Google 会使用 AI 技术将内容翻译成您偏好的语言。AI 翻译可能包含错误。

获取批量文本嵌入推理结果

批量获取响应是一种高效发送大量非延迟时间敏感类嵌入请求的方法。与在线响应（一次只能有一个输入请求）不同，您可以在单个批量请求中发送多个嵌入请求。与对 Gemini Enterprise Agent Platform 中的表格数据执行批量推理的方式类似，您可以确定输出位置、添加输入，然后响应会异步填充到输出位置。

Gemini 嵌入批量预测（预览版）

Gemini 嵌入批量推理（预览版）引入了更新的输入和输出架构。每个批量作业最多支持 1,000,000 行，每个项目最多支持 10,000 个并发批量作业排入队列。

如需了解端点和可用位置，请参阅位置。

JSONL 输入示例

Gemini Embedding 001

gemini-embedding-001 模型支持为每行配置任务类型和标题。

{"key": "id_1", "request": {"content": {"parts": [{"text": "Hello World"}]}}, "embed_content_config":{"output_dimensionality": 768, "title": "some_title", "task_type": "RETRIEVAL_DOCUMENT"}}
{"key": "id_2", "request": {"content": {"parts": [{"text": "Hello World again"}]}}, "embed_content_config":{"output_dimensionality": 768, "title": "some_title_2", "task_type": "RETRIEVAL_DOCUMENT"}}

Gemini Embedding 2

gemini-embedding-2 模型要求将任务类型和标题直接内嵌在提示中。

{"key": "id_1", "request": {"content": {"parts": [{"text": "hello world again"}]}}, "embed_content_config":{"output_dimensionality": 768}}
{"key": "id_2", "request": {"content": {"parts": [{"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/image/benchmark.jpeg", "mimeType": "image/jpeg"}}]}}, "embed_content_config":{"output_dimensionality": 768}}

JSONL 输出示例

{"key": "id_1", "request": {...}, "response": {"tokenCount": "2", "embedding": {"values": [-0.015, 0.024, ...]}}}
{"key": "id_2", "request": {...}, "response": {"tokenCount": "3", "embedding": {"values": [-0.075, 0.031, ...]}}}

其他资源

如需了解 gemini-embedding-001 上可用的配置选项，请参阅文本嵌入指南。
如需了解 gemini-embedding-2 上可用的配置选项，请参阅多模态嵌入指南。

如需详细了解如何针对 Gemini 嵌入模型使用批量推理，请参阅批量预测 API 参考文档。

旧版嵌入批量预测

支持批量推理的文本嵌入模型

所有稳定版旧版文本嵌入模型都支持批量推理，并且在生产环境中完全受支持。如需查看完整的嵌入模型列表，请参阅嵌入模型和版本。

准备输入

批量请求的输入是可以存储在 BigQuery 表中或作为 JSON 行 (JSONL) 文件存储在 Cloud Storage 中的提示列表。每个请求最多可包含 30,000 项提示。

JSONL 示例

本部分介绍如何设置 JSONL 输入和输出的格式。

JSONL 输入示例

{"content":"Give a short description of a machine learning model:"}
{"content":"Best recipe for banana bread:"}

JSONL 输出示例

{"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 示例

本部分介绍如何设置 BigQuery 输入和输出的格式。

BigQuery 输入示例

此示例展示了一个单列 BigQuery 表。

内容
“提供机器学习模型的简短说明：”
“最适合香蕉面包的食谱：”

BigQuery 输出示例

内容	预测	status
“提供机器学习模型的简短说明：”	'[{"embeddings": { "statistics":{"token_count":8,"truncated":false}, "Values":[0.1,....] } } ]'
“最适合香蕉面包的食谱：”	'[{"embeddings": { "statistics":{"token_count":3,"truncated":false}, "Values":[0.2,....] } } ]'

请求批量响应

批量生成任务可能需要一些时间才能完成，具体取决于提交的输入数据项数量。

REST

如需使用 Agent Platform API 测试文本提示，请向发布方模型端点发送 POST 请求。

在使用任何请求数据之前，请先进行以下替换：

PROJECT_ID：您的 Google Cloud 项目的 ID。
BP_JOB_NAME：作业名称。
INPUT_URI：输入源 URI。这是 BigQuery 表 URI 或 Cloud Storage 中的 JSONL 文件 URI。
OUTPUT_URI：输出目标 URI。

HTTP 方法和网址：

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/batchPredictionJobs

请求 JSON 正文：

{
    "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"
    }
  }
}

如需发送请求，请选择以下方式之一：

curl

注意：以下命令假定您已使用您的用户账号通过运行 gcloud init 或 gcloud auth login 登录 gcloud CLI，或者使用了 Cloud Shell，这会使您自动登录 gcloud CLI。您可以运行 gcloud auth list 来检查当前活跃的账号。

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

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

注意：以下命令假定您已使用您的用户账号通过运行 gcloud init 或 gcloud auth login 登录 gcloud CLI。您可以运行 gcloud auth list 来检查当前活跃的账号。

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$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

