This example uses the following table and index:

CREATE TABLE IF NOT EXISTS cycling.comments_vs (
  record_id timeuuid,
  id uuid,
  commenter text,
  comment text,
  comment_vector VECTOR <FLOAT, 5>,
  created_at timestamp,
  PRIMARY KEY (id, created_at)
  ON cycling.comments_vs(comment_vector) USING 'StorageAttachedIndex';

Simple query

To query data using Vector Search, use a SELECT query:

  • CQL

  • Result

SELECT * FROM cycling.comments_vs 
    ORDER BY comment_vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] 
    LIMIT 3;
 id                                   | created_at                      | comment                                | comment_vector               | commenter | record_id
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 | LATE RIDERS SHOULD NOT DELAY THE START | [0.9, 0.54, 0.12, 0.1, 0.95] |      Alex | 616e77e0-22a2-11ee-b99d-1f350647414a
 c7fceba0-c141-4207-9494-a29f9809de6f | 2017-02-17 08:43:20.234000+0000 |      Glad you ran the race in the rain | [0.3, 0.34, 0.2, 0.78, 0.25] |       Amy | 6170c1d0-22a2-11ee-b99d-1f350647414a
 c7fceba0-c141-4207-9494-a29f9809de6f | 2017-04-01 13:43:08.030000+0000 |                Last climb was a killer |   [0.3, 0.75, 0.2, 0.2, 0.5] |       Amy | 62105d30-22a2-11ee-b99d-1f350647414a

The limit has to be 1,000 or fewer.

Scrolling to the right on the results shows the comments from the table that most closely matched the embeddings used for the query.

Similarity query

To obtain the similarity calculation of the best scoring node closest to the query data as part of the results, use a modified SELECT query:

SELECT  comment, similarity_cosine(comment_vector, [0.2, 0.15, 0.3, 0.2, 0.05]) 
    FROM cycling.comments_vs
    ORDER BY comment_vector ANN OF [0.1, 0.15, 0.3, 0.12, 0.05] 
    LIMIT 3;
 comment                                       | system.similarity_cosine(comment_vector, [0.2, 0.15, 0.3, 0.2, 0.05])
             Second rest stop was out of water |                                                              0.949701
 The gift certificate for winning was the best |                                                               0.86062
 The gift certificate for winning was the best |                                                               0.86062

(3 rows)

The supported functions for this type of query are:

  • similarity_dot_product

  • similarity_cosine

  • similarity_euclidean

with the parameters of (<vector_column>, <embedding_value>). Both parameters represent vectors.

Vector Search utilizes Approximate Nearest Neighbor (ANN) that in most cases yields results almost as good as the exact match. The scaling is superior to Exact Nearest Neighbor (KNN).

Least-similar searches are not supported.

Vector Search works optimally on tables with no overwrites or deletions of the comment_vector column. For an comment_vector column with changes, expect slower search results.

The embeddings were randomly generated in this example. Generally, you would run both your source documents/contents through an embeddings generator, as well as the query you were asking to match. This example is simply to show the mechanics of how to use CQL to create vector search data objects.

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