feat(ai): 完善AI网关服务并添加LLM降级模式

config.py 加openai_api_key/base_url/dev_mode

新建llm_client.py httpx异步调OpenAI REST API

main.py 业务路由加/ai前缀+降级模式+readyz端点

Gateway添加/notifications和/ai路由

docs: known-issues记录P5三服务经验

docs/troubleshooting/known-issues.md

@@ -255,30 +255,35 @@
 ### 2.7 messaging（TS/NestJS，P5）
 
 | 场景          | 技术/规则                                                          |
-| -------------- | -------------------------------------------------------------------- |
+| ------------- | ------------------------------------------------------------------ |
 | 消息 CRUD     | 会话/消息 + 调 Push Gateway 推送 + 通知偏好                        |
-| 通知批量化     | `createNotifications(items)` 单次 INSERT，沿用旧项目 dispatcher 模式 |
+| 通知批量化    | `createBatch(items)` 单次 INSERT，沿用旧项目 dispatcher 模式       |
 | 多渠道        | 站内/SMS/邮件/微信，in_app 批量 + 其他渠道并行                     |
-| fan-out 分页   | `getAllUserIds(limit=1000, offset)` 分页遍历                         |
-| 撤回不乐观更新 | 需服务端返回判断 2 分钟窗口                                          |
+| fan-out 分页  | `listByUserWithPagination(userId, page, pageSize)` 分页查询        |
+| ES 降级       | ES_URL 未设置时 esClient=null，safeIndex/safeSearch 跳过返回空结果 |
+| Push 推送降级 | PUSH_GATEWAY_URL 未设置或连接失败时 try/catch 跳过，不影响 DB 写入 |
+| db 常量导出   | database.ts 导出 `db` 常量替代 `getDb()` 函数                      |
 
 ### 2.8 push-gateway（Go，P5）
 
 | 场景             | 技术/规则                                                        |
-| ---------------- | ----------------------------------------------- |
-| WebSocket 长连接 | 单节点支撑 10w+ 连接，业务服务只需发 Kafka 消息 |
-| 跨实例同步       | Redis PubSub                                    |
+| ---------------- | ---------------------------------------------------------------- |
+| WebSocket 长连接 | 单节点支撑 10w+ 连接，业务服务只需调 /internal/push              |
+| 跨实例同步       | Redis PubSub（RedisURL 配置，预留 P6 实现）                      |
 | 离线消息         | 仅推在线用户，离线消息存 MySQL，上线时拉取                       |
+| 并发写修复       | send chan + 单写协程模式，避免 gorilla/websocket 并发写竞争      |
+| DEV_MODE 鉴权    | DEV_MODE=true 时接受 dev-token，生产环境必须 JWT 校验            |
+| 广播端点         | POST /internal/broadcast，body {event, data}，调用 hub.Broadcast |
 
 ### 2.9 ai-gateway（Python/FastAPI，P5）
 
 | 场景              | 技术/规则                                                       |
-| ----------------- | ------------------------------------------- |
-| LLM Provider 适配 | OpenAI/Anthropic，langchain/litellm 生态    |
-| Prompt 模板管理   | 版本管理友好                                |
+| ----------------- | --------------------------------------------------------------- |
+| LLM Provider 适配 | OpenAI 兼容 REST API（httpx 异步），不引入 openai SDK           |
+| 降级模式          | API key 为空或调用失败时返回骨架响应，标记 degraded: true       |
 | 流式 SSE          | AI 网关 → BFF → 前端三层透传，BFF 不缓冲                        |
-| 用量计费          | 按 token 计费                               |
-| AI 模块纯服务端   | Zod 验证 + 失败降级返回空（沿用旧项目模式） |
+| 路由前缀          | 业务路由加 /ai 前缀（APIRouter prefix="/ai"），Gateway 代理 /ai |
+| dev_mode tracer   | dev_mode=true 时跳过 OTel exporter 初始化                       |
 
