All Gemini 1.5 models will be retired on September 24, 2025. To avoid service disruption, update to a newer model (for example, gemini-2.5-flash-lite). Learn more.

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代码执行

代码执行是一种工具，可让模型生成并运行 Python 代码。模型可以从代码执行结果中迭代学习，直到获得最终输出。

您可以使用代码执行功能来构建可受益于基于代码的推理并生成文本输出的功能。例如，您可以使用代码执行功能来求解方程式或处理文本。您还可以使用代码执行环境中包含的库来执行更专业的任务。

与您提供给模型的所有工具一样，模型会决定何时使用代码执行工具。

跳转到代码实现

代码执行与函数调用的比较

代码执行和函数调用是两个类似的功能。通常，如果模型可以处理您的使用场景，您应优先使用代码执行。代码执行的使用也更简单，只需启用即可。

以下是代码执行与函数调用之间的一些其他区别：

代码执行	函数调用
如果您希望模型为您编写和运行 Python 代码并返回结果，请使用代码执行功能。	如果您已有要在本地运行的自有函数，请使用函数调用。
通过代码执行，模型可以在固定隔离的环境的 API 后端中运行代码。	通过函数调用，您可以在所需的任何环境中运行模型请求的函数。
代码执行会在单个请求中进行解析。虽然您可以选择将代码执行与聊天功能搭配使用，但这不是必需的。	函数调用需要额外发送一个请求，以返回每次函数调用的输出。因此，您必须使用聊天功能。

支持的模型

gemini-2.5-pro
gemini-2.5-flash
gemini-2.5-flash-lite
gemini-2.0-flash-001（及其自动更新的别名 gemini-2.0-flash）
gemini-2.0-flash-live-preview-04-09

使用代码执行

您可以使用代码执行功能处理纯文本输入和多模态输入，但响应始终仅为文本或代码。

准备工作

点击您的 Gemini API 提供商，以查看此页面上特定于提供商的内容和代码。

如果您尚未完成入门指南，请先完成该指南。该指南介绍了如何设置 Firebase 项目、将应用连接到 Firebase、添加 SDK、为所选的 Gemini API 提供程序初始化后端服务，以及创建 GenerativeModel 实例。

如需测试和迭代提示，甚至获取生成的代码段，我们建议使用 Google AI Studio。

启用代码执行功能

在试用此示例之前，请完成本指南的准备工作部分，以设置您的项目和应用。
在该部分中，您还需要点击所选Gemini API提供商对应的按钮，以便在此页面上看到特定于提供商的内容。

创建 GenerativeModel 实例时，请提供 CodeExecution 作为模型可用于生成回答的工具。这样一来，模型就可以生成并运行 Python 代码。

Swift


import FirebaseAI

// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [.codeExecution()]
)

let prompt = """
What is the sum of the first 50 prime numbers?
Generate and run code for the calculation, and make sure you get all 50.
"""

let response = try await model.generateContent(prompt)

guard let candidate = response.candidates.first else {
  print("No candidates in response.")
  return
}
for part in candidate.content.parts {
  if let textPart = part as? TextPart {
    print("Text = \(textPart.text)")
  } else if let executableCode = part as? ExecutableCodePart {
    print("Code = \(executableCode.code), Language = \(executableCode.language)")
  } else if let executionResult = part as? CodeExecutionResultPart {
    print("Outcome = \(executionResult.outcome), Result = \(executionResult.output ?? "no output")")
  }
}

Kotlin


// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools = listOf(Tool.codeExecution())
)

val prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."

val response = model.generateContent(prompt)

response.candidates.first().content.parts.forEach {
    if(it is TextPart) {
        println("Text = ${it.text}")
    }
    if(it is ExecutableCodePart) {
        println("Code = ${it.code}, Language = ${it.language}")
    }
    if(it is CodeExecutionResultPart) {
       println("Outcome = ${it.outcome}, Result = ${it.output}")
    }
}

Java


// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide code execution as a tool that the model can use to generate its response.
                        List.of(Tool.codeExecution()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

String 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.";

Content prompt = new Content.Builder()
    .addText(text)
    .build();

ListenableFuture response = model.generateContent(prompt);

