在聊天场景中使用 Gemini API 通过外部函数调用生成文本回答

使用 Gemini API 通过外部函数调用生成文本回答。此示例演示的是包含两个函数和两个连续提示的聊天场景。

代码示例

Node.js

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 Node.js 设置说明执行操作。 如需了解详情，请参阅 Vertex AI Node.js 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。

const {
  VertexAI,
  FunctionDeclarationSchemaType,
} = require('@google-cloud/vertexai');

const functionDeclarations = [
  {
    function_declarations: [
      {
        name: 'get_current_weather',
        description: 'get weather in a given location',
        parameters: {
          type: FunctionDeclarationSchemaType.OBJECT,
          properties: {
            location: {type: FunctionDeclarationSchemaType.STRING},
            unit: {
              type: FunctionDeclarationSchemaType.STRING,
              enum: ['celsius', 'fahrenheit'],
            },
          },
          required: ['location'],
        },
      },
    ],
  },
];

const functionResponseParts = [
  {
    functionResponse: {
      name: 'get_current_weather',
      response: {name: 'get_current_weather', content: {weather: 'super nice'}},
    },
  },
];

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function functionCallingStreamChat(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-2.0-flash-001'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  // Instantiate the model
  const generativeModel = vertexAI.getGenerativeModel({
    model: model,
  });

  // Create a chat session and pass your function declarations
  const chat = generativeModel.startChat({
    tools: functionDeclarations,
  });

  const chatInput1 = 'What is the weather in Boston?';

  // This should include a functionCall response from the model
  const result1 = await chat.sendMessageStream(chatInput1);
  for await (const item of result1.stream) {
    console.log(item.candidates[0]);
  }
  await result1.response;

  // Send a follow up message with a FunctionResponse
  const result2 = await chat.sendMessageStream(functionResponseParts);
  for await (const item of result2.stream) {
    console.log(item.candidates[0]);
  }

  // This should include a text response from the model using the response content
  // provided above
  const response2 = await result2.response;
  console.log(response2.candidates[0].content.parts[0].text);
}

后续步骤

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