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LLM输出的内容先通过正则清理多余内容

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ouyangyouzhang
2025-08-28 14:29:19 +08:00
parent 660b5f6561
commit abc868225a
5 changed files with 95 additions and 119 deletions
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rag2_0/intent_recognition/DataModels.py
+40 -39
View File
@@ -132,9 +132,48 @@ class QueryRewrite(BaseModel):
"rewrite": "问题改写"
}
字段说明：
rewrite 类型：str 描述：问题改写之后的内容
"rewrite" 类型：str 描述：问题改写之后的内容
"""
# 意图优化环节数据模型
class StepBackPrompt(BaseModel):
"""后退提示数据模型"""
original_query: str = Field(description="原始查询")
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")
follow_up_query: str = Field(description="基于历史对话生成的独立问题")
class HypotheticalDocument(BaseModel):
"""假设文档数据模型"""
original_query: str = Field(description="原始查询")
hypothetical_answer: str = Field(description="假设性回答")
class MultiQuestions(BaseModel):
"""多问题查询数据模型"""
original_query: str = Field(description="原始查询")
sub_questions: List[str] = Field(description="从不同角度生成的子问题列表")
##########################槽位模型###########################
class SlotBase(BaseModel):
"""槽位基础模型"""
@@ -329,41 +368,3 @@ class IntentAndSlotResult(BaseModel):
ProblemDiagnosisSlots,
OtherSlots
]
# 意图优化环节数据模型
class StepBackPrompt(BaseModel):
"""后退提示数据模型"""
original_query: str = Field(description="原始查询")
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")
follow_up_query: str = Field(description="基于历史对话生成的独立问题")
class HypotheticalDocument(BaseModel):
"""假设文档数据模型"""
original_query: str = Field(description="原始查询")
hypothetical_answer: str = Field(description="假设性回答")
class MultiQuestions(BaseModel):
"""多问题查询数据模型"""
original_query: str = Field(description="原始查询")
sub_questions: List[str] = Field(description="从不同角度生成的子问题列表")
rag2_0/intent_recognition/IntentRecognition.py
+37 -34
View File
@@ -170,6 +170,24 @@ class AsyncIntentRecognizer:
except Exception as e:
raise RuntimeError(f"加载后缀关键词失败: {e}") from e
def _clean_llm_json(self, content: str) -> str:
"""
统一清洗LLM返回的JSON字符串：
1) 去首尾空白
2) 去除<think>…</think>段
3) 通过第一个"{"与最后一个"}"裁剪内容，移除首尾多余字符
4) 压缩所有空白字符
"""
content = content.strip()
content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
# 裁剪到最外层花括号范围
start = content.find('{')
end = content.rfind('}')
if start != -1 and end != -1 and start < end:
content = content[start:end+1]
content = re.sub(r'\s+', '', content)
return content
async def _classify_intent_async(self, query: str, conversation_context: str = "",
chat_history: List[Dict[str, str]] = None,
previous_slots: Dict[str, Any] = None) -> Classification:
@@ -194,13 +212,11 @@ class AsyncIntentRecognizer:
# 解析输出
try:
# 异步调用LLM
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 = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"})
# 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)
clean_output = self._clean_llm_json(response.content)
parsed_output = classification_parser.parse(clean_output)
# 计算并打印耗时
@@ -264,13 +280,11 @@ 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, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
# 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, response_format={"type": "json_object"})
# 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)
clean_output = self._clean_llm_json(response.content)
parsed_output = terms_list_parser.parse(clean_output)
return parsed_output.terms
@@ -341,20 +355,16 @@ class AsyncIntentRecognizer:
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，如果没有提取到内容返回空的JSON
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)
# response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"})
clean_output = self._clean_llm_json(response.content)
parsed_output = JsonOutputParser().parse(clean_output)
# 计算并打印耗时
@@ -393,11 +403,9 @@ class AsyncIntentRecognizer:
# 解析输出
try:
# 异步调用LLM
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)
# response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
response = await self._llm.ainvoke(formatted_prompt,response_format={"type": "json_object"})
clean_output = self._clean_llm_json(response.content)
parsed_output = query_rewrite_parser.parse(clean_output)
end_time = time.time()
process_time=end_time-start_time
@@ -642,9 +650,7 @@ class AsyncIntentRecognizer:
# 异步调用LLM
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)
clean_output = self._clean_llm_json(response.content)
# 尝试解析LLM响应
parsed_output = slot_parser.parse(clean_output)
return parsed_output
@@ -677,22 +683,18 @@ class AsyncIntentRecognizer:
try:
# 异步调用LLM
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 = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
response = await self._llm.ainvoke(formatted_prompt, response_format={"type": "json_object"})
# 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)
clean_output = self._clean_llm_json(response.content)
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
logging.info(f"后退提示生成耗时统计 - 总耗时: {step_back_time:.2f}秒")
return parsed_output
except Exception as e:
# 如果解析失败，返回原始查询作为后退提示
logging.error(f"后退提示生成失败: {e}", exc_info=True)
return StepBackPrompt(original_query=query, can_use_back_prompt=False, step_back_query=[query])
raise RuntimeError(f"解析后退提示结果时出错: {e}") from e
async def _find_matching_software_docs_async(self, query: str, soft_name: str,
chat_history: List[Dict[str, str]] = None,
@@ -739,7 +741,8 @@ class AsyncIntentRecognizer:
try:
# 异步调用LLM
start_time = time.time()
response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
# response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"}, extra_body={"enable_thinking": False})
response = await self._llm.ainvoke(prompt, response_format={"type": "json_object"})
end_time = time.time()
# 解析JSON响应