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__":

nvidia_router.py

@@ -0,0 +1,318 @@
+"""
+NVIDIA Prompt Task & Complexity Classifier Router
+手动加载自定义多头模型，支持3-tier路由
+
+模型: nvidia/prompt-task-and-complexity-classifier (184M参数)
+架构: DeBERTa-v3-base backbone + 8个分类头
+输出: task_type(12类), creativity(3类), reasoning(2类), 
+      domain_knowledge(4类), complexity_score 等多维度
+"""
+
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, DebertaV2Model, AutoConfig
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+from typing import Dict, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ClassificationHead(nn.Module):
+    """单个分类头"""
+    def __init__(self, input_dim: int, num_classes: int, dropout: float = 0.2):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(input_dim, num_classes)
+    
+    def forward(self, x):
+        x = self.dropout(x)
+        return self.fc(x)
+
+
+class NvidiaMultiHeadClassifier(nn.Module):
+    """
+    NVIDIA 多头分类器
+    DeBERTa backbone + 8个独立分类头
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        
+        # DeBERTa backbone
+        self.backbone = DebertaV2Model.from_pretrained(
+            config.base_model, 
+            ignore_mismatched_sizes=True
+        )
+        
+        hidden_size = 768  # DeBERTa-v3-base
+        dropout = config.fc_dropout if hasattr(config, 'fc_dropout') else 0.2
+        
+        # 8个分类头 (与 state_dict 中的 head_0 ~ head_7 对应)
+        target_sizes = config.target_sizes
+        self.head_0 = ClassificationHead(hidden_size, target_sizes["task_type"], dropout)       # 12类
+        self.head_1 = ClassificationHead(hidden_size, target_sizes["creativity_scope"], dropout)  # 3类
+        self.head_2 = ClassificationHead(hidden_size, target_sizes["reasoning"], dropout)         # 2类
+        self.head_3 = ClassificationHead(hidden_size, target_sizes["contextual_knowledge"], dropout) # 2类
+        self.head_4 = ClassificationHead(hidden_size, target_sizes["number_of_few_shots"], dropout)  # 6类
+        self.head_5 = ClassificationHead(hidden_size, target_sizes["domain_knowledge"], dropout)     # 4类
+        self.head_6 = ClassificationHead(hidden_size, target_sizes["no_label_reason"], dropout)      # 1类
+        self.head_7 = ClassificationHead(hidden_size, target_sizes["constraint_ct"], dropout)        # 2类
+        
+        # Head 名称映射
+        self.head_names = [
+            "task_type",            # head_0: 12类
+            "creativity_scope",     # head_1: 3类
+            "reasoning",            # head_2: 2类
+            "contextual_knowledge", # head_3: 2类
+            "number_of_few_shots",  # head_4: 6类
+            "domain_knowledge",     # head_5: 4类
+            "no_label_reason",      # head_6: 1类
+            "constraint_ct",        # head_7: 2类
+        ]
+    
+    def forward(self, input_ids, attention_mask=None, token_type_ids=None):
+        outputs = self.backbone(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids
+        )
+        # 使用 [CLS] token 的隐层
+        cls_output = outputs.last_hidden_state[:, 0]
+        
+        # 各头输出
+        head_outputs = {
+            "task_type": self.head_0(cls_output),
+            "creativity_scope": self.head_1(cls_output),
+            "reasoning": self.head_2(cls_output),
+            "contextual_knowledge": self.head_3(cls_output),
+            "number_of_few_shots": self.head_4(cls_output),
+            "domain_knowledge": self.head_5(cls_output),
+            "no_label_reason": self.head_6(cls_output),
+            "constraint_ct": self.head_7(cls_output),
+        }
+        return head_outputs
+
+
+class NvidiaComplexityRouter:
+    """NVIDIA 多头分类器路由封装"""
+    
+    MODEL_NAME = "nvidia/prompt-task-and-complexity-classifier"
+    
+    # Task type 映射
+    TASK_TYPE_MAP = {
