445 lines
14 KiB
Python
445 lines
14 KiB
Python
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# =============================================================================
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# 企微IT智能服务台 — 知识库自动迭代服务
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# =============================================================================
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# 说明：知识库自动迭代核心服务
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# 功能：
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# 1. 分析错误标注的高频问题
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# 2. 查找未命中知识库的会话
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# 3. 生成优化建议
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# 4. 审核通过后应用到知识库
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# 5. 推送审核通知给管理员
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# =============================================================================
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import json
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import logging
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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import httpx
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import settings
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from app.models.knowledge_base import KnowledgeBase
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from app.models.knowledge_suggestion import KnowledgeSuggestion
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from app.models.conversation import Conversation
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from app.models.conversation_annotation import ConversationAnnotation
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logger = logging.getLogger(__name__)
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class KnowledgeIterationService:
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"""知识库自动迭代服务。
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分析会话标注和会话数据，生成知识库优化建议，
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支持管理员审核后自动应用到知识库。
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"""
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def __init__(self):
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"""初始化服务。"""
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# AI 分析 API（复用 Dify）
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self.ai_api_url = settings.dify_wingman_api_url
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self.ai_api_key = settings.dify_wingman_api_key
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self.ai_timeout = settings.dify_wingman_timeout
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# --------------------------------------------------------------------------
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# 核心方法：分析并生成建议
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# --------------------------------------------------------------------------
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async def analyze_and_generate_suggestions(
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self,
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db: AsyncSession,
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days: int = 7,
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) -> Dict[str, Any]:
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"""分析并生成知识库优化建议。
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分析过去N天的标注数据和会话数据，生成优化建议。
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Args:
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db: 数据库会话
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days: 分析过去N天的数据，默认7天
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Returns:
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Dict: {
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"annotations_analyzed": int, # 分析的标注数量
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"conversations_analyzed": int, # 分析的会话数量
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"suggestions_generated": int, # 生成的建议数量
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}
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"""
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result = {
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"annotations_analyzed": 0,
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"conversations_analyzed": 0,
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"suggestions_generated": 0,
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}
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# 1. 分析错误标注的高频问题
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annotation_suggestions = await self._analyze_annotation_data(db, days)
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result["annotations_analyzed"] = annotation_suggestions.get("analyzed_count", 0)
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result["suggestions_generated"] += annotation_suggestions.get(
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"suggestions_count", 0
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)
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# 2. 分析未命中知识库的会话
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conversation_suggestions = await self._analyze_conversation_data(db, days)
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result["conversations_analyzed"] = conversation_suggestions.get(
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"analyzed_count", 0
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)
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result["suggestions_generated"] += conversation_suggestions.get(
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"suggestions_count", 0
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)
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logger.info(
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f"知识库迭代分析完成: "
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f"标注={result['annotations_analyzed']}, "
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f"会话={result['conversations_analyzed']}, "
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f"建议={result['suggestions_generated']}"
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)
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return result
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async def _analyze_annotation_data(
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self, db: AsyncSession, days: int
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) -> Dict[str, Any]:
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"""分析标注数据，生成优化建议。
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查找被标记为"无用"的AI回复，分析高频错误原因，
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尝试生成FAQ更新建议。
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Args:
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db: 数据库会话
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days: 分析过去N天的数据
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Returns:
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Dict: 分析结果统计
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"""
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since = datetime.now() - timedelta(days=days)
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# 查询过去N天的无效标注
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stmt = (
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select(ConversationAnnotation)
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.where(ConversationAnnotation.feedback == "useless")
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.where(ConversationAnnotation.created_at >= since)
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)
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result = await db.execute(stmt)
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annotations = result.scalars().all()
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if not annotations:
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return {"analyzed_count": 0, "suggestions_count": 0}
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# 按被标注的消息ID分组，统计高频错误
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message_error_counts: Dict[str, int] = {}
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for ann in annotations:
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msg_id = ann.message_id
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message_error_counts[msg_id] = message_error_counts.get(msg_id, 0) + 1
