工具

代码执行工具

代码执行工具使 Grok 能够实时编写和执行 Python 代码，极大地扩展了其超越文本生成的能力。这一强大功能允许 Grok 执行精确计算、复杂数据分析、统计计算以及解决仅通过文本无法解决的数学问题。

主要功能

数学计算：精确解决复杂方程式、执行统计分析并处理数值计算
数据分析：处理数据集，从提示中提取洞察
金融建模：构建金融模型、计算风险指标并执行量化分析
科学计算：处理科学计算、模拟和数据转换
代码生成与测试：实时编写、测试和调试 Python 代码片段

何时使用代码执行

代码执行工具在以下场景中特别有价值：

数值问题：当您需要精确计算而非近似值时
数据处理：分析来自提示的复杂数据
复杂逻辑：需要中间结果的多步计算
验证：双重检查数学结果或验证假设

SDK 支持

代码执行工具在多个 SDK 和 API 中可用，但命名约定不同：

SDK/API	工具名称	描述
xAI SDK	`code_execution`	原生 xAI SDK 实现
OpenAI Responses API	`code_interpreter`	兼容 OpenAI API 格式
Vercel AI SDK	`xai.tools.codeExecution()`	Vercel AI SDK 集成

此工具也支持所有与 Responses API 兼容的 SDK。

实现示例

以下是全面的示例，展示如何在不同平台和用例中集成代码执行工具。

基础计算

python

import os

from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import code_execution

client = Client(api_key=os.getenv("XAI_API_KEY"))
chat = client.chat.create(
    model="grok-4.5",  # reasoning model
    tools=[code_execution()],
    include=["verbose_streaming"],
)

# Ask for a mathematical calculation
chat.append(user("Calculate the compound interest for $10,000 at 5% annually for 10 years"))

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nFinal Response:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)
print("\\n\\nServer Side Tool Calls:")
print(response.tool_calls)

python

import os
from openai import OpenAI

api_key = os.getenv("XAI_API_KEY")
client = OpenAI(
    api_key=api_key,
    base_url="https://api.x.ai/v1",
)

response = client.responses.create(
    model="grok-4.5",
    input=[
        {
            "role": "user",
            "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years",
        },
    ],
    tools=[
        {
            "type": "code_interpreter",
        },
    ],
)

print(response)

python

import os
import requests

url = "https://api.x.ai/v1/responses"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('XAI_API_KEY')}"
}
payload = {
    "model": "grok-4.5",
    "input": [
        {
            "role": "user",
            "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years"
        }
    ],
    "tools": [
        {
            "type": "code_interpreter",
        }
    ]
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())

bash

curl https://api.x.ai/v1/responses \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer $XAI_API_KEY" \\
  -d '{
  "model": "grok-4.5",
  "input": [
    {
      "role": "user",
      "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years"
    }
  ],
  "tools": [
    {
      "type": "code_interpreter"
    }
  ]
}'

javascript

import { xai } from '@ai-sdk/xai';
import { generateText } from 'ai';

const { text } = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: 'Calculate the compound interest for $10,000 at 5% annually for 10 years',
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
});

console.log(text);

数据分析

python

import os
from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import code_execution

client = Client(api_key=os.getenv("XAI_API_KEY"))

# Multi-turn conversation with data analysis
chat = client.chat.create(
    model="grok-4.5",  # reasoning model
    tools=[code_execution()],
    include=["verbose_streaming"],
)

# Step 1: Load and analyze data
chat.append(user("""
I have sales data for Q1-Q4: [120000, 135000, 98000, 156000].
Please analyze this data and create a visualization showing:
1. Quarterly trends
2. Growth rates
3. Statistical summary
"""))

print("##### Step 1: Data Analysis #####\\n")

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nAnalysis Results:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)

chat.append(response)

# Step 2: Follow-up analysis
chat.append(user("Now predict Q1 next year using linear regression"))

print("\\n\\n##### Step 2: Prediction Analysis #####\\n")

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nPrediction Results:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)
print("\\n\\nServer Side Tool Calls:")
print(response.tool_calls)

javascript

import { xai } from '@ai-sdk/xai';
import { generateText } from 'ai';

// Step 1: Load and analyze data
const step1 = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: \`I have sales data for Q1-Q4: [120000, 135000, 98000, 156000].
Please analyze this data and create a visualization showing:
1. Quarterly trends
2. Growth rates
3. Statistical summary\`,
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
});

console.log('##### Step 1: Data Analysis #####');
console.log(step1.text);

// Step 2: Follow-up analysis using previousResponseId
const step2 = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: 'Now predict Q1 next year using linear regression',
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
  providerOptions: {
    xai: {
      previousResponseId: step1.response.id,
    },
  },
});

console.log('##### Step 2: Prediction Analysis #####');
console.log(step2.text);

最佳实践

1. 请求要具体

提供清晰、详细的指令，说明您希望代码完成什么任务：

python

# Good: Specific and clear
"Calculate the correlation matrix for these variables and highlight correlations above 0.7"

# Avoid: Vague requests  
"Analyze this data"

2. 提供上下文和数据格式

始终指定数据格式和任何数据约束，并提供尽可能多的上下文：

python

# Good: Includes data format and requirements
"""
Here's my CSV data with columns: date, revenue, costs
Please calculate monthly profit margins and identify the best-performing month.
Data: [['2024-01', 50000, 35000], ['2024-02', 55000, 38000], ...]
"""

3. 使用适当的模型设置

Temperature：对于数学计算，使用较低值（0.0-0.3）
Model：使用推理模型如 grok-4.5 以获得更好的代码生成效果

常见用例

金融分析

python

# Portfolio optimization, risk calculations, option pricing
"Calculate the Sharpe ratio for a portfolio with returns [0.12, 0.08, -0.03, 0.15] and risk-free rate 0.02"

统计分析

python

# Hypothesis testing, regression analysis, probability distributions
"Perform a t-test to compare these two groups and interpret the p-value: Group A: [23, 25, 28, 30], Group B: [20, 22, 24, 26]"

科学计算

python

# Simulations, numerical methods, equation solving
"Solve this differential equation using numerical methods: dy/dx = x^2 + y, with initial condition y(0) = 1"

限制与注意事项

执行环境：代码在沙盒 Python 环境中运行，预装了常用库
时间限制：复杂计算可能有执行时间限制
内存使用：大数据集可能会遇到内存限制
包可用性：大多数流行的 Python 包（NumPy、Pandas、Matplotlib、SciPy）都可用
文件 I/O：出于安全原因，文件系统访问有限

安全说明

代码执行在安全、隔离的环境中发生
无外部网络或文件系统访问权限
临时执行上下文，不会在请求之间持久化
所有计算都是无状态且安全的

工具 ​

代码执行工具 ​

主要功能 ​

何时使用代码执行 ​

SDK 支持 ​

实现示例 ​

基础计算 ​

数据分析 ​

最佳实践 ​

1. 请求要具体 ​

2. 提供上下文和数据格式 ​

3. 使用适当的模型设置 ​

常见用例 ​

金融分析 ​

统计分析 ​

科学计算 ​

限制与注意事项 ​

安全说明 ​

工具

代码执行工具

主要功能

何时使用代码执行

SDK 支持

实现示例

基础计算

数据分析

最佳实践

1. 请求要具体

2. 提供上下文和数据格式

3. 使用适当的模型设置

常见用例

金融分析

统计分析

科学计算

限制与注意事项

安全说明