This is a Query Augmented Gneration application that enhances SQL query generation using LLama3-8B. The application connects to a PostgreSQL database and allows users to interact with a natural language interface to query database information. The UI is managed via the streamlit framework.

Follow these steps to set up and run the application:

1. Python 3.x installed on your system.
2. pip package manager installed.
3. Hadoop and Spark installed on your system (here is available a guide for the installation over Windows: "https://medium.com/@deepaksrawat1906/a-step-by-step-guide-to-installing-pyspark-on-windows-3589f0139a30")
4. PostgreSQL database instance with necessary access credentials

1. Clone the repository:

git clone https://github.com/ADP2000/Big_Data_Project
cd Big_Data_Project

2. Install dependencies:

pip install -r requirements.txt

Set up environment variables:

1. Create a .env file in the root directory.

2. Add the following variable to .env:

GROQ_API_KEY = API_KEY_GROQ

replace API_KEY_GROQ with your api key groq available via the groq cloud service

1. Run the Streamlit app:

streamlit run app.py

2. Your default web browser will open with the application running. If not, visit http://localhost:8501 in your browser.

• Upon running the application, you will see a form to enter your PostgreSQL database connection details (DB NAME, DB USER, DB PASSWORD, DB HOST, DB PORT).
• After submitting valid database connection details, you can interact with the natural language interface to query the database.
• Example queries you can try:
  • "What table and his attributes there are in this database?"
  • "Count the average number of rows for each tables on database."
  • "Count the number of rows on database."

• Ensure your PostgreSQL database is accessible from the network where you run this application.
• This application uses Streamlit for the web interface, SQLAlchemy for database connectivity, and Spark for SQL querying.