ChatGPT Plus / Pro + Codex 全栈自动化开发实战：2026年9月2日 从智能体工作流到企业级代码交付的完整技术指南

1. 引言：ChatGPT Plus / Pro + Codex 的开发者新范式

2026 年，软件开发正经历一场由大语言模型驱动的深刻变革。ChatGPT Plus / Pro 与 Codex 的组合，已经从简单的「对话问答 + 代码补全」演进为覆盖需求分析、架构设计、编码实现、测试验证、部署运维的全链路智能开发平台。本文基于 2026 年 9 月 2 日的最新版本特性，从工程实践角度系统解析这一组合的技术能力、API 编程模型、智能体编排策略与真实落地案例。

需要特别说明的是，本文聚焦于纯技术实现与工程方法论，不涉及任何订阅、套餐或充值相关内容。我们将以开发者视角，探讨如何通过编程方式最大化释放 ChatGPT Plus / Pro 与 Codex 的协同生产力。

2. 平台能力全景与技术架构

2.1 ChatGPT Plus / Pro 的模型分层与能力边界

ChatGPT Plus 与 Pro 在模型访问权限、上下文窗口与推理深度上存在梯度差异，但共享统一的 API 基础设施。Plus 用户可调用 GPT-4o 系列多模态模型，Pro 用户则解锁 o1 系列深度推理模型与更高频次的资源配额。

# 模型能力矩阵与选型参考
MODEL_REGISTRY = {
    "gpt-4o": {
        "context_window": 128_000,
        "multimodal": True,          # 图像/文本/音频输入
        "code_interpreter": True,    # 内置代码执行沙箱
        "reasoning": "standard",
        "typical_latency_ms": 800
    },
    "gpt-4o-mini": {
        "context_window": 128_000,
        "multimodal": True,
        "code_interpreter": True,
        "reasoning": "lightweight",
        "typical_latency_ms": 350
    },
    "o1": {
        "context_window": 200_000,
        "multimodal": False,
        "code_interpreter": True,
        "reasoning": "advanced",
        "typical_latency_ms": 5000
    },
    "o1-pro": {
        "context_window": 200_000,
        "multimodal": False,
        "code_interpreter": True,
        "reasoning": "deep",
        "typical_latency_ms": 12000
    }
}

def select_model(task: str) -> str:
    """根据任务复杂度选择最合适的模型"""
    if task in ("simple_qa", "formatting", "extraction"):
        return "gpt-4o-mini"
    if task in ("code_generation", "refactoring", "documentation"):
        return "gpt-4o"
    if task in ("architecture_design", "complex_debugging"):
        return "o1"
    if task in ("security_audit", "algorithm_design"):
        return "o1-pro"
    return "gpt-4o"

2.2 Codex 智能体执行环境

Codex 已进化为具备完整沙箱执行能力的自主智能体。它能在隔离容器中运行代码、读写文件系统、执行测试命令、安装依赖，并将每一步执行结果实时回传主对话线程。这种「可执行」的对话模型，是传统纯文本 LLM 无法替代的核心差异。
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纯推理/文本生成
通过
失败
开发者任务请求
ChatGPT 主模型

语义理解与规划
Codex 沙箱环境
直接返回结果
代码生成与文件操作
容器内执行与测试
执行结果验证
结构化结果回传
错误诊断与自动修复
主对话线程汇总

2.3 组合架构的协同价值

ChatGPT Plus / Pro 负责「思考」，Codex 负责「执行」。前者擅长需求理解、方案设计、代码审查等认知密集型任务；后者擅长代码落地、环境操作、测试运行等执行密集型任务。二者通过 API 层无缝衔接，形成完整的「规划-执行-验证」闭环。

3. 开发环境搭建与 API 接入

3.1 凭证管理与安全配置

所有程序化调用都需要通过 OpenAI 平台获取 API Key。强烈建议使用环境变量或密钥管理服务，避免硬编码泄露。

# .env 文件示例
export OPENAI_API_KEY="sk-proj-your-key-here"
export OPENAI_ORG_ID="org-your-org-id"
export CODEX_SANDBOX_TIMEOUT="120"

