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@@ -29,6 +29,7 @@ try:
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from foundation.ai.models.model_handler import model_handler as mh
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from foundation.ai.agent.generate.model_generate import generate_model_client
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from core.construction_review.component.reviewers.utils.prompt_loader import prompt_loader
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+ from core.construction_review.component.reviewers.utils.inter_tool import InterTool
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from foundation.observability.logger.loggering import server_logger as logger
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except ImportError as e:
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logger.warning(f"Warning: 无法导入依赖: {e}")
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@@ -37,6 +38,7 @@ except ImportError as e:
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generate_model_client = None
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prompt_loader = None
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logger = None
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+ InterTool = None
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class TextProcessor:
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@@ -108,6 +110,54 @@ class ResultFormatter:
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return out
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+class StandardizedResponseProcessor:
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+ """标准化响应处理器 - 统一为outline_reviewer.py格式"""
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+
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+ def __init__(self):
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+ if InterTool:
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+ self.inter_tool = InterTool()
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+ else:
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+ self.inter_tool = None
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+
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+ def process_llm_response(self, response_text: str, check_name: str = "编制依据检查") -> List[Dict[str, Any]]:
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+ """
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+ 处理LLM响应，返回标准格式
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+
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+ Args:
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+ response_text: LLM原始响应文本
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+ check_name: 检查项名称
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+
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+ Returns:
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+ List[Dict]: 标准格式的审查结果列表
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+ """
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+ if not self.inter_tool:
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+ logger.warning("InterTool未初始化，返回空结果")
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+ return []
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+
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+ try:
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+ # 使用inter_tool提取JSON数据
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+ json_data = self.inter_tool._extract_json_data(response_text)
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+ parsed_result = []
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+
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+ if json_data and isinstance(json_data, list):
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+ for item in json_data:
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+ parsed_result.append(self.inter_tool._create_issue_item(item, check_name))
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+ elif json_data and isinstance(json_data, dict):
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+ parsed_result.append(self.inter_tool._create_issue_item(json_data, check_name))
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+
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+ return parsed_result
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+
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+ except Exception as e:
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+ logger.error(f"处理LLM响应失败: {str(e)}")
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+ # 返回一个错误条目
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+ return [{
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+ "check_item": check_name,
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+ "check_result": {"error": str(e)},
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+ "exist_issue": True,
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+ "risk_info": {"risk_level": "medium"}
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+ }]
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+
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+
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class MessageBuilder:
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"""消息构建工具类"""
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@@ -376,6 +426,7 @@ class BasisReviewService:
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self.llm_client = LLMReviewClient()
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self.text_processor = TextProcessor()
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self.result_formatter = ResultFormatter()
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+ self.response_processor = StandardizedResponseProcessor() # 新增标准化处理器
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# 确保使用最新的prompt_loader实例
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from core.construction_review.component.reviewers.utils.prompt_loader import PromptLoader
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fresh_prompt_loader = PromptLoader()
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@@ -448,6 +499,51 @@ class BasisReviewService:
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return [{"name": name, "is_standard": False, "status": "", "meg": f"批次处理失败: {str(e)}"}
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for name in basis_items]
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+ async def review_batch_standardized(self,
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+ basis_items: List[str],
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+ collection_name: str = "already_basis",
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+ filters: Optional[Dict[str, Any]] = None,
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+ min_score: float = 0.3,
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+ top_k_each: int = 3,
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+ ) -> List[Dict[str, Any]]:
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+ """
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+ 异步批次审查（返回outline_reviewer.py标准格式）
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+
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+ Returns:
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+ List[Dict]: 标准格式审查结果，包含check_item, check_result, exist_issue, risk_info
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+ """
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+ basis_items = [x for x in (basis_items or []) if isinstance(x, str) and x.strip()]
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+ if not basis_items:
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+ return []
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+
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+ async with self._semaphore:
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+ try:
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+ # 构建提示词模板和用户内容（不使用搜索结果，简化处理）
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+ prompt_template = self.message_builder.get_prompt_template()
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+ combined_content = "\n".join([f"{i+1}. {item}" for i, item in enumerate(basis_items)])
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+
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+ user_content = f"请审查以下编制依据：\n{combined_content}"
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+ trace_id = f"prep_basis_standardized_{int(time.time())}"
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+ llm_out = await self.llm_client.review_basis(prompt_template, user_content, trace_id)
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+
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+ # 使用标准化处理器处理响应
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+ standardized_result = self.response_processor.process_llm_response(llm_out, "编制依据规范性检查")
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+
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+ # 统计问题数量
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+ issue_count = sum(1 for item in standardized_result if item.get('exist_issue', False))
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+ logger.info(f"编制依据批次审查完成：总计 {len(basis_items)} 项，发现问题 {issue_count} 项")
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+
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+ return standardized_result
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+
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+ except Exception as e:
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+ logger.error(f" 标准化批次处理失败: {e}")
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+ return [{
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+ "check_item": "编制依据规范性检查",
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+ "check_result": {"error": str(e), "basis_items": basis_items},
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+ "exist_issue": True,
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+ "risk_info": {"risk_level": "high"}
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+ }]
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+
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async def _async_search_basis(
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self,
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basis: str,
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