Build RAG from Scratch - Complete PDF Q&A System with Qdrant & Ollama (Build Sunday Ep. 2)

Large Language Models (LLMs) are trained on general internet data, meaning they lack access to your private files and are highly prone to hallucinations when questioned on specific domain data. In the second episode of #BuildSunday, we build a local Retrieval-Augmented Generation (RAG) system from scratch—allowing you to chat with any PDF completely locally, for free, with zero data leaving your machine! We break down the system architecture step-by-step: 1️⃣ Document Ingestion: Extracting raw text from documents using PyMuPDF (fitz). 2️⃣ Chunking & Overlapping: Splitting raw text into 800-1200 character chunks, with a 50-character overlap to maintain context across chunk boundaries (solving the "onset" split problem). 3️⃣ Semantic Embeddings: Converting text chunks into 1D vectors using sentence-transformers from Hugging Face. 4️⃣ Vector Database: Storing and indexing embeddings in a local Qdrant Vector DB running inside Docker. 5️⃣ Retrieval & Similarity Search: Pulling the Top-K matching document chunks dynamically when a query is submitted. 6️⃣ Context-Grounded Generation: Constructing custom prompts and querying a local Llama-3.2 (3B) model served via Ollama. Everything runs 100% locally on your computer—no API keys, no paywalls, and complete data privacy. 📂 GET THE CODE: Clone the repository and run the CLI chatbot in under 5 minutes: 👉 GitHub Repository: https://github.com/thejabirhussain/Im... 🛠️ PREREQUISITES & SERVICES SETUP: 1. Docker: Run Qdrant Vector Database docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/qdrant_storage:/qdrant/storage:z qdrant/qdrant 2. Ollama: Serve Llama-3.2 locally ollama pull llama3.2 ollama run llama3.2 🔔 Subscribe to the channel for weekly end-to-end coding builds on Sundays (Build Sunday) and deep-dive mathematical AI lectures on Wednesdays (In-Depth Lectures)! 00:00 - Welcome & Episode 1 Recap 01:08 - The 3 Types of LLMs: Proprietary vs. Open-Source vs. Open-Parameter 04:30 - Project Overview: PDF Q&A RAG System 05:50 - RAG Architecture Whiteboard Explanation 08:50 - Why LLMs Hallucinate on Private Data 13:30 - RAG vs. Fine-Tuning: Cost, Updates, and Transparency 18:48 - Ingestion Pipeline: Extractor, Chunker, & Overlap 22:24 - Visualizing Vector Embeddings 25:00 - Retrieval & Semantic Search Mechanics 29:00 - Tech Stack: PyMuPDF, sentence-transformers, Qdrant, Ollama 30:40 - pdfrag Project Code Architecture 32:30 - Dockerizing Qdrant Vector Database 34:30 - Serving Llama-3.2 (3B) Locally with Ollama 37:00 - What is Chunk Overlapping? (The "onset" cutoff example) 40:00 - Implementing Ingestion and Retrieval Scripts 51:30 - Live Demo: PDF Ingestion & Semantic Chunking 53:00 - Running Semantic Queries in the CLI 56:00 - Understanding Retrieval Citations & Output 58:00 - Teaser for Episode 3: Vector Databases in Detail #RAG #Llama3 #Qdrant #Ollama #AIEngineering #Python #VectorDatabase #HuggingFace #Docker #MachineLearning #SystemDesign #OpenSource #SoftwareArchitecture