LangChain Tutorial - The Core Building Blocks - LLMs, JSON Output, RAGs, Tools and Observability

Master the fundamental building blocks of LangChain through this comprehensive 33-minute tutorial that breaks down complex AI agent development into essential engineering components. Learn to work with multiple LLM providers including OpenAI, Gemini, and Qwen3, while understanding token usage and cost management. Build a simple chatbot using messages, ChatPromptTemplate, and chat history management, then advance to structured JSON output generation using Pydantic models. Explore document processing by loading external PDF files and implementing a simple RAG (Retrieval-Augmented Generation) system with embeddings and vector stores for PDF chat functionality. Discover tool calling capabilities including tool documentation, execution, and ToolMessage handling, while gaining proficiency in debugging and tracing applications using MLFlow for observability. Follow along with hands-on notebook setup and step-by-step implementation of each core abstraction that makes LLM applications maintainable and scalable, preparing you to build more complex AI agents with a solid foundation in LangChain's ecosystem.