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🤖 Natural Language to SQL Query Generator

A Streamlit app that converts English questions into SQL queries using AI models. Simply ask questions about your data and get instant results!

🚀 Quick Setup

  1. Clone and navigate to the project:
git clone [email protected]:mdarshad1000/Text-2-SQL.git
cd [email protected]:mdarshad1000/Text-2-SQL.git
  1. Create .env file with your API keys:
OPENAI_API_KEY=your_key_here
GEMINI_API_KEY=your_key_here
  1. Start the app:
docker compose up -d

Visit http://0.0.0.0:8501 in your browser.

💡 How to Use

  1. Select an AI model (GPT-4, Llama3.2:latest, or Gemini) {{ If you want to use the Llama3.2 model, ensure the Ollama server is running. }}
  2. Type your question (e.g., "Show me all sales from last month")
  3. Click "Generate Results"
  4. View your data in tables or charts
  5. Download results as CSV if needed

🏗️ Code Structure & Documentation

Folder Structure

`Chinook_PostgreSql.sql`: SQL script to create database schema
`Dockerfile`: Docker file to build Docker image
`README.md`: This README file
`app.py`: Main application file
`config.py`: Configuration file for LLM models
`docker-compose.yml`: Docker Compose file to run Docker image
`llm_integration.py`: Abstract base class for LLM integrations
`query_executor.py`: Database executor class
`requirements.txt`: Python dependencies

Classes

LLMQueryGenerator (Abstract Base Class)

Base class for all AI model integrations.

  • generate_sql_from_nl(db_schema, nl_query): Abstract method to convert natural language to SQL

OpenAIQueryGenerator

Handles GPT-4 integration.

  • __init__(model, sys_prompt, user_prompt): Initializes with model settings
  • generate_sql_from_nl(db_schema, nl_query): Generates SQL using OpenAI API

OllamaQueryGenerator

Manages Llama3.2:latest integration.

  • __init__(model, sys_prompt, user_prompt): Sets up Ollama configuration
  • generate_sql_from_nl(db_schema, nl_query): Generates SQL using Ollama

GeminiQueryGenerator

Handles Google's Gemini integration.

  • __init__(model, sys_prompt, user_prompt): Configures Gemini settings
  • generate_sql_from_nl(db_schema, nl_query): Generates SQL using Gemini API

DatabaseExecutor

Manages database operations.

  • connect(): Establishes database connection
  • disconnect(): Closes database connection
  • execute_query(query): Runs SQL queries and returns results as DataFrame
  • get_schema_info(): Returns database structure information

Workflow

  1. Initialization

    • App starts and creates DatabaseExecutor instance
    • Initializes AI models (GPT-4, Llama3.2, Gemini)

  2. User Interaction

    • User selects AI model
    • Views database schema
    • Enters natural language question

  3. Query Processing

    User Question → AI Model → SQL Query → Database → Results → Visualization

  4. Data Display

    • Results shown in table format
    • Optional visualization in charts
    • CSV export available

🛠️ Troubleshooting

If something's not working:

  1. Check container status: docker compose ps
  2. View logs: docker compose logs app
  3. Restart if needed: docker compose restart

📝 Notes

  • Uses Chinook sample database (digital media store data)
  • Supports basic data visualization
  • Downloads available in CSV format