您应该收到类似以下内容的 JSON 响应：

{
  "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": {}
}

响应包含批量作业的唯一标识符。您可以使用 BATCH_JOB_ID 轮询批量作业的状态，直到作业 state 为 JOB_STATE_SUCCEEDED。例如：

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

安装

pip install --upgrade google-genai

如需了解详情，请参阅 SDK 参考文档。

设置环境变量以将 Google Gen AI SDK 与 Vertex AI 搭配使用：

# 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_ENTERPRISE=True

import time

from google import genai
from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))
# TODO(developer): Update and un-comment below line
# output_uri = "gs://your-bucket/your-prefix"

# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.batches.Batches.create
job = client.batches.create(
    model="text-embedding-005",
    # Source link: https://storage.cloud.google.com/cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl
    src="gs://cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl",
    config=CreateBatchJobConfig(dest=output_uri),
)
print(f"Job name: {job.name}")
print(f"Job state: {job.state}")
# Example response:
# Job name: projects/.../locations/.../batchPredictionJobs/9876453210000000000
# Job state: JOB_STATE_PENDING

# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
completed_states = {
    JobState.JOB_STATE_SUCCEEDED,
    JobState.JOB_STATE_FAILED,
    JobState.JOB_STATE_CANCELLED,
    JobState.JOB_STATE_PAUSED,
}

while job.state not in completed_states:
    time.sleep(30)
    job = client.batches.get(name=job.name)
    print(f"Job state: {job.state}")
    if job.state == JobState.JOB_STATE_FAILED:
        print(f"Error: {job.error}")
        break

# Example response:
# Job state: JOB_STATE_PENDING
# Job state: JOB_STATE_RUNNING
# Job state: JOB_STATE_RUNNING
# ...
# Job state: JOB_STATE_SUCCEEDED

Go

了解如何安装或更新 Go。

如需了解详情，请参阅 SDK 参考文档。

设置环境变量以将 Google Gen AI SDK 与 Vertex AI 搭配使用：

# 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_ENTERPRISE=True

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

	"google.golang.org/genai"
)

// generateBatchEmbeddings shows how to run a batch embeddings prediction job.
func generateBatchEmbeddings(w io.Writer, outputURI string) error {
	// outputURI = "gs://your-bucket/your-prefix"
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}
	modelName := "text-embedding-005"
	// See the documentation: https://pkg.go.dev/google.golang.org/genai#Batches.Create
	job, err := client.Batches.Create(ctx,
		modelName,
		&genai.BatchJobSource{
			Format: "jsonl",
			// Source link: https://storage.cloud.google.com/cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl
			GCSURI: []string{"gs://cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl"},
		},
		&genai.CreateBatchJobConfig{
			Dest: &genai.BatchJobDestination{
				Format: "jsonl",
				GCSURI: outputURI,
			},
		},
	)
	if err != nil {
		return fmt.Errorf("failed to create batch job: %w", err)
	}

	fmt.Fprintf(w, "Job name: %s\n", job.Name)
	fmt.Fprintf(w, "Job state: %s\n", job.State)
	// Example response:
	//  Job name: projects/{PROJECT_ID}/locations/us-central1/batchPredictionJobs/9876453210000000000
	//  Job state: JOB_STATE_PENDING

	// See the documentation: https://pkg.go.dev/google.golang.org/genai#BatchJob
	completedStates := map[genai.JobState]bool{
		genai.JobStateSucceeded: true,
		genai.JobStateFailed:    true,
		genai.JobStateCancelled: true,
		genai.JobStatePaused:    true,
	}

	// Poll until job finishes
	for !completedStates[job.State] {
		time.Sleep(30 * time.Second)
		job, err = client.Batches.Get(ctx, job.Name, nil)
		if err != nil {
			return fmt.Errorf("failed to get batch job: %w", err)
		}
		fmt.Fprintf(w, "Job state: %s\n", job.State)

		if job.State == genai.JobStateFailed {
			fmt.Fprintf(w, "Error: %+v\n", job.Error)
			break
		}
	}

	//  Example response:
	//    Job state: JOB_STATE_PENDING
	//    Job state: JOB_STATE_RUNNING
	//    Job state: JOB_STATE_RUNNING
	//    ...
	//    Job state: JOB_STATE_SUCCEEDED

	return nil
}

Node.js

安装

npm install @google/genai

如需了解详情，请参阅 SDK 参考文档。

设置环境变量以将 Google Gen AI SDK 与 Vertex AI 搭配使用：

# 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_ENTERPRISE=True

const {GoogleGenAI} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION =
  process.env.GOOGLE_CLOUD_LOCATION || 'us-central1';
const OUTPUT_URI = 'gs://your-bucket/your-prefix';

async function runBatchPredictionJob(
  outputUri = OUTPUT_URI,
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
    httpOptions: {
      apiVersion: 'v1',
    },
  });

  // See the documentation: https://googleapis.github.io/js-genai/release_docs/classes/batches.Batches.html
  let job = await client.batches.create({
    model: 'text-embedding-005',
    // Source link: https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
    src: 'gs://cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl',
    config: {
      dest: outputUri,
    },
  });

  console.log(`Job name: ${job.name}`);
  console.log(`Job state: ${job.state}`);