 ### 2.10 shared-proto（契约包）
 
@@ -322,7 +327,8 @@
 > 按时间倒序，50 条上限。AI 发现更好方案时可更新本节。
 
 | 日期       | 时间 | 模块                            | 做了什么 + 学到什么                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| ---------- | ---- | ------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------- | ---- | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 2026-07-09 | 中午 | msg/push-gateway/ai/api-gateway | **P5 沟通与 AI 阶段三服务完善**：(1) msg 服务修复：database.ts 导出 db 常量；env.ts JWT_SECRET/ES_URL 改 optional 加 DEV_MODE/PUSH_GATEWAY_URL；elasticsearch.ts ES 降级（esClient=null 时 safeIndex/safeSearch 跳过）；notifications.service.ts 加 createBatch + listByUserWithPagination + Push Gateway 推送调用（try/catch 降级）；新建 msg-init.sql 2 张表。(2) push-gateway 完善：hub.go 重写用 send chan + 单写协程模式修复 gorilla/websocket 并发写竞争；handler.go 加 DEV_MODE dev-token 支持 + broadcast 端点；config.go 加 DevMode/RedisURL。(3) ai 服务完善：config.py 加 openai_api_key/base_url/dev_mode；新建 llm_client.py（httpx 异步调 OpenAI REST API）；main.py 加 /ai 前缀 + 降级模式（无 key 返回骨架 + degraded: true）+ /readyz 端点。(4) Gateway 路由扩展：/notifications → msg，/ai → ai 服务。**学到**：gorilla/websocket 不支持并发写，必须用 send chan 串行化所有写入；FastAPI APIRouter prefix 与 Gateway 代理路径要协调（ai 服务加 /ai 前缀，Gateway 代理 /ai/*path）；LLM 降级策略统一返回 degraded 标记，调用方据此判断是否路由流量。                                                                                                                                                                                                                                                                                                                                                                                                                                           |
 | 2026-07-09 | 上午 | content/api-gateway             | **P4 内容分析服务端到端打通**：(1) content 服务系统性修复：database.ts 导出 db 常量；env.ts JWT_SECRET/ES_URL/NEO4J_URL/NEO4J_PASSWORD 改 optional 加 DEV_MODE；neo4j.ts driver 惰性创建+try/catch+connectionTimeout:3000；health/lifecycle 改用 Drizzle；global-error.filter 移除 @types/express 依赖；textbooks.schema 修复 integer→int + 导出 NewTextbook/NewChapter 类型；textbooks.controller 移除 body as any + 加 PUT/DELETE。(2) 新建 3 模块：chapters（CRUD + 按 textbook 查询）、knowledge-points（CRUD + Neo4j 前置依赖图非阻塞查询）、questions（CRUD + 4 种题型校验）。(3) Gateway 路由扩展：textbooks/chapters/knowledge-points/questions 四组路由。(4) 数据库：content-init.sql 4 张表。(5) E2E 验证：POST /textbooks 201 → POST /chapters 201（字段用 order 非 orderNum）→ POST /knowledge-points 201（Neo4j 不可用 MySQL 正常写入）→ POST /questions 201 → GET 各列表 200。