Merge branch 'newsnow' into main

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

@@ -23,7 +23,9 @@ class LLMProvider(LLMProviderBase):
         self.bot_id = str(config.get("bot_id"))
         self.user_id = str(config.get("user_id"))
         self.session_conversation_map = {}  # 存储session_id和conversation_id的映射
-        check_model_key("CozeLLM", self.personal_access_token)
+        model_key_msg = check_model_key("CozeLLM", self.personal_access_token)
+        if model_key_msg:
+            logger.bind(tag=TAG).error(model_key_msg)
 
     def response(self, session_id, dialogue, **kwargs):
         coze_api_token = self.personal_access_token

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

@@ -15,7 +15,9 @@ class LLMProvider(LLMProviderBase):
         self.mode = config.get("mode", "chat-messages")
         self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip("/")
         self.session_conversation_map = {}  # 存储session_id和conversation_id的映射
-        check_model_key("DifyLLM", self.api_key)
+        model_key_msg = check_model_key("DifyLLM", self.api_key)
+        if model_key_msg:
+            logger.bind(tag=TAG).error(model_key_msg)
 
     def response(self, session_id, dialogue, **kwargs):
         try:

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

@@ -14,7 +14,9 @@ class LLMProvider(LLMProviderBase):
         self.base_url = config.get("base_url")
         self.detail = config.get("detail", False)
         self.variables = config.get("variables", {})
-        check_model_key("FastGPTLLM", self.api_key)
+        model_key_msg = check_model_key("FastGPTLLM", self.api_key)
+        if model_key_msg:
+            logger.bind(tag=TAG).error(model_key_msg)
 
     def response(self, session_id, dialogue, **kwargs):
         try:

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

@@ -73,8 +73,9 @@ class LLMProvider(LLMProviderBase):
         http_proxy = cfg.get("http_proxy")
         https_proxy = cfg.get("https_proxy")
 
-        if not check_model_key("LLM", self.api_key):
-            raise ValueError("无效的Gemini API Key，请检查是否配置正确")
+        model_key_msg = check_model_key("LLM", self.api_key)
+        if model_key_msg:
+            log.bind(tag=TAG).error(model_key_msg)
 
         if http_proxy or https_proxy:
             log.bind(tag=TAG).info(

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

@@ -25,20 +25,27 @@ class LLMProvider(LLMProviderBase):
             "max_tokens": (500, int),
             "temperature": (0.7, lambda x: round(float(x), 1)),
             "top_p": (1.0, lambda x: round(float(x), 1)),
-            "frequency_penalty": (0, lambda x: round(float(x), 1))
+            "frequency_penalty": (0, lambda x: round(float(x), 1)),
         }
 
         for param, (default, converter) in param_defaults.items():
             value = config.get(param)
             try:
-                setattr(self, param, converter(value) if value not in (None, "") else default)
+                setattr(
+                    self,
+                    param,
+                    converter(value) if value not in (None, "") else default,
+                )
             except (ValueError, TypeError):
                 setattr(self, param, default)
 
         logger.debug(
-            f"意图识别参数初始化: {self.temperature}, {self.max_tokens}, {self.top_p}, {self.frequency_penalty}")
+            f"意图识别参数初始化: {self.temperature}, {self.max_tokens}, {self.top_p}, {self.frequency_penalty}"
+        )
 
-        check_model_key("LLM", self.api_key)
+        model_key_msg = check_model_key("LLM", self.api_key)
+        if model_key_msg:
+            logger.bind(tag=TAG).error(model_key_msg)
         self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url, timeout=httpx.Timeout(self.timeout))
 
     def response(self, session_id, dialogue, **kwargs):
@@ -50,7 +57,9 @@ class LLMProvider(LLMProviderBase):
                 max_tokens=kwargs.get("max_tokens", self.max_tokens),
                 temperature=kwargs.get("temperature", self.temperature),
                 top_p=kwargs.get("top_p", self.top_p),
-                frequency_penalty=kwargs.get("frequency_penalty", self.frequency_penalty),
+                frequency_penalty=kwargs.get(
+                    "frequency_penalty", self.frequency_penalty
+                ),
             )
 
             is_active = True
@@ -88,12 +97,14 @@ class LLMProvider(LLMProviderBase):
             for chunk in stream:
                 # 检查是否存在有效的choice且content不为空
                 if getattr(chunk, "choices", None):
-                    yield chunk.choices[0].delta.content, chunk.choices[0].delta.tool_calls
+                    yield chunk.choices[0].delta.content, chunk.choices[
+                        0
+                    ].delta.tool_calls
                 # 存在 CompletionUsage 消息时，生成 Token 消耗 log
-                elif isinstance(getattr(chunk, 'usage', None), CompletionUsage):
-                    usage_info = getattr(chunk, 'usage', None)
+                elif isinstance(getattr(chunk, "usage", None), CompletionUsage):
+                    usage_info = getattr(chunk, "usage", None)
                     logger.bind(tag=TAG).info(
-                        f"Token 消耗：输入 {getattr(usage_info, 'prompt_tokens', '未知')}，" 
+                        f"Token 消耗：输入 {getattr(usage_info, 'prompt_tokens', '未知')}，"
                         f"输出 {getattr(usage_info, 'completion_tokens', '未知')}，"
                         f"共计 {getattr(usage_info, 'total_tokens', '未知')}"
                     )