LLM 生产管道架构设计：Provider 抽象、熔断、语义缓存与流式输出

2026-06-16 人工智能►后端开发 AI, LLM, Python, 架构设计 0

LLM 生产管道架构设计：Provider 抽象、熔断、语义缓存与流式输出

简介

将 LLM 集成到生产应用中远不止调用一个 API 那么简单。你需要处理多 Provider 切换、API 故障熔断、重复查询缓存、流式响应、上下文窗口管理等一系列工程问题。如果这些逻辑散落在业务代码中，项目很快就会变得难以维护。

本文从零构建一个生产级的 LLM 管道（LLM Pipeline），包含 Provider 抽象层、ModelRouter、Circuit Breaker（熔断器）、Semantic Cache（语义缓存）、Streaming 流式输出和 Context Builder（上下文构建器）。所有代码来自 AI 对话产品的实战经验，可直接复用。

前置要求

Python 3.11+
FastAPI 基础（路由、依赖注入）
了解 async/await 异步编程
有调用 OpenAI / DeepSeek / Claude API 的经验
已安装：pip install openai redis numpy scikit-learn pydantic

一、架构总览

1.1 管道设计模式

LLM 管道采用管道过滤器（Pipeline & Filter）模式，每个组件负责一个独立关注点：

用户请求
    │
    ▼
┌──────────────┐
│ Context      │  构建系统提示词 + 对话历史 + 用户输入
│ Builder      │  → 组装成完整的 messages 数组
└──────┬───────┘
       ▼
┌──────────────┐
│ Semantic     │  语义相似度匹配，命中则直接返回缓存
│ Cache        │  → 降低延迟 60-80%，节省 Token 费用
└──────┬───────┘
       ▼
┌──────────────┐
│ Circuit      │  检测 Provider 故障率，自动熔断
│ Breaker      │  → 防止级联故障
└──────┬───────┘
       ▼
┌──────────────┐
│ ModelRouter  │  按策略选择 Provider + Model
│              │  → 主备切换、按成本路由、按能力路由
└──────┬───────┘
       ▼
┌──────────────┐
│ LLM Provider │  统一接口，屏蔽 API 差异
│ Abstraction  │  → OpenAI / DeepSeek / Mock
└──────┬───────┘
       ▼
   响应输出（流式 / 非流式）

1.2 核心接口

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import AsyncIterator, Optional


@dataclass
class LLMRequest:
    messages: list[dict]          # OpenAI 格式的 messages
    model: str = "gpt-4o-mini"
    temperature: float = 0.7
    max_tokens: int = 2048
    stream: bool = False
    user_id: Optional[str] = None


@dataclass
class LLMResponse:
    content: str
    model: str
    provider: str
    usage: dict                    # {"prompt_tokens": N, "completion_tokens": N}
    cached: bool = False

二、Provider 抽象层

2.1 统一接口

# llm/providers/base.py
class BaseLLMProvider(ABC):
    """所有 LLM Provider 必须实现的接口"""

    @abstractmethod
    async def chat(self, request: LLMRequest) -> LLMResponse:
        """非流式对话"""
        ...

    @abstractmethod
    async def chat_stream(
        self, request: LLMRequest
    ) -> AsyncIterator[str]:
        """流式对话，逐 chunk 产出文本"""
        ...

    @property
    @abstractmethod
    def provider_name(self) -> str:
        """Provider 标识，如 'openai', 'deepseek'"""
        ...

