LangChain Quickstart with Local LLM | Ollama, Pydantic Structured Output, Tool Use, MLflow Tracing

LangChain hit version 1 and now has easier to use and streamlined API. In this video, you'll learn how to swap between LLM providers without rewriting code. How to use Pydantic to get valid JSON output every time and create/execute custom tools. LangChain docs: https://docs.langchain.com/ AI Academy: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 0:00 - The "Fragile AI Script" Problem 1:22 - Local Setup with Ollama & uv 2:55 - LLM Abstraction (init_chat_model) 4:19 - Prompt Templates as Functions 6:14 - Structured Output with Pydantic 8:45 - Implementing Tool Calling 12:20 - Bonus Tip Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join