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veer-mehta

fathom-mcp

by veer-mehta
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fathom-mcp

문서 RAG 시스템: 문서 사이트를 크롤링 → 로컬에서 청크 분할 + 임베딩(HuggingFace) → Postgres+pgvector에 저장 → MCP 서버, REST API, 웹 UI를 통한 시맨틱 검색.

아키텍처

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 --> C

Related MCP server: MCPDocSearch

빠른 시작

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://127.0.0.1:8000

npm (클론 불필요)

npx @fathom-mcp/server            # first run installs ~5GB deps, then instant
npx @fathom-mcp/server --api      # REST API + web UI

MCP 도구

add_documentation · search_documentation · list_sources · get_ingest_status · add_local_docs

REST API

엔드포인트

설명

GET /search?q=...

시맨틱 검색

GET /sources

인덱싱된 소스

POST /upload

파일 업로드

POST /upload-folder

로컬 폴더 인덱싱

GET /llm-chat?q=...

문서와 채팅

GET /about

시스템 정보

OpenCode

~/.config/opencode/opencode.jsonc에 추가하세요:

{
  "mcp": {
    "fathom-mcp": {
      "type": "local",
      "command": ["npx", "-y", "@fathom-mcp/server"]
    }
  }
}

설정

~/.fathom-mcp/.env — 원격 임베딩(Jina, OpenAI 등)을 사용하려면 EMBEDDING_PROVIDER=api와 EMBEDDING_API_KEY를 설정하고, HuggingFace를 사용하려면 local로 두세요.

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