Generate text embeddings by using the ML.GENERATE_EMBEDDING function

Console

1. In the Google Cloud console, go to the BigQuerypage.

   Go to the BigQuery page

2. In the Explorerpane, click your project name.

3. Click more_vert View actions > Create dataset.

4. On the Create datasetpage, do the following:

   • For Dataset ID, type a name for the dataset.

   • For Location type, select a location for the dataset.

   • Click Create dataset.

bq

1. To create a new dataset, use the bq mk command with the --location flag:

   bq --location= LOCATION 
   mk -d DATASET_ID 
   

   Replace the following:

   • LOCATION : the dataset's location .
   • DATASET_ID is the ID of the dataset that you're creating.

2. Confirm that the dataset was created:

   bq  
   ls

Console

1. Go to the BigQuerypage.

   Go to BigQuery

2. In the Explorerpane, click add Add data:

   

   The Add datadialog opens.

3. In the Filter Bypane, in the Data Source Typesection, select Business Applications.

   Alternatively, in the Search for data sourcesfield, you can enter Vertex AI .

4. In the Featured data sourcessection, click Vertex AI.

5. Click the Vertex AI Models: BigQuery Federationsolution card.

6. In the Connection typelist, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).

7. In the Connection IDfield, enter a name for your connection.

8. Click Create connection.

9. Click Go to connection.

10. In the Connection infopane, copy the service account ID for use in a later step.

bq

1. In a command-line environment, create a connection:

   bq  
   mk  
   --connection  
   --location = 
    REGION 
     
   --project_id = 
    PROJECT_ID 
     
    \ 
     
   --connection_type = 
   CLOUD_RESOURCE  
    CONNECTION_ID 
   

   The --project_id parameter overrides the default project.

   Replace the following:

   • REGION : your connection region
   • PROJECT_ID : your Google Cloud project ID
   • CONNECTION_ID : an ID for your connection

   When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

   Troubleshooting: If you get the following connection error, update the Google Cloud SDK :

   Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...

2. Retrieve and copy the service account ID for use in a later step:

   bq  
   show  
   --connection  
    PROJECT_ID 
   . REGION 
   . CONNECTION_ID 
   

   The output is similar to the following:

   name                          properties
   1234. REGION 
   . CONNECTION_ID 
   {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}

Terraform

Use the google_bigquery_connection resource.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

The following example creates a Cloud resource connection named my_cloud_resource_connection in the US region:

 # This queries the provider for project information.
data "google_project" "default" {}

# This creates a cloud resource connection in the US region named my_cloud_resource_connection.
# Note: The cloud resource nested object has only one output field - serviceAccountId.
resource "google_bigquery_connection" "default" {
  connection_id = "my_cloud_resource_connection"
  project       = data.google_project.default.project_id
  location      = "US"
  cloud_resource {}
} 

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell

1. Launch Cloud Shell .

2. Set the default Google Cloud project where you want to apply your Terraform configurations.

   You only need to run this command once per project, and you can run it in any directory.

   export GOOGLE_CLOUD_PROJECT= PROJECT_ID 
   

   Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module ).

1. In Cloud Shell , create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf . In this tutorial, the file is referred to as main.tf .

   mkdir DIRECTORY 
   && cd DIRECTORY 
   && touch main.tf

2. If you are following a tutorial, you can copy the sample code in each section or step.

   Copy the sample code into the newly created main.tf .

   Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

3. Review and modify the sample parameters to apply to your environment.
4. Save your changes.
5. Initialize Terraform. You only need to do this once per directory.

   terraform init

   Optionally, to use the latest Google provider version, include the -upgrade option:

   terraform init -upgrade

Apply the changes

1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations:

   terraform plan

   Make corrections to the configuration as necessary.

2. Apply the Terraform configuration by running the following command and entering yes at the prompt:

   terraform apply

   Wait until Terraform displays the "Apply complete!" message.

3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.

Console

1. Go to the IAM & Adminpage.

   Go to IAM & Admin

2. Click person_add Grant access.

   The Add principalsdialog opens.

3. In the New principalsfield, enter the service account ID that you copied earlier.

4. In the Select a rolefield, select Vertex AI, and then select Vertex AI User.

5. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command :

gcloud projects add-iam-policy-binding ' PROJECT_NUMBER 
' --member='serviceAccount: MEMBER 
' --role='roles/aiplatform.user' --condition=None

Replace the following:

• PROJECT_NUMBER : your project number
• MEMBER : the service account ID that you copied earlier

Vertex AI text

Generate text embeddings by using a remote model over a Vertex AI text embedding model:

 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 ` PROJECT_ID 
. DATASET_ID 
. MODEL_NAME 
` 
 , 
  
