Complete RAG Crash Course With Langchain In 2 Hours

This is a complete course on learning Generative ai and agentic with Langchain and Langgraph. We have included all the topics from RAG, vectorless rag, Deep agents, Guardrails, LLM Evaluation and LLM Gateways Techniques Github Links Langchain : https://github.com/krishnaik06/Langchain-V1-Crash-Course Langgraph: https://github.com/krishnaik06/Agentic-LanggraphCrash-course RAG: https://github.com/krishnaik06/RAG-Tutorials Vectorless RAG: https://github.com/krishnaik06/RAG-Tutorials/blob/main/PageIndex_Vectorless_RAG_CrashCourse%20(1).ipynb Deep Agents: https://drive.google.com/file/d/1SVjvgqvKfF-FPAIqpEZdKMQLhe13NLFD/view Guardrails : https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/updatedlangchain/langchain_guardrails_crash_course.ipynb LLM Evals : https://github.com/krishnaik06/RAG-Tutorials/blob/main/1-rag_evaluation.ipynb LLM Gateways: https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/llm_gateway_tutorial.ipynb Timestamp 00:00:00 Introduction 00:02:31 Langchain Course 02:35:12 Langraph Course 05:02:29 RAG Course 07:10:43 Vectorless RAG 08:02:11 Deep Agents 08:45:43 Guardrails 09:22:55 LLM Evaluation 10:30:25 LLM Gateways ------------------------------------------------------------------- Learn from us visit https://krishnaik.in/liveclasses

Learn to build, debug, optimize, and scale RAG systems for production. 🚀 Free Production AI Starter Kit: https://bit.ly/production-ai-pack This course teaches what tutorials skip: why 90% of RAG projects fail and how to fix them. 💻 Code, Parts 1-5: https://github.com/pdichone/production-course-main-code 💻 Code, Part 6: https://github.com/pdichone/fcc-production-rag-part-6 Paulo's channel: @vincibits ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Chapters ⭐️ 0:00:00 Intro 0:01:44 Full RAG Overview 0:08:27 Development Environment Setup 0:15:35 Document Loader - Overview 0:28:27 Document Processing Pipeline - RAG Indexing Pipeline 0:48:12 Embedding Dimensions - Deep Dive 1:01:05 Hands-on - Create a Vector DB Using Chroma 1:17:48 Similarity Search with Scores 1:24:32 Building a Basic RAG System 1:33:16 Debugging RAG Systems 1:53:46 Hybrid Search 1:13:49 Token Budgeting 2:21:10 Observability - Introduction 2:29:56 LangSmith Setup 2:37:56 RAG Optimization 3:12:58 Scaling RAG Systems 3:23:35 The Real Costs of Vector Search 3:33:17 Production Hosting 3:36:00 Supabase and PGVector - Set up and Introduction 4:04:41 Three Pillars of Production Visibility 4:16:11 Production Project 4:34:36 Set up the Security Layer 4:16:11 Set up the LangGraph Agent and the FastAPI API - Testing and LangSmith Observability Dashboard 5:27:46 Test the Security Layer 5:41:36 Security Checklist 6:06:09 Advanced RAG Topics - Long Context Models vs RAG 6:14:29 Contextual Retrieval 6:24:26 Late Chunking vs Early Chunking 6:42:04 Agentic RAG - Self-Correcting Retrieval 7:04:45 GraphRAG - Multi-hop Reasoning 7:24:28 Multimodal RAG - ColPali - Vision-Based Document RAG 7:34:45 Summary - Advanced RAG (Current State) 7:37:02 RAG Evolution - Overview 7:38:35 Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Euron - https://euron.one/ Course Link : https://euron.one/course/rag-masters For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Step into the world of Retrieval-Augmented Generation (RAG) pipelines with this complete guide! This video breaks down RAG concepts into easy-to-follow steps while showing you how to build real, working pipelines. Whether you’re just starting your AI journey or looking to strengthen your expertise in retrieval-based systems, this video is packed with practical insights and hands-on coding. What you’ll discover in this tutorial: - RAG Fundamentals: Learn how RAG works, from architecture to workflow. - Tools in Action: Explore vector databases, embeddings, LangChain, and other essentials. - Hands-On Projects: Apply RAG to solve real-world use cases. - Prompt Engineering: Learn how to optimize responses and handle private datasets. - Deployment Made Simple: Deploy RAG apps using Streamlit, Render, or AWS Elastic Beanstalk. Why this video is worth your time: - Beginner-friendly yet detailed