Previsão de texto em lote com o modelo Gemini

Fazer a previsão de texto em lote usando o modelo Gemini e retornar o local de saída.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

Exemplo de código

Go

Antes de testar esse exemplo, siga as instruções de configuração para Go no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Go.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

import (
	"context"
	"fmt"
	"io"
	"time"

	aiplatform "cloud.google.com/go/aiplatform/apiv1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1/aiplatformpb"

	"google.golang.org/api/option"
	"google.golang.org/protobuf/types/known/structpb"
)

// batchPredictGCS submits a batch prediction job using GCS data source as its input
func batchPredictGCS(w io.Writer, projectID, location string, inputURIs []string, outputURI string) error {
	// location := "us-central1"
	// inputURIs := []string{"gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"}
	// outputURI := "gs://<cloud-bucket-name>/<prefix-name>"
	modelName := "gemini-1.5-pro-002"
	jobName := "batch-predict-gcs-test-001"

	ctx := context.Background()
	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	client, err := aiplatform.NewJobClient(ctx, option.WithEndpoint(apiEndpoint))
	if err != nil {
		return fmt.Errorf("unable to create aiplatform client: %w", err)
	}
	defer client.Close()

	modelParameters, err := structpb.NewValue(map[string]interface{}{
		"temperature":     0.2,
		"maxOutputTokens": 200,
	})
	if err != nil {
		return fmt.Errorf("unable to convert model parameters to protobuf value: %w", err)
	}

	req := &aiplatformpb.CreateBatchPredictionJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		BatchPredictionJob: &aiplatformpb.BatchPredictionJob{
			DisplayName:     jobName,
			Model:           fmt.Sprintf("publishers/google/models/%s", modelName),
			ModelParameters: modelParameters,
			// Check the API reference for `BatchPredictionJob` for supported input and output formats:
			// https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.BatchPredictionJob
			InputConfig: &aiplatformpb.BatchPredictionJob_InputConfig{
				Source: &aiplatformpb.BatchPredictionJob_InputConfig_GcsSource{
					GcsSource: &aiplatformpb.GcsSource{
						Uris: inputURIs,
					},
				},
				InstancesFormat: "jsonl",
			},
			OutputConfig: &aiplatformpb.BatchPredictionJob_OutputConfig{
				Destination: &aiplatformpb.BatchPredictionJob_OutputConfig_GcsDestination{
					GcsDestination: &aiplatformpb.GcsDestination{
						OutputUriPrefix: outputURI,
					},
				},
				PredictionsFormat: "jsonl",
			},
		},
	}

	job, err := client.CreateBatchPredictionJob(ctx, req)
	if err != nil {
		return err
	}
	fullJobId := job.GetName()
	fmt.Fprintf(w, "submitted batch predict job for model %q\n", job.GetModel())
	fmt.Fprintf(w, "job id: %q\n", fullJobId)
	fmt.Fprintf(w, "job state: %s\n", job.GetState())
	// Example response:
	// submitted batch predict job for model "publishers/google/models/gemini-1.5-pro-002"
	// job id: "projects/.../locations/.../batchPredictionJobs/1234567890000000000"
	// job state: JOB_STATE_PENDING

	for {
		time.Sleep(5 * time.Second)

		job, err := client.GetBatchPredictionJob(ctx, &aiplatformpb.GetBatchPredictionJobRequest{
			Name: fullJobId,
		})
		if err != nil {
			return fmt.Errorf("error: couldn't get updated job state: %w", err)
		}

		if job.GetEndTime() != nil {
			fmt.Fprintf(w, "batch predict job finished with state %s\n", job.GetState())
			break
		} else {
			fmt.Fprintf(w, "batch predict job is running... job state is %s\n", job.GetState())
		}
	}

	return nil
}

Python

Antes de testar esse exemplo, siga as instruções de configuração para Python no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Python.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

import time
import vertexai

from vertexai.batch_prediction import BatchPredictionJob

# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"

# Initialize vertexai
vertexai.init(project=PROJECT_ID, location="us-central1")

input_uri = "gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"

# Submit a batch prediction job with Gemini model
batch_prediction_job = BatchPredictionJob.submit(
    source_model="gemini-1.5-flash-002",
    input_dataset=input_uri,
    output_uri_prefix=output_uri,
)

# Check job status
print(f"Job resource name: {batch_prediction_job.resource_name}")
print(f"Model resource name with the job: {batch_prediction_job.model_name}")
print(f"Job state: {batch_prediction_job.state.name}")

# Refresh the job until complete
while not batch_prediction_job.has_ended:
    time.sleep(5)
    batch_prediction_job.refresh()

# Check if the job succeeds
if batch_prediction_job.has_succeeded:
    print("Job succeeded!")
else:
    print(f"Job failed: {batch_prediction_job.error}")

# Check the location of the output
print(f"Job output location: {batch_prediction_job.output_location}")

# Example response:
#  Job output location: gs://your-bucket/gen-ai-batch-prediction/prediction-model-year-month-day-hour:minute:second.12345

A seguir

Para pesquisar e filtrar exemplos de código de outros Google Cloud produtos, consulte a pesquisa de exemplos de código doGoogle Cloud .