支持流式

src/llm_models/model_client/gemini_client.py

@@ -246,12 +246,14 @@ async def _default_stream_response_handler(
     _fc_delta_buffer = io.StringIO()  # 正式内容缓冲区，用于存储接收到的正式内容
     _tool_calls_buffer: list[tuple[str, str, dict]] = []  # 工具调用缓冲区，用于存储接收到的工具调用
     _usage_record = None  # 使用情况记录
+    last_resp: GenerateContentResponse | None = None  # 保存最后一个 chunk
 
     def _insure_buffer_closed():
         if _fc_delta_buffer and not _fc_delta_buffer.closed:
             _fc_delta_buffer.close()
 
     async for chunk in resp_stream:
+        last_resp = chunk  # 保存最后一个响应
         # 检查是否有中断量
         if interrupt_flag and interrupt_flag.is_set():
             # 如果中断量被设置，则抛出ReqAbortException
@@ -270,13 +272,32 @@ async def _default_stream_response_handler(
                 (chunk.usage_metadata.candidates_token_count or 0) + (chunk.usage_metadata.thoughts_token_count or 0),
                 chunk.usage_metadata.total_token_count or 0,
             )
+
     try:
-        return _build_stream_api_resp(
+        api_response = _build_stream_api_resp(
             _fc_delta_buffer,
             _tool_calls_buffer,
-        ), _usage_record
+        )
+
+        # 检查是否因为 max_tokens 截断
+        if last_resp and last_resp.candidates:
+            c0 = last_resp.candidates[0]
+            reason = getattr(c0, "finish_reason", None) or getattr(c0, "finishReason", None)
+            if reason and "MAX_TOKENS" in str(reason):
+                if api_response.content and api_response.content.strip():
+                    logger.warning(
+                        "⚠ Gemini 响应因达到 max_tokens 限制被部分截断，\n"
+                        "    可能会对回复内容造成影响，建议修改模型 max_tokens 配置！"
+                    )
+                else:
+                    logger.warning(
+                        "⚠ Gemini 响应因达到 max_tokens 限制被截断，\n"
+                        "    请修改模型 max_tokens 配置！"
+                    )
+
+        return api_response, _usage_record
+
     except Exception:
-        # 确保缓冲区被关闭
         _insure_buffer_closed()
         raise