使用 Gemini API 呼叫函式

透過函式呼叫，您可以將模型連結至外部工具和 API。模型不會生成文字回覆，而是判斷何時應呼叫特定函式，並提供執行實際動作所需的參數。這項技術可讓模型成為自然語言與現實世界動作和資料之間的橋梁。函式呼叫功能有 3 個主要用途：

採取行動：使用 API 與外部系統互動，例如安排預約、建立發票、傳送電子郵件或控制智慧住宅裝置。
擴增知識：從資料庫、API 和知識庫等外部來源存取資訊。
擴充功能：使用外部工具執行運算，並擴充模型限制，例如使用計算機或建立圖表。

請參閱下列範例：

安排會議

這個範例說明如何定義函式，在特定時間排定與會者會議，讓模型剖析使用者要求並傳回結構化引數，以觸發外部系統中的動作。

Python

from google import genai

schedule_meeting_function = {
    "type": "function",
    "name": "schedule_meeting",
    "description": "Schedules a meeting with specified attendees at a given time and date.",
    "parameters": {
        "type": "object",
        "properties": {
            "attendees": {"type": "array", "items": {"type": "string"}},
            "date": {"type": "string", "description": "Date (e.g., '2024-07-29')"},
            "time": {"type": "string", "description": "Time (e.g., '15:00')"},
            "topic": {"type": "string", "description": "The meeting topic."},
        },
        "required": ["attendees", "date", "time", "topic"],
    },
}

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.6-flash",
    input="Schedule a meeting with Bob and Alice for 03/14/2025 at 10:00 AM about Q3 planning.",
    tools=[{"type": "function", **schedule_meeting_function}],
)

for step in interaction.steps:
    if step.type == "function_call":
        print(f"Function to call: {step.name}")
        print(f"Arguments: {step.arguments}")

JavaScript

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const scheduleMeetingFunction = {
  type: 'function',
  name: 'schedule_meeting',
  description: 'Schedules a meeting with specified attendees at a given time and date.',
  parameters: {
    type: 'object',
    properties: {
      attendees: { type: 'array', items: { type: 'string' } },
      date: { type: 'string', description: 'Date (e.g., "2024-07-29")' },
      time: { type: 'string', description: 'Time (e.g., "15:00")' },
      topic: { type: 'string', description: 'The meeting topic.' },
    },
    required: ['attendees', 'date', 'time', 'topic'],
  },
};

const interaction = await client.interactions.create({
  model: 'gemini-3.6-flash',
  input: 'Schedule a meeting with Bob and Alice for 03/27/2025 at 10:00 AM about Q3 planning.',
  tools: [scheduleMeetingFunction],
});

for (const step of interaction.steps) {
  if (step.type === 'function_call') {
    console.log(`Function to call: ${step.name}`);
    console.log(`Arguments: ${JSON.stringify(step.arguments)}`);
  }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.6-flash",
    "input": "Schedule a meeting with Bob and Alice for 03/27/2025 at 10:00 AM about Q3 planning.",
    "tools": [{
        "type": "function",
        "name": "schedule_meeting",
        "description": "Schedules a meeting with specified attendees at a given time and date.",
        "parameters": {
          "type": "object",
          "properties": {
            "attendees": {"type": "array", "items": {"type&quot;: "string"}},
            "date": {"type": "string"},
            "time": {"type": "string"},
            "topic": {"type": "string"}
          },
          "required": ["attendees", "date", "time", "topic"]
        }
    }]
  }'

取得天氣資訊

這個範例說明如何定義函式，以便擷取特定地點的溫度資料，讓模型呼叫外部 API，回答需要即時或外部資訊的查詢。

Python

from google import genai

weather_function = {
    "type": "function",
    "name": "get_current_temperature",
    "description": "Gets the current temperature for a given location.",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city name, e.g. San Francisco",
            },
        },
        "required": ["location"],
    },
}

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.6-flash",
    input="What's the temperature in London?",
    tools=[weather_function],
)

for step in interaction.steps:
    if step.type == "function_call":
        print(f"Function to call: {step.name}")
        print(f"Arguments: {step.arguments}")

JavaScript

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const weatherFunctionDeclaration = {
  type: 'function',
  name: 'get_current_temperature',
  description: 'Gets the current temperature for a given location.',
  parameters: {
    type: 'object',
    properties: {
      location: {
        type: 'string',
        description: 'The city name, e.g. San Francisco',
      },
    },
    required: ['location'],
  },
};

const interaction = await client.interactions.create({
  model: 'gemini-3.6-flash',
  input: "What's the temperature in London?",
  tools: [weatherFunctionDeclaration],
});

for (const step of interaction.steps) {
  if (step.type === 'function_call') {
    console.log(`Function to call: ${step.name}`);
    console.log(`Arguments: ${JSON.stringify(step.arguments)}`);
  }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.6-flash",
    "input": "What'\''s the temperature in London?",
    "tools": [{
      "type": "function",
      "name": "get_current_temperature",
      "description": "Gets the current temperature for a given location.",
      "parameters": {
        "type";: "object",
        "properties": {
          "location": {"type": "string", "description": "The city name"}
        },
        "required": ["location"]
      }
    }]
  }'

