# 大模型全链路生产保障体系 - 核心架构调度
from enum import Enum
from typing import Dict, List, Optional
# 定义生产场景枚举
class ModelEnv(Enum):
    OFFLINE = "offline"  # 离线训练/微调场景
    ONLINE = "online"    # 线上推理生产场景
# 核心保障体系总控类
class LLMPrdSystem:
    def __init__(self):
        # 初始化三大核心体系
        self.eval_system = ModelEvalSystem()       # 评测体系
        self.trace_system = LinkTraceSystem()     # 链路追踪体系
        self.guarantee_system = PrdGuaranteeSystem() # 生产保障体系
        self.env: ModelEnv = ModelEnv.OFFLINE
    def set_env(self, env: str) -> None:
        """设置运行环境，切换管控策略"""
        self.env = ModelEnv(env)
        print(f"【体系切换】当前生效环境：{self.env.value}")
    def full_link_guard(self, request_data: Dict) -> Dict:
        """全链路保障核心调度：评测-追踪-保障一体化执行"""
        # 1. 前置评测校验
        eval_result = self.eval_system.eval_check(request_data, self.env.value)
        if not eval_result["pass"]:
            return {"code": 400, "msg": "模型校验不通过", "data": eval_result}
        
        # 2. 全链路追踪埋点
        trace_id = self.trace_system.trace_start(request_data)
        
        # 3. 生产安全保障管控
        guard_result = self.guarantee_system.risk_control(request_data, self.env.value)
        
        # 4. 链路收尾记录
        self.trace_system.trace_end(trace_id, guard_result)
        return {"code": 200, "msg": "执行成功", "trace_id": trace_id, "data": guard_result}
# 空基类，后续章节逐步实现完整能力
class ModelEvalSystem:
    def eval_check(self, data: Dict, env: str) -> Dict:
        return {"pass": True, "score": 90, "env": env}
class LinkTraceSystem:
    def trace_start(self, data: Dict) -> str:
        import uuid
        return str(uuid.uuid4())
    def trace_end(self, trace_id: str, result: Dict) -> None:
        pass
class PrdGuaranteeSystem:
    def risk_control(self, data: Dict, env: str) -> Dict:
        return {"risk_pass": True, "control_strategy": "normal"}
# 体系初始化测试
if __name__ == "__main__":
    llm_prd = LLMPrdSystem()
    llm_prd.set_env("online")
    test_req = {"prompt": "AI业务查询", "user_id": "123456", "model_version": "v2.0"}
    res = llm_prd.full_link_guard(test_req)
    print("全链路保障执行结果：", res)

# 大模型全维度评测体系实现
import time
import re
from typing import Dict, List, Tuple
class LLMFullEvaluator:
    def __init__(self):
        # 初始化评测阈值
        self.quality_threshold = 85    # 质量合格分数线
        self.cost_threshold = 0.02    # 单条推理成本阈值
        self.safe_pattern = re.compile(r"手机号|身份证|银行卡|隐私地址")
    def eval_quality(self, model_output: str, standard_ans: str) -> float:
        """质量评测：计算输出匹配度、流畅度、幻觉规避得分"""
        # 简单相似度评分（实战可替换为bert相似度、llm评分）
        same_char = set(model_output) & set(standard_ans)
        score = len(same_char) / len(set(standard_ans)) * 100
        # 限制分数区间
        return round(min(score, 100), 2)
    def eval_safe(self, content: str) -> Tuple[bool, List[str]]:
        """安全评测：检测敏感数据泄露"""
        risk_words = self.safe_pattern.findall(content)
        if risk_words:
            return False, risk_words
        return True, []
    def eval_cost(self, token_num: int, cost_per_token: float = 0.0001) -> float:
        """成本评测：计算单条推理成本"""
        return round(token_num * cost_per_token, 4)
    def offline_eval(self, test_datas: List[Dict]) -> Dict:
        """离线批量评测：模型上线前全量检测"""
        total_score = 0
        risk_count = 0
        cost_list = []
        for data in test_datas:
            score = self.eval_quality(data["output"], data["standard"])
            safe_pass, _ = self.eval_safe(data["output"])
            cost = self.eval_cost(len(data["output"]))
            total_score += score
            if not safe_pass:
                risk_count += 1
            cost_list.append(cost)
        # 计算综合指标
        avg_score = round(total_score / len(test_datas), 2)
        avg_cost = round(sum(cost_list) / len(cost_list), 4)
        safe_rate = round((len(test_datas) - risk_count) / len(test_datas) * 100, 2)
        # 上线判定
        pass_flag = avg_score >= self.quality_threshold and avg_cost <= self.cost_threshold and safe_rate == 100
        return {
            "avg_quality_score": avg_score,
            "safe_rate": safe_rate,
            "avg_cost": avg_cost,
            "pass_online": pass_flag,
            "sample_num": len(test_datas)
        }
    def online_eval(self, req_data: Dict) -> Dict:
        """线上单条实时评测"""
        score = self.eval_quality(req_data["output"], req_data.get("standard", ""))
        safe_pass, risk_words = self.eval_safe(req_data["output"])
        cost = self.eval_cost(len(req_data["output"]))
        # 线上实时判定
        return {
            "request_id": req_data["request_id"],
            "quality_score": score,
            "safe_pass": safe_pass,
            "risk_words": risk_words,
            "cost": cost,
            "alarm": not safe_pass or score < self.quality_threshold
        }
# 评测体系测试
if __name__ == "__main__":
    evaluator = LLMFullEvaluator()
    # 离线测试数据
    offline_test = [
        {"output": "人工智能助力企业数字化转型", "standard": "AI助力企业数字化升级"},
        {"output": "大模型生产保障体系包含评测、追踪、兜底能力", "standard": "大模型保障体系含评测、链路追踪、生产兜底"}
    ]
    print("离线评测结果：", evaluator.offline_eval(offline_test))
    
