diff --git a/main/xiaozhi-server/core/providers/llm/ollama/ollama.py b/main/xiaozhi-server/core/providers/llm/ollama/ollama.py
index fb34ced5..aad6354f 100644
--- a/main/xiaozhi-server/core/providers/llm/ollama/ollama.py
+++ b/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 '' in content:
+ # 将内容添加到缓冲区
+ buffer += content
+
+ # 处理缓冲区中的标签
+ while '' in buffer and '' in buffer:
+ # 找到完整的标签并移除
+ pre = buffer.split('', 1)[0]
+ post = buffer.split('', 1)[1]
+ buffer = pre + post
+
+ # 处理只有开始标签的情况
+ if '' in buffer:
is_active = False
- content = content.split('')[0]
- if '' in content:
+ buffer = buffer.split('', 1)[0]
+
+ # 处理只有结束标签的情况
+ if '' in buffer:
is_active = True
- content = content.split('')[-1]
- if is_active:
- yield content
+ buffer = buffer.split('', 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 '' in buffer and '' in buffer:
+ # 找到完整的标签并移除
+ pre = buffer.split('', 1)[0]
+ post = buffer.split('', 1)[1]
+ buffer = pre + post
+
+ # 处理只有开始标签的情况
+ if '' in buffer:
+ is_active = False
+ buffer = buffer.split('', 1)[0]
+
+ # 处理只有结束标签的情况
+ if '' in buffer:
+ is_active = True
+ buffer = buffer.split('', 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}")