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knowledge-rag

knowledge-rag

Active·★ 86·MIT·Updated 2026-05-26

★ Trending★ RAG / Knowledge Base★ API Integration

Drop docs, search instantly from Claude Code — 12 MCP tools, 20 format parsers, hybrid search + reranking. Zero servers, zero API keys, 100% local.

Knowledge RAG is a fully local, zero-cloud document retrieval system that lets Claude Code search your PDFs, markdown, code, and notebooks natively. It indexes up to 1800+ files in under 3 minutes, using hybrid search (BM25 + semantic vectors + cross-encoder reranking) via 12 MCP tools. Everything runs locally via ONNX, no Docker, Ollama, or API keys required.

#bm25#chromadb#claude#claude-code#document-search#embeddings#fastembed#hybrid-search

$ Install

$ pip install knowledge-rag

↗ Visit site ★ GitHub

01

Features

01Hybrid Search with BM25 + semantic vectors + cross-encoder reranking

02Optional GPU acceleration for 5-10x faster indexing

0320 format parsers (MD, PDF, DOCX, code, etc.)

04YAML configuration with domain-specific presets

0512 MCP tools for full CRUD, search, and evaluation

02

Compatibility

Windows

Windows

Verified via docs

Linux

Linux

Verified via docs

macOS

macOS

Verified via docs

03

Quick start

1

$ pip install knowledge-rag

04

Use cases

↳Personal document retrieval and knowledge base for developers

↳Security research document indexing and querying

↳Offline documentation assistant for sensitive environments

05

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Related searches

knowledge-rag Alternatives Best RAG / Knowledge Base Tools 2026 Open Source RAG / Knowledge Base knowledge-rag Tutorial knowledge-rag Vs Competitors bm25 chromadb claude

Comments

Log in to leave a comment

R
Rebel NguyenMay 10, 2026
RAG from Claude Code without leaving the terminal is a killer productivity pattern
R
Riley WhiteMay 6, 2026
Drop docs and search instantly — the 12 MCP tools cover the full retrieval workflow
O
Oaklyn JohnsonApr 22, 2026
20 format parsers means you can feed it almost any document type without preprocessing
Quinn RiveraMar 4, 2026
Used for codebase documentation search, retrieval quality is noticeably better than naive embedding

On this page

01Features 02Compatibility 03Quick start 04Use cases 05Alternatives

Stats

GitHub Stars★ 86

Last commit4d ago

StatusActive

LicenseMIT

CategoryRAG / Knowledge Base

Trend (30d)

+3.4↑ 0.7%

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