Transform natural language queries into SQL with AI-powered intelligence and real-time database insights.

See the GIFs above for quick feature highlights, or watch the full demo video for a complete walkthrough!

• AI-Powered Query Generation: Convert natural language to SQL using advanced language models
• Semantic Search: Intelligent table and column matching using embeddings
• Real-time Visualization: Interactive charts and data insights
• SQL Security: Built-in protection against dangerous SQL operations
• Banking Domain Focus: Specialized for financial data analysis
• Modern UI: Clean, responsive interface with real-time feedback
• Multi-Architecture: Streamlit backend + Next.js frontend

text-2-sql/
├── backend/                 # Streamlit API & Core Logic
│   ├── main.py             # Main Streamlit application
│   ├── flask_api.py        # REST API endpoints
│   ├── core/               # Core utilities & config
│   ├── nodes/              # Processing nodes
│   ├── prompts/            # AI prompt templates
│   └── qdrant/            # Vector database for embeddings
├── frontend/               # Next.js React Application
│   ├── app/               # App router pages
│   ├── components/        # Reusable UI components
│   └── utils/             # API utilities
└── samlple_queries/       # Example queries

Entity Relationship Diagram showing the banking database structure

• Python 3.8+
• Node.js 18+
• PostgreSQL database
• OpenAI API key

1. Clone the repository

   git clone https://github.com/yourusername/text-2-sql.git
   cd text-2-sql

2. Install Python dependencies

   cd backend
   pip install streamlit flask psycopg2-binary openai qdrant-client sentence-transformers pyyaml

3. Configure the database

   • Set up PostgreSQL with a banking_data database
   • Update backend/core/config.yaml with your database credentials

4. Set up environment variables

   export OPENAI_API_KEY="your-openai-api-key"

5. Start the backend

   streamlit run main.py

1. Install Node.js dependencies

   cd frontend
   npm install

2. Start the development server

   npm run dev

3. Open your browser Navigate to http://localhost:3000

Try these natural language queries:

• "Which branches have the highest total account balances?"
• "How has the total account balance at each branch changed month over month?"
• "Show me customers with loans above $50,000"
• "What's the average transaction amount by account type?"

1. Natural Language Input: Type your question in plain English
2. AI Processing: The system analyzes your query and generates SQL
3. Results Display: View formatted results with charts and insights
4. SQL Transparency: See the generated SQL for verification

openai:
  api_key: "your-openai-api-key"
  org_id: "your-org-id"

postgres:
  host: "localhost"
  database: "banking_data"
  user: "postgres"
  password: "your-password"
  port: 5432

settings:
  page_title: "Text-to-SQL Agent"
  max_query_timeout: 30
  max_result_rows: 1000

The system includes built-in protection against:

• INSERT operations
• UPDATE operations
• DELETE operations
• DROP operations
• ALTER operations
• TRUNCATE operations
• CREATE operations

• Streamlit: Web application framework
• OpenAI GPT: Natural language processing
• PostgreSQL: Database management
• Qdrant: Vector database for embeddings
• Sentence Transformers: Text embedding models

• Next.js 15: React framework
• Tailwind CSS: Styling
• Chart.js: Data visualization
• Clerk: Authentication
• React Table: Data tables

The system is designed for banking data analysis with tables including:

• Customer information
• Account balances
• Transaction history
• Branch data
• Loan information

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