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Get Started with Vector Search via SQL
Learn how to quickly get started with Vector Search in TiDB using SQL statements to power your generative AI applications.

Get Started with Vector Search via SQL

TiDB extends MySQL syntax to support Vector Search and introduce new Vector data types and several vector functions.

This tutorial demonstrates how to get started with TiDB Vector Search just using SQL statements. You will learn how to use the MySQL command-line client to complete the following operations:

  • Connect to your TiDB cluster.
  • Create a vector table.
  • Store vector embeddings.
  • Perform vector search queries.

Warning:

The vector search feature is experimental. It is not recommended that you use it in the production environment. This feature might be changed without prior notice. If you find a bug, you can report an issue on GitHub.

Note:

The vector search feature is only available for TiDB Self-Managed clusters and TiDB Cloud Serverless clusters.

Prerequisites

To complete this tutorial, you need:

If you don't have a TiDB cluster, you can create one as follows:

If you don't have a TiDB cluster, you can create one as follows:

Get started

Step 1. Connect to the TiDB cluster

Connect to your TiDB cluster depending on the TiDB deployment option you've selected.

  1. Navigate to the Clusters page, and then click the name of your target cluster to go to its overview page.

  2. Click Connect in the upper-right corner. A connection dialog is displayed.

  3. In the connection dialog, select MySQL CLI from the Connect With drop-down list and keep the default setting of the Connection Type as Public.

  4. If you have not set a password yet, click Generate Password to generate a random password.

  5. Copy the connection command and paste it into your terminal. The following is an example for macOS:

    mysql -u '<prefix>.root' -h '<host>' -P 4000 -D 'test' --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/ssl/cert.pem -p'<password>'

After your TiDB Self-Managed cluster is started, execute your cluster connection command in the terminal.

The following is an example connection command for macOS:

mysql --comments --host 127.0.0.1 --port 4000 -u root

Step 2. Create a vector table

When creating a table, you can define a column as a vector column by specifying the VECTOR data type.

For example, to create a table embedded_documents with a three-dimensional VECTOR column, execute the following SQL statements using your MySQL CLI:

USE test;
CREATE TABLE embedded_documents (
    id        INT       PRIMARY KEY,
    -- Column to store the original content of the document.
    document  TEXT,
    -- Column to store the vector representation of the document.
    embedding VECTOR(3)
);

The expected output is as follows:

Query OK, 0 rows affected (0.27 sec)

Step 3. Insert vector embeddings to the table

Insert three documents with their vector embeddings into the embedded_documents table:

INSERT INTO embedded_documents
VALUES
    (1, 'dog', '[1,2,1]'),
    (2, 'fish', '[1,2,4]'),
    (3, 'tree', '[1,0,0]');

The expected output is as follows:

Query OK, 3 rows affected (0.15 sec)
Records: 3  Duplicates: 0  Warnings: 0

Note

This example simplifies the dimensions of the vector embeddings and uses only 3-dimensional vectors for demonstration purposes.

In real-world applications, embedding models often produce vector embeddings with hundreds or thousands of dimensions.

Step 4. Query the vector table

To verify that the documents have been inserted correctly, query the embedded_documents table:

SELECT * FROM embedded_documents;

The expected output is as follows:

+----+----------+-----------+
| id | document | embedding |
+----+----------+-----------+
|  1 | dog      | [1,2,1]   |
|  2 | fish     | [1,2,4]   |
|  3 | tree     | [1,0,0]   |
+----+----------+-----------+
3 rows in set (0.15 sec)

Step 5. Perform a vector search query

Similar to full-text search, users provide search terms to the application when using vector search.

In this example, the search term is "a swimming animal", and its corresponding vector embedding is assumed to be [1,2,3]. In practical applications, you need to use an embedding model to convert the user's search term into a vector embedding.

Execute the following SQL statement, and TiDB will identify the top three documents closest to [1,2,3] by calculating and sorting the cosine distances (vec_cosine_distance) between the vector embeddings in the table.

SELECT id, document, vec_cosine_distance(embedding, '[1,2,3]') AS distance
FROM embedded_documents
ORDER BY distance
LIMIT 3;

The expected output is as follows:

+----+----------+---------------------+
| id | document | distance            |
+----+----------+---------------------+
|  2 | fish     | 0.00853986601633272 |
|  1 | dog      | 0.12712843905603044 |
|  3 | tree     |  0.7327387580875756 |
+----+----------+---------------------+
3 rows in set (0.15 sec)

The three terms in the search results are sorted by their respective distance from the queried vector: the smaller the distance, the more relevant the corresponding document.

Therefore, according to the output, the swimming animal is most likely a fish, or a dog with a gift for swimming.

See also