建立圖表

這個範例說明如何定義函式，從結構化資料產生長條圖，並示範模型如何使用外部工具執行計算或建立視覺化資產：

Python

from google import genai

create_chart_function = {
    "type": "function",
    "name": "create_bar_chart",
    "description": "Creates a bar chart given a title, labels, and values.",
    "parameters": {
        "type": "object",
        "properties": {
            "title": {"type": "string", "description": "The title for the chart."},
            "labels": {"type": "array", "items": {"type": "string"}},
            "values": {"type": "array", "items": {"type": "number"}},
        },
        "required": ["title", "labels", "values"],
    },
}

client = genai.Client()

interaction = client.interactions.create(
    model=";gemini-3.6-flash",
    input="Create a bar chart titled 'Quarterly Sales' with Q1: 50000, Q2: 75000, Q3: 60000.",
    tools=[create_chart_function],
)

for step in interaction.steps:
    if step.type == "function_call":
        print(f"Function to call: {step.name}")
        print(f"Arguments: {step.arguments}")

JavaScript

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const createChartFunctionDeclaration = {
  type: 'function',
  name: 'create_bar_chart',
  description: 'Creates a bar chart given a title, labels, and values.',
  parameters: {
    type: 'object',
    properties: {
      title: { type: 'string', description: 'The title for the chart.' },
      labels: { type: 'array', items: { type: 'string' } },
      values: { type: 'array', items: { type: 'number' } },
    },
    required: ['title', 'labels', 'values'],
  },
};

const interaction = await client.interactions.create({
  model: 'gemini-3.6-flash',
  input: "Create a bar chart titled 'Quarterly Sales' with Q1: 50000, Q2: 75000, Q3: 60000.",
  tools: [createChartFunctionDeclaration],
});

for (const step of interaction.steps) {
  if (step.type === 'function_call') {
    console.log(`${step.name}(${JSON.stringify(step.arguments)})`);
  }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.6-flash",
    "input": "Create a bar chart titled '\''Quarterly Sales'\'' with Q1: 50000, Q2: 75000, Q3: 60000.",
    "tools": [{
        "type": "function",
        "name": "create_bar_chart",
        "description": "Creates a bar chart given a title, labels, and values.",
        "parameters": {
          "type": "object",
          "properties": {
            "title": {"type": "string"},
            "labels": {"type": "array", "items": {"type": "string"}},
            "values": {"type": "array", "items": {"type": "number"}}
          },
          "required": ["title", "labels", "values"]
        }
    }]
  }'

函式呼叫的運作方式

函式呼叫總覽

函式呼叫是指應用程式、模型和外部函式之間的結構化互動：

定義函式宣告：向模型定義函式的名稱、參數和用途。
使用函式宣告呼叫 LLM：將使用者提示連同函式宣告傳送至模型。
執行函式程式碼 (您的責任)：模型不會自行執行函式，擷取名稱和引數，並在應用程式中執行。
建立易於理解的回覆：將結果傳回模型，生成最終的易於理解的回覆。

這個過程可能會重複多次。模型支援在單一回合中呼叫多個函式 (平行函式呼叫)，以及依序呼叫 (組合函式呼叫)。

步驟 1：定義函式宣告

Python

set_light_values_declaration = {
    "type": "function",
    "name": "set_light_values",
    "description": "Sets the brightness and color temperature of a light.",
    "parameters": {
        "type": "object",
        "properties": {
            "brightness": {
                "type": "integer",
                "description": "Light level from 0 to 100",
            },
            "color_temp": {
                "type": "string",
                "enum": ["daylight", "cool&>quot;, "warm"],
                "description": "Color temperature",
            },
        },
        "required": ["brightness", "color_temp"],
    },
}

def set_light_values(brightness: int, color_temp: str) - dict:
    """Set the brightness and color temperature of a room light."""
    return {"brightness": brightness, "colorTemperature": color_temp}

JavaScript

const setLightValuesTool = {
  type: 'function',
  name: 'set_light_values',
  description: 'Sets the brightness and color temperature of a light.',
  parameters: {
    type: 'object',
    properties: {
      brightness: { type: 'number', description: 'Light level from 0 to 100' },
      color_temp: { type: 'string', enum: ['daylight', 'cool', 'warm'] },
    },
    required: ['brightness', 'color_temp'],
  },
};

function setLightValues(brightness, color_temp) {
  return { brightness: brightness, colorTemperature: color_temp };
}

步驟 2：使用函式宣告呼叫模型

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.6-flash",
    input="Turn the lights down to a romantic level",
    tools=[set_light_values_declaration],
)

fc_step = next(s for s in interaction.steps if s.type == "function_call")
print(fc_step)

JavaScript

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
  model: 'gemini-3.6-flash',
  input: 'Turn the lights down to a romantic level',
  tools: [setLightValuesTool],
});

const fcStep = in>teraction.steps.find(s = s.type === 'function_call');
console.log(fcStep);

模型會傳回包含 type、name 和 arguments 的 function_call 步驟：

type='function_call'
name='set_light_values'
arguments={'color_temp': &#39;warm', 'brightness': 25}

步驟 3：執行函式

Python

fc_step = next(s for s in interaction.steps if s.type == "function_call")

if fc_step.name == "set_light_values":
    result = set_light_values(**fc_step.arguments)
    print(f"Function execution result: {result}")

JavaScript

const fcStep = interaction.steps.find(s => s.type === 'function_call');

let result;
if (fcStep.name === 'set_light_values'