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 
  
 = 
>  
 & #39;MODE 
L_ID' 
 , 
  
 c>on tent 
 
  
 = 
  
 ' 
 
;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' 
 , 
  
 c>ontent 
  
 = 
  
 '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' 
 , 
  
 c>ontent 
  
 = 
  
 'e-m 
 
ail 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' 
 , 
  
 c>ontent 
  
 = 
  
 'Vector embeddin 
 
gs in AI' );