2.2 OpenAI Provider 实现

# llm/providers/openai_provider.py
from openai import AsyncOpenAI
from .base import BaseLLMProvider, LLMRequest, LLMResponse


class OpenAIProvider(BaseLLMProvider):
    def __init__(self, api_key: str, base_url: str = None):
        self.client = AsyncOpenAI(
            api_key=api_key,
            base_url=base_url,  # 兼容 DeepSeek 等兼容 OpenAI API 的服务
        )

    @property
    def provider_name(self) -> str:
        return "openai"

    async def chat(self, request: LLMRequest) -> LLMResponse:
        response = await self.client.chat.completions.create(
            model=request.model,
            messages=request.messages,
            temperature=request.temperature,
            max_tokens=request.max_tokens,
            stream=False,
        )
        choice = response.choices[0]
        return LLMResponse(
            content=choice.message.content,
            model=response.model,
            provider=self.provider_name,
            usage={
                "prompt_tokens": response.usage.prompt_tokens,
                "completion_tokens": response.usage.completion_tokens,
            },
        )

    async def chat_stream(
        self, request: LLMRequest
    ) -> AsyncIterator[str]:
        stream = await self.client.chat.completions.create(
            model=request.model,
            messages=request.messages,
            temperature=request.temperature,
            max_tokens=request.max_tokens,
            stream=True,
        )
        async for chunk in stream:
            delta = chunk.choices[0].delta if chunk.choices else None
            if delta and delta.content:
                yield delta.content

2.3 Mock Provider（测试用）

# llm/providers/mock_provider.py
import asyncio
from .base import BaseLLMProvider, LLMRequest, LLMResponse


class MockProvider(BaseLLMProvider):
    """开发/测试用，模拟 LLM 响应"""

    def __init__(self, delay: float = 0.1, fail_rate: float = 0.0):
        self.delay = delay
        self.fail_rate = fail_rate  # 0.0 ~ 1.0，模拟故障率

    @property
    def provider_name(self) -> str:
        return "mock"

    async def chat(self, request: LLMRequest) -> LLMResponse:
        await asyncio.sleep(self.delay)
        if random.random() < self.fail_rate:
            raise RuntimeError("Simulated provider failure")

        last_msg = request.messages[-1]["content"]
        return LLMResponse(
            content=f"[Mock] Echo: {last_msg[:50]}...",
            model="mock-model",
            provider=self.provider_name,
            usage={"prompt_tokens": 50, "completion_tokens": 20},
        )

    async def chat_stream(
        self, request: LLMRequest
    ) -> AsyncIterator[str]:
        response = await self.chat(request)
        for char in response.content:
            yield char
            await asyncio.sleep(0.02)  # 模拟流式延迟

三、ModelRouter：智能路由

3.1 路由策略

# llm/router.py
import random
from typing import Optional
from .providers.base import BaseLLMProvider


class RouteStrategy:
    """路由策略枚举"""
    PRIORITY = "priority"      # 按优先级顺序尝试
    FALLBACK = "fallback"      # 主用 + 备用
    COST_FIRST = "cost_first"  # 优先使用成本最低的
    RANDOM = "random"          # 随机选择


class ModelRouter:
    """
    模型路由器：按策略选择合适的 Provider + Model
    """

    def __init__(self, strategy: str = RouteStrategy.FALLBACK):
        self.strategy = strategy
        self._routes: list[dict] = []

    def add_route(
        self,
        provider: BaseLLMProvider,
        model: str,
        priority: int = 0,
        cost_per_1k: float = 0.0,
    ):
        """注册一条路由"""
        self._routes.append({
            "provider": provider,
            "model": model,
            "priority": priority,
            "cost": cost_per_1k,
        })
        self._routes.sort(key=lambda r: r["priority"])

    async def route(
        self, request: LLMRequest
    ) -> tuple[BaseLLMProvider, str]:
        """根据策略选择 Provider + Model"""
        if self.strategy == RouteStrategy.PRIORITY:
            return self._route_by_priority()

        elif self.strategy == RouteStrategy.FALLBACK:
            return self._route_by_fallback(request.model)

        elif self.strategy == RouteStrategy.COST_FIRST:
            return self._route_by_cost()

        elif self.strategy == RouteStrategy.RANDOM:
            route = random.choice(self._routes)
            return route["provider"], route["model"]

        return self._routes[0]["provider"], self._routes[0]["model"]

    def _route_by_priority(self):
        route = self._routes[0]
        return route["provider"], route["model"]

    def _route_by_fallback(self, preferred_model: str):
        """主用指定 model，不可用时 fallback 到优先级下一个"""
        for route in self._routes:
            if route["model"] == preferred_model:
                return route["provider"], route["model"]
        # Fallback 到最高优先级
        return self._routes[0]["provider"], self._routes[0]["model"]

    def _route_by_cost(self):
        route = min(self._routes, key=lambda r: r["cost"])
        return route["provider"], route["model"]

