ChatGoogle生成式AI

谷歌AI 提供了多种不同的聊天模型，包括强大的Gemini系列。有关最新模型、功能、上下文窗口等信息，请访问谷歌AI文档。这将帮助您开始使用 ChatGoogleGenerativeAI 聊天模型。有关所有 ChatGoogleGenerativeAI 功能和配置的详细文档，请访问 API 参考。

概述

集成详情

类	包	本地	可序列化	PY 支持	下载量	版本
ChatGoogleGenerativeAI	@langchain/google-genai	❌	✅	✅

模型功能

请参阅下表标题中的链接，了解如何使用特定功能。

工具调用	结构化输出	JSON模式	图像输入	音频输入	视频输入	令牌级流	令牌使用	Logprobs
✅	✅	❌	✅	✅	✅	✅	✅	❌

设置

您可以通过 @langchain/google-genai 集成包中的 ChatGoogleGenerativeAI 类访问 Google 的 gemini 和 gemini-vision 模型，以及其他 LangChain 中的生成模型。

您还可以通过LangChain VertexAI和VertexAI-web集成访问Google的gemini模型系列。点击此处阅读文档。

凭证

在此处获取API密钥：https://ai.google.dev/tutorials/setup 然后设置 GOOGLE_API_KEY 环境变量：

export GOOGLE_API_KEY="your-api-key"

如果您想获取模型调用的自动化跟踪，您也可以通过取消以下注释来设置您的 LangSmith API 密钥：

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

安装

LangChain ChatGoogleGenerativeAI 集成位于 @langchain/google-genai 包中：

npm install @langchain/google-genai @langchain/core

实例化

现在我们可以实例化我们的模型对象并生成聊天补全：

import { ChatGoogleGenerativeAI } from "@langchain/google-genai"

const llm = new ChatGoogleGenerativeAI({
    model: "gemini-1.5-pro",
    temperature: 0,
    maxRetries: 2,
    // other params...
})

调用

const aiMsg = await llm.invoke([
    [
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ],
    ["human", "I love programming."],
])
aiMsg

AIMessage {
  "content": "J'adore programmer. \n",
  "additional_kwargs": {
    "finishReason": "STOP",
    "index": 0,
    "safetyRatings": [
      {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HARASSMENT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "probability": "NEGLIGIBLE"
      }
    ]
  },
  "response_metadata": {
    "finishReason": "STOP",
    "index": 0,
    "safetyRatings": [
      {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HARASSMENT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "probability": "NEGLIGIBLE"
      }
    ]
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 21,
    "output_tokens": 5,
    "total_tokens": 26
  }
}

console.log(aiMsg.content)

J'adore programmer.

安全设置

Gemini模型具有默认的安全设置，可以被覆盖。如果您从模型接收到大量的“安全警告”，您可以尝试调整模型的安全设置属性。例如，要关闭危险内容的阻止，您可以导入@google/generative-ai包中的枚举，然后按照以下方式构建您的LLM：

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import { HarmBlockThreshold, HarmCategory } from "@google/generative-ai";

const llmWithSafetySettings = new ChatGoogleGenerativeAI({
  model: "gemini-1.5-pro",
  temperature: 0,
  safetySettings: [
    {
      category: HarmCategory.HARM_CATEGORY_HARASSMENT,
      threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    },
  ],
  // other params...
});

工具调用

使用 Google AI 的工具调用与使用其他模型进行工具调用大致相同如工具调用其他模型，但在模式上有一些限制。谷歌AI API不允许工具模式包含具有未知属性的对象。例如，以下Zod模式将引发错误： const invalidSchema = z.object({ properties: z.record(z.unknown()) }); 并且 const invalidSchema2 = z.record(z.unknown()); 相反，您应该明确定义对象字段的属性。以下是一个示例：

import { tool } from "@langchain/core/tools";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import * as z from "zod";