  // Example response:
  //  Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
  //  Job state: JOB_STATE_PENDING

  const completedStates = new Set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_PAUSED',
  ]);

  while (!completedStates.has(job.state)) {
    await new Promise(resolve => setTimeout(resolve, 30000));
    job = await client.batches.get({name: job.name});
    console.log(`Job state: ${job.state}`);
  }

  // Example response:
  //  Job state: JOB_STATE_PENDING
  //  Job state: JOB_STATE_RUNNING
  //  Job state: JOB_STATE_RUNNING
  //  ...
  //  Job state: JOB_STATE_SUCCEEDED

  return job.state;
}

Java

了解如何安装或更新 Java。

如需了解详情，请参阅 SDK 参考文档。

设置环境变量以将 Google Gen AI SDK 与 Vertex AI 搭配使用：

# 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_ENTERPRISE=True


import static com.google.genai.types.JobState.Known.JOB_STATE_CANCELLED;
import static com.google.genai.types.JobState.Known.JOB_STATE_FAILED;
import static com.google.genai.types.JobState.Known.JOB_STATE_PAUSED;
import static com.google.genai.types.JobState.Known.JOB_STATE_SUCCEEDED;

import com.google.genai.Client;
import com.google.genai.types.BatchJob;
import com.google.genai.types.BatchJobDestination;
import com.google.genai.types.BatchJobSource;
import com.google.genai.types.CreateBatchJobConfig;
import com.google.genai.types.GetBatchJobConfig;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.JobState;
import java.util.EnumSet;
import java.util.Set;
import java.util.concurrent.TimeUnit;

public class BatchPredictionEmbeddingsWithGcs {

  public static void main(String[] args) throws InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "text-embedding-005";
    String outputGcsUri = "gs://your-bucket/your-prefix";
    createBatchJob(modelId, outputGcsUri);
  }

  // Creates a batch prediction job with embedding model and Google Cloud Storage.
  public static JobState createBatchJob(String modelId, String outputGcsUri)
      throws InterruptedException {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("us-central1")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      // See the documentation:
      // https://googleapis.github.io/java-genai/javadoc/com/google/genai/Batches.html
      BatchJobSource batchJobSource =
          BatchJobSource.builder()
              // Source link:
              // https://storage.cloud.google.com/cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl
              .gcsUri("gs://cloud-samples-data/generative-ai/embeddings/embeddings_input.jsonl")
              .format("jsonl")
              .build();

      CreateBatchJobConfig batchJobConfig =
          CreateBatchJobConfig.builder()
              .displayName("your-display-name")
              .dest(BatchJobDestination.builder().gcsUri(outputGcsUri).format("jsonl").build())
              .build();

      BatchJob batchJob = client.batches.create(modelId, batchJobSource, batchJobConfig);

      String jobName =
          batchJob.name().orElseThrow(() -> new IllegalStateException("Missing job name"));
      JobState jobState =
          batchJob.state().orElseThrow(() -> new IllegalStateException("Missing job state"));
      System.out.println("Job name: " + jobName);
      System.out.println("Job state: " + jobState);
      // Job name: projects/.../locations/.../batchPredictionJobs/6205497615459549184
      // Job state: JOB_STATE_PENDING

      // See the documentation:
      // https://googleapis.github.io/java-genai/javadoc/com/google/genai/types/BatchJob.html
      Set<JobState.Known> completedStates =
          EnumSet.of(JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_PAUSED);

      while (!completedStates.contains(jobState.knownEnum())) {
        TimeUnit.SECONDS.sleep(30);
        batchJob = client.batches.get(jobName, GetBatchJobConfig.builder().build());
        jobState =
            batchJob
                .state()
                .orElseThrow(() -> new IllegalStateException("Missing job state during polling"));
        System.out.println("Job state: " + jobState);
      }
      // Example response:
      // Job state: JOB_STATE_QUEUED
      // Job state: JOB_STATE_RUNNING
      // ...
      // Job state: JOB_STATE_SUCCEEDED
      return jobState;
    }
  }
}

检索批量输出

批量推理任务完成后，输出存储在您在请求中指定的 Cloud Storage 存储桶或 BigQuery 表中。

后续步骤

了解如何获取文本嵌入。

获取批量文本嵌入推理结果 使用集合让一切井井有条 根据您的偏好保存内容并对其进行分类。

Gemini 嵌入批量预测（预览版）

JSONL 输入示例

Gemini Embedding 001

Gemini Embedding 2

JSONL 输出示例

其他资源

旧版嵌入批量预测

支持批量推理的文本嵌入模型

准备输入

JSONL 示例

JSONL 输入示例

JSONL 输出示例

BigQuery 示例

BigQuery 输入示例

BigQuery 输出示例

请求批量响应

REST

curl

PowerShell

Python

安装

Go

Node.js

安装

Java

检索批量输出

后续步骤

获取批量文本嵌入推理结果