修改为直接使用OpenAIChat

src/utils/llm.py

@@ -136,91 +136,3 @@ class DeepSeekR1ChatOllama(ChatOllama):
         if "**JSON Response:**" in content:
             content = content.split("**JSON Response:**")[-1]
         return AIMessage(content=content, reasoning_content=reasoning_content)
-
-
-class SiliconFlowChat(ChatOpenAI):
-    """Wrapper for SiliconFlow Chat API, fully compatible with OpenAI-spec format."""
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-
-        # Ensure the API client is initialized with SiliconFlow's endpoint and key
-        self.client = OpenAI(
-            api_key=kwargs.get("api_key"),
-            base_url=kwargs.get("base_url")
-        )
-
-    async def ainvoke(
-            self,
-            input: LanguageModelInput,
-            config: Optional[RunnableConfig] = None,
-            *,
-            stop: Optional[List[str]] = None,
-            **kwargs: Any,
-    ) -> AIMessage:
-        """Async call SiliconFlow API."""
-
-        # Convert input messages into OpenAI-compatible format
-        message_history = []
-        for input_msg in input:
-            if isinstance(input_msg, SystemMessage):
-                message_history.append({"role": "system", "content": input_msg.content})
-            elif isinstance(input_msg, AIMessage):
-                message_history.append({"role": "assistant", "content": input_msg.content})
-            else:  # HumanMessage or similar
-                message_history.append({"role": "user", "content": input_msg.content})
-
-        # Send request to SiliconFlow API (OpenAI-spec endpoint)
-        response = await self.client.chat.completions.create(
-            model=self.model_name,
-            messages=message_history,
-            stop=stop,
-            **kwargs,
-        )
-
-        # Extract the AI response (SiliconFlow's response must match OpenAI format)
-        if hasattr(response.choices[0].message, "reasoning_content"):
-            reasoning_content = response.choices[0].message.reasoning_content
-        else:
-            reasoning_content = None
-
-        content = response.choices[0].message.content
-        return AIMessage(content=content, reasoning_content=reasoning_content)  # Return reasoning_content if needed
-
-    def invoke(
-            self,
-            input: LanguageModelInput,
-            config: Optional[RunnableConfig] = None,
-            *,
-            stop: Optional[List[str]] = None,
-            **kwargs: Any,
-    ) -> AIMessage:
-        """Sync call SiliconFlow API."""
-
-        # Same conversion as async version
-        message_history = []
-        for input_msg in input:
-            if isinstance(input_msg, SystemMessage):
-                message_history.append({"role": "system", "content": input_msg.content})
-            elif isinstance(input_msg, AIMessage):
-                message_history.append({"role": "assistant", "content": input_msg.content})
-            else:
-                message_history.append({"role": "user", "content": input_msg.content})
-
-        # Sync call
-        response = self.client.chat.completions.create(
-            model=self.model_name,
-            messages=message_history,
-            stop=stop,
-            **kwargs,
-        )
-
-        # Handle reasoning_content (if supported)
-        reasoning_content = None
-        if hasattr(response.choices[0].message, "reasoning_content"):
-            reasoning_content = response.choices[0].message.reasoning_content
-
-        return AIMessage(
-            content=response.choices[0].message.content,
-            reasoning_content=reasoning_content,  # Only if SiliconFlow supports it
-        )

src/utils/utils.py

@@ -14,7 +14,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_ollama import ChatOllama
 from langchain_openai import AzureChatOpenAI, ChatOpenAI
 
-from .llm import DeepSeekR1ChatOpenAI, DeepSeekR1ChatOllama,SiliconFlowChat
+from .llm import DeepSeekR1ChatOpenAI, DeepSeekR1ChatOllama
 
 PROVIDER_DISPLAY_NAMES = {
     "openai": "OpenAI",
@@ -177,13 +177,11 @@ def get_llm_model(provider: str, **kwargs):
             base_url = os.getenv("SiliconFLOW_ENDPOINT", "")
         else:
             base_url = kwargs.get("base_url")
-        return SiliconFlowChat(
+        return ChatOpenAI(
             api_key=api_key,
             base_url=base_url,
             model_name=kwargs.get("model_name", "Qwen/QwQ-32B"),
             temperature=kwargs.get("temperature", 0.0),
-            max_tokens=kwargs.get("max_tokens", 512),
-            frequency_penalty=kwargs.get("frequency_penalty", 0.5),
         )
     else:
         raise ValueError(f"Unsupported provider: {provider}")