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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.

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  1. Create embeddings

Your data (documents, images, or structured data) must be converted to embeddings. We recommend creating embeddings using the OpenAI Embeddings API or using the open-source Python library SentenceTransformers. 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.

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  1. 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: 
CREATE TABLE images
(
	`_file` LowCardinality(String),
	`caption` String,
	`image_embedding` Array(Float32)
)
ENGINE = MergeTree;
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: 
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.

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Further Reading

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