https://github.com/luiscarbonel1991/nlp2sql

Enterprise-ready Natural Language to SQL converter with multi-provider AI support (OpenAI, Anthropic, Gemini). Built for production scale databases (1000+ tables) with Clean Architecture.
https://github.com/luiscarbonel1991/nlp2sql

ai anthropic clean-architecture database gemini llms mcp-server natural-language npl openai query-generation sql

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

Host: GitHub
URL: https://github.com/luiscarbonel1991/nlp2sql
Owner: luiscarbonel1991
Created: 2025-07-15T22:36:02.000Z (9 months ago)
Default Branch: main
Last Pushed: 2025-08-27T04:05:36.000Z (7 months ago)
Last Synced: 2025-10-08T23:24:55.944Z (6 months ago)
Topics: ai, anthropic, clean-architecture, database, gemini, llms, mcp-server, natural-language, npl, openai, query-generation, sql
Language: Python
Homepage:
Size: 744 KB
Stars: 2
Watchers: 2
Forks: 1
Open Issues: 5
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md

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# 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](docs/ARCHITECTURE.md) | Component diagram and data flow |

| [API Reference](docs/API.md) | Python API and CLI command reference |

| [Configuration](docs/CONFIGURATION.md) | Environment variables and schema filters |

| [Enterprise Guide](docs/ENTERPRISE.md) | Large-scale deployment and migration |

| [Redshift Support](docs/Redshift.md) | Amazon Redshift setup and examples |

| [Contributing](CONTRIBUTING.md) | Contribution guidelines |

## Installation

```bash

# 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 an API Key

```bash

export OPENAI_API_KEY="your-openai-key"

# or ANTHROPIC_API_KEY, GOOGLE_API_KEY

```

### 2. Connect and Ask

```python

import asyncio

import nlp2sql

from nlp2sql import ProviderConfig

async def main():

    nlp = await nlp2sql.connect(

        "postgresql://user:pass@localhost:5432/mydb",

        provider=ProviderConfig(provider="openai", api_key="sk-..."),

    )

    result = await nlp.ask("Show me all active users")

    print(result.sql)

    print(result.confidence)

    print(result.is_valid)

asyncio.run(main())

```

`connect()` auto-detects the database type from the URL, loads the schema, and builds the FAISS embedding index. Subsequent `ask()` calls reuse everything from disk cache.

### 3. Few-Shot Examples

Pass a list of dicts -- `connect()` handles embedding and indexing automatically:

```python

nlp = await nlp2sql.connect(

    "redshift://user:pass@host:5439/db",

    provider=ProviderConfig(provider="openai", api_key="sk-..."),

    schema="dwh_data_share_llm",

    examples=[

        {

            "question": "Total revenue last month?",

            "sql": "SELECT SUM(revenue) FROM sales WHERE date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')",

            "database_type": "redshift",

        },

    ],

)

result = await nlp.ask("Show me total sales this quarter")

```

### 4. Schema Filtering (Large Databases)

```python

nlp = await nlp2sql.connect(

    "postgresql://localhost/enterprise",

    provider=ProviderConfig(provider="anthropic", api_key="sk-ant-..."),

    schema_filters={

        "include_schemas": ["sales", "finance"],

        "exclude_system_tables": True,

    },

)

```

### 5. Custom Model and Temperature

```python

nlp = await nlp2sql.connect(

    "postgresql://localhost/mydb",

    provider=ProviderConfig(

        provider="openai",

        api_key="sk-...",

        model="gpt-4o",

        temperature=0.0,

        max_tokens=4000,

    ),

)

```

### 6. CLI

```bash

nlp2sql query \

  --database-url postgresql://user:pass@localhost:5432/mydb \

  --question "Show all active users" \

  --explain

nlp2sql inspect --database-url postgresql://localhost/mydb

```

### Advanced: Direct Service Access

For full control over the lifecycle, the lower-level API is still available:

```python

from nlp2sql import create_and_initialize_service, ProviderConfig, DatabaseType

service = await create_and_initialize_service(

    database_url="postgresql://localhost/mydb",

    provider_config=ProviderConfig(provider="openai", api_key="sk-..."),

    database_type=DatabaseType.POSTGRES,

)

result = await service.generate_sql("Count total users", database_type=DatabaseType.POSTGRES)

```

## 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](docs/ARCHITECTURE.md) for the detailed flow with method references and design decisions.

## Provider Comparison

| Provider | Default Model | Context Size | Best For |

|----------|--------------|-------------|----------|

| OpenAI | gpt-4o-mini | 128K | Cost-effective, fast |

| Anthropic | claude-sonnet-4-20250514 | 200K | Large schemas |

| Google Gemini | gemini-2.0-flash | 1M | High volume |

All models are configurable via `ProviderConfig(model="...")`. See [Configuration](docs/CONFIGURATION.md) for details.

## 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/

├── client.py       # DSL: connect() + NLP2SQL class (recommended entry point)

├── core/           # Pure Python: entities, ProviderConfig, QueryResult, sql_safety, sql_keywords

├── ports/          # Interfaces: AIProviderPort, SchemaRepositoryPort, EmbeddingProviderPort,

│                   #   ExampleRepositoryPort, QuerySafetyPort, QueryValidatorPort, CachePort

├── adapters/       # Implementations: OpenAI, Anthropic, Gemini, PostgreSQL, Redshift,

│                   #   RegexQueryValidator

├── services/       # Orchestration: QueryGenerationService

├── schema/         # Schema management: SchemaManager, SchemaAnalyzer, SchemaEmbeddingManager,

│                   #   ExampleStore

├── config/         # Pydantic Settings (centralized defaults)

└── exceptions/     # Exception hierarchy (NLP2SQLException -> 8 subclasses)

```

See [Architecture](docs/ARCHITECTURE.md) for the full component diagram, data flow, and design decisions.

## Development

```bash

# 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.

```json

{

  "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](mcp_server/README.md) for complete setup.

## Contributing

We welcome contributions. See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

MIT License - see [LICENSE](LICENSE).

## Author

**Luis Carbonel** - [@luiscarbonel1991](https://github.com/luiscarbonel1991)

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