Update ollama.py

为本地ollama部署qwen3大语言模型添加适配！在识别到启用为qwen3大模型的时候自动添加no_think停用推理模式，并过滤<think></think>标签转入给TTS（因为qwen3虽然用no_think停用推理后还是会输出<think></think>转入到TTS）

main/xiaozhi-server/core/providers/llm/ollama/ollama.py

@@ -21,27 +21,67 @@ class LLMProvider(LLMProviderBase):
             api_key="ollama"  # Ollama doesn't need an API key but OpenAI client requires one
         )
 
+        # 检查是否是qwen3模型
+        self.is_qwen3 = self.model_name and self.model_name.lower().startswith("qwen3")
+
     def response(self, session_id, dialogue):
         try:
+            # 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
+            if self.is_qwen3:
+                # 复制对话列表，避免修改原始对话
+                dialogue_copy = dialogue.copy()
+
+                # 找到最后一条用户消息
+                for i in range(len(dialogue_copy) - 1, -1, -1):
+                    if dialogue_copy[i]["role"] == "user":
+                        # 在用户消息前添加/no_think指令
+                        dialogue_copy[i]["content"] = "/no_think " + dialogue_copy[i]["content"]
+                        logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
+                        break
+
+                # 使用修改后的对话
+                dialogue = dialogue_copy
+
             responses = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=dialogue,
                 stream=True
             )
-            is_active=True
+            is_active = True
+            # 用于处理跨chunk的标签
+            buffer = ""
+
             for chunk in responses:
                 try:
                     delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
                     content = delta.content if hasattr(delta, 'content') else ''
+
                     if content:
-                        if '<think>' in content:
+                        # 将内容添加到缓冲区
+                        buffer += content
+
+                        # 处理缓冲区中的标签
+                        while '<think>' in buffer and '</think>' in buffer:
+                            # 找到完整的<think></think>标签并移除
+                            pre = buffer.split('<think>', 1)[0]
+                            post = buffer.split('</think>', 1)[1]
+                            buffer = pre + post
+
+                        # 处理只有开始标签的情况
+                        if '<think>' in buffer:
                             is_active = False
-                            content = content.split('<think>')[0]
-                        if '</think>' in content:
+                            buffer = buffer.split('<think>', 1)[0]
+
+                        # 处理只有结束标签的情况
+                        if '</think>' in buffer:
                             is_active = True
-                            content = content.split('</think>')[-1]
-                        if is_active:
-                            yield content
+                            buffer = buffer.split('</think>', 1)[1]
+
+                        # 如果当前处于活动状态且缓冲区有内容，则输出
+                        if is_active and buffer:
+                            yield buffer
+                            buffer = ""  # 清空缓冲区
+
                 except Exception as e:
                     logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
 
@@ -51,6 +91,22 @@ class LLMProvider(LLMProviderBase):
 
     def response_with_functions(self, session_id, dialogue, functions=None):
         try:
+            # 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
+            if self.is_qwen3:
+                # 复制对话列表，避免修改原始对话
+                dialogue_copy = dialogue.copy()
+
+                # 找到最后一条用户消息
+                for i in range(len(dialogue_copy) - 1, -1, -1):
+                    if dialogue_copy[i]["role"] == "user":
+                        # 在用户消息前添加/no_think指令
+                        dialogue_copy[i]["content"] = "/no_think " + dialogue_copy[i]["content"]
+                        logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
+                        break
+
+                # 使用修改后的对话
+                dialogue = dialogue_copy
+
             stream = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=dialogue,
@@ -58,8 +114,49 @@ class LLMProvider(LLMProviderBase):
                 tools=functions,
             )
 
+            is_active = True
+            buffer = ""
+
             for chunk in stream:
-                yield chunk.choices[0].delta.content, chunk.choices[0].delta.tool_calls
+                try:
+                    delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
+                    content = delta.content if hasattr(delta, 'content') else None
+                    tool_calls = delta.tool_calls if hasattr(delta, 'tool_calls') else None
+
+                    # 如果是工具调用，直接传递
+                    if tool_calls:
+                        yield None, tool_calls
+                        continue
+
+                    # 处理文本内容
+                    if content:
+                        # 将内容添加到缓冲区
+                        buffer += content
+
+                        # 处理缓冲区中的标签
+                        while '<think>' in buffer and '</think>' in buffer:
+                            # 找到完整的<think></think>标签并移除
+                            pre = buffer.split('<think>', 1)[0]
+                            post = buffer.split('</think>', 1)[1]
+                            buffer = pre + post
+
+                        # 处理只有开始标签的情况
+                        if '<think>' in buffer:
+                            is_active = False
+                            buffer = buffer.split('<think>', 1)[0]
+
+                        # 处理只有结束标签的情况
+                        if '</think>' in buffer:
+                            is_active = True
+                            buffer = buffer.split('</think>', 1)[1]
+
+                        # 如果当前处于活动状态且缓冲区有内容，则输出
+                        if is_active and buffer:
+                            yield buffer, None
+                            buffer = ""  # 清空缓冲区
+                except Exception as e:
+                    logger.bind(tag=TAG).error(f"Error processing function chunk: {e}")
+                    continue
 
         except Exception as e:
             logger.bind(tag=TAG).error(f"Error in Ollama function call: {e}")