**学到**：Drizzle schema TS 字段名与 DB 列名解耦（order→order_num），API 请求体用 TS 字段名；Neo4j 不可用时必须 driver=null（不设 NEO4J_URL），否则每次请求尝试连接拖慢响应；neo4j-driver safeCreateNode 用 try/catch 非阻塞，MySQL 数据始终先落库。                                                                                                                                                                                                                                                                                                                                                                |
 | 2026-07-09 | 凌晨 | core-edu/api-gateway            | **P3 核心教学服务端到端打通**：(1) core-edu 服务系统性修复 13 项：database.ts 导出 db 常量替代 getDb()；env.ts JWT_SECRET 改 optional 加 DEV_MODE；kafka.ts connectKafka 加 try/catch 不阻塞启动；main.ts 去全局 /api 前缀 + connectKafka 改 void 非阻塞；app.module 移除未用 AuthMiddleware/ClassesesModule 加 HealthModule；3 个 controller 路由去前缀去 UseGuards 从 x-user-id 读身份；exams/homework service datetime 列 ISO 字符串转 Date 修复 drizzle toISOString 错误；修正 10 处相对 import 路径；health/lifecycle 改用 Drizzle 原生查询；新增 core-edu-init.sql 4 张表。(2) Gateway 路由扩展：发现 internal/routing/routing.go 是死代码（未被 main 引用），真正路由在 main.go；在 main.go 添加 exams/homework/grades 三组路由（无尾斜杠+通配符）；删除 routing.go；config.go 加 CoreEduServiceURL。(3) DEV_MODE 环境变量问题：Go 不自动加载 .env，必须在启动前 export DEV_MODE=true 否则 dev-token 被拒 401。(4) E2E 验证：POST /exams 201 → GET /exams/:id 200 → GET /exams/class/:id 200 → POST /homework 201 → POST /grades 201 → Outbox 3 条事件正确写入（exam.failed 因 Kafka 未启动，homework/grade pending）。**学到**：drizzle datetime 列需 Date 对象不是 ISO 字符串（mapToDriverValue 调 toISOString）；Go 项目 .env 不会自动加载需显式 export 或 godotenv 库；NestJS controller 路由前缀与 Gateway 代理路径要协调（Gateway 去掉 /api/v1 后转发，controller 用裸路径如 'exams'）；Outbox 模式业务事务同写验证通过，Kafka 未启动时事件 status=failed 但业务数据已落库。                       |
 | 2026-07-09 | 上午 | iam/teacher-bff/teacher-portal  | **P2 身份阶段完整实现**：(1) Gateway 公开路径白名单（register/login/refresh）解决无 token 死锁。(2) IAM schema 扩展：users 加 dataScope，新增 role_viewports 表。(3) RBAC 端点 4 个 GET。(4) 视口按 requiredPermission 过滤 + sortOrder 排序；getEffectivePermissions 用 Set 去重。(5) JWT payload 含 dataScope，register 自动分配 teacher 角色。(6) 种子数据 7 权限+12 映射+7 视口。(7) Teacher BFF 视口聚合。(8) 前端：lib/auth.ts + login + AppShell + (app) 路由组 + dashboard + classes（真实 JWT）+ 根重定向。(9) E2E 全链路通过。**学到**：Next.js 路由组 (app) 不影响 URL，/login 与 /dashboard 共存只后者套壳；fetch headers 函数返回 Record<string,string> 避免 TS2769；ESLint 9 需 flat config 留 P6；AppShell aside 用 flex flex-col + mt-auto 比 absolute 稳健。                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |

services/ai/src/ai/config.py

@@ -7,12 +7,27 @@ class Settings(BaseSettings):
     """应用配置."""
 
     port: int = 3008
+    # LLM 配置（可选，为空时降级返回骨架响应）
     openai_api_key: str = ""
+    openai_base_url: str = "https://api.openai.com/v1"
     anthropic_api_key: str = ""
+    # 开发模式：true 时跳过 OTel exporter 初始化，避免本地无 collector 时报错
+    dev_mode: str = "false"
+    # 可观测性
     otel_endpoint: str = "http://localhost:4318"
     log_level: str = "info"
 
     model_config = {"env_file": ".env", "env_prefix": ""}
 
+    @property
+    def is_dev(self) -> bool:
+        """是否处于开发模式."""
+        return self.dev_mode.lower() == "true"
+
+    @property
+    def llm_available(self) -> bool:
+        """LLM 是否可用（至少一个 provider 配置了 API key）."""
+        return bool(self.openai_api_key or self.anthropic_api_key)
+
 
 settings = Settings()

services/ai/src/ai/llm_client.py

@@ -0,0 +1,137 @@
+"""LLM 客户端 - 使用 httpx 直接调用 OpenAI 兼容 REST API。
+
+设计要点：
+- 不依赖 openai SDK，纯 httpx 异步调用
+- api_key 为空或调用失败时返回 None / yield 降级骨架数据
+- 调用方据此决定是否进入降级路径
+"""
+
+from collections.abc import AsyncGenerator
+from typing import Any
+
+import httpx
+import structlog
+
+logger = structlog.get_logger()
+
+# 非流式请求默认超时（秒）
+DEFAULT_TIMEOUT: float = 30.0
+# 流式请求建立连接超时（秒）；读取通过迭代器控制
+STREAM_CONNECT_TIMEOUT: float = 30.0
+# 流式读取单次 chunk 超时（秒）
+STREAM_READ_TIMEOUT: float = 60.0
+
+
+def _build_url(base_url: str) -> str:
+    """拼接 chat completions 端点 URL."""
+    return f"{base_url.rstrip('/')}/chat/completions"
+
+
+def _build_headers(api_key: str) -> dict[str, str]:
+    """构建请求头."""
+    return {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    }
+
+
+async def chat_completion(
+    messages: list[dict[str, Any]],
+    model: str,
+    temperature: float,
+    api_key: str,
+    base_url: str,
+) -> dict[str, Any] | None:
+    """非流式调用 LLM。
+
+    Returns:
+        OpenAI 兼容的响应 dict；api_key 为空或调用失败时返回 None（由调用方降级）。
+    """
+    if not api_key:
+        logger.warning("llm_chat_completion_no_api_key_degraded")
+        return None
+
+    url = _build_url(base_url)
+    headers = _build_headers(api_key)
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": temperature,
+        "stream": False,
+    }
+
+    try:
+        async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as client:
+            resp = await client.post(url, json=payload, headers=headers)
+            resp.raise_for_status()
+            return resp.json()
+    except httpx.HTTPStatusError as exc:
+        logger.error(
+            "llm_chat_completion_http_error",
+            status_code=exc.response.status_code,
+            body=exc.response.text[:500],
+        )
+        return None
+    except Exception as exc:  # noqa: BLE001 - 顶层兜底，所有异常均降级
+        logger.error("llm_chat_completion_failed", error=str(exc))
+        return None
+
+
+async def chat_completion_stream(
+    messages: list[dict[str, Any]],
+    model: str,
+    temperature: float,
+    api_key: str,
+    base_url: str,
+) -> AsyncGenerator[str, None]:
+    """流式调用 LLM，以 SSE 格式（``data: <chunk>\\n\\n``）yield。
+
+    api_key 为空或调用失败时 yield 降级骨架数据，保证下游始终能消费。
+    """
+    if not api_key:
+        logger.warning("llm_stream_no_api_key_degraded")
+        yield (
+            'data: {"choices":[{"delta":{"content":"[degraded] LLM API key not configured"}}]}\n\n'
+        )
+        yield "data: [DONE]\n\n"
+        return
+
+    url = _build_url(base_url)
+    headers = _build_headers(api_key)
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": temperature,
+        "stream": True,
+    }
+
+    timeout = httpx.Timeout(
+        connect=STREAM_CONNECT_TIMEOUT,
+        read=STREAM_READ_TIMEOUT,
+        write=STREAM_CONNECT_TIMEOUT,
+        pool=STREAM_CONNECT_TIMEOUT,
+    )
+
+    try:
+        async with (
+            httpx.AsyncClient(timeout=timeout) as client,
+            client.stream("POST", url, json=payload, headers=headers) as resp,
+        ):
+            resp.raise_for_status()
+            async for line in resp.aiter_lines():
+                if not line or not line.startswith("data: "):
+                    continue
+                yield f"{line}\n\n"
+                if line.strip() == "data: [DONE]":
+                    return
+    except httpx.HTTPStatusError as exc:
+        logger.error(
+            "llm_stream_http_error_degraded",
+            status_code=exc.response.status_code,
+        )
+        yield 'data: {"choices":[{"delta":{"content":"[degraded] LLM stream HTTP error"}}]}\n\n'
+        yield "data: [DONE]\n\n"
+    except Exception as exc:  # noqa: BLE001 - 顶层兜底，所有异常均降级
+        logger.error("llm_stream_failed_degraded", error=str(exc))
+        yield 'data: {"choices":[{"delta":{"content":"[degraded] LLM stream error"}}]}\n\n'
+        yield "data: [DONE]\n\n"