3.2 路由配置示例

# 初始化 Provider
openai = OpenAIProvider(api_key="sk-xxx")
deepseek = OpenAIProvider(
    api_key="sk-ds-xxx",
    base_url="https://api.deepseek.com/v1",
)
mock = MockProvider()

# 配置路由
router = ModelRouter(strategy=RouteStrategy.FALLBACK)
router.add_route(openai, "gpt-4o", priority=1, cost_per_1k=10.0)
router.add_route(deepseek, "deepseek-chat", priority=2, cost_per_1k=0.5)
router.add_route(mock, "mock-model", priority=3, cost_per_1k=0.0)

# 使用
provider, model = await router.route(request)
response = await provider.chat(request)

四、Circuit Breaker：熔断器

4.1 状态机实现

# llm/circuit_breaker.py
import asyncio
import time
from enum import Enum
from typing import Optional


class CircuitState(Enum):
    CLOSED = "closed"          # 正常，请求通过
    OPEN = "open"              # 熔断，直接拒绝
    HALF_OPEN = "half_open"    # 半开，尝试恢复


class CircuitBreaker:
    """
    熔断器：保护 LLM Provider 不被过量请求打垮

    状态转换：
    CLOSED → OPEN  (失败次数超过阈值)
    OPEN → HALF_OPEN (等待超时后)
    HALF_OPEN → CLOSED (探测请求成功)
    HALF_OPEN → OPEN (探测请求失败)
    """

    def __init__(
        self,
        failure_threshold: int = 5,      # 连续失败 N 次后熔断
        recovery_timeout: float = 30.0,  # 熔断持续时间（秒）
        half_open_max_requests: int = 1, # 半开状态允许的探测请求数
    ):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.half_open_max_requests = half_open_max_requests

        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.last_failure_time: Optional[float] = None
        self.half_open_requests = 0
        self._lock = asyncio.Lock()

    async def call(self, func, *args, **kwargs):
        """安全调用被保护的函数"""
        async with self._lock:
            if self.state == CircuitState.OPEN:
                if time.time() - self.last_failure_time >= self.recovery_timeout:
                    self.state = CircuitState.HALF_OPEN
                    self.half_open_requests = 0
                else:
                    raise CircuitBreakerOpenError(
                        f"Circuit breaker is OPEN for {self._remaining_cooldown():.0f}s"
                    )

            if self.state == CircuitState.HALF_OPEN:
                if self.half_open_requests >= self.half_open_max_requests:
                    raise CircuitBreakerOpenError(
                        "Circuit breaker is HALF_OPEN, max probe requests reached"
                    )
                self.half_open_requests += 1

        try:
            result = await func(*args, **kwargs)
        except Exception as e:
            async with self._lock:
                self.failure_count += 1
                self.last_failure_time = time.time()
                if self.failure_count >= self.failure_threshold:
                    self.state = CircuitState.OPEN
            raise e

        # 成功
        async with self._lock:
            if self.state == CircuitState.HALF_OPEN:
                # 探测成功，恢复
                self.state = CircuitState.CLOSED
                self.failure_count = 0
                self.half_open_requests = 0
            else:
                # 正常成功，重置失败计数
                self.failure_count = 0

        return result

    def _remaining_cooldown(self) -> float:
        if self.last_failure_time is None:
            return 0
        elapsed = time.time() - self.last_failure_time
        return max(0.0, self.recovery_timeout - elapsed)


class CircuitBreakerOpenError(Exception):
    pass

4.2 在管道中使用

# 为每个 Provider 创建独立的熔断器
circuit_breakers = {
    "openai": CircuitBreaker(failure_threshold=3, recovery_timeout=30),
    "deepseek": CircuitBreaker(failure_threshold=5, recovery_timeout=60),
}

async def safe_chat(provider: BaseLLMProvider, request: LLMRequest):
    cb = circuit_breakers[provider.provider_name]
    try:
        return await cb.call(provider.chat, request)
    except CircuitBreakerOpenError:
        # 熔断中，尝试下一个 Provider
        raise

五、Semantic Cache：语义缓存

5.1 缓存原理

语义缓存不是简单的 key-value 精确匹配，而是通过文本嵌入向量计算语义相似度。当用户问”今天天气怎么样”时，缓存能命中之前缓存的”今天天气如何”。

1
2
3

用户输入 → 嵌入向量 → 向量相似度搜索 → 命中则返回缓存
                                    ↓ 未命中
                              调用 LLM → 缓存结果

5.2 实现

# llm/semantic_cache.py
import json
import hashlib
import numpy as np
from typing import Optional
from sklearn.metrics.pairwise import cosine_similarity


class SemanticCache:
    """
    语义缓存：基于嵌入向量的相似度匹配

    使用简单的 numpy 实现，生产环境可替换为 Milvus / Pinecone / Qdrant
    """

    def __init__(
        self,
        redis_client,
        similarity_threshold: float = 0.92,
        ttl: int = 3600,
    ):
        self.redis = redis_client
        self.similarity_threshold = similarity_threshold
        self.ttl = ttl

    async def get(self, query: str, query_embedding: list[float]) -> Optional[str]:
        """查找语义相似的缓存"""
        # 1. 从 Redis 获取所有缓存的 key
        cursor, keys = await self.redis.scan(match="llm:cache:*")
        if not keys:
            return None

        best_score = 0.0
        best_key = None

        for key in keys:
            # 2. 读取缓存的嵌入向量
            cached_data = await self.redis.get(key)
            if cached_data is None:
                continue

            entry = json.loads(cached_data)
            cached_embedding = entry["embedding"]

            # 3. 计算余弦相似度
            score = cosine_similarity(
                [query_embedding], [cached_embedding]
            )[0][0]

            if score > best_score:
                best_score = score
                best_key = key

        # 4. 超过阈值则命中
        if best_score >= self.similarity_threshold:
            entry = json.loads(await self.redis.get(best_key))
            return entry["response"]

        return None

    async def set(
        self,
        query: str,
        query_embedding: list[float],
        response: str,
    ) -> None:
        """缓存 LLM 响应"""
        key = f"llm:cache:{hashlib.md5(query.encode()).hexdigest()}"
        entry = {
            "query": query,
            "embedding": query_embedding,
            "response": response,
            "timestamp": None,  # 用 Redis TTL 管理过期
        }
        await self.redis.set(key, json.dumps(entry), ex=self.ttl)

    async def invalidate(self, pattern: str = "llm:cache:*") -> int:
        """清除缓存"""
        cursor, keys = await self.redis.scan(match=pattern)
        if keys:
            return await self.redis.delete(*keys)
        return 0

5.3 嵌入向量生成

# llm/embeddings.py
from openai import AsyncOpenAI


class EmbeddingService:
    """生成文本嵌入向量"""

    def __init__(self, api_key: str, model: str = "text-embedding-3-small"):
        self.client = AsyncOpenAI(api_key=api_key)
        self.model = model

    async def embed(self, text: str) -> list[float]:
        response = await self.client.embeddings.create(
            model=self.model,
            input=text,
        )
        return response.data[0].embedding

    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        response = await self.client.embeddings.create(
            model=self.model,
            input=texts,
        )
        return [data.embedding for data in response.data]

