Getting responses in a batch is a way to efficiently send large numbers of non-latency sensitive embeddings requests. Different from getting online responses, where you are limited to one input request at a time, you can send a large number of LLM requests in a single batch request. Similar to how batch inference is done for tabular data in Vertex AI , you determine your output location, add your input, and your responses asynchronously populate into your output location.
Text embeddings models that support batch inferences
All stable versions of text embedding models support batch inferences with the exception of Gemini embeddings (gemini-embedding-001). Stable versions are fully supported for production environments. To view the full list of embedding models, see Embedding model and versions .
Prepare your inputs
The input for batch requests are a list of prompts that can either be stored in a BigQuery table or as a JSON Lines (JSONL) file in Cloud Storage. Each request can include up to 30,000 prompts.
JSONL example
This section shows examples of how to format JSONL input and output.
JSONL input example
{
"content"
:
"Give a short description of a machine learning model:"
}
{
"content"
:
"Best recipe for banana bread:"
}
JSONL output example
{
"instance"
:{
"content"
:
"Give..."
},
"predictions"
:
[{
"embeddings"
:{
"statistics"
:{
"token_count"
:
8
,
"truncated"
:
false
},
"values"
:[
0.2
,
....
]}}],
"status"
:
""
}
{
"instance"
:{
"content"
:
"Best..."
},
"predictions"
:
[{
"embeddings"
:{
"statistics"
:{
"token_count"
:
3
,
"truncated"
:
false
},
"values"
:[
0.1
,
....
]}}],
"status"
:
""
}
BigQuery example
This section shows examples of how to format BigQuery input and output.
BigQuery input example
This example shows a single column BigQuery table.
| content |
|---|
| "Give a short description of a machine learning model:" |
| "Best recipe for banana bread:" |
BigQuery output example
| content | predictions | status |
|---|---|---|
|
"Give a short description of a machine learning model:"
|
' [{ "embeddings" : { "statistics" :{ "token_count" : 8 , "truncated" : false }, "Values" :[ 0.1 , .... ] } } ] ' |
|
|
"Best recipe for banana bread:"
|
' [{ "embeddings" : { "statistics" :{ "token_count" : 3 , "truncated" : false }, "Values" :[ 0.2 , .... ] } } ] ' |
Request a batch response
Depending on the number of input items that you've submitted, a batch generation task can take some time to complete.
REST
To test a text prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.
Before using any of the request data, make the following replacements:
- PROJECT_ID : The ID of your Google Cloud project.
- BP_JOB_NAME : The job name.
- INPUT_URI : The input source URI. This is either a BigQuery table URI or a JSONL file URI in Cloud Storage.
- OUTPUT_URI : Output target URI.
HTTP method and URL:
POST https://us-central1-aiplatform.googleapis.com/v1/projects/ PROJECT_ID /locations/us-central1/batchPredictionJobs
Request JSON body:
{ "name": " BP_JOB_NAME ", "displayName": " BP_JOB_NAME ", "model": " publishers/google/models/textembedding-gecko ", "inputConfig": { "instancesFormat":"bigquery", "bigquerySource":{ "inputUri" : " INPUT_URI " } }, "outputConfig": { "predictionsFormat":"bigquery", "bigqueryDestination":{ "outputUri": " OUTPUT_URI " } } }
To send your request, choose one of these options:
curl
Save the request body in a file named request.json
,
and execute the following command:
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://us-central1-aiplatform.googleapis.com/v1/projects/ PROJECT_ID /locations/us-central1/batchPredictionJobs"
PowerShell
Save the request body in a file named request.json
,
and execute the following command:
$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }
Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/ PROJECT_ID /locations/us-central1/batchPredictionJobs" | Select-Object -Expand Content
You should receive a JSON response similar to the following:
{
"name": "projects/123456789012/locations/us-central1/batchPredictionJobs/1234567890123456789",
"displayName": "BP_sample_publisher_BQ_20230712_134650",
"model": "projects/{PROJECT_ID}/locations/us-central1/models/textembedding-gecko",
"inputConfig": {
"instancesFormat": "bigquery",
"bigquerySource": {
"inputUri": "bq://project_name.dataset_name.text_input"
}
},
"modelParameters": {},
"outputConfig": {
"predictionsFormat": "bigquery",
"bigqueryDestination": {
"outputUri": "bq://project_name.llm_dataset.embedding_out_BP_sample_publisher_BQ_20230712_134650"
}
},
"state": "JOB_STATE_PENDING",
"createTime": "2023-07-12T20:46:52.148717Z",
"updateTime": "2023-07-12T20:46:52.148717Z",
"labels": {
"owner": "sample_owner",
"product": "llm"
},
"modelVersionId": "1",
"modelMonitoringStatus": {}
}
The response includes a unique identifier for the batch job.
You can poll for the status of the batch job using
the BATCH_JOB_ID
until the job state
is JOB_STATE_SUCCEEDED
. For example:
curl \ -X GET \ -H "Authorization: Bearer $( gcloud auth print-access-token ) " \ -H "Content-Type: application/json" \ https://us-central1-aiplatform.googleapis.com/v1/projects/ PROJECT_ID /locations/us-central1/batchPredictionJobs/ BATCH_JOB_ID
Python
Install
pip install --upgrade google-genai
To learn more, see the SDK reference documentation .
Set environment variables to use the Gen AI SDK with Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT = GOOGLE_CLOUD_PROJECT export GOOGLE_CLOUD_LOCATION = us-central1 export GOOGLE_GENAI_USE_VERTEXAI = True
Go
Learn how to install or update the Go .
To learn more, see the SDK reference documentation .
Set environment variables to use the Gen AI SDK with Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT = GOOGLE_CLOUD_PROJECT export GOOGLE_CLOUD_LOCATION = us-central1 export GOOGLE_GENAI_USE_VERTEXAI = True
Node.js
Install
npm install @google/genai
To learn more, see the SDK reference documentation .
Set environment variables to use the Gen AI SDK with Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT = GOOGLE_CLOUD_PROJECT export GOOGLE_CLOUD_LOCATION = us-central1 export GOOGLE_GENAI_USE_VERTEXAI = True
Java
Learn how to install or update the Java .
To learn more, see the SDK reference documentation .
Set environment variables to use the Gen AI SDK with Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT = GOOGLE_CLOUD_PROJECT export GOOGLE_CLOUD_LOCATION = us-central1 export GOOGLE_GENAI_USE_VERTEXAI = True
Retrieve batch output
When a batch inference task is complete, the output is stored in the Cloud Storage bucket or BigQuery table that you specified in your request.
What's next
- Learn how to get text embeddings .
