-1-ISPs.php. Edge providers associated with Tier 1 ISPs may have additional competitive advantages through the\nISPs’ ability to send content to any part of the internet for free. Edge providers associated with other ISPs may have to\npay or barter with Tier 1 or other ISPs to access certain destinations. Details on how Tier 1 ISPs compete with other\nISPs are beyond the scope of this report.\n11 See https://www.att.com/gen/general?pid=7462 for more information on the digital and communications\ninfrastructure owned by AT&T. AT&T has stated that it considers its television subscription service to be a “video\nservice” under the Communications Act of 1934, as amended, rather than a cable service. See AT&T Inc., SEC Form\n10-K for the year ending December 31, 2014, p. 3.\n12 Edmund Lee and Cecilia King, “U.S. Loses Appeal Seeking to Block AT&T-Time Warner Merger,” New York\nTimes, February 26, 2019, https://www.nytimes.com/2019/02/26/business/media/att-time-warner-appeal.html.\n13 Ibid; see CRS In Focus IF10526, AT&T-Time Warner Merger Overview, by Dana A. Scherer, for more information\non the merger and the court case.\n14 Helen Coster and Kenneth Li, “Behind AT&T’s Plan to Take on Netflix, Apple, and Disney with HBO Max,”\nCompetition on the Edge of the Internet\nCongressional Research Service 5\nComcast. Comcast is an ISP, a cable television service, and a voice service provider. In 2011,\nComcast became the majority owner of NBCUniversal, which owns television networks and\nbroadcast stations, and thus obtained minority ownership of Hulu, an edge provider that streams\nvideo programming to subscribers.15 In 2019, Walt Disney Company obtained “full operational\ncontrol” of Hulu, but Comcast retained its 33% financial stake.16 Comcast also announced plans\nto launch its own video streaming service, Peacock. Comcast reportedly plans to offer three\nsubscription options for Peacock: a free option supported by ads, a premium version with more\nprogramming for a fee, and the premium version with no ads for a higher fee.17 The premium\nversion is to be offered for free to subscribers of Comcast and Cox Communications.\nVerizon. Verizon owns part of the internet backbone and is considered a Tier 1 ISP.18 It is also a\nmobile carrier, and offers video, voice, and ISP services. In 2015, Verizon acquired AOL, an ISP\nand edge provider, and in 2016, it acquired the core business of Yahoo, an edge provider.19 It\ncombined the edge provider products from these acquisitions—such as Yahoo Finance,\nHuffington Post, TechCrunch, and Engadget—in 2017 to create Oath.20\nExamples of Edge Providers Becoming ISPs\nGoogle. Google is the largest subsidiary of the company Alphabet.21 It offers multiple products,\nincluding a search engine, email server, word processing, video streaming, and\nmapping/navigation system.22 Google generally relies on other ISPs to deliver its content, but\nentered the ISP market in 2010 when it announced Google Fiber. Google Fiber provides\nbroadband internet service and video programming.23 Beginning in 2016, it suspended or ended\nsome of its projects; as of October 2019, it had installed fiber optic cables in 18 cities.24\nReuters, October 25, 2019, https://www.reuters.com/article/us-media-at-t-hbo-max-focus/behind-atts-plan-to-take-on-\nnetflix-apple-and-disney-with-hbo-max-idUSKBN1X4163.\n15 Yinka Adegoke and Dan Levine, “Comcast Completes NBC Universal Merger,” Reuters, January 29, 2011,\nhttps://www.reuters.com/article/us-comcast-nbc/comcast-completes-nbc-universal-merger-\nidUSTRE70S2WZ20110129.\n16 Lauren Feiner, Christine Wang, and Alex Sherman, “Disney to Take Full Control over Hulu, Comcast Has Option to\nSell Its Stake in 5 years,” CNBC, May 14, 2019, https://www.cnbc.com/2019/05/14/comcast-has-agreed-to-sell-its-\nstake-in-hulu-in-5-years.html.\n17 Gerry Smith, “NBC’s Peacock Bets Viewers Will Watch Ads to Stream for Free,” Bloomberg, January 16, 2020,\nhttps://www.bloomberg.com/news/articles/2020-01-16/nbc-s-peacock-bets-consumers-will-watch-ads-to-stream-for-\nfree.\n18 DrPeering.net. “Who Are the Tier 1 ISPs?” accessed on December 4, 2019, https://drpeering.net/FAQ/Who-are-the-\nTier-1-ISPs.php.\n19 Verizon, “Mergers & Acquisitions,” accessed on October 28, 2019, https://www.verizon.com/about/timeline-\ncategories/mergers-acquisitions.\n20 Tracey Lien, “Verizon Buys Yahoo for $4.8 Billion, and It’s Giving Yahoo’s Brand Another Chance,” Los Angeles\nTimes, July 25, 2016, https://www.latimes.com/business/technology/la-fi-verizon-buys-yahoo-20160725-snap-\nstory.html.\n21 Larry Page, “G Is for Google,” Google Official Blog, August 10, 2015,\nhttps://googleblog.blogspot.com/2015/08/google-alphabet.html.\n22 Google, “Our Products,” accessed on November 16, 2019, https://about.google/products.\n23 Google, “Think Big with a Gig: Our Experimental Fiber Network,” February 10, 2010,\nhttps://googleblog.blogspot.com/2010/02/think-big-with-gig-our-experimental.html.\n24 Jack Nicas, “Google’s High-Speed Web Plans Hit Snags,” Wall Street Journal, August 15, 2016,\nhttps://www.wsj.com/articles/googles-high-speed-web-plans-hit-snags-1471193165; Lauren Feiner, “Google Fiber’s\nHigh-Speed Internet Service Is Leaving Louisville After Ripping up Roads and Leaving Cables Exposed,” CNBC,\nFebruary 7, 2019, https://www.cnbc.com/2019/02/07/google-fiber-pulls-out-of-louisville.html; Google, “Our Cities,”\nCompetition on the Edge of the Internet\nCongressional Research Service 6\nFacebook. As it attracted more users, Facebook expanded from providing an online platform that\nconnects users to an online platform suitable for various activities, including fundraising,\nmessaging, and commerce. In 2018, a spokesman confirmed that Facebook was pursuing another\nproject, dubbed Athena.25 Athena is an experimental satellite that would beam internet access\nthrough radio signals. If successful, Athena would enable Facebook to become an ISP.\nAmazon. In addition to being a major online retailer, Amazon offers information technology\ninfrastructure services through Amazon Web Services.26 In 2019, Amazon confirmed plans—\ndubbed Project Kuiper—to launch 3,236 satellites into low-Earth orbit to provide broadband\ninternet across the world. If successful, Project Kuiper would enable Amazon to become an ISP.27","full_prompt":"use only the context you are provided to answer. include every isp mentioned. use bullet points, then no more than 25 words to explain. focus on direct actions made.\nwhat have isps done to transition into edge providers?\n\nExamples of ISPs Becoming Edge Providers\nAT&T. AT&T owns part of the internet backbone and is considered a Tier 1 ISP, meaning it has\nfree access to the entire U.S. internet region.10 It is also a mobile carrier and provides voice\nservices and video programming.11 In 2018, AT&T acquired Time Warner, a content creator that\nowns HBO and its affiliated edge provider HBO NOW, as well as other cable channels.12 The\nDOJ unsuccessfully attempted to block the merger.13 AT&T has announced plans to introduce a\nnew edge provider—HBO Max—to stream video programming for no extra charge to AT&T\ncustomers who are also HBO subscribers; other customers will reportedly be charged a\nsubscription fee.14\n10 DrPeering.net. “Who Are the Tier 1 ISPs?” accessed on December 4, 2019, https://drpeering.net/FAQ/Who-are-the-\nTier-1-ISPs.php. Edge providers associated with Tier 1 ISPs may have additional competitive advantages through the\nISPs’ ability to send content to any part of the internet for free. Edge providers associated with other ISPs may have to\npay or barter with Tier 1 or other ISPs to access certain destinations. Details on how Tier 1 ISPs compete with other\nISPs are beyond the scope of this report.\n11 See https://www.att.com/gen/general?pid=7462 for more information on the digital and communications\ninfrastructure owned by AT&T. AT&T has stated that it considers its television subscription service to be a “video\nservice” under the Communications Act of 1934, as amended, rather than a cable service. See AT&T Inc., SEC Form\n10-K for the year ending December 31, 2014, p. 3.\n12 Edmund Lee and Cecilia King, “U.S. Loses Appeal Seeking to Block AT&T-Time Warner Merger,” New York\nTimes, February 26, 2019, https://www.nytimes.com/2019/02/26/business/media/att-time-warner-appeal.html.\n13 Ibid; see CRS In Focus IF10526, AT&T-Time Warner Merger Overview, by Dana A. Scherer, for more information\non the merger and the court case.\n14 Helen Coster and Kenneth Li, “Behind AT&T’s Plan to Take on Netflix, Apple, and Disney with HBO Max,”\nCompetition on the Edge of the Internet\nCongressional Research Service 5\nComcast. Comcast is an ISP, a cable television service, and a voice service provider. In 2011,\nComcast became the majority owner of NBCUniversal, which owns television networks and\nbroadcast stations, and thus obtained minority ownership of Hulu, an edge provider that streams\nvideo programming to subscribers.15 In 2019, Walt Disney Company obtained “full operational\ncontrol” of Hulu, but Comcast retained its 33% financial stake.16 Comcast also announced plans\nto launch its own video streaming service, Peacock. Comcast reportedly plans to offer three\nsubscription options for Peacock: a free option supported by ads, a premium version with more\nprogramming for a fee, and the premium version with no ads for a higher fee.17 The premium\nversion is to be offered for free to subscribers of Comcast and Cox Communications.\nVerizon. Verizon owns part of the internet backbone and is considered a Tier 1 ISP.18 It is also a\nmobile carrier, and offers video, voice, and ISP services. In 2015, Verizon acquired AOL, an ISP\nand edge provider, and in 2016, it acquired the core business of Yahoo, an edge provider.19 It\ncombined the edge provider products from these acquisitions—such as Yahoo Finance,\nHuffington Post, TechCrunch, and Engadget—in 2017 to create Oath.20\nExamples of Edge Providers Becoming ISPs\nGoogle. Google is the largest subsidiary of the company Alphabet.21 It offers multiple products,\nincluding a search engine, email server, word processing, video streaming, and\nmapping/navigation system.22 Google generally relies on other ISPs to deliver its content, but\nentered the ISP market in 2010 when it announced Google Fiber. Google Fiber provides\nbroadband internet service and video programming.23 Beginning in 2016, it suspended or ended\nsome of its projects; as of October 2019, it had installed fiber optic cables in 18 cities.24\nReuters, October 25, 2019, https://www.reuters.com/article/us-media-at-t-hbo-max-focus/behind-atts-plan-to-take-on-\nnetflix-apple-and-disney-with-hbo-max-idUSKBN1X4163.\n15 Yinka Adegoke and Dan Levine, “Comcast Completes NBC Universal Merger,” Reuters, January 29, 2011,\nhttps://www.reuters.com/article/us-comcast-nbc/comcast-completes-nbc-universal-merger-\nidUSTRE70S2WZ20110129.\n16 Lauren Feiner, Christine Wang, and Alex Sherman, “Disney to Take Full Control over Hulu, Comcast Has Option to\nSell Its Stake in 5 years,” CNBC, May 14, 2019, https://www.cnbc.com/2019/05/14/comcast-has-agreed-to-sell-its-\nstake-in-hulu-in-5-years.html.\n17 Gerry Smith, “NBC’s Peacock Bets Viewers Will Watch Ads to Stream for Free,” Bloomberg, January 16, 2020,\nhttps://www.bloomberg.com/news/articles/2020-01-16/nbc-s-peacock-bets-consumers-will-watch-ads-to-stream-for-\nfree.\n18 DrPeering.net. “Who Are the Tier 1 ISPs?” accessed on December 4, 2019, https://drpeering.net/FAQ/Who-are-the-\nTier-1-ISPs.php.\n19 Verizon, “Mergers & Acquisitions,” accessed on October 28, 2019, https://www.verizon.com/about/timeline-\ncategories/mergers-acquisitions.\n20 Tracey Lien, “Verizon Buys Yahoo for $4.8 Billion, and It’s Giving Yahoo’s Brand Another Chance,” Los Angeles\nTimes, July 25, 2016, https://www.latimes.com/business/technology/la-fi-verizon-buys-yahoo-20160725-snap-\nstory.html.\n21 Larry Page, “G Is for Google,” Google Official Blog, August 10, 2015,\nhttps://googleblog.blogspot.com/2015/08/google-alphabet.html.\n22 Google, “Our Products,” accessed on November 16, 2019, https://about.google/products.\n23 Google, “Think Big with a Gig: Our Experimental Fiber Network,” February 10, 2010,\nhttps://googleblog.blogspot.com/2010/02/think-big-with-gig-our-experimental.html.\n24 Jack Nicas, “Google’s High-Speed Web Plans Hit Snags,” Wall Street Journal, August 15, 2016,\nhttps://www.wsj.com/articles/googles-high-speed-web-plans-hit-snags-1471193165; Lauren Feiner, “Google Fiber’s\nHigh-Speed Internet Service Is Leaving Louisville After Ripping up Roads and Leaving Cables Exposed,” CNBC,\nFebruary 7, 2019, https://www.cnbc.com/2019/02/07/google-fiber-pulls-out-of-louisville.html; Google, “Our Cities,”\nCompetition on the Edge of the Internet\nCongressional Research Service 6\nFacebook. As it attracted more users, Facebook expanded from providing an online platform that\nconnects users to an online platform suitable for various activities, including fundraising,\nmessaging, and commerce. In 2018, a spokesman confirmed that Facebook was pursuing another\nproject, dubbed Athena.25 Athena is an experimental satellite that would beam internet access\nthrough radio signals. If successful, Athena would enable Facebook to become an ISP.\nAmazon. In addition to being a major online retailer, Amazon offers information technology\ninfrastructure services through Amazon Web Services.26 In 2019, Amazon confirmed plans—\ndubbed Project Kuiper—to launch 3,236 satellites into low-Earth orbit to provide broadband\ninternet across the world. If successful, Project Kuiper would enable Amazon to become an ISP.27","domain":"Internet/Technology","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":780} +{"system_instruction":"This task requires you to answer questions based solely on the information provided in the prompt. You are not allowed to use any external resources or prior knowledge. Give your answer in bullet points with the proper noun and key word bolded, followed by a short explanation with no, unasked for information.","user_request":"What states, mentioned in the text, have enacted some type of prohibition or restriction on price rises during proclaimed emergencies and specifically mention the key word,\"fuel\", by name.","context_document":"State Price-Gouging Laws\nMany states have enacted some type of prohibition or limitation on price increases during\ndeclared emergencies. Generally, these state laws take one of two basic forms. Some states\nprohibit the sale of goods and services at what are deemed to be “unconscionable” or “excessive”\nprices in the area and during the period of a designated emergency. Other states have established a\nmaximum permissible increase in the prices for retail goods during a designated emergency\nperiod. Many statutes of both kinds include an exemption if price increases are the result of\nincreased costs incurred for procuring the goods or services in question.\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 2\nExamples of State Statutes\nProhibitions on “Excessive” or “Unconscionable” Pricing\nOne common way that states address price gouging is to ban prices that are considered to be (for\nexample) “excessive” or “unconscionable,” as defined in the statute or left to the discretion of the\ncourts. These statutes generally bar such increases during designated emergency periods. The\nprocess for emergency designation is also usually defined in the statute. Frequently, the state’s\ngovernor is granted authority to designate an emergency during which the price limitations are in\nplace.\nFor example, the New York statute provides that:\nDuring any abnormal disruption of the market for consumer goods and services vital and\nnecessary for the health, safety and welfare of consumers, no party within the chain of\ndistribution of such consumer goods or services or both shall sell or offer to sell any such\ngoods or services or both for an amount which represents an unconscionably excessive\nprice.5\nThe statute defines abnormal disruption of the market as a real or threatened change to the market\n“resulting from stress of weather, convulsion of nature, failure or shortage of electric power or\nother source of energy, strike, civil disorder, war, military action, national or local emergency …\nwhich results in the declaration of a state of emergency by the governor.”6 The statute provides\nonly for criminal liability and leaves the ultimate decision as to whether a price is\n“unconscionably excessive” to prosecutors (for charging purposes) and to the courts, with no\nseparate cause of action created for private parties. As guidance in such cases, the statute notes\nthat if there is a “gross disparity” between the price during the disruption and the price prior to the\ndisruption, or if the price “grossly exceeds” the price at which the same or similar goods are\navailable in the area, such disparity will be considered prima facie evidence that a price is\nunconscionable.7\nSimilarly, Florida’s statute bars “unconscionable pricing” during declared states of emergency.8\nIf\nthe amount being charged represents a “gross disparity” from the average price at which the\nproduct or service was sold in the usual course of business (or available in the “trade area”)\nduring the 30 days immediately prior to a declaration of a state of emergency, it is considered\nprima facie evidence of “unconscionable pricing,” which constitutes an “unlawful act or\npractice.”\n9 However, pricing is not considered unconscionable if the increase is attributable to\nadditional costs incurred by the seller or is the result of national or international market trends.10\nAs with the New York statute, the Florida statute offers guidance, but the question of whether\ncertain prices during an emergency are deemed “unconscionable” is ultimately left to the courts.\nMany state price-gouging laws are triggered only by a declaration of emergency in response to\nlocalized conditions. Thus, they will generally not apply after a declared emergency ends or in\nareas not directly affected by a particular emergency or natural disaster. However, at least two\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 3\nstates have laws prohibiting excessive pricing that impose liability even without a declaration of\nany type of emergency. Maine law prohibits “unjust or unreasonable” profits in the sale,\nexchange, or handling of necessities, defined to include fuel.11 Michigan’s consumer protection\nact simply prohibits “charging the consumer a price that is grossly in excess of the price at which\nsimilar property or services are sold.”\n12\nProhibitions of Price Increases Beyond a Certain Percentage\nIn contrast to a general ban on “excessive” or “unconscionable” pricing, some state statutes leave\nless to the courts’ discretion and instead place limits on price increases of certain goods during\nemergencies.\nFor example, California’s anti-price-gouging statute states that for a period of 30 days following\nthe proclamation of a state of emergency by the President of the United States or the governor of\nCalifornia or the declaration of a local emergency by the relevant executive officer, it is unlawful\nto sell or offer certain goods and services (including emergency and medical supplies, building\nand transportation materials, fuel, etc.) at a price more than 10% higher than the price of the good\nprior to the proclamation of emergency.13 As a defense, a seller can show that the price increase\nwas directly attributable to additional costs imposed on it by the supplier of the goods or\nadditional costs for the labor and material used to provide the services.14 The prohibition lasts for\n30 days from the date of issuance of the emergency proclamation.15\nWest Virginia has also adopted an anti-price-gouging measure based on caps to percentage\nincreases in price during times of emergency. The West Virginia statute provides that upon a\ndeclaration of a state of emergency by the President of the United States, the governor, or the\nstate legislature, it is unlawful to sell or offer to sell certain critical goods and services “for a price\ngreater than ten percent above the price charged by that person for those goods and services on\nthe tenth day immediately preceding the declaration of emergency.”