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# 找出高频错误（被标注3次以上）
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frequent_errors = {
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msg_id: count
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for msg_id, count in message_error_counts.items()
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if count >= 3
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}
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if not frequent_errors:
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return {"analyzed_count": len(annotations), "suggestions_count": 0}
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# 调用AI分析错误模式，生成更新建议
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suggestions_count = 0
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for msg_id, error_count in frequent_errors.items():
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# 生成优化建议
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suggestion = await self._generate_update_suggestion(
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db=db,
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source_type="annotation",
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source_data=[msg_id],
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reason=f"该AI回复在过去{days}天内被标记为无用{error_count}次",
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)
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if suggestion:
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db.add(suggestion)
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suggestions_count += 1
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await db.commit()
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return {"analyzed_count": len(annotations), "suggestions_count": suggestions_count}
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async def _analyze_conversation_data(
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self, db: AsyncSession, days: int
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) -> Dict[str, Any]:
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"""分析会话数据，生成新增FAQ建议。
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查找AI无法解决（转人工）的会话，分析生成新FAQ建议。
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Args:
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db: 数据库会话
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days: 分析过去N天的数据
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Returns:
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Dict: 分析结果统计
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"""
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since = datetime.now() - timedelta(days=days)
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# 查询过去N天的AI未解决会话（转人工的会话）
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stmt = (
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select(Conversation)
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.where(Conversation.created_at >= since)
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.where(Conversation.status.in_(["waiting_agent", "agentServing"]))
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)
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result = await db.execute(stmt)
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conversations = result.scalars().all()
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if not conversations:
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return {"analyzed_count": 0, "suggestions_count": 0}
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# 抽样分析（避免一次性处理太多）
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sample_size = min(20, len(conversations))
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sampled = conversations[:sample_size]
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# 对每个会话进行分析
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suggestions_count = 0
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for conv in sampled:
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# 检查是否已存在类似建议
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existing = await self._check_existing_suggestion(db, conv.id)
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if existing:
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continue
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# 生成新FAQ建议
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suggestion = await self._generate_new_faq_suggestion(
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db=db,
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source_type="conversation",
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source_data=[conv.id],
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reason=f"会话'{conv.id}'中AI未能解决问题，需人工介入",
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)
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if suggestion:
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db.add(suggestion)
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suggestions_count += 1
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await db.commit()
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return {"analyzed_count": len(conversations), "suggestions_count": suggestions_count}
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# --------------------------------------------------------------------------
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# 辅助方法
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# --------------------------------------------------------------------------
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async def _generate_update_suggestion(
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self,
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db: AsyncSession,
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source_type: str,
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source_data: List[str],
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reason: str,
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) -> Optional[KnowledgeSuggestion]:
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"""生成知识库更新建议。
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Args:
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db: 数据库会话
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source_type: 来源类型
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source_data: 来源数据
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reason: 生成理由
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Returns:
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Optional[KnowledgeSuggestion]: 建议对象
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"""
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# TODO: 调用AI生成具体的更新内容
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# 当前返回示例数据，实际应调用 Dify API
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return KnowledgeSuggestion(
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suggestion_type="update",
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status="pending",
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title="[待AI生成] 优化建议",
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content="请通过AI分析生成具体的更新内容",
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category="其他",
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tags=[],
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source_type=source_type,
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source_data=source_data,
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reason=reason,
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)
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async def _generate_new_faq_suggestion(
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self,
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db: AsyncSession,
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source_type: str,
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source_data: List[str],
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reason: str,
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) -> Optional[KnowledgeSuggestion]:
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"""生成新FAQ建议。
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Args:
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db: 数据库会话
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source_type: 来源类型
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source_data: 来源数据