services/ai/src/ai/main.py

@@ -1,9 +1,11 @@
 """AI 网关服务入口."""
 
+from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
+from typing import Any
 
 import structlog
-from fastapi import FastAPI
+from fastapi import APIRouter, FastAPI
 from fastapi.responses import StreamingResponse
 from opentelemetry import trace
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
@@ -12,25 +14,45 @@ from opentelemetry.sdk.trace.export import BatchSpanProcessor
 from prometheus_client import make_asgi_app
 from pydantic import BaseModel
 
+from .config import settings
+from .llm_client import chat_completion, chat_completion_stream
+
 logger = structlog.get_logger()
 tracer = trace.get_tracer(__name__)
 
 
 def init_tracer() -> None:
-    """初始化 OpenTelemetry."""
+    """初始化 OpenTelemetry.
+
+    endpoint 从 settings.otel_endpoint 读取；dev_mode=true 时跳过 exporter
+    初始化，避免本地无 collector 时报错。
+    """
+    if settings.is_dev:
+        logger.info("dev_mode_tracer_skipped", dev_mode=settings.dev_mode)
+        return
+
     provider = TracerProvider()
-    exporter = OTLPSpanExporter(endpoint="http://localhost:4318/v1/traces")
+    endpoint = f"{settings.otel_endpoint.rstrip('/')}/v1/traces"
+    exporter = OTLPSpanExporter(endpoint=endpoint)
     provider.add_span_processor(BatchSpanProcessor(exporter))
     trace.set_tracer_provider(provider)
+    logger.info("tracer_initialized", otel_endpoint=endpoint)
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     """应用生命周期."""
     init_tracer()
-    logger.info("ai service starting")
+    logger.info(
+        "ai_service_starting",
+        llm_available=settings.llm_available,
+        dev_mode=settings.is_dev,
+        openai_base_url=settings.openai_base_url,
+    )
+    if not settings.llm_available:
+        logger.warning("ai_service_llm_degraded_no_api_key")
     yield
-    logger.info("ai service stopping")
+    logger.info("ai_service_stopping")
 
 
 app = FastAPI(
@@ -41,11 +63,14 @@ app = FastAPI(
 
 app.mount("/metrics", make_asgi_app())
 
+# 业务路由加 /ai 前缀，Gateway 代理 /api/v1/ai/* → /ai/*
+router = APIRouter(prefix="/ai")
+
 
 class ChatRequest(BaseModel):
     """聊天请求."""
 
-    messages: list[dict]
+    messages: list[dict[str, Any]]
     model: str = "gpt-4o-mini"
     temperature: float = 0.7
     stream: bool = False
@@ -56,56 +81,157 @@ class ChatResponse(BaseModel):
 
     content: str
     model: str
-    usage: dict
+    usage: dict[str, Any]
+    degraded: bool = False
+
+
+def _extract_content(result: dict[str, Any] | None) -> tuple[str, str, dict[str, Any]]:
+    """从 OpenAI 响应中抽取 (content, model, usage)。"""
+    if result is None:
+        return "", "", {}
+    choices = result.get("choices", [])
+    content = ""
+    if choices:
+        content = choices[0].get("message", {}).get("content", "") or ""
+    model = result.get("model", "") or ""
+    usage = result.get("usage", {}) or {}
+    return content, model, usage
 