    # 线上实时测试
    online_test = {"request_id": "test_001", "output": "用户身份证号110xxxx存在隐私风险"}
    print("线上评测结果：", evaluator.online_eval(online_test))

# 大模型全链路追踪体系实现
import uuid
import time
from dataclasses import dataclass, asdict
from typing import Dict, Optional
# 定义链路追踪数据结构
@dataclass
class TraceNode:
    node_name: str       # 节点层级名称
    start_time: float    # 节点开始时间
    end_time: float = 0 # 节点结束时间
    cost_time: float = 0# 节点耗时
    status: str = "success" # 节点状态
    error_msg: str = "" # 异常信息
@dataclass
class LLMTraceInfo:
    trace_id: str        # 全局唯一追踪ID
    request_id: str      # 业务请求ID
    user_id: str         # 用户ID
    model_version: str   # 模型版本
    total_cost: float = 0 # 总耗时
    token_in: int = 0    # 输入Token数
    token_out: int = 0   # 输出Token数
    nodes: Dict[str, TraceNode] = None # 各层级节点信息
class LLMLinkTracer:
    def __init__(self):
        self.trace_cache = {}  # 临时缓存追踪链路（实战替换为redis/日志中心）
    def start_trace(self, request_info: Dict) -> str:
        """开启全链路追踪，生成唯一ID"""
        trace_id = str(uuid.uuid4())
        trace_info = LLMTraceInfo(
            trace_id=trace_id,
            request_id=request_info["request_id"],
            user_id=request_info["user_id"],
            model_version=request_info["model_version"],
            nodes={}
        )
        self.trace_cache[trace_id] = trace_info
        return trace_id
    def start_node(self, trace_id: str, node_name: str) -> None:
        """开启单个链路节点监控"""
        if trace_id not in self.trace_cache:
            return
        node = TraceNode(node_name=node_name, start_time=time.time())
        self.trace_cache[trace_id].nodes[node_name] = node
    def end_node(self, trace_id: str, node_name: str, error_msg: str = "") -> None:
        """结束单个节点监控，统计耗时与状态"""
        if trace_id not in self.trace_cache or node_name not in self.trace_cache[trace_id].nodes:
            return
        node = self.trace_cache[trace_id].nodes[node_name]
        node.end_time = time.time()
        node.cost_time = round((node.end_time - node.start_time) * 1000, 2)
        if error_msg:
            node.status = "fail"
            node.error_msg = error_msg
    def update_token(self, trace_id: str, token_in: int, token_out: int) -> None:
        """更新Token成本指标"""
        if trace_id not in self.trace_cache:
            return
        trace = self.trace_cache[trace_id]
        trace.token_in = token_in
        trace.token_out = token_out
    def finish_trace(self, trace_id: str) -> Dict:
        """结束全链路追踪，生成完整追踪报告"""
        if trace_id not in self.trace_cache:
            return {"trace_id": trace_id, "status": "not found"}
        trace = self.trace_cache[trace_id]
        # 统计总耗时
        total_cost = sum([node.cost_time for node in trace.nodes.values()])
        trace.total_cost = round(total_cost, 2)
        # 转为字典输出
        trace_result = asdict(trace)
        # 清除临时缓存
        del self.trace_cache[trace_id]
        return trace_result
# 链路追踪完整测试流程
if __name__ == "__main__":
    tracer = LLMLinkTracer()
    # 模拟请求信息
    req = {"request_id": "req_002", "user_id": "user_123", "model_version": "v2.0"}
    # 开启追踪
    trace_id = tracer.start_trace(req)
    # 1. 接入层节点
    tracer.start_node(trace_id, "access_layer")
    time.sleep(0.02)
    tracer.end_node(trace_id, "access_layer")
    # 2. 预处理层节点
    tracer.start_node(trace_id, "preprocess_layer")
    time.sleep(0.05)
    tracer.end_node(trace_id, "preprocess_layer")
    # 3. 推理层节点
    tracer.start_node(trace_id, "infer_layer")
    time.sleep(0.2)
    tracer.update_token(trace_id, token_in=128, token_out=256)
    tracer.end_node(trace_id, "infer_layer")
    # 4. 后处理层节点
    tracer.start_node(trace_id, "postprocess_layer")
    time.sleep(0.03)
    tracer.end_node(trace_id, "postprocess_layer")
    # 生成完整追踪报告
    print("全链路追踪报告：", tracer.finish_trace(trace_id))

