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.

Learn how LangChain Runnables work and how to compose flexible, reusable, and scalable LLM workflows using sequences, parallel execution, branching logic, and LCEL syntax.

Learn how RAG combines retrieval-based and generative models to create accurate, up-to-date, and scalable LLM workflows using LangChain’s document loaders, retrievers, and vector stores.

Learn how to split large texts into manageable chunks for LLMs using various splitting strategies, improving embeddings, retrieval, and summarization tasks with LangChain’s Text Splitters.

Learn how vector stores work in LangChain, from embeddings and similarity search to using Chroma for storing, querying, filtering, and managing high-dimensional vector data in RAG and recommendation systems.

Understand how retrievers work in LangChain, explore different retriever types, and learn how to use MMR and Contextual Compression to improve relevance, diversity, and efficiency in RAG pipelines.

Learn how Retrieval Augmented Generation (RAG) works in LangChain with clear architecture diagrams and a complete YouTube summarizer chatbot implementation using Chroma, Ollama, and runnable chains.