@vijay-fs
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Coffee MCP Server is a robust FastAPI-based API server for asynchronous document extraction and processing. It extracts text and tables from PDFs and images using OCR, generates text embeddings via OpenAI or Anthropic, and stores job results in MongoDB for persistent tracking. It is designed for developers who need to automate document data extraction at scale.
The server requires Python 3.8+, a running MongoDB instance, Tesseract OCR, and Poppler. After cloning the repository, install system and Python dependencies, configure environment variables in a .env file (including MONGODB_URI, OPENAI_API_KEY, and optionally ANTHROPIC_API_KEY), and start the server with uvicorn app:app --host localhost --port 8000 --reload. Documents are submitted via the POST /v1/extract_data endpoint; the returned job_id is used to poll GET /v1/extract_data_job for status and retrieve results from GET /v1/extract_data_result.
It extracts text using OCR, detects and extracts tables, and generates text embeddings. Processing runs asynchronously on a background thread, enabling page‑by‑page updates for large PDFs.
You need Python 3.8+, a running MongoDB instance, Tesseract OCR engine, and Poppler. API keys for OpenAI (required) and optionally Anthropic are set in a `.env` file.
All job metadata, progress, extracted text, tables, and embeddings are stored in MongoDB. The database and collection are configured via the `MONGODB_URI` and `MONGODB_DATABASE` environment variables.
The server exposes a RESTful HTTP API via FastAPI/Uvicorn. No built‑in authentication is described; API keys for embedding providers are stored locally in the environment file. CORS is configured on the server.
Processing very large documents (500+ pages) requires sufficient memory for OCR operations. The background thread architecture keeps the API responsive, and debug images can be disabled by omitting the `RAGNOR_DEBUG_IMAGES_PATH` variable.
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