代码顾问 - Anthropic

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	内容
System	你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。识别代码可以变得更高效、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能够提升代码的性能。优化后的代码应保持与原始代码相同的功能，同时展示改进的效率。
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序列更加内存高效。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

import anthropic

client = anthropic.Anthropic(  # 默认为os.environ.get("ANTHROPIC_API_KEY")
    api_key="my_api_key",
)
message = client.messages.create(
    model="claude-opus-4-20250514",
    max_tokens=1000,
    temperature=0,
    system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
    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)

import anthropic

client = anthropic.Anthropic(  # 默认为os.environ.get("ANTHROPIC_API_KEY")
    api_key="my_api_key",
)
message = client.messages.create(
    model="claude-opus-4-20250514",
    max_tokens=1000,
    temperature=0,
    system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
    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)

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

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

const msg = await anthropic.messages.create({
  model: "claude-opus-4-20250514",
  max_tokens: 1000,
  temperature: 0,
  system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
  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);

from anthropic import AnthropicBedrock

# 有关身份验证选项，请参阅https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
client = AnthropicBedrock()

message = client.messages.create(
    model="anthropic.claude-opus-4-20250514-v1:0",
    max_tokens=1000,
    temperature=0,
    system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
    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)

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

// 有关身份验证选项，请参阅https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
const client = new AnthropicBedrock();

const msg = await client.messages.create({
  model: "anthropic.claude-opus-4-20250514-v1:0",
  max_tokens: 1000,
  temperature: 0,
  system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
  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);

from anthropic import AnthropicVertex

client = AnthropicVertex()

message = client.messages.create(
    model="claude-3-7-sonnet-v1@20250219",
    max_tokens=1000,
    temperature=0,
    system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
    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)

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

// 从`CLOUD_ML_REGION`和`ANTHROPIC_VERTEX_PROJECT_ID`环境变量读取。
// 此外，还通过标准的`google-auth-library`流程。
const client = new AnthropicVertex();

const msg = await client.messages.create({
model: "claude-3-7-sonnet-v1@20250219",
max_tokens: 1000,
temperature: 0,
system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
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);

资源

​示例输出

​API请求

示例输出

API请求