Futures.addCallback(response, new FutureCallback() {
   @Override
public void onSuccess(GenerateContentResponse response)   {
    // Access the first candidate's content parts
    List parts = response.getCandidates().get(0).getContent().getParts();
    for (Part part : parts) {
        if (part instanceof TextPart) {
            TextPart textPart = (TextPart) part;
            System.out.println("Text = " + textPart.getText());
        } else if (part instanceof ExecutableCodePart) {
            ExecutableCodePart codePart = (ExecutableCodePart) part;
            System.out.println("Code = " + codePart.getCode() + ", Language = " + codePart.getLanguage());
        } else if (part instanceof CodeExecutionResultPart) {
            CodeExecutionResultPart resultPart = (CodeExecutionResultPart) part;
            System.out.println("Outcome = " + resultPart.getOutcome() + ", Result = " + resultPart.getOutput());
        }
    }
}

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools: [{ codeExecution: {} }]
  }
);

const prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."

const result = await model.generateContent(prompt);
const response = await result.response;

const parts = response.candidates?.[0].content.parts;

if (parts) {
  parts.forEach((part) => {
    if (part.text) {
        console.log(`Text: ${part.text}`);
    } else if (part.executableCode) {
      console.log(
        `Code: ${part.executableCode.code}, Language: ${part.executableCode.language}`
      );
    } else if (part.codeExecutionResult) {
      console.log(
        `Outcome: ${part.codeExecutionResult.outcome}, Result: ${part.codeExecutionResult.output}`
      );
    }
  });
}

Dart

我们将在下一个版本中支持 Flutter。

Unity

Unity 支持将在其下一个版本中推出。

了解如何选择适合您的应用场景和应用的模型。

查看输出示例

输出可能如下所示，为了便于阅读，已对其进行格式设置：

Okay, I need to calculate the sum of the first 50 prime numbers. Here's how I'll
approach this:

1.  **Generate Prime Numbers:** I'll use an iterative method to find prime
    numbers. I'll start with 2 and check if each subsequent number is divisible
    by any number between 2 and its square root. If not, it's a prime.
2.  **Store Primes:** I'll store the prime numbers in a list until I have 50 of
    them.
3.  **Calculate the Sum:**  Finally, I'll sum the prime numbers in the list.

Here's the Python code to do this:

def is_prime(n):
  """Efficiently checks if a number is prime."""
  if n <= 1:
    return False
  if n <= 3:
    return True
  if n % 2 == 0 or n % 3 == 0:
    return False
  i = 5
  while i * i <= n:
    if n % i == 0 or n % (i + 2) == 0:
      return False
    i += 6
  return True

primes = []
num = 2
while len(primes) < 50:
  if is_prime(num):
    primes.append(num)
  num += 1

sum_of_primes = sum(primes)
print(f'{primes=}')
print(f'{sum_of_primes=}')

primes=[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67,
71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151,
157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229]
sum_of_primes=5117

The sum of the first 50 prime numbers is 5117.

此输出结合了模型在使用代码执行功能时返回的多个内容部分：

text：模型生成的内嵌文本
executableCode：模型生成的旨在执行的代码
codeExecutionResult：执行的代码的结果

这些部分的命名惯例因编程语言而异。

在对话中使用代码执行

您还可以在对话中使用代码执行：

Swift


import FirebaseAI

// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [.codeExecution()]
)

let prompt = """
What is the sum of the first 50 prime numbers?
Generate and run code for the calculation, and make sure you get all 50.
"""
let chat = model.startChat()

let response = try await chat.sendMessage(prompt)

guard let candidate = response.candidates.first else {
  print("No candidates in response.")
  return
}
for part in candidate.content.parts {
  if let textPart = part as? TextPart {
    print("Text = \(textPart.text)")
  } else if let executableCode = part as? ExecutableCodePart {
    print("Code = \(executableCode.code), Language = \(executableCode.language)")
  } else if let executionResult = part as? CodeExecutionResultPart {
    print("Outcome = \(executionResult.outcome), Result = \(executionResult.output ?? "no output")")
  }
}

Kotlin


// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools = listOf(Tool.codeExecution())
)

val prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."
val chat = model.startChat()
val response = chat.sendMessage(prompt)

response.candidates.first().content.parts.forEach {
    if(it is TextPart) {
        println("Text = ${it.text}")
    }
    if(it is ExecutableCodePart) {
        println("Code = ${it.code}, Language = ${it.language}")
    }
    if(it is CodeExecutionResultPart) {
       println("Outcome = ${it.outcome}, Result = ${it.output}")
    }
}

Java


// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide code execution as a tool that the model can use to generate its response.
                        List.of(Tool.codeExecution()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
String 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.";

Content prompt = new Content.Builder()
    .addText(text)
    .build();

ChatFutures chat = model.startChat();
ListenableFuture response = chat.sendMessage(prompt);