// Define your tool
const fakeBrowserTool = tool((_) => {
  return "The search result is xyz..."
}, {
  name: "browser_tool",
  description: "Useful for when you need to find something on the web or summarize a webpage.",
  schema: z.object({
    url: z.string().describe("The URL of the webpage to search."),
    query: z.string().optional().describe("An optional search query to use."),
  }),
})

const llmWithTool = new ChatGoogleGenerativeAI({
  model: "gemini-pro",
}).bindTools([fakeBrowserTool]) // Bind your tools to the model

const toolRes = await llmWithTool.invoke([
  [
    "human",
    "Search the web and tell me what the weather will be like tonight in new york. use a popular weather website",
  ],
]);

console.log(toolRes.tool_calls);

[
  {
    name: 'browser_tool',
    args: {
      url: 'https://www.weather.com',
      query: 'weather tonight in new york'
    },
    type: 'tool_call'
  }
]

内置谷歌搜索检索

Google 还提供内置的搜索工具，您可以使用它将内容生成基于现实世界的信息。以下是如何使用它的一个示例：

import { DynamicRetrievalMode, GoogleSearchRetrievalTool } from "@google/generative-ai";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const searchRetrievalTool: GoogleSearchRetrievalTool = {
  googleSearchRetrieval: {
    dynamicRetrievalConfig: {
      mode: DynamicRetrievalMode.MODE_DYNAMIC,
      dynamicThreshold: 0.7, // default is 0.7
    }
  }
};
const searchRetrievalModel = new ChatGoogleGenerativeAI({
  model: "gemini-1.5-pro",
  temperature: 0,
  maxRetries: 0,
}).bindTools([searchRetrievalTool]);

const searchRetrievalResult = await searchRetrievalModel.invoke("Who won the 2024 MLB World Series?");

console.log(searchRetrievalResult.content);

The Los Angeles Dodgers won the 2024 World Series, defeating the New York Yankees in Game 5 on October 30, 2024, by a score of 7-6. This victory marks the Dodgers' eighth World Series title and their first in a full season since 1988.  They achieved this win by overcoming a 5-0 deficit, making them the first team in World Series history to win a clinching game after being behind by such a margin.  The Dodgers also became the first team in MLB postseason history to overcome a five-run deficit, fall behind again, and still win.  Walker Buehler earned the save in the final game, securing the championship for the Dodgers.

响应还包括关于搜索结果的元数据：

console.dir(searchRetrievalResult.response_metadata?.groundingMetadata, { depth: null });

