Turn Any LLM Into an Expert 📚 RAG Coding Crash Course

What if you could take any language model and turn it into an expert by giving it fast access to documents? 🤔 Big thanks to HubSpot for sponsoring this video 🙌 ⭐ Check out HubSpot's FREE AI Agents Cheat Sheet: https://clickhubspot.com/0acb23 In this beginner-friendly RAG (Retrieval-Augmented Generation) project, we will build a local AI system that can read custom documents, search through them intelligently, and answer questions using knowledge that was never part of the original model! 📚⚡ To make things fun, we'll use a collection of fictional investigation files inspired by The Lord Of The Rings and The Matrix — turning an ordinary LLM into a specialized AI detective capable of solving mysteries hidden throughout our documents: analyzing suspicious characters, secret reports, and hidden clues like a real pro! 🧙‍♂️🕶️ Unlike Fine Tuning, we won't train the model at all! Instead, we will allow a tiny and not-so-smart model to search through documents very quickly, retrieve the most relevant information, and use it to generate intelligent answers in real time — as tough we are dealing with a giant super-smart model that requires way more resources and computing power. 😎 By the end of this tutorial, you'll have a complete local RAG application running on your own computer using Python, LangChain, Ollama and FAISS — and you'll finally understand why Fine Tuning and RAG are not the same thing! 📚 What You'll Learn: What RAG (Retrieval-Augmented Generation) actually is Loading PDF documents into Python Document chunking and text splitting Creating embeddings from text Storing embeddings with FAISS Retrieving relevant context from a vector database Building a complete RAG pipeline with LangChain Running local LLMs with Ollama Answering questions using custom knowledge 🛠 Tools Used: Python Ollama Qwen2.5:1.5B (small enough to run on CPU) BGE-M3 LangChain FAISS Jupyter Lab 🚨 Are you an educator? Feel free to use my video slides and project idea in your classes! 🙂 I've included a high-quality PDF containing all the slides from this video, which you can download directly from YouTube. The complete source code, notebook, and project files are also available on GitHub. You're welcome to use them in your lectures, workshops, and classroom activities. Happy teaching! 💚 🔎 Resources & Helpful Tutorials: ⭐ Full Code and Starter Files on GitHub: https://github.com/MariyaSha/rag_ollama ⭐ My WSL and Conda Tutorial:    • My Go-To Python Setup! 🐍 WSL + Conda Minif...   ⭐ My Hyperparameter Tuning with Sklearn Tutorial:    • Simple Machine Learning Code Tutorial for ...   ⭐ Classroom Slides (PDF): Download directly from YouTube 📄 ⭐ Ollama and FAISS GPU links in the pinned comment 📌 💻 WSL Environment Setup: conda create -n rag_env python=3.12 conda activate rag_env pip install \ langchain \ langchain-community \ langchain-ollama \ langchain-text-splitters \ faiss-cpu \ pypdf \ jupyter ⚙️ Module Imports: from langchain_community.document_loaders import PyPDFLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_community.vectorstores import FAISS from langchain_ollama import OllamaEmbeddings, ChatOllama from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser import os ⏰ Timestamps ⏰ 00:00 Build a Local RAG Pipeline with Ollama 01:16 Environment Setup (Ollama, LangChain & FAISS) 04:29 Load PDF Documents for RAG 07:24 HubSpot AI Agents Cheat Sheet 09:05 Chunking Documents for RAG 11:18 Create Embeddings & Build a FAISS Vector Database 13:32 Retrieve Relevant Documents with FAISS 15:26 Connect the Chat Model to Your RAG Pipeline 20:09 RAG Best Practices 20:16 Add Chat History to a RAG Chatbot 20:54 Give Your LLM Model Identity (a System Prompt) 21:27 Run RAG on a GPU with Ollama & FAISS 21:49 Load a Saved FAISS Vector Database 22:07 Hyperparameter Tuning for RAG 22:14 Final Thoughts - Is Elrond Guilty? #python #pythonprogramming #LLM #AI #RAG #LangChain #Ollama #FAISS #MachineLearning #ArtificialIntelligence #PythonTutorial