 
 @app.get("/healthz")
-async def healthz():
-    """健康检查."""
+async def healthz() -> dict[str, Any]:
+    """健康检查（liveness）."""
     return {"status": "ok", "service": "ai"}
 
 
-@app.post("/chat", response_model=ChatResponse)
-async def chat(req: ChatRequest):
-    """LLM 聊天接口."""
-    with tracer.start_as_current_span("ai_chat"):
-        # P5 骨架：实际调用 OpenAI/Anthropic API
-        # 需要从环境变量获取 API key
+@app.get("/readyz")
+async def readyz() -> dict[str, Any]:
+    """就绪检查（readiness）.
+
+    LLM 未配置时仍返回 200，但标记 degraded=true，调用方可据此判断是否路由流量。
+    """
+    llm_configured = settings.llm_available
     return {
-            "content": "P5 skeleton - LLM integration pending",
-            "model": req.model,
-            "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+        "status": "ok",
+        "service": "ai",
+        "llm_configured": llm_configured,
+        "degraded": not llm_configured,
+        "openai_base_url": settings.openai_base_url,
     }
 
 
-@app.post("/chat/stream")
-async def chat_stream(req: ChatRequest):
-    """流式聊天（SSE）."""
+@router.post("/chat", response_model=ChatResponse)
+async def chat(req: ChatRequest) -> ChatResponse:
+    """LLM 聊天接口（无 API key 时降级返回骨架响应）."""
+    with tracer.start_as_current_span("ai_chat"):
+        result = await chat_completion(
+            messages=req.messages,
+            model=req.model,
+            temperature=req.temperature,
+            api_key=settings.openai_api_key,
+            base_url=settings.openai_base_url,
+        )
+        if result is None:
+            logger.warning("chat_degraded", model=req.model)
+            return ChatResponse(
+                content="[degraded] LLM unavailable - returning skeleton response",
+                model=req.model,
+                usage={"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+                degraded=True,
+            )
+        content, model, usage = _extract_content(result)
+        return ChatResponse(
+            content=content,
+            model=model or req.model,
+            usage=usage,
+            degraded=False,
+        )
 
-    async def generate():
+
+@router.post("/chat/stream")
+async def chat_stream(req: ChatRequest) -> StreamingResponse:
+    """流式聊天（SSE，无 API key 时降级返回骨架 SSE）."""
+
+    async def generate() -> AsyncGenerator[str, None]:
         with tracer.start_as_current_span("ai_chat_stream"):
-            # P5 骨架：流式调用 LLM
-            yield "data: P5 skeleton\n\n"
-            yield "data: [DONE]\n\n"
+            async for chunk in chat_completion_stream(
+                messages=req.messages,
+                model=req.model,
+                temperature=req.temperature,
+                api_key=settings.openai_api_key,
+                base_url=settings.openai_base_url,
+            ):
+                yield chunk
 
     return StreamingResponse(generate(), media_type="text/event-stream")
 
 
-@app.post("/generate/question")
-async def generate_question(prompt: str):
-    """生成题目."""
+@router.post("/generate/question")
+async def generate_question(prompt: str) -> dict[str, Any]:
+    """生成题目（无 API key 时降级返回骨架）."""
     with tracer.start_as_current_span("generate_question"):
+        messages = [
+            {
+                "role": "system",
+                "content": "You are an educational question generator. "
+                "Generate a clear, concise question based on the user's prompt.",
+            },
+            {"role": "user", "content": prompt},
+        ]
+        result = await chat_completion(
+            messages=messages,
+            model="gpt-4o-mini",
+            temperature=0.7,
+            api_key=settings.openai_api_key,
+            base_url=settings.openai_base_url,
+        )
+        if result is None:
+            logger.warning("generate_question_degraded", prompt=prompt[:100])
             return {
                 "success": True,
-            "data": {"question": "P5 skeleton - question generation pending"},
+                "data": {"question": "[degraded] question generation skeleton"},
+                "degraded": True,
+            }
+        content, _, _ = _extract_content(result)
+        return {
+            "success": True,
+            "data": {"question": content},
+            "degraded": False,
         }
 
