feat: Improve streaming LLM token usage reporting by adding input estimation, requesting usage via `stream_options`, and providing fallback estimation.

backend/app/services/llm/adapters/litellm_adapter.py

@@ -229,6 +229,9 @@ class LiteLLMAdapter(BaseLLMAdapter):
         
         messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
         
+        # 🔥 估算输入 token 数量（用于在无法获取真实 usage 时进行估算）
+        input_tokens_estimate = sum(estimate_tokens(msg["content"]) for msg in messages)
+        
         kwargs = {
             "model": self._litellm_model,
             "messages": messages,
@@ -238,6 +241,11 @@ class LiteLLMAdapter(BaseLLMAdapter):
             "stream": True,  # 启用流式输出
         }
         
+        # 🔥 对于支持的模型，请求在流式输出中包含 usage 信息
+        # OpenAI API 支持 stream_options
+        if self.config.provider in [LLMProvider.OPENAI, LLMProvider.DEEPSEEK]:
+            kwargs["stream_options"] = {"include_usage": True}
+        
         if self.config.api_key and self.config.api_key != "ollama":
             kwargs["api_key"] = self.config.api_key
         
@@ -247,11 +255,21 @@ class LiteLLMAdapter(BaseLLMAdapter):
         kwargs["timeout"] = self.config.timeout
         
         accumulated_content = ""
+        final_usage = None  # 🔥 存储最终的 usage 信息
         
         try:
             response = await litellm.acompletion(**kwargs)
             
             async for chunk in response:
+                # 🔥 检查是否有 usage 信息（某些 API 会在最后的 chunk 中包含）
+                if hasattr(chunk, "usage") and chunk.usage:
+                    final_usage = {
+                        "prompt_tokens": chunk.usage.prompt_tokens or 0,
+                        "completion_tokens": chunk.usage.completion_tokens or 0,
+                        "total_tokens": chunk.usage.total_tokens or 0,
+                    }
+                    logger.debug(f"Got usage from chunk: {final_usage}")
+                
                 if not chunk.choices:
                     continue
                 
@@ -269,27 +287,36 @@ class LiteLLMAdapter(BaseLLMAdapter):
                 
                 if finish_reason:
                     # 流式完成
-                    usage = None
+                    # 🔥 如果没有从 chunk 获取到 usage，进行估算
-                    if hasattr(chunk, "usage") and chunk.usage:
+                    if not final_usage:
-                        usage = {
+                        output_tokens_estimate = estimate_tokens(accumulated_content)
-                            "prompt_tokens": chunk.usage.prompt_tokens or 0,
+                        final_usage = {
-                            "completion_tokens": chunk.usage.completion_tokens or 0,
+                            "prompt_tokens": input_tokens_estimate,
-                            "total_tokens": chunk.usage.total_tokens or 0,
+                            "completion_tokens": output_tokens_estimate,
+                            "total_tokens": input_tokens_estimate + output_tokens_estimate,
                         }
+                        logger.debug(f"Estimated usage: {final_usage}")
                     
                     yield {
                         "type": "done",
                         "content": accumulated_content,
-                        "usage": usage,
+                        "usage": final_usage,
                         "finish_reason": finish_reason,
                     }
                     break
                     
         except Exception as e:
+            # 🔥 即使出错，也尝试返回估算的 usage
+            output_tokens_estimate = estimate_tokens(accumulated_content) if accumulated_content else 0
             yield {
                 "type": "error",
                 "error": str(e),
                 "accumulated": accumulated_content,
+                "usage": {
+                    "prompt_tokens": input_tokens_estimate,
+                    "completion_tokens": output_tokens_estimate,
+                    "total_tokens": input_tokens_estimate + output_tokens_estimate,
+                } if accumulated_content else None,
             }
 
     async def validate_config(self) -> bool: