refactor(chat): overhaul response processing for robustness and integrity

This commit redesigns the LLM response handling pipeline to gracefully manage complex, long, and structured outputs, shifting from a restrictive filtering model to a more resilient and content-aware approach.

The previous implementation suffered from brittleness, often discarding valid responses or corrupting formatted content. This refactor introduces two core changes to ensure response integrity:

1.  **Adaptive Message Merging:** Replaces the rigid message count and length limits, which caused the silent dropping of valid long-form answers. The new logic dynamically merges the shortest adjacent sentences if a response is overly fragmented, ensuring the full thought is preserved while adhering to display limits.

2.  **Content-Aware Pre-processing:** Implements a protection mechanism for special text formats. It now identifies and isolates Markdown code blocks, LaTeX-style math equations, and other code-like segments using placeholders before splitting or typo generation. This prevents formatting corruption and ensures technical content is rendered correctly.

Additionally, the LLM prompt for splitting has been refined to emphasize logical coherence over simple fragmentation.

Finally, this commit also hardens the `LLMRequest` class against race conditions by adding an `asyncio.Lock` to serialize generation requests, improving the system's stability under concurrent workloads.

src/llm_models/utils_model.py

@@ -802,6 +802,7 @@ class LLMRequest:
             for model in self.model_for_task.model_list
         }
         """模型使用量记录"""
+        self._lock = asyncio.Lock()
 
         # 初始化辅助类
         self._model_selector = _ModelSelector(self.model_for_task.model_list, self.model_usage)
@@ -930,42 +931,43 @@ class LLMRequest:
         tools: list[dict[str, Any]] | None = None,
         raise_when_empty: bool = True,
     ) -> tuple[str, tuple[str, str, list[ToolCall] | None]]:
-        """
-        执行单次文本生成请求的内部方法。
-        这是 `generate_response_async` 的核心实现，处理单个请求的完整生命周期，
-        包括工具构建、故障转移执行和用量记录。
+        async with self._lock:
+            """
+            执行单次文本生成请求的内部方法。
+            这是 `generate_response_async` 的核心实现，处理单个请求的完整生命周期，
+            包括工具构建、故障转移执行和用量记录。
 
-        Args:
-            prompt (str): 用户的提示。
-            temperature (Optional[float]): 生成温度。
-            max_tokens (Optional[int]): 最大生成令牌数。
-            tools (Optional[List[Dict[str, Any]]]): 可用工具列表。
-            raise_when_empty (bool): 如果响应为空是否引发异常。
+            Args:
+                prompt (str): 用户的提示。
+                temperature (Optional[float]): 生成温度。
+                max_tokens (Optional[int]): 最大生成令牌数。
+                tools (Optional[List[Dict[str, Any]]]): 可用工具列表。
+                raise_when_empty (bool): 如果响应为空是否引发异常。
 
-        Returns:
-            Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
-                (响应内容, (推理过程, 模型名称, 工具调用))
-        """
-        start_time = time.time()
-        tool_options = await self._build_tool_options(tools)
+            Returns:
+                Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
+                    (响应内容, (推理过程, 模型名称, 工具调用))
+            """
+            start_time = time.time()
+            tool_options = await self._build_tool_options(tools)
 
-        response, model_info = await self._strategy.execute_with_failover(
-            RequestType.RESPONSE,
-            raise_when_empty=raise_when_empty,
-            prompt=prompt,  # 传递原始prompt，由strategy处理
-            tool_options=tool_options,
-            temperature=self.model_for_task.temperature if temperature is None else temperature,
-            max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
-        )
+            response, model_info = await self._strategy.execute_with_failover(
+                RequestType.RESPONSE,
+                raise_when_empty=raise_when_empty,
+                prompt=prompt,  # 传递原始prompt，由strategy处理
+                tool_options=tool_options,
+                temperature=self.model_for_task.temperature if temperature is None else temperature,
+                max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
+            )
 
-        await self._record_usage(model_info, response.usage, time.time() - start_time, "/chat/completions")
+            await self._record_usage(model_info, response.usage, time.time() - start_time, "/chat/completions")
 
-        if not response.content and not response.tool_calls:
-            if raise_when_empty:
-                raise RuntimeError("所选模型生成了空回复。")
-            response.content = "生成的响应为空"
+            if not response.content and not response.tool_calls:
+                if raise_when_empty:
+                    raise RuntimeError("所选模型生成了空回复。")
+                response.content = "生成的响应为空"
 
-        return response.content or "", (response.reasoning_content or "", model_info.name, response.tool_calls)
+            return response.content or "", (response.reasoning_content or "", model_info.name, response.tool_calls)
 
     async def get_embedding(self, embedding_input: str) -> tuple[list[float], str]:
         """