What is Promptql

PROMPTQL is a FastAPI service that acts as a natural language query gateway, converting natural language queries into SQL and NoSQL queries using LangChain and Claude.

Use cases

Use cases for PROMPTQL include enabling non-technical users to query databases using natural language, integrating with applications that require dynamic query generation, and simplifying data retrieval processes in business applications.

How to use

To use PROMPTQL, clone the repository, set up a virtual environment, install the required dependencies, configure your database settings in the .env file, and run the application using Uvicorn. Access the API documentation at http://localhost:8000/docs.

Key features

Key features of PROMPTQL include support for multiple databases (PostgreSQL, MongoDB, MySQL, SQLite), natural language to SQL/NoSQL query conversion, automatic schema introspection, query validation, health check endpoints, comprehensive test suite, API documentation with Swagger UI, monitoring and metrics, rate limiting, and enhanced error handling.

Where to use

PROMPTQL can be used in various fields such as data analysis, business intelligence, and application development where natural language processing and database interaction are required.

Natural Language Query Gateway

A FastAPI service that converts natural language queries to SQL/NoSQL queries using LangChain and Claude.

Features

• Support for multiple databases:
  • PostgreSQL
  • MongoDB
  • MySQL
  • SQLite
• Natural language to SQL/NoSQL query conversion
• Automatic schema introspection
• Query validation and error handling
• Health check endpoints
• Comprehensive test suite
• API documentation with Swagger UI
• New: Monitoring and metrics
• New: Rate limiting and security features
• New: Enhanced error handling and logging

Prerequisites

• Python 3.10+
• One or more of the following databases:
  • PostgreSQL
  • MongoDB
  • MySQL
  • SQLite
• Anthropic API key

Installation

1. Clone the repository:

git clone <repository-url>
cd PROMPTQL

2. Create and activate a virtual environment:

# Windows
python -m venv .env
.env\Scripts\activate

# Linux/macOS
python -m venv .env
source .env/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file:

cp .env.example .env

5. Configure your environment variables in .env:

# Database Type (required)
DB_TYPE=postgres  # Options: postgres, mongodb, mysql, sqlite

# Database Settings
DB_HOST=localhost
DB_PORT=5432  # Default ports: PostgreSQL=5432, MongoDB=27017, MySQL=3306
DB_USER=your_username
DB_PASSWORD=your_password
DB_NAME=your_database

# SQLite Specific
DB_PATH=./data/database.db  # Only for SQLite

# API Settings
ANTHROPIC_API_KEY=your_api_key

# Security Settings
API_KEY=your_api_key
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_WINDOW=60
ALLOWED_ORIGINS=["http://localhost:3000"]
ENABLE_DOCS=true

Running the Application

1. Start the server:

uvicorn app.main:app --reload

2. Access the API documentation:

http://localhost:8000/docs

API Endpoints

Health Check

GET /health

Metrics

GET /metrics

Schema Information

GET /schema/{source}

Query Execution

POST /query
Content-Type: application/json
X-API-Key: your_api_key

{
    "query": "Show me all users who signed up this month",
    "source": "sql"
}

Supported Databases

GET /supported-databases

Security Features

• API key authentication
• Rate limiting
• CORS protection
• Security headers
• Input validation
• Error handling

Monitoring

The application provides the following monitoring capabilities:

• Request logging
• Query execution metrics
• Database connection status
• LLM request tracking
• Performance metrics

Testing

1. Run all tests:

pytest

2. Run specific test file:

pytest tests/test_api.py

3. Run with coverage:

pytest --cov=app tests/

Example Queries

SQL (PostgreSQL/MySQL/SQLite)

{
  "query": "Find all users older than 25",
  "source": "sql"
}

MongoDB

{
  "query": {
    "collection": "users",
    "age": {
      "$gt": 25
    },
    "interests": "coding"
  },
  "source": "mongodb"
}

Development

Project Structure

PROMPTQL/
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── config/
│   │   ├── __init__.py
│   │   └── settings.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── errors.py
│   │   ├── llm.py
│   │   ├── monitoring.py
│   │   ├── security.py
│   │   └── validation.py
│   ├── databases/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── postgres.py
│   │   ├── mongodb.py
│   │   ├── mysql.py
│   │   └── sqlite.py
│   └── models/
│       ├── __init__.py
│       └── schemas.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   └── test_api.py
├── .env.example
├── requirements.txt
└── README.md

Adding a New Database

1. Create a new adapter in app/databases/
2. Implement the BaseDB interface
3. Add database-specific settings to app/config/settings.py
4. Update the database instances in app/main.py
5. Add test cases in tests/

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT