自 2025 年 4 月 29 日起，Gemini 1.5 Pro 和 Gemini 1.5 Flash 模型將無法用於先前未使用這些模型的專案，包括新專案。詳情請參閱「模型版本和生命週期」。

本頁面由 Cloud Translation API 翻譯而成。

程式碼執行

有了 Gemini API 的程式碼執行功能，模型可生成以及執行 Python 程式碼，並根據結果反覆試驗學習，直到生成最終輸出內容。透過這個程式碼執行功能，您能建構應用程式來生成文字輸出內容，並在其中運用以程式碼為基礎的推理技術。舉例來說，您可以在應用程式中使用程式碼執行功能，解開方程式或處理文字。

Gemini API 提供程式碼執行工具，與函式呼叫類似。將程式碼執行功能新增為工具後，模型會決定何時使用這項工具。

程式碼執行環境包含下列程式庫。您無法安裝自己的程式庫。

支援的模型

下列模型支援執行程式碼：

開始執行程式碼

本節假設您已完成 Gemini API 快速入門導覽課程中顯示的設定和配置步驟。

啟用模型上的程式碼執行功能

您可以啟用基本程式碼執行功能，如下所示：

Python

安裝

pip install --upgrade google-genai

詳情請參閱 SDK 參考說明文件。

設定環境變數，透過 Vertex AI 使用 Gen AI SDK：

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

from google import genai
from google.genai.types import (
    HttpOptions,
    Tool,
    ToolCodeExecution,
    GenerateContentConfig,
)

client = genai.Client(http_options=HttpOptions(api_version="v1"))
model_id = "gemini-2.5-flash"

code_execution_tool = Tool(code_execution=ToolCodeExecution())
response = client.models.generate_content(
    model=model_id,
    contents="Calculate 20th fibonacci number. Then find the nearest palindrome to it.",
    config=GenerateContentConfig(
        tools=[code_execution_tool],
        temperature=0,
    ),
)
print("# Code:")
print(response.executable_code)
print("# Outcome:")
print(response.code_execution_result)

# Example response:
# # Code:
# def fibonacci(n):
#     if n <= 0:
#         return 0
#     elif n == 1:
#         return 1
#     else:
#         a, b = 0, 1
#         for _ in range(2, n + 1):
#             a, b = b, a + b
#         return b
#
# fib_20 = fibonacci(20)
# print(f'{fib_20=}')
#
# # Outcome:
# fib_20=6765

Go

瞭解如何安裝或更新 Go。

詳情請參閱 SDK 參考說明文件。

設定環境變數，透過 Vertex AI 使用 Gen AI SDK：

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

import (
	"context"
	"fmt"
	"io"

	genai "google.golang.org/genai"
)

// generateWithCodeExec shows how to generate text using the code execution tool.
func generateWithCodeExec(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	prompt := "Calculate 20th fibonacci number. Then find the nearest palindrome to it."
	contents := []*genai.Content{
		{Parts: []*genai.Part{
			{Text: prompt},
		},
			Role: "user"},
	}
	config := &genai.GenerateContentConfig{
		Tools: []*genai.Tool{
			{CodeExecution: &genai.ToolCodeExecution{}},
		},
		Temperature: genai.Ptr(float32(0.0)),
	}
	modelName := "gemini-2.5-flash"

	resp, err := client.Models.GenerateContent(ctx, modelName, contents, config)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	for _, p := range resp.Candidates[0].Content.Parts {
		if p.Text != "" {
			fmt.Fprintf(w, "Gemini: %s", p.Text)
		}
		if p.ExecutableCode != nil {
			fmt.Fprintf(w, "Language: %s\n%s\n", p.ExecutableCode.Language, p.ExecutableCode.Code)
		}
		if p.CodeExecutionResult != nil {
			fmt.Fprintf(w, "Outcome: %s\n%s\n", p.CodeExecutionResult.Outcome, p.CodeExecutionResult.Output)
		}
	}

	// Example response:
	// Gemini: Okay, I can do that. First, I'll calculate the 20th Fibonacci number. Then, I need ...
	//
	// Language: PYTHON
	//
	// def fibonacci(n):
	//    ...
	//
	// fib_20 = fibonacci(20)
	// print(f'{fib_20=}')
	//
	// Outcome: OUTCOME_OK
	// fib_20=6765
	//
	// Now that I have the 20th Fibonacci number (6765), I need to find the nearest palindrome. ...
	// ...

