Document Loaders
Every retrieval pipeline starts by turning some external source — a PDF, a web page, a database table — into LangChain's shared unit: the Document.
Text Splitters
Embedding models and context windows both have limits, so a loaded Document almost always needs to be broken into smaller pieces — chunks — before it can be indexed. Splitting is where retrieval quality is won or lost; get it wrong and no amount of prompt tuning fixes it downstream.
Embeddings
An embedding model turns text into a fixed-length vector of numbers positioned so that semantically similar text ends up nearby in that vector space. That's the entire mechanism retrieval is built on: instead of matching keywords, you compare vectors.
Retrievers
A retriever is the interface between a query and a candidate set of relevant chunks. Every vector store exposes one through as_retriever, which returns a VectorStoreRetriever — a Runnable, so it composes with | like anything else in this reference.
RAG Pipeline
Retrieval-Augmented Generation combines everything in this section into one flow: load documents, split them, embed them into a searchable index, then at query time retrieve the relevant chunks and hand them to a model alongside the question.