Knowledge RAG
Your docs, your machine, zero cloud. Claude Code searches them natively.
Drop your PDFs, markdown, code, notebooks — 1800+ files, 39K chunks, indexed in under 3 minutes.
Hybrid search (BM25 + semantic vectors + cross-encoder reranking) through 13 MCP tools.
Everything runs locally via ONNX. No Docker, no Ollama, no API keys, no data leaves your machine.
pip install knowledge-rag → restart Claude Code → search_knowledge("your query")13 MCP Tools | Hybrid Search + Reranking | 20 File Formats | Optional NVIDIA GPU | 100% Local
What's New | Supported Formats | Installation | Configuration | API Reference | Architecture
Star History
What's New in v4.2.0
Search Performance & Output Quality (v4.2.0)
128× faster BM25 search — replaced rank-bm25 full-corpus scan with a custom inverted-index implementation. Only documents containing query terms are scored, using numpy.argpartition for O(n) top-k selection. Adjacent chunk fetching now uses a single batched ChromaDB call instead of N round-trips, and an O(1) reverse lookup (_source_to_docid) eliminates linear scans.
