Text-to-SQL Query Demo

Overview

This project demonstrates a Language Model (LLM) that can interpret natural language queries and generate corresponding PostgreSQL queries. The project is implemented in Python and uses the langchain package to interface with a suitable language model, which could be any of the GPT models provided by OpenAI or other language models that support the same interface.

Files:

• 'llm.py': Contains the core logic for converting text to SQL queries.
• 'main.py': A Streamlit application that provides a user-friendly interface for interacting with the model.

Features

• Natural Language Understanding: Accepts user input in natural language and interprets the intent behind the text.
• SQL Generation: Produces PostgreSQL queries based on the provided input.
• Schema Aware: Takes a database schema as input to generate context-specific queries.
• Interactive Chat: Utilizes a chat interface for receiving user input and displaying the generated SQL queries.
• State Management: Maintains conversation state to allow for context-aware query generation.
• Review Mode: A mechanism to review and ensure that the generated content strictly contains SQL queries.