🤖 Blue Collar Connect AI

Intelligent RAG Chatbot for Blue-Collar Workers

Providing real-time assistance on labor laws, wages, job schemes, and worker rights

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🎯 Overview

Blue Collar Connect AI is a Retrieval-Augmented Generation (RAG) system designed for blue-collar workers. It provides intelligent, context-aware responses about labor laws, wages, government schemes, workplace rights, and career guidance using Google's Gemini language model with advanced document retrieval.

🌟 Key Features

• Labor Law Expertise – Worker rights and regulations
• Wage Intelligence – Salary data and negotiation guidance
• Government Schemes – Support programs and benefits
• Safety Information – Workplace safety protocols
• Multilingual Support – Multiple regional languages

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🛠️ Technology Stack

Component	Technology	Purpose
API Framework	FastAPI	High-performance async API server
LLM Engine	Google Gemini	Advanced language understanding
RAG Framework	LangChain	Document processing & retrieval
Workflow Engine	LangGraph	Complex conversation flows
Vector Database	ChromaDB	Semantic search & embeddings

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Google Cloud Project with Vertex AI enabled
• Google Cloud credentials

Installation

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

# Install dependencies
pip install -r requirements.txt

Configuration

Create .env file:

# Google Cloud
GOOGLE_CLOUD_PROJECT=your-gcp-project-id
GOOGLE_API_KEY=your-google-api-key
GOOGLE_MODEL_ID=gemini-1.5-pro

# ChromaDB
CHROMA_DB_PATH=./data/chroma_db
COLLECTION_NAME=blue_collar_knowledge

# API
API_HOST=0.0.0.0
API_PORT=8000

Initialize & Start

# Initialize knowledge base
python scripts/initialize_db.py

# Start server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

🎉 Server running at http://localhost:8000

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📡 API Endpoints

Chat Interface

POST /chat

# Request
{
    "message": "What are my rights regarding overtime pay?",
    "user_id": "user123",
    "session_id": "session456"
}

# Response
{
    "response": "According to labor law, you are entitled to...",
    "sources": [...],
    "session_id": "session456"
}

POST /chat/stream

Real-time streaming responses using Server-Sent Events.

Management

• GET /health - System health check
• POST /knowledge/upload - Upload documents
• GET /knowledge/stats - Knowledge base statistics

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📁 Project Structure

ai/
├── main.py                    # FastAPI entry point
├── requirements.txt           # Dependencies
├── src/                      # Source code
│   ├── rag_engine.py         # Core RAG implementation
│   ├── models.py             # Data models
│   └── config.py             # Configuration
├── routers/                  # API routes
│   ├── chat.py               # Chat endpoints
│   └── health.py             # Health checks
├── data/                     # Data storage
│   ├── documents/            # Source documents
│   └── chroma_db/            # Vector database
└── tests/                    # Test suite

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🚀 Deployment

Docker

docker build -t blue-collar-ai .
docker run -p 8000:8000 blue-collar-ai

Google Cloud Run

gcloud run deploy blue-collar-ai \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

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📊 Performance

• Response Time: < 2 seconds
• Throughput: 100+ requests/minute
• Memory Usage: < 2GB
• Vector Search: < 100ms

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🔧 Troubleshooting

Common Issues

ChromaDB Connection

rm -rf data/chroma_db
python scripts/initialize_db.py

Authentication Errors

gcloud auth application-default login

Slow Responses

# Adjust in config
CHUNK_SIZE=500
MAX_RETRIEVED_DOCS=3
SIMILARITY_THRESHOLD=0.8

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🧪 Testing

# Install test dependencies
pip install pytest pytest-asyncio httpx

# Run tests
pytest tests/ -v

# With coverage
pytest --cov=src tests/

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🆘 Support

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📄 License

MIT License - see LICENSE file for details.

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🤖 Empowering workers through intelligent AI assistance 🤖