读取接口内层嵌套BUG修复
This commit is contained in:
lixinyue
@@ -26,22 +26,33 @@ class VerificationNodeService(LLMServiceMixin):
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# 创建全局服务实例
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verification_service = VerificationNodeService()
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async def Verify_prompt(state: ReadState,messages_deal):
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async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
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"""处理验证结果并生成输出格式"""
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storage_type = state.get('storage_type', '')
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user_rag_memory_id = state.get('user_rag_memory_id', '')
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data = state.get('data', '')
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# 将 VerificationItem 对象转换为字典列表
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verified_data = []
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if messages_deal.expansion_issue:
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for item in messages_deal.expansion_issue:
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if hasattr(item, 'model_dump'):
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verified_data.append(item.model_dump())
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elif isinstance(item, dict):
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verified_data.append(item)
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Verify_result = {
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"status": messages_deal.split_result,
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"verified_data": messages_deal.expansion_issue,
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"verified_data": verified_data,
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"storage_type": storage_type,
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"user_rag_memory_id": user_rag_memory_id,
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"_intermediate": {
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"type": "verification",
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"title": "Data Verification",
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"result": messages_deal.split_result,
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"reason": messages_deal.reason,
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"query": data,
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"verified_count": len(messages_deal.expansion_issue),
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"reason": messages_deal.reason or "验证完成",
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"query": messages_deal.query,
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"verified_count": len(verified_data),
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"storage_type": storage_type,
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"user_rag_memory_id": user_rag_memory_id
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}
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@@ -71,11 +82,16 @@ async def Verify(state: ReadState):
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}
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logger.info("Verify: 开始渲染模板")
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# 生成 JSON schema 以指导 LLM 输出正确格式
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json_schema = VerificationResult.model_json_schema()
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system_prompt = await verification_service.template_service.render_template(
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template_name='split_verify_prompt.jinja2',
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operation_name='split_verify_prompt',
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history=history,
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sentence=messages
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sentence=messages,
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json_schema=json_schema
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)
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logger.info(f"Verify: 模板渲染完成，prompt length={len(system_prompt)}")
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@@ -92,10 +108,11 @@ async def Verify(state: ReadState):
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system_prompt=system_prompt,
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response_model=VerificationResult,
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fallback_value={
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"query": content, # 添加必填的 query 字段
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"split_result": "fail",
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"query": content,
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"history": history if isinstance(history, list) else [],
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"expansion_issue": [],
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"reason": "验证失败"
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"split_result": "failed",
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"reason": "验证失败或超时"
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}
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),
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timeout=150.0 # 150秒超时
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@@ -105,8 +122,9 @@ async def Verify(state: ReadState):
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logger.error("Verify: LLM 调用超时（150秒），使用 fallback 值")
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structured = VerificationResult(
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query=content,
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split_result="fail",
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history=history if isinstance(history, list) else [],
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expansion_issue=[],
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split_result="failed",
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reason="LLM调用超时"
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)
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@@ -4,11 +4,29 @@ from typing import List, Optional, Dict, Any
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from pydantic import BaseModel, Field
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class VerificationItem(BaseModel):
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"""Individual verification item for a query-answer pair."""
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query_small: str = Field(..., description="子问题")
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answer_small: str = Field(..., description="子问题的回答")
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status: str = Field(..., description="验证状态：True 或 False")
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query_answer: str = Field(..., description="问题的答案（与 answer_small 相同）")
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class VerificationResult(BaseModel):
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"""Result model for verification operation."""
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query: str
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expansion_issue: List[Dict[str, Any]]
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split_result: str
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reason: Optional[str] = None
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history: List[Dict[str, Any]] = Field(default_factory=list)
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query: str = Field(..., description="原始查询问题")
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history: List[Dict[str, Any]] = Field(default_factory=list, description="历史对话记录")
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expansion_issue: List[VerificationItem] = Field(
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default_factory=list,
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description="验证后的数据列表，包含所有通过验证的问答对"
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)
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split_result: str = Field(
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...,
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description="验证结果状态：success（expansion_issue 非空）或 failed（expansion_issue 为空）"
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)
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reason: Optional[str] = Field(
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None,
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description="验证结果的说明和分析"
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)
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@@ -162,35 +162,22 @@ class OptimizedLLMService:
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return fallback_value
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elif isinstance(fallback_value, dict):
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return response_model(**fallback_value)
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elif isinstance(fallback_value, list):
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# 对于 RootModel[List[...]] 类型，直接传入列表
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if hasattr(response_model, 'model_fields') and 'root' in response_model.model_fields:
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return response_model(root=fallback_value)
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# 或者尝试直接传入（Pydantic v2 的 RootModel 支持）
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return response_model(fallback_value)
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# 尝试创建空的响应模型
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# 检查是否是 RootModel 类型（通过检查 __pydantic_root_model__ 属性）
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if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__:
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# RootModel类型 - 传入空列表
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logger.debug(f"创建 RootModel 类型的空响应: {response_model.__name__}")
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if hasattr(response_model, 'root'):
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# RootModel类型
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return response_model([])
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else:
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# 普通BaseModel类型 - 尝试无参数构造
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logger.debug(f"创建普通 BaseModel 类型的空响应: {response_model.__name__}")
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# 普通BaseModel类型
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return response_model()
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except Exception as e:
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logger.error(f"创建降级响应失败: {e}", exc_info=True)
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logger.error(f"创建降级响应失败: {e}")
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# 最后的降级策略
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try:
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if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__:
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return response_model([])
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else:
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return response_model()
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except Exception as final_error:
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logger.error(f"最终降级策略也失败: {final_error}")
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raise
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if hasattr(response_model, 'root'):
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return response_model([])
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else:
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return response_model()
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def clear_cache(self):
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"""清理客户端缓存"""
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@@ -42,19 +42,33 @@
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如果状态是TRUE保留这条数据，否则需不需要这条数据
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### 第五步 输出格式
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按照json的形式输出
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{"data":"Query":原来Query的字段，"history":原来的history字段，
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"expansion_issue":以为列表的形式存储验证之后的数据比如[
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{"query_small": query_small,
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"answer_small": answer_small,,
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"status": 回答的结果是否符合query_small，填写状态,
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"query_answer": answer_small},
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{"query":"原来Query的字段",
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"history":"原来的history字段",
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"expansion_issue":以列表的形式存储验证之后的数据比如[
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{
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"query_small": "张曼婷生日是什么时候？",
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"answer_small": "张曼婷喜欢绘画。",
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"status": "True",
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"query_answer": "张曼 婷喜欢绘画。"
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},{}......]
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,
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"split_result":如果expansion_issue是空的列表返回failed，不是空列表返回success,
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"reason": 为以上分析完之后的结果给一个说明
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}
