nlmdb 1.3.3

Natural Language Model Database - Query databases using natural language

NLMDB: Natural Language & MCP-powered Database

Query your databases using natural language through the Model Context Protocol (MCP) approach. NLMDB provides a simple API for interacting with databases using either OpenAI or Hugging Face models.

✨ Features

• 💬 Query databases using natural language
• 🔄 Support for both OpenAI and Hugging Face models
• 🔒 Enhanced privacy options with local Hugging Face models
• 📊 Automatic schema extraction
• 📝 Clean, professional responses
• 🧩 Simple, intuitive API

🚀 Installation

pip install nlmdb

🏁 Quick Start

Using OpenAI

from nlmdb import dbagent

# Initialize the agent with your API key and database path
response = dbagent(
    api_key="your-openai-api-key",
    db_path="path/to/your/database.db",
    query="What tables are in the database and what columns do they have?"
)

print(response["output"])

Using Hugging Face

from nlmdb import dbagent_private

# Initialize the agent with your Hugging Face token and model name
response = dbagent_private(
    hf_config=("your-huggingface-token", "model-repo-name"),
    db_path="path/to/your/database.db",
    query="What tables are in the database and what columns do they have?"
)

print(response["output"])

🔒 Privacy and Data Security

NLMDB offers enhanced privacy options through its support for Hugging Face models:

Enhanced Privacy with Hugging Face Models

When using dbagent_private with use_local=True, all processing happens locally on your machine, ensuring your database schema and query data never leave your environment:

response = dbagent_private(
    hf_config=("your-huggingface-token", "model-repo-name"),
    db_path="path/to/your/database.db",
    query="What tables are in the database?",
    use_local=True  # Ensures all processing happens locally
)

Data Security Considerations

• OpenAI Integration: When using dbagent with OpenAI models, database schema and queries are sent to OpenAI's API. While only schema information and not actual data is shared, consider privacy implications.

• Hugging Face Cloud API: Using dbagent_private without use_local=True sends queries to Hugging Face's Inference API.

• Local Processing: For maximum privacy, use dbagent_private with use_local=True to keep all processing on your machine.

• No Data Storage: NLMDB does not store or log your database contents, queries, or responses.

🔄 Model Comparison

Feature	OpenAI Models (dbagent)	Hugging Face Models (dbagent_private)
SQL Generation Quality	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Privacy	⭐⭐	⭐⭐⭐⭐⭐ (with use_local=True)
Cost	💰💰💰	💰 (self-hosted) / 💰💰 (HF API)
Offline Usage	❌	✅ (with use_local=True)
Setup Complexity	Simple	Moderate
Resource Requirements	Minimal (Cloud-based)	High (for local models)
Speed	Fast	Varies (depends on hardware)
Customizability	Limited	Extensive

🧩 Advanced Usage

Running with Verbose Output

You can enable verbose output to see the SQL queries being generated and executed:

response = dbagent(
    api_key="your-openai-api-key",
    db_path="path/to/your/database.db",
    query="How many customers do we have?",
    verbose=True
)

Using Local Hugging Face Models

For improved performance, privacy, or when working offline, you can run Hugging Face models locally:

response = dbagent_private(
    hf_config=("your-huggingface-token", "model-repo-name"),
    db_path="path/to/your/database.db",
    query="What tables are in the database?",
    use_local=True  # This will download and run the model locally
)

Customizing Model Parameters

You can customize the behavior of the language model by passing additional parameters:

model_kwargs = {
    "temperature": 0.2,
    "max_new_tokens": 1024,
    "repetition_penalty": 1.1
}

response = dbagent_private(
    hf_config=("your-huggingface-token", "mistralai/Mixtral-8x7B-Instruct-v0.1"),
    db_path="path/to/your/database.db",
    query="Summarize the sales data for the last quarter",
    model_kwargs=model_kwargs
)

🔍 Choosing the Right Model

Recommended Hugging Face Models

Model	Performance	Resource Usage	Best For
mistralai/Mixtral-8x7B-Instruct-v0.1	⭐⭐⭐⭐⭐	🖥️🖥️🖥️🖥️	Best overall SQL generation
meta-llama/Llama-2-7b-chat-hf	⭐⭐⭐⭐	🖥️🖥️🖥️	Balance of performance and resources
Qwen/Qwen2-7B-Instruct	⭐⭐⭐	🖥️🖥️	Efficient for simpler queries

📊 Supported Databases

Currently, NLMDB supports:

• SQLite ✅

Future releases will add support for:

• PostgreSQL 🔜
• MySQL 🔜
• Microsoft SQL Server 🔜

⚙️ Requirements

• Python 3.8+
• openai>=1.0.0
• langchain>=0.1.0
• langchain-core>=0.1.0
• langchain-community>=0.0.0
• langchain-huggingface>=0.0.1 (for Hugging Face integration)

📜 License

MIT

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🙏 Acknowledgements

This library is built on top of:

• LangChain
• OpenAI API
• Hugging Face Inference API
• SQLite