How do vector stores work in LangChain?

A vector store in LangChain stores text (documents, chunks) as high-dimensional embedding vectors so you can perform semantic similarity search — finding documents whose meaning is close to a query, even if the exact words don't match. Every vector store integrates an embedding model and a storage backend.

The standard workflow:

1. Embed documents with an embedding model (OpenAIEmbeddings, HuggingFaceEmbeddings, etc.)
2. Store the vectors in a vector database (FAISS, Chroma, Pinecone, Weaviate, PGVector)
3. At query time, embed the query and retrieve the k nearest vectors

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

# Create store from documents
vectorstore = FAISS.from_documents(docs, embeddings)

# Similarity search
results = vectorstore.similarity_search("How does LangChain work?", k=4)

# Use as a retriever in a chain
retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 5})

Search types: similarity returns the k most similar documents; mmr (Maximal Marginal Relevance) balances similarity with diversity to avoid returning near-duplicate chunks. Most production vector stores (Pinecone, Weaviate, Qdrant) support metadata filtering so you can scope searches to a subset of documents.

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