Blood Bank AI Chatbot

An intelligent AI-powered chatbot that helps users learn about blood donation, find donation centers, and get real-time information about blood supply status. Built with AWS Bedrock Knowledge Base and a modern React frontend.

Demo

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(a) is for informational purposes only,

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(a) as-is and without warranties or representations of any kind,

(b) not suitable for production environments, or on production or other critical data, and

(c) to include shortcuts in order to support rapid prototyping such as, but not limited to, relaxed authentication and authorization and a lack of strict adherence to security best practices.

All work produced is open source. More information can be found in the GitHub repo.

Index

Description	Link
Overview	Overview
Architecture	Architecture
Detailed Architecture	Architecture Deep Dive
Deployment	Deployment Guide
User Guide	User Guide
API Documentation	API Documentation
Modification Guide	Modification Guide
Credits	Credits
License	License

Overview

Blood Bank AI Chatbot is a conversational AI assistant designed to provide comprehensive information about blood donation. It enables users to get instant, accurate answers about eligibility, donation process, blood center locations, and current blood supply status through natural language conversations.

Key Features

• AI-Powered Conversations using AWS Bedrock Models
• Knowledge Base Integration with blood donation documents and website content
• Bilingual Support for English and Spanish users
• Real-time Information with daily content updates
• Source Citations with links to authoritative information
• Admin Dashboard for monitoring conversations and managing data sources
• Blood Center Locator integration for finding nearby donation locations
• Responsive Design optimized for both desktop and mobile devices

Architecture Diagram

The application implements a serverless architecture on AWS, combining:

• Frontend: React application hosted on AWS Amplify with built-in CDN
• Backend: AWS CDK-deployed infrastructure with Lambda Function URL (no API Gateway)
• AI Layer: AWS Bedrock Knowledge Base with 3 data sources (documents, website, daily-sync)
• Data Storage: S3 for documents, DynamoDB for conversation history, OpenSearch Serverless for vector embeddings
• Authentication: Amazon Cognito for admin dashboard access
• Orchestration: Step Functions for sequential data source synchronization
• Automation: EventBridge for daily sync scheduling (2 PM EST / 7 PM UTC)

For a detailed deep dive into the architecture, see docs/architectureDeepDive.md.

Deployment

For detailed deployment instructions, including prerequisites and step-by-step guides, see docs/deploymentGuide.md.

Quick Start

# Clone the repository
git clone https://github.com/your-org/America-Blood-Centers-chatbot.git
cd America-Blood-Centers-chatbot/Backend

# Run the deployment script
chmod +x deploy.sh
./deploy.sh

User Guide

For detailed usage instructions with screenshots, see docs/userGuide.md.

API Documentation

For complete API reference including chat endpoints, admin APIs, and authentication, see docs/APIDoc.md.

Modification Guide

For developers looking to extend or customize this project, see docs/modificationGuide.md.

Directory Structure

├── Backend/
│   ├── bin/
│   │   └── bedrock-stack.ts        # CDK app entry point
│   ├── lambda/
│   │   ├── chat-lambda/            # Main chat handler with streaming
│   │   └── sync-operations/        # Data source management
│   ├── lib/
│   │   └── bedrock-chatbot-stack.ts # Main CDK stack definition
│   ├── data-sources/               # Knowledge base content
│   ├── deploy.sh                   # One-command deployment script
│   ├── cdk.json
│   ├── package.json
│   └── tsconfig.json
├── Frontend/
│   ├── src/
│   │   ├── admin/                  # Admin login and dashboard
│   │   ├── Components/             # Reusable UI components
│   │   ├── services/               # API and authentication services
│   │   ├── utilities/              # Constants and helper functions
│   │   ├── App.js                  # Main application component
│   │   └── index.js                # Application entry point
│   ├── public/
│   │   ├── logo.png               # Blood Bank logo
│   │   └── index.html
│   └── package.json
├── docs/
│   ├── architectureDeepDive.md
│   ├── deploymentGuide.md
│   ├── userGuide.md
│   ├── APIDoc.md
│   ├── modificationGuide.md
│   ├── media/                      # Screenshots and diagrams
│   └── Architecture_Diagram.png # Architecture diagram
├── LICENSE
└── README.md

Features

Core Functionality

• Intelligent Q&A: Natural language processing for blood donation questions
• Bilingual Support: Full English and Spanish language support
• Source Attribution: Every response includes citations to authoritative sources
• Real-time Updates: Daily synchronization of blood supply and center information

Data Sources

• PDF Documents: Official guidelines, statistics, and educational materials
• Website Content: Live content from americasblood.org including donation centers
• Daily Sync: Automated updates of time-sensitive information

Admin Features

• Conversation Monitoring: View and analyze chat interactions
• Data Source Management: Manual sync triggers and status monitoring
• System Health: Real-time status of all system components
• Analytics Dashboard: Usage statistics and popular questions

Technical Features

• Serverless Architecture: Auto-scaling AWS Lambda functions
• Vector Search: Semantic search using Amazon Bedrock Knowledge Base
• Advanced PDF Processing: Bedrock Data Automation with multimodal support
• Secure Authentication: Amazon Cognito for admin access

Data Flow

1. User Interaction: User sends question through React frontend hosted on Amplify
2. Lambda Function URL: Frontend calls Lambda directly (no API Gateway)
3. Knowledge Base Query: Lambda queries Bedrock Knowledge Base using retrieve_and_generate API
4. Vector Search: OpenSearch Serverless performs semantic search on embeddings (1536 dimensions)
5. AI Response: Bedrock generates streaming response using Claude Sonnet with retrieved context
6. Source Attribution: System adds citations and streams formatted response via SSE

Security & Compliance

• Data Privacy: No personal information stored in conversation logs
• Secure Authentication: JWT-based admin authentication via Cognito
• Encrypted Communication: All data encrypted in transit and at rest
• Access Control: Fine-grained IAM permissions for all AWS resources

Credits

This application was developed for Blood Bank to support their mission of ensuring a safe and adequate blood supply for patients in need.

Built with:

• AWS Bedrock for AI/ML capabilities
• React and Material-UI for the frontend
• AWS CDK for infrastructure as code
• OpenSearch Serverless for vector search

License

This project is licensed under the MIT License - see the LICENSE file for details.