🛠️ SQL Query Assistant

A natural language to SQL query interface powered by LangChain and OpenAI, with an interactive Streamlit frontend for intuitive SQL generation.

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🌟 Features

Natural Language Querying: Convert plain English into SQL queries
Interactive Query Builder: Pre-built query templates for easy selection
Real-Time Execution: Run queries instantly and view results
Custom Query Parameters:

• Sort order (ASC/DESC)
• Result limits
• Time-based filtering
  Results Visualization: View in tables and export to CSV
  Smart Error Handling: Detects and corrects SQL errors

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🔧 Prerequisites

• Python 3.13+
• SQL Server with ODBC Driver 17
• OpenAI API key

📦 Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

3. Set up your environment variables:

OPENAI_API_KEY=your_api_key_here
DB_SERVER=your_server_name
DB_NAME=your_database_name
DB_USER=your_database_user
DB_PASSWORD=your_database_password
AI_MODEL=gpt-4-turbo-preview

🚀 Usage

1. Start the application:

streamlit run app/streamlit_app.py

2. Select a query category or create a custom query
3. Customize query parameters (sort order, result limit, time filter)
4. Click "Generate Query" to execute

🏗️ Project Structure

app/
├── streamlit_app.py            # Main Streamlit application
├── agent/
│   ├── analyze_schema.py       # Schema retrieval & analysis
│   ├── handle_tool_error.py    # SQL error handling & correction
│   ├── create_agent.py         # LangChain agent setup
│   ├── execute_query.py        # Query execution logic
│   ├── generate_query.py       # SQL query generation from natural language
│   ├── query_database.py       # Manages full query pipeline
│   └── state.py                # Tracks state & workflow

Running API Locally with Uvicorn

uvicorn server:app --host 0.0.0.0 --port 8000

Test the API at:

http://localhost:8000/docs

Running The Application in a Docker Container

1. Set up the environment variables in the .env file

   Important: -- (DB_SERVER should be "host.docker.internal") -- (DB_USER and DB_PASSWORD must be set to the credentials of an authorized database user)

OPENAI_API_KEY=your_api_key_here
DB_SERVER=host.docker.internal
DB_NAME=your_DB_NAME
DB_USER=your_DB_USER
DB_PASSWORD=your_DB_PASSWORD

2. Build and run the Docker container

docker build -t sql-query-assistant .
docker run -d --env-file .env -p 8000:8000 sql-query-assistant

🛠️ Technical Details

Architecture

• Uses LangGraph for workflow management
• Implements a state machine pattern for query processing
• Leverages OpenAI's language models for query generation
• Uses Streamlit for the user interface
• Connects to SQL Server via ODBC

Query Pipeline

1. Schema Analysis
2. Query Generation
3. Query Execution
4. Error Handling & Correction
5. Result Formatting