RAG Customer Support Chatbot

A Retrieval-Augmented Generation (RAG) chatbot built using:

• ChromaDB for vector storage
• Amazon Bedrock (Claude 3 Sonnet) for LLM
• HuggingFace Embeddings (all-MiniLM-L6-v2)
• Streamlit for the user interface

Setup

Using Conda

1. Create and activate the Conda environment:

conda create -n rag-chatbot python=3.10 -y
conda activate rag-chatbot
pip install -r requirements.txt

Configuration

1. Create a .env file with your AWS credentials:

AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_DEFAULT_REGION=your_region

2. Place your documentation files in the docs directory

Running the Application

Start the chatbot with network access:

streamlit run app.py --server.address 0.0.0.0 --server.port 8501

Then access the application in your browser:

1. If using SSH port forwarding:

   ssh -L 8501:localhost:8501 your-username@your-server-ip

   Then open: http://localhost:8501

2. Or access directly using server IP:

   http://your-server-ip:8501
   

Note: Make sure port 8501 is open in your server's firewall/security group.