Skip to content

README.md

Latest commit

 

History

History
78 lines (48 loc) · 1.69 KB

README.md

File metadata and controls

78 lines (48 loc) · 1.69 KB

🚀ContextCore-Domain-Aware-RAG-Chatbot

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

📌 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.

✨ 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

🛠️ Tech Stack

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

🏗️ Architecture

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

│ Text Chunking (Splitter)   │

         │
         ▼

│ HuggingFace Embeddings │

      │
      ▼

│ Chroma Vector Database │

      │
      ▼

 │ Retriever       │

           │
           ▼

 │ Groq LLM (LLaMA 3)    │

      │
      ▼

✅ Final Answer + Sources