Principle:Langchain ai Langchain Retriever Integration
| Knowledge Sources | |
|---|---|
| Domains | Information_Retrieval, RAG |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
A bridge pattern that wraps a vector store as a LangChain Retriever for seamless composition in LCEL chains and RAG pipelines.
Description
The VectorStoreRetriever wraps a VectorStore instance and exposes it as a BaseRetriever, making it composable in LCEL chains. It supports configurable search types (similarity, MMR, similarity_score_threshold) and passes search parameters to the underlying vector store.
Usage
Convert a vector store to a retriever when building RAG chains that combine retrieval with LLM generation.
Theoretical Basis
# RAG pipeline pattern (not real code)
retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 5})
chain = retriever | format_docs | prompt | llm | output_parser
result = chain.invoke("user question")
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