Are you tired of getting only garbage instead of Markdown from PDFs? Yeah, same.

Originally published on Medium

That's why we built llm-food — a FastAPI-based service that converts documents and URLs into clean, LLM-friendly Markdown. It supports batch processing, integrates with Google's Gemini for blazing-fast PDF OCR, and wraps everything in a CLI and Python client that doesn't make you cry.

tl;dr

• Converts PDFs, DOCX, RTF, PPTX, and HTML/web pages into Markdown.
• Synchronous and async modes via HTTP API, CLI, and Python client.
• Batch PDFs go through Gemini Batch Prediction API for high throughput ($1 for ~6,000 pages).
• Tracks batch jobs in DuckDB.
• Fully dockerized. Optional auth. One-liner CLI.

Why?

Extracting clean text from PDFs is still a mess.

You've probably seen dockling, marker, or pymupdf4llm. They're okay — but they're either slow, resource-hungry, or AGPL-licensed (ouch!). If you just want clean Markdown to fine-tune or RAG with your LLM, it's way more effort than it should be.

Enter Gemini Batch Prediction. It's fast and insanely cheap — $1 per 6,000 pages. But it's not dev-friendly.

So we wrapped it all up in a neat little microservice: llm-food.

What You Get

Features

• Convert:
  • PDFs (Gemini, pymupdf4llm, or pypdf)
  • DOC/DOCX (via mammoth)
  • RTF (via striprtf)
  • PPTX (python-pptx)
  • HTML or URLs (trafilatura)
• Run as:
  • FastAPI server (sync and async modes)
  • CLI (llm-food convert-file my.pdf)
  • Python client
• Async batch jobs with task tracking (DuckDB-based).
• Docker-ready, with optional Bearer token auth.
• Extensible and configurable.

Batch Mode: Fast, Cheap, Parallel

PDFs get chunked and sent to Gemini's Batch API. You just upload files and give it a GCS path — we handle the queuing, status tracking, and Markdown export.

Other formats (DOCX, RTF, PPTX) are handled individually as background tasks.

How to Use It

Install

pip install 'llm-food[server]'          # Full server
pip install 'llm-food[server,pymupdf]'  # Optional: if you want pymupdf backend

Or just the client:

uv add llm-food

Start the Server

llm-food-serve

API lives at http://localhost:8000/docs.

Want Docker?

docker build -t llm-food .
docker run -d -p 8000:8000 --env-file .env llm-food

CLI Usage

llm-food convert-file ./paper.pdf
llm-food convert-url https://example.com
llm-food batch-create doc1.pdf doc2.pdf gs://my-bucket/outputs/
llm-food batch-status <task_id>
llm-food batch-results <task_id>

Set server URL/token via env:

export LLMFOOD_SERVERURL=http://my-host:8000
export LLMFOOD_APITOKEN=secret

Auth & Config

Add a .env file to configure:

• GCS credentials
• Gemini API location/project
• File size limits
• PDF backend
• Auth token

Run locally or on a containerized server.

Roadmap

• Support for transcription of media files (videos and audios)
• YouTube crawling and batch transcription
• Configurable automatic chunking

Final Thoughts

Building a RAG pipeline, a search agent, or just need clean Markdown from messy enterprise docs? llm-food gets you there fast.

But let's be real: raw Markdown is just raw ingredients. We're cooking up an enterprise-grade engine that builds custom synthetic datasets and trains LLMs tailored to your docs. Join the waitlist or drop us a line if that's what you're hungry for.