Generate vector embeddings with model endpoint management
Stay organized with collections
Save and categorize content based on your preferences.
This page describes how to register an AI model endpoint and generate 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 endpointCONTENT: 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');