Self-hosted documentation RAG pipeline that serves semantic search to LLM agents over the Model Context Protocol.
A self-hosted documentation RAG pipeline for LLM agents — crawl static docs sites, embed them locally with pgvector, and serve semantic search over the Model Context Protocol.
self-docs gives your coding agents (Cursor, Claude Code, Antigravity, or any MCP client) a private, always-current reference library. It crawls upstream documentation sites, chunks and embeds them locally — no third-party embedding API — and exposes hybrid semantic search as MCP tools over streamable HTTP.
- Why self-docs - Architecture - Quickstart — Local Development - Go CLI & Progressive Disclosure Skill (doc-cli) - Quickstart — Production (Home-Lab + Traefik) - MCP Tools & REST Endpoints - Managing Sources - Documentation - Development - License
- Local-first embeddings. FastEmbed runs in-process on CPU (ONNX, no GPU/torch); documentation never leaves your network. The model is selectable from a registry (config/models.yaml) — make configure derives the vector dimension and container memory limits from your choice. Default: mixedbread-ai/mxbai-embed-large-v1 (1024-dim). - Hybrid retrieval. Vector similarity + per-source-language Postgres full-text search over pgvector, so exact terms and semantic matches both surface. - Efficient re-crawling. Sources can prefer a site's llms.txt index over HTML crawling, and re-syncs use HTTP conditional GET (ETag/If-Modified-Since) to skip unchanged pages before download — see ADR-003. - Agent-native. Ships as MCP tools (searchdocs, listdocsources, proposedocsource) over streamable HTTP — wire it into any MCP client. - Operator-friendly. Crawl targets live in
From the project README.
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