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Your RAG system from Episode 1 had two big limitations: hardcoded text and zero memory. Every question was independent — ask a follow-up like "Does it carry over?" and the AI has no idea what "it" means. In this episode, we fix both. You'll load real PDF and text files, add conversation memory so the chatbot handles follow-ups naturally, and see similarity scores that tell you how confident each retrieval is. By the end, you'll have a conversational RAG system — the exact pattern used in production chatbots at companies handling millions of conversations. What you'll learn: 00:00 RAG Episode 2: Chat With Real PDFs and Remember Every Question 01:25 Setup — Getting Everything Ready 02:17 Creating Real Documents to Work With 06:49 Loading Real Files — PDF Loader and Text Loader 08:37 Splitting, Embedding, and Storing — The Pipeline You Know 10:11 Similarity Search With Scores — How Confident Is the Match? 11:51 Building the Basic RAG Chain — Quick Refresher 12:56 The Problem — Why Basic RAG Can't Handle Conversations 14:48 Conversational RAG — Adding Memory That Actually Works 17:39 The Moment of Truth — Follow-Up Questions That Actually Work 18:36 Interactive Conversational RAG Demo 19:43 What You Just Built — And What's Coming Next Tech stack (100% free): LangChain — Framework Groq (Llama 3.3 70B) — LLM (free API) HuggingFace (all-MiniLM-L6-v2) — Embeddings (free, local) FAISS — Vector store (free, local) pypdf — PDF parsing Key concepts covered: PyPDFLoader and TextLoader for real files Similarity scores (distance-based confidence) History-aware retrieval (question rewriting) Chat memory management Two-stage conversational RAG pipeline Download the notebook: https://github.com/notebooklearnings/... Watch Episode 1 first: • Make ChatGPT Read Your Documents — RAG Tut... RAG Series — NoteBook Learnings: Ep 1: RAG From Scratch Ep 2: Real PDFs + Conversational RAG (this video) Ep 3: Advanced Retrieval Strategies Ep 4: Multi-Modal RAG Ep 5: Production RAG Deployment Subscribe and hit the bell so you don't miss Episode 3! #RAG #ConversationalRAG #LangChain #Python #AI #MachineLearning