Build a RAG Pipeline from Scratch | LangChain + OpenAI + FAISS | Ask Questions to Any PDF скачать в хорошем качестве

Build a RAG Pipeline from Scratch | LangChain + OpenAI + FAISS | Ask Questions to Any PDF

Build a complete RAG (Retrieval-Augmented Generation) pipeline from scratch in 15 minutes — using LangChain, OpenAI, and FAISS. In this hands-on tutorial, I take India's Union Budget 2025-26 (a 50+ page PDF) and build a system that can answer any question about it in under 5 seconds. No copy-pasting. No Ctrl+F. Just ask in plain English and get precise, sourced answers. RAG is an open-book exam for AI — instead of making the LLM know everything, we let it look things up. What you'll learn: → What RAG is and why LLMs need it → How to load, chunk, and embed a PDF → How embeddings and vector search work (explained simply) → Building a complete pipeline: PDF → Chunks → FAISS → GPT-4o-mini → Answer → Live demo: asking real questions about the Union Budget ⏱️ Timestamps: 0:00 — Watch what this pipeline can do 0:45 — What is RAG? (The Open-Book Exam Analogy) 2:30 — The Architecture: PDF to Answers 3:30 — Setup: Install & Configure 5:00 — Load the PDF & Chunk It 7:30 — Embeddings & FAISS Vector Store 10:00 — Ask Questions: Live Demo 17:52 — What We Just Built (Full Recap) 18:55 — Limitations & What's Next 📓 Free Colab Notebook (run it yourself): https://github.com/prashantnair2050/y... 📺 Previous: What is Agentic AI? Explained Simply →    • What is Agentic AI? Explained Simply | The...   📺 Next: LangChain vs LangGraph — When to Use What → [LINK WHEN READY] 🔗 Connect: LinkedIn:   / prashant-solution-architect   Instagram: @pn_riderbysoul Corporate Training: prashant@vnex.in #RAG #LangChain #GenAI #FAISS #OpenAI #RAGPipeline #RetrievalAugmentedGeneration #AITutorial #PrashantNair #LangChainTutorial