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sqlrag

sqlrag helps users to query SQL databases using natural language. It leverages LLM and embeddings to interpret user queries and generate appropriate SQL statements.

Features

  • Natural language interface for SQL queries
  • Returns both query results and the generated SQL statement
  • Currently only supports SQLite database
  • Currently only supports Ollama for language model and embeddings

Note: This project is currently under development.

Setup and Running

  1. Clone the repository:

    git clone https://github.com/yourusername/sqlrag.git
cd sqlrag

  2. Install dependencies:

    pip install -r requirements.txt

  3. Configure .env file:

    DB = <sqlite_db_name>
LLM_MODEL = <ollama_model_name>
EMBEDDING_MODEL = <ollama_embedding_model_name>

  4. Run the script:

    python sqlrag.py

Install Ollama

You can follow the instructions on the Ollama GitHub repository to install and run Ollama on your system.

Sample SQLite database:

Create:

python create_database.py

Check:

python check_tables.py

TODO

  • Add support for SQLite database
  • Add support for CSV file
  • Add support for PostgreSQL database
  • Add support for MySQL database
  • Implement basic web interface
  • Integrate basic data visualization
  • Add query history tracking
  • Add basic authentication
  • Data validation for CSV files
  • Multiple CSV files support

Contributing

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