enough for intermediate learners. - Hands-on coding and project-based learning. - Clear, structured explanations to help you actually build and deploy a RAG system. CHAPTERS: 00:00 - Announcements 01:36 - What is Retrieval Augmented Generation (RAG) 05:09 - How RAG Works 10:47 - Understanding Retrieval Augmented Generation 14:20 - Problems Solved by RAG 21:13 - Overview of RAG Pipeline 22:58 - RAG Pipeline Explained 27:56 - Generating Embeddings 38:35 - Preparing Your Own Data for RAG 41:02 - Creating Text Files for Data 49:53 - Creating Embeddings from Data 1:11:40 - Querying from Vector Database 1:14:47 - Final Operation: How RA Works 1:31:56 - Deploying Code in Streamlit 1:32:51 - Setting Up Application Directory 1:33:14 - Creating app.py File 1:37:00 - Developing app.py 1:41:34 - Creating Environment for Streamlit 1:46:28 - Testing Streamlit Application 1:50:04 - Deploying Application on Streamlit 1:50:34 - Deploying Application on Render 1:50:40 - Deploying Application on AWS Elastic Beanstalk 2:00:24 - Streamlit Deployment Hands-On 2:29:54 - Introduction to Document Loading 2:32:41 - Text Loader Overview 2:35:21 - Loading CSV Files 2:36:00 - Loading PDF Files 2:46:29 - Chunking and Splitting Data 3:05:14 - Lecture 2 Overview 3:10:28 - Cosine Similarity and Normalization 3:19:59 - Practical Cosine Similarity 3:26:01 - Introduction to Vector Databases 3:31:52 - Understanding Vector Representation 3:37:19 - Cosine Similarity Explained 4:11:28 - Step 2: Creating Embeddings 4:18:25 - Step 3: Creating Embedding Arrays 4:40:29 - Inserting Data into ChromaDB 4:43:14 - Querying ChromaDB 4:46:38 - Updating Records in ChromaDB 4:49:24 - Adding Metadata Information 4:56:22 - Persisting Collections in ChromaDB 5:03:20 - Pinecone Insert Operations 5:31:16 - Connecting to BayesVector 6:13:20 - Lecture 2: End to End ALM Chain 6:18:44 - Project Setup Process 6:24:08 - System Setup for ALM Chain 6:25:36 - Accessing LM and Embeddings 7:10:07 - Multi-Agent System with Self-Routing 7:12:05 - Accessing LLM in ALM 7:19:55 - Creating a Tool for ALM 7:22:08 - Creating an Agent in ALM 7:23:41 - Creating a Routing Agent 7:34:01 - Introduction to (LCEL) 8:03:02 - Setting the Entry Point in ALM 8:10:33 - Multi-Agent System Overview 8:16:08 - Creating Context Files 8:18:37 - Researcher Node in ALM 8:24:45 - Synthesizer Node Overview 8:27:20 - Classifier Node in ALM 8:28:45 - Finalizer Node Overview 8:42:01 - Understanding Prompting Techniques 8:43:20 - Crafting Effective Prompts 8:53:20 - Few-Shot Prompting Techniques 9:00:48 - Output Format Instructions 9:04:27 - Chain of Thought (COT) Prompting 9:09:24 - Explicit Anchoring Techniques 9:50:37 - Project Setup Process 9:54:41 - Obtaining URI API Key 9:57:48 - Storing and Retrieving Vectors 10:43:58 - Deploying the Chatbot Application 10:45:58 - Testing the Deployed Chatbot Roadmap for you : AI /Data Science Pro Level Expert Roadmap - https://euron.one/roadmap/c9361831-c806-45e2-b65c-c3f4c6cd2fa4 NLP expert Roadmap - https://euron.one/roadmap/300bc526-ed55-42e3-9072-43aca6c3ba4f Data Analytics / Business Analytics Expert Roadmap - https://euron.one/roadmap/920278e8-e3c0-4763-a135-ebed66074853 Big Data / Data Engineering Expert Roadmap - https://euron.one/roadmap/98c8db49-2eab-44b7-8fba-7f2b2575ec83 Computer Vision Roadmap - https://euron.one/roadmap/d8281277-5cfd-4498-bbff-4135aa178897 Deep Learning Roadmap - https://euron.one/roadmap/1495a7ba-4297-4cc9-8d68-5460cafb90ca Generative AI Roadmap - https://euron.one/roadmap/2380f611-7475-4343-b7f7-22b765710604 Machine Learning Expert Roadmap - https://euron.one/roadmap/ff514391-328e-4863-b810-0a5c5db6a170 Android- https://play.google.com/store/apps/details?id=com.euron.one&hl=en IOS - https://apps.apple.com/in/app/euron-your-learning-app/id6741360597

