perf: 合并嵌套的if条件

packages/thinking_filter/main.py

@@ -24,47 +24,48 @@ class R1Filter(Star):
     @filter.on_llm_response()
     async def resp(self, event: AstrMessageEvent, response: LLMResponse):
         if self.display_reasoning_text:
+            # 显示推理内容的处理逻辑
             if (
                 response
                 and response.raw_completion
                 and isinstance(response.raw_completion, ChatCompletion)
+                and len(response.raw_completion.choices) > 0
+                and response.raw_completion.choices[0].message
             ):
-                if (
-                    len(response.raw_completion.choices)
-                    and response.raw_completion.choices[0].message
+                message = response.raw_completion.choices[0].message
+                reasoning_content = ""  # 初始化 reasoning_content
+
+                # 检查 Groq deepseek-r1-distill-llama-70b 模型的 'reasoning' 属性
+                if hasattr(message, "reasoning") and message.reasoning:
+                    reasoning_content = message.reasoning
+                # 检查 DeepSeek deepseek-reasoner 模型的 'reasoning_content'
+                elif (
+                    hasattr(message, "reasoning_content") and message.reasoning_content
                 ):
-                    message = response.raw_completion.choices[0].message
-                    reasoning_content = ""  # 初始化 reasoning_content
-
-                    # 检查 Groq deepseek-r1-distill-llama-70b模型的 'reasoning' 属性
-                    if hasattr(message, "reasoning") and message.reasoning:
-                        reasoning_content = message.reasoning
-                    # 检查 DeepSeek deepseek-reasoner模型的 'reasoning_content'
-                    elif (
-                        hasattr(message, "reasoning_content")
-                        and message.reasoning_content
-                    ):
-                        reasoning_content = message.reasoning_content
-
-                    if reasoning_content:
-                        response.completion_text = (
-                            f"🤔思考：{reasoning_content}\n\n{message.content}"
-                        )
-                    else:
-                        response.completion_text = message.content
+                    reasoning_content = message.reasoning_content
 
+                if reasoning_content:
+                    response.completion_text = (
+                        f"🤔思考：{reasoning_content}\n\n{message.content}"
+                    )
+                else:
+                    response.completion_text = message.content
         else:
-            # DeepSeek 官方的模型的思考存在了 reason_content 字段因此不需要过滤
+            # 过滤推理标签的处理逻辑
             completion_text = response.completion_text
-            # 适配 ollama deepseek-r1 模型
+
+            # 检查并移除 <think> 标签
             if r"<think>" in completion_text or r"</think>" in completion_text:
+                # 移除配对的标签及其内容
                 completion_text = re.sub(
                     r"<think>.*?</think>", "", completion_text, flags=re.DOTALL
                 ).strip()
-                # 可能有单标签情况
+
+                # 移除可能残留的单个标签
                 completion_text = (
                     completion_text.replace(r"<think>", "")
                     .replace(r"</think>", "")
                     .strip()
                 )
+
             response.completion_text = completion_text