{
  searchEntryPoint: {
    renderedContent: '<style>\n' +
      '.container {\n' +
      '  align-items: center;\n' +
      '  border-radius: 8px;\n' +
      '  display: flex;\n' +
      '  font-family: Google Sans, Roboto, sans-serif;\n' +
      '  font-size: 14px;\n' +
      '  line-height: 20px;\n' +
      '  padding: 8px 12px;\n' +
      '}\n' +
      '.chip {\n' +
      '  display: inline-block;\n' +
      '  border: solid 1px;\n' +
      '  border-radius: 16px;\n' +
      '  min-width: 14px;\n' +
      '  padding: 5px 16px;\n' +
      '  text-align: center;\n' +
      '  user-select: none;\n' +
      '  margin: 0 8px;\n' +
      '  -webkit-tap-highlight-color: transparent;\n' +
      '}\n' +
      '.carousel {\n' +
      '  overflow: auto;\n' +
      '  scrollbar-width: none;\n' +
      '  white-space: nowrap;\n' +
      '  margin-right: -12px;\n' +
      '}\n' +
      '.headline {\n' +
      '  display: flex;\n' +
      '  margin-right: 4px;\n' +
      '}\n' +
      '.gradient-container {\n' +
      '  position: relative;\n' +
      '}\n' +
      '.gradient {\n' +
      '  position: absolute;\n' +
      '  transform: translate(3px, -9px);\n' +
      '  height: 36px;\n' +
      '  width: 9px;\n' +
      '}\n' +
      '@media (prefers-color-scheme: light) {\n' +
      '  .container {\n' +
      '    background-color: #fafafa;\n' +
      '    box-shadow: 0 0 0 1px #0000000f;\n' +
      '  }\n' +
      '  .headline-label {\n' +
      '    color: #1f1f1f;\n' +
      '  }\n' +
      '  .chip {\n' +
      '    background-color: #ffffff;\n' +
      '    border-color: #d2d2d2;\n' +
      '    color: #5e5e5e;\n' +
      '    text-decoration: none;\n' +
      '  }\n' +
      '  .chip:hover {\n' +
      '    background-color: #f2f2f2;\n' +
      '  }\n' +
      '  .chip:focus {\n' +
      '    background-color: #f2f2f2;\n' +
      '  }\n' +
      '  .chip:active {\n' +
      '    background-color: #d8d8d8;\n' +
      '    border-color: #b6b6b6;\n' +
      '  }\n' +
      '  .logo-dark {\n' +
      '    display: none;\n' +
      '  }\n' +
      '  .gradient {\n' +
      '    background: linear-gradient(90deg, #fafafa 15%, #fafafa00 100%);\n' +
      '  }\n' +
      '}\n' +
      '@media (prefers-color-scheme: dark) {\n' +
      '  .container {\n' +
      '    background-color: #1f1f1f;\n' +
      '    box-shadow: 0 0 0 1px #ffffff26;\n' +
      '  }\n' +
      '  .headline-label {\n' +
      '    color: #fff;\n' +
      '  }\n' +
      '  .chip {\n' +
      '    background-color: #2c2c2c;\n' +
      '    border-color: #3c4043;\n' +
      '    color: #fff;\n' +
      '    text-decoration: none;\n' +
      '  }\n' +
      '  .chip:hover {\n' +
      '    background-color: #353536;\n' +
      '  }\n' +
      '  .chip:focus {\n' +
      '    background-color: #353536;\n' +
      '  }\n' +
      '  .chip:active {\n' +
      '    background-color: #464849;\n' +
      '    border-color: #53575b;\n' +
      '  }\n' +
      '  .logo-light {\n' +
      '    display: none;\n' +
      '  }\n' +
      '  .gradient {\n' +
      '    background: linear-gradient(90deg, #1f1f1f 15%, #1f1f1f00 100%);\n' +
      '  }\n' +
      '}\n' +
      '</style>\n' +
      '<div class="container">\n' +
      '  <div class="headline">\n' +
      '    <svg class="logo-light" width="18" height="18" viewBox="9 9 35 35" fill="none" xmlns="http://www.w3.org/2000/svg">\n' +
      '      <path fill-rule="evenodd" clip-rule="evenodd" d="M42.8622 27.0064C42.8622 25.7839 42.7525 24.6084 42.5487 23.4799H26.3109V30.1568H35.5897C35.1821 32.3041 33.9596 34.1222 32.1258 35.3448V39.6864H37.7213C40.9814 36.677 42.8622 32.2571 42.8622 27.0064V27.0064Z" fill="#4285F4"/>\n' +
