🧠 Cognitive RAG Assistant

AI-Powered Retrieval-Augmented Generation (RAG) Knowledge Assistant

Document Chunking • TF-IDF Retrieval • Cosine Similarity Search • Groq LLaMA 3.3

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📖 Overview

Cognitive RAG Assistant is a modern Retrieval-Augmented Generation (RAG) system that allows users to upload or paste documents, automatically index and chunk content, retrieve the most relevant information using TF-IDF and cosine similarity, and generate contextual answers powered by Groq's LLaMA 3.3 model.

Unlike traditional chatbots that rely solely on pre-trained knowledge, this system grounds every response in the provided document, ensuring accurate and context-aware answers.

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✨ Features

📄 Document Processing

• Smart document chunking
• Automatic text segmentation
• Context preservation through chunk overlap
• Real-time indexing pipeline

🔍 Retrieval Engine

• TF-IDF based ranking
• Cosine similarity scoring
• Top-K chunk retrieval
• Highlighted source chunks

🤖 AI-Powered Responses

• Groq LLaMA 3.3 integration
• Context-aware answer generation
• Hallucination reduction
• Source-grounded responses

📊 Interactive Visualization

• Retrieval pipeline monitoring
• Chunk activation indicators
• Similarity score tracking
• Document statistics dashboard

📥 Export Support

• Generate professional PDF reports
• Export answers instantly
• Query history preservation

🎨 Modern User Experience

• Fully responsive interface
• Smooth animations with Motion
• Clean professional dashboard
• Interactive retrieval workflow

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🏗️ System Architecture

Document Input
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Document Chunking
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TF-IDF Vectorization
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Cosine Similarity Search
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Top Relevant Chunks
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Groq LLaMA 3.3
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      ▼
Generated Answer

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🛠️ Tech Stack

Frontend

• React
• TypeScript
• Vite
• Tailwind CSS
• Motion
• Lucide Icons

Retrieval Layer

• TF-IDF
• Cosine Similarity
• Custom Chunking Engine

AI Backend

• Groq API
• LLaMA 3.3 70B Versatile

Utilities

• jsPDF
• dotenv

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🚀 Getting Started

Clone Repository

git clone https://github.com/yourusername/cognitive-rag-assistant.git

cd cognitive-rag-assistant

Install Dependencies

npm install

Configure Environment Variables

Create a .env file:

GROQ_API_KEY=your_groq_api_key_here

Start Development Server

npm start

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📸 Screenshots

Document Indexing

Retrieval Visualization and Generated Response

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🎯 Example Questions

AI Trends Dataset

• What is Agentic AI?
• What companies launched AI agents?
• What is the IndiaAI Mission?
• What is the EU AI Act?

Market Dataset

• What companies form the Magnificent Seven?
• Why did NVIDIA cross $3 trillion market cap?
• What caused Bitcoin's surge?

Health Dataset

• How are wearables used in healthcare?
• What is the Ayushman Bharat Digital Mission?
• How is AI accelerating drug discovery?

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🔬 Learning Objectives

This project demonstrates:

• Retrieval-Augmented Generation (RAG)
• Information Retrieval
• Natural Language Processing
• Context Engineering
• Large Language Model Integration
• Vector Search Fundamentals
• Full-Stack AI Application Development

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🤝 Contributions

Contributions, feature suggestions, and improvements are welcome.

If you'd like to improve the retrieval engine, UI/UX, model integration, or documentation, feel free to open an issue or submit a pull request.

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⭐ Support

If you found this project useful, consider giving it a star ⭐ and sharing it with others interested in AI, NLP, and Retrieval-Augmented Generation systems.

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👨‍💻 Author

Supreet Mohapatra

Building intelligent systems, AI-powered applications, and real-world machine learning projects.