GPT-5.5 深度解读：OpenAI 的半官方旗舰模型——Codex 通道抢先体验与本地化部署方案

# 方案一：使用 GPT-5.5 API（闭源旗舰）
import openai

client = openai.OpenAI(api_key="sk-...")
response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "生成一个完整的 Web 应用"}],
    temperature=0.7,
)
# 优点：最强编码能力，稳定可靠
# 缺点：每次调用有成本，数据离开本地，依赖 API 可用性
# 成本预估：~$0.05-0.15/千 tokens（假设定价）

import requests
import json

# 注意：以下接口可能随 OpenAI 政策变化而调整
# 需要有效的 Codex 订阅（FREE 或 PLUS $20/月）

def codex_generate(prompt: str, model: str = "gpt-5.5") -> str:
    """
    通过 Codex 的编程接口调用 GPT-5.5
    需要有效的 Codex 认证 token
    """
    headers = {
        "Authorization": "Bearer YOUR_CODEX_TOKEN",
        "Content-Type": "application/json",
    }
    
    payload = {
        "model": model,
        "messages": [
            {"role": "system", "content": "你是一个专业的编码助手。"},
            {"role": "user", "content": prompt}
        ],
        "max_tokens": 4096,
        "temperature": 0.7,
    }
    
    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers=headers,
        json=payload,
        timeout=60
    )
    
    if response.status_code == 200:
        return response.json()["choices"][0]["message"]["content"]
    else:
        raise Exception(f"API Error: {response.status_code} - {response.text}")

# 示例使用
result = codex_generate(
    "创建一个 Python FastAPI 后端，包含用户注册、登录和 JWT 认证"
)
print(result)

"""
数据分析 Pipeline - 模拟 GPT-5.5 的 Agentic 编码思维
展示从数据加载、清洗、分析到可视化的完整流程
"""
import pandas as pd
import numpy as np
from dataclasses import dataclass, field
from typing import Callable, Optional
from enum import Enum


class DataQuality(Enum):
    CLEAN = "clean"
    NEEDS_REVIEW = "needs_review"
    CORRUPTED = "corrupted"


@dataclass
class PipelineStep:
    """Pipeline 中的单个处理步骤"""
    name: str
    func: Callable
    description: str
    depends_on: list[str] = field(default_factory=list)
    
    def execute(self, data: pd.DataFrame) -> pd.DataFrame:
        try:
            result = self.func(data)
            print(f"✅ [{self.name}] 执行成功")
            return result
        except Exception as e:
            print(f"❌ [{self.name}] 执行失败: {e}")
            raise


class DataPipeline:
    """
    Agentic 风格的数据分析 Pipeline
    自动管理步骤依赖关系和错误恢复
    """
    
    def __init__(self):
        self.steps: dict[str, PipelineStep] = {}
        self.results: dict[str, pd.DataFrame] = {}
    
    def add_step(self, step: PipelineStep):
        self.steps[step.name] = step
    
    def _can_execute(self, step: PipelineStep) -> bool:
        """检查依赖是否满足"""
        return all(dep in self.results for dep in step.depends_on)
    
    def run(self) -> dict[str, pd.DataFrame]:
        """按依赖顺序执行所有步骤"""
        remaining = list(self.steps.values())
        max_iterations = len(remaining) * 2  # 防止死循环
        
        iteration = 0
        while remaining and iteration < max_iterations:
            iteration += 1
            for step in remaining[:]:
                if self._can_execute(step):
                    input_data = (
                        self.results[step.depends_on[-1]]
                        if step.depends_on
                        else pd.DataFrame()
                    )
                    self.results[step.name] = step.execute(input_data)
                    remaining.remove(step)
        
        if remaining:
            print(f"⚠️  未执行的步骤: {[s.name for s in remaining]}")
        
        return self.results


# ===== 定义实际的数据处理步骤 =====

def load_sample_data(df: pd.DataFrame) -> pd.DataFrame:
    """生成模拟数据"""
    np.random.seed(42)
    n = 1000
    return pd.DataFrame({
        "user_id": range(1, n + 1),
        "age": np.random.normal(35, 10, n).astype(int).clip(18, 80),
        "income": np.random.lognormal(10, 0.5, n).round(2),
        "score": np.random.beta(2, 5, n).round(3),
        "category": np.random.choice(["A", "B", "C", "D"], n),
        "is_active": np.random.choice([True, False], n, p=[0.7, 0.3]),
    })


def detect_outliers(df: pd.DataFrame) -> pd.DataFrame:
    """异常值检测"""
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    for col in numeric_cols:
        q1, q3 = df[col].quantile([0.25, 0.75])
        iqr = q3 - q1
        lower, upper = q1 - 1.5 * iqr, q3 + 1.5 * iqr
        outliers = df[(df[col] < lower) | (df[col] > upper)]
        print(f"  {col}: {len(outliers)} 个异常值 ({len(outliers)/len(df)*100:.1f}%)")
    return df


def compute_statistics(df: pd.DataFrame) -> pd.DataFrame:
    """计算分组统计"""
    stats = df.groupby("category").agg(
        count=("user_id", "count"),
        avg_age=("age", "mean"),
        avg_income=("income", "mean"),
        avg_score=("score", "mean"),
        active_rate=("is_active", "mean"),
    ).round(2)
    print("\n📊 分组统计结果:")
    print(stats)
    return stats


# ===== 构建并运行 Pipeline =====

pipeline = DataPipeline()
pipeline.add_step(PipelineStep("load_data", load_sample_data, "加载模拟数据"))
pipeline.add_step(PipelineStep("detect_outliers", detect_outliers, "异常值检测", ["load_data"]))
pipeline.add_step(PipelineStep("compute_stats", compute_statistics, "分组统计", ["load_data"]))

results = pipeline.run()
print(f"\n🎉 Pipeline 完成，共 {len(results)} 个输出")