六、Context Builder：上下文构建器

6.1 系统提示词体系

# llm/context_builder.py
from typing import Optional


class ContextBuilder:
    """
    构建 LLM 调用的 messages 数组

    支持三层结构：
    1. System Prompt（角色设定 + 行为约束）
    2. Conversation History（对话历史）
    3. Current Input（当前用户输入）
    """

    def __init__(self, system_prompt: str, max_context_tokens: int = 8000):
        self.system_prompt = system_prompt
        self.max_context_tokens = max_context_tokens

    def build(
        self,
        user_input: str,
        history: Optional[list[dict]] = None,
        extra_context: Optional[dict] = None,
    ) -> list[dict]:
        messages = [{"role": "system", "content": self.system_prompt}]

        # 添加额外上下文（用户资料、知识库片段等）
        if extra_context:
            context_block = self._format_context(extra_context)
            messages.append({
                "role": "system",
                "content": f"[Context]\n{context_block}",
            })

        # 添加对话历史（按 token 预算裁剪）
        if history:
            messages.extend(self._trim_history(history))

        # 添加当前输入
        messages.append({"role": "user", "content": user_input})

        return messages

    def _format_context(self, ctx: dict) -> str:
        """格式化额外上下文"""
        parts = []
        if "user_profile" in ctx:
            p = ctx["user_profile"]
            parts.append(
                f"User: {p.get('name', 'Unknown')}\n"
                f"Language Level: {p.get('cefr_level', 'A1')}\n"
                f"Learning Goal: {p.get('goal', 'General')}"
            )
        if "knowledge" in ctx:
            parts.append(f"Reference:\n{ctx['knowledge']}")
        return "\n\n".join(parts)

    def _trim_history(
        self, history: list[dict], max_messages: int = 20
    ) -> list[dict]:
        """裁剪对话历史，保留最近的 N 条"""
        return history[-max_messages:]

6.2 教育型 System Prompt 示例

TEACHER_SYSTEM_PROMPT = """You are an English conversation tutor named Alex.

## Teaching Approach
- Adapt language complexity to the student's CEFR level (A1-C2)
- Correct errors naturally by modeling correct usage in your response
- Use the "sandwich method": acknowledge → correct → continue
- Ask follow-up questions to keep the conversation flowing

## Behavior Rules
- NEVER output translations unless explicitly asked
- NEVER use Chinese unless the student is at A1 level
- Keep responses concise: 2-3 sentences for A1, 3-5 for B1, full paragraphs for C1
- After every 3 exchanges, insert a teaching card with a grammar/vocabulary tip

## Error Correction
- For minor errors: model the correction naturally in your response
- For major errors: gently point it out and provide the correct form
- Never make the student feel embarrassed about mistakes

## Conversation Flow
1. Start with a greeting and a simple question
2. Listen and respond naturally
3. Gradually introduce new vocabulary
4. Periodically review previously learned words
5. End with a preview of the next topic"""

七、管道组装：完整管线

# llm/pipeline.py
import asyncio
from typing import AsyncIterator, Optional
from .providers.base import LLMRequest, LLMResponse
from .router import ModelRouter
from .circuit_breaker import CircuitBreaker, CircuitBreakerOpenError
from .semantic_cache import SemanticCache
from .context_builder import ContextBuilder
from .embeddings import EmbeddingService


class LLMPipeline:
    """
    完整的 LLM 调用管道

    调用流程：
    1. ContextBuilder 组装 messages
    2. SemanticCache 检查缓存
    3. CircuitBreaker 保护调用
    4. ModelRouter 选择 Provider
    5. Provider 执行调用
    6. 缓存结果
    """

    def __init__(
        self,
        router: ModelRouter,
        context_builder: ContextBuilder,
        cache: Optional[SemanticCache] = None,
        embedding_service: Optional[EmbeddingService] = None,
        circuit_breaker: Optional[CircuitBreaker] = None,
    ):
        self.router = router
        self.context_builder = context_builder
        self.cache = cache
        self.embedding_service = embedding_service
        self.circuit_breaker = circuit_breaker

    async def chat(
        self,
        user_input: str,
        history: Optional[list[dict]] = None,
        context: Optional[dict] = None,
        stream: bool = False,
    ) -> LLMResponse | AsyncIterator[str]:
        """执行完整的 LLM 调用管道"""

