feat(router): 集成NVIDIA多头分类器实现3-tier智能路由

- 新增nvidia_router.py: 手动加载NVIDIA prompt-task-and-complexity-classifier模型
- DeBERTa-v3-base backbone + 8个分类头(task_type/creativity/reasoning/domain等)
- 综合多维度评分实现simple/medium/complex三级路由
- 映射: simple->qwen-flash, medium->qwen-plus, complex->qwen-max
- main.py切换到NVIDIA路由替代RouteLLM BERT二分类
- 移除LiteLLM依赖解决版本冲突,使用原生httpx调用
- 版本升级至v0.3.0

main.py

@@ -13,7 +13,7 @@ from litellm import acompletion
 import litellm
 
 from config import MODEL_CONFIG, ROUTING_THRESHOLDS, DEFAULT_ROUTING, DASHSCOPE_API_KEY
-from bert_router import get_bert_router, route_with_bert
+from nvidia_router import get_nvidia_router, select_model_by_nvidia
 
 # 配置 LiteLLM 使用 DashScope (Qwen)
 if DASHSCOPE_API_KEY:
@@ -21,15 +21,15 @@ if DASHSCOPE_API_KEY:
     # Qwen 使用 OpenAI 兼容接口，但需要通过 api_base 指定
     litellm.api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1"
 
-# BERT Router 实例（延迟加载）
-_bert_router = None
+# NVIDIA Router 实例（延迟加载）
+_nvidia_router = None
 
 def get_router():
-    """获取 BERT Router 实例（延迟加载）"""
-    global _bert_router
-    if _bert_router is None:
-        _bert_router = get_bert_router()
-    return _bert_router
+    """获取 NVIDIA Router 实例（延迟加载）"""
+    global _nvidia_router
+    if _nvidia_router is None:
+        _nvidia_router = get_nvidia_router()
+    return _nvidia_router
 
 
 # 调用历史记录
@@ -69,8 +69,8 @@ class StatsResponse(BaseModel):
 
 app = FastAPI(
     title="LLM Router MVP",
-    description="基于 LiteLLM 的多提供商路由服务",
-    version="0.2.0",
+    description="基于 LiteLLM + NVIDIA 分类器的多提供商路由服务（支持3-tier智能路由）",
+    version="0.3.0",
 )
 
 
@@ -102,30 +102,26 @@ def select_model_by_length(messages: List[Message]) -> str:
         return DEFAULT_ROUTING["complex"]
 
 
-def select_model_by_bert(messages: List[Message]) -> str:
+def select_model_by_nvidia_classifier(messages: List[Message]) -> str:
     """
-    基于 BERT 分类器选择模型
+    基于 NVIDIA 多头分类器选择模型（3-tier路由）
     
-    BERT 输出: strong / weak
+    NVIDIA 输出: 多维度复杂度评分
     映射到 Qwen 模型:
-    - strong -> qwen-max (复杂任务)
-    - weak -> qwen-flash (简单任务)
+    - simple  -> qwen-flash (简单任务)
+    - medium  -> qwen-plus  (中等任务)
+    - complex -> qwen-max   (复杂任务)
     """
     # 取最后一条用户消息作为查询
     query = messages[-1].content if messages else ""
     
     try:
         router = get_router()
-        complexity = router.predict(query)
-        
-        # BERT 二分类映射到三模型
-        if complexity == "strong":
-            return "qwen-max"
-        else:
-            return "qwen-flash"
+        model = router.select_model(query)
+        return model
     except Exception as e:
-        # BERT 失败时回退到 token 长度策略
-        print(f"BERT routing failed: {e}, falling back to token length")
+        # NVIDIA 分类器失败时回退到 token 长度策略
+        print(f"NVIDIA routing failed: {e}, falling back to token length")
         return select_model_by_length(messages)
 
 
@@ -180,8 +176,8 @@ async def chat_completions(request: ChatRequest):
     if request.model:
         model_key = request.model
     else:
-        # 使用 BERT 智能路由（替代原来的 token 长度路由）
-        model_key = select_model_by_bert(request.messages)
+        # 使用 NVIDIA 多头分类器智能路由（支持3-tier）
+        model_key = select_model_by_nvidia_classifier(request.messages)
     
     # 获取 LiteLLM 模型名称
     provider_model = get_provider_model(model_key)
@@ -293,7 +289,7 @@ async def get_stats():
 @app.get("/health")
 async def health_check():
     """健康检查"""
-    return {"status": "healthy", "version": "0.2.0"}
+    return {"status": "healthy", "version": "0.3.0", "router": "nvidia-3tier"}
 
 
 if __name__ == "__main__":