> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-a804b3ad.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Can you use ClickHouse for vector search?

> Learn how to use ClickHouse for vector search, including storing embeddings and searching with distance functions like cosine similarity.

{frontMatter.description}

<h2 id="clickhouse-for-vector-search">
  ClickHouse for Vector Search!
</h2>

Yes, ClickHouse can perform vector search.

The main advantages of using ClickHouse for vector search compared to using more specialized vector databases include:

* Using ClickHouse's filtering and full-text search capabilities to refine your dataset before performing a search.
* Performing analytics on your datasets.
* Running a `JOIN` against your existing data.
* No need to manage yet another database and complicate your infrastructure.

Here is a quick tutorial on how to use ClickHouse for vector search.

<Steps>
  <Step title="Create embeddings">
    Your data (documents, images, or structured data) must be converted to *embeddings*. We recommend creating embeddings using the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) or using the open-source Python library [SentenceTransformers](https://www.sbert.net/).

    You can think of an embedding as a large array of floating-point numbers that represent your data. [Check out this guide from OpenAI to learn more about embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
  </Step>

  <Step title="Store the embeddings">
    Once you have generated embeddings, you need to store them in ClickHouse. Each embedding should be stored in a separate row and can include metadata for filtering, aggregations, or analytics. Here's an example of a table that can store images with captions:

    ```sql theme={null}
    CREATE TABLE images
    (
    	`_file` LowCardinality(String),
    	`caption` String,
    	`image_embedding` Array(Float32)
    )
    ENGINE = MergeTree;
    ```
  </Step>

  <Step title="Search for related embeddings">
    Let's say you want to search for pictures of dogs in your dataset. You can use a distance function like `cosineDistance` to take an embedding of a dog image and search for related images:

    ```sql theme={null}
    SELECT
        _file,
    	caption,
    	cosineDistance(
            -- An embedding of your "input" dog picture
            [0.5736801028251648, 0.2516217529773712, ...,  -0.6825592517852783],
            image_embedding
        ) AS score
    FROM images
    ORDER BY score ASC
    LIMIT 10
    ```

    This query returns the `_file` names and `caption` of the top 10 images most likely to be related to your provided dog image.
  </Step>
</Steps>

<h2 id="further-reading">
  Further reading
</h2>

To follow a more in-depth tutorial on vector search using ClickHouse, please see:

* [Exact and Approximate Vector Search](/reference/engines/table-engines/mergetree-family/annindexes)