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:
- Python 3.x installed on your system.
- pip package manager installed.
- 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")
- PostgreSQL database instance with necessary access credentials
- Clone the repository:
git clone https://github.com/ADP2000/Big_Data_Project
cd Big_Data_Project- Install dependencies:
pip install -r requirements.txtSet up environment variables:
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Create a .env file in the root directory.
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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
- Run the Streamlit app:
streamlit run app.py- 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.