      '      <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 43.8555C30.9659 43.8555 34.8687 42.3195 37.7213 39.6863L32.1258 35.3447C30.5898 36.3792 28.6306 37.0061 26.3109 37.0061C21.8282 37.0061 18.0195 33.9811 16.6559 29.906H10.9194V34.3573C13.7563 39.9841 19.5712 43.8555 26.3109 43.8555V43.8555Z" fill="#34A853"/>\n' +
      '      <path fill-rule="evenodd" clip-rule="evenodd" d="M16.6559 29.8904C16.3111 28.8559 16.1074 27.7588 16.1074 26.6146C16.1074 25.4704 16.3111 24.3733 16.6559 23.3388V18.8875H10.9194C9.74388 21.2072 9.06992 23.8247 9.06992 26.6146C9.06992 29.4045 9.74388 32.022 10.9194 34.3417L15.3864 30.8621L16.6559 29.8904V29.8904Z" fill="#FBBC05"/>\n' +
      '      <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 16.2386C28.85 16.2386 31.107 17.1164 32.9095 18.8091L37.8466 13.8719C34.853 11.082 30.9659 9.3736 26.3109 9.3736C19.5712 9.3736 13.7563 13.245 10.9194 18.8875L16.6559 23.3388C18.0195 19.2636 21.8282 16.2386 26.3109 16.2386V16.2386Z" fill="#EA4335"/>\n' +
      '    </svg>\n' +
      '    <svg class="logo-dark" width="18" height="18" viewBox="0 0 48 48" xmlns="http://www.w3.org/2000/svg">\n' +
      '      <circle cx="24" cy="23" fill="#FFF" r="22"/>\n' +
      '      <path d="M33.76 34.26c2.75-2.56 4.49-6.37 4.49-11.26 0-.89-.08-1.84-.29-3H24.01v5.99h8.03c-.4 2.02-1.5 3.56-3.07 4.56v.75l3.91 2.97h.88z" fill="#4285F4"/>\n' +
      '      <path d="M15.58 25.77A8.845 8.845 0 0 0 24 31.86c1.92 0 3.62-.46 4.97-1.31l4.79 3.71C31.14 36.7 27.65 38 24 38c-5.93 0-11.01-3.4-13.45-8.36l.17-1.01 4.06-2.85h.8z" fill="#34A853"/>\n' +
      '      <path d="M15.59 20.21a8.864 8.864 0 0 0 0 5.58l-5.03 3.86c-.98-2-1.53-4.25-1.53-6.64 0-2.39.55-4.64 1.53-6.64l1-.22 3.81 2.98.22 1.08z" fill="#FBBC05"/>\n' +
      '      <path d="M24 14.14c2.11 0 4.02.75 5.52 1.98l4.36-4.36C31.22 9.43 27.81 8 24 8c-5.93 0-11.01 3.4-13.45 8.36l5.03 3.85A8.86 8.86 0 0 1 24 14.14z" fill="#EA4335"/>\n' +
      '    </svg>\n' +
      '    <div class="gradient-container"><div class="gradient"></div></div>\n' +
      '  </div>\n' +
      '  <div class="carousel">\n' +
      '    <a class="chip" href="https://vertexaisearch.cloud.google.com/grounding-api-redirect/AZnLMfyXqJN3K4FKueRIZDY2Owjs5Rw4dqgDOc6ZjYKsFo4GgENxLktR2sPHtNUuEBIUeqmUYc3jz9pLRq2cgSpc-4EoGBwQSTTpKk71CX7revnXUa54r9LxcxKgYxrUNBm5HpEm6JDNeJykc6NacPYv43M2wgkrhHCHCzHRyjEP2YR0Pxq4JQMUuOrLeTAYWB9oUb87FE5ksfuB6gimqO5-6uS3psR6">who won the 2024 mlb world series</a>\n' +
      '  </div>\n' +
      '</div>\n'
  },
  groundingChunks: [
    {
      web: {
        uri: 'https://vertexaisearch.cloud.google.com/grounding-api-redirect/AZnLMfwvs0gpiM4BbIcNXZnnp4d4ED_rLnIYz2ZwM-lwFnoUxXNlKzy7ZSbbs_E27yhARG6Gx2AuW7DsoqkWPfDFMqPdXfvG3n0qFOQxQ4MBQ9Ox9mTk3KH5KPRJ79m8V118RQRyhi6oK5qg5-fLQunXUVn_a42K7eMk7Kjb8VpZ4onl8Glv1lQQsAK7YWyYkQ7WkTHDHVGB-vrL2U2yRQ==',
        title: 'foxsports.com'
      }
    },
    {
      web: {
        uri: 'https://vertexaisearch.cloud.google.com/grounding-api-redirect/AZnLMfwxwBq8VYgKAhf3UC8U6U5D-i0lK4TwP-2Jf8ClqB-sI0iptm9GxgeaH1iHFbSi-j_C3UqYj8Ok0YDTyvg87S7JamU48pndrd467ZQbI2sI0yWxsCCZ_dosXHwemBHFL5TW2hbAqasq93CfJ09cp1jU',
        title: 'mlb.com'
      }
    }
  ],
  groundingSupports: [
    {
      segment: {
        endIndex: 131,
        text: 'The Los Angeles Dodgers won the 2024 World Series, defeating the New York Yankees in Game 5 on October 30, 2024, by a score of 7-6.'
      },
      groundingChunkIndices: [ 0, 1 ],
      confidenceScores: [ 0.7652759, 0.7652759 ]
    },
    {
      segment: {
        startIndex: 401,
        endIndex: 531,
        text: 'The Dodgers also became the first team in MLB postseason history to overcome a five-run deficit, fall behind again, and still win.'
      },
      groundingChunkIndices: [ 1 ],
      confidenceScores: [ 0.8487609 ]
    }
  ],
  retrievalMetadata: { googleSearchDynamicRetrievalScore: 0.93359375 },
  webSearchQueries: [ 'who won the 2024 mlb world series' ]
}

