Text-to-SQL: Natural Language Querying for Databases

✨ About This Project

Welcome to the Text-to-SQL project! 🚀 This repository enables you to effortlessly convert natural language queries into structured SQL commands, allowing users to interact with databases without needing to write complex SQL syntax.

Leveraging LangChain and OpenAI, this project translates user questions into meaningful SQL queries that can be executed on PostgreSQL databases. This tool makes database querying as simple as asking a question in plain English.

🌟 Key Features:

• Natural Language to SQL: Convert questions like "Show me the top 10 products by price" into SQL queries.
• Integration with PostgreSQL: Connects to PostgreSQL databases, runs SQL queries, and retrieves results.
• Flask API: Exposes a simple REST API for submitting questions and receiving answers in real-time.
• Dynamic Database Connections: Establish database connections on-the-fly using credentials passed via API.

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🧠 How It Works

This project uses LangChain and OpenAI's GPT-3.5 model to:

1. Parse user questions and generate corresponding SQL queries.
2. Execute these queries on a connected PostgreSQL database.
3. Return the results to the user, rephrased for clarity and relevance.

It supports dynamic interaction with any PostgreSQL database, making it ideal for building intelligent applications, chatbots, and data assistants.

🛠️ Tech Stack:

• LangChain for query generation
• OpenAI GPT-3.5 for natural language processing
• Flask for serving a REST API
• PostgreSQL for database interactions
• Requests for API-based configuration retrieval

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🧪 How to Use

1. Clone the Repository

git clone https://github.com/ravindrakush11/Text2SQL.git
cd Text2SQL

2. Install Dependencies

pip install -r requirements.txt

3. Set Up Environment Variables

You need to set up the following environment variables:

• OPENAI_KEY — Your OpenAI API key
• LANGCHAIN_TRACING_V2 — Optional: Set this for LangChain tracing
• LANGCHAIN_API_KEY — Optional: For additional LangChain configuration

4. Start the Flask Application

Run the following command to start the Flask server:

python text2sql.py

By default, the server runs on port 84. You can now interact with the application via the API.

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📝 API Endpoints

1. /db_connection [POST]

Use this endpoint to establish a connection to your PostgreSQL database by providing the necessary credentials.

Request Body:

{
    "hostname": "your-db-hostname",
    "port": "5432",
    "username": "your-db-username",
    "password": "your-db-password",
    "database": "your-db-name"
}

Response:

{
    "status": 200,
    "message": "Connection established successfully"
}

2. /answer_sql [POST]

Submit a user question to be transformed into an SQL query and executed on the connected PostgreSQL database.

Request Body:

{
    "question": "What are the top 5 products by sales?"
}

Response:

{
    "status": 200,
    "info": {
        "query": "SELECT * FROM products ORDER BY sales DESC LIMIT 5",
        "table": "products"
    },
    "information": "Here are the top 5 products by sales..."
}

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⚙️ Configuration

The database connection is established by retrieving credentials from an API endpoint or by directly passing the connection details.

1. Modify the get_db_config() function to fetch database credentials from a different source or API if needed.
2. You can also tweak the prompt templates in the execute_question() function to adjust how the SQL query is formatted or refined.