Chess Bot Application

This is a Chess Bot application that uses a combination of natural language processing models, including DistilBERT for embeddings and LLaMA 3 for text generation, to answer chess-related questions. The bot retrieves relevant information from a PDF textbook on chess, chunks the text, retrieves the most relevant pieces using FAISS, and generates a response using the LLaMA 3 model through AWS Bedrock.

Features

• Text Extraction: Extracts text from a chess textbook in PDF format.
• Text Chunking: Splits the extracted text into manageable chunks for processing.
• FAISS Indexing: Retrieves top-k relevant document chunks based on query embeddings.
• LLaMA 3 Integration: Uses LLaMA 3 to generate responses to chess-related queries.
• Flask Frontend: A simple web-based frontend where users can input chess queries and receive responses.

How It Works

1. Extracts text from a chess PDF textbook using PyPDF2.
2. Chunks the text into smaller segments to work within model constraints.
3. Generates embeddings for the text chunks using DistilBERT.
4. Stores embeddings in a FAISS index to enable fast similarity search.
5. Retrieves the top-k similar chunks based on the user’s query.
6. Calls the LLaMA 3 model via AWS Bedrock to generate a detailed response based on the query and retrieved context.

Technologies Used

• Flask: Web framework to serve the app.
• DistilBERT: For generating embeddings.
• LLaMA 3: For generating detailed text responses via AWS Bedrock.
• FAISS: For efficient similarity search and retrieval.
• AWS Bedrock: For hosting and invoking the LLaMA 3 model.
• Docker: Containerization for easy deployment.

Setup Instructions

1. Clone the Repository

git clone https://github.com/siddharth130500/RAG_chess_bot.git
cd chess-bot

2. Install Dependencies

Ensure you have Python 3.10 or later installed. Install the required Python dependencies:

pip install -r requirements.txt

3. Set Up Environment Variables

The application requires AWS credentials to interact with the LLaMA 3 model via AWS Bedrock. Create a .env file in the root directory of the project with the following:

AWS_ACCESS_KEY_ID=your_access_key_id
AWS_SECRET_ACCESS_KEY=your_secret_access_key

4. Run the Application

Start the Flask server locally:

python app.py

The application will be accessible at http://localhost:5000.

5. Docker Setup

The Docker image for the Chess Bot application is available on DockerHub.

DockerHub Image

You can pull the pre-built Docker image from DockerHub:

docker pull siddharth130500/chess-bot-app:latest

Run the Docker Container

Ensure that your .env file is mapped into the Docker container for AWS authentication:

docker run -d -p 5000:5000 -v /path/to/your/.env:/app/.env siddharth130500/chess-bot-app

6. Usage

1. Open a web browser and go to http://localhost:5000.
2. Enter a chess-related question in the input field.
3. Submit the query and wait for the response generated by the LLaMA 3 model.

Automated start and stop for the Docker container

For automated container start and stop mechanisms to optimize resource usage, we can use a CGI-based interface to start the container on demand and monitor for inactivity to stop the container automatically.

Features

• Automated Start: The container starts automatically when accessed through a browser via a CGI script.
• Inactivity Monitoring: A script monitors for inactivity and stops the container after a defined timeout period.
• Nginx Reverse Proxy: Nginx is configured as a reverse proxy to route requests seamlessly to the application.

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File Overview

1. start_docker_container.sh

• Purpose: Starts the Docker container if not already running.
• Key Features:
  • Checks the status of the container using its name (siddharth130500/chess-bot-app).
  • Starts the container on port 5000 and mounts the .env file.
  • Sets up a cron job to monitor inactivity using timeout_check.sh.
• Usage: Automatically triggered by start_container.cgi.

2. timeout_check.sh

• Purpose: Stops the Docker container after a specified period of inactivity.
• Key Features:
  • Monitors /var/log/nginx/access.log for the last access timestamp.
  • Compares the last access time to the current time and stops the container if inactivity exceeds 120 seconds.
  • Removes the associated cron job upon stopping the container.
• Usage: Triggered by the cron job set in start_docker_container.sh.

3. start_container.cgi

• Purpose: A CGI script to start the container when accessed via the browser.
• Key Features:
  • Executes start_docker_container.sh.
  • Displays a confirmation message and redirects to the application (http://localhost:5000/) after a short delay.
• Usage: Access via the URL http://localhost/cgi-bin/start_container.cgi.

4. test.cgi

• Purpose: A test CGI script to verify CGI execution.
• Key Features:
  • Outputs a test message (Test CGI Script Works) in the browser.
• Usage: Access via the URL http://localhost/cgi-bin/test.cgi.

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Nginx Configuration

To set up Nginx as a reverse proxy for the Chess Bot application:

1. Install Nginx

sudo apt update
sudo apt install nginx

2. Configure Nginx

Edit the Nginx configuration file:

sudo nano /etc/nginx/sites-available/default

Add the following configuration:

server {
    listen 80;

    server_name localhost;

    location / {
        proxy_pass http://localhost:5000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }

    location /cgi-bin/ {
        root /var/www/html; # Adjust this to your CGI scripts directory
        fastcgi_pass unix:/run/fcgiwrap.socket;
        include fastcgi_params;
        fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
    }
}

3. Enable CGI Execution

Install and configure fcgiwrap:

sudo apt install fcgiwrap
sudo systemctl enable fcgiwrap
sudo systemctl start fcgiwrap

4. Restart Nginx

sudo systemctl restart nginx

Access the app via the URL http://localhost/cgi-bin/start_container.cgi.