Generate vector embeddings with model endpoint management

MySQL | PostgreSQL | SQL Server

This page describes how to register an AI model endpoint and generating vector embeddings with model endpoint management. To use AI models in production environments, see Build generative AI applications using Cloud SQL and Work with vector embeddings.

After the model endpoints are added and registered in model endpoint management, you can reference them using the model ID to generate embeddings.

Before you begin

Make sure that you complete the following actions:

Register your model endpoint with model endpoint management. For more information, see Register and call remote AI models using model endpoint management.
Create or update your Cloud SQL instance so that the instance can integrate with Vertex AI. For more information, see Enable database integration with Vertex AI.

Generate embeddings

Use the google_ml.embedding() SQL function to call the registered model endpoint with the text embedding model type to generate embeddings.

To call the model and generate embeddings, use the following SQL query:

SELECT
  google_ml.embedding(
    model_id => 'MODEL_ID',
    content => 'CONTENT');

Replace the following:

MODEL_ID: the model ID you defined when registering the model endpoint
CONTENT: the text to translate into a vector embedding

Examples

Some examples for generating embeddings using registered model endpoints are listed in this section.

Text embedding models with built-in support

To generate embeddings for a registered textembedding-gecko@002 model endpoint, run the following statement:

    SELECT
      google_ml.embedding(
        model_id => 'textembedding-gecko@002',
        content => 'Cloud SQL is a managed, cloud-hosted SQL database service');