\n16 West Virginia also provides\nan exception for price increases attributable to increased costs on the seller imposed by the\nsupplier or to added costs of providing the goods or services during the emergency.17\nSome states use language barring “unconscionable” or “excessive” pricing in a manner similar to\nthe state statutes described in the previous section but define these terms with hard caps instead of\nleaving their exact definition to the discretion of the courts. For example, the Alabama statute\nmakes it unlawful for anyone to “impose unconscionable prices for the sale or rental of any\ncommodity or rental facility during the period of a declared state of emergency.”\n18 However, it\nprovides that prima facie evidence of unconscionable pricing exists “if any person, during a state\nof emergency declared pursuant to the powers granted to the Governor, charges a price that\nexceeds, by an amount equal to or in excess of 25%, the average price at which the same or\nsimilar commodity or rental facility was obtainable in the affected area during the last 30 days\n\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 4\nimmediately prior to the declared state of emergency.”\n19 As with most other state price-gouging\nstatutes, the statute does not apply if the price increase is attributable to reasonable costs incurred\nby the seller in connection with the rental or sale of the commodity.20\nA few other states have imposed caps on price increases during emergencies even tighter than the\none imposed by the aforementioned statutes. Some state statutes ban any price increase during\nperiods of emergency. For example, in Georgia, it is considered an “unlawful, unfair and\ndeceptive trade practice” for anyone doing business in an areas where a state of emergency has\nbeen declared to\nsell or offer for sale at retail any goods or services identified by the Governor in the\ndeclaration of the state of emergency necessary to preserve, protect, or sustain the life,\nhealth, or safety of persons or their property at a price higher than the price at which such\ngoods were sold or offered for sale immediately prior to the declaration of a state of\nemergency.21\nAs with other state gouging statutes, the Georgia statute provides an exception for price increases\nthat reflect “an increase in cost of the goods or services to the person selling the goods or services\nor an increase in the cost of transporting the goods or services into the area.”\n\n","full_prompt":"This task requires you to answer questions based solely on the information provided in the prompt. You are not allowed to use any external resources or prior knowledge. Give your answer in bullet points with the proper noun and key word bolded, followed by a short explanation with no, unasked for information.\n\nWhat states, mentioned in the text, have enacted some type of prohibition or restriction on price rises during proclaimed emergencies and specifically mention the key word,\"fuel\", by name.\n\nState Price-Gouging Laws\nMany states have enacted some type of prohibition or limitation on price increases during\ndeclared emergencies. Generally, these state laws take one of two basic forms. Some states\nprohibit the sale of goods and services at what are deemed to be “unconscionable” or “excessive”\nprices in the area and during the period of a designated emergency. Other states have established a\nmaximum permissible increase in the prices for retail goods during a designated emergency\nperiod. Many statutes of both kinds include an exemption if price increases are the result of\nincreased costs incurred for procuring the goods or services in question.\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 2\nExamples of State Statutes\nProhibitions on “Excessive” or “Unconscionable” Pricing\nOne common way that states address price gouging is to ban prices that are considered to be (for\nexample) “excessive” or “unconscionable,” as defined in the statute or left to the discretion of the\ncourts. These statutes generally bar such increases during designated emergency periods. The\nprocess for emergency designation is also usually defined in the statute. Frequently, the state’s\ngovernor is granted authority to designate an emergency during which the price limitations are in\nplace.\nFor example, the New York statute provides that:\nDuring any abnormal disruption of the market for consumer goods and services vital and\nnecessary for the health, safety and welfare of consumers, no party within the chain of\ndistribution of such consumer goods or services or both shall sell or offer to sell any such\ngoods or services or both for an amount which represents an unconscionably excessive\nprice.5\nThe statute defines abnormal disruption of the market as a real or threatened change to the market\n“resulting from stress of weather, convulsion of nature, failure or shortage of electric power or\nother source of energy, strike, civil disorder, war, military action, national or local emergency …\nwhich results in the declaration of a state of emergency by the governor.”6 The statute provides\nonly for criminal liability and leaves the ultimate decision as to whether a price is\n“unconscionably excessive” to prosecutors (for charging purposes) and to the courts, with no\nseparate cause of action created for private parties. As guidance in such cases, the statute notes\nthat if there is a “gross disparity” between the price during the disruption and the price prior to the\ndisruption, or if the price “grossly exceeds” the price at which the same or similar goods are\navailable in the area, such disparity will be considered prima facie evidence that a price is\nunconscionable.7\nSimilarly, Florida’s statute bars “unconscionable pricing” during declared states of emergency.8\nIf\nthe amount being charged represents a “gross disparity” from the average price at which the\nproduct or service was sold in the usual course of business (or available in the “trade area”)\nduring the 30 days immediately prior to a declaration of a state of emergency, it is considered\nprima facie evidence of “unconscionable pricing,” which constitutes an “unlawful act or\npractice.”\n9 However, pricing is not considered unconscionable if the increase is attributable to\nadditional costs incurred by the seller or is the result of national or international market trends.10\nAs with the New York statute, the Florida statute offers guidance, but the question of whether\ncertain prices during an emergency are deemed “unconscionable” is ultimately left to the courts.\nMany state price-gouging laws are triggered only by a declaration of emergency in response to\nlocalized conditions. Thus, they will generally not apply after a declared emergency ends or in\nareas not directly affected by a particular emergency or natural disaster. However, at least two\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 3\nstates have laws prohibiting excessive pricing that impose liability even without a declaration of\nany type of emergency. Maine law prohibits “unjust or unreasonable” profits in the sale,\nexchange, or handling of necessities, defined to include fuel.11 Michigan’s consumer protection\nact simply prohibits “charging the consumer a price that is grossly in excess of the price at which\nsimilar property or services are sold.”\n12\nProhibitions of Price Increases Beyond a Certain Percentage\nIn contrast to a general ban on “excessive” or “unconscionable” pricing, some state statutes leave\nless to the courts’ discretion and instead place limits on price increases of certain goods during\nemergencies.\nFor example, California’s anti-price-gouging statute states that for a period of 30 days following\nthe proclamation of a state of emergency by the President of the United States or the governor of\nCalifornia or the declaration of a local emergency by the relevant executive officer, it is unlawful\nto sell or offer certain goods and services (including emergency and medical supplies, building\nand transportation materials, fuel, etc.) at a price more than 10% higher than the price of the good\nprior to the proclamation of emergency.13 As a defense, a seller can show that the price increase\nwas directly attributable to additional costs imposed on it by the supplier of the goods or\nadditional costs for the labor and material used to provide the services.14 The prohibition lasts for\n30 days from the date of issuance of the emergency proclamation.15\nWest Virginia has also adopted an anti-price-gouging measure based on caps to percentage\nincreases in price during times of emergency. The West Virginia statute provides that upon a\ndeclaration of a state of emergency by the President of the United States, the governor, or the\nstate legislature, it is unlawful to sell or offer to sell certain critical goods and services “for a price\ngreater than ten percent above the price charged by that person for those goods and services on\nthe tenth day immediately preceding the declaration of emergency.”\n16 West Virginia also provides\nan exception for price increases attributable to increased costs on the seller imposed by the\nsupplier or to added costs of providing the goods or services during the emergency.17\nSome states use language barring “unconscionable” or “excessive” pricing in a manner similar to\nthe state statutes described in the previous section but define these terms with hard caps instead of\nleaving their exact definition to the discretion of the courts. For example, the Alabama statute\nmakes it unlawful for anyone to “impose unconscionable prices for the sale or rental of any\ncommodity or rental facility during the period of a declared state of emergency.”\n18 However, it\nprovides that prima facie evidence of unconscionable pricing exists “if any person, during a state\nof emergency declared pursuant to the powers granted to the Governor, charges a price that\nexceeds, by an amount equal to or in excess of 25%, the average price at which the same or\nsimilar commodity or rental facility was obtainable in the affected area during the last 30 days\n\n\nGasoline Price Increases: Federal and State Authority to Limit “Price Gouging”\nCongressional Research Service 4\nimmediately prior to the declared state of emergency.”\n19 As with most other state price-gouging\nstatutes, the statute does not apply if the price increase is attributable to reasonable costs incurred\nby the seller in connection with the rental or sale of the commodity.20\nA few other states have imposed caps on price increases during emergencies even tighter than the\none imposed by the aforementioned statutes. Some state statutes ban any price increase during\nperiods of emergency. For example, in Georgia, it is considered an “unlawful, unfair and\ndeceptive trade practice” for anyone doing business in an areas where a state of emergency has\nbeen declared to\nsell or offer for sale at retail any goods or services identified by the Governor in the\ndeclaration of the state of emergency necessary to preserve, protect, or sustain the life,\nhealth, or safety of persons or their property at a price higher than the price at which such\ngoods were sold or offered for sale immediately prior to the declaration of a state of\nemergency.21\nAs with other state gouging statutes, the Georgia statute provides an exception for price increases\nthat reflect “an increase in cost of the goods or services to the person selling the goods or services\nor an increase in the cost of transporting the goods or services into the area.”\n\n","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":795} +{"system_instruction":"Formulate your answer using only the provided text; do not draw from any outside sources.","user_request":"What is HR 4319?","context_document":"Background on the 2024 Farmworker Protection Rule\nDOL indicates that the purpose of the Farmworker Protection Rule is to strengthen “protections for\nagricultural workers,” enhance the agency’s “capabilities to monitor H-2A program compliance and take\nnecessary enforcement actions against program violators,” and ensure that “hiring H-2A workers does not\nadversely affect the wages and working conditions of similarly employed workers” in the United States.\nThe rule amends existing regulations and includes provisions that encompass six areas: (1) “protections\nfor worker voice and empowerment,” (2) “clarification of termination for cause,” (3) “immediate effective\ndate for updated adverse effect wage rate,” (4) “enhanced transparency for job opportunity and foreign\nlabor recruitment,” (5) “enhanced transparency and protections for agricultural workers,” and (6)\n“enhanced integrity and enforcement capabilities.”\nIn the pending litigation, the first set of provisions, i.e., “protections for worker voice and empowerment”\nis most relevant. This set revises 20 C.F.R. § 655.135(h) and adds two new subsections, (m) and (n). DOL\nhas stated that these provisions aim to protect H-2A workers by “explicitly protecting certain activities all\nworkers must be able to engage in without fear of intimidation, threats, and other forms of retaliation”;\nsafeguarding “collective action and concerted activity for mutual aid and protection”; allowing workers to\ndecline to listen to “employer speech regarding protected activities without fear of retaliation”; permitting\nworkers to “designate a representative of their choosing in certain interviews”; and authorizing workers to\n“invite or accept guests to worker housing.” The rule states that it “does not require employers to\nrecognize labor organizations or to engage in any collective bargaining activities such as those that may\nbe required by the [National Labor Relations Act].” The National Labor Relations Act (NLRA) is a law\nthat gives collective bargaining rights to workers who qualify as “employees” under the definition in the\nstatute. The NLRA explicitly excludes agricultural workers from the definition of “employee.”\nKansas v. U.S. Department of Labor\nOn June 10, 2024, Kansas and 16 other states, a trade association of growers, and a private farm filed a\ncomplaint against DOL in the U.S. District Court for the Southern District of Georgia, arguing, among\nother things, that the Farmworker Protection Rule violates the NLRA because it gives H-2A agricultural\nworkers collective bargaining rights when the NLRA explicitly excludes agricultural workers from having\nthose rights. The plaintiffs subsequently filed a motion for a preliminary injunction and temporary\nrestraining order seeking a stay of the effective date of the Farmworker Protection Rule or, in the\nalternative, a temporary restraining order until the court grants an injunction. The court held a hearing on\nthe motion on August 2, 2024, and on August 26, 2024, the federal district court judge granted the\nplaintiffs’ motion for a preliminary injunction.\nPlaintiffs’ Arguments\nThe arguments below were raised in the plaintiffs’ motion for preliminary injunction. This Sidebar does\nnot cover every argument the plaintiffs advanced.\nThe Rule Violates the NLRA\nThe plaintiffs argued that the rule is not in accordance with existing law and that DOL is providing\ncollective bargaining protection to H-2A workers. According to the plaintiffs, parts of the rule are almost\na direct copy of certain provisions in the NLRA, such as those regarding unfair labor practices and\nrepresentatives and elections. The plaintiffs acknowledged that the rule does not expressly declare that H2A workers have a right to unionize and collectively bargain, but they claim that the protections conferred\nby the rule effectively confer such rights in contravention of the NLRA.\nThe Rule Exceeds DOL’s Authority Under the INA\nThe plaintiffs also argued that DOL has very limited authority to issue regulations under 8 U.S.C. § 1188.\nSpecifically, the plaintiffs state that Section 1188(a), which is the part of the statute DOL relied on to\npromulgate the rule, is being misinterpreted by the agency. According to the plaintiffs, DOL is supposed\nto neutralize any adverse effects from an influx of H-2A workers and not necessarily take affirmative\nsteps to improve the working conditions for H-2A workers. In addition, according to the plaintiffs,\nSection 1188(a) does not explicitly give DOL rulemaking authority.\nThe plaintiffs filed this lawsuit before the Supreme Court’s decision in Loper Bright Enterprises v.\nRaimondo, which overturned the Chevron doctrine. The Chevron doctrine directed courts to defer to an\nagency’s reasonable interpretation of ambiguous statutes the agency administers. The plaintiffs argued\nthat because Congress’s intent was clear in 8 U.S.C. § 1188, DOL was not entitled to Chevron deference.\nRelatedly, the plaintiffs pointed out that DOL relies on caselaw that existed before the Supreme Court\noverruled the Chevron doctrine rather than on the statute itself.\nDOL’s Arguments\nThe arguments below were raised in DOL’s response to the plaintiffs’ motion for preliminary injunction.\nThis Sidebar does not cover every argument DOL advanced.\nThe Rule Does Not Violate the NLRA\nIn summary, DOL argued that the rule does not require employers to recognize unions or engage in\ncollective bargaining and is therefore not in violation of the NLRA. According to DOL, the rule expands\non existing H-2A anti-discrimination provisions, and individuals who fall outside the NLRA’s definition\nof “employee” can still be protected by other statutes and regulations. DOL states that the rule does just\nthat by granting protections to those not covered by the NLRA. Finally, DOL argues that the rule and the\nNLRA do not conflict with one another.\nThe Rule Is a Proper Exercise of DOL’s Statutory Obligation\nDOL responded to the plaintiffs’ argument that the rule exceeded its authority by stating that the INA\ngrants it rulemaking authority. DOL pointed out that provisions in 8 U.S.C. § 1188 expressly reference\nDOL regulations and that Congress authorized it to implement the mission of the statute through\nregulation. Further, DOL argued that H-2A workers will become more attractive to U.S. employers if they\nreceive fewer protections than U.S. workers and that this in turn will “adversely affect” U.S. workers. The\ngoal of the rule, according to DOL, is to place H-2A workers on similar footing as U.S. workers to prevent an adverse effect in the long run. Lastly, DOL maintained that it has historically understood the\n“adverse effect” requirement “as requiring parity between the terms and conditions of employment\nprovided to H-2A workers ... and as establishing a baseline ‘acceptable’ standard for working conditions\nbelow which [U.S. workers] would be adversely affected.”\nDOL filed its response after the Supreme Court announced the overruling of Chevron in Loper Bright\nEnterprises. Citing Loper Bright Enterprises in a footnote, DOL argued that the best reading of Section\n1188 was that Congress had delegated to DOL broad, discretionary authority to take action to prevent\nadverse effects to workers in the United States. The agency claimed that the rule is an appropriate\nexercise of this discretionary authority, including because the rule “ensures that agricultural employers\ncannot use the H-2A workforce to undermine workers in the United States who seek better wages and\nworking conditions.”","full_prompt":"Formulate your answer using only the provided text; do not draw from any outside sources.