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On this page

#  Vector stores

info

Head to [ Integrations ](/docs/integrations/vectorstores/) for documentation
on built-in integrations with 3rd-party 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.

![Diagram illustrating the process of vector stores: 1. Load source data, 2.
Query vector store, 3. Retrieve &#39;most similar&#39;
results.](/assets/images/vector_stores-125d1675d58cfb46ce9054c9019fea72.jpg)

##  Get started  ​

This walkthrough showcases basic functionality related to vector stores. A key
part of working with vector stores is creating the vector to put in them,
which is usually created via embeddings. Therefore, it is recommended that you
familiarize yourself with the [ text embedding model
](/docs/modules/data_connection/text_embedding