Simple RAG Template

This is a starter repo for an easy and quick Retrieval-Augmented Generation (RAG) system.

Purpose

This application demonstrates a basic implementation of a RAG system, which combines the power of large language models with a custom knowledge base. It uses Streamlit for the frontend interface.

How it Works

A Jupyter Notebook is available rag_tutorial.ipynb if needed.

Requirements

• Python 3.7+
• Create a new virtual environment
• Install the required Python libraries:

  pip install -r requirements.txt
  

• Add your OpenAI API key to the environment variable: OPENAI_API_KEY

Create a Vector Database

Run loader.py to create the Vector Database and vectorize your PDF documents:

python app/loader.py

This script will create a ChromaDB instance, which is an open-source embedding database. For more information, visit: https://docs.trychroma.com/

Run the Application (not available)

To start the Streamlit app, run:

streamlit run app/app.py

Features

• PDF document ingestion and vectorization
• Natural language querying of the knowledge base
• Integration with OpenAI's language models for response generation

Customization

You can customize this template by:

• Adding more document types for ingestion
• Implementing different embedding models
• Enhancing the user interface with additional Streamlit components

Limitations

• Currently only supports PDF documents
• Requires an OpenAI API key

This readme was written by Claude 3.5 and checked by me.