This project implements a Retrieval-Augmented Generation (RAG) system to enable conversational interaction with a database using natural language. The system translates user queries into SQL to retrieve relevant information from the database, which is then used by a large language model to generate a natural and accurate response.
Key Features:
- Natural Language to SQL: Converts user prompts into executable SQL queries.
- Database Interaction: Directly queries a local database (
student.db). - RAG Architecture: Integrates a RAG pipeline to enhance response accuracy and relevance.
Project Structure:
app.py: Main application script.main.py: Core logic for the RAG system.sqlite.py: Handles database operations.student.db: The SQLite database used for the project.requirements.txt: Lists all necessary dependencies.