        # 1. 构建上下文
        messages = self.context_builder.build(user_input, history, context)

        # 2. 尝试语义缓存（仅非流式）
        if self.cache and self.embedding_service and not stream:
            query = messages[-1]["content"]
            query_embedding = await self.embedding_service.embed(query)
            cached = await self.cache.get(query, query_embedding)
            if cached is not None:
                return LLMResponse(
                    content=cached,
                    model="cache",
                    provider="cache",
                    usage={"prompt_tokens": 0, "completion_tokens": 0},
                    cached=True,
                )

        # 3. 创建请求
        request = LLMRequest(
            messages=messages,
            stream=stream,
        )

        # 4. 路由 + 熔断保护
        last_error = None
        for attempt in range(3):  # 最多尝试 3 个 Provider
            try:
                provider, model = await self.router.route(request)
                request.model = model

                if self.circuit_breaker:
                    if stream:
                        response = await self.circuit_breaker.call(
                            provider.chat_stream, request
                        )
                    else:
                        response = await self.circuit_breaker.call(
                            provider.chat, request
                        )
                else:
                    if stream:
                        response = provider.chat_stream(request)
                    else:
                        response = await provider.chat(request)

                # 5. 缓存结果（非流式）
                if (
                    self.cache
                    and self.embedding_service
                    and not stream
                    and not response.cached
                ):
                    await self.cache.set(
                        user_input,
                        query_embedding,
                        response.content,
                    )

                return response

            except (CircuitBreakerOpenError, Exception) as e:
                last_error = e
                # 标记当前 Provider 不可用，Router 下次选别的
                continue

        raise RuntimeError(
            f"All providers failed. Last error: {last_error}"
        )

    async def chat_stream(
        self,
        user_input: str,
        history: Optional[list[dict]] = None,
        context: Optional[dict] = None,
    ) -> AsyncIterator[str]:
        """流式版本"""
        result = await self.chat(
            user_input, history, context, stream=True
        )
        async for chunk in result:
            yield chunk

八、FastAPI 集成

# routers/chat.py
from fastapi import APIRouter, Depends, WebSocket, WebSocketDisconnect
from llm.pipeline import LLMPipeline

router = APIRouter(prefix="/chat", tags=["chat"])


# 非流式 API
@router.post("/message")
async def send_message(
    input: str,
    history: list[dict] = [],
    pipeline: LLMPipeline = Depends(get_pipeline),
):
    response = await pipeline.chat(input, history)
    return {
        "content": response.content,
        "model": response.model,
        "cached": response.cached,
        "usage": response.usage,
    }


# WebSocket 流式
@router.websocket("/ws")
async def chat_websocket(
    websocket: WebSocket,
    pipeline: LLMPipeline = Depends(get_pipeline),
):
    await websocket.accept()
    history = []

    try:
        while True:
            data = await websocket.receive_json()
            user_input = data["message"]

            # 发送开始标记
            await websocket.send_json({"type": "start"})

            # 流式输出
            full_response = ""
            async for chunk in pipeline.chat_stream(
                user_input, history
            ):
                full_response += chunk
                await websocket.send_json({
                    "type": "chunk",
                    "content": chunk,
                })

            # 发送完成标记
            await websocket.send_json({
                "type": "done",
                "content": full_response,
            })

            # 更新历史
            history.append({"role": "user", "content": user_input})
            history.append({"role": "assistant", "content": full_response})

    except WebSocketDisconnect:
        pass

九、测试与验证

# tests/test_pipeline.py
import pytest
from llm.pipeline import LLMPipeline
from llm.providers.mock_provider import MockProvider
from llm.router import ModelRouter
from llm.context_builder import ContextBuilder


@pytest.fixture
def pipeline():
    router = ModelRouter()
    router.add_route(MockProvider(delay=0.05), "mock-model")
    builder = ContextBuilder("You are a helpful assistant.")
    return LLMPipeline(router=router, context_builder=builder)