# config.py
import os
from dotenv import load_dotenv

load_dotenv()

class Settings:
    OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
    OPENAI_ORG_ID: str = os.getenv("OPENAI_ORG_ID", "")
    DEFAULT_MODEL: str = "gpt-4o"
    CODEX_MODEL: str = "codex-1"
    REQUEST_TIMEOUT: int = 60

settings = Settings()

3.2 安装与初始化官方 SDK

pip install --upgrade openai python-dotenv

from openai import OpenAI
from config import settings

# 统一客户端初始化
client = OpenAI(
    api_key=settings.OPENAI_API_KEY,
    organization=settings.OPENAI_ORG_ID,
    timeout=settings.REQUEST_TIMEOUT,
    max_retries=3
)

# 验证连接
def verify_connection() -> bool:
    try:
        response = client.models.list()
        print(f"连接成功，可用模型数量: {len(response.data)}")
        return True
    except Exception as e:
        print(f"连接失败: {e}")
        return False

4. ChatGPT Plus / Pro 核心 API 编程模式

4.1 对话补全与系统提示工程

def chat_completion(
    user_prompt: str,
    system_prompt: str = "你是一位资深软件架构师。",
    model: str = "gpt-4o",
    temperature: float = 0.7
) -> str:
    """基础对话补全"""
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ],
        temperature=temperature,
        max_tokens=4096
    )
    return response.choices[0].message.content

# 使用示例
architecture_advice = chat_completion(
    "请设计一个高可用的微服务网关架构，要求支持限流、熔断与灰度发布。"
)
print(architecture_advice)

4.2 流式输出与实时交互

def stream_chat(user_prompt: str, model: str = "gpt-4o"):
    """流式输出，适合长文本生成与实时交互场景"""
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": user_prompt}],
        stream=True
    )
    
    full_response = []
    for chunk in stream:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            print(content, end="", flush=True)
            full_response.append(content)
    
    return "".join(full_response)

# 使用示例
result = stream_chat("请详细解释分布式事务的 SAGA 模式，并给出代码示例。")

4.3 结构化输出与 JSON Mode

import json
from typing import Any

def structured_completion(
    prompt: str,
    system_prompt: str = "你是一个配置生成器，只输出合法 JSON。",
    model: str = "gpt-4o"
) -> dict[str, Any]:
    """强制模型输出结构化 JSON"""
    response = client.chat.completions.create(
        model=model,
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": prompt}
        ]
    )
    return json.loads(response.choices[0].message.content)

# 使用示例：生成 Docker Compose 配置
docker_config = structured_completion(
    "生成一个包含 PostgreSQL、Redis、Nginx 的 docker-compose.yml 配置描述"
)
print(json.dumps(docker_config, indent=2, ensure_ascii=False))

4.4 多模态输入处理

import base64

def analyze_image_with_text(image_path: str, question: str) -> str:
    """结合图片与文本的多模态分析"""
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode("utf-8")
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": question},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/png;base64,{image_data}"
                        }
                    }
                ]
            }
        ],
        max_tokens=2048
    )
    return response.choices[0].message.content

# 使用示例：分析系统架构图
analysis = analyze_image_with_text(
    "architecture.png",
    "请分析这张架构图中的组件关系，并指出潜在的单点故障。"
)

5. Codex 智能体深度实战

5.1 会话创建与状态管理

Codex 的核心优势在于持久化会话状态与文件系统访问能力。每个会话都拥有独立的沙箱工作目录。

class CodexSession:
    """Codex 会话管理器"""
    def __init__(self, instructions: str = ""):
        self.client = OpenAI()
        self.session = self.client.beta.codex.sessions.create(
            model="codex-1",
            instructions=instructions or "你是一个全栈开发助手，精通 Python、TypeScript 与 DevOps。"
        )
        self.session_id = self.session.id
        print(f"Codex 会话已创建: {self.session_id}")
    
