Simon
194 lines
6.2 KiB
Python
194 lines
6.2 KiB
Python
# =============================================================================
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# 企微IT智能服务台 — 图谱查询服务
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# =============================================================================
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# 说明：基于Neo4j知识图谱的智能问答服务。
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# 用户问题 → 图谱查询 → 直接返回解决方案（毫秒级响应）
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# 未命中 → 降级到Dify流程
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#
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# 核心功能：
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# 1. 关键词提取（jieba分词 + 停用词过滤）
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# 2. Neo4j模糊匹配Issue节点
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# 3. 查找关联的Action解决方案
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# 4. 返回最高匹配的SolutionResult
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# =============================================================================
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import logging
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from typing import List, Optional
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from pydantic import BaseModel
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logger = logging.getLogger(__name__)
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# 停用词列表（常见的无意义词汇）
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STOPWORDS = {
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"的", "了", "在", "是", "我", "有", "和", "就", "不", "人",
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"都", "一", "一个", "上", "也", "很", "到", "说", "要", "去",
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"你", "会", "着", "没有", "看", "好", "自己", "这", "那",
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"什么", "怎么", "如何", "为什么", "请问", "帮忙", "帮助",
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}
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class SolutionResult(BaseModel):
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"""图谱查询结果 - 解决方案"""
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issue_name: str = ""
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issue_uuid: str = ""
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solution: str = ""
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action_name: str = ""
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confidence: float = 0.0
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class GraphQueryService:
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"""图谱查询服务 - 根据用户问题查找解决方案"""
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def __init__(self, neo4j_client):
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"""初始化图谱查询服务
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Args:
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neo4j_client: Neo4jClient实例
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"""
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self.neo4j_client = neo4j_client
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self._keyword_cache: dict = {} # 简单缓存
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async def find_solution_by_question(
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self, question: str, timeout_ms: int = 100
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) -> Optional[SolutionResult]:
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"""根据用户问题查找解决方案（图谱查询主入口）
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流程：
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1. 提取问题关键词
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2. 遍历关键词查询Neo4j图谱
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3. 获取匹配的Issue及关联的Action
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4. 返回置信度最高的解决方案
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Args:
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question: 用户问题文本
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timeout_ms: 查询超时时间（毫秒），默认100ms
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Returns:
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Optional[SolutionResult]: 匹配的解决方案，未命中返回None
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"""
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if not question or not question.strip():
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return None
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# 1. 提取关键词
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keywords = self._extract_keywords(question)
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if not keywords:
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logger.info(f"图谱查询：无法提取关键词 question={question[:50]}")
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return None
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logger.info(f"图谱查询：keywords={keywords}, question={question[:50]}")
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# 2. 遍历关键词查询图谱（按置信度排序）
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for kw in keywords:
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try:
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result = await self._query_by_keyword(kw)
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if result:
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logger.info(
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f"图谱命中: keyword={kw}, issue={result.issue_name}, "
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f"confidence={result.confidence}"
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)
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return result
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except Exception as e:
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logger.warning(f"图谱查询异常（降级继续）: keyword={kw}, error={e}")
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continue
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logger.info(f"图谱未命中: question={question[:50]}")
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return None
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def _extract_keywords(self, text: str) -> List[str]:
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"""从文本中提取关键词
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使用jieba分词 + 停用词过滤
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Args:
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text: 输入文本
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Returns:
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List[str]: 关键词列表（按出现顺序）
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"""
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# 尝试导入jieba，如果失败则使用简单分词
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try:
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import jieba
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words = jieba.lcut(text)
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except ImportError:
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# 降级：简单按空格和标点分词
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words = text.replace("，", " ").replace("？", " ").replace("？", " ").split()
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# 过滤停用词和短词
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keywords = [
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w.strip()
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for w in words
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if w.strip() and len(w.strip()) >= 2 and w.strip() not in STOPWORDS
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]
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# 返回前5个关键词（避免查询过多）
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return keywords[:5]
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async def _query_by_keyword(self, keyword: str) -> Optional[SolutionResult]:
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"""根据单个关键词查询解决方案
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Args:
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keyword: 搜索关键词
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Returns:
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Optional[SolutionResult]: 匹配的解决方案
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"""
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# 调用neo4j_client的模糊搜索方法
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if not hasattr(self.neo4j_client, "find_issues_by_keyword"):
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logger.warning("neo4j_client没有find_issues_by_keyword方法")
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return None
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issues = await self.neo4j_client.find_issues_by_keyword(keyword, limit=5)
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if not issues:
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return None
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# 获取第一个Issue的详细信息和关联的Action
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issue = issues[0]
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# 查询关联的Action解决方案
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actions = await self.neo4j_client.find_actions_by_issue(issue.uuid)
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if not actions:
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return None
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action = actions[0]
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# 构建返回结果
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return SolutionResult(
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issue_name=issue.name,
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issue_uuid=issue.uuid,
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solution=action.description or action.name,
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action_name=action.name,
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confidence=0.8, # 简化处理，默认0.8
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)
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# 全局单例
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_graph_query_service: Optional[GraphQueryService] = None
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async def get_graph_query_service(neo4j_client=None) -> Optional[GraphQueryService]:
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"""获取GraphQueryService单例（异步版本）
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Args:
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neo4j_client: Neo4jClient实例，不传则自动获取
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Returns:
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Optional[GraphQueryService]: 图谱查询服务实例，Neo4j不可用时返回None
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"""
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global _graph_query_service
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if _graph_query_service is None:
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if neo4j_client is None:
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from app.services.neo4j_client import get_neo4j_client
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neo4j_client = await get_neo4j_client()
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# 如果neo4j_client为None（图服务不可用），直接返回None
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if neo4j_client is None:
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return None
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_graph_query_service = GraphQueryService(neo4j_client)
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return _graph_query_service
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