Chat Application - HR Policy Q&A Assistant

A full-stack chat application featuring a RAG (Retrieval-Augmented Generation) powered chatbot that answers questions about HR policies. The application consists of a Flask backend API and a Next.js frontend client.

🚀 Features

• 🤖 RAG-based Chatbot: Uses Retrieval-Augmented Generation to answer questions based on HR policy documents
• 🔍 Vector Search: Leverages ChromaDB for semantic search and document retrieval
• 🎨 Modern Frontend: Next.js 16 with React 19, TypeScript, and Tailwind CSS
• 🏗️ Clean Architecture: Implements controller-service pattern for maintainable code structure
• 🔐 Environment-based Configuration: Secure configuration management using environment variables
• 📊 Health Check Endpoint: Monitor application health status
• 🚀 RESTful API: Well-structured API endpoints for chat interactions

📁 Project Structure

chat-application/
├── chat-bot-langchain-latest/    # Backend Flask API
│   ├── app/
│   │   ├── config/                # Application configuration
│   │   ├── controllers/           # Request controllers
│   │   ├── models/                # Pydantic models
│   │   ├── routes/                # API routes
│   │   └── services/              # Business logic & RAG agent
│   ├── chroma_db/                 # ChromaDB persistent storage
│   ├── main.py                    # Application entry point
│   ├── pyproject.toml             # Python dependencies
│   └── docker-compose.yml         # Docker configuration
│
└── chat-client/                   # Frontend Next.js client
    ├── app/                      # Next.js app directory
    ├── public/                  # Static assets
    ├── package.json             # Node.js dependencies
    └── tsconfig.json            # TypeScript configuration

🛠️ Tech Stack

Backend

• Framework: Flask 3.1.2+
• LLM: OpenAI GPT-4.1-mini
• Vector Database: ChromaDB
• Embeddings: OpenAI text-embedding-3-large
• Language Chain: LangChain
• Validation: Pydantic
• Python: 3.13+

Frontend

• Framework: Next.js 16.1.1
• UI Library: React 19.2.3
• Language: TypeScript 5+
• Styling: Tailwind CSS 4
• Package Manager: Bun

📋 Prerequisites

• Python 3.13 or higher
• Node.js 20+ (or Bun)
• OpenAI API Key
• Git

🔧 Installation

Backend Setup

1. Navigate to the backend directory:

cd chat-bot-langchain-latest

2. Install dependencies using uv (recommended) or pip:

# Using uv
uv pip install -e .

# Or using pip
pip install -e .

3. Create a .env file in the backend directory:

OPENAI_API_KEY=your_openai_api_key_here
PORT=5000
FLASK_ENV=development

4. (Optional) Set up ChromaDB using Docker:

docker-compose -f chroma-docker-compose.yml up -d

Frontend Setup

1. Navigate to the frontend directory:

cd chat-client

2. Install dependencies:

# Using bun (recommended)
bun install

# Or using npm
npm install

🚀 Running the Application

Start Backend Server

cd chat-bot-langchain-latest
python main.py

The backend API will be available at http://localhost:5000

Start Frontend Client

cd chat-client
bun dev
# or
npm run dev

The frontend will be available at http://localhost:3000

📡 API Endpoints

Chat Endpoint

POST /api/v1/ask
Content-Type: application/json

{
  "question": "What is the leave policy for employees?"
}

Response:

{
  "answer": "Based on the HR policy document...",
  "status": "success"
}

Health Check

GET /api/v1/health

Response:

{
  "status": "healthy",
  "message": "Service is running"
}

🔄 How RAG Works

1. Document Processing: HR policy PDFs are split into chunks and converted to vector embeddings
2. Vector Storage: Embeddings stored in ChromaDB for fast similarity search
3. Query Processing: User questions are converted to embeddings
4. Retrieval: System finds the top 3 most relevant document chunks
5. Generation: GPT-4.1-mini generates answer using retrieved context

🐳 Docker Support

Backend with Docker

cd chat-bot-langchain-latest
docker-compose up -d

ChromaDB with Docker

cd chat-bot-langchain-latest
docker-compose -f chroma-docker-compose.yml up -d

🧪 Testing

Backend Testing

cd chat-bot-langchain-latest
python testing/testing.py

📝 Environment Variables

Backend (.env)

• OPENAI_API_KEY: Your OpenAI API key (required)
• PORT: Server port (default: 5000)
• FLASK_ENV: Environment mode (development/production)

Frontend

Configure API endpoint in your frontend code to point to the backend URL.

🐛 Troubleshooting

Common Issues

1. OpenAI API Key Error: Ensure OPENAI_API_KEY is set in your .env file
2. ChromaDB Connection Error: Check if chroma_db directory has proper permissions
3. Port Already in Use: Change the PORT in your .env file
4. Module Not Found: Ensure all dependencies are installed using uv pip install -e . or pip install -e .

📚 Development

Adding New Features

Backend:

1. New Route: Add route in app/routes/
2. New Controller: Create controller in app/controllers/
3. New Service: Add business logic in app/services/
4. New Model: Define schema in app/models/schema.py

Frontend:

1. Add new pages in app/ directory
2. Create components as needed
3. Update API calls to match backend endpoints

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📞 Support

For issues and questions, please open an issue on the repository.

👤 Author

Sudip Das

• GitHub: @dassudip2001

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Note: This project was created as part of the IBM SkillsBuild Winter Certification Program – Applied Artificial Intelligence.