An AI-powered medical documentation and coding assistant that leverages OpenAI, Azure Cognitive Search, and Firebase to streamline medical scribing processes.

ScribingAgent is a Python-based application designed to assist with medical documentation and coding tasks. It combines modern AI capabilities with cloud services to provide intelligent medical information processing and documentation support.

• AI-Powered Medical Documentation: Utilizes OpenAI and LangChain for intelligent text processing
• Azure Cognitive Search Integration: Enables powerful search capabilities across medical data
• Firebase Backend: Provides real-time data storage and authentication
• Streamlit Interface: User-friendly web interface for easy interaction
• Medical Coding Support: Specialized tools for medical coding workflows

• Language: Python
• AI/ML: OpenAI API, LangChain (v0.0.272)
• Cloud Services:
  • Azure Cognitive Search (v11.4.0b8)
  • Firebase Admin SDK
• Frontend: Streamlit
• Data Processing: Pandas
• PDF Generation: WeasyPrint

1. Clone the repository:

git clone https://github.com/shivamd1810/scribingAgent.git
cd scribingAgent

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the root directory and add your API keys:

OPENAI_API_KEY=your_openai_api_key
AZURE_SEARCH_ENDPOINT=your_azure_endpoint
AZURE_SEARCH_KEY=your_azure_key
# Add other required environment variables

4. Configure Firebase:

• Place your Firebase service account credentials in the appropriate location
• Update firebase.json with your project configuration

Run the application:

streamlit run app.py

The application will start on http://localhost:8501 by default.

scribingAgent/
├── app.py                    # Main application entry point
├── azureCognitiveSearch.py   # Azure search functionality
├── firebaseFunctions.py      # Firebase operations
├── prompt.py                 # AI prompt management
├── tools.py                  # Utility functions
├── medicalCoding/           # Medical coding specific modules
├── requirements.txt         # Python dependencies
├── firebase.json           # Firebase configuration
└── README.md              # This file

• app.py: Main Streamlit application that provides the user interface
• azureCognitiveSearch.py: Handles integration with Azure Cognitive Search for medical data retrieval
• firebaseFunctions.py: Manages Firebase operations including data storage and retrieval
• prompt.py: Contains prompt engineering logic for AI interactions
• tools.py: Utility functions and helper methods

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