Qwen3-32B JSON格式化输出不稳定，微调输出格式

api_key.txt

@@ -1,46 +1,3 @@
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rag2_0/intent_recognition/DataModels.py

@@ -107,9 +107,34 @@ class Classification(BaseModel):
     vertical_classification:str = Field(description="垂直领域一级分类")
     sub_classification:str = Field(description="一级分类下的二级分类")
 
+    @classmethod
+    def get_format_instructions(cls):
+        return """
+        格式如下,必须严格以纯JSON格式输出
+        {
+            "vertical_classification": "垂直领域一级分类",
+            "sub_classification": "一级分类下的二级分类"
+        }
+        字段说明：
+        vertical_classification 类型：str 描述：垂直领域一级分类
+        sub_classification 类型：str 描述：一级分类下的二级分类
+
+        """
+
 class QueryRewrite(BaseModel):
     rewrite:str = Field(description="问题改写")
 
+    @classmethod
+    def get_format_instructions(cls):
+        return """
+        格式如下：必须严格以纯JSON格式输出
+        {
+            "rewrite": "问题改写"
+        }
+        字段说明：
+        rewrite 类型：str 描述：问题改写之后的内容
+        """
+
 ##########################槽位模型###########################
 class SlotBase(BaseModel):
     """槽位基础模型"""
@@ -312,6 +337,22 @@ class StepBackPrompt(BaseModel):
     can_use_back_prompt: bool = Field(description="原始查询是否可以进行后退提示(true/false),如果原始查询没有限定词或其他限定词语，则不能进行后退提示")
     step_back_query: List[str] = Field(description="后退提示生成的抽象查询(多个)")
 
+    @classmethod
+    def get_format_instructions(cls):
+        return """
+        格式如下，必须严格以纯JSON格式输出
+        {
+            "original_query": "原始查询",
+            "can_use_back_prompt": "原始查询是否可以进行后退提示(true/false),如果原始查询没有限定词或其他限定词语，则不能进行后退提示",
+            "step_back_query": "后退提示生成的抽象查询(多个)"
+        }
+        字段说明：
+        original_query 类型：str 描述：用户输入的原始查询
+        can_use_back_prompt 类型：bool 描述：原始查询是否可以进行后退提示(true/false),如果原始查询没有限定词或其他限定词语，则不能进行后退提示
+        step_back_query 类型：List[str] 描述：后退提示生成的抽象查询(多个)
+        """
+        
+
 class FollowUpQuestions(BaseModel):
     """后续问题数据模型"""
     original_query: str = Field(description="原始查询")

rag2_0/intent_recognition/IntentRecognition.py

@@ -188,17 +188,19 @@ class AsyncIntentRecognizer:
         classification_parser = PydanticOutputParser(pydantic_object=Classification)
         formatted_prompt = classification_prompt.format(user_input=query, 
                                                         classification_info=classification_info, 
-                                                        output_format=classification_parser.get_format_instructions(),
+                                                        output_format=Classification.get_format_instructions(),
                                                         # conversation_context=conversation_context,
                                                         chat_history=json.dumps(chat_history, ensure_ascii=False))
         # 解析输出
         try:
             # 异步调用LLM
-            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
 
             # 尝试直接解析JSON响应
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            clean_output = re.sub(r'\s+', '', clean_output)
             parsed_output = classification_parser.parse(clean_output)
             
             # 计算并打印耗时
@@ -262,11 +264,13 @@ class AsyncIntentRecognizer:
             formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
             
             # 异步调用LLM
-            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # 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()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            clean_output = re.sub(r'\s+', '', clean_output)
             parsed_output = terms_list_parser.parse(clean_output)
             return parsed_output.terms
 
@@ -335,18 +339,22 @@ class AsyncIntentRecognizer:
         </chat_history>
 
         1、请从当前提问内容中提取电力造价行中定额编码、定额名称、清单编码、清单名称
-        2、请勿随机编造，如果没有提取到，返回空内容
+        2、请勿随机编造，如果没有提取到内容返回空的JSON
-        3、返回结果为json格式
+        3、返回结果为json格式,必须严格以纯JSON格式输出
+        ```json
         {{
             "dinge_info_list":{{"dinge_code_list":["xxxx","xxxx"], "dinge_name_list":["xxxx","xxxx"]}},
             "qingdan_info":{{"qingdan_code_list":["xxxx","xxxx"], "qingdan_name_list":["xxxx","xxxx"]}}
         }}
+        ```json
         """
 
         try:
             response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # response = await self._llm.ainvoke(prompt, extra_body={"enable_thinking": False})
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            clean_output = re.sub(r'\s+', '', clean_output)
             parsed_output = JsonOutputParser().parse(clean_output)
             
             # 计算并打印耗时
@@ -378,16 +386,18 @@ class AsyncIntentRecognizer:
         keywords_str = json.dumps(terms_dict, ensure_ascii=False)
         query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
         formatted_prompt = query_rewrite_prompt_pro.format(query=query, 
-                                                         output_format=query_rewrite_parser.get_format_instructions(),
+                                                         output_format=QueryRewrite.get_format_instructions(),
                                                          keywords=keywords_str,
                                                          chat_history=chat_history,
                                                          context=context)
         # 解析输出
         try:
             # 异步调用LLM
-            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # 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)
+            clean_output = re.sub(r'\s+', '', clean_output)
             parsed_output = query_rewrite_parser.parse(clean_output)
             end_time = time.time()
             process_time=end_time-start_time
@@ -630,9 +640,11 @@ class AsyncIntentRecognizer:
         )
         try:
             # 异步调用LLM
-            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # 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)
+            clean_output = re.sub(r'\s+', '', clean_output)
             # 尝试解析LLM响应
             parsed_output = slot_parser.parse(clean_output)
             return parsed_output
@@ -660,16 +672,18 @@ class AsyncIntentRecognizer:
             query=query,
             chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
             # conversation_context=conversation_context,
-            output_format=step_back_parser.get_format_instructions()
+            output_format=StepBackPrompt.get_format_instructions()
         )
         
         try:
             # 异步调用LLM
-            response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
+            response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
+            # 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)
+            clean_output = re.sub(r'\s+', '', clean_output)
             parsed_output = step_back_parser.parse(clean_output)
             step_back_end_time = time.time()
             step_back_time = step_back_end_time - step_back_start_time

rag2_0/intent_recognition/PromptTemplates.py

@@ -298,7 +298,7 @@ step_back_prompt = """
 后退问题: 
 {{
     "original_query": "2023版本如何在Windows 11系统上导入单位工程量清单？",
-    "can_use_back_prompt": True,
+    "can_use_back_prompt": true,
     "step_back_query": ["如何在Windows 11系统上导入单位工程量清单？", "如何导入单位工程量清单？"]
 }}
 
@@ -306,7 +306,7 @@ step_back_prompt = """
 后退问题: 
 {{
     "original_query": "某个设备更换后，如何在系统中更新对应的定额？",
-    "can_use_back_prompt": True,
+    "can_use_back_prompt": true,
     "step_back_query": ["如何更新设备对应的定额？", "如何更新定额？"]
 }}