RAG (Retrieval Augmented Generation) Application

This repository contains a Streamlit-based chatbot application that uses Retrieval Augmented Generation (RAG) to provide informative responses based on document retrieval.

Setup Instructions

Windows Setup

1. Make sure you have Python installed on your system.
2. Open Command Prompt or PowerShell.
3. Navigate to the project directory.
4. Run the setup script:

start.bat

This script will:

• Create a virtual environment called venv
• Activate the virtual environment
• Install the required dependencies from requirements.txt
• Create a .streamlit directory with a basic secrets.toml file

Mac/Linux Setup

1. Make sure you have Python installed on your system.
2. Open Terminal.
3. Navigate to the project directory.
4. Make the script executable and run it:

chmod +x start.sh
./start.sh

This script will:

• Create a virtual environment called venv
• Activate the virtual environment
• Install the required dependencies from requirements.txt
• Create a .streamlit directory with a basic secrets.toml file

Running the Application

After completing the setup, you can run the Streamlit application:

1. Ensure your virtual environment is activated:
   • Windows: venv\Scripts\activate
   • Mac/Linux: source venv/bin/activate
2. Run the Streamlit application:

streamlit run app.py

3. The application will be available in your web browser at http://localhost:8501

Configuration

• API keys for OpenAI and other services can be configured in secrets.toml
• Google service account credentials are stored in service_account.json

Features

This application demonstrates various RAG implementations including:

• Basic LLM integration
• Vector search using LangChain
• Google Vertex AI integration
• Azure Search AI integration
• RAG evaluation techniques