A
licenseNot graded
qualityF
maintenanceEnables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
21
63
MIT
fathom-mcp
by veer-mehta
fathom-mcp
Dokumentations-RAG-System: eine Dokumentationsseite crawlen → in Chunks aufteilen + lokal einbetten (HuggingFace) → in Postgres+pgvector speichern → semantische Suche über MCP-Server, REST-API und Web-UI.
Architektur
flowchart LR
A[Browser] --> B[API Server]
B --> D[Web UI]
B --> C[(Postgres + pgvector)]
B --> G[Ingestion Pipeline]
G --> H[Scraper Subprocess]
G --> C
I[AI Client<br/>Claude, Cursor] -->|MCP over stdio| F[MCP Server]
F --> CRelated MCP server: MCPDocSearch
Schnellstart
git clone ... fathom-mcp && cd fathom-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[local]"
docker compose up -d
cp .env.example .env # set LLM_API_KEY
.venv/bin/docs-mcp-api # http://127.0.0.1:8000npm (kein Klonen erforderlich)
npx @fathom-mcp/server # first run installs ~5GB deps, then instant
npx @fathom-mcp/server --api # REST API + web UIMCP-Tools
add_documentation · search_documentation · list_sources · get_ingest_status · add_local_docs
REST API
Endpunkt | Was |
| Semantische Suche |
| Indizierte Quellen |
| Dateien hochladen |
| Lokalen Ordner indizieren |
| Mit Dokumenten chatten |
| Systeminformationen |
OpenCode
Zu ~/.config/opencode/opencode.jsonc hinzufügen:
{
"mcp": {
"fathom-mcp": {
"type": "local",
"command": ["npx", "-y", "@fathom-mcp/server"]
}
}
}Konfiguration
~/.fathom-mcp/.env — setze EMBEDDING_PROVIDER=api und EMBEDDING_API_KEY für Remote-Embeddings (Jina, OpenAI, usw.) oder belasse den Wert für HuggingFace auf local.
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