\n\nProvided text:\nThe Court’s Order on the Motion for Preliminary Injunction\nOn August 26, 2024, a federal district court judge granted the plaintiffs’ motion for preliminary\ninjunction. The judge found that the plaintiffs met their burden to show that they were entitled to\npreliminary relief. First, the judge held that the plaintiffs were likely to succeed on the merits of their\ncase. The judge initially determined that the rule falls within DOL’s rulemaking authority under 8 U.S.C.\n§ 1188 but found that the rule conflicts with the NLRA. Specifically, the judge stated that DOL had “not\nshown a consequential difference between the rights protected by the [rule] and those given to\nnonagricultural workers by the NLRA,” that the rule “creates a right not previously bestowed by\nCongress,” and that DOL failed to show that Congress intended to give agricultural workers a right to\nparticipate in collective bargaining. The judge further found that just because DOL has rulemaking\nauthority does not mean it can “create law or protect newly-created rights of agricultural workers.”\nTherefore, the court held that the plaintiffs were likely to succeed on the merits of their claim. The judge\nfurther held that the plaintiffs met their burden with regard to the other factors needed to support a\npreliminary injunction.\nThe judge also found that, although the plaintiffs were entitled to preliminary relief, that relief should be\nnarrowly tailored and party-specific. According to the court, nationwide relief is generally disfavored, as\n“national uniformity is not a proper consideration,” and a nationwide injunction in this case is\nunwarranted. The judge determined that the court is able to provide a tailored preliminary injunction that\naddresses the plaintiffs’ harms and can offer relief “without issuing a nationwide injunction.” DOL filed a\nmotion for reconsideration of the scope of the judge’s order, but the motion was denied.\nConsiderations for Congress\nMembers of Congress have taken differing views on the Farmworker Protection Rule. Before the rule was\nfinalized, several Members of Congress wrote a letter in November 2023 to Acting DOL Secretary Su and\nDHS Secretary Mayorkas in support of the rule, stating that the rule represents an opportunity to improve\nworking conditions for H-2A workers and “improve enforcement capabilities of agencies against abusive\nemployers.” Following the rule’s publication in April 2024, Representative Scott Franklin introduced a\nresolution of disapproval under the Congressional Review Act to rescind the rule, H.J. Res. 135. This\nresolution would prohibit DOL from any future similar rulemaking. He and the co-sponsors maintain that\nthe rule will increase costs for agricultural producers and allow H-2A workers to unionize.\nThere are other options if Congress chooses to respond to DOL’s Farmworker Protection Rule. First,\nCongress may consider amending the NLRA’s definition of “employee” to include agricultural workers,\nthereby allowing H-2A agricultural workers to receive collective bargaining rights. Alternatively,\nCongress could amend the NLRA and other laws to authorize or prohibit different labor requirements\ncontained in the Farmworker Protection Rule that are not expressly addressed under existing statutes.\nCongress could also consider making changes to the H-2A visa program itself. For example, the\nAffordable and Secure Food Act (S. 4069) in the 118th Congress would, among other things, reform the\nH-2A visa program by adding worker protections and by providing visas for year-round jobs. A similar\nbill, the Farm Workforce Modernization Act of 2023 (H.R. 4319), has been introduced in the House\nduring this Congress. Earlier versions of this bill introduced in the 116th and 117th Congresses passed the\nHouse.\n\nWhat is HR 4319?","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":798} +{"system_instruction":"In a 3-5 sentence paragraph based solely on the provided context block, answer the user's question. Outside knowledge is strictly prohibited.","user_request":"What are the benefits and/or drawbacks of this acquisition?","context_document":" Contact: Corporate Communications, USJ Co.\n 81-6-6465-3333\nUS MEDIA GIANT, COMCAST NBCUNIVERSAL\nTO PURCHASE 51% OWNERSHIP OF USJ CO., LTD.\nOSAKA (Sept. 28, 2015) – USJ Co., Ltd., the operating company of Universal Studios Japan, announced today that\nComcast NBCUniversal agreed to purchase 51% of ownership of USJ from the current shareholders. This acquisition\nwill show the strong commitment of Comcast NBCUniversal to grow and evolve Universal Studios Japan and as we\nwork with NBCUniversal and its Universal Parks & Resorts division, the entire group’s global strategy in theme park\nbusiness will accelerate.\nAlso today, Glenn Gumpel, who served as Chief Executive Officer of USJ since 2004, announced to step down from\nthe current position effective when the transaction closes. Universal Parks & Resorts has named Jean-Louis Bonnier\nas the new Chief Executive Officer.\nGlenn Gumpel said, “Universal Studios Japan will continue to progress along with its basic policies such as the\nsuccessful marketing strategy which has boosted the attendance these recent years and look forward to even further\ngrowth utilizing a financial strength and a great platform Comcast NBCUniversal will give.”\nAbout Universal Studios Japan\nBring You the Best of the Worldas a theme park where its guests can have the world’s best experiences and create\nthe world’s best memories, Universal Studios Japan offers the world-class entertainment such as authentic attractions\nand shows, based on not only Hollywood blockbusters but also very popular world class entertainment brands, and a\nvariety of seasonal events entertain its guests to the fullest fun.\nIn recent years, Universal Studios Japan has constantly offered new entertainment one after another such as\nUniversal Wonederland area where family guests enjoy meeting with popular characters, Universal Cool Japan event\noffering attractions themed on world-renowned Japanese entertainment brands, and The Wizarding World of Harry\nPotter which has been gathering attention of both domestic and international guests. These efforts resulted in not only\na record-high attendance made in FY 2014 but also positioning of the Park as a prominent entertainment and leisure\nlandmark drawing much greater number of guests from distant areas in Japan as well as overseas.\nAbout Comcast:\nComcast Corporation (Nasdaq: CMCSA, CMCSK) is a global media and technology company with two primary\nbusinesses, Comcast Cable and NBCUniversal. Comcast Cable is one of the nation's largest video, high-speed Internet\nand phone providers to residential customers under the XFINITY brand and also provides these services to businesses.\nAbout NBCUniversal:\nNBCUniversal owns and operates a valuable portfolio of news and entertainment television networks, a premier motion \npicture company, significant television production operations, a leading television stations group, world-renowned\ntheme parks, and a suite of leading Internet-based businesses. NBCUniversal is a subsidiary of Comcast Corporation.\nAbout Universal Parks & Resorts:\nUniversal Parks & Resorts, a unit of Comcast NBCUniversal, offers guests around the globe today’s most relevant and\npopular entertainment experiences. With three-time Academy Award winner Steven Spielberg as creative consultant, its\ntheme parks are known for immersive experiences that feature some of the world’s most thrilling and technologically\nadvanced film- and television-based attractions.\nComcast NBCUniversal wholly owns Universal Studios Hollywood, which includes Universal CityWalk Hollywood. It\nalso owns Universal Orlando Resort, a world-class destination resort featuring two theme parks (Universal Studios\nFlorida and Universal’s Islands of Adventure), four resort hotels, and Universal CityWalk Orlando. Comcast\nNBCUniversal also has license agreements with Universal Studios Japan in Osaka, Japan and Universal Studios\nSingapore at Resorts World Sentosa, Singapore. In addition, Comcast NBCUniversal has recently announced plans for a\ntheme park in Beijing and an indoor theme park to be developed as part of the Galactica Park project in Moscow.\n＊ ＊ ＊\nUniversal Studios Japan aims for the world’s best entertainment, a place where memories that lasts a lifetime are\nmade.\nPlease call the information center （Tel : 0570-20-0606） for any general information in regards to Universal\nStudios Japan. The Official Universal Studios Japan website can be accessed via computer, cell phone and smart\nphone.\n＊ ＊ ＊","full_prompt":"Context Block: Contact: Corporate Communications, USJ Co.\n 81-6-6465-3333\nUS MEDIA GIANT, COMCAST NBCUNIVERSAL\nTO PURCHASE 51% OWNERSHIP OF USJ CO., LTD.\nOSAKA (Sept. 28, 2015) – USJ Co., Ltd., the operating company of Universal Studios Japan, announced today that\nComcast NBCUniversal agreed to purchase 51% of ownership of USJ from the current shareholders. This acquisition\nwill show the strong commitment of Comcast NBCUniversal to grow and evolve Universal Studios Japan and as we\nwork with NBCUniversal and its Universal Parks & Resorts division, the entire group’s global strategy in theme park\nbusiness will accelerate.\nAlso today, Glenn Gumpel, who served as Chief Executive Officer of USJ since 2004, announced to step down from\nthe current position effective when the transaction closes. Universal Parks & Resorts has named Jean-Louis Bonnier\nas the new Chief Executive Officer.\nGlenn Gumpel said, “Universal Studios Japan will continue to progress along with its basic policies such as the\nsuccessful marketing strategy which has boosted the attendance these recent years and look forward to even further\ngrowth utilizing a financial strength and a great platform Comcast NBCUniversal will give.”\nAbout Universal Studios Japan\nBring You the Best of the Worldas a theme park where its guests can have the world’s best experiences and create\nthe world’s best memories, Universal Studios Japan offers the world-class entertainment such as authentic attractions\nand shows, based on not only Hollywood blockbusters but also very popular world class entertainment brands, and a\nvariety of seasonal events entertain its guests to the fullest fun.\nIn recent years, Universal Studios Japan has constantly offered new entertainment one after another such as\nUniversal Wonederland area where family guests enjoy meeting with popular characters, Universal Cool Japan event\noffering attractions themed on world-renowned Japanese entertainment brands, and The Wizarding World of Harry\nPotter which has been gathering attention of both domestic and international guests. These efforts resulted in not only\na record-high attendance made in FY 2014 but also positioning of the Park as a prominent entertainment and leisure\nlandmark drawing much greater number of guests from distant areas in Japan as well as overseas.\nAbout Comcast:\nComcast Corporation (Nasdaq: CMCSA, CMCSK) is a global media and technology company with two primary\nbusinesses, Comcast Cable and NBCUniversal. Comcast Cable is one of the nation's largest video, high-speed Internet\nand phone providers to residential customers under the XFINITY brand and also provides these services to businesses.\nAbout NBCUniversal:\nNBCUniversal owns and operates a valuable portfolio of news and entertainment television networks, a premier motion \npicture company, significant television production operations, a leading television stations group, world-renowned\ntheme parks, and a suite of leading Internet-based businesses. NBCUniversal is a subsidiary of Comcast Corporation.\nAbout Universal Parks & Resorts:\nUniversal Parks & Resorts, a unit of Comcast NBCUniversal, offers guests around the globe today’s most relevant and\npopular entertainment experiences. With three-time Academy Award winner Steven Spielberg as creative consultant, its\ntheme parks are known for immersive experiences that feature some of the world’s most thrilling and technologically\nadvanced film- and television-based attractions.\nComcast NBCUniversal wholly owns Universal Studios Hollywood, which includes Universal CityWalk Hollywood. It\nalso owns Universal Orlando Resort, a world-class destination resort featuring two theme parks (Universal Studios\nFlorida and Universal’s Islands of Adventure), four resort hotels, and Universal CityWalk Orlando. Comcast\nNBCUniversal also has license agreements with Universal Studios Japan in Osaka, Japan and Universal Studios\nSingapore at Resorts World Sentosa, Singapore. In addition, Comcast NBCUniversal has recently announced plans for a\ntheme park in Beijing and an indoor theme park to be developed as part of the Galactica Park project in Moscow.\n＊ ＊ ＊\nUniversal Studios Japan aims for the world’s best entertainment, a place where memories that lasts a lifetime are\nmade.\nPlease call the information center （Tel : 0570-20-0606） for any general information in regards to Universal\nStudios Japan. The Official Universal Studios Japan website can be accessed via computer, cell phone and smart\nphone.\n＊ ＊ ＊\n\nSystem Instructions: In a 3-5 sentence paragraph based solely on the provided context block, answer the user's question. Outside knowledge is strictly prohibited.\n\nQuestion: Can you explain the relationship between all the companies mentioned here in simple terms, including subsidiaries, etc.?","domain":"Financial","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":815} +{"system_instruction":"Provide a concise answer (less than 100 words), using only the information provided below.","user_request":"In the context of the Gender Recognition Act 2004, what makes something a gender-specific offence?","context_document":"3 Evidence\n(1) An application under section 1(1)(a) must include either—\n(a) a report made by a registered medical practitioner practising in the\nfield of gender dysphoria and a report made by another registered\nmedical practitioner (who may, but need not, practise in that field), or\n(b) a report made by a chartered psychologist practising in that field and a\nreport made by a registered medical practitioner (who may, but need\nnot, practise in that field).\n(2) But subsection (1) is not complied with unless a report required by that\nsubsection and made by—\n(a) a registered medical practitioner, or\n(b) a chartered psychologist,\npractising in the field of gender dysphoria includes details of the diagnosis of\nthe applicant’s gender dysphoria.\n(3) And subsection (1) is not complied with in a case where—\n(a) the applicant has undergone or is undergoing treatment for the\npurpose of modifying sexual characteristics, or\n(b) treatment for that purpose has been prescribed or planned for the\napplicant,\nunless at least one of the reports required by that subsection includes details of\nit.\n(4) An application under section 1(1)(a) must also include a statutory declaration\nby the applicant that the applicant meets the conditions in section 2(1)(b) and\n(c).\n(5) An application under section 1(1)(b) must include evidence that the applicant\nhas changed gender under the law of an approved country or territory.\nGender Recognition Act 2004 (c. 7) 3\n(6) Any application under section 1(1) must include—\n(a) a statutory declaration as to whether or not the applicant is married,\n(b) any other information or evidence required by an order made by the\nSecretary of State, and\n(c) any other information or evidence which the Panel which is to\ndetermine the application may require,\nand may include any other information or evidence which the applicant wishes\nto include.\n(7) The Secretary of State may not make an order under subsection (6)(b) without\nconsulting the Scottish Ministers and the Department of Finance and Personnel\nin Northern Ireland.\n(8) If the Panel which is to determine the application requires inform","full_prompt":"What evidence is required to obtain a Gender Recognition Certificate in the UK?\n\nProvide a concise answer (less than 100 words), using only the information provided below.\n\n\"3 Evidence\n(1) An application under section 1(1)(a) must include either—\n(a) a report made by a registered medical practitioner practising in the\nfield of gender dysphoria and a report made by another registered\nmedical practitioner (who may, but need not, practise in that field), or\n(b) a report made by a chartered psychologist practising in that field and a\nreport made by a registered medical practitioner (who may, but need\nnot, practise in that field).\n(2) But subsection (1) is not complied with unless a report required by that\nsubsection and made by—\n(a) a registered medical practitioner, or\n(b) a chartered psychologist,\npractising in the field of gender dysphoria includes details of the diagnosis of\nthe applicant’s gender dysphoria.\n(3) And subsection (1) is not complied with in a case where—\n(a) the applicant has undergone or is undergoing treatment for the\npurpose of modifying sexual characteristics, or\n(b) treatment for that purpose has been prescribed or planned for the\napplicant,\nunless at least one of the reports required by that subsection includes details of\nit.\n(4) An application under section 1(1)(a) must also include a statutory declaration\nby the applicant that the applicant meets the conditions in section 2(1)(b) and\n(c).\n(5) An application under section 1(1)(b) must include evidence that the applicant\nhas changed gender under the law of an approved country or territory.\nGender Recognition Act 2004 (c. 7) 3\n(6) Any application under section 1(1) must include—\n(a) a statutory declaration as to whether or not the applicant is married,\n(b) any other information or evidence required by an order made by the\nSecretary of State, and\n(c) any other information or evidence which the Panel which is to\ndetermine the application may require,\nand may include any other information or evidence which the applicant wishes\nto include.\n(7) The Secretary of State may not make an order under subsection (6)(b) without\nconsulting the Scottish Ministers and the Department of Finance and Personnel\nin Northern Ireland.\n(8) If the Panel which is to determine the application requires inform\"","domain":"Legal","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":822} +{"system_instruction":"Respond to questions or requests using only the information contained in the text that is provided to you.","user_request":"Summarize and list the cases used to support the policy in this document in chronological order.","context_document":"Attorney Fees The Freedom of Information Act is one of more than a hundred different federal statutes that contain a \"fee-shifting\" provision permitting the trial court to award reasonable attorney fees and litigation costs to a plaintiff who has \"substantially prevailed.\"1 The FOIA's attorney fees provision requires courts to engage in a two-step substantive inquiry. The court must determine first if the plaintiff is eligible for an award of fees and/or costs and it must then determine if the plaintiff is entitled to the award.2 Even if a plaintiff meets both of these tests, the award of fees and costs is entirely within the discretion of the court.3 Threshold Issues The FOIA's attorney fees provision limits an award to fees and costs incurred in litigating a case brought pursuant to the FOIA;4 accordingly, fees and other costs are generally 1 5 U.S.C. § 552(a)(4)(E)(i) (2006), amended by OPEN Government Act of 2007, Pub. L. No. 110-175, 121 Stat. 2524. 2 See, e.g., Tax Analysts v. DOJ, 965 F.2d 1092, 1093 (D.C. Cir. 1992); Church of Scientology v. USPS, 700 F.2d 486, 489 (9th Cir. 1983); see also Wheeler v. IRS, 37 F. Supp. 2d 407, 411 n.1 (W.D. Pa. 1998) (\"The test for whether the court should award a FOIA plaintiff litigation costs is the same as the test for whether attorney fees should be awarded.