Wealth Portfolio RAG Agent

A comprehensive Natural Language Cross-Platform Data Query System for wealth portfolio management, specifically designed for high net worth individuals including film stars and sports personalities with 100+ crore investments.

🚀 Features

Core Capabilities

• Natural Language Processing: Query complex portfolio data using plain English
• Multi-Database Integration: Seamlessly connects MongoDB (client profiles) and MySQL (transactions)
• AI-Powered Analytics: LangChain-powered RAG system for intelligent responses
• Real-time Visualizations: Interactive charts and graphs using Chart.js
• Modern UI: Beautiful, responsive interface built with React and Tailwind CSS

Business Intelligence Queries

• "What are the top five portfolios of our wealth members?"
• "Give me the breakup of portfolio values per relationship manager."
• "Tell me the top relationship managers in my firm"
• "Which clients are the highest holders of [specific stock]?"
• Risk distribution analysis across portfolios
• Portfolio concentration and diversification metrics

🏗️ Architecture

Backend (Python)

• FastAPI: High-performance API framework
• LangChain: RAG implementation with Cohere integration
• MongoDB: Client profiles and portfolio holdings
• MySQL: Transaction data and trading history
• ChromaDB: Vector database for domain knowledge
• Authentication: JWT-based security system

Frontend (React TypeScript)

• React 18: Modern component-based UI
• TypeScript: Type-safe development
• Tailwind CSS: Utility-first styling
• Chart.js: Interactive data visualizations
• Axios: API communication
• React Router: Navigation and routing

📊 Data Model

MongoDB Collections

// clients collection
{
  client_id: "CL001",
  name: "Client Name",
  type: "Film Star" | "Sports Personality",
  risk_appetite: "Conservative" | "Moderate" | "Aggressive",
  total_portfolio_value: 15000000000, // 150 crores
  relationship_manager_id: "RM001",
  // ... other fields
}

// portfolio_holdings collection
{
  client_id: "CL001",
  stock_symbol: "RELIANCE",
  stock_name: "Reliance Industries",
  quantity: 50000,
  current_value: 120000000,
  // ... other fields
}

MySQL Tables

-- transactions table
CREATE TABLE transactions (
  id INT PRIMARY KEY AUTO_INCREMENT,
  client_id VARCHAR(50) NOT NULL,
  transaction_type VARCHAR(20) NOT NULL, -- BUY/SELL
  stock_symbol VARCHAR(20) NOT NULL,
  quantity INT NOT NULL,
  price DECIMAL(10,2) NOT NULL,
  total_amount DECIMAL(15,2) NOT NULL,
  transaction_date DATETIME NOT NULL,
  relationship_manager_id VARCHAR(20) NOT NULL
);

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• MongoDB
• MySQL
• Cohere API Key

Backend Setup

# Navigate to backend directory
cd backend

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

# Install dependencies
pip install -r requirements.txt

# Setup environment variables
cp .env.example .env
# Edit .env with your database URLs and Cohere API key

# Run the application
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm start

Database Setup

# MongoDB (using Docker)
docker run -d -p 27017:27017 --name mongodb mongo:latest

# MySQL (using Docker)
docker run -d -p 3306:3306 --name mysql \
  -e MYSQL_ROOT_PASSWORD=password \
  -e MYSQL_DATABASE=wealth_transactions \
  mysql:8.0

🔐 Authentication

The system includes a simple authentication system with three demo roles:

• admin/admin123: Full access to all features
• manager/manager123: Portfolio management access
• analyst/analyst123: Read-only analytics access

🎯 Business Use Cases

Portfolio Management

• Track and analyze 100+ crore portfolios for celebrities
• Monitor risk distribution across client segments
• Relationship manager performance tracking
• Asset allocation optimization

Risk Management

• Portfolio concentration analysis
• Risk appetite alignment checking
• Diversification metrics
• Regulatory compliance monitoring

Client Analytics

• Top performing portfolios identification
• Client segmentation by investment patterns
• Historical performance tracking
• Custom reporting and insights

🔮 Advanced Features

RAG Implementation

• Domain-specific knowledge base in ChromaDB
• Contextual query understanding
• Multi-source data fusion
• Intelligent response generation

Visualization Engine

• Real-time portfolio charts
• Risk distribution visualizations
• Performance trend analysis
• Interactive dashboards

Natural Language Processing

• Complex query parsing
• Intent recognition
• Context-aware responses
• Conversational memory

🏆 Technical Highlights

Performance Optimizations

• Async database connections
• Connection pooling
• Efficient query patterns
• Caching strategies

Security Features

• JWT authentication
• CORS protection
• Input validation
• SQL injection prevention

Scalability

• Microservices architecture
• Database abstraction layers
• Horizontal scaling support
• Load balancing ready

🔧 Development

Running Tests

# Backend tests
cd backend
pytest

# Frontend tests
cd frontend
npm test

Building for Production

# Backend
cd backend
docker build -t wealth-portfolio-backend .

# Frontend
cd frontend
npm run build

📈 Future Enhancements

MCP (Model Context Protocol) Integration

• Enhanced context management
• Improved scalability
• Better multi-modal support
• Advanced reasoning capabilities

Additional Features

• Real-time market data integration
• Automated portfolio rebalancing
• Advanced risk modeling
• Mobile application
• API rate limiting
• Advanced caching
• Multi-tenant support

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

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

Project Screenshots

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