Mastered LangChain LLMs with Code | LangChain Full Course 2025

Video Description

GitHub: https://github.com/Sumit-Kumar-Dash/Langchain-Tutorial/tree/main Langchain Tutorial: https://www.youtube.com/watch?v=xLjyeeCNHrI&list=PLAgxe7DpTXmei0IV7-u9Zt9aoWP3eAHRg&index=1&t=3s&pp=gAQBiAQB Welcome to Part 3 of LangChain Tutorial — we dive deep into how LangChain handles LLMs (Text Completion Models) — from string-in, string-out to modern Chat Models like OpenAI and Gemini 💻 Complete code walkthrough included to help you implement everything step-by-step. 🔥LinkedIn: https://www.linkedin.com/in/sumit-kumar-dash-315378140/ 🔗 Related Playlists : Generative AI Full Course 2025: https://www.youtube.com/watch?v=3uMQnWCIkDg&list=PLAgxe7DpTXmdwTd1m6em5xeFCcUN6tvWm&index=1&t=505s&pp=gAQBiAQB Official Data Scientist Roadmap 2025: https://www.youtube.com/watch?v=BW49QAfLunQ&list=PLAgxe7DpTXmdAbTjrvIdt63LVQWP3j9Tx&index=1&pp=gAQBiAQB Crack Data Science Interview: https://www.youtube.com/playlist?list=PLAgxe7DpTXmcd3VEwRwEd_MQioAaXXcMN Timestamps: 00:00 – Welcome! What You’ll Learn About LangChain LLMs 00:25 – What Exactly Are LLMs? (Simple Breakdown) 01:30 – Types of LLMs You Must Know 🔍 02:50 – Open-Source LLMs Explained (Ollama, Mistral & More) 03:45 – What Are Training Parameters in LLMs? 06:05 – How Hyperparameters Shape Model Performance 06:50 – Learning Rate Demystified — The Speed of Learning 07:50 – Batch Size: Why It Can Make or Break Training 10:15 – Epochs Explained Simply (Stop Overfitting Fast) 11:40 – What Are Inference Parameters in LLMs? 12:40 – How Temperature Controls Creativity in Models 🔥 13:40 – Max Tokens: The Secret Behind Model Output Length 14:33 – top_p vs top_k — Which One Should You Use? 16:35 – Quantization Explained: Faster, Smaller, Smarter Models ⚡ 18:10 – Closed-Source LLMs: Why They Still Dominate 19:10 – Cloud-Based LLMs: When and Why to Use Them 20:30 – LangChain LLM Implementation Overview 22:17 – Full Coding Demo Starts Here 22:30 – BaseLLM vs ChatLLM: The Real Difference 24:50 – How to Securely Pass API Keys in LangChain 🔑 25:30 – Using Environment Variables the Right Way 30:05 – getpass() Method: Hide Your API Keys Like a Pro 31:20 – OpenAI Models Overview 33:50 – OpenAI LLMs in Action 35:55 – OpenAI ChatModels Explained 37:15 – Understanding OpenAI Metadata 38:10 – How to Adjust Temperature in OpenAI Models 39:15 – Gemini Models (Google’s LLM Explained) 39:55 – What Is Google Generative AI LLM? 40:18 – ChatGoogleGenerativeAI in LangChain 40:49 – Gemini Metadata Deep Dive 41:15 – Advanced Langchain Inferencing Parameters 43:08 – How to Initialize Any LLM in One Line ⚙️ 44:25 – Using LangChain with Open-Source Models 45:05 – Hugging Face Endpoints + LangChain Integration 47:55 – Hugging Face Pipelines in Action 🚀 52:50 – Quantized Model Implementation for Speed 53:20 – Final Takeaways & What’s Coming Next 🎯 #langchain #python #llm #generativeai #datascience 👉 Don’t forget to like, share & subscribe 🔔 SKD Neuron that breakdown to help others avoid the same pitfalls!