Introduction to LangChain: Concepts, Models & LLM Integration
A comprehensive introduction to LangChain explaining its core concepts, supported model types, and hands-on integration with Ollama LLMs, chat models, and embedding models.
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Prompt Engineering in LangChain: Templates, Messages & ChatPromptTemplate
Learn how to control and structure prompts in LangChain using PromptTemplate, message classes, and ChatPromptTemplate to build safe, consistent, and context-aware chat applications.
Structured Output in LangChain: Pydantic Models & Output Parsers
Learn how to enforce structured outputs in LangChain using Pydantic schemas, with_structured_output, and output parsers to build reliable, production-ready LLM applications.
Chains in LangChain: Sequential, Parallel & Conditional Workflows
Learn how to compose powerful LLM workflows in LangChain using chains, including sequential, parallel, and conditional execution patterns with real-world examples.
Runnables in LangChain: Building Modular & Composable LLM Workflows
Learn how LangChain Runnables work and how to compose flexible, reusable, and scalable LLM workflows using sequences, parallel execution, branching logic, and LCEL syntax.
RAG in LangChain: Introduction and Loaders
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.
Text Splitters in LangChain: Efficiently Chunking Large Documents for LLMs
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.
Vector Stores in LangChain: Embeddings, Semantic Search & Similarity Retrieval
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.
Retrievers in LangChain: Data Sources, MMR & Contextual Compression
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.
Retrieval Augmented Generation (RAG) in LangChain: Architecture & Practical Example
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.