Qdrant RAG Plugin

Hybrid search RAG (Retrieval Augmented Generation) using Qdrant's built-in fastembed integration for dense and BM25 sparse embeddings.

Features

• Hybrid Search: Dense vector (semantic) + BM25 sparse (keyword) via native Qdrant RRF fusion
• fastembed Native: No external embedding dependencies — Qdrant client handles everything
• Implements RAG Interface: Compatible with Vision Agents RAG base class

Installation

uv add "vision-agents[qdrant]"
# or directly
uv add vision-agents-plugins-qdrant

Usage

from vision_agents.plugins import qdrant

# Initialize RAG (connects to local Qdrant by default)
rag = qdrant.QdrantRAG(collection="my-knowledge")
await rag.add_directory("./knowledge")

# Hybrid search (default)
results = await rag.search("How does the chat API work?")

# Vector-only search
results = await rag.search("How does the chat API work?", mode="vector")

# BM25 search
results = await rag.search("chat API pricing", mode="bm25")

# Or use convenience function
rag = await qdrant.create_rag(
    collection="product-knowledge",
    knowledge_dir="./knowledge"
)

Configuration

Environment Variables

• QDRANT_API_KEY: Qdrant API key (for Qdrant Cloud; not needed for local)

Running Qdrant locally

docker run -p 6333:6333 qdrant/qdrant

Dependencies

• qdrant-client[fastembed]: Qdrant async client with built-in fastembed support
• langchain-text-splitters: Text chunking utilities

References

• Qdrant Hybrid Queries
• fastembed Models