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Introduction This tutorial is inspired by a YouTube video that demonstrates how to set up a chatbot using the Llama 3 LLM model and Streamlit and Groq. Additionally, this guide enhances the project by integrating a PostgreSQL database to manage chat histories more effectively using Alembic for database migrations and SQLAlchemy for database interactions.

First of all, you need to download the chat bot’s code from my GitHub repo: aarriitt666/local_chat_bot: Local Chat Bot based on Groq, Streamlit, llama 3, and Postgres. (github.com)

Prerequisites

• Sign up for an account at Groq Console and generate an API key.
• Install PostgreSQL on your computer. Installation guides can be found on the PostgreSQL official website.

Configuration Steps

1. Setting up PostgreSQL
   1. On Windows, navigate to your PostgreSQL installation directory (e.g., C:\Program Files\PostgreSQL\16\data).
   2. Open pg_hba.conf in a text editor and add the following lines to enable external connections: host all all 0.0.0.0/0 scram-sha-256
2. Prepare Your Python Environment
   1. Navigate to your project directory and set up a Python virtual environment: python -m venv env
   2. Activate the virtual environment: source env/bin/activate
   3. Install required Python packages: pip install -r requirements.txt
3. Database and Alembic Setup
   1. Ensure your .env file contains the correct DATABASE_URL and your GROQ_API_KEY.
   2. Initialize the Alembic configuration to handle database migrations: alembic upgrade head
4. Running the Chatbot
   1. Start your chatbot with Streamlit: streamlit run chatbot.py
   2. The application will start, and you can interact with your chatbot through the web interface provided by Streamlit.

Enhancements

• Increased Memory Capacity: Adjust the MAX_BOT_MEMORY_SIZE in settings.py to increase the number of conversations the chatbot can remember, testing up to 2000 if your hardware supports it.
• Testing Persistence: To ensure that the chatbot retains memory, ask it to remember your name, restart the application, and then ask it again to see if it recalls your name.

Tools for Monitoring

• Use pgAdmin to connect to your PostgreSQL database to monitor and manage the stored chat histories.

Conclusion This setup not only allows you to build a powerful chatbot using the Llama 3 model but also enhances its capabilities by integrating a local database to store conversation histories, thus improving the persistence and scalability of the chatbot.

Additional Resources

• PostgreSQL Documentation
• Streamlit Documentation
• Alembic Documentation

By following these steps, you can create a sophisticated chatbot with enhanced memory capabilities suitable for more complex interaction scenarios.