SQL RAG – AI-Powered Multi-Mode Database Assistant

SQL RAG is an intelligent AI application that enables users to interact with databases and knowledge sources using natural language. Instead of writing complex SQL queries manually, users can simply ask questions in plain English and receive accurate SQL queries, database results, contextual explanations, and AI-powered insights.

The application integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to understand user intent, retrieve relevant schema and contextual information, generate optimized SQL queries, and provide reliable responses. It offers four specialized AI modes, allowing users to choose the best experience for their specific task.

🚀 AI Modes

🤖 Assistant Mode

General-purpose AI assistant for answering questions and providing guidance.
Explains SQL queries, database concepts, and technical topics in natural language.

📊 SQL Analytics Mode

Converts natural language into optimized SQL queries.
Executes queries on the connected database.
Displays query results with AI-generated explanations and insights.

📚 Knowledge Base Mode

Uses Retrieval-Augmented Generation (RAG) to search indexed documents and knowledge sources.
Retrieves relevant context before generating accurate, grounded responses.
Reduces hallucinations by answering from trusted information.

🔀 Hybrid AI Mode

Combines SQL Analytics and Knowledge Base retrieval in a single workflow.
Retrieves contextual information alongside database records to generate richer, more informative answers.
Ideal for complex analytical and decision-making tasks.

✨ Key Features

Natural Language to SQL Conversion
Multi-Mode AI Experience
Retrieval-Augmented Generation (RAG)
Intelligent Database Querying
Semantic Search using Vector Embeddings
AI-Powered Query Explanation
Conversational Chat Interface
Context-Aware Response Generation
Error Handling for Invalid SQL Queries
Interactive and User-Friendly Web Interface

🛠️ Technology Stack

Python
Streamlit
LangChain
OpenAI GPT
SQLite
FAISS Vector Store
Pandas
SQLAlchemy
Git & GitHub