RAG Application with Llama3 (Ollama) and Streamlit

This is a Retrieval-Augmented Generation (RAG) application that processes legal document PDFs, stores them in a vector database using Chroma, and retrieves relevant information using Llama3 from Ollama.

Features

• Upload Legal Documents: Accepts PDFs and extracts text.
• Vector Search with Chroma: Uses embeddings to store and retrieve relevant document sections.
• Local AI Processing with Llama3: Runs Llama3 locally using Ollama for fast, private inference.
• Streamlit UI: A simple web interface to interact with the AI model.

Installation Guide

1. Install Dependencies

Before running the application, install the required dependencies using the provided requirements.txt file:

pip install -r requirements.txt

2. Install and Run Ollama (Llama3) Locally

MacOS

# Install Ollama
brew install ollama

# Start Ollama service
ollama serve

# Pull the Llama3 model
ollama pull llama3

Windows

1. Download and install Ollama for Windows.
2. Open PowerShell and start the service:

   ollama serve

3. Pull the Llama3 model:

   ollama pull llama3

3. Set Up ChromaDB

Ensure ChromaDB is set up for vector storage:

mkdir chroma

4. Run the Application

To start the Streamlit web application, run:

streamlit run app.py

To populate the database with legal documents:

python populate_database.py --reset

To query the AI through CLI:

python query.py "Your legal question here"