update

backend/app/services/llm/document_summarizer.py

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+# src/services/llm/document_summarizer.py
+"""文档摘要生成服务 - LLM生成法规文档摘要"""
+
+from typing import Dict, Optional
+from dataclasses import dataclass
+from loguru import logger
+
+from app.services.llm import get_llm_client, BaseLLMClient
+from app.services.rag.prompt_templates import get_prompt_template
+from app.config.settings import settings
+
+
+@dataclass
+class DocumentSummary:
+    """文档摘要结果"""
+    doc_name: str
+    summary: str
+    applicable_scope: str
+    key_clauses: list
+    key_terms: list
+    compliance_points: list
+    model: str
+    latency_ms: int
+    error: Optional[str] = None
+
+    @property
+    def is_success(self) -> bool:
+        return self.error is None
+
+
+class DocumentSummarizer:
+    """
+    文档摘要生成器
+
+    功能：
+    - 生成法规文档的核心要点摘要
+    - 提取适用范围
+    - 突出关键条款
+    - 列出合规要点
+
+    使用示例：
+        summarizer = DocumentSummarizer()
+        result = summarizer.summarize("GB 7258-2017", markdown_content)
+        print(result.summary)
+    """
+
+    def __init__(
+        self,
+        provider: str = None,
+        model: str = None,
+        max_tokens: int = None
+    ):
+        """
+        初始化摘要生成器
+
+        Args:
+            provider: LLM提供商
+            model: LLM模型名称
+            max_tokens: 最大输出token数
+        """
+        self.provider = provider or settings.llm_provider
+        self.model = model or settings.llm_model
+        self.max_tokens = max_tokens or settings.rag_summary_max_tokens
+
+        # LLM客户端（延迟加载）
+        self.llm: Optional[BaseLLMClient] = None
+
+        logger.info(f"摘要生成器初始化: provider={self.provider}, model={self.model}")
+
+    def _init_llm(self):
+        """延迟初始化LLM"""
+        if self.llm is None:
+            self.llm = get_llm_client(
+                provider=self.provider,
+                model=self.model
+            )
+
+    def summarize(
+        self,
+        doc_name: str,
+        content: str,
+        regulation_type: str = "",
+        max_tokens: Optional[int] = None
+    ) -> DocumentSummary:
+        """
+        生成文档摘要
+
+        Args:
+            doc_name: 文档名称
+            content: 文档内容（Markdown格式）
+            regulation_type: 法规类型
+            max_tokens: 最大输出token数
+
+        Returns:
+            DocumentSummary: 摘要结果
+        """
+        import time
+        start_time = time.time()
+
+        logger.info(f"生成文档摘要: {doc_name}")
+
+        try:
+            self._init_llm()
+
+            # 使用摘要模板
+            template = get_prompt_template("document_summary")
+
+            # 构建用户消息
+            user_content = template.user_template.format(
+                doc_name=doc_name,
+                content=content[:8000]  # 截取前8000字符（避免超出token限制）
+            )
+
+            # 调用LLM
+            response = self.llm.chat(
+                messages=[
+                    {"role": "system", "content": template.system_prompt},
+                    {"role": "user", "content": user_content}
+                ],
+                max_tokens=max_tokens or self.max_tokens,
+                temperature=0.3  # 低温度保证摘要准确性
+            )
+
+            latency_ms = int((time.time() - start_time) * 1000)
+
+            if not response.is_success:
+                return DocumentSummary(
+                    doc_name=doc_name,
+                    summary="",
+                    applicable_scope="",
+                    key_clauses=[],
+                    key_terms=[],
+                    compliance_points=[],
+                    model=self.model,
+                    latency_ms=latency_ms,
+                    error=response.error
+                )
+
+            # 解析摘要结构
+            summary_data = self._parse_summary(response.content)
+
+            logger.success(f"摘要生成完成: {doc_name}, {latency_ms}ms")
+
+            return DocumentSummary(
+                doc_name=doc_name,
+                summary=summary_data.get("summary", response.content),
+                applicable_scope=summary_data.get("applicable_scope", ""),
+                key_clauses=summary_data.get("key_clauses", []),
+                key_terms=summary_data.get("key_terms", []),
+                compliance_points=summary_data.get("compliance_points", []),
+                model=response.model,
+                latency_ms=latency_ms
+            )
+
+        except Exception as e:
+            logger.error(f"摘要生成失败: {e}")
+            return DocumentSummary(
+                doc_name=doc_name,
+                summary="",
+                applicable_scope="",
+                key_clauses=[],
+                key_terms=[],
+                compliance_points=[],
+                model=self.model,
+                latency_ms=0,
+                error=str(e)
+            )
+
+    def _parse_summary(self, content: str) -> Dict:
+        """解析摘要内容（提取结构化信息）"""
+        result = {
+            "summary": content,
+            "applicable_scope": "",
+            "key_clauses": [],
+            "key_terms": [],
+            "compliance_points": []
+        }
+
+        # 简单解析（提取关键信息）
+        lines = content.split("\n")
+
+        for line in lines:
+            line = line.strip()
+
+            # 提取适用范围
+            if "适用范围" in line or "适用对象" in line:
+                result["applicable_scope"] = line.split("：")[-1].strip() if "：" in line else line.split(":")[-1].strip()
+
+            # 提取关键条款
+            if line.startswith("- 【条款") or line.startswith("【条款"):
+                result["key_clauses"].append(line)
+
+            # 提取关键术语
+            if "关键术语" in line or "术语定义" in line:
+                # 继续读取后续几行
+                pass
+
+            # 提取合规要点
+            if "合规要点" in line or "必须满足" in line:
+                pass
+
+        return result
+
+    def batch_summarize(
+        self,
+        documents: list
+    ) -> list:
+        """
+        批量生成摘要
+
+        Args:
+            documents: 文档列表 [{"doc_name": str, "content": str}, ...]
+
+        Returns:
+            list: 摘要结果列表
+        """
+        results = []
+        for doc in documents:
+            result = self.summarize(doc["doc_name"], doc["content"])
+            results.append(result)
+        return results
+
+
+def summarize_document(
+    doc_name: str,
+    content: str,
+    **kwargs
+) -> DocumentSummary:
+    """便捷函数：生成文档摘要"""
+    summarizer = DocumentSummarizer(**kwargs)
+    return summarizer.summarize(doc_name, content)