nlp2sql 0.2.0rc6

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

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

Features

• Multiple AI Providers: OpenAI, Anthropic Claude, Google Gemini - no vendor lock-in
• Database Support: PostgreSQL, Amazon Redshift
• Large Schema Handling: Vector embeddings and intelligent filtering for 1000+ tables
• Smart Caching: Query and schema embedding caching for improved performance
• Async Support: Full async/await support
• Clean Architecture: Ports & Adapters pattern for maintainability

Documentation

Document	Description
Architecture	Component diagram and data flow
API Reference	Python API and CLI command reference
Configuration	Environment variables and schema filters
Enterprise Guide	Large-scale deployment and migration
Redshift Support	Amazon Redshift setup and examples
Contributing	Contribution guidelines

Installation

# With UV (recommended)
uv add nlp2sql

# With pip
pip install nlp2sql

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

# With embeddings
pip install nlp2sql[embeddings-local]   # Local embeddings (free)
pip install nlp2sql[embeddings-openai]  # OpenAI embeddings

Quick Start

1. Set Environment Variables

# At least one AI provider key required
export OPENAI_API_KEY="your-openai-key"
# export ANTHROPIC_API_KEY="your-anthropic-key"
# export GOOGLE_API_KEY="your-google-key"

2. One-Line Usage

import asyncio
import os
from nlp2sql import generate_sql_from_db

async def main():
    result = await generate_sql_from_db(
        database_url="postgresql://user:pass@localhost:5432/mydb",
        question="Show me all active users",
        ai_provider="openai",
        api_key=os.getenv("OPENAI_API_KEY")
    )
    print(result['sql'])
    print(f"Confidence: {result['confidence']}")

asyncio.run(main())

3. Pre-Initialized Service (Better Performance)

from nlp2sql import create_and_initialize_service

async def main():
    # Initialize once
    service = await create_and_initialize_service(
        database_url="postgresql://user:pass@localhost:5432/mydb",
        ai_provider="openai",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use multiple times
    result1 = await service.generate_sql("Count total users")
    result2 = await service.generate_sql("Show recent orders")

4. Large Database with Schema Filtering

from nlp2sql import create_and_initialize_service

service = await create_and_initialize_service(
    database_url="postgresql://localhost/enterprise",
    ai_provider="anthropic",  # Best for large schemas (200K context)
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    schema_filters={
        "include_schemas": ["sales", "finance"],
        "exclude_system_tables": True
    }
)

result = await service.generate_sql("Show revenue by month")

5. CLI Usage

# Generate SQL
nlp2sql query \
  --database-url postgresql://user:pass@localhost:5432/mydb \
  --question "Show all active users" \
  --explain

# Inspect schema
nlp2sql inspect --database-url postgresql://localhost/mydb

# Benchmark providers
nlp2sql benchmark --database-url postgresql://localhost/mydb

How It Works

Question ──► Cache check ──► Schema retrieval ──► Relevance filtering ──► Context building ──► AI generation ──► Validation
                                    │                     │                      │
                              SchemaRepository    FAISS + TF-IDF hybrid   Reuses precomputed
                              (+ disk cache)      + batch scoring          relevance scores

1. Schema retrieval -- Fetches tables from database via SchemaRepository (with disk cache for Redshift)
2. Relevance filtering -- FAISS dense search + TF-IDF sparse search (50/50 hybrid) finds candidate tables; batch scoring refines with precomputed embeddings
3. Context building -- Builds optimized schema context within token limits, reusing scores from step 2 (zero additional embedding calls)
4. SQL generation -- AI provider (OpenAI, Anthropic, or Gemini) generates SQL from question + schema context
5. Validation -- SQL syntax and safety checks before returning results

See Architecture for the detailed flow with method references and design decisions.

Provider Comparison

Provider	Context Size	Best For
OpenAI GPT-4	128K	Complex reasoning
Anthropic Claude	200K	Large schemas
Google Gemini	1M	High volume, cost efficiency

See Configuration for detailed provider setup.

Architecture

Clean Architecture (Ports & Adapters) with three layers: core entities, port interfaces, and adapter implementations. The schema management layer uses FAISS + TF-IDF hybrid search for relevance filtering at scale.

nlp2sql/
├── core/           # Business entities (pure Python, no dependencies)
├── ports/          # Interfaces (AIProviderPort, SchemaRepositoryPort, EmbeddingProviderPort)
├── adapters/       # Implementations (OpenAI, Anthropic, Gemini, PostgreSQL, Redshift)
├── services/       # Orchestration (QueryGenerationService)
├── schema/         # Schema management (SchemaManager, SchemaAnalyzer, SchemaEmbeddingManager)
├── config/         # Pydantic Settings configuration
└── exceptions/     # Custom exception hierarchy

See Architecture for the full component diagram, data flow, and design decisions.

Development

# Clone and install
git clone https://github.com/luiscarbonel1991/nlp2sql.git
cd nlp2sql
uv sync

# Start test databases
cd docker && docker-compose up -d

# Run tests
uv run pytest

# Code quality
uv run ruff format .
uv run ruff check .
uv run mypy src/

MCP Server

nlp2sql includes a Model Context Protocol server for AI assistant integration.

{
  "mcpServers": {
    "nlp2sql": {
      "command": "python",
      "args": ["/path/to/nlp2sql/mcp_server/server.py"],
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "NLP2SQL_DEFAULT_DB_URL": "postgresql://user:pass@localhost:5432/mydb"
      }
    }
  }
}

Tools: ask_database, explore_schema, run_sql, list_databases, explain_sql

See mcp_server/README.md for complete setup.

Contributing

We welcome contributions. See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE.

Author

Luis Carbonel - @luiscarbonel1991