Skip to content

Latest commit

 

History

History
61 lines (38 loc) · 1.59 KB

README.md

File metadata and controls

61 lines (38 loc) · 1.59 KB

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.