This project is a Retrieval-Augmented Generation (RAG) application designed to streamline interactions with a SQL database. By harnessing the power of few-shot learning techniques, the application adapts a Large Language Model (LLM) to the specific database structure. Gemini-Pro, a language model from Google, is utilized for this purpose. The front end of the application is developed using Streamlit, providing an intuitive interface for users to seamlessly interact with the database. This project serves as a complement to the concepts discussed in the accompanying YouTube video, offering a practical implementation of the discussed techniques.

• SQL Query Generation: The application is capable of generating SQL queries based on user input. Users can input their queries or questions, and the application utilizes the underlying LLM to generate corresponding SQL queries tailored to the database schema.

• Few-Shot Learning: Few-shot learning techniques are employed to fine-tune the LLM for the specific database schema. By providing a small number of example-query pairs and leveraging the schema information, the LLM learns to generalize and generate accurate SQL queries for a wide range of user inputs.

• Schema Adaptation: The application dynamically adapts to changes in the database schema. By analyzing the schema structure, the LLM adjusts its query generation process to accommodate modifications or updates in the database schema, ensuring robust performance over time.

• Streamlit Interface: The front end of the application is developed using Streamlit, a user-friendly framework for building interactive web applications. The Streamlit interface provides users with an intuitive and responsive platform to interact with the SQL database, facilitating seamless query formulation and data exploration.

The application utilizes a SQLite database containing information about movie ratings, earnings, and genres. You can find the database used in this project by downloading it from the following link: Kaggle.

To install the RAG SQL Reader application, follow these steps:

Clone the repository:

git clone https://github.com/Eduardovasquezn/rag-sql-reader.git

Navigate to the project directory:

cd rag-sql-reader

Create and activate virtual environment:

python -m venv venv
venv/Scripts/activate

Install the required Python libraries:

pip install -r requirements.txt

Create a .env file using .env-example as a template:

cp .env-example .env

In the .env file, insert your Google API Key:

GOOGLE_API_KEY=your_google_api_key

Run the main application script:

streamlit run src/app.py

Contributions to this project are welcome! Feel free to submit bug reports, feature requests, or pull requests to help improve the functionality and usability of the RAG SQL Reader application.

This application is intended for educational and informational purposes only.

If you found the RAG SQL Reader helpful and enjoyed using it, consider subscribing to my YouTube channel for more tutorials, tips, and projects related to Python, AI, and web development. Your support helps me create more valuable content for the community!