🧠 ContextIQ - RAG Chatbot 🤖

Overview • Features • How It Works • Installation • Usage • Tech Stack

🌟 Overview

ContextIQ is a sleek, AI-powered chatbot that allows you to chat with your files and URLs using Retrieval-Augmented Generation (RAG). With a responsive UI, document upload capabilities, and conversational memory, ContextIQ helps you extract contextually relevant answers from any source you provide.

🔍 Upload files, add URLs, and let the bot fetch accurate answers from your custom context.

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✨ Features

• 🧠 Conversational memory with multi-turn chat history
• 📄 PDF, DOCX, and TXT document upload support
• 🌐 URL ingestion with BeautifulSoup scraping
• 🔎 ChromaDB vector search with OpenAI embeddings
• 💬 Chatbot-style frontend UI (typing effect + alignment)
• ⚡ Powered by FastAPI backend and React + Tailwind frontend

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🛠️ How It Works

flowchart TD
    A[User Input: Chat / Upload / URL] --> B[React Frontend]
    B --> C[FastAPI Backend]
    C --> D1[Document Upload]
    C --> D2[URL Scraping]
    D1 --> E1[Parse and Chunk Documents]
    D2 --> E2[Extract Web Content]
    E1 --> F[Generate Embeddings via OpenAI]
    E2 --> F
    F --> G[Store in Chroma Vector DB]
    A --> H[Retrieve Relevant Context via RAG]
    G --> H
    H --> I[Generate Answer]
    I --> J[Return Answer to Frontend]
    J --> K[Display Response]

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🚀 Installation

📦 Backend Setup

git clone https://github.com/het0814/ContextIQ-rag-chatbot.git
cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install Python dependencies
pip install -r requirements.txt

# Add your OpenAI key
export OPENAI_API_KEY=your-api-key
uvicorn main:app --reload

💻 Frontend Setup

cd ../frontend
npm install
npm run dev

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💡 Usage

🔗 Upload a URL

POST /add-url
{ "url": "https://www.bbc.com/news" }

📁 Upload a Document

POST /upload-document
(file = .pdf / .docx / .txt)

💬 Ask a Question

POST /generate-response
{ "query": "What’s the latest news?" }

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🧱 Tech Stack

Layer	Tech Used
Frontend	React, Tailwind CSS, Framer Motion
Backend	FastAPI, Python
LLM	OpenAI GPT-4o (via LangChain)
Embeddings	OpenAI Embeddings
Vector DB	ChromaDB
Memory	ConversationBufferMemory

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🧠 Future Roadmap

• Markdown rendering in responses
• Source highlighting per answer
• Multi-user chat sessions
• Dark mode toggle
• Response citation UI

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👥 Contributing

Contributions are welcome!
To contribute:

1. Fork this repo
2. Create a branch (git checkout -b feature/add-citation)
3. Commit changes
4. Push (git push origin feature/add-citation)
5. Open a pull request 🚀

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📄 License

This project is under the MIT License. Feel free to use it in personal or commercial projects.
See LICENSE for full terms.

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⭐ Star this project if you like it ; contributions welcome!