diff --git a/rag2_0/demo/intent_recognition_example.py b/rag2_0/demo/intent_recognition_example.py
index 0f55823..2981b7e 100755
--- a/rag2_0/demo/intent_recognition_example.py
+++ b/rag2_0/demo/intent_recognition_example.py
@@ -204,41 +204,7 @@ class QueryRewriteProcessor:
enable_query_expansion=True,
use_jieba=True,
cur_soft_name=current_softname))
-
- # 提取分类信息
- classification = result["classification"]
- original_query = result["rewrite"]["rewrite"]
- query_list = result["query_expand"]["all"]
- # 将字典转换为Classification对象
- classification_obj = Classification(**classification)
-
- # 根据enable_retrieval参数决定是否进行文档检索
- retrieved_doc = None
-
-
- retrieved_doc_titles=[]
- if retrieved_doc:
- retrieved_doc_titles=[doc["title"].split("/")[-1] for doc in retrieved_doc]
- # 提取槽位填充信息
- slot_filling = result.get("slot_filling", {})
- slot_filling_str = ""
- if slot_filling and "filled_data" in slot_filling:
- # 格式化槽位填充结果
- slot_filling_str = json.dumps({
- "is_complete": slot_filling.get("is_complete", False),
- "missing_slots": slot_filling.get("missing_slots", {}),
- "filled_data": slot_filling.get("filled_data", {})
- }, ensure_ascii=False, indent=2)
-
- # 处理成功，返回结果
- return {
- "问题": query,
- "问题分类": f"{classification['vertical_classification']} - {classification['sub_classification']}",
- "问题改写": result["rewrite"]["rewrite"],
- "槽位信息": slot_filling_str,
- "检索的文档": "\n".join(retrieved_doc_titles),
- "检索的内容": json.dumps(retrieved_doc, ensure_ascii=False, indent=2) if retrieved_doc else "",
- }
+ return result
except Exception as e:
logging.error(f"处理问题 '{query}' 时出错: ",exc_info=True)
retry_count += 1
@@ -424,19 +390,25 @@ def main():
logging.info(f"所有处理完成，最终结果已保存至: {output_file}")
else:
logging.info(f"文档检索功能状态: 已启用")
- for idx, query in enumerate(examples):
- if query.strip() == "":
- continue
- query="怎么调整报表顺序"
+ nCount=0
+ while True:
+ query="请问怎么在南网版概算工程软件版里面查施工费？"
conversation_context={
- "current_softname": "储能计价通C1软件"
+ "current_softname": "电力建设计价通软件"
}
- # 在调试模式下使用完整的参数
- print(json.dumps(processor.process_query(
+ result = processor.process_query(
query,
conversation_context=conversation_context,
enable_retrieval=True
- ), ensure_ascii=False, indent=2))
+ )
+ # 在调试模式下使用完整的参数
+ # print(json.dumps(processor.process_query(
+ # query,
+ # conversation_context=conversation_context,
+ # enable_retrieval=True
+ # ), ensure_ascii=False, indent=2))
+ nCount+=1
+ print("测试数量: ", nCount)
def setup_logging():
# 配置日志输出到控制台
diff --git a/rag2_0/intent_recognition/DataModels.py b/rag2_0/intent_recognition/DataModels.py
index 60d6cec..1ed0085 100755
--- a/rag2_0/intent_recognition/DataModels.py
+++ b/rag2_0/intent_recognition/DataModels.py
@@ -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")
\ No newline at end of file
diff --git a/rag2_0/intent_recognition/IntentRecognition.py b/rag2_0/intent_recognition/IntentRecognition.py
index 06d6977..14cbe9e 100755
--- a/rag2_0/intent_recognition/IntentRecognition.py
+++ b/rag2_0/intent_recognition/IntentRecognition.py
@@ -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…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'.*?', '', 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'.*?', '', 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'.*?', '', 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'.*?', '', 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'.*?', '', 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'.*?', '', 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'.*?', '', 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
diff --git a/rag2_0/tool/APIKeyManager.py b/rag2_0/tool/APIKeyManager.py
index 2926f0d..0767e77 100755
--- a/rag2_0/tool/APIKeyManager.py
+++ b/rag2_0/tool/APIKeyManager.py
@@ -30,7 +30,7 @@ class APIKeyManager:
# 密钥使用计数和上次使用时间
_key_usage: Dict[str, Dict] = {}
# 当前正在使用的密钥索引
- _current_index = 0
+ _current_index = -1
api_file_path = "api_key.txt"
diff --git a/rag2_0/tool/ModelTool.py b/rag2_0/tool/ModelTool.py
index d223f42..be9daad 100755
--- a/rag2_0/tool/ModelTool.py
+++ b/rag2_0/tool/ModelTool.py
@@ -200,7 +200,7 @@ class OpenAiLLM:
except Exception as e:
retry_count += 1
if retry_count == max_retries:
- raise RuntimeError(f"OpenAiLLM:invoke:error:{str(e)}.api_key:{api_key}") from e
+ raise RuntimeError(f"OpenAiLLM:invoke:error:{str(e)}") from e
else:
time.sleep(5*retry_count) # 重试前等待5秒*重试次数
else:
@@ -215,7 +215,7 @@ class OpenAiLLM:
)
return completion.choices[0].message
except Exception as e:
- raise RuntimeError(f"OpenAiLLM:invoke:error:{str(e)}.api_key:{api_key}") from e
+ raise RuntimeError(f"OpenAiLLM:invoke:error:{str(e)}") from e
async def ainvoke(self, user_prompt="你是谁？", **extra_kwargs):
"""异步调用OpenAI API"""