创建上下文缓存

创建上下文缓存，以减少包含相同词元数量输入的重复请求的费用。

代码示例

C#

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 C# 设置说明执行操作。 如需了解详情，请参阅 Vertex AI C# 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。

using Google.Cloud.AIPlatform.V1Beta1;
using Google.Protobuf.WellKnownTypes;
using System;
using System.Threading.Tasks;

public class CreateContextCache
{
    public async Task<CachedContentName> Create(string projectId)
    {
        var client = await new GenAiCacheServiceClientBuilder
        {
            Endpoint = "us-central1-aiplatform.googleapis.com"
        }.BuildAsync();

        var request = new CreateCachedContentRequest
        {
            Parent = $"projects/{projectId}/locations/us-central1",
            CachedContent = new CachedContent
            {
                Model = $"projects/{projectId}/locations/us-central1/publishers/google/models/gemini-2.0-flash-001",
                SystemInstruction = new Content
                {
                    Parts =
                    {
                        new Part { Text = "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 =
                {
                    new Content
                    {
                        Role = "USER",
                        Parts =
                        {
                            new Part { FileData = new() { MimeType = "application/pdf", FileUri = "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf" } },
                            new Part { FileData = new() { MimeType = "application/pdf", FileUri = "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf" } }
                        }
                    }
                },
                Ttl = Duration.FromTimeSpan(TimeSpan.FromMinutes(60))
            }
        };

        var cachedContent = await client.CreateCachedContentAsync(request);
        Console.WriteLine($"Created cache: {cachedContent.CachedContentName}");
        return cachedContent.CachedContentName;
    }
}

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"
	"fmt"
	"io"
	"time"

	"cloud.google.com/go/vertexai/genai"
)

// createContextCache shows how to create a cached content, and returns its name.
func createContextCache(w io.Writer, projectID, location, modelName string) (string, error) {
	// location := "us-central1"
	// modelName := "gemini-2.0-flash-001"
	ctx := context.Background()

	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.
    `

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return "", fmt.Errorf("unable to create client: %w", err)
	}
	defer client.Close()

	// These PDF are viewable at
	//   https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf
	//   https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf

	part1 := genai.FileData{
		MIMEType: "application/pdf",
		FileURI:  "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",
	}

	part2 := genai.FileData{
		MIMEType: "application/pdf",
		FileURI:  "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
	}

	content := &genai.CachedContent{
		Model: modelName,
		SystemInstruction: &genai.Content{
			Parts: []genai.Part{genai.Text(systemInstruction)},
		},
		Expiration: genai.ExpireTimeOrTTL{TTL: 60 * time.Minute},
		Contents: []*genai.Content{
			{
				Role:  "user",
				Parts: []genai.Part{part1, part2},
			},
		},
	}

	result, err := client.CreateCachedContent(ctx, content)
	if err != nil {
		return "", fmt.Errorf("CreateCachedContent: %w", err)
	}
	fmt.Fprint(w, result.Name)
	return result.Name, nil
}

后续步骤

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