\"). 3 See, e.g., Lissner v. U.S. Customs Serv., 56 F. App'x 330, 331 (9th Cir. 2002) (stating that review of attorney fee award is for abuse of discretion); Anderson v. HHS, 80 F.3d 1500, 1504 (10th Cir. 1996) (\"Assessment of attorney's fees in an FOIA case is discretionary with the district court.\"); Detroit Free Press, Inc. v. DOJ, 73 F.3d 93, 98 (6th Cir. 1996) (\"We review the court's determination [to grant fees] for an abuse of discretion.\"); Young v. Dir., No. 92-2561, 1993 WL 305970, at *2 (4th Cir. 1993) (noting that court has discretion to deny fees even if eligibility threshold is met); Maynard v. CIA, 986 F.2d 547, 567 (1st Cir. 1993) (holding that a decision on whether to award attorney fees \"will be reversed only for an abuse of . . . discretion\"); Tax Analysts, 965 F.2d at 1094 (\"sifting of those [fee] criteria over the facts of a case is a matter of district court discretion\"); Hersh & Hersh v. HHS, No. 06-4234, 2008 WL 2725497, at *1 (N.D. Cal. July 10, 2008) (\"If a plaintiff demonstrates eligibility for fees, the district court may then, in the exercise of its discretion, determine that the plaintiff is entitled to an award of fees and costs.\"); Bangor Hydro-Elec. Co. v. U.S. Dep't of the Interior, 903 F. Supp. 160, 170 (D. Me. 1995) (\"Awards of litigation costs and attorney fees under FOIA are left to the sound discretion of the trial court.\"). 4 See Nichols v. Pierce, 740 F.2d 1249, 1252-54 (D.C. Cir. 1984) (refusing to award fees for (continued...) not awarded for services rendered at the administrative level.5 Furthermore, the Court of Appeals for the District of Columbia Circuit has held that FOIA litigation costs related to disputes with third parties, \"who are not within the government's authority or control, with respect to litigation issues that were neither raised nor pursued by the government, cannot form the basis of a fee award under 5 U.S.C. § 552(a)(4)(E).\"6 A threshold eligibility matter concerns precisely who can qualify for an award of attorney fees. The D.C. Circuit has found that the Supreme Court's decision in Kay v. Ehrler7 establishes that subsection (a)(4)(E)(i) of the FOIA does not authorize the award of fees to a pro se non-attorney plaintiff, because \"the word 'attorney,' when used in the context of a feeshifting statute, does not encompass a layperson proceeding on his own behalf.\"8 In order to 4 (...continued) plaintiff's success under Administrative Procedure Act, 5 U.S.C. §§ 701-706 (2006), resulting in order to agency to issue regulations, despite plaintiff's claim of victory under FOIA subsection (a)(1)), because Complaint failed to assert claim under or rely specifically on FOIA). 5 See AutoAlliance Int'l, Inc. v. U.S. Customs Serv., No. 02-72369, slip op. at 3 (E.D. Mich. Mar. 23, 2004) (denying attorney fees for time spent on \"administrative appeals that should have been completed prior to filing suit\"); Inst. for Wildlife Prot. v. U.S. Fish & Wildlife Serv., No. 02-6178, slip op. at 6 (D. Or. Dec. 3, 2003) (deducting hours spent on FOIA administrative process for fee-calculation purposes); Nw. Coal. for Alternatives to Pesticides v. Browner, 965 F. Supp. 59, 65 (D.D.C. 1997) (\"FOIA does not authorize fees for work performed at the administrative stage.\"); Associated Gen. Contractors v. EPA, 488 F. Supp. 861, 864 (D. Nev. 1980) (concluding that attorney fees are unavailable for work performed at administrative level); cf. Kennedy v. Andrus, 459 F. Supp. 240, 244 (D.D.C. 1978) (rejecting attorney fees claim for services rendered at administrative level under Privacy Act, 5 U.S.C. § 552a (2006)), aff'd, 612 F.2d 586 (D.C. Cir. 1980) (unpublished table decision). But see Or. Natural Desert Ass'n v. Gutierrez, 442 F. Supp. 2d 1096, 1101 (D. Or. 2006) (awarding fees for work performed at the administrative level, on the rationale that \"exhaustion of remedies is required and provides a sufficient record for the civil action\") (appeal pending); McCoy v. BOP, No. 03-383, 2005 WL 1972600, at *4 (E.D. Ky. Aug. 16, 2005) (permitting fees for work on plaintiff's administrative appeal, on the rationale that it \"was necessary to exhaust administrative remedies\"), reconsideration denied, No. 03-383 (E.D. Ky. Oct. 6, 2005); cf. Tule River Conservancy v. U.S. Forest Serv., No. 97-5720, slip op. at 16-17 (E.D. Cal. Sept. 12, 2000) (allowing attorney fees for pre-litigation research on \"how to exhaust [plaintiff's] administration remedies prior to filing suit\" and on \"how to file FOIA complaint\"). 6 Judicial Watch, Inc. v. U.S. Dep't of Commerce, 470 F.3d 363, 373 (D.C. Cir. 2006). 7 499 U.S. 432 (1991). 8 Benavides v. BOP, 993 F.2d 257, 259 (D.C. Cir. 1993) (explaining Kay decision); see Bensman v. U.S. Fish & Wildlife Serv., 49 F. App'x 646, 647 (7th Cir. 2002) (\"Even when a pro se litigant performs the same tasks as an attorney, he is not entitled to reimbursement for his time.\"); Sukup v. EOUSA, No. 02-0355, 2007 WL 2405716, at *1 (D.D.C. Aug. 23, 2007) (\"Pro se plaintiffs may not recover attorney's fees under the FOIA.\"); Deichman v. United States, No. 2:05cv680, 2006 WL 3000448, at *7 (E.D. Va. Oct. 20, 2006) (holding that pro see litigant cannot (continued...) be eligible for attorney fees, therefore, a FOIA plaintiff must have a representational relationship with an attorney.9 Furthermore, Kay indicated that no award of attorney fees should be made to a pro se plaintiff who also is an attorney. 10 Because the fee-shifting provision of the FOIA was intended \"'to encourage potential claimants to seek legal advice before commencing litigation,'\"11 and because a pro se attorney, by definition, does not seek out the \"'detached and objective perspective necessary'\" to litigate his FOIA case,12 the overwhelming majority of courts have agreed with Kay and have held that a pro se attorney is not eligible for a fee award that otherwise would have had to be paid to counsel.13 This is particularly so because 8 (...continued) recover attorney fees under FOIA); Lair v. Dep't of the Treasury, No. 03-827, 2005 WL 645228, at *6 (D.D.C. Mar. 21, 2005) (explaining that \"pro-se non-attorney . . . may not collect attorney fees\" (citing Benavides)), reconsideration denied, 2005 WL 1330722 (D.D.C. June 3, 2005). 9 See Kooritzky v. Herman, 178 F.3d 1315, 1323 (D.C. Cir. 1999) (holding that for all similarly worded fee-shifting statutes, \"the term 'attorney' contemplates an agency relationship between a litigant and an independent lawyer\"); see also Blazy v. Tenet, 194 F.3d 90, 94 (D.C. Cir. 1999) (concluding that attorney need not file formal appearance in order for litigant to claim fees for consultations, so long as attorney-client relationship existed) (Privacy Act case); cf. Anderson v. U.S. Dep't of the Treasury, 648 F.2d 1, 3 (D.C. Cir. 1979) (indicating that when an organization litigates through in-house counsel, any payable attorney fees should not \"exceed[] the expenses incurred by [that party] in terms of [in-house counsel] salaries and other out-of-pocket expenses\"). ","full_prompt":"Respond to questions or requests using only the information contained in the text that is provided to you.\n\nSummarize and list the cases used to support the policy in this document in chronological order.\n\nAttorney Fees The Freedom of Information Act is one of more than a hundred different federal statutes that contain a \"fee-shifting\" provision permitting the trial court to award reasonable attorney fees and litigation costs to a plaintiff who has \"substantially prevailed.\"1 The FOIA's attorney fees provision requires courts to engage in a two-step substantive inquiry. The court must determine first if the plaintiff is eligible for an award of fees and/or costs and it must then determine if the plaintiff is entitled to the award.2 Even if a plaintiff meets both of these tests, the award of fees and costs is entirely within the discretion of the court.3 Threshold Issues The FOIA's attorney fees provision limits an award to fees and costs incurred in litigating a case brought pursuant to the FOIA;4 accordingly, fees and other costs are generally 1 5 U.S.C. § 552(a)(4)(E)(i) (2006), amended by OPEN Government Act of 2007, Pub. L. No. 110-175, 121 Stat. 2524. 2 See, e.g., Tax Analysts v. DOJ, 965 F.2d 1092, 1093 (D.C. Cir. 1992); Church of Scientology v. USPS, 700 F.2d 486, 489 (9th Cir. 1983); see also Wheeler v. IRS, 37 F. Supp. 2d 407, 411 n.1 (W.D. Pa. 1998) (\"The test for whether the court should award a FOIA plaintiff litigation costs is the same as the test for whether attorney fees should be awarded.\"). 3 See, e.g., Lissner v. U.S. Customs Serv., 56 F. App'x 330, 331 (9th Cir. 2002) (stating that review of attorney fee award is for abuse of discretion); Anderson v. HHS, 80 F.3d 1500, 1504 (10th Cir. 1996) (\"Assessment of attorney's fees in an FOIA case is discretionary with the district court.\"); Detroit Free Press, Inc. v. DOJ, 73 F.3d 93, 98 (6th Cir. 1996) (\"We review the court's determination [to grant fees] for an abuse of discretion.\"); Young v. Dir., No. 92-2561, 1993 WL 305970, at *2 (4th Cir. 1993) (noting that court has discretion to deny fees even if eligibility threshold is met); Maynard v. CIA, 986 F.2d 547, 567 (1st Cir. 1993) (holding that a decision on whether to award attorney fees \"will be reversed only for an abuse of . . . discretion\"); Tax Analysts, 965 F.2d at 1094 (\"sifting of those [fee] criteria over the facts of a case is a matter of district court discretion\"); Hersh & Hersh v. HHS, No. 06-4234, 2008 WL 2725497, at *1 (N.D. Cal. July 10, 2008) (\"If a plaintiff demonstrates eligibility for fees, the district court may then, in the exercise of its discretion, determine that the plaintiff is entitled to an award of fees and costs.\"); Bangor Hydro-Elec. Co. v. U.S. Dep't of the Interior, 903 F. Supp. 160, 170 (D. Me. 1995) (\"Awards of litigation costs and attorney fees under FOIA are left to the sound discretion of the trial court.\"). 4 See Nichols v. Pierce, 740 F.2d 1249, 1252-54 (D.C. Cir. 1984) (refusing to award fees for (continued...) not awarded for services rendered at the administrative level.5 Furthermore, the Court of Appeals for the District of Columbia Circuit has held that FOIA litigation costs related to disputes with third parties, \"who are not within the government's authority or control, with respect to litigation issues that were neither raised nor pursued by the government, cannot form the basis of a fee award under 5 U.S.C. § 552(a)(4)(E).\"6 A threshold eligibility matter concerns precisely who can qualify for an award of attorney fees. The D.C. Circuit has found that the Supreme Court's decision in Kay v. Ehrler7 establishes that subsection (a)(4)(E)(i) of the FOIA does not authorize the award of fees to a pro se non-attorney plaintiff, because \"the word 'attorney,' when used in the context of a feeshifting statute, does not encompass a layperson proceeding on his own behalf.\"8 In order to 4 (...continued) plaintiff's success under Administrative Procedure Act, 5 U.S.C. §§ 701-706 (2006), resulting in order to agency to issue regulations, despite plaintiff's claim of victory under FOIA subsection (a)(1)), because Complaint failed to assert claim under or rely specifically on FOIA). 5 See AutoAlliance Int'l, Inc. v. U.S. Customs Serv., No. 02-72369, slip op. at 3 (E.D. Mich. Mar. 23, 2004) (denying attorney fees for time spent on \"administrative appeals that should have been completed prior to filing suit\"); Inst. for Wildlife Prot. v. U.S. Fish & Wildlife Serv., No. 02-6178, slip op. at 6 (D. Or. Dec. 3, 2003) (deducting hours spent on FOIA administrative process for fee-calculation purposes); Nw. Coal. for Alternatives to Pesticides v. Browner, 965 F. Supp. 59, 65 (D.D.C. 1997) (\"FOIA does not authorize fees for work performed at the administrative stage.\"); Associated Gen. Contractors v. EPA, 488 F. Supp. 861, 864 (D. Nev. 1980) (concluding that attorney fees are unavailable for work performed at administrative level); cf. Kennedy v. Andrus, 459 F. Supp. 240, 244 (D.D.C. 1978) (rejecting attorney fees claim for services rendered at administrative level under Privacy Act, 5 U.S.C. § 552a (2006)), aff'd, 612 F.2d 586 (D.C. Cir. 1980) (unpublished table decision). But see Or. Natural Desert Ass'n v. Gutierrez, 442 F. Supp. 2d 1096, 1101 (D. Or. 2006) (awarding fees for work performed at the administrative level, on the rationale that \"exhaustion of remedies is required and provides a sufficient record for the civil action\") (appeal pending); McCoy v. BOP, No. 03-383, 2005 WL 1972600, at *4 (E.D. Ky. Aug. 16, 2005) (permitting fees for work on plaintiff's administrative appeal, on the rationale that it \"was necessary to exhaust administrative remedies\"), reconsideration denied, No. 03-383 (E.D. Ky. Oct. 6, 2005); cf. Tule River Conservancy v. U.S. Forest Serv., No. 97-5720, slip op. at 16-17 (E.D. Cal. Sept. 12, 2000) (allowing attorney fees for pre-litigation research on \"how to exhaust [plaintiff's] administration remedies prior to filing suit\" and on \"how to file FOIA complaint\"). 6 Judicial Watch, Inc. v. U.S. Dep't of Commerce, 470 F.3d 363, 373 (D.C. Cir. 2006). 7 499 U.S. 432 (1991). 8 Benavides v. BOP, 993 F.2d 257, 259 (D.C. Cir. 1993) (explaining Kay decision); see Bensman v. U.S. Fish & Wildlife Serv., 49 F. App'x 646, 647 (7th Cir. 2002) (\"Even when a pro se litigant performs the same tasks as an attorney, he is not entitled to reimbursement for his time.\"); Sukup v. EOUSA, No. 02-0355, 2007 WL 2405716, at *1 (D.D.C. Aug. 23, 2007) (\"Pro se plaintiffs may not recover attorney's fees under the FOIA.\"); Deichman v. United States, No. 2:05cv680, 2006 WL 3000448, at *7 (E.D. Va. Oct. 20, 2006) (holding that pro see litigant cannot (continued...) be eligible for attorney fees, therefore, a FOIA plaintiff must have a representational relationship with an attorney.9 Furthermore, Kay indicated that no award of attorney fees should be made to a pro se plaintiff who also is an attorney. 10 Because the fee-shifting provision of the FOIA was intended \"'to encourage potential claimants to seek legal advice before commencing litigation,'\"11 and because a pro se attorney, by definition, does not seek out the \"'detached and objective perspective necessary'\" to litigate his FOIA case,12 the overwhelming majority of courts have agreed with Kay and have held that a pro se attorney is not eligible for a fee award that otherwise would have had to be paid to counsel.13 This is particularly so because 8 (...continued) recover attorney fees under FOIA); Lair v. Dep't of the Treasury, No. 03-827, 2005 WL 645228, at *6 (D.D.C. Mar. 21, 2005) (explaining that \"pro-se non-attorney . . . may not collect attorney fees\" (citing Benavides)), reconsideration denied, 2005 WL 1330722 (D.D.C. June 3, 2005). 9 See Kooritzky v. Herman, 178 F.3d 1315, 1323 (D.C. Cir. 1999) (holding that for all similarly worded fee-shifting statutes, \"the term 'attorney' contemplates an agency relationship between a litigant and an independent lawyer\"); see also Blazy v. Tenet, 194 F.3d 90, 94 (D.C. Cir. 1999) (concluding that attorney need not file formal appearance in order for litigant to claim fees for consultations, so long as attorney-client relationship existed) (Privacy Act case); cf. Anderson v. U.S. Dep't of the Treasury, 648 F.2d 1, 3 (D.C. Cir. 1979) (indicating that when an organization litigates through in-house counsel, any payable attorney fees should not \"exceed[] the expenses incurred by [that party] in terms of [in-house counsel] salaries and other out-of-pocket expenses\"). ","domain":"Legal","type":"Summarize & Format","high_level_type":"Text Transformation","__index_level_0__":829} +{"system_instruction":"This task requires you to answer questions based solely on the information provided in the prompt and context block. You are not allowed to use any external resources or prior knowledge.","user_request":"What was the first circuits ruling on the United States v Evans?","context_document":"Funding Limitations on Medical Marijuana Prosecutions In each fiscal year since FY2015, Congress has included provisions in appropriations acts that prohibit DOJ from using appropriated funds to prevent certain states and territories and the District of Columbia from “implementing their own laws that authorize the use, distribution, possession, or cultivation of medical marijuana.” The FY2024 provision lists 52 jurisdictions, including every U.S. jurisdiction that had legalized medical cannabis use at the time it was enacted. On its face, the appropriations rider bars DOJ from taking legal action against the states directly in order to prevent them from promulgating or enforcing medical marijuana laws. In addition, federal courts have interpreted the rider to prohibit certain federal prosecutions of private individuals or organizations that Congressional Research Service 3 produce, distribute, or possess marijuana in accordance with state medical marijuana laws. In those cases, criminal defendants have invoked the rider before trial, seeking either the dismissal of their indictments or injunctions barring prosecution. By contrast, courts have generally declined to apply the rider outside the context of initial criminal prosecutions. For instance, the Ninth Circuit has held that the provision does not “impact[ ] the ability of a federal district court to restrict the use of medical marijuana as a condition of probation.” In the 2016 case United States v. McIntosh, the U.S. Court of Appeals for the Ninth Circuit considered the circumstances in which the appropriations rider bars CSA prosecution of marijuana-related activities. The court held that the rider prohibits the federal government only from preventing the implementation of those specific rules of state law that authorize the use, distribution, possession, or cultivation of medical marijuana. DOJ does not prevent the implementation of [such rules] when it prosecutes individuals who engage in conduct unauthorized under state medical marijuana laws. Individuals who do not strictly comply with all state-law conditions regarding the use, distribution, possession, and cultivation of medical marijuana have engaged in conduct that is unauthorized, and prosecuting such individuals does not violate [the rider]. Relying on McIntosh, the Ninth Circuit has issued several decisions allowing federal prosecution of individuals who did not “strictly comply” with state medical marijuana laws, notwithstanding the appropriations rider, and several district courts have followed that reasoning. As one example, in United States v. Evans, the Ninth Circuit upheld the prosecution of two individuals involved in the production of medical marijuana who smoked marijuana as they processed plants for sale. Although state law permitted medical marijuana use by “qualifying patients,” the court concluded that the defendants failed to show they were qualifying patients, and thus they could be prosecuted because their personal marijuana use did not strictly comply with state medical marijuana law. In the 2022 case United States v. Bilodeau, the U.S. Court of Appeals for the First Circuit also considered the scope of the appropriations rider. The defendants in Bilodeau were registered with the State of Maine to produce medical marijuana, but DOJ alleged that they distributed large quantities of marijuana to individuals who were not qualifying patients under Maine law, including recipients in other states. Following indictment for criminal CSA violations, the defendants sought to invoke the appropriations rider to bar their prosecutions. They argued that the rider “must be read to preclude the DOJ, under most circumstances, from prosecuting persons who possess state licenses to partake in medical