撤回之前使用langchain_openai调用模型的逻辑。因为暂时无法解决调用Qwen3禁用思考模式的问题

rag2_0/intent_recognition/IntentRecognition.py

@@ -19,8 +19,6 @@ import jieba
 import time
 import threading
 
-from langchain_openai import ChatOpenAI
-
 from .PromptTemplates import (classification_prompt, query_rewrite_prompt_pro, 
                               extract_nouns_prompt, classification_info, 
                               slot_filling_prompt, step_back_prompt, 
@@ -34,10 +32,7 @@ from .DataModels import (
     StepBackPrompt, HypotheticalDocument
 )
 from .ProfessionalNounVector import ProfessionalNounRetriever, AsyncProfessionalNounRetriever
-from rag2_0.tool.APIKeyManager import APIKeyManager
+from rag2_0.tool.ModelTool import OpenAiLLM
-
-TEMPERATURE = 0.4
-TOP_P = 0.7
 
 class AsyncIntentRecognizer:
     SOFT_WIKI_PATH = "data/wiki_data"
@@ -64,7 +59,17 @@ class AsyncIntentRecognizer:
             model_name: 要使用的模型名称
             vector_index_dir: 向量索引目录，如果为None则使用默认目录
         """
+        base_url = os.getenv("OPENAI_API_BASE")
+        model_name = os.getenv("MODEL_NAME", "gpt-3.5-turbo")
+        # 初始化LLM
+        llm_params = {
+            "temperature": 0.4,  # 降低随机性，使结果更确定
+            "top_p": 0.7,
+            "model": model_name,
+            "base_url": base_url
+        }
         
+        self._llm = OpenAiLLM(**llm_params) 
         # 加载suffix关键词
         self._suffix_keywords = self._load_suffix_keywords()
         # 加载软件词条名称库
@@ -189,15 +194,7 @@ class AsyncIntentRecognizer:
         # 解析输出
         try:
             # 异步调用LLM
-            llm = ChatOpenAI(
+            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-            llm.with_structured_output(Classification)
-            response = await llm.ainvoke(formatted_prompt)
 
             # 尝试直接解析JSON响应
             response.content = response.content.strip()
@@ -264,17 +261,8 @@ class AsyncIntentRecognizer:
             terms_list_parser = PydanticOutputParser(pydantic_object=TermList)
             formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
             
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-            llm.with_structured_output(TermList)
-
             # 异步调用LLM
-            response = await llm.ainvoke(formatted_prompt)
+            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
             
             # 尝试使用Pydantic解析器解析TermList
             response.content = response.content.strip()
@@ -356,16 +344,7 @@ class AsyncIntentRecognizer:
         """
 
         try:
-
+            response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-                
-            response = await llm.ainvoke(prompt, response_format={"type": "json_object"})
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             parsed_output = JsonOutputParser().parse(clean_output)
@@ -405,17 +384,8 @@ class AsyncIntentRecognizer:
                                                          context=context)
         # 解析输出
         try:
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-            llm.with_structured_output(QueryRewrite)
-
             # 异步调用LLM
-            response = await llm.ainvoke(formatted_prompt)
+            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             parsed_output = query_rewrite_parser.parse(clean_output)
@@ -659,18 +629,8 @@ class AsyncIntentRecognizer:
             previous_slots=json.dumps(previous_slots,ensure_ascii=False),
         )
         try:
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-            llm.with_structured_output(slot_model_class)
-
-
             # 异步调用LLM
-            response = await llm.ainvoke(formatted_prompt)
+            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             # 尝试解析LLM响应
@@ -704,17 +664,10 @@ class AsyncIntentRecognizer:
         )
         
         try:
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P
-            )
-            llm.with_structured_output(StepBackPrompt)
-
             # 异步调用LLM
-            response = await llm.ainvoke(formatted_prompt)
+            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            
+            # 解析输出
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             parsed_output = step_back_parser.parse(clean_output)
@@ -770,18 +723,9 @@ class AsyncIntentRecognizer:
         """
         
         try:
-
-            llm = ChatOpenAI(
-                api_key=APIKeyManager.get_api_key(),
-                openai_api_base=os.getenv("OPENAI_API_BASE"),
-                model_name=os.getenv("MODEL_NAME"),
-                temperature=TEMPERATURE,
-                top_p=TOP_P,
-            )
-
             # 异步调用LLM
             start_time = time.time()
-            response = await llm.ainvoke(prompt, response_format={"type": "json_object"})
+            response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
             end_time = time.time()
             
             # 解析JSON响应

rag2_0/tool/ModelTool.py

@@ -217,7 +217,7 @@ class OpenAiLLM:
             except Exception as e:
                 raise RuntimeError(f"OpenAiLLM:invoke:error:{str(e)}.api_key:{api_key}") from e
 
-    async def invoke_async(self, user_prompt="你是谁？", need_retry=True, **extra_kwargs):
+    async def ainvoke(self, user_prompt="你是谁？", **extra_kwargs):
         """异步调用OpenAI API"""
         max_retries = 3
         retry_count = 0
@@ -231,38 +231,17 @@ class OpenAiLLM:
             timeout = httpx.Timeout(300.0) 
             kwargs["timeout"] = timeout
 
-        if need_retry:
+        try:
-            while retry_count < max_retries:
+            api_key = APIKeyManager.get_api_key()  
-                try:
+            async with AsyncOpenAI(api_key=api_key, base_url=self._url) as client:
-                    api_key = APIKeyManager.get_api_key()  
+                completion = await client.chat.completions.create(
-                    # 使用异步客户端
+                    model=self._model,
-                    async with AsyncOpenAI(api_key=api_key, base_url=self._url) as client:
+                    messages=[{'role': 'user', 'content': user_prompt}],
-                        # 创建异步Completion请求
+                    **kwargs
-                        completion = await client.chat.completions.create(
+                )
-                            model=self._model,
+                return completion.choices[0].message
-                            messages=[{'role': 'user', 'content': user_prompt}],
+        except Exception as e:
-                            **kwargs
+            raise RuntimeError(f"OpenAiLLM:ainvoke:error:{str(e)}") from e
-                        )
-                        return completion.choices[0].message
-                    
-                except Exception as e:
-                    retry_count += 1
-                    if retry_count == max_retries:
-                        raise RuntimeError(f"OpenAiLLM:invoke_async:error:{str(e)}.api_key:{api_key}") from e
-                    else:
-                        await asyncio.sleep(5*retry_count)  # 异步等待
-        else:
-            try:
-                api_key = APIKeyManager.get_api_key()  
-                async with AsyncOpenAI(api_key=api_key, base_url=self._url) as client:
-                    completion = await client.chat.completions.create(
-                        model=self._model,
-                        messages=[{'role': 'user', 'content': user_prompt}],
-                        **kwargs
-                    )
-                    return completion.choices[0].message
-            except Exception as e:
-                raise RuntimeError(f"OpenAiLLM:invoke_async:error:{str(e)}.api_key:{api_key}") from e
 
 if __name__ == "__main__":
     # 测试重排模型
@@ -291,7 +270,7 @@ if __name__ == "__main__":
         
         # 测试异步LLM调用
         llm = OpenAiLLM()
-        response = await llm.invoke_async("你好，请简单介绍一下自己")
+        response = await llm.ainvoke("你好，请简单介绍一下自己")
         print(f"异步LLM响应: {response.content}")
     
     # 如果需要运行异步测试，取消下面的注释