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VOX

VOX: YOUR VOICE. YOUR DATA. YOUR WAY.

VOX - Voice-Enabled SQL Assistant

VOX is an intelligent voice-activated SQL assistant that allows users to interact with databases using natural language. It combines speech recognition, natural language processing, and database management to provide a seamless voice-controlled database exploration experience.

Features

  • Voice-to-Text Conversion: High-accuracy speech recognition using Deepgram
  • Natural Language Processing: Intelligent query understanding and context management
  • Multi-Database Support: Seamlessly switch between different databases
  • Interactive Voice Responses: Natural text-to-speech responses using Deepgram's voice synthesis
  • Real-time Visual Feedback: Audio visualization and status indicators
  • Context-Aware Conversations: Maintains conversation history and entity memory
  • Mode-Based Processing: Intelligent handling of different conversation modes (Database, Creative, Explanation)

Tech Stack

  • Backend:

    • Python with FastAPI for the server
    • SQLAlchemy for database management
    • Langchain for LLM integration
    • Deepgram for speech recognition and synthesis
    • OpenAI GPT for natural language processing

  • Frontend:

    • Pure JavaScript for client-side logic
    • WebSocket for real-time communication
    • HTML5 Audio API for voice processing
    • CSS3 for modern, responsive design

Setup

  1. Environment Setup:

    # Clone the repository
git clone [repository-url]
cd vox-assistant

# 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

  2. Environment Variables: Create a .env file with:

    DEEPGRAM_API_KEY=your_deepgram_key
OPENAI_API_KEY=your_openai_key
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_HOST=your_db_host

  3. Database Configuration:

    • Ensure MySQL is installed and running
    • Create necessary databases
    • Grant appropriate permissions to the DB user

  4. Running the Application:

    # Start the server
python server.py

# Open index.html in a modern web browser

Usage

  1. Click the microphone button to start voice input
  2. Speak your database query naturally
  3. VOX will process your request and respond both visually and aurally
  4. Use commands like "switch to [database]" to change databases
  5. Ask questions about data, request explanations, or engage in general conversation

Architecture

The system follows a modular architecture:

  • assistant.py: Core voice assistant logic
  • server.py: FastAPI server implementation
  • mode_tracker.py: Conversation mode management
  • Frontend files (index.html, script.js, style.css): User interface

Future Improvements for Advanced Data Analytics

1. Enhanced Analytics Capabilities

  • Predictive Analytics Integration:

    • Time series forecasting for trend analysis
    • Anomaly detection in data patterns
    • Predictive modeling for business metrics

  • Advanced Visualization:

    • Interactive dashboards with D3.js
    • Real-time data visualization
    • Custom chart types for specific data analysis

2. Data Processing Enhancements

  • ETL Pipeline Integration:

    • Automated data cleaning and transformation
    • Real-time data processing
    • Data quality monitoring

  • Big Data Support:

    • Integration with big data platforms (Hadoop, Spark)
    • Distributed query processing
    • Handling of unstructured data

3. AI/ML Capabilities

  • Advanced NLP Features:

    • Sentiment analysis of text data
    • Entity relationship extraction
    • Topic modeling for text analytics

  • Machine Learning Integration:

    • Automated feature selection
    • Model training and deployment
    • A/B testing framework

4. Reporting and Business Intelligence

  • Advanced Reporting:

    • Automated report generation
    • Custom report templates
    • Scheduled analytics jobs

  • Business Intelligence:

    • KPI tracking and monitoring
    • Industry benchmark comparisons
    • ROI analysis tools

5. Data Governance and Security

  • Enhanced Security:

    • Row-level security
    • Column-level encryption
    • Audit logging for compliance

  • Data Governance:

    • Data lineage tracking
    • Metadata management
    • Compliance reporting

6. Performance Optimization

  • Query Optimization:

    • Query performance analysis
    • Automated index management
    • Cache optimization

  • Scalability:

    • Horizontal scaling support
    • Load balancing
    • Connection pooling

Contributing

We welcome contributions! Please see our contributing guidelines for more details.

License

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