🎓 Student Assistant Bot with RAG 📚

A powerful AI-powered Student Assistant Bot that leverages RAG (Retrieval-Augmented Generation) to answer student queries with document-grounded responses. Designed to integrate seamlessly with educational content and provide reliable, contextual answers!

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🚀 Features

• 📄 Document-based Query Understanding
  Retrieves information from PDFs, notes, or course materials.

• 🧠 LLM-Powered Reasoning
  Combines document retrieval with language model understanding using Retrieval-Augmented Generation.

• 🎯 Accurate and Contextual
  Provides source-backed answers to improve trust and accuracy.

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📦 Tech Stack

Component	Tech Used
💬 LLM	OpenAI / OpenRouter
📚 RAG Upload	Qdrant / docling
🗂️ Document Loader	Docling / Embedding Models
⚙️ Backend	Python
🌐 Frontend	Streamlit

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🧠 How It Works

1. Ingest Documents
   Academic materials (PDFs, text, etc.) are split into chunks and indexed.

2. User Asks a Question
   The bot receives a student query (e.g., "What is deadlock in OS?").

3. Retriever Finds Relevant Chunks
   The retriever scans vectorized content to fetch top-matching passages.

4. Generator Forms the Answer
   The LLM reads the relevant chunks and generates a precise, helpful answer.

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🛠️ Setup Instructions

1. 🔧 Install Dependencies

pip install -r requirements.txt