AI PDF Assistant – RAG Chatbot using LlamaIndex & Qdrant

Overview

AI PDF Assistant is an intelligent document question-answering application built using Retrieval-Augmented Generation (RAG). Users can upload PDF documents and ask natural language questions. Instead of generating answers from general knowledge, the system retrieves relevant document content using vector search and provides accurate, context-aware responses.

Problem Statement

Searching through lengthy PDF documents manually is time-consuming. Traditional keyword search often fails to understand the meaning of a user's question.

Solution

This project indexes uploaded PDF documents into a vector database using embeddings. When a question is asked, the system retrieves the most relevant document chunks and uses an AI model to generate accurate answers grounded in the uploaded content.

Key Features

• Upload PDF documents
• Automatic document parsing
• Semantic search using embeddings
• Retrieval-Augmented Generation (RAG)
• LlamaIndex document indexing
• Qdrant vector database
• Streamlit web interface
• Context-aware AI responses

Technology Stack

• Python
• Streamlit
• LlamaIndex
• Qdrant
• Sentence Transformers
• Hugging Face
• PyPDF

Skills Demonstrated

This project demonstrates practical experience with Large Language Models (LLMs), Retrieval-Augmented Generation, vector databases, semantic search, document indexing, prompt engineering, and AI application development.