(Yet another) LLM PDF Summarizer 📝

Here's an extensible and production-ready PDF summarizer that you can run anywhere! The frontend uses streamlit, which communicates with a FastAPI backend powered by Hamilton. You give it a PDF file via the browser app and it returns you a text summary using the OpenAI API. If you want, you skip the browser inteface and directly access the /summarize endpoint with your document! Everything is containerized using Docker, so you should be able to run it where you please 🏃.

Why build this project?

This project shows how easy it is to productionize Hamilton. Its function-centric declarative approach makes the code easy to read and extend. We invite you to clone the repo and customize to your needs! We are happy to help you via Slack and are excited to see what you build 😁

Here are a few ideas:

• Modify the streamlit file_uploader to allow sending batches of files through the UI
• Add PDF parsing and preprocessing to reduce number of tokens sent to OpenAI
• Add Hamilton functions to gather metadata (file length, number of tokens, language, etc.) and return it via SummaryResponse
• Support other file formats; use the @config.when() decorator to add alternatives to the raw_text() function for PDFs
• Extract structured data from PDFs using open source models from the HuggingFace Hub.

The Hamilton execution DAG powering the backend

Setup

1. Clone this repository git clone https://github.com/dagworks-inc/hamilton.git
2. Move to the directory cd hamilton/examples/LLM_Workflows/pdf_summarizer
3. Create a .env (next to README.md and docker-compose.yaml) and add your OpenAI API key in such that OPENAI_API_KEY=YOUR_API_KEY
4. Build docker images docker compose build
5. Create docker containers docker compose up -d
6. Go to http://localhost:8080/docs to see if the FastAPI server is running
7. Go to http://localhost:8081/ to view the Streamlit app
8. If you make changes, you need to rebuild the docker images, so do docker compose up -d --build.
9. To stop the containers do docker compose down.
10. To look at the logs, your docker application should allow you to view them, or you can do docker compose logs -f to tail the logs (ctrl+c to stop tailing the logs).

Connecting to DAGWorks

1. Create a DAGWorks account at www.dagworks.io - follow the instructions to set up a project.
2. Add your DAGWorks API Key to the .env file. E.g. DAGWORKS_API_KEY=YOUR_API_KEY
3. Uncomment dagworks-sdk in requirements.txt.
4. Uncomment the lines in server.py to replace sync_dr with the DAGWorks Driver.
5. Rebuild the docker images docker compose up -d --build.

Running on Spark!

Yes, that‘s right, you can also run the exact same code on spark! It’s just a oneline code change. See the run_on_spark README for more details.