    def execute(self, task: str) -> list:
        """向会话发送任务并收集执行事件"""
        response = self.client.beta.codex.sessions.respond(
            session_id=self.session_id,
            content=task
        )
        return self._parse_events(response)
    
    def _parse_events(self, response) -> list:
        """解析 Codex 返回的事件流"""
        events = []
        for event in response.events:
            if event.type == "code_execution":
                events.append({
                    "type": "execution",
                    "code": event.code,
                    "output": event.output,
                    "exit_code": event.exit_code
                })
            elif event.type == "file_change":
                events.append({
                    "type": "file",
                    "path": event.path,
                    "action": event.action
                })
            elif event.type == "message":
                events.append({
                    "type": "message",
                    "content": event.content
                })
        return events
    
    def close(self):
        """关闭会话"""
        self.client.beta.codex.sessions.delete(session_id=self.session_id)
        print(f"会话 {self.session_id} 已关闭")

# 使用示例
codex = CodexSession("你负责创建并测试一个 FastAPI 项目。")
events = codex.execute("""
请完成以下任务：
1. 创建项目结构 src/ 和 tests/
2. 实现一个带数据库连接的 FastAPI 应用
3. 编写单元测试并运行
4. 输出测试结果
""")
for event in events:
    print(event)
codex.close()

5.2 文件系统操作与项目脚手架生成

def scaffold_project(project_name: str, framework: str = "fastapi") -> dict:
    """使用 Codex 自动生成完整项目脚手架"""
    codex = CodexSession(f"你是项目脚手架生成专家，擅长 {framework}。")
    
    task = f"""
    在当前工作目录创建名为 {project_name} 的项目：
    1. 初始化 {framework} 项目结构
    2. 创建主应用入口文件
    3. 生成依赖管理文件（requirements.txt 或 pyproject.toml）
    4. 创建基础配置文件（.env.example, .gitignore）
    5. 编写一个健康检查接口
    6. 运行测试确认项目可启动
    """
    
    events = codex.execute(task)
    codex.close()
    
    # 汇总文件变更
    files_created = [
        e["path"] for e in events 
        if e["type"] == "file" and e["action"] == "create"
    ]
    return {"files": files_created, "events": events}

# 使用示例
result = scaffold_project("my-api-service")
print(f"已创建 {len(result['files'])} 个文件")

5.3 代码修复与迭代优化

def auto_fix_code(code: str, error_message: str) -> str:
    """利用 Codex 自动修复代码错误"""
    codex = CodexSession("你是代码修复专家，擅长分析错误并给出修复方案。")
    
    task = f"""
    以下代码执行时出现错误，请分析原因并修复：
    
    代码：
    ```python
    {code}
    ```
    
    错误信息：
    ```
    {error_message}
    ```
    
    请直接输出修复后的完整代码，并简要说明修复原因。
    """
    
    events = codex.execute(task)
    codex.close()
    
    # 提取修复后的代码
    for event in reversed(events):
        if event["type"] == "message":
            return event["content"]
    return ""

# 使用示例
buggy_code = """
def calculate_average(numbers):
    total = sum(numbers)
    return total / len(numbers)  # 空列表会除零
"""
fixed = auto_fix_code(buggy_code, "ZeroDivisionError: division by zero")
print(fixed)

6. 高级智能体编排与工作流设计

6.1 规划-执行-验证三阶段架构

将 ChatGPT 的规划能力与 Codex 的执行能力组合，构建健壮的自动化流水线：

from dataclasses import dataclass
from typing import Callable

@dataclass
class TaskResult:
    """任务执行结果"""
    status: str          # success / failed / needs_review
    plan: str
    code: str = ""
    test_output: str = ""
    review_comments: str = ""

class DevAgentPipeline:
    """规划-执行-验证流水线"""
    def __init__(self):
        self.planner = OpenAI()   # ChatGPT 负责规划
        self.executor = OpenAI()  # Codex 负责执行
    
