nlp2sql 0.2.0rc2

Enterprise-ready Natural Language to SQL converter with multi-provider support. Built for production scale (1000+ tables) with Clean Architecture.

nlp2sql

Enterprise-ready Natural Language to SQL converter with multi-provider support

A powerful Python library for converting natural language queries to optimized SQL using multiple AI providers. Built with Clean Architecture principles for enterprise-scale applications handling 1000+ table databases.

🚀 Why nlp2sql?

Unlike academic frameworks focused on composability, nlp2sql is built for enterprise production environments from day one:

• 🏢 Enterprise Scale: Handle databases with 1000+ tables efficiently
• 🤖 Multi-Provider Native: OpenAI, Anthropic, Gemini support - no vendor lock-in
• ⚡ Production Ready: Advanced caching, async support, schema optimization
• 🛠️ Developer First: Professional CLI, Docker setup, automated installation
• 🏗️ Clean Architecture: Maintainable, testable, extensible codebase
• 📊 Performance Focused: Benchmarking, schema filtering, vector embeddings

✨ Features

• 🤖 Multiple AI Providers: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI
• 🗄️ Database Support: PostgreSQL (with MySQL, SQLite, Oracle, MSSQL coming soon)
• 📊 Large Schema Handling: Advanced strategies for databases with 1000+ tables
• ⚡ Smart Caching: Intelligent result caching for improved performance
• 🔍 Query Optimization: Built-in SQL query optimization
• 🧠 Schema Analysis: AI-powered relevance scoring and schema compression
• 🔍 Vector Embeddings: Semantic search for schema elements
• 📈 Token Management: Efficient token usage across different providers
• ⚡ Async Support: Full async/await support for better performance
• 🏗️ Clean Architecture: Ports & Adapters pattern for maintainability

🚀 Quick Start

Installation

# Install with UV (recommended)
uv add nlp2sql

# Or with pip
pip install nlp2sql

# Release candidate with latest features (multi-provider support)
pip install nlp2sql==0.2.0rc1

# With specific providers
pip install nlp2sql[anthropic,gemini]  # Multiple providers
pip install nlp2sql[all-providers]     # All providers

One-Line Usage (Simplest)

import asyncio
import os
from nlp2sql import generate_sql_from_db

async def main():
    # Automatic provider detection
    providers = [
        {"name": "openai", "key": os.getenv("OPENAI_API_KEY")},
        {"name": "anthropic", "key": os.getenv("ANTHROPIC_API_KEY")},
        {"name": "gemini", "key": os.getenv("GOOGLE_API_KEY")}
    ]
    
    # Use first available provider
    selected = next((p for p in providers if p["key"]), None)
    if not selected:
        raise ValueError("No API key found. Set OPENAI_API_KEY, ANTHROPIC_API_KEY, or GOOGLE_API_KEY")
    
    result = await generate_sql_from_db(
        database_url="postgresql://testuser:testpass@localhost:5432/testdb",
        question="Show me all active users",
        ai_provider=selected["name"],
        api_key=selected["key"]
    )
    print(result['sql'])

asyncio.run(main())

Pre-Initialized Service (Better Performance)

import asyncio
import os
from nlp2sql import create_and_initialize_service

async def main():
    # Smart provider detection
    api_key = os.getenv("OPENAI_API_KEY") or os.getenv("ANTHROPIC_API_KEY") or os.getenv("GOOGLE_API_KEY")
    provider = "openai" if os.getenv("OPENAI_API_KEY") else \
               "anthropic" if os.getenv("ANTHROPIC_API_KEY") else "gemini"
    
    # Initialize once with Docker test database
    service = await create_and_initialize_service(
        database_url="postgresql://testuser:testpass@localhost:5432/testdb",
        ai_provider=provider,
        api_key=api_key
    )
    
    # Use multiple times
    result1 = await service.generate_sql("Count total users")
    result2 = await service.generate_sql("Find inactive accounts")
    result3 = await service.generate_sql("Show user registration trends")
    
    print(f"Using {provider} provider")
    for i, result in enumerate([result1, result2, result3], 1):
        print(f"Query {i}: {result['sql']}")

asyncio.run(main())

Manual Service Creation (Full Control)

import asyncio
import os
from nlp2sql import create_query_service, DatabaseType

async def main():
    # Create service with schema filtering for large databases
    service = create_query_service(
        database_url="postgresql://demo:demo123@localhost:5433/enterprise",
        ai_provider="anthropic",  # Good for large schemas
        api_key=os.getenv("ANTHROPIC_API_KEY"),
        schema_filters={
            "include_schemas": ["sales", "finance"],
            "exclude_system_tables": True
        }
    )
    
    # Initialize (loads schema automatically)
    await service.initialize(DatabaseType.POSTGRES)
    
    # Generate SQL
    result = await service.generate_sql(
        question="Show revenue by month for the sales team",
        database_type=DatabaseType.POSTGRES
    )
    
    print(f"SQL: {result['sql']}")
    print(f"Confidence: {result['confidence']}")
    print(f"Explanation: {result['explanation']}")
    print(f"Valid: {result['validation']['is_valid']}")

asyncio.run(main())

🤖 Multiple AI Providers Support

nlp2sql supports multiple AI providers - you're not locked into OpenAI!

