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 prediction 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 predictions
All stable versions of text embedding models support batch predictions, except for textembedding-gecko-multilingual@001. Stable versions are versions which are no
longer in preview and are fully supported for production environments. To see the
full list of supported 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
Vertex AI SDK for Python
To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.
Retrieve batch output
When a batch prediction 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.