Autonomous Agent for Portfolio Data Retrieval

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Portfolio Assistant is an advanced AI agent that provides real-time portfolio data acquisition and semantic synthesis. This autonomous system uses Large Language Models (LLMs) to intelligently answer questions about projects, skills, and experience while incorporating custom tool-calling mechanisms for enhanced flexibility.

Key Features:

• 🧠 Intelligent question answering about portfolio content
• 🔄 API key rotation for robust performance
• 📝 Automated logging of user interactions
• 📱 Push notifications for important events
• ⚙️ Custom tool integration for extended functionality

How It Works

graph TD
    A[User] -->|HTTP Request| B[FastAPI]
    B --> C[PortfolioAgent]
    C --> D{Gemini API}
    D -->| Knowledge Retrieval | E[Portfolio Content]
    D -->| Tool Calling | F[Record Details]
    D -->| Tool Calling | G[Log Questions]
    F --> H[Pushbullet]
    G --> H
    D --> I[Generate Response]
    I --> B
    B -->|HTTP Response| A

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The system architecture consists of:

1. FastAPI Server: Handles HTTP requests and CORS
2. Portfolio Assistant: Core agent processing logic
3. Gemini API: LLM for knowledge retrieval and response generation
4. Tool Integration: Custom functions for recording user data
5. Notification System: Pushbullet integration for alerts

Key Technologies

• Python 3.9+
• FastAPI (Web framework)
• Gemini API (LLM service)
• Pushbullet (Notification service)
• Pydantic (Data validation)
• dotenv (Environment management)

Setup and Installation

Prerequisites

• Python 3.9 or higher
• Gemini API keys (set as environment variables)
• Pushbullet API key (optional for notifications)

Installation Steps

1. Clone the repository:

git clone https://github.com/yourusername/portfolio-agent.git
cd portfolio-agent

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file with your credentials:

KNOWLEDGE_URL="https://your-portfolio-content.com"
GEMINI_API_KEY_1="your-gemini-key-1"
GEMINI_API_KEY_2="your-gemini-key-2"
PUSHBULLET_API_KEY="your-pushbullet-key"

5. Run the application:

uvicorn main:app --reload

Configuration

Environment Variable	Description	Required
KNOWLEDGE_URL	URL to portfolio content	Yes
GEMINI_API_KEY_1	Gemini API key 1	At least one
GEMINI_API_KEY_2	Gemini API key 2	Optional
PUSHBULLET_API_KEY	Pushbullet API key	No
CHAT_LOG_PATH	Path to chat logs	No

API Documentation

POST /chat

Send a message to the portfolio assistant:

Request:

{
  "message": "What projects have you worked on?",
  "history": [
    ["Hello", "Hi! How can I help?"]
  ]
}

Response:

{
  "response": "I've worked on several projects including..."
}

Project Structure

autonomous-portfolio-agent/

├── portfolio_agent/
│   ├── agent.py           # Core AI agent logic
│   ├── tools.py           # Custom tool-calling functions
│   ├── config.py          # Environment configuration
│   ├── logger.py          # Structured logging
│   ├── notifier.py        # Push notification system
├── main.py                # FastAPI server entry point
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables
└── README.md

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a pull request

License

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

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