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.
RAG
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.