LLM

A comprehensive introduction to LangChain explaining its core concepts, supported model types, and hands-on integration with Ollama LLMs, chat models, and embedding models.

Learn how to control and structure prompts in LangChain using PromptTemplate, message classes, and ChatPromptTemplate to build safe, consistent, and context-aware chat applications.

Learn how to enforce structured outputs in LangChain using Pydantic schemas, with_structured_output, and output parsers to build reliable, production-ready LLM applications.

Learn how to compose powerful LLM workflows in LangChain using chains, including sequential, parallel, and conditional execution patterns with real-world examples.

Understand how LangChain tools and agents work together. This guide covers built-in and custom tools, tool binding, tool calling, execution flow, and ReAct agents with real Python examples.