+        0: "Brainstorming", 1: "Chatbot", 2: "Classification",
+        3: "Closed QA", 4: "Code Generation", 5: "Extraction",
+        6: "Open QA", 7: "Other", 8: "Rewrite",
+        9: "Summarization", 10: "Text Generation", 11: "Unknown"
+    }
+    
+    # Domain knowledge 映射 
+    DOMAIN_MAP = {0: "High", 1: "Low", 2: "Medium", 3: "No"}
+    
+    # Creativity 映射
+    CREATIVITY_MAP = {0: "High", 1: "Low", 2: "No"}
+    
+    def __init__(self, device: str = "cpu"):
+        self.device = device
+        self.tokenizer = None
+        self.model = None
+        self.config = None
+        self._initialized = False
+    
+    def initialize(self):
+        """延迟加载模型"""
+        if self._initialized:
+            return
+        
+        logger.info(f"Loading NVIDIA classifier: {self.MODEL_NAME}")
+        
+        # 1. 加载 config
+        self.config = AutoConfig.from_pretrained(self.MODEL_NAME)
+        
+        # 2. 加载 tokenizer (slow模式，兼容性好)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL_NAME, use_fast=False)
+        
+        # 3. 构建模型并加载权重
+        self.model = NvidiaMultiHeadClassifier(self.config)
+        
+        model_path = hf_hub_download(self.MODEL_NAME, "model.safetensors")
+        state_dict = load_file(model_path)
+        self.model.load_state_dict(state_dict, strict=False)
+        
+        self.model.to(self.device)
+        self.model.eval()
+        self._initialized = True
+        logger.info("NVIDIA classifier loaded successfully")
+    
+    def predict(self, query: str) -> Dict:
+        """
+        预测查询的多维度特征
+        
+        Returns:
+            {
+                "tier": "simple" | "medium" | "complex",
+                "complexity_score": float (0-1),
+                "task_type": str,
+                "domain_knowledge": str,
+                "reasoning": bool,
+                "creativity": str
+            }
+        """
+        if not self._initialized:
+            self.initialize()
+        
+        inputs = self.tokenizer(
+            query, return_tensors="pt", truncation=True, max_length=512, padding=True
+        )
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        
+        # 解析各头输出
+        task_type_idx = torch.argmax(outputs["task_type"], dim=-1).item()
+        task_type = self.TASK_TYPE_MAP.get(task_type_idx, "Unknown")
+        
+        domain_idx = torch.argmax(outputs["domain_knowledge"], dim=-1).item()
+        domain = self.DOMAIN_MAP.get(domain_idx, "Unknown")
+        
+        creativity_idx = torch.argmax(outputs["creativity_scope"], dim=-1).item()
+        creativity = self.CREATIVITY_MAP.get(creativity_idx, "Unknown")
+        
+        reasoning_idx = torch.argmax(outputs["reasoning"], dim=-1).item()
+        needs_reasoning = reasoning_idx == 1
+        
+        # 计算综合复杂度评分 (0-1)
+        complexity_score = self._compute_complexity_score(
+            domain=domain,
+            creativity=creativity,
+            needs_reasoning=needs_reasoning,
+            task_type=task_type
+        )
+        
+        tier = self._score_to_tier(complexity_score)
+        
+        return {
+            "tier": tier,
+            "complexity_score": complexity_score,
+            "task_type": task_type,
+            "domain_knowledge": domain,
+            "reasoning": needs_reasoning,
+            "creativity": creativity,
+        }
+    
+    def _compute_complexity_score(self, domain, creativity, needs_reasoning, task_type) -> float:
+        """
+        综合多维度计算复杂度评分 (0-1)
+        
+        权重:
+        - domain_knowledge: 40% (High=1.0, Medium=0.6, Low=0.3, No=0.0)
+        - reasoning: 30% (Yes=1.0, No=0.0)
+        - creativity: 20% (High=1.0, Low=0.4, No=0.0)
+        - task_type: 10% (Code=0.8, QA=0.5, Chatbot=0.2, ...)