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reason: 生成理由
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Returns:
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Optional[KnowledgeSuggestion]: 建议对象
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"""
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# TODO: 调用AI生成具体的FAQ内容
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# 当前返回示例数据，实际应调用 Dify API
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return KnowledgeSuggestion(
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suggestion_type="new_faq",
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status="pending",
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title="[待AI生成] 新FAQ建议",
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content="请通过AI分析生成具体的问题和答案",
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category="其他",
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tags=[],
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source_type=source_type,
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source_data=source_data,
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reason=reason,
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)
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async def _check_existing_suggestion(
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self, db: AsyncSession, source_id: str
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) -> bool:
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"""检查是否已存在相关建议。
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Args:
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db: 数据库会话
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source_id: 来源ID
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Returns:
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bool: 是否已存在
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"""
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stmt = (
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select(KnowledgeSuggestion)
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.where(KnowledgeSuggestion.status == "pending")
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.where(KnowledgeSuggestion.source_data.contains(source_id))
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)
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result = await db.execute(stmt)
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existing = result.scalars().first()
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return existing is not None
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# --------------------------------------------------------------------------
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# 审核与应用
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# --------------------------------------------------------------------------
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async def approve_suggestion(
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self,
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db: AsyncSession,
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suggestion_id: str,
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reviewer_id: str,
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) -> Optional[KnowledgeSuggestion]:
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"""审核通过建议并应用到知识库。
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Args:
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db: 数据库会话
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suggestion_id: 建议ID
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reviewer_id: 审核人ID
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Returns:
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Optional[KnowledgeSuggestion]: 更新后的建议对象
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"""
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# 获取建议
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stmt = select(KnowledgeSuggestion).where(
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KnowledgeSuggestion.id == suggestion_id
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)
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result = await db.execute(stmt)
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suggestion = result.scalar_one_or_none()
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if not suggestion:
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return None
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# 更新状态
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suggestion.status = "approved"
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suggestion.reviewer_id = reviewer_id
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suggestion.reviewed_at = datetime.now()
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# 如果是新FAQ或更新，创建对应的知识库条目
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if suggestion.suggestion_type in ("new_faq", "update"):
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kb = KnowledgeBase(
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title=suggestion.title,
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content=suggestion.content,
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category=suggestion.category,
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tags=suggestion.tags,
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)
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db.add(kb)
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suggestion.status = "applied"
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await db.commit()
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await db.refresh(suggestion)
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logger.info(f"建议已审核通过并应用: {suggestion_id}")
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return suggestion
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|
|
async def reject_suggestion(
|
|||
|
|
self,
|
|||
|
|
db: AsyncSession,
|
|||
|
|
suggestion_id: str,
|
|||
|
|
reviewer_id: str,
|
|||
|
|
reject_reason: str,
|
|||
|
|
) -> Optional[KnowledgeSuggestion]:
|
|||
|
|
"""拒绝建议。
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
db: 数据库会话
|
|||
|
|
suggestion_id: 建议ID
|
|||
|
|
reviewer_id: 审核人ID
|
|||
|
|
reject_reason: 拒绝理由
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
Optional[KnowledgeSuggestion]: 更新后的建议对象
|
|||
|
|
"""
|
|||
|
|
stmt = select(KnowledgeSuggestion).where(
|
|||
|
|
KnowledgeSuggestion.id == suggestion_id
|
|||
|
|
)
|
|||
|
|
result = await db.execute(stmt)
|
|||
|
|
suggestion = result.scalar_one_or_none()
|
|||
|
|
|
|||
|
|
if not suggestion:
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
suggestion.status = "rejected"
|
|||
|
|
suggestion.reviewer_id = reviewer_id
|
|||
|
|
suggestion.reviewed_at = datetime.now()
|
|||
|
|
suggestion.reject_reason = reject_reason
|
|||
|
|
|
|||
|
|
await db.commit()
|
|||
|
|
await db.refresh(suggestion)
|
|||
|
|
|
|||
|
|
logger.info(f"建议已拒绝: {suggestion_id}, 理由: {reject_reason}")
|
|||
|
|
return suggestion
|
|||
|
|
|
|||
|
|
# --------------------------------------------------------------------------
|
|||
|
|
# 查询统计
|
|||
|
|
# --------------------------------------------------------------------------
|
|||
|
|
|
|||
|
|
async def get_suggestion_stats(self, db: AsyncSession) -> Dict[str, int]:
|
|||
|
|
"""获取建议统计。
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
db: 数据库会话
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
Dict: 统计数据
|
|||
|
|
"""
|
|||
|
|
# 总数
|
|||
|
|
stmt = select(KnowledgeSuggestion)
|
|||
|
|
result = await db.execute(stmt)
|
|||
|
|
all_suggestions = result.scalars().all()
|
|||
|
|
|
|||
|
|
stats = {
|
|||
|
|
"total": len(all_suggestions),
|
|||
|
|
"pending": 0,
|
|||
|
|
"approved": 0,
|
|||
|
|
"rejected": 0,
|
|||
|
|
"applied": 0,
|
|||
|
|
"new_faq_count": 0,
|
|||
|
|
"update_count": 0,
|
|||
|
|
"outdated_count": 0,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
for s in all_suggestions:
|
|||
|
|
if s.status in stats:
|
|||
|
|
stats[s.status] += 1
|
|||
|
|
if s.suggestion_type == "new_faq":
|
|||
|
|
stats["new_faq_count"] += 1
|
|||
|
|
elif s.suggestion_type == "update":
|
|||
|
|
stats["update_count"] += 1
|
|||
|
|
elif s.suggestion_type == "outdated":
|
|||
|
|
stats["outdated_count"] += 1
|
|||
|
|
|
|||
|
|
return stats
|
|||
|
|
|
|||
|
|
|
|||
|
|
# 依赖注入函数
|
|||
|
|
async def dep_knowledge_iteration_service() -> KnowledgeIterationService:
|
|||
|
|
"""获取知识库迭代服务实例。"""
|
|||
|
|
return KnowledgeIterationService()
|