 { 
 TABLE 
  
  PROJECT_ID 
 
 . 
  DATASET_ID 
 
 . 
  TABLE_NAME 
 
  
 | 
  
 ( 
  CONTENT_QUERY 
 
 ) 
 } 
 , 
  
 STRUCT 
 ( 
  FLATTEN_JSON 
 
  
 AS 
  
 flatten_json_output 
 , 
  
  TASK_TYPE 
 
  
 AS 
  
 task_type 
 , 
  
  OUTPUT_DIMENSIONALITY 
 
  
 AS 
  
 output_dimensionality 
 ) 
 ); 

Replace the following:

• PROJECT_ID : your project ID.
• DATASET_ID : the ID of the dataset that contains the model.
• MODEL_NAME : the name of the remote model over an embedding model.
• TABLE_NAME : the name of the table that contains the text to embed. This table must have a column that's named content , or you can use an alias to use a differently named column.
• CONTENT_QUERY : a query whose result contains a STRING column called content .
• FLATTEN_JSON : a BOOL value that indicates whether to parse the embedding into a separate column. The default value is TRUE .
• TASK_TYPE : a STRING literal that specifies the intended downstream application to help the model produce better quality embeddings. TASK_TYPE accepts the following values:
  • RETRIEVAL_QUERY : specifies that the given text is a query in a search or retrieval setting.
  • RETRIEVAL_DOCUMENT : specifies that the given text is a document in a search or retrieval setting.

    When using this task type, it is helpful to include the document title in the query statement in order to improve embedding quality. The document title must be in a column either named title or aliased as title , for example:

      
     SELECT 
      
     * 
      
     FROM 
      
     ML 
     . 
     GENERATE_EMBEDDING 
     ( 
      
     MODEL 
      
      mydataset 
     . 
     embedding_model 
     
     , 
      
     ( 
     SELECT 
      
     abstract 
      
     as 
      
     content 
     , 
      
     header 
      
     as 
      
     title 
     , 
      
     publication_number 
      
     FROM 
      
      mydataset 
     . 
     publications 
     
     ), 
      
     STRUCT 
     ( 
     TRUE 
      
     AS 
      
     flatten_json_output 
     , 
      
     'RETRIEVAL_DOCUMENT' 
      
     as 
      
     task_type 
     ) 
      
     ); 
      
    

    Specifying the title column in the input query populates the title field of the request body sent to the model. If you specify a title value when using any other task type, that input is ignored and has no effect on the embedding results.

  • SEMANTIC_SIMILARITY : specifies that the given text will be used for Semantic Textual Similarity (STS).
  • CLASSIFICATION : specifies that the embeddings will be used for classification.
  • CLUSTERING : specifies that the embeddings will be used for clustering.
  • QUESTION_ANSWERING : specifies that the embeddings will be used for question answering.
  • FACT_VERIFICATION : specifies that the embeddings will be used for fact verification.
  • CODE_RETRIEVAL_QUERY : specifies that the embeddings will be used for code retrieval.
• OUTPUT_DIMENSIONALITY : an INT64 value that specifies the number of dimensions to use when generating embeddings. For example, if you specify 256 AS output_dimensionality , then the ml_generate_embedding_result output column contains 256 embeddings for each input value.

  For remote models over gemini-embedding-001 models, the OUTPUT_DIMENSIONALITY value must be in the range [1, 3072] . The default value is 3072 . For remote models over text-embedding or text-multilingual-embedding models, the OUTPUT_DIMENSIONALITY value must be in the range [1, 768] . The default value is 768 .

  If you are using a remote model over a text-embedding model, the text-embedding model version must be text-embedding-004 or later. If you are using a remote model over a text-multilingual-embedding model, the text-multilingual-embedding model version must be text-multilingual-embedding-002 or later.

Example: embed text in a table

The following example shows a request to embed the content column of the text_data table:

 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 `mydataset.embedding_model` 
 , 
  
 TABLE 
  
 mydataset 
 . 
 text_data 
 , 
  
 STRUCT 
 ( 
 TRUE 
  
 AS 
  
 flatten_json_output 
 , 
  
 'CLASSIFICATION' 
  
 AS 
  
 task_type 
 ) 
  
 ); 

Open text

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

Generate text embeddings by using a remote model over an open embedding model:

 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 ` PROJECT_ID 
. DATASET_ID 
. MODEL_NAME 
` 
 , 
  
 { 
 TABLE 
  
  PROJECT_ID 
 
 . 
  DATASET_ID 
 
 . 
  TABLE_NAME 
 
  
 | 
  
 ( 
  CONTENT_QUERY 
 
 ) 
 } 
 , 
  
 STRUCT 
 ( 
  FLATTEN_JSON 
 
  
 AS 
  
 flatten_json_output 
 ) 
 ); 

Replace the following:

• PROJECT_ID : your project ID.
• DATASET_ID : the ID of the dataset that contains the model.
• MODEL_NAME : the name of the remote model over an embedding model.
• TABLE_NAME : the name of the table that contains the text to embed. This table must have a column that's named content , or you can use an alias to use a differently named column.
• CONTENT_QUERY : a query whose result contains a STRING column called content .
• FLATTEN_JSON : a BOOL value that indicates whether to parse the embedding into a separate column. The default value is TRUE .

