🐾 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