Qwen3-32B JSON格式化输出不稳定，微调输出格式
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@@ -101,6 +58,49 @@ sk-ufmqbuplpjvzzlzohvsxertwgnguhipsbajxnxecvvccozly
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@@ -107,9 +107,34 @@ class Classification(BaseModel):
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vertical_classification:str = Field(description="垂直领域一级分类")
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sub_classification:str = Field(description="一级分类下的二级分类")
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@classmethod
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def get_format_instructions(cls):
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return """
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格式如下,必须严格以纯JSON格式输出
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{
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"vertical_classification": "垂直领域一级分类",
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"sub_classification": "一级分类下的二级分类"
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}
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字段说明：
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vertical_classification 类型：str 描述：垂直领域一级分类
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sub_classification 类型：str 描述：一级分类下的二级分类
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"""
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class QueryRewrite(BaseModel):
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rewrite:str = Field(description="问题改写")
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@classmethod
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def get_format_instructions(cls):
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return """
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格式如下：必须严格以纯JSON格式输出
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{
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"rewrite": "问题改写"
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}
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字段说明：
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rewrite 类型：str 描述：问题改写之后的内容
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"""
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##########################槽位模型###########################
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class SlotBase(BaseModel):
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"""槽位基础模型"""
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@@ -312,6 +337,22 @@ class StepBackPrompt(BaseModel):
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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")
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step_back_query: List[str] = Field(description="后退提示生成的抽象查询(多个)")
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@classmethod
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def get_format_instructions(cls):
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return """
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格式如下，必须严格以纯JSON格式输出
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{
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"original_query": "原始查询",
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"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",
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"step_back_query": "后退提示生成的抽象查询(多个)"
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}
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字段说明：
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original_query 类型：str 描述：用户输入的原始查询
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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
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step_back_query 类型：List[str] 描述：后退提示生成的抽象查询(多个)
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"""
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class FollowUpQuestions(BaseModel):
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"""后续问题数据模型"""
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original_query: str = Field(description="原始查询")
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@@ -188,17 +188,19 @@ class AsyncIntentRecognizer:
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classification_parser = PydanticOutputParser(pydantic_object=Classification)
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formatted_prompt = classification_prompt.format(user_input=query,
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classification_info=classification_info,
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output_format=classification_parser.get_format_instructions(),
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output_format=Classification.get_format_instructions(),
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# conversation_context=conversation_context,
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chat_history=json.dumps(chat_history, ensure_ascii=False))
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# 解析输出
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try:
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# 异步调用LLM
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response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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# 尝试直接解析JSON响应
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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parsed_output = classification_parser.parse(clean_output)
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# 计算并打印耗时
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@@ -262,11 +264,13 @@ class AsyncIntentRecognizer:
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formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
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# 异步调用LLM
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response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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# 尝试使用Pydantic解析器解析TermList
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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parsed_output = terms_list_parser.parse(clean_output)
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return parsed_output.terms
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@@ -335,18 +339,22 @@ class AsyncIntentRecognizer:
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</chat_history>
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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
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2、请勿随机编造，如果没有提取到，返回空内容
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3、返回结果为json格式
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2、请勿随机编造，如果没有提取到内容返回空的JSON
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3、返回结果为json格式,必须严格以纯JSON格式输出
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```json
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{{
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"dinge_info_list":{{"dinge_code_list":["xxxx","xxxx"], "dinge_name_list":["xxxx","xxxx"]}},
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"qingdan_info":{{"qingdan_code_list":["xxxx","xxxx"], "qingdan_name_list":["xxxx","xxxx"]}}
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}}
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```json
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"""
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try:
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response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(prompt, extra_body={"enable_thinking": False})
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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parsed_output = JsonOutputParser().parse(clean_output)
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# 计算并打印耗时
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@@ -378,16 +386,18 @@ class AsyncIntentRecognizer:
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keywords_str = json.dumps(terms_dict, ensure_ascii=False)
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query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
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formatted_prompt = query_rewrite_prompt_pro.format(query=query,
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output_format=query_rewrite_parser.get_format_instructions(),
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output_format=QueryRewrite.get_format_instructions(),
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keywords=keywords_str,
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chat_history=chat_history,
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context=context)
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# 解析输出
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try:
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# 异步调用LLM
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response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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parsed_output = query_rewrite_parser.parse(clean_output)
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end_time = time.time()
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process_time=end_time-start_time
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@@ -630,9 +640,11 @@ class AsyncIntentRecognizer:
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)
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try:
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# 异步调用LLM
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response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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# 尝试解析LLM响应
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parsed_output = slot_parser.parse(clean_output)
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return parsed_output
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@@ -660,16 +672,18 @@ class AsyncIntentRecognizer:
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query=query,
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chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
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# conversation_context=conversation_context,
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output_format=step_back_parser.get_format_instructions()
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output_format=StepBackPrompt.get_format_instructions()
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)
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try:
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# 异步调用LLM
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response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
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# response = await self._llm.ainvoke(formatted_prompt, extra_body={"enable_thinking": False})
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# 解析输出
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response.content = response.content.strip()
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clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
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clean_output = re.sub(r'\s+', '', clean_output)
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parsed_output = step_back_parser.parse(clean_output)
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step_back_end_time = time.time()
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step_back_time = step_back_end_time - step_back_start_time
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@@ -298,7 +298,7 @@ step_back_prompt = """
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后退问题:
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{{
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"original_query": "2023版本如何在Windows 11系统上导入单位工程量清单？",
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"can_use_back_prompt": True,
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"can_use_back_prompt": true,
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"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？"]
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}}
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@@ -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？"]
|
||||
}}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user