    def plan_task(self, requirement: str) -> str:
        """阶段一：ChatGPT 生成详细实施计划"""
        response = self.planner.chat.completions.create(
            model="o1",
            messages=[
                {
                    "role": "system",
                    "content": """
                    你是资深技术负责人。请将需求拆解为：
                    1. 技术选型与架构决策
                    2. 分步骤实施计划（含依赖关系）
                    3. 潜在风险与应对策略
                    4. 验收标准
                    """
                },
                {"role": "user", "content": requirement}
            ]
        )
        return response.choices[0].message.content
    
    def execute_plan(self, plan: str) -> str:
        """阶段二：Codex 按计划执行"""
        session = self.executor.beta.codex.sessions.create(
            instructions="严格按照计划执行，每步完成后验证结果。"
        )
        response = self.executor.beta.codex.sessions.respond(
            session_id=session.id,
            content=f"请执行以下计划并输出结果：\n\n{plan}"
        )
        return self._collect_output(response)
    
    def review_result(self, code: str, plan: str) -> str:
        """阶段三：ChatGPT 审查执行结果"""
        response = self.planner.chat.completions.create(
            model="gpt-4o",
            messages=[
                {
                    "role": "system",
                    "content": "你是代码审查专家，对照计划检查实现质量。"
                },
                {
                    "role": "user",
                    "content": f"计划：\n{plan}\n\n实现代码：\n{code}"
                }
            ]
        )
        return response.choices[0].message.content
    
    def run(self, requirement: str) -> TaskResult:
        """执行完整流水线"""
        # 1. 规划
        plan = self.plan_task(requirement)
        print("=== 实施计划 ===")
        print(plan)
        
        # 2. 执行
        execution_output = self.execute_plan(plan)
        print("=== 执行输出 ===")
        print(execution_output)
        
        # 3. 审查
        review = self.review_result(execution_output, plan)
        print("=== 审查意见 ===")
        print(review)
        
        return TaskResult(
            status="success",
            plan=plan,
            test_output=execution_output,
            review_comments=review
        )

# 使用示例
pipeline = DevAgentPipeline()
result = pipeline.run(
    "实现一个带 JWT 认证的 Flask REST API，包含用户注册、登录和资料查询接口。"
)

6.2 函数调用与外部工具集成

import json

# 定义工具函数
def get_weather(city: str) -> str:
    """模拟天气查询"""
    weather_data = {
        "北京": "晴，25°C",
        "上海": "多云，28°C",
        "深圳": "雷阵雨，30°C"
    }
    return weather_data.get(city, f"{city}：暂无数据")

def calculate_expression(expression: str) -> float:
    """安全计算数学表达式"""
    import ast
    try:
        tree = ast.parse(expression, mode="eval")
        return eval(compile(tree, "<string>", "eval"))
    except Exception as e:
        return f"计算错误: {e}"

# 工具注册表
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "查询指定城市的实时天气",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "城市名称"}
                },
                "required": ["city"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "calculate_expression",
            "description": "计算数学表达式",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {"type": "string", "description": "数学表达式"}
                },
                "required": ["expression"]
            }
        }
    }
]

def execute_tool_call(tool_name: str, arguments: str) -> str:
    """执行工具调用"""
    args = json.loads(arguments)
    if tool_name == "get_weather":
        return get_weather(args["city"])
    elif tool_name == "calculate_expression":
        return str(calculate_expression(args["expression"]))
    return "未知工具"

def chat_with_tools(user_message: str) -> str:
    """带工具调用的对话"""
    messages = [{"role": "user", "content": user_message}]
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=TOOLS,
        tool_choice="auto"
    )
    
    # 处理工具调用
    while response.choices[0].message.tool_calls:
        tool_calls = response.choices[0].message.tool_calls
        messages.append(response.choices[0].message)
        
        for tool_call in tool_calls:
            result = execute_tool_call(
                tool_call.function.name,
                tool_call.function.arguments
            )
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": result
            })
        