	return nil
}

Node.js

安裝

npm install @google/genai

詳情請參閱 SDK 參考說明文件。

設定環境變數，透過 Vertex AI 使用 Gen AI SDK：

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

const {GoogleGenAI} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';

async function generateContent(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const ai = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const response = await ai.models.generateContent({
    model: 'gemini-2.5-flash-preview-05-20',
    contents:
      'What is the sum of the first 50 prime numbers? Generate and run code for the calculation, and make sure you get all 50.',
    config: {
      tools: [{codeExecution: {}}],
      temperature: 0,
    },
  });

  console.debug(response.executableCode);
  console.debug(response.codeExecutionResult);

  return response.codeExecutionResult;
}

REST

使用任何要求資料之前，請先替換以下項目：

GENERATE_RESPONSE_METHOD：您希望模型生成的回應類型。選擇生成模型回覆傳回方式的方法：
- streamGenerateContent：在生成回覆時串流傳輸，減少人類觀眾的延遲感。
- generateContent：系統會在完整生成回覆後傳回。
LOCATION：處理要求的區域。可用的選項包括：
按一下即可展開可用地區的部分清單
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID：您的專案 ID。
MODEL_ID：要使用的模型 ID。
ROLE：與內容相關聯的對話角色。即使是單輪對話，也必須指定角色。可接受的值包括：
- USER：指定您傳送的內容。
- MODEL：指定模型的回覆。
```
TEXT
```
要加入提示的文字指令。

如要傳送要求，請選擇以下其中一個選項：

curl

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI，或使用 Cloud Shell，自動登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中。在終端機中執行下列指令，在目前目錄中建立或覆寫這個檔案：

cat > request.json << 'EOF'
{
  "tools": [{'codeExecution': {}}],
  "contents": {
    "role": "ROLE",
    "parts": { "text": "TEXT" }
  },
}
EOF

接著，請執行下列指令來傳送 REST 要求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD"

PowerShell

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中。在終端機中執行下列指令，在目前目錄中建立或覆寫這個檔案：

@'
{
  "tools": [{'codeExecution': {}}],
  "contents": {
    "role": "ROLE",
    "parts": { "text": "TEXT" }
  },
}
'@  | Out-File -FilePath request.json -Encoding utf8

接著，請執行下列指令來傳送 REST 要求：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD" | Select-Object -Expand Content

您應該會收到類似如下的 JSON 回應。

回應

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "Okay, I understand. You want me to calculate the sum of the first 10 positive integers and to use code to do so. Here's my plan: I will use a loop to add the numbers from 1 to 10 and then return the final sum.\n\n"
          },
          {
            "executableCode": {
              "language": "PYTHON",
              "code": "\ntotal = 0\nfor i in range(1, 11):\n    total += i\nprint(f'{total=}')\n"
            }
          },
          {
            "codeExecutionResult": {
              "outcome": "OUTCOME_OK",
              "output": "total=55\n"
            }
          },
          {
            "text": "The sum of the first 10 positive numbers is 55.\n"
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.19436789,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.17441037
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.0685376,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.14903527
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.23231025,
          "severity": "HARM_SEVERITY_LOW",
          "severityScore": 0.2436427
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.08269742,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.10818888
        }
      ],
      "score": -0.50845032930374146,
      "avgLogprobs": -0.0046222757209431042
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 34,
    "candidatesTokenCount": 110,
    "totalTokenCount": 144,
    "billablePromptUsage": {
      "textCount": 119
    },
    "trafficType": "ON_DEMAND"
  },
  "modelVersion": "gemini-2.0-flash-001",
  "createTime": "2024-12-09T23:33:47.842964Z",
  "responseId": "W35XZ9S5M6acmecP3vDFkQU"
}

在對話中使用程式碼執行功能

你也可以在對話中使用程式碼執行功能。

REST

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://aiplatform.googleapis.com/v1/projects/test-project/locations/global/publishers/google/models/gemini-2.0-flash-001:generateContent -d \
$'{
    "tools": [{'code_execution': {}}],
    "contents": [
      {
        "role": "user",
        "parts": {
          "text": "Can you print \"Hello world!\"?"
        }
      },
      {
        "role": "model",
        "parts": [
          {
            "text": ""
          },
          {
            "executable_code": {
              "language": "PYTHON",
              "code": "\nprint(\"hello world!\")\n"
            }
          },
          {
            "code_execution_result": {
              "outcome": "OUTCOME_OK",
              "output": "hello world!\n"
            }
          },
          {
            "text": "I have printed \"hello world!\" using the provided python code block. \n"
          }
        ],
      },
      {
        "role": "user",
        "parts": {
          "text": "What is the sum of the first 50 prime numbers? Generate and run code for the calculation, and make sure you get all 50."
        }
      }
    ]
  }'