将 Google 搜索工具与 Gemini 多模态模型搭配使用
使用集合让一切井井有条
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此示例演示了如何将 Google 搜索工具与 Gemini 搭配使用,根据搜索查询生成文本。
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[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],[],[],[],null,["# Using Google Search Tool with Gemini Multimodal\n\nThis sample demonstrates how to use the Google Search tool with Gemini to generate text based on a search query.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Grounding](/vertex-ai/generative-ai/docs/model-reference/grounding)\n- [Grounding with Google Search](/vertex-ai/generative-ai/docs/grounding/grounding-with-google-search)\n\nCode sample\n-----------\n\n### Go\n\n\nBefore trying this sample, follow the Go setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Go API\nreference documentation](/go/docs/reference/cloud.google.com/go/aiplatform/latest/apiv1).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import (\n \t\"context\"\n \t\"fmt\"\n \t\"io\"\n\n \tgenai \"google.golang.org/genai\"\n )\n\n // generateWithGoogleSearch shows how to generate text using Google Search.\n func generateWithGoogleSearch(w io.Writer) error {\n \tctx := context.Background()\n\n \tclient, err := genai.NewClient(ctx, &genai.ClientConfig{\n \t\tHTTPOptions: genai.HTTPOptions{APIVersion: \"v1\"},\n \t})\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to create genai client: %w\", err)\n \t}\n\n \tmodelName := \"gemini-2.5-flash\"\n \tcontents := []*genai.Content{\n \t\t{Parts: []*genai.Part{\n \t\t\t{Text: \"When is the next total solar eclipse in the United States?\"},\n \t\t},\n \t\t\tRole: \"user\"},\n \t}\n \tconfig := &genai.GenerateContentConfig{\n \t\tTools: []*genai.Tool{\n \t\t\t{GoogleSearch: &genai.GoogleSearch{}},\n \t\t},\n \t}\n\n \tresp, err := client.Models.GenerateContent(ctx, modelName, contents, config)\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to generate content: %w\", err)\n \t}\n\n \trespText := resp.Text()\n\n \tfmt.Fprintln(w, respText)\n\n \t// Example response:\n \t// The next total solar eclipse in the United States will occur on March 30, 2033, but it will only ...\n\n \treturn nil\n }\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google import genai\n from google.genai.types import (\n GenerateContentConfig,\n GoogleSearch,\n HttpOptions,\n Tool,\n )\n\n client = genai.Client(http_options=HttpOptions(api_version=\"v1\"))\n\n response = client.models.generate_content(\n model=\"gemini-2.5-flash\",\n contents=\"When is the next total solar eclipse in the United States?\",\n config=GenerateContentConfig(\n tools=[\n # Use Google Search Tool\n Tool(google_search=GoogleSearch())\n ],\n ),\n )\n\n print(response.text)\n # Example response:\n # 'The next total solar eclipse in the United States will occur on ...'\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]