        # 继续对话
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=TOOLS
        )
    
    return response.choices[0].message.content

# 使用示例
print(chat_with_tools("北京天气怎么样？顺便算一下 (15+27)*3 等于多少"))

6.3 上下文管理与记忆优化

from collections import deque
import tiktoken

class ConversationMemory:
    """基于 token 预算的对话记忆管理"""
    def __init__(self, max_tokens: int = 12000):
        self.history: deque = deque()
        self.max_tokens = max_tokens
        self.encoder = tiktoken.encoding_for_model("gpt-4o")
    
    def _count_tokens(self, messages: list) -> int:
        """估算消息 token 数"""
        total = 0
        for msg in messages:
            total += len(self.encoder.encode(msg["content"]))
            total += 4  # 每条消息的格式开销
        return total
    
    def add(self, role: str, content: str):
        """添加消息并自动裁剪"""
        self.history.append({"role": role, "content": content})
        self._trim()
    
    def _trim(self):
        """裁剪超出预算的历史消息"""
        while self._count_tokens(list(self.history)) > self.max_tokens:
            if len(self.history) <= 2:
                break
            # 保留 system 消息，丢弃最早的对话
            if self.history[0]["role"] == "system":
                self.history[1]  # 跳过 system
            self.history.popleft()
    
    def summarize_old_messages(self) -> str:
        """将早期消息压缩为摘要"""
        if len(self.history) < 10:
            return ""
        
        # 取前 80% 消息生成摘要
        split_idx = int(len(self.history) * 0.8)
        old_messages = list(self.history)[:split_idx]
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": "将以下对话压缩为简洁摘要，保留关键决策与信息。"
                },
                {
                    "role": "user",
                    "content": str(old_messages)
                }
            ]
        )
        summary = response.choices[0].message.content
        
        # 用摘要替换旧消息
        self.history = deque(
            [{"role": "system", "content": f"历史摘要: {summary}"}]
            + list(self.history)[split_idx:]
        )
        return summary
    
    def get_messages(self) -> list:
        """获取当前完整消息列表"""
        return list(self.history)

# 使用示例
memory = ConversationMemory(max_tokens=8000)
memory.add("system", "你是一个数据分析助手。")
memory.add("user", "请分析这份销售数据。")
memory.add("assistant", "好的，请提供数据文件。")
# ... 持续对话

7. 企业级实战案例：智能代码审查与 CI/CD 集成

7.1 系统架构设计

构建一个结合 ChatGPT 语义审查与 Codex 静态分析的自动化代码审查系统，并集成到 CI/CD 流水线：
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建议级
Git Push 触发
CI 拉取代码 Diff
ChatGPT 语义审查

逻辑/安全/规范
Codex 静态分析

pylint/bandit/mypy
审查结果合并
问题严重度
PR 标记失败
PR 评论提醒
开发者修复
合并代码

7.2 核心实现代码

import subprocess
import json
from pathlib import Path
from typing import Optional

class AICodeReviewBot:
    """AI 驱动的自动化代码审查机器人"""
    
    def __init__(self, repo_path: str = "."):
        self.repo_path = Path(repo_path)
        self.client = OpenAI()
        self.review_results = []
    
    def get_git_diff(self, base_branch: str = "main") -> str:
        """获取当前分支相对基准分支的代码变更"""
        result = subprocess.run(
            ["git", "diff", f"{base_branch}...HEAD"],
            cwd=self.repo_path,
            capture_output=True,
            text=True
        )
        if result.returncode != 0:
            raise RuntimeError(f"Git diff 失败: {result.stderr}")
        return result.stdout
    
    def semantic_review(self, diff: str) -> dict:
        """使用 ChatGPT 进行语义级代码审查"""
        response = self.client.chat.completions.create(
            model="o1",
            response_format={"type": "json_object"},
            messages=[
                {
                    "role": "system",
                    "content": """
                    你是资深代码审查专家。请审查代码变更并输出 JSON：
                    {
                        "blocking_issues": [{"file": "", "line": 0, "issue": "", "severity": "critical"}],
                        "suggestions": [{"file": "", "issue": "", "suggestion": ""}],
                        "summary": "总体评价"
                    }
                    关注：逻辑错误、安全漏洞、性能问题、并发风险、代码规范。
                    """
                },
                {
                    "role": "user",
                    "content": f"请审查以下代码变更：\n\n{diff}"
                }
            ]
        )
        return json.loads(response.choices[0].message.content)
    
