refactor: update QwenChatModel to use BaseChatOpenAI and remove unused methods

--bug=1052269 --user=刘瑞斌 【模型】对接千问模型，设置联网参数，maxkb对答不生效。通过python代码调用是可以的。 https://www.tapd.cn/57709429/s/1662132

Author: liuruibin

apps/setting/models_provider/impl/qwen_model_provider/model/image.py

@@ -7,6 +7,9 @@
 
 
 class QwenVLChatModel(MaxKBBaseModel, BaseChatOpenAI):
+    @staticmethod
+    def is_cache_model():
+        return False
 
     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):

apps/setting/models_provider/impl/qwen_model_provider/model/llm.py

@@ -6,20 +6,13 @@
     @date：2024/4/28 11:44
     @desc:
 """
-from typing import List, Dict, Optional, Iterator, Any, cast
-
-from langchain_community.chat_models import ChatTongyi
-from langchain_community.llms.tongyi import generate_with_last_element_mark
-from langchain_core.callbacks import CallbackManagerForLLMRun
-from langchain_core.language_models import LanguageModelInput
-from langchain_core.messages import BaseMessage
-from langchain_core.outputs import ChatGenerationChunk, ChatGeneration
-from langchain_core.runnables import RunnableConfig, ensure_config
+from typing import Dict
 
 from setting.models_provider.base_model_provider import MaxKBBaseModel
+from setting.models_provider.impl.base_chat_open_ai import BaseChatOpenAI
 
 
-class QwenChatModel(MaxKBBaseModel, ChatTongyi):
+class QwenChatModel(MaxKBBaseModel, BaseChatOpenAI):
     @staticmethod
     def is_cache_model():
         return False
@@ -29,81 +22,10 @@ def new_instance(model_type, model_name, model_credential: Dict[str, object], **
         optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
         chat_tong_yi = QwenChatModel(
             model_name=model_name,
-            dashscope_api_key=model_credential.get('api_key'),
-            model_kwargs=optional_params,
+            openai_api_key=model_credential.get('api_key'),
+            openai_api_base='https://dashscope.aliyuncs.com/compatible-mode/v1',
+            streaming=True,
+            stream_usage=True,
+            **optional_params,
         )
         return chat_tong_yi
-
-    usage_metadata: dict = {}
-
-    def get_last_generation_info(self) -> Optional[Dict[str, Any]]:
-        return self.usage_metadata
-
-    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
-        return self.usage_metadata.get('input_tokens', 0)
-
-    def get_num_tokens(self, text: str) -> int:
-        return self.usage_metadata.get('output_tokens', 0)
-
-    def _stream(
-            self,
-            messages: List[BaseMessage],
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> Iterator[ChatGenerationChunk]:
-        params: Dict[str, Any] = self._invocation_params(
-            messages=messages, stop=stop, stream=True, **kwargs
-        )
-
-        for stream_resp, is_last_chunk in generate_with_last_element_mark(
-                self.stream_completion_with_retry(**params)
-        ):
-            choice = stream_resp["output"]["choices"][0]
-            message = choice["message"]
-            if (
-                    choice["finish_reason"] == "stop"
-                    and message["content"] == ""
-            ) or (choice["finish_reason"] == "length"):
-                token_usage = stream_resp["usage"]
-                self.usage_metadata = token_usage
-            if (
-                    choice["finish_reason"] == "null"
-                    and message["content"] == ""
-                    and "tool_calls" not in message
-            ):
-                continue
-
-            chunk = ChatGenerationChunk(
-                **self._chat_generation_from_qwen_resp(
-                    stream_resp, is_chunk=True, is_last_chunk=is_last_chunk
-                )
-            )
-            if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
-            yield chunk
-
-    def invoke(
-            self,
-            input: LanguageModelInput,
-            config: Optional[RunnableConfig] = None,
-            *,
-            stop: Optional[List[str]] = None,
-            **kwargs: Any,
-    ) -> BaseMessage:
-        config = ensure_config(config)
-        chat_result = cast(
-            ChatGeneration,
-            self.generate_prompt(
-                [self._convert_input(input)],
-                stop=stop,
-                callbacks=config.get("callbacks"),
-                tags=config.get("tags"),
-                metadata=config.get("metadata"),
-                run_name=config.get("run_name"),
-                run_id=config.pop("run_id", None),
-                **kwargs,
-            ).generations[0][0],
-        ).message
-        self.usage_metadata = chat_result.response_metadata['token_usage']
-        return chat_result