awesome-llm-apps/llama3_local_rag
2024-05-01 15:40:34 -05:00
..
llama3_local_rag.py Added new demo 2024-05-01 15:40:34 -05:00
README.md Added new demo 2024-05-01 15:40:34 -05:00
requirements.txt Added new demo 2024-05-01 15:40:34 -05:00

💻 Local Lllama-3 with RAG

Streamlit app that allows you to chat with any webpage using local Llama-3 and Retrieval Augmented Generation (RAG). This app runs entirely on your computer, making it 100% free and without the need for an internet connection.

Features

  • Input a webpage URL
  • Ask questions about the content of the webpage
  • Get accurate answers using RAG and the Llama-3 model running locally on your computer

How to get Started?

  1. Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Get your OpenAI API Key
  • Sign up for an OpenAI account (or the LLM provider of your choice) and obtain your API key.
  1. Run the Streamlit App
streamlit run llama3_local_rag.py

How it Works?

  • The app loads the webpage data using WebBaseLoader and splits it into chunks using RecursiveCharacterTextSplitter.
  • It creates Ollama embeddings and a vector store using Chroma.
  • The app sets up a RAG (Retrieval-Augmented Generation) chain, which retrieves relevant documents based on the user's question.
  • The Llama-3 model is called to generate an answer using the retrieved context.
  • The app displays the answer to the user's question.