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

> That's why we built llm-food — a FastAPI-based service that converts documents and URLs into clean, LLM-friendly Markdown with batch processing support.

> Originally published on [Medium](https://medium.com/altai-dev/are-you-tired-of-getting-only-garbage-instead-of-markdown-from-pdfs-yeah-same-f7816fe2f87b)

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

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

Or just the client:

```bash
uv add llm-food
```

### Start the Server

```bash
llm-food-serve
```

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

Want Docker?

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

### CLI Usage

```bash
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:

```bash
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](https://altai.dev/) or drop us a line if that's what you're hungry for.

Source: https://altai.dev/blog/llm-food-pdf-to-markdown/