A newer version of the Streamlit SDK is available: 1.59.1
title: GenAI Text2SQL Analytics Assistant
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.45.1
app_file: app.py
pinned: false
license: mit
🤖 GenAI Text2SQL Analytics Assistant
An advanced Generative AI-powered analytics assistant that converts natural language questions into executable SQL queries and retrieves insights from a live relational database.
Built using LangChain, Groq LLMs, SQLAlchemy, SQLite, and Streamlit.
🚀 Features
- Natural Language to SQL Conversion
- Live Database Query Execution
- AI-Powered Business Analytics
- SQL Query Transparency
- Interactive Analytics Dashboard
- CSV Export Functionality
- Automatic SQL Cleaning & Validation
- Multi-table SQL Reasoning
- Conversational Analytics Experience
- Real-time Data Insights
🧠 How It Works
The application does not train a custom AI model.
Instead, it uses a Large Language Model (LLM) to:
- Understand the user's business question
- Generate optimized SQL queries dynamically
- Execute queries on a live SQLite database
- Return analytics results and business insights
🏗️ Architecture
User Query
↓
LLM (Groq + LangChain)
↓
SQL Query Generation
↓
SQLite Database Execution
↓
Analytics Results
↓
AI Business Summary + Visualization
🛠️ Tech Stack
| Category | Technology |
|---|---|
| LLM Provider | Groq |
| Framework | LangChain |
| Database | SQLite |
| ORM | SQLAlchemy |
| Frontend | Streamlit |
| Data Generation | Faker |
| Data Processing | Pandas |
| Language | Python |
📂 Project Structure
TEXT2SQL-GENAI/
│
├── agents/
│ ├── sql_agent.py
│ └── langchain_sql_agent.py
│
├── database/
│ ├── create_db.py
│ ├── seed_data.py
│ └── ecommerce.db
│
├── prompts/
│ └── sql_prompt.py
│
├── utils/
│ ├── sql_utils.py
│ └── query_engine.py
│
├── app.py
├── requirements.txt
├── .env
└── README.md
🗄️ Database Schema
The project uses a relational e-commerce database containing:
- Customers
- Products
- Employees
- Orders
- Order Items
The database was populated with synthetic business data using Faker.
📊 Example Questions
Top 5 products by revenue
Which employee handled the most orders?
Average order value by city
Show revenue by product category
Top customers by spending
🔥 Advanced Features Implemented
✅ Schema-Aware Prompting
Injected database schema directly into prompts to improve SQL accuracy.
✅ SQL Cleaning
Automatically removes markdown formatting and cleans generated SQL.
✅ Error Handling & Retry Logic
Detects invalid SQL queries and retries with correction prompts.
✅ LangChain SQL Agent
Implemented autonomous SQL reasoning using LangChain SQL Agent.
✅ AI Business Summaries
Generates human-readable business insights from query results.
✅ Interactive Analytics Dashboard
Built using Streamlit with:
- Data tables
- SQL visibility
- CSV export
- Charts and visualizations
📈 Sample Analytics Capabilities
- Revenue Analysis
- Customer Segmentation
- Product Performance
- Sales Trends
- Employee Performance Metrics
- Business Intelligence Reporting
⚙️ Installation
1️⃣ Clone Repository
git clone <your_repo_url>
cd TEXT2SQL-GENAI
2️⃣ Create Virtual Environment
Windows
python -m venv venv
venv\Scripts\activate
3️⃣ Install Dependencies
pip install -r requirements.txt
4️⃣ Configure Environment Variables
Create .env
GROQ_API_KEY=your_api_key_here
▶️ Running The Project
Step 1 — Create Database
python database/create_db.py
Step 2 — Seed Database
python database/seed_data.py
Step 3 — Run Streamlit App
streamlit run app.py
🌟 Key Learning Outcomes
- Generative AI Application Development
- Prompt Engineering
- LangChain Agent Workflows
- SQL Query Generation
- Database Integration with LLMs
- Conversational Analytics Systems
- Streamlit Dashboard Development
- AI-powered Business Intelligence
🎯 Future Improvements
- PostgreSQL Support
- Authentication System
- Query History
- Role-Based Access Control
- Real-time Streaming Responses
- Data Upload & Auto Analysis
- Multi-turn Conversational Memory
- Advanced Chart Visualizations
📌 Important Note
This project does not fine-tune or train an LLM.
Instead, it demonstrates:
- LLM orchestration
- schema-aware prompting
- live SQL generation
- autonomous database querying
- AI analytics workflows
which closely reflects real-world enterprise GenAI systems.
👨💻 Author
Mohd Faizanullah