# 大模型生产保障体系：灰度、降级、回滚、安全隔离
from enum import Enum
from typing import Dict, Optional
# 定义降级级别
class DegradeLevel(Enum):
    NORMAL = 0    # 正常模式
    LIMIT = 1     # 限流降级
    SIMPLE = 2    # 能力降级
    FALLBACK = 3  # 兜底降级
class LLMProductionGuard:
    def __init__(self):
        self.current_version = "v1.0"    # 当前稳定版本
        self.gray_version = "v2.0"       # 灰度版本
        self.gray_rate = 0.1             # 灰度流量比例
        self.degrade_level = DegradeLevel.NORMAL
        # 业务隔离配置
        self.business_isolate = {
            "core": ["service_01", "service_02"],
            "normal": ["service_03"],
            "test": ["test_01"]
        }
    def security_isolate(self, business_type: str, req_data: Dict) -> Dict:
        """安全隔离：业务流量隔离+敏感数据脱敏"""
        # 1. 业务集群隔离校验
        if business_type not in self.business_isolate.keys():
            return {"pass": False, "msg": "非法业务渠道"}
        
        # 2. 敏感数据脱敏
        sensitive_keys = ["id_card", "phone", "bank_card"]
        for key in sensitive_keys:
            if key in req_data:
                req_data[key] = req_data[key][:3] + "****" + req_data[key][-4:]
        return {"pass": True, "msg": "隔离校验通过", "data": req_data}
    def gray_switch(self, request_id: str) -> str:
        """灰度流量分发：按比例分配新旧版本"""
        # 简单哈希实现流量均匀分发
        hash_val = int(request_id[-4:], 16) / 0xFFFF
        if hash_val < self.gray_rate:
            return self.gray_version
        return self.current_version
    def degrade_control(self, qps: int, max_qps: int = 100) -> DegradeLevel:
        """动态降级策略：根据流量压力调整降级级别"""
        if qps < max_qps * 0.7:
            self.degrade_level = DegradeLevel.NORMAL
        elif qps < max_qps * 0.9:
            self.degrade_level = DegradeLevel.LIMIT
        elif qps < max_qps:
            self.degrade_level = DegradeLevel.SIMPLE
        else:
            self.degrade_level = DegradeLevel.FALLBACK
        return self.degrade_level
    def rollback_version(self) -> Dict:
        """故障一键回滚至稳定版本"""
        old_gray = self.gray_version
        self.gray_version = self.current_version
        self.gray_rate = 0
        self.degrade_level = DegradeLevel.NORMAL
        return {
            "rollback_success": True,
            "rollback_from": old_gray,
            "rollback_to": self.current_version,
            "status": "stable"
        }
# 生产保障能力测试
if __name__ == "__main__":
    guard = LLMProductionGuard()
    
    # 1. 安全隔离测试
    test_req = {"phone": "13812345678", "business_type": "core"}
    print("安全隔离结果：", guard.security_isolate("core", test_req))
    
    # 2. 灰度分发测试
    print("灰度版本分发：", guard.gray_switch("req_003"))
    
    # 3. 动态降级测试
    print("流量降级策略：", guard.degrade_control(95))
    
    # 4. 版本回滚测试
    print("故障回滚结果：", guard.rollback_version())