multilingual-rag-mcp

# multilingual-rag-mcp Multilingual RAG MCP server for local document search. Query in one language, find content in another. Built for the common case where you talk to AI agents in English but your documents are in Russian (or any other language). The multilingual embedding model maps semantically similar concepts across 50+ languages to the same vector space — no translation step needed. ## Install ### pip from PyPI ```bash pip install multilingual-rag-mcp ``` ### One-liner with uvx (no install needed) ```bash uvx --from multilingual-rag-mcp rag-mcp index ./docs/ ``` ### pip from GitHub (latest) ```bash pip install git+https://github.com/aliaksandr-kazarez/multilingual-rag-mcp.git ``` ## Quick start ### 1. Index your documents ```bash rag-mcp index ~/documents/ ``` ### 2. Add to Claude Code ```bash claude mcp add rag -- uvx --from multilingual-rag-mcp rag-mcp ``` Set the document paths via env vars: ```bash claude mcp add rag \ -e RAG_DOCS=$HOME/documents \ -- uvx --from multilingual-rag-mcp rag-mcp ``` If installed locally (pip install), use the simpler form: ```bash claude mcp add rag -e RAG_DOCS=$HOME/documents -- rag-mcp ``` ### 3. Search From Claude Code, the `search` tool handles cross-lingual queries automatically: - "search for protein recommendations" finds Russian articles about белок - "найди рецепты" finds recipe content regardless of language ## MCP tools | Tool | Description | |------|-------------| | `search(query, n=5)` | Semantic search across all indexed documents | | `get_document(path)` | Retrieve full document content | | `list_documents(filter?)` | List indexed documents, optionally filtered | | `reindex()` | Re-index all configured document directories | | `stats()` | Index statistics (documents, chunks, categories) | ## Configuration | Env var | Default | Description | |---------|---------|-------------| | `RAG_DOCS` | — | Comma-separated paths to document directories | | `RAG_DATA` | `~/.local/share/rag-mcp/`…