代码执行

Google 生成式 AI 也支持代码执行。使用内置的 CodeExecutionTool，您可以令模型生成代码，执行它，并将结果用于最终的完成：

import { CodeExecutionTool } from "@google/generative-ai";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const codeExecutionTool: CodeExecutionTool = {
  codeExecution: {}, // Simply pass an empty object to enable it.
};
const codeExecutionModel = new ChatGoogleGenerativeAI({
  model: "gemini-1.5-pro",
  temperature: 0,
  maxRetries: 0,
}).bindTools([codeExecutionTool]);

const codeExecutionResult = await codeExecutionModel.invoke("Use code execution to find the sum of the first and last 3 numbers in the following list: [1, 2, 3, 72638, 8, 727, 4, 5, 6]");

console.dir(codeExecutionResult.content, { depth: null });

[
  {
    type: 'text',
    text: "Here's how to find the sum of the first and last three numbers in the given list using Python:\n" +
      '\n'
  },
  {
    type: 'executableCode',
    executableCode: {
      language: 'PYTHON',
      code: '\n' +
        'my_list = [1, 2, 3, 72638, 8, 727, 4, 5, 6]\n' +
        '\n' +
        'first_three_sum = sum(my_list[:3])\n' +
        'last_three_sum = sum(my_list[-3:])\n' +
        'total_sum = first_three_sum + last_three_sum\n' +
        '\n' +
        'print(f"{first_three_sum=}")\n' +
        'print(f"{last_three_sum=}")\n' +
        'print(f"{total_sum=}")\n' +
        '\n'
    }
  },
  {
    type: 'codeExecutionResult',
    codeExecutionResult: {
      outcome: 'OUTCOME_OK',
      output: 'first_three_sum=6\nlast_three_sum=15\ntotal_sum=21\n'
    }
  },
  {
    type: 'text',
    text: 'Therefore, the sum of the first three numbers (1, 2, 3) is 6, the sum of the last three numbers (4, 5, 6) is 15, and their total sum is 21.\n'
  }
]

您还可以将此生成内容作为聊天历史传递回模型：

const codeExecutionExplanation = await codeExecutionModel.invoke([
  codeExecutionResult,
  {
    role: "user",
    content: "Please explain the question I asked, the code you wrote, and the answer you got.",
  }
])

console.log(codeExecutionExplanation.content);

You asked for the sum of the first three and the last three numbers in the list `[1, 2, 3, 72638, 8, 727, 4, 5, 6]`.

Here's a breakdown of the code:

1. **`my_list = [1, 2, 3, 72638, 8, 727, 4, 5, 6]`**: This line defines the list of numbers you provided.

2. **`first_three_sum = sum(my_list[:3])`**: This calculates the sum of the first three numbers.  `my_list[:3]` is a slice of the list that takes elements from the beginning up to (but not including) the index 3.  So, it takes elements at indices 0, 1, and 2, which are 1, 2, and 3. The `sum()` function then adds these numbers together.

3. **`last_three_sum = sum(my_list[-3:])`**: This calculates the sum of the last three numbers. `my_list[-3:]` is a slice that takes elements starting from the third element from the end and goes to the end of the list. So it takes elements at indices -3, -2, and -1 which correspond to 4, 5, and 6. The `sum()` function adds these numbers.

4. **`total_sum = first_three_sum + last_three_sum`**: This adds the sum of the first three numbers and the sum of the last three numbers to get the final result.