Supported Providers

# OpenAI GPT-4 (default)
service = await create_and_initialize_service(
    database_url="postgresql://testuser:testpass@localhost:5432/testdb",
    ai_provider="openai",
    api_key="your-openai-key"
)

# Anthropic Claude
service = await create_and_initialize_service(
    database_url="postgresql://testuser:testpass@localhost:5432/testdb", 
    ai_provider="anthropic",
    api_key="your-anthropic-key"
)

# Google Gemini
service = await create_and_initialize_service(
    database_url="postgresql://testuser:testpass@localhost:5432/testdb",
    ai_provider="gemini", 
    api_key="your-google-key"
)

Provider Comparison

Provider	Context Size	Cost/1K tokens	Best For
OpenAI GPT-4	128K	$0.030	Complex reasoning
Anthropic Claude	200K	$0.015	Large schemas
Google Gemini	1M	$0.001	High volume/cost

📊 Large Schema Support

For databases with 1000+ tables, use schema filters:

# Basic filtering
filters = {
    "exclude_system_tables": True,
    "exclude_tables": ["audit_log", "temp_data", "migration_history"]
}

service = await create_and_initialize_service(
    database_url="postgresql://demo:demo123@localhost:5433/enterprise",
    api_key="your-api-key",
    schema_filters=filters
)

# Business domain filtering
business_filters = {
    "include_tables": [
        "users", "customers", "orders", "products",
        "invoices", "payments", "addresses"
    ],
    "exclude_system_tables": True
}

# Multi-schema filtering for enterprise databases
enterprise_filters = {
    "include_schemas": ["sales", "hr", "finance"],
    "exclude_schemas": ["archive", "temp"],
    "include_tables": ["customers", "orders", "employees", "transactions"],
    "exclude_tables": ["audit_logs", "system_logs"],
    "exclude_system_tables": True
}

🏗️ Architecture

nlp2sql follows Clean Architecture principles with clear separation of concerns:

nlp2sql/
├── core/           # Business entities and domain logic
├── ports/          # Interfaces/abstractions
├── adapters/       # External service implementations
├── services/       # Application services
├── schema/         # Schema management strategies
├── config/         # Configuration management
└── exceptions/     # Custom exceptions

Configuration

Environment Variables

# AI Provider API Keys (at least one required)
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export GOOGLE_API_KEY="your-google-key"  # Note: GOOGLE_API_KEY, not GEMINI_API_KEY

# Database (Docker test databases)
export DATABASE_URL="postgresql://testuser:testpass@localhost:5432/testdb"  # Simple DB
# export DATABASE_URL="postgresql://demo:demo123@localhost:5433/enterprise"  # Large DB

# Optional Settings
export NLP2SQL_MAX_SCHEMA_TOKENS=8000
export NLP2SQL_CACHE_ENABLED=true
export NLP2SQL_LOG_LEVEL=INFO

Development

Setup Development Environment

# Clone repository
git clone https://github.com/luiscarbonel1991/nlp2sql.git
cd nlp2sql

# Install dependencies
uv sync

# Setup Docker test databases
cd docker
docker-compose up -d
cd ..

# Test CLI with Docker database
export OPENAI_API_KEY=your-key
uv run nlp2sql query \
  --database-url "postgresql://testuser:testpass@localhost:5432/testdb" \
  --question "How many users are there?" \
  --provider openai

# Run tests
uv run pytest

# Format code
uv run ruff format .

# Lint code
uv run ruff check .

# Type checking
uv run mypy src/

🏢 Enterprise Use Cases

Data Analytics Teams

• Large Schema Navigation: Query enterprise databases with 1000+ tables
• Multi-Tenant Support: Schema filtering for different business units
• Performance Optimization: Intelligent caching and query optimization

DevOps & Platform Teams

• Multi-Provider Strategy: Avoid vendor lock-in, optimize costs
• Infrastructure as Code: Docker setup, automated deployment
• Monitoring & Benchmarking: Performance tracking across providers

Business Intelligence

• Self-Service Analytics: Non-technical users query databases naturally
• Audit & Compliance: Explainable queries with confidence scoring
• Cost Management: Provider comparison and optimization

📊 Performance & Scale

Metric	nlp2sql	Typical Framework
Max Tables Supported	1000+	~100
AI Providers	3+ (OpenAI, Anthropic, Gemini)	Usually 1
Query Cache	✅ Advanced	❌ Basic/None
Schema Optimization	✅ Vector embeddings	❌ Manual
Enterprise CLI	✅ Professional	❌ Basic/None
Docker Setup	✅ Production-ready	❌ Manual

🔄 Migration from Other Frameworks

Coming from other NLP-to-SQL frameworks? nlp2sql provides:

• Drop-in replacement for most common patterns
• Enhanced performance with minimal code changes
• Additional features without breaking existing workflows

See our Migration Guide for framework-specific instructions.

🤝 Contributing

We welcome contributions! This project follows enterprise development practices:

• Clean Architecture patterns
• Comprehensive testing
• Type safety with mypy
• Code formatting with black/ruff

See CONTRIBUTING.md for guidelines.

📄 License

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

👨‍💻 Author & Maintainer

Luis Carbonel - Initial work and ongoing development

• GitHub: @luiscarbonel1991
• Email: [e-mail]

Built with enterprise needs in mind, refined through real-world production use cases.