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version. Instructor Andrej was a founding member at OpenAI (2015) and then Sr. Director of AI at Tesla (2017-2022), and is now a founder at Eureka Labs, which is building an AI-native school. His goal in this video is to raise knowledge and understanding of the state of the art in AI, and empower people to effectively use the latest and greatest in their work. Find more at https://karpathy.ai/ and https://x.com/karpathy Chapters 00:00:00 introduction 00:01:00 pretraining data (internet) 00:07:47 tokenization 00:14:27 neural network I/O 00:20:11 neural network internals 00:26:01 inference 00:31:09 GPT-2: training and inference 00:42:52 Llama 3.1 base model inference 00:59:23 pretraining to post-training 01:01:06 post-training data (conversations) 01:20:32 hallucinations, tool use, knowledge/working memory 01:41:46 knowledge of self 01:46:56 models need tokens to think 02:01:11 tokenization revisited: models struggle with spelling 02:04:53 jagged intelligence 02:07:28 supervised finetuning to reinforcement learning 02:14:42 reinforcement learning 02:27:47 DeepSeek-R1 02:42:07 AlphaGo 02:48:26 reinforcement learning from human feedback (RLHF) 03:09:39 preview of things to come 03:15:15 keeping track of LLMs 03:18:34 where to find LLMs 03:21:46 grand summary Links - ChatGPT https://chatgpt.com/ - FineWeb (pretraining dataset): https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 - Tiktokenizer: https://tiktokenizer.vercel.app/ - Transformer Neural Net 3D visualizer: https://bbycroft.net/llm - llm.c Let's Reproduce GPT-2 https://github.com/karpathy/llm.c/discussions/677 - Llama 3 paper from Meta: https://arxiv.org/abs/2407.21783 - Hyperbolic, for inference of base model: https://app.hyperbolic.xyz/ - InstructGPT paper on SFT: https://arxiv.org/abs/2203.02155 - HuggingFace inference playground: https://huggingface.co/spaces/huggingface/inference-playground - DeepSeek-R1 paper: https://arxiv.org/abs/2501.12948 - TogetherAI Playground for open model inference: https://api.together.xyz/playground - AlphaGo paper (PDF): https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf - AlphaGo Move 37 video: https://www.youtube.com/watch?v=HT-UZkiOLv8 - LM Arena for model rankings: https://lmarena.ai/ - AI News Newsletter: https://buttondown.com/ainews - LMStudio for local inference https://lmstudio.ai/ - The visualization UI I was using in the video: https://excalidraw.com/ - The specific file of Excalidraw we built up: https://drive.google.com/file/d/1EZh5hNDzxMMy05uLhVryk061QYQGTxiN/view?usp=sharing - Discord channel for Eureka Labs and this video: https://discord.gg/3zy8kqD9Cp Educational Use Licensing This video is freely available for educational and internal training purposes. Educators, students, schools, universities, nonprofit institutions, businesses, and individual learners may use this content freely for lessons, courses, internal training, and learning activities, provided they do not engage in commercial resale, redistribution, external commercial use, or modify content to misrepresent its intent.

In this video, you will learn how to build a full Duolingo clone in React Native using a workflow called Practical Vibe Coding. AI has turned months of work into a single weekend; allowing you to ship a real apps without writing a single line of code. The app isn't the point; the workflow is. Let’s go. Stream: https://jsm.dev/lingua-stream Vision Agents: https://jsm.dev/lingua-vision Clerk: https://jsm.dev/lingua-clerk PostHog: https://jsm.dev/lingua-posthog CodeRabbit: https://jsm.dev/lingua-coderabbit 📘 Practical Vibe Coding Guide: https://jsm.dev/lingua-ebook 📋 Join the AI Course Waitlist: https://jsm.dev/lingua-ai 🎁 Video Kit (Code, Assets, Prompts): https://jsm.dev/lingua-kit ⭐ Join JS Mastery Pro: https://jsm.dev/lingua-jsm 🤖 Android Studio/Windows Guide: https://jsm.dev/lingua-android ➤ Links not working? Some regions may need a VPN. https://discord.com/invite/n6EdbFJ https://twitter.com/jsmasterypro https://instagram.com/javascriptmastery https://linkedin.com/company/javascriptmastery Business Inquiries: contact@jsmastery.pro Timestamps: 00:00:00 — Introduction 00:03:07 — Crash Course - AGENTS.md 00:15:13 — Crash Course - When the AI’s Knowledge Ends 00:23:47 — Setup 00:28:37 — AGENTS.md Setup 00:38:01 — Nativewind Setup 00:47:13 — Design Theme 00:58:26 — Onboarding Screen 01:08:58 — Auth Screen 01:15:45 — Clerk 01:27:04 — Content 01:33:27 — Language Screen 01:39:01 — State Management 01:45:16 — Bottom Tab Navigation 01:51:30 — Home UI 02:00:23 — PostHog 02:14:57 — Lesson UI 02:19:55 — Audio Lesson UI 02:25:25 — Stream 02:28:17 — XCode & Android Studio Setup 02:39:35 — Stream Integration 02:47:51 — Vision Agent 02:53:45 — AI Teacher 03:04:56 — AI Teacher Improvements 03:15:29 — Live Captions 03:28:58 — More PostHog 03:38:05 — End