5. **`print(f"{first_three_sum=}")`**, **`print(f"{last_three_sum=}")`**, and **`print(f"{total_sum=}")`**: These lines print the calculated sums in a clear and readable format.

The output of the code was:

* `first_three_sum=6`
* `last_three_sum=15`
* `total_sum=21`

Therefore, the answer to your question is 21.

上下文缓存

上下文缓存允许您一次性将一些内容传递给模型，缓存输入令牌，然后在后续请求中引用这些缓存的令牌以降低成本。您可以使用 GoogleAICacheManager 类创建一个 CachedContent 对象，然后将 CachedContent 对象传递给您的 ChatGoogleGenerativeAIModel，并通过 enableCachedContent() 方法进行传递。

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import {
  GoogleAICacheManager,
  GoogleAIFileManager,
} from "@google/generative-ai/server";

const fileManager = new GoogleAIFileManager(process.env.GOOGLE_API_KEY);
const cacheManager = new GoogleAICacheManager(process.env.GOOGLE_API_KEY);

// uploads file for caching
const pathToVideoFile = "/path/to/video/file";
const displayName = "example-video";
const fileResult = await fileManager.uploadFile(pathToVideoFile, {
    displayName,
    mimeType: "video/mp4",
});

// creates cached content AFTER uploading is finished
const cachedContent = await cacheManager.create({
    model: "models/gemini-1.5-flash-001",
    displayName: displayName,
    systemInstruction: "You are an expert video analyzer, and your job is to answer " +
      "the user's query based on the video file you have access to.",
    contents: [
        {
            role: "user",
            parts: [
                {
                    fileData: {
                        mimeType: fileResult.file.mimeType,
                        fileUri: fileResult.file.uri,
                    },
                },
            ],
        },
    ],
    ttlSeconds: 300,
});

// passes cached video to model
const model = new ChatGoogleGenerativeAI({});
model.useCachedContent(cachedContent);

// invokes model with cached video
await model.invoke("Summarize the video");

注意

上下文缓存支持 Gemini 1.5 Pro 和 Gemini 1.5 Flash。上下文缓存仅适用于具有固定版本的稳定模型（例如，gemini-1.5-pro-001）。您必须包含版本后缀（例如，gemini-1.5-pro-001 中的 -001）。
上下文缓存的最低输入令牌计数为 32,768，最高计数与给定模型的最高计数相同。

Gemini 提示 FAQs

截至本文撰写时间（2023/12/12），Gemini对其接受的提示类型和结构有一些限制。具体来说：

当提供多模态（图像）输入时，您最多只能发送1条“人类”（用户）类型的消息。您不能发送多条消息（尽管单条人类消息可能有多个内容条目）
系统消息不支持原生，如果存在，将与第一条人类消息合并。
对于常规聊天对话，消息必须遵循人类/人工智能/人类/人工智能交替的模式。您不能连续提供2条人工智能或人类消息。
如果消息违反了LLM的安全检查，可能会被阻止。在这种情况下，模型将返回空响应。

API 参考文档

有关所有ChatGoogleGenerativeAI功能和配置的详细文档，请参阅API参考。

在GitHub上编辑此页面的源代码。

通过MCP将这些文档编程连接到Claude、VSCode等，以获取实时答案。

热门提供商

通用集成

RAG 集成

概述

集成详情

模型功能

设置

凭证

安装

实例化

调用

安全设置

工具调用

内置谷歌搜索检索

代码执行

上下文缓存

Gemini 提示 FAQs

API 参考文档

热门提供商

通用集成

RAG 集成

​概述

​集成详情

​模型功能

​设置

​凭证

​安装

​实例化

​调用

​安全设置

​工具调用

​内置谷歌搜索检索

​代码执行

​上下文缓存

​Gemini 提示 FAQs

​API 参考文档

概述

集成详情

模型功能

设置

凭证

安装

实例化

调用

安全设置

工具调用

内置谷歌搜索检索

代码执行

上下文缓存

Gemini 提示 FAQs

API 参考文档