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"query_small": "子问题",
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"answer_small": "子问题的回答",
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"status": "True或False，表示回答是否符合query_small",
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"query_answer": "问题的答案（与answer_small相同）"
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},
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{
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"query_small": "张曼婷生日是什么时候？",
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"answer_small": "张曼婷喜欢绘画。",
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"status": "False",
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"query_answer": "张曼婷喜欢绘画。"
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}
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],
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"split_result":"如果expansion_issue是空的列表返回failed，不是空列表返回success",
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"reason": "为以上分析完之后的结果给一个说明"
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}
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**输出格式要求**
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**CRITICAL JSON FORMATTING REQUIREMENTS:**
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1. Use only standard ASCII double quotes (") for JSON structure - never use Chinese quotation marks ("") or other Unicode quotes
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2. If the extracted statement text contains quotation marks, escape them properly using backslashes (\")
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3. Ensure all JSON strings are properly closed and comma-separated
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4. Do not include line breaks within JSON string values
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5. The output language should always be the same as the input language
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**JSON Schema:**
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{{ json_schema }}
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@@ -100,41 +100,6 @@ class OpenAIClient(LLMClient):
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logger.error(f"LLM 调用失败: {e}")
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raise LLMClientException(f"LLM 调用失败: {e}") from e
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async def response(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""
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简单响应接口实现（用于fallback机制）
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Args:
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messages: 消息列表
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**kwargs: 额外参数
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Returns:
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LLM 响应文本
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Raises:
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LLMClientException: LLM 调用失败
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"""
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try:
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template = """{messages}"""
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prompt = ChatPromptTemplate.from_template(template)
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chain = prompt | self.client
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# 添加 Langfuse 回调（如果可用）
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config = {}
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if self.langfuse_handler:
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config["callbacks"] = [self.langfuse_handler]
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response = await chain.ainvoke({"messages": messages}, config=config)
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# 提取文本内容
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if hasattr(response, "content"):
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return str(response.content)
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return str(response)
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except Exception as e:
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logger.error(f"LLM 调用失败: {e}")
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raise LLMClientException(f"LLM 调用失败: {e}") from e
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async def response_structured(
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self,
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messages: List[Dict[str, str]],
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@@ -166,206 +131,44 @@ class OpenAIClient(LLMClient):
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if self.langfuse_handler:
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config["callbacks"] = [self.langfuse_handler]
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template = """{question}"""
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prompt = ChatPromptTemplate.from_template(template)
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# 对于 DashScope 等不支持 with_structured_output 的模型，优先使用手动JSON解析
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# 这样可以避免不必要的尝试和错误
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if self.provider: #.lower() == "dashscope"
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logger.info("DashScope 模型，直接使用手动JSON解析方法")
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try:
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# 获取原始响应，添加超时保护
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chain = prompt | self.client
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response = await asyncio.wait_for(
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chain.ainvoke({"question": question_text}, config=config),
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timeout=self.timeout
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)
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# 提取响应文本
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response_text = ""
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if hasattr(response, "content"):
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response_text = str(response.content)
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else:
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response_text = str(response)
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logger.debug(f"LLM原始响应长度: {len(response_text)}")
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# 尝试提取JSON内容
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json_text = response_text.strip()
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# 如果响应包含markdown代码块，提取其中的JSON