    def static_analysis(self) -> dict:
        """使用 Codex 执行静态分析工具链"""
        session = self.client.beta.codex.sessions.create(
            instructions="""
            你是静态分析专家。请执行以下工具并汇总结果：
            1. pylint（代码规范）
            2. bandit（安全漏洞）
            3. mypy（类型检查）
            4. pytest（单元测试）
            """
        )
        
        response = self.client.beta.codex.sessions.respond(
            session_id=session.id,
            content=f"""
            请对 {self.repo_path} 目录执行完整的静态分析：
            1. 运行 pylint --output-format=json
            2. 运行 bandit -r .
            3. 运行 mypy .
            4. 运行 pytest --tb=short
            5. 汇总所有发现的问题，按严重程度排序
            """
        )
        
        # 收集执行结果
        analysis_output = []
        for event in response.events:
            if event.type == "code_execution":
                analysis_output.append({
                    "tool": event.code.split("\n")[0][:50],
                    "output": event.output
                })
        
        return {"results": analysis_output}
    
    def merge_reports(self, semantic: dict, static: dict) -> dict:
        """合并语义审查与静态分析结果"""
        merged = {
            "blocking_issues": semantic.get("blocking_issues", []),
            "suggestions": semantic.get("suggestions", []),
            "static_findings": [],
            "summary": semantic.get("summary", "")
        }
        
        # 解析静态分析输出
        for result in static.get("results", []):
            output = result.get("output", "")
            if "pylint" in result.get("tool", "").lower():
                merged["static_findings"].append({
                    "tool": "pylint",
                    "detail": output[:500]
                })
            elif "bandit" in result.get("tool", "").lower():
                merged["static_findings"].append({
                    "tool": "bandit",
                    "detail": output[:500]
                })
        
        return merged
    
    def generate_pr_comment(self, report: dict) -> str:
        """生成 PR 评论"""
        lines = ["## 🤖 AI 代码审查报告", ""]
        
        if report["blocking_issues"]:
            lines.append("### 🚫 阻断性问题")
            for issue in report["blocking_issues"][:10]:
                lines.append(
                    f"- **{issue.get('file', '未知')}**:{issue.get('line', '?')} "
                    f"- {issue['issue']} (严重度: {issue.get('severity', 'high')})"
                )
            lines.append("")
        
        if report["suggestions"]:
            lines.append("### 💡 改进建议")
            for suggestion in report["suggestions"][:10]:
                lines.append(f"- {suggestion['issue']}: {suggestion.get('suggestion', '')}")
            lines.append("")
        
        if report["static_findings"]:
            lines.append("### 🔍 静态分析发现")
            for finding in report["static_findings"]:
                lines.append(f"- **{finding['tool']}**: {finding['detail'][:200]}")
            lines.append("")
        
        lines.append(f"### 📋 总结\n{report['summary']}")
        return "\n".join(lines)
    
    def run_review(self) -> str:
        """执行完整审查流程"""
        print("1. 获取代码变更...")
        diff = self.get_git_diff()
        
        print("2. ChatGPT 语义审查中...")
        semantic_result = self.semantic_review(diff)
        
        print("3. Codex 静态分析中...")
        static_result = self.static_analysis()
        
        print("4. 合并审查报告...")
        merged_report = self.merge_reports(semantic_result, static_result)
        
        print("5. 生成 PR 评论...")
        comment = self.generate_pr_comment(merged_report)
        
        return comment

# 使用示例
bot = AICodeReviewBot(repo_path="./my-project")
review_comment = bot.run_review()
print(review_comment)

7.3 CI/CD 集成配置

# .github/workflows/ai-review.yml
name: AI Code Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
      
      - name: Install dependencies
        run: |
          pip install openai pylint bandit mypy pytest
      
