feat: 多专业小模型+路由模型系统 MVP（mock 全链路 + FastAPI 网关 + 论文调研）

router_system/fallback.py

@@ -0,0 +1,99 @@
+"""大模型回退层：Mock 与 OpenAI 兼容 API 两种后端。"""
+from __future__ import annotations
+
+import asyncio
+from typing import Dict, Optional
+
+from .models import ExpertResponse
+
+
+class FallbackProvider:
+    name: str = "fallback"
+
+    async def generate(self, query: str) -> ExpertResponse:
+        raise NotImplementedError
+
+
+class MockFallback(FallbackProvider):
+    """确定性 mock 大模型：标识为 fallback，便于测试升级路径。"""
+
+    def __init__(self, model: str = "mock-large"):
+        self.model = model
+        self.name = f"fallback-{model}"
+
+    async def generate(self, query: str) -> ExpertResponse:
+        await asyncio.sleep(0.002)
+        body = (
+            f"（大模型回退）「{query}」\n\n"
+            "这是一条来自大模型回退路径的完整回答。\n"
+            "要点：\n"
+            "1. 对复杂/跨域任务给出综合推理\n"
+            "2. 补充领域专家未覆盖的上下文\n"
+            "3. 给出可执行的后续建议\n"
+        )
+        return ExpertResponse(
+            text=body,
+            model_used=self.model,
+            latency_ms=2.0,
+            tokens=120,
+            cost_est=2.0 * 120 / 1_000_000,
+        )
+
+
+class APIFallback(FallbackProvider):
+    """OpenAI 兼容大模型 API（如 DeepSeek / OpenAI / 本地 vLLM）。"""
+
+    def __init__(self, model: str, base_url: str, api_key: str):
+        self.model = model
+        self.base_url = base_url.rstrip("/")
+        self.api_key = api_key
+        self.name = f"fallback-{model}"
+        self._client = None
+
+    def _get_client(self):
+        if self._client is None:
+            import httpx
+            self._client = httpx.AsyncClient(timeout=90.0)
+        return self._client
+
+    async def generate(self, query: str) -> ExpertResponse:
+        client = self._get_client()
+        resp = await client.post(
+            f"{self.base_url}/chat/completions",
+            headers={"Authorization": f"Bearer {self.api_key}"},
+            json={
+                "model": self.model,
+                "messages": [{"role": "user", "content": query}],
+                "temperature": 0.3,
+                "max_tokens": 2048,
+            },
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        body = data["choices"][0]["message"]["content"]
+        usage = data.get("usage", {})
+        tokens = usage.get("completion_tokens", int(len(body) / 2.2))
+        return ExpertResponse(
+            text=body,
+            model_used=self.model,
+            latency_ms=0.0,
+            tokens=tokens,
+            cost_est=2.0 * tokens / 1_000_000,
+        )
+
+
+def build_fallback(cfg: Dict) -> FallbackProvider:
+    """cfg 为 fallback 段配置。"""
+    ftype = cfg.get("type", "mock")
+    model = cfg.get("model", "deepseek-chat")
+    if ftype == "mock":
+        return MockFallback(model=model)
+    if ftype == "api":
+        import os
+        api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY"))
+        if not api_key:
+            raise RuntimeError(
+                f"APIFallback 缺少 API Key：请设置环境变量 {cfg.get('api_key_env')} 或配置 api_key"
+            )
+        return APIFallback(model, cfg.get("base_url", "https://api.deepseek.com/v1"), api_key)
+    raise ValueError(f"未知 fallback 类型: {ftype}（支持 mock | api）")␍