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if "```json" in json_text:
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json_text = json_text.split("```json")[1].split("```")[0].strip()
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elif "```" in json_text:
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json_text = json_text.split("```")[1].split("```")[0].strip()
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# 尝试修复常见的JSON格式问题
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# 1. 移除可能的BOM标记
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json_text = json_text.lstrip('\ufeff')
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# 2. 如果JSON被截断（缺少结尾的 ] 或 }），尝试修复
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if json_text.startswith('[') and not json_text.rstrip().endswith(']'):
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logger.warning("检测到JSON数组被截断，尝试修复")
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# 找到最后一个完整的对象
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last_complete_brace = json_text.rfind('}')
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if last_complete_brace > 0:
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json_text = json_text[:last_complete_brace + 1] + ']'
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logger.info(f"修复后的JSON长度: {len(json_text)}")
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elif json_text.startswith('{') and not json_text.rstrip().endswith('}'):
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logger.warning("检测到JSON对象被截断，尝试修复")
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# 找到最后一个完整的字段
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last_complete_brace = json_text.rfind('}')
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if last_complete_brace > 0:
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json_text = json_text[:last_complete_brace + 1]
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logger.info(f"修复后的JSON长度: {len(json_text)}")
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# 解析JSON
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try:
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parsed_dict = json.loads(json_text)
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logger.debug(f"JSON解析成功，类型: {type(parsed_dict)}")
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# 如果是列表，记录第一个元素的结构
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if isinstance(parsed_dict, list) and len(parsed_dict) > 0:
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logger.debug(f"第一个元素的键: {list(parsed_dict[0].keys()) if isinstance(parsed_dict[0], dict) else 'not a dict'}")
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# 尝试字段映射转换（处理LLM返回格式不匹配的情况）
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if isinstance(parsed_dict, list):
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transformed_list = []
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for item in parsed_dict:
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if isinstance(item, dict):
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transformed_item = {}
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# 常见的字段映射规则
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field_mappings = {
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'question': ['extended_question', 'question', 'query'],
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'original_question': ['original_question', 'original', 'source_question'],
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'extended_question': ['extended_question', 'question', 'query', 'extended'],
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'type': ['type', 'category', 'question_type'],
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'reason': ['reason', 'explanation', 'rationale'],
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'query': ['query', 'question', 'text'],
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'split_result': ['split_result', 'result', 'status'],
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'expansion_issue': ['expansion_issue', 'issues', 'expansions'],
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}
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# 对于每个期望的字段，尝试从多个可能的源字段中获取
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for target_field, source_fields in field_mappings.items():
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for source_field in source_fields:
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if source_field in item:
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transformed_item[target_field] = item[source_field]
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break
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# 特殊处理：如果只有 'question' 但缺少 'original_question' 和 'extended_question'
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if 'question' in item and 'original_question' not in transformed_item:
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transformed_item['original_question'] = item['question']
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if 'question' in item and 'extended_question' not in transformed_item:
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transformed_item['extended_question'] = item['question']
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# 保留原始字段（如果没有被映射）
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for key, value in item.items():
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if key not in transformed_item:
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transformed_item[key] = value
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transformed_list.append(transformed_item)
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else:
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transformed_list.append(item)
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logger.info(f"字段映射完成，尝试重新验证")
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logger.debug(f"转换后的数据: {transformed_list}")
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try:
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return response_model.model_validate(transformed_list)
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except Exception as retry_error:
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logger.error(f"字段映射后仍然验证失败: {retry_error}")
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logger.error(f"完整的LLM响应: {response_text}")
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logger.error(f"原始解析字典: {parsed_dict}")
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logger.error(f"转换后的字典: {transformed_list}")
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raise
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else:
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# 非列表类型，记录并抛出原始错误
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logger.error(f"完整的LLM响应: {response_text}")
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logger.error(f"解析后的字典: {parsed_dict}")
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raise
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except json.JSONDecodeError as je:
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logger.error(f"JSON解析失败: {je}")
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logger.error(f"问题位置附近的文本: {json_text[max(0, je.pos-50):min(len(json_text), je.pos+50)]}")
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# 尝试更激进的修复：逐行解析，找到有效的JSON部分
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logger.info("尝试逐行解析JSON")
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lines = json_text.split('\n')
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for i in range(len(lines), 0, -1):
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try:
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partial_json = '\n'.join(lines[:i])
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if partial_json.startswith('['):
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partial_json = partial_json.rstrip().rstrip(',') + ']'
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elif partial_json.startswith('{'):
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partial_json = partial_json.rstrip().rstrip(',') + '}'
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parsed_dict = json.loads(partial_json)
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logger.info(f"成功解析部分JSON（前{i}行）")
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return response_model.model_validate(parsed_dict)
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except:
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continue
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|
||||
# 如果所有尝试都失败，抛出原始错误
|
||||
raise LLMClientException(f"JSON解析失败: {je}") from je
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.error(f"LLM调用超时（{self.timeout}秒）")
|
||||
raise LLMClientException(f"LLM调用超时（{self.timeout}秒）")
|
||||
except LLMClientException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"手动JSON解析失败: {e}", exc_info=True)
|
||||
raise LLMClientException(f"手动JSON解析失败: {e}") from e
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# 方法 1: 使用 PydanticOutputParser（适用于支持的模型）
|
||||
# 方法 1: 使用 PydanticOutputParser
|
||||
if PydanticOutputParser is not None:
|
||||
try:
|
||||
parser = PydanticOutputParser(pydantic_object=response_model)
|
||||
format_instructions = parser.get_format_instructions()
|
||||
prompt_with_instructions = ChatPromptTemplate.from_template(
|
||||
prompt = ChatPromptTemplate.from_template(
|
||||
"{question}\n{format_instructions}"
|
||||
)
|
||||
chain = prompt_with_instructions | self.client | parser
|
||||
chain = prompt | self.client | parser
|
||||
|
||||
parsed = await asyncio.wait_for(
|
||||
chain.ainvoke(
|
||||
{
|
||||
"question": question_text,
|
||||
"format_instructions": format_instructions,
|
||||
},
|
||||
config=config
|
||||
),
|
||||
timeout=self.timeout
|
||||
parsed = await chain.ainvoke(
|
||||
{
|
||||
"question": question_text,
|
||||
"format_instructions": format_instructions,
|
||||
},
|
||||
config=config
|
||||
)
|
||||
|
||||
logger.debug(f"使用 PydanticOutputParser 解析成功")
|
||||
return parsed
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.error(f"PydanticOutputParser 调用超时（{self.timeout}秒）")
|
||||
raise LLMClientException(f"LLM调用超时（{self.timeout}秒）")
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"PydanticOutputParser 解析失败，尝试其他方法: {e}"
|
||||
)
|
||||
|
||||
# 方法 2: 使用 LangChain 的 with_structured_output (如果支持)
|
||||
with_so = getattr(self.client, "with_structured_output", None)
|
||||
|
||||
if callable(with_so):
|
||||
try:
|
||||
# 方法 2: 使用 LangChain 的 with_structured_output
|
||||
template = """{question}"""
|
||||
prompt = ChatPromptTemplate.from_template(template)
|
||||
|
||||
try:
|
||||
with_so = getattr(self.client, "with_structured_output", None)
|
||||
|
||||
if callable(with_so):
|
||||
structured_chain = prompt | with_so(response_model, strict=True)
|
||||
parsed = await asyncio.wait_for(
|
||||
structured_chain.ainvoke(
|
||||
{"question": question_text},
|
||||
config=config
|
||||
),
|
||||
timeout=self.timeout
|
||||
parsed = await structured_chain.ainvoke(
|
||||
{"question": question_text},
|
||||
config=config
|
||||
)
|
||||
|
||||
# 验证并返回结果
|
||||
@@ -378,60 +181,14 @@ class OpenAIClient(LLMClient):
|
||||
# 尝试从 JSON 解析
|
||||
return response_model.model_validate_json(json.dumps(parsed))
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.error(f"with_structured_output 调用超时（{self.timeout}秒）")
|
||||
raise LLMClientException(f"LLM调用超时（{self.timeout}秒）")
|
||||
except NotImplementedError:
|
||||
logger.warning(
|
||||
f"模型 {self.model_name} 不支持 with_structured_output，使用手动JSON解析"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"with_structured_output 失败: {e}，尝试手动解析")
|
||||
|
||||
# 方法 3: 手动JSON解析（fallback方法）
|
||||
logger.info("使用手动JSON解析方法（fallback）")
|
||||
try:
|
||||
# 获取原始响应
|
||||
chain = prompt | self.client
|
||||
response = await asyncio.wait_for(
|
||||
chain.ainvoke({"question": question_text}, config=config),
|
||||
timeout=self.timeout
|
||||
)
|
||||
|
||||
# 提取响应文本
|
||||
response_text = ""
|
||||
if hasattr(response, "content"):
|
||||
response_text = str(response.content)
|
||||
else:
|
||||
response_text = str(response)
|
||||
|
||||
logger.debug(f"LLM原始响应: {response_text[:500]}...")
|
||||
|
||||
# 尝试提取JSON内容
|
||||
json_text = response_text.strip()
|
||||
|
||||
# 如果响应包含markdown代码块，提取其中的JSON
|
||||
if "```json" in json_text:
|
||||
json_text = json_text.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in json_text:
|
||||
json_text = json_text.split("```")[1].split("```")[0].strip()
|
||||
|
||||
# 解析JSON
|
||||
parsed_dict = json.loads(json_text)
|
||||
logger.debug(f"JSON解析成功: {parsed_dict}")
|
||||
|
||||
# 验证并创建Pydantic模型
|
||||
return response_model.model_validate(parsed_dict)
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.error(f"手动JSON解析调用超时（{self.timeout}秒）")
|
||||
raise LLMClientException(f"LLM调用超时（{self.timeout}秒）")
|
||||
except json.JSONDecodeError as je:
|
||||
logger.error(f"JSON解析失败: {je}, 原始文本: {json_text[:200]}...")
|
||||
raise LLMClientException(f"JSON解析失败: {je}") from je
|
||||
except Exception as e:
|
||||
logger.error(f"手动JSON解析失败: {e}")
|
||||
raise LLMClientException(f"手动JSON解析失败: {e}") from e
|
||||
logger.error(f"结构化输出失败: {e}")
|
||||
raise LLMClientException(f"结构化输出失败: {e}") from e
|
||||
|
||||
# 如果所有方法都失败，抛出异常
|
||||
raise LLMClientException(
|
||||
"无法生成结构化输出，所有解析方法均失败"
|
||||
)
|
||||
|
||||
except LLMClientException:
|
||||
raise
|
||||
|
||||
Reference in New Issue
Block a user