AI Agent Practice

A Python-based AI agent system that provides various NLP capabilities including summarization, article writing, and data sanitization. The system uses Ollama as the LLM backend and provides a Streamlit-based web interface for easy interaction.

Author

Arpit Agarwal arpit.dev@outlook.com

Features

Features

• Text Summarization
• Article Writing
• Data Sanitization
• Real-time Validation
• Streamlit-based Web Interface
• Logging and Error Handling

Project Structure

ai-agent-practice/
├── agents/                 # Core AI agent implementations
│   ├── __init__.py        # Agent manager and initialization
│   ├── agent_base.py      # Base class for all agents
│   ├── summarize_tool.py  # Text summarization agent
│   ├── article_tool.py    # Article writing agent
│   ├── sanitize_data.py   # Data sanitization agent
│   ├── write_article_validator_agent.py
│   ├── sanitize_data_validator_agent.py
│   ├── summary_validator_agent.py
│   └── refiner_agent.py
├── utils/                 # Utility modules
│   └── logger.py          # Custom logging implementation
├── app.py                # Streamlit web application
├── requirements.txt       # Project dependencies
└── logs/                 # Application logs

Installation

1. Clone the repository:

git clone <repository-url>
cd ai-agent-practice

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install Ollama and download the model:

brew install ollama
ollama pull llama3

Running the Application

1. Start the application:

streamlit run app.py

2. Open your web browser and navigate to http://localhost:8501

Usage

The application provides a simple web interface with three main functionalities:

1. Summarize: Enter text to get a concise summary
2. Write Article: Generate articles based on your input
3. Sanitize Data: Clean and process your data

Each operation includes real-time validation to ensure quality.

Error Handling

The application includes comprehensive error handling with:

• Retry mechanism for LLM calls
• Detailed logging
• User-friendly error messages

Logging

The application uses a custom logging system that:

• Logs to both console and file
• Rotates logs automatically
• Includes timestamps and log levels

Logs are stored in the logs directory.

Contributing

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.

Acknowledgments

• Ollama for providing the LLM backend
• Streamlit for the web interface