marijuana activity.” DOJ instead urged the court to apply the Ninth Circuit’s standard, allowing prosecution unless the defendants could show that they acted in strict compliance with state medical marijuana laws. The First Circuit declined to adopt either of the proposed tests. As an initial matter, the court agreed with the Ninth Circuit that the rider means “DOJ may not spend funds to bring prosecutions if doing so prevents a state from giving practical effect to its medical marijuana laws.” However, the panel declined to adopt the Ninth Circuit’s holding that the rider bars prosecution only in cases where defendants strictly complied with state law. The court noted that the text of the rider does not explicitly require strict compliance with state law and that, given the complexity of state marijuana regulations, “the potential for technical noncompliance [with state law] is real enough that no person through any reasonable effort could always assure strict compliance.” Thus, the First Circuit concluded that requiring strict compliance with state law would likely chill state-legal medical marijuana activities and prevent the states from giving effect to their medical marijuana laws. On the other hand, the court also rejected the defendants’ more expansive reading of the rider, reasoning that “Congress surely did not intend for the rider to provide a safe harbor to all caregivers with facially valid documents without regard for blatantly illegitimate activity.” Ultimately, while the First Circuit held that the rider bars CSA prosecution in at least some cases where the defendant has committed minor technical violations of state medical marijuana laws, it declined to Congressional Research Service 4 “fully define [the] precise boundaries” of its alternative standard. On the record before it, the court concluded that “the defendants’ cultivation, possession, and distribution of marijuana aimed at supplying persons whom no defendant ever thought were qualifying patients under Maine law” and that a CSA conviction in those circumstances would not “prevent Maine’s medical marijuana laws from having their intended practical effect.” Considerations for Congress It remains to be seen whether and how the difference in reasoning between the Ninth Circuit and the First Circuit will make a practical difference in federal marijuana prosecutions. In theory, the First Circuit’s analysis could make it easier for defendants to invoke the appropriations rider to bar federal prosecutions, because they could do so even if they had not been in strict compliance with state law. In practice, however, resource limitations and enforcement priorities have historically meant that federal marijuana prosecutions target only individuals and organizations that have clearly not complied with state law. Thus, one of the First Circuit judges who considered Bilodeau agreed with the panel’s interpretation of the rider but wrote a concurrence noting that, in practice, the First Circuit’s standard might not be “materially different from the one that the Ninth Circuit applied.” While the medical marijuana appropriations rider restricts DOJ’s ability to bring some marijuana prosecutions, its effect is limited in several ways. First, marijuana-related activities that fall outside the scope of the appropriations rider remain subject to prosecution under the CSA. By its terms, the rider applies only to state laws related to medical marijuana; it does not bar prosecution of any activities related to recreational marijuana, even if those activities are permitted under state law. Second, as the Ninth Circuit has explained, even where the rider does apply, it “does not provide immunity from prosecution for federal marijuana offenses”—it simply restricts DOJ’s ability to expend funds to enforce federal law for as long as it remains in effect. If Congress instead opted to repeal the rider or allow it to lapse, DOJ would be able to prosecute future CSA violations as well as past violations that occurred while the rider was in effect, subject to the applicable statute of limitations. Third, participants in the cannabis industry may face numerous collateral consequences arising from the federal prohibition of marijuana in areas including bankruptcy, taxation, and immigration. Many of those legal consequences attach regardless of whether a person is charged with or convicted of a CSA offense, meaning the rider would not affect them. Because the medical marijuana appropriations rider applies to marijuana specifically, regardless of how the substance is classified under the CSA, rescheduling marijuana would not affect the rider. Congress has the authority to enact legislation to clarify or alter the scope of the appropriations rider, repeal the rider, or decline to include it in future appropriations laws. For instance, Congress could amend the rider to specify whether strict compliance with state medical marijuana law is required in order to bar prosecution under the CSA or provide a different standard that DOJ and the courts should apply. Beyond the appropriations context, Congress could also consider other changes to federal marijuana law that would affect its interaction with state law. Such changes could take the form of more stringent marijuana regulation—for instance, through increased DOJ funding to prosecute CSA violations or limiting federal funds for states that legalize marijuana. In contrast, most recent proposals before Congress seek to relax federal restrictions on marijuana or mitigate the disparity between federal and state marijuana regulation.","full_prompt":"System Instructions: [This task requires you to answer questions based solely on the information provided in the prompt and context block. You are not allowed to use any external resources or prior knowledge.]\nQuestion: [What was the first circuits ruling on the United States v Evans?]\n\nContext Block: [Funding Limitations on Medical Marijuana Prosecutions In each fiscal year since FY2015, Congress has included provisions in appropriations acts that prohibit DOJ from using appropriated funds to prevent certain states and territories and the District of Columbia from “implementing their own laws that authorize the use, distribution, possession, or cultivation of medical marijuana.” The FY2024 provision lists 52 jurisdictions, including every U.S. jurisdiction that had legalized medical cannabis use at the time it was enacted. On its face, the appropriations rider bars DOJ from taking legal action against the states directly in order to prevent them from promulgating or enforcing medical marijuana laws. In addition, federal courts have interpreted the rider to prohibit certain federal prosecutions of private individuals or organizations that Congressional Research Service 3 produce, distribute, or possess marijuana in accordance with state medical marijuana laws. In those cases, criminal defendants have invoked the rider before trial, seeking either the dismissal of their indictments or injunctions barring prosecution. By contrast, courts have generally declined to apply the rider outside the context of initial criminal prosecutions. For instance, the Ninth Circuit has held that the provision does not “impact[ ] the ability of a federal district court to restrict the use of medical marijuana as a condition of probation.” In the 2016 case United States v. McIntosh, the U.S. Court of Appeals for the Ninth Circuit considered the circumstances in which the appropriations rider bars CSA prosecution of marijuana-related activities. The court held that the rider prohibits the federal government only from preventing the implementation of those specific rules of state law that authorize the use, distribution, possession, or cultivation of medical marijuana. DOJ does not prevent the implementation of [such rules] when it prosecutes individuals who engage in conduct unauthorized under state medical marijuana laws. Individuals who do not strictly comply with all state-law conditions regarding the use, distribution, possession, and cultivation of medical marijuana have engaged in conduct that is unauthorized, and prosecuting such individuals does not violate [the rider]. Relying on McIntosh, the Ninth Circuit has issued several decisions allowing federal prosecution of individuals who did not “strictly comply” with state medical marijuana laws, notwithstanding the appropriations rider, and several district courts have followed that reasoning. As one example, in United States v. Evans, the Ninth Circuit upheld the prosecution of two individuals involved in the production of medical marijuana who smoked marijuana as they processed plants for sale. Although state law permitted medical marijuana use by “qualifying patients,” the court concluded that the defendants failed to show they were qualifying patients, and thus they could be prosecuted because their personal marijuana use did not strictly comply with state medical marijuana law. In the 2022 case United States v. Bilodeau, the U.S. Court of Appeals for the First Circuit also considered the scope of the appropriations rider. The defendants in Bilodeau were registered with the State of Maine to produce medical marijuana, but DOJ alleged that they distributed large quantities of marijuana to individuals who were not qualifying patients under Maine law, including recipients in other states. Following indictment for criminal CSA violations, the defendants sought to invoke the appropriations rider to bar their prosecutions. They argued that the rider “must be read to preclude the DOJ, under most circumstances, from prosecuting persons who possess state licenses to partake in medical marijuana activity.” DOJ instead urged the court to apply the Ninth Circuit’s standard, allowing prosecution unless the defendants could show that they acted in strict compliance with state medical marijuana laws. The First Circuit declined to adopt either of the proposed tests. As an initial matter, the court agreed with the Ninth Circuit that the rider means “DOJ may not spend funds to bring prosecutions if doing so prevents a state from giving practical effect to its medical marijuana laws.” However, the panel declined to adopt the Ninth Circuit’s holding that the rider bars prosecution only in cases where defendants strictly complied with state law. The court noted that the text of the rider does not explicitly require strict compliance with state law and that, given the complexity of state marijuana regulations, “the potential for technical noncompliance [with state law] is real enough that no person through any reasonable effort could always assure strict compliance.” Thus, the First Circuit concluded that requiring strict compliance with state law would likely chill state-legal medical marijuana activities and prevent the states from giving effect to their medical marijuana laws. On the other hand, the court also rejected the defendants’ more expansive reading of the rider, reasoning that “Congress surely did not intend for the rider to provide a safe harbor to all caregivers with facially valid documents without regard for blatantly illegitimate activity.” Ultimately, while the First Circuit held that the rider bars CSA prosecution in at least some cases where the defendant has committed minor technical violations of state medical marijuana laws, it declined to Congressional Research Service 4 “fully define [the] precise boundaries” of its alternative standard. On the record before it, the court concluded that “the defendants’ cultivation, possession, and distribution of marijuana aimed at supplying persons whom no defendant ever thought were qualifying patients under Maine law” and that a CSA conviction in those circumstances would not “prevent Maine’s medical marijuana laws from having their intended practical effect.” Considerations for Congress It remains to be seen whether and how the difference in reasoning between the Ninth Circuit and the First Circuit will make a practical difference in federal marijuana prosecutions. In theory, the First Circuit’s analysis could make it easier for defendants to invoke the appropriations rider to bar federal prosecutions, because they could do so even if they had not been in strict compliance with state law. In practice, however, resource limitations and enforcement priorities have historically meant that federal marijuana prosecutions target only individuals and organizations that have clearly not complied with state law. Thus, one of the First Circuit judges who considered Bilodeau agreed with the panel’s interpretation of the rider but wrote a concurrence noting that, in practice, the First Circuit’s standard might not be “materially different from the one that the Ninth Circuit applied.” While the medical marijuana appropriations rider restricts DOJ’s ability to bring some marijuana prosecutions, its effect is limited in several ways. First, marijuana-related activities that fall outside the scope of the appropriations rider remain subject to prosecution under the CSA. By its terms, the rider applies only to state laws related to medical marijuana; it does not bar prosecution of any activities related to recreational marijuana, even if those activities are permitted under state law. Second, as the Ninth Circuit has explained, even where the rider does apply, it “does not provide immunity from prosecution for federal marijuana offenses”—it simply restricts DOJ’s ability to expend funds to enforce federal law for as long as it remains in effect. If Congress instead opted to repeal the rider or allow it to lapse, DOJ would be able to prosecute future CSA violations as well as past violations that occurred while the rider was in effect, subject to the applicable statute of limitations. Third, participants in the cannabis industry may face numerous collateral consequences arising from the federal prohibition of marijuana in areas including bankruptcy, taxation, and immigration. Many of those legal consequences attach regardless of whether a person is charged with or convicted of a CSA offense, meaning the rider would not affect them. Because the medical marijuana appropriations rider applies to marijuana specifically, regardless of how the substance is classified under the CSA, rescheduling marijuana would not affect the rider. Congress has the authority to enact legislation to clarify or alter the scope of the appropriations rider, repeal the rider, or decline to include it in future appropriations laws. For instance, Congress could amend the rider to specify whether strict compliance with state medical marijuana law is required in order to bar prosecution under the CSA or provide a different standard that DOJ and the courts should apply. Beyond the appropriations context, Congress could also consider other changes to federal marijuana law that would affect its interaction with state law. Such changes could take the form of more stringent marijuana regulation—for instance, through increased DOJ funding to prosecute CSA violations or limiting federal funds for states that legalize marijuana. In contrast, most recent proposals before Congress seek to relax federal restrictions on marijuana or mitigate the disparity between federal and state marijuana regulation. ]","domain":"Legal","type":"Fact Finding","high_level_type":"Q&A","__index_level_0__":833} +{"system_instruction":"Solely utilize information found in the text within the prompt to answer, do not rely on any other information when drawing conclusions. Try to avoid using complex legal terms, simplify for easier reading where possible.","user_request":"Give the names of all of the courts in which Smith's case has been considered according to the context document.","context_document":"Before trial, Smith moved to dismiss the indictment for lack of venue, citing the Constitution’s Venue Clause, Art. III, §2, cl. 3, and its Vicinage Clause, Amdt. 6. Smith argued that trial in the Northern District of Florida was improper because he had accessed StrikeLines’ website from his home in Mobile (in the Southern District of Alabama) and the servers storing StrikeLines’ data were located in Orlando (in the Middle District of Florida). The District Court concluded that factual disputes related to venue should be resolved by the jury and denied Smith’s motion to dismiss without prejudice. The jury found Smith guilty, and Smith moved for a judgment of acquittal based on improper venue. See Fed. Rule Crim. Proc. 29. The District Court denied the motion, reasoning that the effects of Smith’s crime were felt at StrikeLines’ headquarters, located in the Northern District of Florida. On appeal, the Eleventh Circuit determined that venue was improper, but disagreed with Smith that a trial in an improper venue barred reprosecution. The Eleventh Circuit therefore vacated Smith’s conviction for theft of trade secrets. Held: The Constitution permits the retrial of a defendant following a trial in an improper venue conducted before a jury drawn from the wrong district. Pp. 3–16. (a) Except as prohibited by the Double Jeopardy Clause, it “has long been the rule that when a defendant obtains a reversal of a prior, unsatisfied conviction, he may be retried in the normal course of events.” United States v. Ewell, 383 U. S. 116, 121. In all circumstances outside of the Speedy Trial Clause, the strongest appropriate remedy for trial error is a new trial, not a judgment barring reprosecution. Pp. 3–4. 2 SMITH v. UNITED STATES Syllabus (1) Text and precedent provide no basis for concluding that violations of the Venue and Vicinage Clauses are exceptions to the retrial rule. The Venue Clause mandates that the “Trial of all Crimes . . . shall be held in the State where the . . . Crimes shall have been committed.” Art. III, §2, cl. 3. Nothing about this language suggests that a new trial in the proper venue is not an adequate remedy for its violation. Smith primarily argues that the Venue Clause aims to prevent the infliction of additional harm on a defendant who has already undergone the hardship of an initial trial in a distant and improper place. But the mere burden of a second trial has never justified an exemption from the retrial rule. See Ewell, 383 U. S., at 121. Indeed, while the most convenient trial venue for a defendant would presumably be where he lives, the Venue Clause is keyed to the location of the alleged crimes. The Clause does not allow “variation . . . for convenience of the . . . accused,” Johnston v. United States, 351 U. S. 215, 221, and this Court has repeatedly rejected objections based on the hardships created when a defendant is prosecuted far from home.","full_prompt":"Solely utilize information found in the text within the prompt to answer, do not rely on any other information when drawing conclusions. Try to avoid using complex legal terms, simplify for easier reading where possible.\n\nBefore trial, Smith moved to dismiss the indictment for lack of venue, citing the Constitution’s Venue Clause, Art. III, §2, cl. 3, and its Vicinage Clause, Amdt. 6. Smith argued that trial in the Northern District of Florida was improper because he had accessed StrikeLines’ website from his home in Mobile (in the Southern District of Alabama) and the servers storing StrikeLines’ data were located in Orlando (in the Middle District of Florida). The District Court concluded that factual disputes related to venue should be resolved by the jury and denied Smith’s motion to dismiss without prejudice. The jury found Smith guilty, and Smith moved for a judgment of acquittal based on improper venue. See Fed. Rule Crim. Proc. 29. The District Court denied the motion, reasoning that the effects of Smith’s crime were felt at StrikeLines’ headquarters, located in the Northern District of Florida. On appeal, the Eleventh Circuit determined that venue was improper, but disagreed with Smith that a trial in an improper venue barred reprosecution. The Eleventh Circuit therefore vacated Smith’s conviction for theft of trade secrets. Held: The Constitution permits the retrial of a defendant following a trial in an improper venue conducted before a jury drawn from the wrong district. Pp. 3–16. (a) Except as prohibited by the Double Jeopardy Clause, it “has long been the rule that when a defendant obtains a reversal of a prior, unsatisfied conviction, he may be retried in the normal course of events.” United States v. Ewell, 383 U. S. 116, 121. In all circumstances outside of the Speedy Trial Clause, the strongest appropriate remedy for trial error is a new trial, not a judgment barring reprosecution. Pp. 3–4. 