 
-@app.post("/optimize/expression")
-async def optimize_expression(text: str):
-    """优化表达."""
+@router.post("/optimize/expression")
+async def optimize_expression(text: str) -> dict[str, Any]:
+    """优化表达（无 API key 时降级返回骨架）."""
     with tracer.start_as_current_span("optimize_expression"):
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a writing assistant. "
+                "Optimize the user's text for clarity, conciseness, and tone.",
+            },
+            {"role": "user", "content": text},
+        ]
+        result = await chat_completion(
+            messages=messages,
+            model="gpt-4o-mini",
+            temperature=0.5,
+            api_key=settings.openai_api_key,
+            base_url=settings.openai_base_url,
+        )
+        if result is None:
+            logger.warning("optimize_expression_degraded", text=text[:100])
             return {
                 "success": True,
-            "data": {"optimized": "P5 skeleton - expression optimization pending"},
+                "data": {"optimized": "[degraded] expression optimization skeleton"},
+                "degraded": True,
             }
+        content, _, _ = _extract_content(result)
+        return {
+            "success": True,
+            "data": {"optimized": content},
+            "degraded": False,
+        }
+
+
+app.include_router(router)

services/api-gateway/internal/config/config.go

@@ -16,6 +16,8 @@ type Config struct {
 	CoreEduServiceURL string
 	ContentServiceURL string
 	DataAnaServiceURL string
+	MsgServiceURL     string
+	AiServiceURL      string
 	OTLPEndpoint      string
 	LogLevel          string
 	DevMode           bool
@@ -33,6 +35,8 @@ func Load() *Config {
 		CoreEduServiceURL: getEnv("CORE_EDU_SERVICE_URL", "http://localhost:3004"),
 		ContentServiceURL: getEnv("CONTENT_SERVICE_URL", "http://localhost:3005"),
 		DataAnaServiceURL: getEnv("DATA_ANA_SERVICE_URL", "http://localhost:3006"),
+		MsgServiceURL:     getEnv("MSG_SERVICE_URL", "http://localhost:3007"),
+		AiServiceURL:      getEnv("AI_SERVICE_URL", "http://localhost:3008"),
 		OTLPEndpoint:      getEnv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318"),
 		LogLevel:          getEnv("LOG_LEVEL", "info"),
 		DevMode:           getEnvBool("DEV_MODE", false),

services/api-gateway/main.go

@@ -109,6 +109,24 @@ func main() {
 		api.Any("/questions", contentHandler)
 		api.Any("/questions/*path", contentHandler)
 
+		// msg 服务路由（通知/消息）
+		msgProxy, err := proxy.NewProxy(cfg.MsgServiceURL)
+		if err != nil {
+			log.Fatalf("failed to create msg proxy: %v", err)
+		}
+		msgHandler := proxy.ProxyHandler(msgProxy)
+		api.Any("/notifications", msgHandler)
+		api.Any("/notifications/*path", msgHandler)
+
+		// ai 服务路由（AI 聊天/生成/优化）
+		aiProxy, err := proxy.NewProxy(cfg.AiServiceURL)
+		if err != nil {
+			log.Fatalf("failed to create ai proxy: %v", err)
+		}
+		aiHandler := proxy.ProxyHandler(aiProxy)
+		api.Any("/ai", aiHandler)
+		api.Any("/ai/*path", aiHandler)
+
 		// data-ana 服务路由（学情诊断/错题本）
 		dataAnaProxy, err := proxy.NewProxy(cfg.DataAnaServiceURL)
 		if err != nil {