Vertex AI multimodal

Generate text embeddings by using a remote model over a Vertex AI multimodal embedding model:

 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 ` PROJECT_ID 
. DATASET_ID 
. MODEL_NAME 
` 
 , 
  
 { 
 TABLE 
  
  PROJECT_ID 
 
 . 
  DATASET_ID 
 
 . 
  TABLE_NAME 
 
  
 | 
  
 ( 
  CONTENT_QUERY 
 
 ) 
 } 
 , 
  
 STRUCT 
 ( 
  FLATTEN_JSON 
 
  
 AS 
  
 flatten_json_output， 
  
  OUTPUT_DIMENSIONALITY 
 
  
 AS 
  
 output_dimensionality 
 ) 
 ); 

Replace the following:

• PROJECT_ID : your project ID.
• DATASET_ID : the ID of the dataset that contains the model.
• MODEL_NAME : the name of the remote model over a multimodalembedding@001 model.
• TABLE_NAME : the name of the table that contains the text to embed. This table must have a column that's named content , or you can use an alias to use a differently named column.
• CONTENT_QUERY : a query whose result contains a STRING column called content .
• FLATTEN_JSON : a BOOL that indicates whether to parse the embedding into a separate column. The default value is TRUE .
• OUTPUT_DIMENSIONALITY : an INT64 value that specifies the number of dimensions to use when generating embeddings. Valid values are 128 , 256 , 512 , and 1408 . The default value is 1408 . For example, if you specify 256 AS output_dimensionality , then the ml_generate_embedding_result output column contains 256 embeddings for each input value.

Example: use embeddings to rank semantic similarity

The following example embeds a collection of movie reviews and orders them by cosine distance to the review "This movie was average" using the VECTOR_SEARCH function . A smaller distance indicates more semantic similarity.

For more information about vector search and vector index, see Introduction to vector search .

 CREATE 
  
 TEMPORARY 
  
 TABLE 
  
 movie_review_embeddings 
  
 AS 
  
 ( 
  
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 `bqml_tutorial.embedding_model` 
 , 
  
 ( 
  
 SELECT 
  
 "This movie was fantastic" 
  
 AS 
  
 content 
  
 UNION 
  
 ALL 
  
 SELECT 
  
 "This was the best movie I've ever seen!!" 
  
 AS 
  
 content 
  
 UNION 
  
 ALL 
  
 SELECT 
  
 "This movie was just okay..." 
  
 AS 
  
 content 
  
 UNION 
  
 ALL 
  
 SELECT 
  
 "This movie was terrible." 
  
 AS 
  
 content 
  
 ), 
  
 STRUCT 
 ( 
 TRUE 
  
 AS 
  
 flatten_json_output 
 ) 
  
 ) 
 ); 
 WITH 
  
 average_review_embedding 
  
 AS 
  
 ( 
  
 SELECT 
  
 ml_generate_embedding_result 
  
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 `bqml_tutorial.embedding_model` 
 , 
  
 ( 
 SELECT 
  
 "This movie was average" 
  
 AS 
  
 content 
 ), 
  
 STRUCT 
 ( 
 TRUE 
  
 AS 
  
 flatten_json_output 
 ) 
  
 ) 
 ) 
 SELECT 
  
 base 
 . 
 content 
  
 AS 
  
 content 
 , 
  
 distance 
  
 AS 
  
 distance_to_average_review 
 FROM 
  
 VECTOR_SEARCH 
 ( 
  
 TABLE 
  
 movie_review_embeddings 
 , 
  
 "ml_generate_embedding_result" 
 , 
  
 ( 
 SELECT 
  
 ml_generate_embedding_result 
  
 FROM 
  
 average_review_embedding 
 ), 
  
 distance_type 
 => 
 "COSINE" 
 , 
  
 top_k 
 =>- 
 1 
  
 ) 
 ORDER 
  
 BY 
  
 distance_to_average_review 
 ; 

The result is the following:

+------------------------------------------+----------------------------+
| content                                  | distance_to_average_review |
+------------------------------------------+----------------------------+
| This movie was just okay...              | 0.062789813467745592       |
| This movie was fantastic                 |  0.18579561313064263       |
| This movie was terrible.                 |  0.35707466240930985       |
| This was the best movie I've ever seen!! |  0.41844932504542975       |
+------------------------------------------+----------------------------+