InsightIQ

An AI-powered business analytics dashboard that enables users to query and visualize data using natural language. Built with React, FastAPI, LangChain, and Groq.

Features

Frontend

• Natural Language Queries: Ask questions in plain English like "What's our churn this month?"
• Interactive Visualizations: Beautiful charts and graphs using Recharts
• Modern UI: Responsive design with shadcn/ui components
• Real-time Insights: Instant AI-generated explanations of data

Backend

• NL2SQL Pipeline: Convert natural language to SQL using Groq AI
• CSV Upload & Processing: Automatic schema generation from uploaded files
• DuckDB Integration: Fast analytical database for data processing
• Dashboard Builder: Create custom dashboards with drag-and-drop widgets
• Query History: Track and retrieve past queries

Architecture

InsightIQ/
├── frontend/          # React + TypeScript + Vite
│   ├── src/
│   │   ├── components/    # UI components
│   │   ├── pages/         # Page components
│   │   └── hooks/         # Custom hooks
│   └── package.json
├── backend/           # FastAPI + LangChain + Groq
│   ├── app/
│   │   ├── api/          # API routes
│   │   ├── services/     # AI and data services
│   │   └── models/       # Database models
│   └── requirements.txt
└── README.md

Tech Stack

Frontend

• React 18 - UI library
• TypeScript - Type-safe JavaScript
• Vite - Fast build tool
• Tailwind CSS - Utility-first CSS framework
• shadcn/ui - Beautiful UI components
• Recharts - Charting library

Backend

• FastAPI - Modern Python web framework
• LangChain - AI application framework
• Groq - Fast AI inference for NL2SQL
• PostgreSQL - Primary database
• DuckDB - Analytical database
• SQLAlchemy - ORM and database toolkit

Quick Start

Prerequisites

• Node.js (v16+)
• Python 3.8+
• PostgreSQL
• Groq API key

Frontend Setup

1. Navigate to frontend directory:

   cd frontend

2. Install dependencies:

   npm install
   # or
   bun install

3. Start development server:

   npm run dev

4. Open browser: http://localhost:5173

Backend Setup

1. Navigate to backend directory:

   cd backend

2. Create virtual environment:

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment:

   cp env.example .env
   # Edit .env with your configuration

5. Start the server:

   python start.py

6. API Documentation: http://localhost:8000/docs

Usage Examples

1. Upload Data

Upload a CSV file through the web interface or API:

curl -X POST "http://localhost:8000/api/v1/upload/csv" \
  -F "file=@sales_data.csv" \
  -F "dataset_name=Sales Data"

2. Ask Questions

Use natural language to query your data:

• "What's our monthly revenue trend?"
• "Show me customer churn by region"
• "Which products are selling best?"

3. Create Dashboards

Build custom dashboards with your insights and share them with your team.

License

This project is licensed under the MIT License.