🤖 RagChat — AI Chatbot with RAG

A full-stack AI chatbot powered by Groq (Llama 3.3 70B) with file upload Q&A, persistent memory, and a beautiful dark neon UI with sidebar.

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📌 Table of Contents

• Features
• Preview
• Project Structure
• Getting Started
• API Endpoints
• Tech Stack
• How It Works
• Author

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✨ Features

Feature	Description
💬 Multi-turn Chat	Remembers full conversation history
📄 File Q&A	Upload PDF, TXT, DOCX and ask questions
🧠 Personal Memory	Always knows who you are via memory.json
💾 Persistent History	All conversations saved to JSON files
🗂️ Recent Chats	Switch between saved conversations in sidebar
🎨 Dark Neon UI	Beautiful blue neon interface with sidebar
⚡ Free & Fast	Powered by Groq free tier (14,400 req/day)
🔄 Session Management	Create, switch, and delete conversations

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🖥️ Preview

╔══════════════════════════════════════════════════════════════╗
║  🤖 Haider AI        │  Haider Assistant                     ║
║                      │                                        ║
║  + New conversation  │  ┌─────────────────────────────────┐   ║
║                      │  │ AI: Hello! I am Haider           │  ║
║  RECENT CHATS        │  │     Assistant. Ask me anything.  │  ║
║  ▶ What is Python?   │  └─────────────────────────────────┘  ║
║  ▶ Explain FastAPI   │                                       ║
║                      │         ┌──────────────────┐          ║
║                      │         │ You: Hello!      │          ║
║                      │         └──────────────────┘          ║
║                      │  ┌─────────────────────────────────┐  ║
║                      │  │ AI: Hello! How can I help you?  │  ║
║                      │  └─────────────────────────────────┘  ║
║                      │                                        ║
║  Haider AI           │  [ 📄 ] [ Message Haider AI...  ] [▶] ║
╚══════════════════════════════════════════════════════════════╝

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🗂️ Project Structure

ragchat/
├── 📄 main.py              # FastAPI routes & endpoints
├── ⚙️  config.py            # Settings, API key, system prompt
├── 📐 models.py            # Pydantic data models
├── 🤖 chat_service.py      # Groq AI integration
├── 💾 history_store.py     # Chat history management
├── 📁 file_handler.py      # PDF, TXT, DOCX text extraction
├── 🧠 memory.json          # Personal memory about the user
├── 🎨 chatbot_ui.html      # Frontend dark neon UI
├── 🔒 .env                 # API keys (never commit!)
├── 📋 .env.example         # Template for environment variables
├── 📦 requirements.txt     # Python dependencies
└── 💬 chat_histories/      # Saved conversations (auto-created)

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🚀 Getting Started

Prerequisites

• Python 3.9+
• Free Groq API key → console.groq.com

1. Clone the repository

git clone https://github.com/HaiderNeuralNet/Ragchat-Haider.git
cd Ragchat-Haider

2. Install dependencies

pip install -r requirements.txt

3. Set up environment variables

Create a .env file in the root folder:

GROQ_API_KEY=gsk_your_key_here
SYSTEM_PROMPT=You are a helpful assistant.

Get your free API key at: https://console.groq.com

4. Configure personal memory (optional)

Edit memory.json to personalize the AI:

{
  "name": "Haider",
  "profession": "Software Developer",
  "location": "Pakistan",
  "interests": ["AI", "chatbots", "modern UI"],
  "current_project": "AI Chatbot with FastAPI and Groq"
}

5. Run the app

python -m uvicorn main:app --port 8000

6. Open in browser

http://127.0.0.1:8000

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📡 API Endpoints

Method	Endpoint	Description
GET	/	Serve the chat UI
POST	/chat	Send a message
POST	/chat/stream	Streaming chat response
POST	/upload	Upload a file (PDF/TXT/DOCX)
GET	/history/{session_id}	Get chat history
DELETE	/history/{session_id}	Clear chat history
GET	/sessions	Get all saved sessions
GET	/health	Health check

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🛠️ Tech Stack

Layer	Technology	Purpose
Backend	Python, FastAPI	REST API server
AI Model	Groq — Llama 3.3 70B	AI responses (free)
Frontend	HTML, CSS, JavaScript	Chat UI
File Parsing	PyPDF2, python-docx	Extract text from files
Storage	JSON files	Save chat history
Server	Uvicorn	ASGI web server

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📦 Requirements

fastapi
uvicorn[standard]
groq
python-dotenv
sse-starlette
python-multipart
PyPDF2
python-docx

Install all with:

pip install -r requirements.txt

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💡 How It Works

Chat Flow

User types message
        ↓
FastAPI receives request  (main.py)
        ↓
Load full conversation history  (history_store.py)
        ↓
Send history + system prompt to Groq  (chat_service.py)
        ↓
Groq (Llama 3.3 70B) generates reply
        ↓
Save reply to JSON file  (history_store.py)
        ↓
Display reply in browser  (chatbot_ui.html)

File Q&A Flow

User uploads PDF / TXT / DOCX
        ↓
Extract all text  (file_handler.py)
        ↓
Inject text into chat history as context
        ↓
User asks questions → AI answers based on file

Why Multi-turn Memory Works

Every request includes FULL conversation history:

Turn 1: [user: "hi"]
Turn 2: [user: "hi", ai: "hello!", user: "what is Python?"]
Turn 3: [user: "hi", ai: "hello!", ..., user: "give an example"]

AI always sees everything → remembers context perfectly ✅

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🔒 Security

• ✅ Use .env.example to share required variables
• ✅ Regenerate your API key if accidentally exposed
• ✅ File uploads are processed and deleted immediately

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📄 License

This project is licensed under the MIT License — feel free to use, modify, and share.

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👤 Author

Haider

• 🐙 GitHub: @HaiderNeuralNet
• 💼 Project: Ragchat-Haider

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⭐ Support

If you found this project helpful, please give it a ⭐ star on GitHub!

It helps others discover the project and motivates continued development. 🚀

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Built with ❤️ by Haider — learning AI by building real projects