+        """
+        domain_scores = {"High": 1.0, "Medium": 0.6, "Low": 0.3, "No": 0.0}
+        creativity_scores = {"High": 1.0, "Low": 0.4, "No": 0.0}
+        task_complexity = {
+            "Code Generation": 0.8, "Text Generation": 0.7,
+            "Summarization": 0.6, "Rewrite": 0.5,
+            "Open QA": 0.5, "Closed QA": 0.4,
+            "Classification": 0.3, "Extraction": 0.3,
+            "Brainstorming": 0.6, "Chatbot": 0.2,
+            "Other": 0.5, "Unknown": 0.5,
+        }
+        
+        score = (
+            0.4 * domain_scores.get(domain, 0.5) +
+            0.3 * (1.0 if needs_reasoning else 0.0) +
+            0.2 * creativity_scores.get(creativity, 0.5) +
+            0.1 * task_complexity.get(task_type, 0.5)
+        )
+        return round(score, 3)
+    
+    def _score_to_tier(self, score: float) -> str:
+        if score < 0.35:
+            return "simple"
+        elif score < 0.65:
+            return "medium"
+        else:
+            return "complex"
+    
+    def select_model(self, query: str) -> str:
+        """直接返回推荐的模型名称"""
+        result = self.predict(query)
+        model_map = {
+            "simple": "qwen-flash",
+            "medium": "qwen-plus",
+            "complex": "qwen-max"
+        }
+        return model_map[result["tier"]]
+    
+    def benchmark(self, queries: list) -> Dict:
+        """批量测试"""
+        import time
+        results = []
+        for query in queries:
+            start = time.time()
+            result = self.predict(query)
+            elapsed = (time.time() - start) * 1000
+            results.append({
+                "query": query[:50],
+                "tier": result["tier"],
+                "score": result["complexity_score"],
+                "task": result["task_type"],
+                "domain": result["domain_knowledge"],
+                "reasoning": result["reasoning"],
+                "time_ms": round(elapsed, 1)
+            })
+        
+        times = [r["time_ms"] for r in results]
+        return {
+            "avg_ms": round(sum(times) / len(times), 1),
+            "results": results
+        }
+
+
+# 全局单例
+_router_instance: Optional[NvidiaComplexityRouter] = None
+
+def get_nvidia_router() -> NvidiaComplexityRouter:
+    global _router_instance
+    if _router_instance is None:
+        _router_instance = NvidiaComplexityRouter()
+    return _router_instance
+
+def select_model_by_nvidia(query: str) -> str:
+    return get_nvidia_router().select_model(query)
+
+
+if __name__ == "__main__":
+    test_queries = [
+        "你好",
+        "What is 2+2?",
+        "Explain quantum computing principles in detail",
+        "Write a quicksort algorithm in Python with error handling",
+        "Analyze this 10-page research paper and summarize the key innovations",
+        "Rewrite this sentence to be more concise",
+        "Generate a creative story about a robot",
+    ]
+    
+    router = NvidiaComplexityRouter()
+    
+    print("=" * 80)
+    print("NVIDIA Prompt Task & Complexity Classifier - 3-Tier Router Test")
+    print("=" * 80)
+    
+    for query in test_queries:
+        result = router.predict(query)
+        model = router.select_model(query)
+        print(f"\nQuery: {query}")
+        print(f"  Tier:       {result['tier']}")
+        print(f"  Score:      {result['complexity_score']}")
+        print(f"  Task:       {result['task_type']}")
+        print(f"  Domain:     {result['domain_knowledge']}")
+        print(f"  Reasoning:  {result['reasoning']}")
+        print(f"  Creativity: {result['creativity']}")
+        print(f"  -> Model:   {model}")

requirements.txt

@@ -7,5 +7,7 @@ httpx>=0.25.0
 python-dotenv>=1.0.0
 transformers>=4.30.0
 torch>=2.0.0
+# NVIDIA Multi-head Classifier for 3-tier routing
+# nvidia/prompt-task-and-complexity-classifier will be loaded via transformers
 pytest>=7.4.0
 pytest-asyncio>=0.21.0