로컬 첫 RAG (리발 증강) MCP 서버. 에 그것을 문서의 폴더와 MCP 클라이언트 (Claude Code, Cursor, Codex, …) 하이브리드 검색 - semantic 벡터 유사성 플러스 키워드 향상 정확한 용어 — 그 내용에.

두 가지를 제외하고 기계를 잎이 없습니다. 한 번 embedding-model 첫 번째 사용에서 다운로드, 그리고 명시 ingest_url] 당신이 그것을 요청할 때 호출 웹 페이지를 읽으십시오. 로컬 파일, 색인 및 쿼리를 수행하지 않는 네트워크.

파이썬, MCP-native 아날로그입니다. shinpr/mcp-local-rag (TypeScript), fastmcp에 건축하는, 빠른, 및 LanceDB.

특징

-Hybrid search — 키워드로 융합된 벡터 유사성(fastembed/ONNX) Weighted Reciprocal Rank Fusion의 LanceDB BM25 전체 텍스트 검색 그래서 정확한 식별자 및 오류 코드 표면 semantically 비슷한 패스워드

• 모든 언급, 계산 — find_mentions 모든 문서 목록 단어 또는 구문을 포함, 상위 몇 가지, 그리고 얼마나 많은 문서를 말한다 검색, 그래서 “전체 목록”, "많은"및 "그것은 없습니다"전체를 얻을 이름 * 자주 묻는 질문.

minirag-mcp

A local-first RAG (retrieval-augmented generation) MCP server. Point it at a folder of documents and it gives your MCP client (Claude Code, Cursor, Codex, …) hybrid search — semantic vector similarity plus a keyword boost for exact terms — over that content.

Nothing leaves your machine except two things: the one-time embedding-model download on first use, and the explicit ingest_url call when you ask it to fetch a web page. Ingesting local files, indexing, and querying never touch the network.

It is a Python, MCP-native analog of shinpr/mcp-local-rag (TypeScript), built on fastmcp, fastembed, and LanceDB.

Features

• Hybrid search — vector similarity (fastembed/ONNX) fused with keyword ranking (LanceDB BM25 full-text search) by weighted Reciprocal Rank Fusion, so exact identifiers and error codes surface alongside semantically similar passages.
• Every mention, counted — find_mentions lists every document that contains a word or phrase, not the top few, and says how many documents it searched, so “list all”, “how many” and “it is not there” get a complete answer. See Finding every mention.
• Look-alike documents named — a result whose document shares most of its text with other indexed documents, such as another version of it or a sibling from the same template, names them and how much text they share. See Look-alike documents.
• Filenames are searchable — keyword search covers document titles as well as body text, and an informative filename becomes the document’s title when the document’s own heading is boilerplate. In many real document sets the filename is the only place the document code and subject appear at all. See Titles and filenames.
• Multilingual by default — the default embedding model covers 50+ languages, so English and Russian corpora both work out of the box.
• Converted once, not re-read every chat — a PDF handed to an assistant is read again, page by page, in every new conversation, and scanned pages cost the most. Here each document is converted (and OCRed) once at ingest, and a question brings back only the sections that answer it, so it costs those sections rather than the whole file.