akasharidas / LLM-Learning-Assistant

LLM Learning Assistant: A tool to help you quickly comprehend long-form content

LLM Learning Assistant: Long-Form Content Summarization and Q&A using LLMs and Retrieval-Augmented Generation

Have a 2-hour long Huberman Lab episode queued up but wish you could get a summary and ask follow-up questions instead? This project is for you!

Features ✨

• Generates a bulleted summary and answers questions based on information retrieved from the source content
• Supports long-form content via Map-Reduce
• Transcribes audio using OpenAI Whisper, or fetches manually-created transript directly if available
• Supports OpenAI GPT models via API, or Llama 2 running locally on CPU or GPU via llama.cpp

Usage 🔨

For long-form content, I recommend using models with at least 16k context length, such as gpt-3.5-turbo-16k.

1. If using GPT: Place your OpenAI API key in a .env file as OPENAI_API_KEY=sk-***...
2. If using Llama 2: Compile and install llama-cpp-python for your desired backend following the instructions.
3. pip install -r requirements.txt
4. python run_summary_qa.py --help to see the commandline options for running it

Example