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建议改进以优化Python代码性能。

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	内容
System	您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。
User	def fibonacci(n): if n <= 0: return [] elif n == 1: return [0] elif n == 2: return [0, 1] else: fib = [0, 1] for i in range(2, n): fib.append(fib[i-1] + fib[i-2]) return fib

​

示例输出

Python

def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b

解释：使用带有yield的生成器函数对于生成斐波那契数列更节省内存。它避免了在列表中创建和存储整个序列。相反，它根据需要即时生成每个数字。

---

​

API请求

Python

import anthropic

client = anthropic.Anthropic(  # defaults to os.environ.get("ANTHROPIC_API_KEY")
    api_key="my_api_key",
)
message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1000,
    temperature=0,
    system="您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib",
                }
            ],
        }
    ],
)
print(message.content)

TypeScript

import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({
  apiKey: "my_api_key", // defaults to process.env["ANTHROPIC_API_KEY"]
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-5",
  max_tokens: 1000,
  temperature: 0,
  system: "您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
  messages: [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "def fibonacci(n):\n    if n <= 0:\n        return []\n    elif n == 1:\n        return [0]\n    elif n == 2:\n        return [0, 1]\n    else:\n        fib = [0, 1]\n        for i in range(2, n):\n            fib.append(fib[i-1] + fib[i-2])\n    return fib"
        }
      ]
    }
  ]
});
console.log(msg);

AWS Bedrock Python

from anthropic import AnthropicBedrock

# See https://docs.claude.com/claude/reference/claude-on-amazon-bedrock
# for authentication options
client = AnthropicBedrock()

message = client.messages.create(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    max_tokens=1000,
    temperature=0,
    system="您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "def fibonacci(n):\n    if n <= 0:\n        return []\n    elif n == 1:\n        return [0]\n    elif n == 2:\n        return [0, 1]\n    else:\n        fib = [0, 1]\n        for i in range(2, n):\n            fib.append(fib[i-1] + fib[i-2])\n    return fib"
                }
            ]
        }
    ]
)
print(message.content)

AWS Bedrock TypeScript

import AnthropicBedrock from "@anthropic-ai/bedrock-sdk";

// See https://docs.claude.com/claude/reference/claude-on-amazon-bedrock
// for authentication options
const client = new AnthropicBedrock();

const msg = await client.messages.create({
  model: "anthropic.claude-sonnet-4-5-20250929-v1:0",
  max_tokens: 1000,
  temperature: 0,
  system: "您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
  messages: [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "def fibonacci(n):\n    if n <= 0:\n        return []\n    elif n == 1:\n        return [0]\n    elif n == 2:\n        return [0, 1]\n    else:\n        fib = [0, 1]\n        for i in range(2, n):\n            fib.append(fib[i-1] + fib[i-2])\n    return fib"
        }
      ]
    }
  ]
});
console.log(msg);

Vertex AI Python

from anthropic import AnthropicVertex

client = AnthropicVertex()

message = client.messages.create(
    model="claude-sonnet-4@20250514",
    max_tokens=1000,
    temperature=0,
    system="您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "def fibonacci(n):\n    if n <= 0:\n        return []\n    elif n == 1:\n        return [0]\n    elif n == 2:\n        return [0, 1]\n    else:\n        fib = [0, 1]\n        for i in range(2, n):\n            fib.append(fib[i-1] + fib[i-2])\n    return fib"
                }
            ]
        }
    ]
)
print(message.content)

Vertex AI TypeScript

import { AnthropicVertex } from '@anthropic-ai/vertex-sdk';

// Reads from the `CLOUD_ML_REGION` & `ANTHROPIC_VERTEX_PROJECT_ID` environment variables.
// Additionally goes through the standard `google-auth-library` flow.
const client = new AnthropicVertex();

const msg = await client.messages.create({
model: "claude-sonnet-4@20250514",
max_tokens: 1000,
temperature: 0,
system: "您的任务是分析提供的Python代码片段并建议改进以优化其性能。识别可以使代码更高效、更快速或更少资源密集的区域。提供具体的优化建议，并解释这些更改如何增强代码的性能。优化后的代码应保持与原始代码相同的功能，同时展现出改进的效率。",
messages: [
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
});
console.log(msg);