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如需查看包含此代码示例的详细文档，请参阅以下内容：

创建上下文缓存

代码示例

Go

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Go 设置说明执行操作。如需了解详情，请参阅 Vertex AI Go API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭证。如需了解详情，请参阅为本地开发环境设置身份验证。

import (
	"context"
	"encoding/json"
	"fmt"
	"io"
	"time"

	genai "google.golang.org/genai"
)

// createContentCache shows how to create a content cache with an expiration parameter.
func createContentCache(w io.Writer) (string, error) {
	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 := "gemini-2.5-flash"

	systemInstruction := "You are an expert researcher. You always stick to the facts " +
		"in the sources provided, and never make up new facts. " +
		"Now look at these research papers, and answer the following questions."

	cacheContents := []*genai.Content{
		{
			Parts: []*genai.Part{
				{FileData: &genai.FileData{
					FileURI:  "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",
					MIMEType: "application/pdf",
				}},
				{FileData: &genai.FileData{
					FileURI:  "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
					MIMEType: "application/pdf",
				}},
			},
			Role: "user",
		},
	}
	config := &genai.CreateCachedContentConfig{
		Contents: cacheContents,
		SystemInstruction: &genai.Content{
			Parts: []*genai.Part{
				{Text: systemInstruction},
			},
		},
		DisplayName: "example-cache",
		TTL:         time.Duration(time.Duration.Seconds(86400)),
	}

	res, err := client.Caches.Create(ctx, modelName, config)
	if err != nil {
		return "", fmt.Errorf("failed to create content cache: %w", err)
	}

	cachedContent, err := json.MarshalIndent(res, "", "  ")
	if err != nil {
		return "", fmt.Errorf("failed to marshal cache info: %w", err)
	}

	// See the documentation: https://pkg.go.dev/google.golang.org/genai#CachedContent
	fmt.Fprintln(w, string(cachedContent))

	// Example response:
	// {
	//   "name": "projects/111111111111/locations/us-central1/cachedContents/1111111111111111111",
	//   "displayName": "example-cache",
	//   "model": "projects/111111111111/locations/us-central1/publishers/google/models/gemini-2.5-flash",
	//   "createTime": "2025-02-18T15:05:08.29468Z",
	//   "updateTime": "2025-02-18T15:05:08.29468Z",
	//   "expireTime": "2025-02-19T15:05:08.280828Z",
	//   "usageMetadata": {
	//     "imageCount": 167,
	//     "textCount": 153,
	//     "totalTokenCount": 43125
	//   }
	// }

	return res.Name, nil
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭证。如需了解详情，请参阅为本地开发环境设置身份验证。


import com.google.genai.Client;
import com.google.genai.types.CachedContent;
import com.google.genai.types.Content;
import com.google.genai.types.CreateCachedContentConfig;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.Part;
import java.time.Duration;
import java.util.Optional;

public class ContentCacheCreateWithTextGcsPdf {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash";
    contentCacheCreateWithTextGcsPdf(modelId);
  }

  // Creates a cached content using text and gcs pdfs files
  public static Optional<String> contentCacheCreateWithTextGcsPdf(String modelId) {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("global")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      // Set the system instruction
      Content systemInstruction =
          Content.fromParts(
              Part.fromText(
                  "You are an expert researcher. You always stick to the facts"
                      + " in the sources provided, and never make up new facts.\n"
                      + "Now look at these research papers, and answer the following questions."));

      // Set pdf files
      Content contents =
          Content.fromParts(
              Part.fromUri(
                  "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf", "application/pdf"),
              Part.fromUri(
                  "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf", "application/pdf"));

      // Configuration for cached content using pdfs files and text
      CreateCachedContentConfig config =
          CreateCachedContentConfig.builder()
              .systemInstruction(systemInstruction)
              .contents(contents)
              .displayName("example-cache")
              .ttl(Duration.ofSeconds(86400))
              .build();

      CachedContent cachedContent = client.caches.create(modelId, config);
      cachedContent.name().ifPresent(System.out::println);
      cachedContent.usageMetadata().ifPresent(System.out::println);
      // Example response:
      // projects/111111111111/locations/global/cachedContents/1111111111111111111
      // CachedContentUsageMetadata{audioDurationSeconds=Optional.empty, imageCount=Optional[167],
      // textCount=Optional[153], totalTokenCount=Optional[43125],
      // videoDurationSeconds=Optional.empty}
      return cachedContent.name();
    }
  }
}

Python

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭证。如需了解详情，请参阅为本地开发环境设置身份验证。

from google import genai
from google.genai.types import Content, CreateCachedContentConfig, HttpOptions, Part

client = genai.Client(http_options=HttpOptions(api_version="v1"))

system_instruction = """
You are an expert researcher. You always stick to the facts in the sources provided, and never make up new facts.
Now look at these research papers, and answer the following questions.
"""

contents = [
    Content(
        role="user",
        parts=[
            Part.from_uri(
                file_uri="gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",
                mime_type="application/pdf",
            ),
            Part.from_uri(
                file_uri="gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
                mime_type="application/pdf",
            ),
        ],
    )
]

content_cache = client.caches.create(
    model="gemini-2.5-flash",
    config=CreateCachedContentConfig(
        contents=contents,
        system_instruction=system_instruction,
        # (Optional) For enhanced security, the content cache can be encrypted using a Cloud KMS key
        # kms_key_name = "projects/.../locations/us-central1/keyRings/.../cryptoKeys/..."
        display_name="example-cache",
        ttl="86400s",
    ),
)

print(content_cache.name)
print(content_cache.usage_metadata)
# Example response:
#   projects/111111111111/locations/us-central1/cachedContents/1111111111111111111
#   CachedContentUsageMetadata(audio_duration_seconds=None, image_count=167,
#       text_count=153, total_token_count=43130, video_duration_seconds=None)

后续步骤

如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅Google Cloud 示例浏览器。

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深入探索

代码示例

Go

Java

Python

后续步骤

创建上下文缓存