Meeting Assistant - Multi-Agent System

A sophisticated multi-agent system built with Microsoft AutoGen 0.4 for processing meeting recordings. The system transcribes audio, summarizes content, and extracts action items from meetings using specialized agents.

Author

Chaitanya K.K. Vankadaru

Features

• Audio Transcription: Converts meeting audio into text using Azure Speech-to-Text
• Meeting Summarization: Generates concise summaries using OpenAI GPT models
• Action Item Extraction: Identifies tasks, assignees, and deadlines from meeting discussions
• Agent Coordination: Uses Microsoft AutoGen 0.4 for agent communication and orchestration
• Robust Error Handling: Comprehensive error handling and logging
• Configurable: Flexible configuration management using Pydantic
• Well-Tested: Extensive test coverage with pytest
• CI/CD Ready: GitHub Actions workflow for testing, linting, and security checks

Installation

Prerequisites

• Python 3.12
• FFmpeg (for audio processing)
• OpenAI API key
• Azure Speech Services key

Using pip

# Clone the repository
git clone https://github.com/EarthlyAlien/Meeting-Assistant-MultiAgent.git
cd Meeting-Assistant-MultiAgent

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

# Install dependencies
pip install -r requirements.txt

Using Docker

# Build and run with Docker Compose
docker-compose up --build

Configuration

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key
AZURE_SPEECH_KEY=your_azure_speech_key
MODEL_NAME=gpt-4  # Optional, defaults to gpt-4
TEMPERATURE=0.7   # Optional, defaults to 0.7
MAX_TOKENS=1000   # Optional, defaults to 1000
DEBUG=false       # Optional, defaults to false
LOG_LEVEL=INFO    # Optional, defaults to INFO

Usage

Basic Usage

from meeting_assistant import MeetingAssistantOrchestrator

# Initialize the orchestrator
orchestrator = MeetingAssistantOrchestrator()

# Process a meeting recording
results = orchestrator.process_meeting("path/to/meeting.wav")

# Generate a report
report = orchestrator.generate_report(results)

# Save results
orchestrator.save_results(results, "meeting_results.json")

Directory Structure

meeting_assistant/
├── __init__.py
├── config.py           # Configuration management
├── logger.py          # Logging setup
├── orchestrator.py    # Main orchestrator
├── transcription_agent.py
├── summarization_agent.py
└── action_item_extraction_agent.py

tests/
├── __init__.py
├── conftest.py        # Test configuration
└── test_orchestrator.py

logs/                  # Log files
results/              # Processing results
uploads/              # Uploaded audio files

Development

Running Tests

# Install test dependencies
pip install -r requirements.txt

# Run tests with coverage
pytest --cov=./ --cov-report=term-missing

# Run specific test file
pytest tests/test_orchestrator.py -v

Code Quality

# Format code
black .
isort .

# Check code quality
flake8 .

Security Checks

# Install security tools
pip install bandit safety

# Run security checks
bandit -r .
safety check

CI/CD Pipeline

The project includes a comprehensive GitHub Actions workflow that:

1. Tests the code on multiple Python versions (3.9-3.12)
2. Checks code formatting (black, isort)
3. Runs linting (flake8)
4. Performs security checks (bandit, safety)
5. Builds and publishes Docker images
6. Reports test coverage

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and commit: git commit -am 'Add feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

License

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

Acknowledgments

• Microsoft AutoGen
• OpenAI
• Azure Speech Services