English2SQL: Enterprise-Grade Natural Language Interface to Databases

English2SQL is a production-ready interface that democratizes data access by allowing non-technical users to query complex SQL databases using plain English.

By leveraging Schema-Aware RAG (Retrieval Augmented Generation) and a refined LLM pipeline, this system achieves a 9% accuracy improvement over baseline text-to-SQL models, significantly reducing hallucination rates in enterprise environments.

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🚀 Key Features

• Natural Language Querying: Converts complex questions ("Show me top 5 users by spend in NY last month") into optimized SQL.
• Schema-Aware RAG: Dynamically retrieves relevant table schemas and context before generation, ensuring queries are syntactically and semantically correct.
• Enterprise Security:
  • role-based access control (RBAC) via Firebase Auth.
  • Sanitized inputs to prevent SQL injection.
• Interactive Data Visualization: Automatically renders results as dynamic tables or charts based on data type.

🏗️ Architecture

graph TD
    User[User Question] -->|Natural Language| FE[React Frontend]
    FE -->|Auth Token| API[FastAPI Backend]
    
    subgraph "AI Core"
        API -->|Context Retrieval| VectorDB[(Vector Store)]
        VectorDB -->|Relevant Schema| IL[In-Context Learning]
        IL -->|Prompt| LLM[LLM Engine]
        LLM -->|Generated SQL| API
    end
    
    subgraph "Data Layer"
        API -->|Execute SQL| DB[(PostgreSQL)]
        DB -->|JSON Result| API
    end
    
    API -->|Result + Viz Config| FE

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🛠️ Tech Stack

Frontend & Auth (This Repository)

• Framework: React 18 (CRA)
• Styling: Bootstrap 5 + Custom CSS
• Authentication: Firebase (Google OAuth)
• State Management: Jotai

Backend Integration (Connected Service)

• API: FastAPI (Python)
• AI Orchestration: LangChain + OpenAI/Ollama
• Database: PostgreSQL (AWS RDS)

🔮 Roadmap

• Multi-Dialect Support: Add adapters for MySQL, Snowflake, and BigQuery.
• Advanced Visualization: Integrate Vega-Lite for more complex automated charting.
• Voice Interface: Add speech-to-text for hands-free querying.

🎥 Demo

See the system in action converting varied business questions into precise SQL.

📦 Installation (Frontend)

To run this interface locally:

1. Clone the repository

   git clone https://github.com/Rayhanpatel/English2SQL.git
   cd English2SQL

2. Install Dependencies

   npm install

3. Configure Environment Create a .env file with your Firebase config:

   REACT_APP_FIREBASE_API_KEY=your_key
   REACT_APP_FIREBASE_AUTH_DOMAIN=your_domain
   ...

4. Start the App

   npm start

   Open http://localhost:3000 to view it in the browser.

🔌 Backend Setup (Required for API)

To make the "Architecture Diagram" real, you need to run the Python backend which creates a mock SQL environment.

1. Navigate to backend:

   cd backend

2. Install Python Dependencies:

   pip install -r requirements.txt

3. Run the Server:

   python server.py

   The API will be available at http://localhost:5001.

📄 License

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

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Built by Rayhan Patel