Futures.addCallback(response, new FutureCallback() {
   @Override
public void onSuccess(GenerateContentResponse response)   {
    // Access the first candidate's content parts
    List parts = response.getCandidates().get(0).getContent().getParts();
    for (Part part : parts) {
        if (part instanceof TextPart) {
            TextPart textPart = (TextPart) part;
            System.out.println("Text = " + textPart.getText());
        } else if (part instanceof ExecutableCodePart) {
            ExecutableCodePart codePart = (ExecutableCodePart) part;
            System.out.println("Code = " + codePart.getCode() + ", Language = " + codePart.getLanguage());
        } else if (part instanceof CodeExecutionResultPart) {
            CodeExecutionResultPart resultPart = (CodeExecutionResultPart) part;
            System.out.println("Outcome = " + resultPart.getOutcome() + ", Result = " + resultPart.getOutput());
        }
    }
}

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools: [{ codeExecution: {} }]
  }
);

const prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."
const chat = model.startChat()
const result = await chat.sendMessage(prompt);
const response = await result.response;

const parts = response.candidates?.[0].content.parts;

if (parts) {
  parts.forEach((part) => {
    if (part.text) {
        console.log(`Text: ${part.text}`);
    } else if (part.executableCode) {
      console.log(
        `Code: ${part.executableCode.code}, Language: ${part.executableCode.language}`
      );
    } else if (part.codeExecutionResult) {
      console.log(
        `Outcome: ${part.codeExecutionResult.outcome}, Result: ${part.codeExecutionResult.output}`
      );
    }
  });
}

Dart

我们将在下一个版本中支持 Flutter。

Unity

Unity 支持将在其下一个版本中推出。

了解如何选择适合您的应用场景和应用的模型。

价格

启用代码执行功能并将其作为工具提供给模型不会产生额外的费用。如果模型决定使用代码执行，系统会根据您使用的 Gemini 模型，按当前的输入和输出令牌费率向您收费。

下图显示了代码执行的结算模式：

图表：显示了当模型使用代码执行功能时，系统如何收取 token 费用。

以下是模型使用代码执行功能时 token 的结算方式摘要：

原始提示仅收取一次费用。这些 token 会被标记为中间 token，并按输入 token 计费。
生成的代码和执行的代码的结果按以下方式收费：
- 在代码执行期间使用时，它们会被标记为中间令牌，并按输入令牌计费。
- 当它们作为最终响应的一部分包含在内时，会按输出令牌计费。
最终回答中的最终总结按输出 token 计费。

Gemini API 在 API 响应中包含中间 token 数，因此您可以知道为什么系统会针对除初始提示之外的输入 token 向您收取费用。

请注意，生成的代码可以包含文本和多模态输出（例如图片）。

限制和最佳做法

模型只能生成和执行 Python 代码。它无法返回其他制品，例如媒体文件。
代码执行最长可运行 30 秒，超出这一时间即会超时。
在某些情况下，启用代码执行可能会导致模型输出的其他方面（例如，撰写故事）出现回归问题。
代码执行工具不支持文件 URI 作为输入/输出。不过，代码执行工具支持以内嵌字节的形式输入文件和输出图表。利用这些输入和输出功能，您可以上传 CSV 和文本文件，询问有关这些文件的问题，并让系统生成 Matplotlib 图表作为代码执行结果的一部分显示出来。内嵌字节支持的 MIME 类型包括 .cpp、.csv、.java、.jpeg、.js、.png、.py、.ts 和 .xml。

受支持的库

代码执行环境包含以下库。 您无法安装自己的库。

查看 Gemini Developer API 支持的库

attrs
国际象棋
contourpy
fpdf
geopandas
imageio
jinja2
joblib
jsonschema
jsonschema-specifications
lxml
matplotlib
mpmath
numpy
opencv-python
openpyxl
打包
pandas
pillow
protobuf
pylatex
pyparsing
PyPDF2
python-dateutil
python-docx
python-pptx
reportlab
scikit-learn
scipy
seaborn
six
striprtf
sympy
制表
tensorflow
toolz
xlrd

查看 Vertex AI Gemini API 支持的库

就您使用 Firebase AI Logic 的体验提供反馈

代码执行 使用集合让一切井井有条 根据您的偏好保存内容并对其进行分类。

代码执行与函数调用的比较

支持的模型

使用代码执行

准备工作

启用代码执行功能

Swift

Kotlin

Java

Web

Dart

Unity

在对话中使用代码执行

Swift

Kotlin

Java

Web

Dart

Unity

价格

限制和最佳做法

受支持的库

代码执行