An LLM is an incredibly powerful brain that knows everything about humanity and nothing about you or the software you run. This video walks through the four buzzwords that turn that raw brain into a system you can trust: agent harness, loop engineering, LLM Ops, and eval. No jargon, step by step, technical or not. You can build anything. You can learn anything. 💪 Timeline 0:00 Intro: the 4 AI agent buzzwords 0:42 What an AI agent run actually is 1:52 The memory system: procedural, semantic, episodic 3:37 What "harness" really means (the horse) 5:07 Storing and updating memory (databases, skills, summarizer agent) 8:28 Retrieval: RAG, SQL vs semantic search 10:00 Tool calling and why agents loop 10:52 Loop engineering and end-loop guardrails 13:21 A Claude Code hooks example 14:18 Eval and LLM Ops: why you need them 15:55 Tracing every run (Langfuse, LangSmith) 16:43 Evaluation: LLM as a judge 17:20 Diagnosing what broke 18:16 The gate: ship the fix or fix the bug 18:57 Zoom out: the full system 19:53 Wrap up Launch Full-Stack App Template: https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase My AI Agent Startup AutoManus.io is an AI sales lead manager for made-to-order product sales. We embed directly where conversations happen (WhatsApp, email, web chat) to capture inbound, automate follow‑ups, and kill CRM busywork; live with 4 pilots and 2 contracts in motion, pre‑seed backed by Character VC. Focused on US/UK/Germany, raising to expand engineering and scale deployments. ☕️ Buy me a coffee: https://buy.stripe.com/5kA176bA895ggog4gh - X: https://x.com/ShenSeanChen - Sean's Discord: https://discord.gg/tvECErKcFr - AutoManus Discord: https://discord.gg/5HhcNjCR - LinkedIn: https://linkedin.com/in/shen-sean-chen - Github Link: https://github.com/ShenSeanChen - Instagram: https://www.instagram.com/sean_ai_stories Sub Count when launched: 13,050 Top Github Repos - LaunchMVP NextJS,Supabase,Stripe (1015 stars): https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase - ReactNextJSTips (114 stars): https://github.com/ShenSeanChen/yt-react-nextjs-tips - DeepResearchAgentBackend (70 stars): https://github.com/ShenSeanChen/yt-DeepResearch-Backend - SupabaseTipsKanban (64 stars): https://github.com/ShenSeanChen/yt-supabase-tips-kanban - RAGVectorDB (56 stars): https://github.com/ShenSeanChen/yt-rag - KimiK2Claude (37 stars): https://github.com/ShenSeanChen/yt-kimi-k2-claude - DeepResearchAgentFrontend (33 stars): https://github.com/ShenSeanChen/yt-DeepResearch-Frontend - AgenticRAG (13 stars): https://github.com/ShenSeanChen/yt-agentic-rag You Can Build Anything Playlist (Full-Stack Dev Without CS Degrees) - Core7: 95% React/NextJS Use Cases https://www.youtube.com/watch?v=vZzFlAjz4rA&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=16 - Core6: 95% Supabase Use Cases https://www.youtube.com/watch?v=Pnoq2tIif3s&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=15 - Core5: StripeNextJSSupabase https://www.youtube.com/watch?v=ad1BxZufer8&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=8 - Core4: AuthSupabaseGoogle https://www.youtube.com/watch?v=AZAU3ClAWWE&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=6 - Core3: FastapiGCPPro https://www.youtube.com/watch?v=u3sfqpzzo-U&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=5 - Core2: SupabaseHero https://www.youtube.com/watch?v=V0kmgkyPsVQ&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=4 - Core1: LaunchAnApp https://www.youtube.com/watch?v=iQ2izvGG4tQ&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=3 - Launch WhereToMeet w/ GoogleMaps, Calendar, Supabase, Cursor: https://www.youtube.com/watch?v=5xMjX1QxGUY&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=2 - Launch EncourageMe w/ Cursor, Supabase, Vercel https://www.youtube.com/watch?v=hnvL4xa2DHA&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR