� QueryMind

A powerful AI-powered web application built with Flask that enables intelligent document analysis and CSV data exploration using natural language queries.

✨ Features

📚 Document Analysis (RAG)

• Web Document Loading: Load and process documents from any URL
• Intelligent Chunking: Configurable chunk size and overlap for optimal context
• Vector Search: FAISS-powered semantic search for relevant document retrieval
• Contextual Q&A: Ask questions and get AI-generated answers based on document content

📊 CSV Analysis with Natural Language

• Auto Schema Detection: Automatically detects column types and sample values
• Natural Language to SQL: Convert plain English questions to SQL queries using LLM
• DuckDB Execution: Fast in-memory SQL execution on your CSV data
• Smart Explanations: AI-generated explanations of query results
• Export Results: Download analysis results as CSV files

🎨 Modern UI/UX

• Animated Interface: Smooth animations and visual effects
• Glassmorphism Design: Modern glass-effect styling
• Responsive Layout: Works on desktop and mobile devices
• Real-time Feedback: Typing indicators and loading animations
• Dark Theme: Eye-friendly dark color scheme

🛠️ Tech Stack

Category	Technologies
Backend	Flask, Python 3.10+
AI/ML	LangChain, Groq LLM, Sentence Transformers
Vector Store	FAISS
Database	DuckDB (in-memory SQL)
Frontend	HTML5, CSS3, JavaScript
Embeddings	all-MiniLM-L6-v2

📁 Project Structure

querymind/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (API keys)
├── .gitignore            # Git ignore rules
├── README.md             # This file
├── static/
│   ├── style.css         # Styles with animations
│   └── script.js         # Frontend JavaScript
├── templates/
│   └── index.html        # Main HTML template
└── myenv/                # Virtual environment

🚀 Getting Started

Prerequisites

• Python 3.10 or higher
• Groq API Key (Get one here)

Installation

1. Clone the repository

   git clone <repository-url>
   cd querymind

2. Create and activate virtual environment

   # Windows
   python -m venv myenv
   .\myenv\Scripts\activate
   
   # Linux/Mac
   python -m venv myenv
   source myenv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Create .env file

   GROQ_API_KEY=your_groq_api_key_here

5. Run the application

   python app.py

6. Open in browser

   http://localhost:5000
   

☁️ Deploying to Render

You can easily deploy QueryMind to Render as a Python Web Service.

Step-by-Step Deployment Guide

1. Prerequisites:

   • Create a free account on Render.
   • Push your code to a GitHub, GitLab, or Bitbucket repository.

2. Create a New Web Service:

   • In the Render Dashboard, click New + and select Web Service.
   • Connect your GitHub repository containing this project.

3. Configure Settings:

   • Name: querymind (or any name you prefer)
   • Region: Choose the region closest to you (e.g., Singapore, Oregon, Frankfurt)
   • Branch: main (or your production branch)
   • Runtime: Python
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app --bind 0.0.0.0:$PORT (This binds the server to Render's dynamic port, or you can leave it blank/default to let Render automatically use the Procfile)

4. Add Environment Variables:

   • Click the Advanced button or go to the Environment tab.
   • Click Add Environment Variable to configure the following keys:
     • GROQ_API_KEY: Your Groq API key (Required)
     • USER_AGENT: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 (Required for LangChain's WebBaseLoader to function correctly without being blocked by websites)
     • PYTHON_VERSION: 3.12.0 (Recommended)

5. Deploy:

   • Click Deploy Web Service.
   • Render will build the environment and launch the Flask application. Once successful, you will receive a public URL (e.g., https://querymind.onrender.com).

📖 Usage Guide

Document Analysis

1. Enter Document URL: Paste the URL of the webpage/document you want to analyze
2. Configure Settings: Adjust chunk size (500-2000) and overlap (100-500)
3. Initialize System: Click "Initialize System" to load and process the document
4. Ask Questions: Type your questions about the document content

CSV Analysis

1. Upload CSV: Click the upload area or drag & drop your CSV file
2. Review Schema: Check the auto-detected columns and data types
3. Preview Data: View the first few rows of your data
4. Ask Questions: Type natural language questions like:
   • "Show me the top 5 sales by region"
   • "What's the average age of customers?"
   • "Count rows where status is 'active'"
5. View Results: See the generated SQL, results, and AI explanation
6. Download: Export results as CSV

🔧 API Endpoints

Endpoint	Method	Description
/	GET	Main application page
/api/initialize-docs	POST	Initialize document system
/api/ask-document	POST	Ask question about documents
/api/upload-csv	POST	Upload CSV file
/api/analyze-csv	POST	Analyze CSV with natural language
/api/download-results	POST	Download analysis results
/api/get-chat-history	GET	Get conversation history
/api/clear-history	POST	Clear chat history

⚙️ Configuration

Available LLM Models

Model	Description
llama-3.1-8b-instant	Fast, efficient responses (default)
llama-3.3-70b-versatile	More capable, detailed responses
llama3-groq-8b-8192-tool-use-preview	Optimized for tool use

Environment Variables

Variable	Required	Description
GROQ_API_KEY	Yes	Your Groq API key
USER_AGENT	No	Custom user agent for web requests

🎨 UI Features

• Animated Gradient Header: Dynamic color-shifting background
• Floating Particles: Ambient particle effects
• Glow Effects: Pulsing glow on interactive elements
• Typing Indicators: Visual feedback during AI processing
• Slide Animations: Smooth message transitions
• 3D Card Effects: Interactive hover states
• Custom Cursor Glow: Follows mouse movement

🔒 Security Notes

• API keys are stored in .env (not committed to git)
• File uploads limited to 16MB
• Only CSV files accepted for data analysis
• Input sanitization for SQL queries

🐛 Troubleshooting

Common Issues

1. Model Decommissioned Error

   • Update model names in app.py and index.html
   • Check Groq Docs for current models

2. GROQ_API_KEY Not Found

   • Ensure .env file exists with valid API key
   • Restart the Flask server after adding the key

3. Document Loading Fails

   • Check if the URL is accessible
   • Some sites block web scraping

4. CSV Upload Fails

   • Ensure file is valid CSV format
   • Check file size is under 16MB

📄 License

This project is licensed under the MIT License.

👨‍💻 Developer

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Made with ❤️ using Flask & LangChain