🧠 Natural Language → SQL AI Agent

An end-to-end AI-powered SQL agent that allows users to query a PostgreSQL (Supabase) database using plain English, with memory, department-wise filtering, and dynamic aggregation — deployed as a Streamlit web app.

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🚀 Features

• 🔤 Ask questions in natural language
• 🧠 Intent detection (highest / lowest / average / select)
• 🏢 Department-wise queries (Engineering, Sales, HR, etc.)
• 📊 Salary analytics:
  • Highest salary
  • Lowest salary
  • Average salary
• 🧾 Session memory (stores last user queries & answers)
• 🌐 Deployed as a web application
• ☁️ Uses Supabase (PostgreSQL) as backend database
• 🤖 Lightweight ML model trained on Text-to-SQL dataset

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🏗️ Architecture Overview

User (Web UI - Streamlit)
        ↓
Natural Language Query
        ↓
Rule-based + ML Intent Detection
        ↓
Business Logic (Python)
        ↓
Supabase (PostgreSQL)
        ↓
Formatted Natural Language Answer
        ↓
Session Memory (Query History)

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🛠️ Tech Stack

• Frontend: Streamlit
• Backend Logic: Python
• Database: Supabase (PostgreSQL)
• ML Model: TF-IDF + Logistic Regression
• Model Training: Kaggle (Text-to-SQL dataset)
• Deployment: Streamlit Cloud

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📂 Project Structure

sql-ai-agent/
│
├── app.py                     # Streamlit web app
├── query_intent_model.pkl     # Trained ML model
├── tfidf_vectorizer.pkl       # Text vectorizer
├── requirements.txt
└── README.md

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🧪 Example Queries

• highest salary employee
• lowest salary in sales
• average salary in engineering
• show employees
• top paid employee in hr

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🧠 How Intent Detection Works

• Rule-based logic handles common business queries (fast & explainable)
• ML model (trained on a Text-to-SQL dataset) acts as a fallback
• This hybrid approach avoids heavy LLMs while remaining reliable and efficient

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🧾 Memory Feature

The agent maintains session-level memory, showing:

• Previous user questions
• Corresponding answers

This makes the agent feel stateful and conversational.

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⚙️ Environment Variables

For local development (.env):

SUPABASE_URL=your_supabase_project_url
SUPABASE_KEY=your_supabase_anon_key

For Streamlit Cloud, the same values are added via Secrets (TOML format).

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🌐 Deployment

The application is deployed using Streamlit Cloud and connects securely to Supabase using environment secrets.

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📈 Future Enhancements

• Multi-table SQL support
• Role-based access control
• Advanced query parsing
• Export results as CSV
• LLM-based SQL generation

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🧑‍💻 Author

Kumar Nihal Generative AI / Agentic AI Engineer

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Causly Ecosystem

This project is part of the Causly Server ecosystem and is intended to evolve with it as the platform develops.