Generate vector embeddings with model endpoint management

Before you begin

Make sure that you have registered your model endpoint with Model endpoint management. For more information, see Register a model endpoint with model endpoint management

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 endpoint are listed in this section.

Text embedding models with built-in support

To generate embeddings for a registered text-embedding-005 model endpoint, run the following statement:

    SELECT
      google_ml.embedding(
        model_id => 'text-embedding-005',
        content => 'AlloyDB is a managed, cloud-hosted SQL database service');

To generate embeddings for a registered text-embedding-ada-002 model endpoint by OpenAI, run the following statement:

    SELECT
      google_ml.embedding(
        model_id => 'text-embedding-ada-002',
        content => 'e-mail spam');

To generate embeddings for a registered text-embedding-3-small or text-embedding-3-large model endpoints by OpenAI, run the following statement:

  SELECT
    google_ml.embedding(
      model_id => 'text-embedding-3-small',
      content => 'Vector embeddings in AI');