LLMs and GenAI: A Guide to Building Your Own RAG Model for Document Interaction
summary
Code walks through a concrete RAG pipeline implemented in Python + LangChain: PDF-to-text extraction, recursive char splitter chunking with overlap, small English embedding model, storing vectors in Qdrant vector DB, and retrieval by cosine similarity before passing top-K context to Llama 3 (demo) or OpenAI models (GPT‑4/4o/3.5). Production notes cover multiprocessing for large ingests, OCR/diagram parsing, showing source PDF pages for validation, and tradeoffs between few‑shot and retrieval approaches.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 1:00Overview
- 1:40What is RAG
- 2:45Architecture
- 4:20Coding
- 8:45Generation
- 9:50Demo
- 12:55Questions
related talks
41mFrom RAGs to riches: Build an AI document interrogation app in 30 mins
57mLLMs gone wild
42mTake Your RAG To The Max!
39mAgentic RAG Platform for AI-Assisted Workflows
01h 34mHow to Construct and Assess Your First LLM Based RAG System
47mBuilding and Testing AI-Agent Powered LLM Applications: A Live Demonstration
36mGraphRAG Explained 🔥 Building Smarter LLMs
46mGenerative AI and Java