fix(llm): 兼容处理部分模型缺失的token用量字段

部分模型（如 embedding 模型）的 API 响应中可能不包含 `completion_tokens` 等完整的用量字段。

此前的直接属性访问会导致 `AttributeError`，从而中断使用记录和统计更新流程。

通过改用 `getattr(usage, "...", 0)` 的方式为缺失的字段提供默认值 0，增强了代码的健壮性，确保系统能够稳定处理来自不同类型模型的响应。

src/llm_models/model_client/base_client.py

@@ -26,13 +26,13 @@ class UsageRecord:
     provider_name: str
     """提供商名称"""
 
-    prompt_tokens: int
+    prompt_tokens: int = 0
     """提示token数"""
 
-    completion_tokens: int
+    completion_tokens: int = 0
     """完成token数"""
 
-    total_tokens: int
+    total_tokens: int = 0
     """总token数"""
 
 

src/llm_models/model_client/openai_client.py

@@ -298,9 +298,9 @@ async def _default_stream_response_handler(
         if event.usage:
             # 如果有使用情况，则将其存储在APIResponse对象中
             _usage_record = (
-                event.usage.prompt_tokens or 0,
+                getattr(event.usage, "prompt_tokens", 0) or 0,
-                event.usage.completion_tokens or 0,
+                getattr(event.usage, "completion_tokens", 0) or 0,
-                event.usage.total_tokens or 0,
+                getattr(event.usage, "total_tokens", 0) or 0,
             )
 
     try:
@@ -368,9 +368,9 @@ def _default_normal_response_parser(
     # 提取Usage信息
     if resp.usage:
         _usage_record = (
-            resp.usage.prompt_tokens or 0,
+            getattr(resp.usage, "prompt_tokens", 0) or 0,
-            resp.usage.completion_tokens or 0,
+            getattr(resp.usage, "completion_tokens", 0) or 0,
-            resp.usage.total_tokens or 0,
+            getattr(resp.usage, "total_tokens", 0) or 0,
         )
     else:
         _usage_record = None
@@ -599,7 +599,7 @@ class OpenaiClient(BaseClient):
                 model_name=model_info.name,
                 provider_name=model_info.api_provider,
                 prompt_tokens=raw_response.usage.prompt_tokens or 0,
-                completion_tokens=raw_response.usage.completion_tokens or 0,  # type: ignore
+                completion_tokens=getattr(raw_response.usage, "completion_tokens", 0) or 0,
                 total_tokens=raw_response.usage.total_tokens or 0,
             )
 

src/llm_models/utils.py

@@ -155,8 +155,12 @@ class LLMUsageRecorder:
         endpoint: str,
         time_cost: float = 0.0,
     ):
-        input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
+        prompt_tokens = getattr(model_usage, "prompt_tokens", 0)
-        output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
+        completion_tokens = getattr(model_usage, "completion_tokens", 0)
+        total_tokens = getattr(model_usage, "total_tokens", 0)
+
+        input_cost = (prompt_tokens / 1000000) * model_info.price_in
+        output_cost = (completion_tokens / 1000000) * model_info.price_out
         round(input_cost + output_cost, 6)
 
         session = None
@@ -170,9 +174,9 @@ class LLMUsageRecorder:
                     user_id=user_id,
                     request_type=request_type,
                     endpoint=endpoint,
-                    prompt_tokens=model_usage.prompt_tokens or 0,
+                    prompt_tokens=prompt_tokens,
-                    completion_tokens=model_usage.completion_tokens or 0,
+                    completion_tokens=completion_tokens,
-                    total_tokens=model_usage.total_tokens or 0,
+                    total_tokens=total_tokens,
                     cost=1.0,
                     time_cost=round(time_cost or 0.0, 3),
                     status="success",
@@ -185,8 +189,8 @@ class LLMUsageRecorder:
             logger.debug(
                 f"Token使用情况 - 模型: {model_usage.model_name}, "
                 f"用户: {user_id}, 类型: {request_type}, "
-                f"提示词: {model_usage.prompt_tokens}, 完成: {model_usage.completion_tokens}, "
+                f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
-                f"总计: {model_usage.total_tokens}"
+                f"总计: {total_tokens}"
             )
         except Exception as e:
             logger.error(f"记录token使用情况失败: {e!s}")

src/llm_models/utils_model.py

@@ -1009,12 +1009,15 @@ class LLMRequest:
             # 步骤1: 更新内存中的统计数据，用于负载均衡
             stats = self.model_usage[model_info.name]
 
+            # 安全地获取 token 使用量, embedding 模型可能不返回 completion_tokens
+            total_tokens = getattr(usage, "total_tokens", 0)
+
             # 计算新的平均延迟
             new_request_count = stats.request_count + 1
             new_avg_latency = (stats.avg_latency * stats.request_count + time_cost) / new_request_count
 
             self.model_usage[model_info.name] = stats._replace(
-                total_tokens=stats.total_tokens + usage.total_tokens,
+                total_tokens=stats.total_tokens + total_tokens,
                 avg_latency=new_avg_latency,
                 request_count=new_request_count,
             )