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