      - name: Run AI Code Review
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: |
          python review_bot.py > review_output.md
      
      - name: Post PR Comment
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            const comment = fs.readFileSync('review_output.md', 'utf8');
            await github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: comment
            });

8. 性能优化与资源管理

8.1 多级缓存策略

import hashlib
import json
import time
from typing import Any, Callable
import redis

class TieredCache:
    """多级缓存：内存 + Redis"""
    
    def __init__(self, redis_host: str = "localhost", redis_port: int = 6379):
        self.memory_cache = {}
        self.memory_ttl = {}
        self.redis = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
        self.default_ttl = 3600  # 1小时
    
    def _generate_key(self, model: str, messages: list, **params) -> str:
        """生成缓存键"""
        content = json.dumps({
            "model": model,
            "messages": messages,
            **params
        }, sort_keys=True)
        return hashlib.sha256(content.encode()).hexdigest()
    
    def get(self, key: str) -> Optional[Any]:
        """从缓存读取"""
        # 1. 内存缓存
        if key in self.memory_cache:
            if time.time() < self.memory_ttl.get(key, 0):
                return self.memory_cache[key]
            else:
                del self.memory_cache[key]
                del self.memory_ttl[key]
        
        # 2. Redis 缓存
        cached = self.redis.get(key)
        if cached:
            # 回填内存缓存
            self.memory_cache[key] = json.loads(cached)
            self.memory_ttl[key] = time.time() + 300  # 内存 TTL 5分钟
            return json.loads(cached)
        
        return None
    
    def set(self, key: str, value: Any, ttl: int = None):
        """写入缓存"""
        ttl = ttl or self.default_ttl
        serialized = json.dumps(value, ensure_ascii=False)
        
        # 写入内存
        self.memory_cache[key] = value
        self.memory_ttl[key] = time.time() + min(ttl, 300)
        
        # 写入 Redis
        self.redis.setex(key, ttl, serialized)
    
    def get_or_compute(
        self,
        model: str,
        messages: list,
        compute_fn: Callable,
        ttl: int = 3600,
        **params
    ) -> Any:
        """缓存优先的计算模式"""
        key = self._generate_key(model, messages, **params)
        
        # 尝试从缓存获取
        cached = self.get(key)
        if cached is not None:
            print(f"缓存命中: {key[:16]}...")
            return cached
        
        # 计算新结果
        print(f"缓存未命中，计算中...")
        result = compute_fn()
        
        # 写入缓存
        self.set(key, result, ttl)
        return result

# 使用示例
cache = TieredCache()

def expensive_api_call():
    """模拟昂贵的 API 调用"""
    time.sleep(2)  # 模拟延迟
    return {"result": "这是计算结果", "timestamp": time.time()}

# 第一次调用（未命中缓存）
result1 = cache.get_or_compute(
    model="gpt-4o",
    messages=[{"role": "user", "content": "复杂查询"}],
    compute_fn=expensive_api_call
)

# 第二次调用（命中缓存）
result2 = cache.get_or_compute(
    model="gpt-4o",
    messages=[{"role": "user", "content": "复杂查询"}],
    compute_fn=expensive_api_call
)

print(f"结果一致: {result1 == result2}")

8.2 并发请求与异步处理

import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Any

class AsyncOpenAIWorker:
    """异步并发请求管理器"""
    
    def __init__(self, max_concurrency: int = 10):
        self.semaphore = asyncio.Semaphore(max_concurrency)
        self.client = OpenAI()
        self.executor = ThreadPoolExecutor(max_workers=max_concurrency)
    
    async def _async_call(self, func, *args, **kwargs):
        """在线程池中执行同步 API 调用"""
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(
            self.executor,
            lambda: func(*args, **kwargs)
        )
    
    async def process_item(
        self,
        item: Dict[str, Any],