2 SMITH v. UNITED STATES Syllabus (1) Text and precedent provide no basis for concluding that violations of the Venue and Vicinage Clauses are exceptions to the retrial rule. The Venue Clause mandates that the “Trial of all Crimes . . . shall be held in the State where the . . . Crimes shall have been committed.” Art. III, §2, cl. 3. Nothing about this language suggests that a new trial in the proper venue is not an adequate remedy for its violation. Smith primarily argues that the Venue Clause aims to prevent the infliction of additional harm on a defendant who has already undergone the hardship of an initial trial in a distant and improper place. But the mere burden of a second trial has never justified an exemption from the retrial rule. See Ewell, 383 U. S., at 121. Indeed, while the most convenient trial venue for a defendant would presumably be where he lives, the Venue Clause is keyed to the location of the alleged crimes. The Clause does not allow “variation . . . for convenience of the . . . accused,” Johnston v. United States, 351 U. S. 215, 221, and this Court has repeatedly rejected objections based on the hardships created when a defendant is prosecuted far from home.\n\nGive the names of all of the courts in which Smith's case has been considered according to the context document.","domain":"Legal","type":"Find & Summarize","high_level_type":"Text Transformation","__index_level_0__":843} diff --git a/python/samples/getting_started/evaluation/self_reflection/self_reflection.py b/python/samples/getting_started/evaluation/self_reflection/self_reflection.py new file mode 100644 index 00000000000..76ed8d6e657 --- /dev/null +++ b/python/samples/getting_started/evaluation/self_reflection/self_reflection.py @@ -0,0 +1,383 @@ +# Copyright (c) Microsoft. All rights reserved. +# type: ignore +import asyncio +import os +import time +import argparse +import pandas as pd +from typing import Any +from dotenv import load_dotenv + +from agent_framework import ChatAgent, ChatMessage +from agent_framework.azure import AzureOpenAIChatClient +from azure.identity import AzureCliCredential +from azure.ai.evaluation import GroundednessEvaluator, AzureOpenAIModelConfiguration + +""" +Self-Reflection LLM Runner + +Reflexion: language agents with verbal reinforcement learning. +Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. +In Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS '23). Curran Associates Inc., Red Hook, NY, USA, Article 377, 8634–8652. +https://arxiv.org/abs/2303.11366 + +This module implements a self-reflection loop for LLM responses using groundedness evaluation. +It loads prompts from a JSONL file, runs them through an LLM with self-reflection, +and saves the results. + + +Usage as CLI: + python self_reflection.py + +Usage as CLI with extra options: + python self_reflection.py --input resources/suboptimal_groundedness_prompts.jsonl \\ + --output resources/results.jsonl \\ + --max-reflections 3 \\ + -n 10 # Optional: process only first 10 prompts +""" + + +DEFAULT_AGENT_MODEL = "gpt-4.1" +DEFAULT_JUDGE_MODEL = "gpt-4.1" + + +def create_groundedness_evaluator(judge_model: str) -> GroundednessEvaluator: + """ + Create a groundedness evaluator. + + Args: + judge_model: Model deployment name for evaluation + Returns: + Configured GroundednessEvaluator + """ + judge_model_config = AzureOpenAIModelConfiguration( + azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"), + api_key=os.environ.get("AZURE_OPENAI_API_KEY"), + api_version="2024-12-01-preview", + azure_deployment=judge_model, + ) + return GroundednessEvaluator(model_config=judge_model_config) + + +async def execute_query_with_self_reflection( + *, + agent: ChatAgent, + full_user_query: str, + context: str, + evaluator: GroundednessEvaluator, + max_self_reflections: int = 3, +) -> dict[str, Any]: + """ + Execute a query with self-reflection loop. + + Args: + agent: ChatAgent instance to use for generating responses + full_user_query: Complete prompt including system prompt, user request, and context + context: Context document for groundedness evaluation + evaluator: Groundedness evaluator function + max_self_reflections: Maximum number of self-reflection iterations + + Returns: + Dictionary containing: + - best_response: The best response achieved + - best_response_score: Best groundedness score + - best_iteration: Iteration number where best score was achieved + - iteration_scores: List of groundedness scores for each iteration + - messages: Full conversation history + - usage_metadata: Token usage information + - num_retries: Number of iterations performed + - total_groundedness_eval_time: Time spent on evaluations (seconds) + - total_end_to_end_time: Total execution time (seconds) + """ + messages = [ChatMessage(role="user", text=full_user_query)] + + best_score = 0 + max_score = 5 + best_response = None + best_iteration = 0 + raw_response = None + total_groundedness_eval_time = 0.0 + start_time = time.time() + iteration_scores = [] # Store all iteration scores in structured format + + for i in range(max_self_reflections): + print(f" Self-reflection iteration {i+1}/{max_self_reflections}...") + + raw_response = await agent.run(messages=messages) + agent_response = raw_response.text + + # Evaluate groundedness + start_time_eval = time.time() + groundedness_res = evaluator( + query=full_user_query, + response=agent_response, + context=context + ) + end_time_eval = time.time() + total_groundedness_eval_time += (end_time_eval - start_time_eval) + + feedback = groundedness_res['groundedness_reason'] + score = int(groundedness_res['groundedness']) + + # Store score in structured format + iteration_scores.append(score) + + # Show groundedness score + print(f" Groundedness score: {score}/{max_score}") + + # Update best response if improved + if score > best_score: + if best_score > 0: + print(f" ✓ Score improved from {best_score} to {score}/{max_score}") + best_score = score + best_response = agent_response + best_iteration = i + 1 + if score == max_score: + print(f" ✓ Perfect groundedness score achieved!") + break + else: + print(f" → No improvement (score: {score}/{max_score}). Trying again...") + + # Add to conversation history + messages.append(ChatMessage(role="assistant", text=agent_response)) + + # Request improvement + reflection_prompt = ( + f"The groundedness score of your response is {score}/{max_score}. " + f"Explanation for score: [{feedback}]. " + f"Reflect on your answer and improve it to get the maximum score of {max_score} " + f"considering the explanation. Now please provide an updated response, taking into " + f"account the feedback, but make your answer sound as if it was your first response. " + f"Don't refer to the feedback in your answer." + ) + messages.append(ChatMessage(role="user", text=reflection_prompt)) + + end_time = time.time() + latency = end_time - start_time + + # Handle edge case where no response improved the score + if best_response is None and raw_response is not None and len(raw_response.messages) > 0: + best_response = raw_response.messages[0].text + best_iteration = i + 1 + + return { + "best_response": best_response, + "best_response_score": best_score, + "best_iteration": best_iteration, + "iteration_scores": iteration_scores, # Structured list of all scores + "messages": [message.to_json() for message in messages], + "num_retries": i + 1, + "total_groundedness_eval_time": total_groundedness_eval_time, + "total_end_to_end_time": latency, + } + + +async def run_self_reflection_batch( + input_file: str, + output_file: str, + agent_model: str = DEFAULT_AGENT_MODEL, + judge_model: str = DEFAULT_JUDGE_MODEL, + max_self_reflections: int = 3, + env_file: str | None = None, + limit: int | None = None +): + """ + Run self-reflection on a batch of prompts. + + Args: + input_file: Path to input JSONL file with prompts + output_file: Path to save output JSONL file + agent_model: Model to use for generating responses + judge_model: Model to use for groundedness evaluation + max_self_reflections: Maximum number of self-reflection iterations + env_file: Optional path to .env file + limit: Optional limit to process only the first N prompts + """ + # Load environment variables + if env_file and os.path.exists(env_file): + load_dotenv(env_file, override=True) + else: + load_dotenv(override=True) + + # Create agent, it loads environment variables AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT automatically + agent = AzureOpenAIChatClient( + credential=AzureCliCredential(), + deployment_name=agent_model, + ).create_agent( + instructions="You are a helpful agent.", + ) + + # Load input data + print(f"Loading prompts from: {input_file}") + df = pd.read_json(input_file, lines=True) + print(f"Loaded {len(df)} prompts") + + # Apply limit if specified + if limit is not None and limit > 0: + df = df.head(limit) + print(f"Processing first {len(df)} prompts (limited by -n {limit})") + + # Validate required columns + required_columns = ['system_instruction', 'user_request', 'context_document', + 'full_prompt', 'domain', 'type', 'high_level_type'] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise ValueError(f"Input file missing required columns: {missing_columns}") + + # Configure clients + print(f"Configuring Azure OpenAI client...") + + print(f"Creating groundedness evaluator with model: {judge_model}") + evaluator = create_groundedness_evaluator(judge_model) + + # Process each prompt + print(f"Max self-reflections: {max_self_reflections}\n") + + results = [] + for counter, (idx, row) in enumerate(df.iterrows(), start=1): + print(f"[{counter}/{len(df)}] Processing prompt {row.get('original_index', idx)}...") + + try: + result = await execute_query_with_self_reflection( + agent=agent, + full_user_query=row['full_prompt'], + context=row['context_document'], + evaluator=evaluator, + max_self_reflections=max_self_reflections, + ) + + # Prepare result data + result_data = { + "original_index": row.get('original_index', idx), + "domain": row['domain'], + "question_type": row['type'], + "high_level_type": row['high_level_type'], + "full_prompt": row['full_prompt'], + "system_prompt": row['system_instruction'], + "user_request": row['user_request'], + "context_document": row['context_document'], + "agent_response_model": agent_model, + "agent_response": result, + "error": None, + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + } + results.append(result_data) + + print(f" ✓ Completed with score: {result['best_response_score']}/5 " + f"(best at iteration {result['best_iteration']}/{result['num_retries']}, " + f"time: {result['total_end_to_end_time']:.1f}s)\n") + + except Exception as e: + print(f" ✗ Error: {str(e)}\n") + + # Save error information + error_data = { + "original_index": row.get('original_index', idx), + "domain": row['domain'], + "question_type": row['type'], + "high_level_type": row['high_level_type'], + "full_prompt": row['full_prompt'], + "system_prompt": row['system_instruction'], + "user_request": row['user_request'], + "context_document": row['context_document'], + "agent_response_model": agent_model, + "agent_response": None, + "error": str(e), + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + } + results.append(error_data) + continue + + # Create DataFrame and save + results_df = pd.DataFrame(results) + + print(f"\nSaving results to: {output_file}") + results_df.to_json(output_file, orient='records', lines=True) + + # Generate detailed summary + successful_runs = results_df[results_df['error'].isna()] + failed_runs = results_df[results_df['error'].notna()] + + print("\n" + "="*60) + print("SUMMARY") + print("="*60) + print(f"Total prompts processed: {len(results_df)}") + print(f" ✓ Successful: {len(successful_runs)}") + print(f" ✗ Failed: {len(failed_runs)}") + + if len(successful_runs) > 0: + # Extract scores and iteration data from nested agent_response dict + best_scores = [r['best_response_score'] for r in successful_runs['agent_response'] if r is not None] + iterations = [r['best_iteration'] for r in successful_runs['agent_response'] if r is not None] + iteration_scores_list = [r['iteration_scores'] for r in successful_runs['agent_response'] if r is not None and 'iteration_scores' in r] + + if best_scores: + avg_score = sum(best_scores) / len(best_scores) + perfect_scores = sum(1 for s in best_scores if s == 5) + print(f"\nGroundedness Scores:") + print(f" Average best score: {avg_score:.2f}/5") + print(f" Perfect scores (5/5): {perfect_scores}/{len(best_scores)} ({100*perfect_scores/len(best_scores):.1f}%)") + + # Calculate improvement metrics + if iteration_scores_list: + first_scores = [scores[0] for scores in iteration_scores_list if len(scores) > 0] + last_scores = [scores[-1] for scores in iteration_scores_list if len(scores) > 0] + improvements = [last - first for first, last in zip(first_scores, last_scores)] + improved_count = sum(1 for imp in improvements if imp > 0) + + if first_scores and last_scores: + avg_first_score = sum(first_scores) / len(first_scores) + avg_last_score = sum(last_scores) / len(last_scores) + avg_improvement = sum(improvements) / len(improvements) + + print(f"\nImprovement Analysis:") + print(f" Average first score: {avg_first_score:.2f}/5") + print(f" Average final score: {avg_last_score:.2f}/5") + print(f" Average improvement: +{avg_improvement:.2f}") + print(f" Responses that improved: {improved_count}/{len(improvements)} ({100*improved_count/len(improvements):.1f}%)") + + # Show iteration statistics + if iterations: + avg_iteration = sum(iterations) / len(iterations) + first_try = sum(1 for it in iterations if it == 1) + print(f"\nIteration Statistics:") + print(f" Average best iteration: {avg_iteration:.2f}") + print(f" Best on first try: {first_try}/{len(iterations)} ({100*first_try/len(iterations):.1f}%)") + + print("="*60) + + +async def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Run self-reflection loop on LLM prompts with groundedness evaluation") + parser.add_argument('--input', '-i', default="resources/suboptimal_groundedness_prompts.jsonl", help='Input JSONL file with prompts') + parser.add_argument('--output', '-o', default="resources/results.jsonl", help='Output JSONL file for results') + parser.add_argument('--agent-model', '-m', default=DEFAULT_AGENT_MODEL, help=f'Agent model deployment name (default: {DEFAULT_AGENT_MODEL})') + parser.add_argument('--judge-model', '-e', default=DEFAULT_JUDGE_MODEL, help=f'Judge model deployment name (default: {DEFAULT_JUDGE_MODEL})') + parser.add_argument('--max-reflections', type=int, default=3, help='Maximum number of self-reflection iterations (default: 3)') + parser.add_argument('--env-file', help='Path to .env file with Azure OpenAI credentials') + parser.add_argument('--limit', '-n', type=int, default=None, help='Process only the first N prompts from the input file') + + args = parser.parse_args() + + # Run the batch processing + try: + await run_self_reflection_batch( + input_file=args.input, + output_file=args.output, + agent_model=args.agent_model, + judge_model=args.judge_model, + max_self_reflections=args.max_reflections, + env_file=args.env_file, + limit=args.limit + ) + print("\n✓ Processing complete!") + + except Exception as e: + print(f"\n✗ Error: {str(e)}") + return 1 + return 0 + + +if __name__ == "__main__": + exit(asyncio.run(main())) diff --git a/python/samples/getting_started/middleware/runtime_context_delegation.py b/python/samples/getting_started/middleware/runtime_context_delegation.py new file mode 100644 index 00000000000..94719396c66 --- /dev/null +++ b/python/samples/getting_started/middleware/runtime_context_delegation.py @@ -0,0 +1,456 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio +from collections.abc import Awaitable, Callable +from typing import Annotated + +from agent_framework import FunctionInvocationContext, ai_function, function_middleware +from agent_framework.openai import OpenAIChatClient +from pydantic import Field + +""" +Runtime Context Delegation Patterns + +This sample demonstrates different patterns for passing runtime context (API tokens, +session data, etc.) to tools and sub-agents. + +Patterns Demonstrated: + +1. **Pattern 1: Single Agent with Middleware & Closure** (Lines 130-180) + - Best for: Single agent with multiple tools + - How: Middleware stores kwargs in container, tools access via closure + - Pros: Simple, explicit state management + - Cons: Requires container instance per agent + +2. **Pattern 2: Hierarchical Agents with kwargs Propagation** (Lines 190-240) + - Best for: Parent-child agent delegation with as_tool() + - How: kwargs automatically propagate through as_tool() wrapper + - Pros: Automatic, works with nested delegation, clean separation + - Cons: None - this is the recommended pattern for hierarchical agents + +3. **Pattern 3: Mixed - Hierarchical with Middleware** (Lines 250-300) + - Best for: Complex scenarios needing both delegation and state management + - How: Combines automatic kwargs propagation with middleware processing + - Pros: Maximum flexibility, can transform/validate context at each level + - Cons: More complex setup + +Key Concepts: +- Runtime Context: Session-specific data like API tokens, user IDs, tenant info +- Middleware: Intercepts function calls to access/modify kwargs +- Closure: Functions capturing variables from outer scope +- kwargs Propagation: Automatic forwarding of runtime context through delegation chains +""" + + +class SessionContextContainer: + """Container for runtime session context accessible via closure.""" + + def __init__(self) -> None: + """Initialize with None values for runtime context.""" + self.api_token: str | None = None + self.user_id: str | None = None + self.session_metadata: dict[str, str] = {} + + async def inject_context_middleware( + self, + context: FunctionInvocationContext, + next: Callable[[FunctionInvocationContext], Awaitable[None]], + ) -> None: + """Middleware that extracts runtime context from kwargs and stores in container. + + This middleware runs before tool execution and makes runtime context + available to tools via the container instance. + """ + # Extract runtime context from kwargs + self.api_token = context.kwargs.get("api_token") + self.user_id = context.kwargs.get("user_id") + self.session_metadata = context.kwargs.get("session_metadata", {}) + + # Log what we captured (for demonstration) + if self.api_token or self.user_id: + print("[Middleware] Captured runtime context:") + print(f" - API Token: {'***' + self.api_token[-4:] if self.api_token else 'None'}") + print(f" - User ID: {self.user_id}") + print(f" - Session Metadata: {self.session_metadata}") + + # Continue to tool execution + await next(context) + + +# Create a container instance that will be shared via closure +runtime_context = SessionContextContainer() + + +@ai_function +async def send_email( + to: Annotated[str, Field(description="Recipient email address")], + subject: Annotated[str, Field(description="Email subject line")], + body: Annotated[str, Field(description="Email body content")], +) -> str: + """Send an email using authenticated API (simulated). + + This function accesses runtime context (API token, user ID) via closure + from the runtime_context container. + """ + # Access runtime context via closure + token = runtime_context.api_token + user_id = runtime_context.user_id + tenant = runtime_context.session_metadata.get("tenant", "unknown") + + print("\n[send_email] Executing with runtime context:") + print(f" - Token: {'***' + token[-4:] if token else 'NOT PROVIDED'}") + print(f" - User ID: {user_id or 'NOT PROVIDED'}") + print(f" - Tenant: {tenant}") + print(f" - To: {to}") + print(f" - Subject: {subject}") + + # Simulate API call with authentication + if not token: + return "ERROR: No API token provided - cannot send email" + + # Simulate sending email + return f"Email sent to {to} from user {user_id} (tenant: {tenant}). Subject: '{subject}'" + + +@ai_function +async def send_notification( + message: Annotated[str, Field(description="Notification message to send")], + priority: Annotated[str, Field(description="Priority level: low, medium, high")] = "medium", +) -> str: + """Send a push notification using authenticated API (simulated). + + This function accesses runtime context via closure from runtime_context. + """ + token = runtime_context.api_token + user_id = runtime_context.user_id + + print("\n[send_notification] Executing with runtime context:") + print(f" - Token: {'***' + token[-4:] if token else 'NOT PROVIDED'}") + print(f" - User ID: {user_id or 'NOT PROVIDED'}") + print(f" - Message: {message}") + print(f" - Priority: {priority}") + + if not token: + return "ERROR: No API token provided - cannot send notification" + + return f"Notification sent to user {user_id} with priority {priority}: {message}" + + +async def pattern_1_single_agent_with_closure() -> None: + """Pattern 1: Single agent with middleware and closure for runtime context.""" + print("\n" + "=" * 70) + print("PATTERN 1: Single Agent with Middleware & Closure") + print("=" * 70) + print("Use case: Single agent with multiple tools sharing runtime context") + print() + + client = OpenAIChatClient(model_id="gpt-4o-mini") + + # Create agent with both tools and shared context via middleware + communication_agent = client.create_agent( + name="communication_agent", + instructions=( + "You are a communication assistant that can send emails and notifications. " + "Use send_email for email tasks and send_notification for notification tasks." + ), + tools=[send_email, send_notification], + # Both tools share the same context container via middleware + middleware=[runtime_context.inject_context_middleware], + ) + + # Test 1: Send email with runtime context + print("\n" + "=" * 70) + print("TEST 1: Email with Runtime Context") + print("=" * 70) + + user_query = ( + "Send an email to john@example.com with subject 'Meeting Tomorrow' and body 'Don't forget our 2pm meeting.'" + ) + print(f"\nUser: {user_query}") + + result1 = await communication_agent.run( + user_query, + # Runtime context passed as kwargs + api_token="sk-test-token-xyz-789", + user_id="user-12345", + session_metadata={"tenant": "acme-corp", "region": "us-west"}, + ) + + print(f"\nAgent: {result1.text}") + + # Test 2: Send notification with different runtime context + print("\n" + "=" * 70) + print("TEST 2: Notification with Different Runtime Context") + print("=" * 70) + + user_query2 = "Send a high priority notification saying 'Your order has shipped!'" + print(f"\nUser: {user_query2}") + + result2 = await communication_agent.run( + user_query2, + # Different runtime context for this request + api_token="sk-prod-token-abc-456", + user_id="user-67890", + session_metadata={"tenant": "store-inc", "region": "eu-central"}, + ) + + print(f"\nAgent: {result2.text}") + + # Test 3: Both email and notification in one request + print("\n" + "=" * 70) + print("TEST 3: Multiple Tools in One Request") + print("=" * 70) + + user_query3 = ( + "Send an email to alice@example.com about the new feature launch " + "and also send a notification to remind about the team meeting." + ) + print(f"\nUser: {user_query3}") + + result3 = await communication_agent.run( + user_query3, + api_token="sk-dev-token-def-123", + user_id="user-11111", + session_metadata={"tenant": "dev-team", "region": "us-east"}, + ) + + print(f"\nAgent: {result3.text}") + + # Test 4: Missing context - show error handling + print("\n" + "=" * 70) + print("TEST 4: Missing Runtime Context (Error Case)") + print("=" * 70) + + user_query4 = "Send an email to test@example.com with subject 'Test'" + print(f"\nUser: {user_query4}") + print("Note: Running WITHOUT api_token to demonstrate error handling") + + result4 = await communication_agent.run( + user_query4, + # Missing api_token - tools should handle gracefully + user_id="user-22222", + ) + + print(f"\nAgent: {result4.text}") + + print("\n✓ Pattern 1 complete - Middleware & closure pattern works for single agents") + + +# Pattern 2: Hierarchical agents with automatic kwargs propagation +# ================================================================ + + +# Create tools for sub-agents (these will use kwargs propagation) +@ai_function +async def send_email_v2( + to: Annotated[str, Field(description="Recipient email")], + subject: Annotated[str, Field(description="Subject")], + body: Annotated[str, Field(description="Body")], +) -> str: + """Send email - demonstrates kwargs propagation pattern.""" + # In this pattern, we can create a middleware to access kwargs + # But for simplicity, we'll just simulate the operation + return f"Email sent to {to} with subject '{subject}'" + + +@ai_function +async def send_sms( + phone: Annotated[str, Field(description="Phone number")], + message: Annotated[str, Field(description="SMS message")], +) -> str: + """Send SMS message.""" + return f"SMS sent to {phone}: {message}" + + +async def pattern_2_hierarchical_with_kwargs_propagation() -> None: + """Pattern 2: Hierarchical agents with automatic kwargs propagation through as_tool().""" + print("\n" + "=" * 70) + print("PATTERN 2: Hierarchical Agents with kwargs Propagation") + print("=" * 70) + print("Use case: Parent agent delegates to specialized sub-agents") + print("Feature: Runtime kwargs automatically propagate through as_tool()") + print() + + # Track kwargs at each level + email_agent_kwargs: dict[str, object] = {} + sms_agent_kwargs: dict[str, object] = {} + + @function_middleware + async def email_kwargs_tracker( + context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]] + ) -> None: + email_agent_kwargs.update(context.kwargs) + print(f"[EmailAgent] Received runtime context: {list(context.kwargs.keys())}") + await next(context) + + @function_middleware + async def sms_kwargs_tracker( + context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]] + ) -> None: + sms_agent_kwargs.update(context.kwargs) + print(f"[SMSAgent] Received runtime context: {list(context.kwargs.keys())}") + await next(context) + + client = OpenAIChatClient(model_id="gpt-4o-mini") + + # Create specialized sub-agents + email_agent = client.create_agent( + name="email_agent", + instructions="You send emails using the send_email_v2 tool.", + tools=[send_email_v2], + middleware=[email_kwargs_tracker], + ) + + sms_agent = client.create_agent( + name="sms_agent", + instructions="You send SMS messages using the send_sms tool.", + tools=[send_sms], + middleware=[sms_kwargs_tracker], + ) + + # Create coordinator that delegates to sub-agents + coordinator = client.create_agent( + name="coordinator", + instructions=( + "You coordinate communication tasks. " + "Use email_sender for emails and sms_sender for SMS. " + "Delegate to the appropriate specialized agent." + ), + tools=[ + email_agent.as_tool( + name="email_sender", + description="Send emails to recipients", + arg_name="task", + ), + sms_agent.as_tool( + name="sms_sender", + description="Send SMS messages", + arg_name="task", + ), + ], + ) + + # Test: Runtime context propagates automatically + print("Test: Send email with runtime context\n") + await coordinator.run( + "Send an email to john@example.com with subject 'Meeting' and body 'See you at 2pm'", + api_token="secret-token-abc", + user_id="user-999", + tenant_id="tenant-acme", + ) + + print(f"\n[Verification] EmailAgent received: {email_agent_kwargs}") + print(f" - api_token: {email_agent_kwargs.get('api_token')}") + print(f" - user_id: {email_agent_kwargs.get('user_id')}") + print(f" - tenant_id: {email_agent_kwargs.get('tenant_id')}") + + print("\n✓ Pattern 2 complete - kwargs automatically propagate through as_tool()") + + +# Pattern 3: Mixed pattern - hierarchical with middleware processing +# =================================================================== + + +class AuthContextMiddleware: + """Middleware that validates and transforms runtime context.""" + + def __init__(self) -> None: + self.validated_tokens: list[str] = [] + + async def validate_and_track( + self, context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]] + ) -> None: + """Validate API token and track usage.""" + api_token = context.kwargs.get("api_token") + + if api_token: + # Simulate token validation + if api_token.startswith("valid-"): + print(f"[AuthMiddleware] ✓ Token validated: ***{api_token[-4:]}") + self.validated_tokens.append(api_token) + else: + print(f"[AuthMiddleware] ✗ Invalid token: {api_token}") + # Could set context.terminate = True to block execution + else: + print("[AuthMiddleware] ⚠ No API token provided") + + await next(context) + + +@ai_function +async def protected_operation(operation: Annotated[str, Field(description="Operation to perform")]) -> str: + """Protected operation that requires authentication.""" + return f"Executed protected operation: {operation}" + + +async def pattern_3_hierarchical_with_middleware() -> None: + """Pattern 3: Hierarchical agents with middleware processing at each level.""" + print("\n" + "=" * 70) + print("PATTERN 3: Hierarchical with Middleware Processing") + print("=" * 70) + print("Use case: Multi-level validation/transformation of runtime context") + print() + + auth_middleware = AuthContextMiddleware() + + client = OpenAIChatClient(model_id="gpt-4o-mini") + + # Sub-agent with validation middleware + protected_agent = client.create_agent( + name="protected_agent", + instructions="You perform protected operations that require authentication.", + tools=[protected_operation], + middleware=[auth_middleware.validate_and_track], + ) + + # Coordinator delegates to protected agent + coordinator = client.create_agent( + name="coordinator", + instructions="You coordinate protected operations. Delegate to protected_executor.", + tools=[ + protected_agent.as_tool( + name="protected_executor", + description="Execute protected operations", + ) + ], + ) + + # Test with valid token + print("Test 1: Valid token\n") + await coordinator.run( + "Execute operation: backup_database", + api_token="valid-token-xyz-789", + user_id="admin-123", + ) + + # Test with invalid token + print("\nTest 2: Invalid token\n") + await coordinator.run( + "Execute operation: delete_records", + api_token="invalid-token-bad", + user_id="user-456", + ) + + print(f"\n[Validation Summary] Validated tokens: {len(auth_middleware.validated_tokens)}") + print("✓ Pattern 3 complete - Middleware can validate/transform context at each level") + + +async def main() -> None: + """Demonstrate all runtime context delegation patterns.""" + print("=" * 70) + print("Runtime Context Delegation Patterns Demo") + print("=" * 70) + print() + + # Run Pattern 1 + await pattern_1_single_agent_with_closure() + + # Run Pattern 2 + await pattern_2_hierarchical_with_kwargs_propagation() + + # Run Pattern 3 + await pattern_3_hierarchical_with_middleware() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/multimodal_input/azure_chat_multimodal.py b/python/samples/getting_started/multimodal_input/azure_chat_multimodal.py index e6c0ffcb879..fb80d65ad14 100644 --- a/python/samples/getting_started/multimodal_input/azure_chat_multimodal.py +++ b/python/samples/getting_started/multimodal_input/azure_chat_multimodal.py @@ -13,6 +13,7 @@ def create_sample_image() -> str: png_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==" return f"data:image/png;base64,{png_data}" + async def test_image() -> None: """Test image analysis with Azure OpenAI.""" # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred @@ -37,5 +38,6 @@ async def main() -> None: print("Testing image analysis (supported by Chat Completions API)") await test_image() + if __name__ == "__main__": asyncio.run(main()) diff --git a/python/samples/getting_started/observability/workflow_observability.py b/python/samples/getting_started/observability/workflow_observability.py index 9b56def216d..b8ef5d10e91 100644 --- a/python/samples/getting_started/observability/workflow_observability.py +++ b/python/samples/getting_started/observability/workflow_observability.py @@ -17,10 +17,25 @@ """ This sample shows the telemetry collected when running a Agent Framework workflow. +This simple workflow consists of two executors arranged sequentially: +1. An executor that converts input text to uppercase. +2. An executor that reverses the uppercase text. + +The workflow receives an initial string message, processes it through the two executors, +and yields the final result. + Telemetry data that the workflow system emits includes: - Overall workflow build & execution spans + - workflow.build (events: build.started, build.validation_completed, build.completed, edge_group.process) + - workflow.run (events: workflow.started, workflow.completed or workflow.error) - Individual executor processing spans + - executor.process (for each executor invocation) - Message publishing between executors + - message.send (for each outbound message) + +Prerequisites: +- Basic understanding of workflow executors, edges, and messages. +- Basic understanding of OpenTelemetry concepts like spans and traces. """ diff --git a/python/samples/getting_started/purview_agent/sample_purview_agent.py b/python/samples/getting_started/purview_agent/sample_purview_agent.py index 4d9b0d612c1..b9518edf082 100644 --- a/python/samples/getting_started/purview_agent/sample_purview_agent.py +++ b/python/samples/getting_started/purview_agent/sample_purview_agent.py @@ -20,7 +20,6 @@ - PURVIEW_CERT_PASSWORD (optional) - PURVIEW_DEFAULT_USER_ID (optional, user ID for Purview evaluation) """ -from __future__ import annotations import asyncio import os @@ -28,18 +27,17 @@ from agent_framework import AgentRunResponse, ChatAgent, ChatMessage, Role from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.microsoft import ( + PurviewChatPolicyMiddleware, + PurviewPolicyMiddleware, + PurviewSettings, +) from azure.identity import ( AzureCliCredential, CertificateCredential, InteractiveBrowserCredential, ) -from agent_framework.microsoft import ( - PurviewPolicyMiddleware, - PurviewChatPolicyMiddleware, - PurviewSettings, -) - JOKER_NAME = "Joker" JOKER_INSTRUCTIONS = "You are good at telling jokes. Keep responses concise." @@ -96,7 +94,6 @@ async def remove(self, key: str) -> None: print(f"[CustomCache] Removed key: {key[:50]}...") - def _get_env(name: str, *, required: bool = True, default: str | None = None) -> str: val = os.environ.get(name, default) if required and not val: @@ -176,7 +173,7 @@ async def run_with_chat_middleware() -> None: deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", default="gpt-4o-mini") user_id = os.environ.get("PURVIEW_DEFAULT_USER_ID") - + chat_client = AzureOpenAIChatClient( deployment_name=deployment, endpoint=endpoint, @@ -216,6 +213,7 @@ async def run_with_chat_middleware() -> None: ) print("Second response (chat middleware):\n", second) + async def run_with_custom_cache_provider() -> None: """Demonstrate implementing and using a custom cache provider.""" endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") @@ -246,7 +244,7 @@ async def run_with_custom_cache_provider() -> None: print("-- Custom Cache Provider Path --") print("Using SimpleDictCacheProvider") - + first: AgentRunResponse = await agent.run( ChatMessage(role=Role.USER, text="Tell me a joke about a programmer.", additional_properties={"user_id": user_id}) ) @@ -256,7 +254,7 @@ async def run_with_custom_cache_provider() -> None: ChatMessage(role=Role.USER, text="That's hilarious! One more?", additional_properties={"user_id": user_id}) ) print("Second response (custom provider):\n", second) - + """Demonstrate using the default built-in cache.""" endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") if not endpoint: @@ -286,7 +284,7 @@ async def run_with_custom_cache_provider() -> None: print("-- Default Cache Path --") print("Using default InMemoryCacheProvider with settings-based configuration") - + first: AgentRunResponse = await agent.run( ChatMessage(role=Role.USER, text="Tell me a joke about AI.", additional_properties={"user_id": user_id}) ) @@ -300,7 +298,7 @@ async def run_with_custom_cache_provider() -> None: async def main() -> None: print("== Purview Agent Sample (Middleware with Automatic Caching) ==") - + try: await run_with_agent_middleware() except Exception as ex: # pragma: no cover - demo resilience diff --git a/python/samples/getting_started/tools/ai_function_from_dict_with_dependency_injection.py b/python/samples/getting_started/tools/ai_function_from_dict_with_dependency_injection.py index bff59f31d20..502f9e3ae3b 100644 --- a/python/samples/getting_started/tools/ai_function_from_dict_with_dependency_injection.py +++ b/python/samples/getting_started/tools/ai_function_from_dict_with_dependency_injection.py @@ -50,7 +50,7 @@ def func(a, b) -> int: # Create the AIFunction tool using dependency injection # The 'definition' dictionary contains the serialized tool configuration, # while the actual function implementation is provided via dependencies. - # + # # Dependency structure: {"ai_function": {"name:add_numbers": {"func": func}}} # - "ai_function": matches the tool type identifier # - "name:add_numbers": instance-specific injection targeting tools with name="add_numbers" diff --git a/python/samples/getting_started/tools/ai_function_with_approval_and_threads.py b/python/samples/getting_started/tools/ai_function_with_approval_and_threads.py index 53e7f0c786d..2da16a2101f 100644 --- a/python/samples/getting_started/tools/ai_function_with_approval_and_threads.py +++ b/python/samples/getting_started/tools/ai_function_with_approval_and_threads.py @@ -45,7 +45,7 @@ async def approval_example() -> None: # Check for approval requests if result.user_input_requests: for request in result.user_input_requests: - print(f"\nApproval needed:") + print("\nApproval needed:") print(f" Function: {request.function_call.name}") print(f" Arguments: {request.function_call.arguments}") @@ -79,12 +79,12 @@ async def rejection_example() -> None: if result.user_input_requests: for request in result.user_input_requests: - print(f"\nApproval needed:") + print("\nApproval needed:") print(f" Function: {request.function_call.name}") print(f" Arguments: {request.function_call.arguments}") # User rejects - print(f" Decision: Rejected") + print(" Decision: Rejected") # Send rejection response rejection_response = request.create_response(approved=False) diff --git a/python/samples/getting_started/workflows/README.md b/python/samples/getting_started/workflows/README.md index ffbeedd7510..ab6d4a10cc3 100644 --- a/python/samples/getting_started/workflows/README.md +++ b/python/samples/getting_started/workflows/README.md @@ -82,9 +82,7 @@ Once comfortable with these, explore the rest of the samples below. ### observability -| Sample | File | Concepts | -|---|---|---| -| Tracing (Basics) | [observability/tracing_basics.py](./observability/tracing_basics.py) | Use basic tracing for workflow telemetry. Refer to this [directory](../observability/) to learn more about observability concepts. | +For observability samples in Agent Framework, see the [observability getting started samples](../observability/README.md). The [sample](../observability/workflow_observability.py) demonstrates integrating observability into workflows. ### orchestration diff --git a/python/samples/getting_started/workflows/observability/tracing_basics.py b/python/samples/getting_started/workflows/observability/tracing_basics.py deleted file mode 100644 index 6914ad4ce24..00000000000 --- a/python/samples/getting_started/workflows/observability/tracing_basics.