Vector stores

One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search for you.

Most vector stores can also store metadata about embedded vectors and support filtering on that metadata before similarity search, allowing you more control over returned documents.

Vector stores can be converted to the retriever interface by doing:

vectorstore = MyVectorStore()
retriever = vectorstore.as_retriever()

For specifics on how to use vector stores, see the relevant how-to guides here.