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.
Prerequisites
To complete this tutorial, you need:
- MySQL command-line client (MySQL CLI) installed on your machine.
- A TiDB Cloud Serverless cluster. Follow creating a TiDB Cloud Serverless cluster to create your own TiDB Cloud cluster if you don't have one.
Get started
Step 1. Connect to the TiDB cluster
Navigate to the Clusters page, and then click the name of your target cluster to go to its overview page.
Click Connect in the upper-right corner. A connection dialog is displayed.
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.
If you have not set a password yet, click Generate Password to generate a random password.
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>'
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
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.