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -import asyncio - -from agent_framework import Executor, WorkflowBuilder, WorkflowContext, get_logger, handler -from agent_framework.observability import setup_observability - -"""Basic tracing workflow sample. - -Sample: Workflow Tracing basics - -A minimal two executor workflow demonstrates built in OpenTelemetry spans when diagnostics are enabled. -The sample raises an error if tracing is not configured. - -Purpose: -- Require diagnostics by checking ENABLE_OTEL and wiring a console exporter. -- Show the span categories produced by a simple graph: - - workflow.build (events: build.started, build.validation_completed, build.completed, edge_group.process) - - workflow.run (events: workflow.started, workflow.completed or workflow.error) - - executor.process (for each executor invocation) - - message.send (for each outbound message) -- Provide a tiny flow that is easy to run and reason about: uppercase then print. - -Prerequisites: -- No external services required for the workflow itself. -""" - -logger = get_logger() - - -class StartExecutor(Executor): - @handler # type: ignore[misc] - async def handle_input(self, message: str, ctx: WorkflowContext[str]) -> None: - # Transform and forward downstream. This produces executor.process and message.send spans. - await ctx.send_message(message.upper()) - - -class EndExecutor(Executor): - @handler # type: ignore[misc] - async def handle_final(self, message: str, ctx: WorkflowContext) -> None: - # Sink executor. The workflow completes when idle with no pending work. - print(f"Final result: {message}") - - -async def main() -> None: - # This will enable tracing and create the necessary tracing, logging and metrics providers - # based on environment variables. - setup_observability() - - # Build a two node graph: StartExecutor -> EndExecutor. The builder emits a workflow.build span. - workflow = ( - WorkflowBuilder() - .add_edge(StartExecutor(id="start"), EndExecutor(id="end")) - .set_start_executor("start") # set_start_executor accepts an executor id string or the instance - .build() - ) # workflow.build span emitted here - - # Run once with a simple payload. You should see workflow.run plus executor and message spans. - await workflow.run("hello tracing") # workflow.run + executor.process and message.send spans - - -if __name__ == "__main__": # pragma: no cover - asyncio.run(main()) diff --git a/python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py b/python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py index 72acb1ab6d1..c7c66d93420 100644 --- a/python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py +++ b/python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py @@ -32,7 +32,7 @@ Prerequisites: - Azure AI/ Azure OpenAI for `AzureOpenAIChatClient` agents. - Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`). -- For visualization export: `pip install agent-framework[viz] --pre` and install GraphViz binaries. +- For visualization export: `pip install graphviz>=0.20.0` and install GraphViz binaries. """ @@ -157,12 +157,10 @@ async def main() -> None: print("DiGraph string: \n=======") print(viz.to_digraph()) print("=======") - try: - # Export the DiGraph visualization as SVG. - svg_file = viz.export(format="svg") - print(f"SVG file saved to: {svg_file}") - except ImportError: - print("Tip: Install 'viz' extra to export workflow visualization: pip install agent-framework[viz] --pre") + + # Export the DiGraph visualization as SVG. + svg_file = viz.export(format="svg") + print(f"SVG file saved to: {svg_file}") # 3) Run with a single prompt async for event in workflow.run_stream("We are launching a new budget-friendly electric bike for urban commuters."): diff --git a/python/samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py b/python/samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py index d08a4ce5f0f..c9b1fb50eec 100644 --- a/python/samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py +++ b/python/samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py @@ -13,32 +13,30 @@ async def run_semantic_kernel() -> None: from azure.identity.aio import AzureCliCredential from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings - async with AzureCliCredential() as credential: - async with AzureAIAgent.create_client(credential=credential) as client: - settings = AzureAIAgentSettings() # Reads env vars for region/deployment. - # SK builds the remote agent definition then wraps it with AzureAIAgent. - definition = await client.agents.create_agent( - model=settings.model_deployment_name, - name="Support", - instructions="Answer customer questions in one paragraph.", - ) - agent = AzureAIAgent(client=client, definition=definition) - response = await agent.get_response("How do I upgrade my plan?") - print("[SK]", response.message.content) + async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client: + settings = AzureAIAgentSettings() # Reads env vars for region/deployment. + # SK builds the remote agent definition then wraps it with AzureAIAgent. + definition = await client.agents.create_agent( + model=settings.model_deployment_name, + name="Support", + instructions="Answer customer questions in one paragraph.", + ) + agent = AzureAIAgent(client=client, definition=definition) + response = await agent.get_response("How do I upgrade my plan?") + print("[SK]", response.message.content) async def run_agent_framework() -> None: - from azure.identity.aio import AzureCliCredential from agent_framework.azure import AzureAIAgentClient + from azure.identity.aio import AzureCliCredential - async with AzureCliCredential() as credential: - async with AzureAIAgentClient(async_credential=credential).create_agent( - name="Support", - instructions="Answer customer questions in one paragraph.", - ) as agent: - # AF client returns an asynchronous context manager for remote agents. - reply = await agent.run("How do I upgrade my plan?") - print("[AF]", reply.text) + async with AzureCliCredential() as credential, AzureAIAgentClient(async_credential=credential).create_agent( + name="Support", + instructions="Answer customer questions in one paragraph.", + ) as agent: + # AF client returns an asynchronous context manager for remote agents. + reply = await agent.run("How do I upgrade my plan?") + print("[AF]", reply.text) async def main() -> None: diff --git a/python/samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py b/python/samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py index cf4a4d8ed07..76cbb3e47bf 100644 --- a/python/samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py +++ b/python/samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py @@ -13,39 +13,37 @@ async def run_semantic_kernel() -> None: from azure.identity.aio import AzureCliCredential from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings - async with AzureCliCredential() as credential: - async with AzureAIAgent.create_client(credential=credential) as client: - settings = AzureAIAgentSettings() - # Register the hosted code interpreter tool with the remote agent. - definition = await client.agents.create_agent( - model=settings.model_deployment_name, - name="Analyst", - instructions="Use the code interpreter for numeric work.", - tools=[{"type": "code_interpreter"}], - ) - agent = AzureAIAgent(client=client, definition=definition) - response = await agent.get_response( - "Use Python to compute 42 ** 2 and explain the result.", - ) - print("[SK]", response.message.content) + async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client: + settings = AzureAIAgentSettings() + # Register the hosted code interpreter tool with the remote agent. + definition = await client.agents.create_agent( + model=settings.model_deployment_name, + name="Analyst", + instructions="Use the code interpreter for numeric work.", + tools=[{"type": "code_interpreter"}], + ) + agent = AzureAIAgent(client=client, definition=definition) + response = await agent.get_response( + "Use Python to compute 42 ** 2 and explain the result.", + ) + print("[SK]", response.message.content) async def run_agent_framework() -> None: - from azure.identity.aio import AzureCliCredential from agent_framework.azure import AzureAIAgentClient, HostedCodeInterpreterTool + from azure.identity.aio import AzureCliCredential - async with AzureCliCredential() as credential: - async with AzureAIAgentClient(async_credential=credential).create_agent( - name="Analyst", - instructions="Use the code interpreter for numeric work.", - tools=[HostedCodeInterpreterTool()], - ) as agent: - # HostedCodeInterpreterTool mirrors the built-in Azure AI capability. - reply = await agent.run( - "Use Python to compute 42 ** 2 and explain the result.", - tool_choice="auto", - ) - print("[AF]", reply.text) + async with AzureCliCredential() as credential, AzureAIAgentClient(async_credential=credential).create_agent( + name="Analyst", + instructions="Use the code interpreter for numeric work.", + tools=[HostedCodeInterpreterTool()], + ) as agent: + # HostedCodeInterpreterTool mirrors the built-in Azure AI capability. + reply = await agent.run( + "Use Python to compute 42 ** 2 and explain the result.", + tool_choice="auto", + ) + print("[AF]", reply.text) async def main() -> None: diff --git a/python/samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py b/python/samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py index 1b63324e34b..d96215d3196 100644 --- a/python/samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py +++ b/python/samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py @@ -8,53 +8,51 @@ async def run_semantic_kernel() -> None: from azure.identity.aio import AzureCliCredential from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread - async with AzureCliCredential() as credential: - async with AzureAIAgent.create_client(credential=credential) as client: - settings = AzureAIAgentSettings() - definition = await client.agents.create_agent( - model=settings.model_deployment_name, - name="Planner", - instructions="Track follow-up questions within the same thread.", - ) - agent = AzureAIAgent(client=client, definition=definition) + async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client: + settings = AzureAIAgentSettings() + definition = await client.agents.create_agent( + model=settings.model_deployment_name, + name="Planner", + instructions="Track follow-up questions within the same thread.", + ) + agent = AzureAIAgent(client=client, definition=definition) - thread: AzureAIAgentThread | None = None - # SK returns the updated AzureAIAgentThread on each response. - first = await agent.get_response("Outline the onboarding checklist.", thread=thread) - thread = first.thread - print("[SK][turn1]", first.message.content) + thread: AzureAIAgentThread | None = None + # SK returns the updated AzureAIAgentThread on each response. + first = await agent.get_response("Outline the onboarding checklist.", thread=thread) + thread = first.thread + print("[SK][turn1]", first.message.content) - second = await agent.get_response( - "Highlight the items that require legal review.", - thread=thread, - ) - print("[SK][turn2]", second.message.content) - if thread is not None: - print("[SK][thread-id]", thread.id) + second = await agent.get_response( + "Highlight the items that require legal review.", + thread=thread, + ) + print("[SK][turn2]", second.message.content) + if thread is not None: + print("[SK][thread-id]", thread.id) async def run_agent_framework() -> None: - from azure.identity.aio import AzureCliCredential from agent_framework.azure import AzureAIAgentClient + from azure.identity.aio import AzureCliCredential - async with AzureCliCredential() as credential: - async with AzureAIAgentClient(async_credential=credential).create_agent( - name="Planner", - instructions="Track follow-up questions within the same thread.", - ) as agent: - thread = agent.get_new_thread() - # AF threads are explicit and can be serialized for external storage. - first = await agent.run("Outline the onboarding checklist.", thread=thread) - print("[AF][turn1]", first.text) + async with AzureCliCredential() as credential, AzureAIAgentClient(async_credential=credential).create_agent( + name="Planner", + instructions="Track follow-up questions within the same thread.", + ) as agent: + thread = agent.get_new_thread() + # AF threads are explicit and can be serialized for external storage. + first = await agent.run("Outline the onboarding checklist.", thread=thread) + print("[AF][turn1]", first.text) - second = await agent.run( - "Highlight the items that require legal review.", - thread=thread, - ) - print("[AF][turn2]", second.text) + second = await agent.run( + "Highlight the items that require legal review.", + thread=thread, + ) + print("[AF][turn2]", second.text) - serialized = await thread.serialize() - print("[AF][thread-json]", serialized) + serialized = await thread.serialize() + print("[AF][thread-json]", serialized) async def main() -> None: diff --git a/python/tests/samples/getting_started/test_agent_samples.py b/python/tests/samples/getting_started/test_agent_samples.py index 710a5604fc8..e1a85951934 100644 --- a/python/tests/samples/getting_started/test_agent_samples.py +++ b/python/tests/samples/getting_started/test_agent_samples.py @@ -7,6 +7,18 @@ import pytest from pytest import MonkeyPatch, mark, param +from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( + mixed_tools_example as azure_ai_with_function_tools_mixed, +) +from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( + tools_on_agent_level as azure_ai_with_function_tools_agent, +) +from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( + tools_on_run_level as azure_ai_with_function_tools_run, +) +from samples.getting_started.agents.azure_ai.azure_ai_with_local_mcp import ( + main as azure_ai_with_local_mcp, +) from samples.getting_started.agents.azure_ai.azure_ai_basic import ( main as azure_ai_basic, @@ -20,18 +32,6 @@ from samples.getting_started.agents.azure_ai.azure_ai_with_explicit_settings import ( main as azure_ai_with_explicit_settings, ) -from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( - mixed_tools_example as azure_ai_with_function_tools_mixed, -) -from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( - tools_on_agent_level as azure_ai_with_function_tools_agent, -) -from samples.getting_started.agents.azure_ai.azure_ai_with_function_tools import ( - tools_on_run_level as azure_ai_with_function_tools_run, -) -from samples.getting_started.agents.azure_ai.azure_ai_with_local_mcp import ( - main as azure_ai_with_local_mcp, -) from samples.getting_started.agents.azure_ai.azure_ai_with_thread import ( main as azure_ai_with_thread, ) diff --git a/python/uv.lock b/python/uv.lock index f0657d5f683..89127de6ec5 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'darwin'", @@ -26,12 +26,14 @@ members = [ "agent-framework", "agent-framework-a2a", "agent-framework-ag-ui", + "agent-framework-aisearch", "agent-framework-anthropic", "agent-framework-azure-ai", "agent-framework-azurefunctions", "agent-framework-chatkit", "agent-framework-copilotstudio", "agent-framework-core", + "agent-framework-declarative", "agent-framework-devui", "agent-framework-lab", "agent-framework-mem0", @@ -45,7 +47,7 @@ overrides = [ [[package]] name = "a2a-sdk" -version = "0.3.12" +version = "0.3.14" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "google-api-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -54,18 +56,18 @@ dependencies = [ { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, { name = "pydantic", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/55/38/b473e04e21960d8eee7b04e35ac35c8e117f3cc2b333e0fa0371b48308a3/a2a_sdk-0.3.12.tar.gz", hash = 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marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-azure-ai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-azurefunctions", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-chatkit", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-copilotstudio", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-devui", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-lab", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-mem0", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-purview", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "agent-framework-redis", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "agent-framework-core", extra = ["all"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, ] [package.dev-dependencies] @@ -129,21 +119,7 @@ docs = [ ] [package.metadata] -requires-dist = [ - { name = "agent-framework-a2a", editable = "packages/a2a" }, - { name = "agent-framework-ag-ui", editable = "packages/ag-ui" }, - { name = "agent-framework-anthropic", editable = "packages/anthropic" }, - { name = "agent-framework-azure-ai", editable = "packages/azure-ai" }, - { name = "agent-framework-azurefunctions", editable = "packages/azurefunctions" }, - { name = "agent-framework-chatkit", editable = "packages/chatkit" }, - { name = "agent-framework-copilotstudio", editable = "packages/copilotstudio" }, - { name = "agent-framework-core", editable = "packages/core" }, - { name = "agent-framework-devui", editable = "packages/devui" }, - { name = "agent-framework-lab", editable = "packages/lab" }, - { name = "agent-framework-mem0", editable = "packages/mem0" }, - { name = "agent-framework-purview", editable = "packages/purview" }, - { name = "agent-framework-redis", editable = "packages/redis" }, -] +requires-dist = [{ name = "agent-framework-core", extras = ["all"], editable = "packages/core" }] [package.metadata.requires-dev] dev = [ @@ -175,7 +151,7 @@ docs = [ [[package]] name = "agent-framework-a2a" -version = "1.0.0b251114" +version = "1.0.0b251120" source = { editable = "packages/a2a" } dependencies = [ { name = "a2a-sdk", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -190,7 +166,7 @@ requires-dist = [ [[package]] name = "agent-framework-ag-ui" -version = "1.0.0b251114" +version = "1.0.0b251120" source = { editable = "packages/ag-ui" } dependencies = [ { name = "ag-ui-protocol", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -218,9 +194,24 @@ requires-dist = [ ] provides-extras = ["dev"] +[[package]] +name = "agent-framework-aisearch" +version = "1.0.0b251118" +source = { editable = "packages/aisearch" } +dependencies = [ + { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "azure-search-documents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, +] + +[package.metadata] +requires-dist = [ + { name = "agent-framework-core", editable = "packages/core" }, + { name = "azure-search-documents", specifier = "==11.7.0b2" }, +] + [[package]] name = "agent-framework-anthropic" -version = "1.0.0b251114" +version = "1.0.0b251120" source = { editable = "packages/anthropic" } dependencies = [ { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -235,7 +226,7 @@ requires-dist = [ [[package]] name = "agent-framework-azure-ai" -version = "1.0.0b251114" +version = "1.0.0b251120" source = { editable = "packages/azure-ai" } dependencies = [ { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -249,12 +240,12 @@ requires-dist = [ { name = "agent-framework-core", editable = "packages/core" }, { name = "aiohttp" }, { name = "azure-ai-agents", specifier = "==1.2.0b5" }, - { name = "azure-ai-projects", specifier = ">=2.0.0b1" }, + { name = "azure-ai-projects", specifier = ">=2.0.0b2" }, ] [[package]] name = "agent-framework-azurefunctions" -version = "1.0.0b251114" +version = "1.0.0b251120" 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