🚀ContextCore-Domain-Aware-RAG-Chatbot

Turn any PDF into an intelligent AI-powered Q&A system using Retrieval-Augmented Generation (RAG).

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📌 Overview

This project implements a Retrieval-Augmented Generation (RAG) pipeline that allows users to ask questions from a PDF document and receive context-aware, accurate answers along with source references.

Instead of relying solely on LLM knowledge, this system retrieves relevant chunks from the document and feeds them into the model — ensuring grounded and reliable responses.

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✨ Features

• 📄 Load PDF directly from a URL
• 🔍 Intelligent text chunking for better retrieval
• 🧠 Semantic search using embeddings
• 🗂️ Vector storage using ChromaDB
• ⚡ Fast inference using Groq LLM
• 🔗 RetrievalQA pipeline with LangChain
• 📌 Returns answers with source context
• 💡 Scalable and modular design

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🛠️ Tech Stack

• Python
• LangChain
• ChromaDB (Vector Database)
• HuggingFace Embeddings
• Groq API (LLM - LLaMA 3)
• Unstructured (PDF Loader)

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🏗️ Architecture

        │   PDF Document     │
        
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                 ▼
  
  │ Unstructured File Loader    │
  
           │
           ▼

│ Text Chunking (Splitter)   │

         │
         ▼

│ HuggingFace Embeddings │

      │
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│ Chroma Vector Database │

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 │ Retriever       │

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 │ Groq LLM (LLaMA 3)    │

      │
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✅ Final Answer + Sources