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🐾 Panther Pathfinder - AWS Bedrock Knowledge Base Chatbot

A modern Streamlit chatbot interface that integrates with AWS Bedrock Knowledge Base for intelligent document retrieval and AI-powered responses.

Features

  • 🤖 AI-Powered Chat: Uses Claude 3 Sonnet via AWS Bedrock for intelligent responses
  • 📚 Knowledge Base Integration: Retrieves relevant documents from your Bedrock Knowledge Base
  • 🔍 Source Attribution: Shows document sources for transparency
  • 🎨 Modern UI: Clean, responsive Streamlit interface
  • ⚙️ Configurable: Adjustable retrieval settings and model parameters

Prerequisites

  • Python 3.8+
  • AWS Account with Bedrock access
  • AWS Bedrock Knowledge Base set up
  • AWS credentials configured

Setup

1. Clone and Setup Environment

# Clone the repository
git clone <your-repo-url>
cd panther-pathfinder-aws

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configure AWS Credentials

You have two options for configuration:

Option A: Environment Variables (Recommended)

# Copy the template
cp .env.template .env

# Edit .env with your actual values
nano .env

Option B: Direct Configuration

Edit config.py and uncomment the AWS_CONFIG section at the bottom, filling in your values.

3. Required AWS Setup

  1. AWS Bedrock Access: Ensure your AWS account has access to Bedrock
  2. Knowledge Base: Create a Bedrock Knowledge Base and note its ID
  3. IAM Permissions: Your AWS credentials need these permissions:
    • bedrock:InvokeModel
    • bedrock:Retrieve
    • bedrock:RetrieveAndGenerate

Configuration

Set these required values in your .env file or config.py:

  • AWS_ACCESS_KEY_ID: Your AWS access key
  • AWS_SECRET_ACCESS_KEY: Your AWS secret key
  • AWS_REGION: AWS region (default: us-east-1)
  • BEDROCK_KNOWLEDGE_BASE_ID: Your Bedrock Knowledge Base ID
  • BEDROCK_MODEL_ID: Model to use (default: Claude 3 Sonnet)

Usage

Run the Application

streamlit run app.py

The application will be available at http://localhost:8501

Using the Chatbot

  1. Ask Questions: Type your questions in the chat input
  2. View Sources: Expand the "Sources" section to see retrieved documents
  3. Adjust Settings: Use the sidebar to configure retrieval parameters
  4. Clear History: Use the sidebar button to reset the conversation

Features in Detail

Knowledge Base Integration

  • Automatically retrieves relevant documents from your Bedrock Knowledge Base
  • Shows relevance scores and source document information
  • Configurable number of retrieved documents (1-10)

AI Response Generation

  • Uses Claude 3 Sonnet for high-quality responses
  • Implements Retrieval-Augmented Generation (RAG) pattern
  • Provides context-aware answers based on your documents

User Interface

  • Modern, responsive design
  • Chat history persistence during session
  • Source attribution for transparency
  • Real-time status indicators

File Structure

panther-pathfinder-aws/
├── app.py                 # Main Streamlit application
├── config.py             # Configuration management
├── requirements.txt      # Python dependencies
├── .env.template        # Environment variables template
├── README.md           # This file
└── venv/              # Virtual environment (created locally)

Troubleshooting

Common Issues

  1. Connection Failed: Check your AWS credentials and region
  2. Knowledge Base Not Found: Verify your Knowledge Base ID
  3. Permission Denied: Ensure your IAM user has required Bedrock permissions
  4. No Results: Check if your Knowledge Base has indexed documents

Error Messages

  • "Failed to initialize Bedrock Knowledge Base": Check AWS configuration
  • "Error querying knowledge base": Verify Knowledge Base ID and permissions
  • "Error generating response": Check model access and quotas

Development

Adding New Features

The application is modular and easy to extend:

  • BedrockKnowledgeBase class: Handles AWS Bedrock interactions
  • main() function: Contains the Streamlit UI logic
  • config.py: Manages configuration and validation

Customization

  • Modify CSS in the main() function for styling changes
  • Adjust model parameters in generate_response_with_rag()
  • Add new sidebar controls for additional features

Current Limitations

  • Due to current LLM limitations, queries are only accurate up to two classes/locations

Roadmap

  • RAG system setup using Amazon Bedrock Knowledge Bases
  • Frontend developed using Streamlit
  • Python script developed to gather information from Hartnell course catalog (failure of web scraper)
  • Gathered information uploaded to S3 buckets
  • Python script developed to automatically generate Google Map link to destination
  • Multilingual support added
  • Better map integration
  • Make the school databases more AI-accessible (right now, it's very user-focused, but data ingestion is difficult for AI)
  • Build on project to add scalability
  • Automate processes to make it easy to implement across campuses

Security Notes

  • Never commit AWS credentials to version control
  • Use environment variables or AWS IAM roles for production
  • Rotate access keys regularly
  • Follow AWS security best practices

License

This project is open source. Please check the license file for details.

Support

For issues and questions:

  1. Check the troubleshooting section above
  2. Review AWS Bedrock documentation
  3. Open an issue in this repository