gptme-rag

ChromaDB-based RAG (Retrieval-Augmented Generation) for gptme agents.

Part of gptme-contrib. Upstreamed from gptme/gptme-rag.

Enhances AI responses by retrieving and incorporating relevant context from your local files using vector/semantic search with ChromaDB.

This is the vector search complement to gptme-wisdom (BM25/SQLite exact-term search). Different approaches for different use cases.

Features

• 📚 Document indexing with ChromaDB (vector storage, semantic search, persistence)
• 🔍 Semantic search with sentence-transformers embeddings
• 📄 Smart document processing (streaming, chunking, reconstruction)
• 👀 File watching and auto-indexing
• 🔌 MCP server for agent integration (gptme-rag mcp)
• 🛠️ CLI interface (gptme-rag index, gptme-rag search)

Quick Start

# Index your documents
gptme-rag index /path/to/documents

# Search with semantic relevance
gptme-rag search "your query"

# Start MCP server (for agent tool integration)
gptme-rag mcp --persist-dir /path/to/index

Development

# Run tests (excluding slow embedding-model tests)
uv run pytest packages/gptme-rag/ -v -m "not slow"

# Run all tests
uv run pytest packages/gptme-rag/ -v

License

MIT