QQL — Qdrant Query Language | SQL-like CLI for Vector Search Engine
Author: Kameshwara Pavan Kumar Mantha
Description: QQL is a SQL-like query language and CLI for Qdrant vector database. Write INSERT, SEARCH, RECOMMEND, and DELETE statements instead of Python SDK calls. Supports hybrid dense+sparse search, cross-encoder reranking, quantization, WHERE filters, and script execution.
Keywords: qdrant, vector database, vector search, SQL query language, CLI, semantic search, hybrid search, embeddings, RAG, fastembed, NLP, dense sparse search

# QQL

SQL-like query language and CLI for Qdrant vector search engine

    # Install
    $ pip install qql-cli

    # Connect to Qdrant
    $ qql connect --url http://localhost:6333

    # Insert a document — text is embedded automatically
    qql> INSERT INTO COLLECTION notes VALUES {'text': 'Qdrant is a vector database', 'year': 2024}
    ✓ Inserted 1 point [3f2e1a4b-...]

    # Search with a WHERE filter
    qql> SEARCH notes SIMILAR TO 'vector storage engines' LIMIT 5 WHERE year >= 2023
    ✓ Found 1 result(s)

    # Hybrid dense+sparse search with cross-encoder reranking
    qql> SEARCH notes SIMILAR TO 'vector databases' LIMIT 5 USING HYBRID RERANK
    ✓ Found 1 result(s) (hybrid, reranked)

## Documentation

### Getting Started

Installation, connecting to Qdrant, your first queries

### INSERT / INSERT BULK

Adding documents, batch inserts, payload types

### SEARCH / SELECT / SCROLL / RECOMMEND

Semantic search, point retrieval, pagination, hybrid search, reranking, recommendations

### WHERE Filters

Full SQL-style filter operators — range, IN, MATCH, AND/OR/NOT

### Collections & Quantization

CREATE, DROP, quantize (scalar/binary/product), payload indexes

### Scripts: EXECUTE / DUMP

Run script files, export collections for backup/migration

### Programmatic Usage

Use QQL as a Python library without the CLI

### Reference

Embedding models, config file, project structure, error guide

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