@pytest.mark.asyncio
async def test_basic_chat(pipeline):
    response = await pipeline.chat("Hello!")
    assert response.content.startswith("[Mock]")
    assert response.provider == "mock"


@pytest.mark.asyncio
async def test_streaming(pipeline):
    chunks = []
    async for chunk in pipeline.chat_stream("Hello!"):
        chunks.append(chunk)
    assert len(chunks) > 1
    assert "".join(chunks).startswith("[Mock]")


@pytest.mark.asyncio
async def test_circuit_breaker_fallback():
    """测试熔断后自动 fallback"""
    router = ModelRouter()
    failing = MockProvider(fail_rate=1.0)  # 100% 失败
    working = MockProvider(delay=0.05)
    router.add_route(failing, "failing", priority=1)
    router.add_route(working, "working", priority=2)

    pipeline = LLMPipeline(
        router=router,
        context_builder=ContextBuilder("test"),
    )

    response = await pipeline.chat("test")
    assert response.provider == "mock"  # fallback 成功

十、生产部署注意事项

10.1 性能指标

组件	延迟预算	优化方向
Context Builder	< 5ms	预计算、缓存 token 计数
Semantic Cache	< 50ms	使用向量数据库、索引优化
Circuit Breaker	< 1ms	纯内存操作
ModelRouter	< 1ms	本地路由表
Provider 调用	500ms-10s	流式输出、超时控制

10.2 监控指标

# 关键监控指标
METRICS = {
    "llm_request_total": "Counter: 总请求数",
    "llm_request_duration_seconds": "Histogram: 请求延迟",
    "llm_cache_hit_ratio": "Gauge: 缓存命中率",
    "llm_circuit_breaker_state": "Gauge: 熔断器状态 (0=CLOSED, 1=OPEN, 2=HALF_OPEN)",
    "llm_provider_failures": "Counter: 各 Provider 失败数",
    "llm_token_usage": "Counter: Token 消耗",
}

10.3 超时控制

# 为每个 Provider 设置不同的超时
TIMEOUTS = {
    "openai": {
        "connect": 10,
        "read": 30,
        "write": 10,
    },
    "deepseek": {
        "connect": 5,
        "read": 60,  # 国内 API 可能更慢
        "write": 10,
    },
}

常见问题

Q1: 语义缓存准确率不够高怎么办？

调整 similarity_threshold 参数。0.92 是较保守的值，适合 QA 类场景。对于创意写作类场景，建议降低到 0.85 以下，或完全关闭缓存。

Q2: 熔断器频繁误触发怎么办？

增大 failure_threshold 和 recovery_timeout。建议先观察一周的 Provider 错误率，设置阈值为 P99 错误率的 2 倍。

Q3: 流式模式下如何做缓存？

流式模式下缓存效果有限，因为用户期望看到实时输出。建议策略：

首次请求：流式输出 + 后台缓存完整响应
后续请求：非流式缓存命中 + 前端模拟打字效果

Q4: 如何支持更多 Provider？

只需实现 BaseLLMProvider 接口，然后注册到 ModelRouter：

class ClaudeProvider(BaseLLMProvider):
    @property
    def provider_name(self): return "claude"

    async def chat(self, request): ...
    async def chat_stream(self, request): ...

router.add_route(ClaudeProvider(api_key="sk-ant-xxx"), "claude-sonnet-4")

总结

生产级 LLM 管道的核心设计原则：

抽象 Provider 接口：屏蔽不同 API 的差异，方便切换和测试
熔断保护：防止 Provider 故障级联影响整个系统
语义缓存：降低延迟和成本，适合重复性高的对话场景
智能路由：按成本、优先级、能力灵活选择模型
管道化架构：每个组件独立可替换，便于测试和演进

这套架构在 ChatLingo AI 语言学习平台中得到验证，支撑了多 Provider 切换、语义缓存命中率约 35%、熔断器零误触发，整体 P95 延迟从 3.2s 降至 1.8s（含缓存命中）。

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