998211c483
- 从服务器拉取完整代码 - 按框架规范整理项目结构 - 配置 Drone CI 测试环境部署 - 包含后端(FastAPI)、前端(Vue3)、管理端 技术栈: Vue3 + TypeScript + FastAPI + MySQL
480 lines
14 KiB
Python
480 lines
14 KiB
Python
"""
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智能工牌能力分析与课程推荐服务 - Python 原生实现
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功能：
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- 分析员工与顾客的对话记录
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- 评估多维度能力得分
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- 基于能力短板推荐课程
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提供稳定可靠的能力分析和课程推荐能力。
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"""
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import json
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.exceptions import ExternalServiceError
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from .ai_service import AIService, AIResponse
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from .llm_json_parser import parse_with_fallback, clean_llm_output
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from .prompts.ability_analysis_prompts import (
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SYSTEM_PROMPT,
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USER_PROMPT,
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ABILITY_ANALYSIS_SCHEMA,
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ABILITY_DIMENSIONS,
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)
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logger = logging.getLogger(__name__)
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# ==================== 数据结构 ====================
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@dataclass
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class AbilityDimension:
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"""能力维度评分"""
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name: str
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score: float
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feedback: str
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@dataclass
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class CourseRecommendation:
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"""课程推荐"""
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course_id: int
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course_name: str
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recommendation_reason: str
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priority: str # high, medium, low
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match_score: float
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@dataclass
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class AbilityAnalysisResult:
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"""能力分析结果"""
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success: bool
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total_score: float = 0.0
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ability_dimensions: List[AbilityDimension] = field(default_factory=list)
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course_recommendations: List[CourseRecommendation] = field(default_factory=list)
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ai_provider: str = ""
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ai_model: str = ""
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ai_tokens: int = 0
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ai_latency_ms: int = 0
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error: str = ""
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def to_dict(self) -> Dict[str, Any]:
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"""转换为字典"""
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return {
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"success": self.success,
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"total_score": self.total_score,
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"ability_dimensions": [
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{"name": d.name, "score": d.score, "feedback": d.feedback}
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for d in self.ability_dimensions
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],
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"course_recommendations": [
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{
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"course_id": c.course_id,
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"course_name": c.course_name,
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"recommendation_reason": c.recommendation_reason,
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"priority": c.priority,
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"match_score": c.match_score,
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}
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for c in self.course_recommendations
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],
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"ai_provider": self.ai_provider,
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"ai_model": self.ai_model,
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"ai_tokens": self.ai_tokens,
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"ai_latency_ms": self.ai_latency_ms,
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"error": self.error,
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}
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@dataclass
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class UserPositionInfo:
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"""用户岗位信息"""
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position_id: int
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position_name: str
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code: str
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description: str
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skills: Optional[Dict[str, Any]]
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level: str
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status: str
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@dataclass
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class CourseInfo:
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"""课程信息"""
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id: int
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name: str
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description: str
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category: str
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tags: Optional[List[str]]
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difficulty_level: int
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duration_hours: float
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# ==================== 服务类 ====================
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class AbilityAnalysisService:
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"""
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智能工牌能力分析服务
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使用 Python 原生实现。
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使用示例：
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```python
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service = AbilityAnalysisService()
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result = await service.analyze(
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db=db_session,
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user_id=1,
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dialogue_history="顾客：你好，我想了解一下你们的服务..."
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)
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print(result.total_score)
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print(result.course_recommendations)
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```
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"""
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def __init__(self):
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"""初始化服务"""
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self.ai_service = AIService(module_code="ability_analysis")
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async def analyze(
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self,
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db: AsyncSession,
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user_id: int,
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dialogue_history: str
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) -> AbilityAnalysisResult:
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"""
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分析员工能力并推荐课程
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Args:
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db: 数据库会话（支持多租户，每个租户传入各自的会话）
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user_id: 用户ID
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dialogue_history: 对话记录
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Returns:
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AbilityAnalysisResult 分析结果
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"""
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try:
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logger.info(f"开始能力分析 - user_id: {user_id}")
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# 1. 验证输入
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if not dialogue_history or not dialogue_history.strip():
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return AbilityAnalysisResult(
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success=False,
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error="对话记录不能为空"
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)
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# 2. 查询用户岗位信息
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user_positions = await self._get_user_positions(db, user_id)
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user_info_str = self._format_user_info(user_positions)
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logger.info(f"用户岗位信息: {len(user_positions)} 个岗位")
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# 3. 查询所有可选课程
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courses = await self._get_published_courses(db)
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courses_str = self._format_courses(courses)
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logger.info(f"可选课程: {len(courses)} 门")
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# 4. 调用 AI 分析
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ai_response = await self._call_ai_analysis(
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dialogue_history=dialogue_history,
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user_info=user_info_str,
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courses=courses_str
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)
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logger.info(
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f"AI 分析完成 - provider: {ai_response.provider}, "
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f"tokens: {ai_response.total_tokens}, latency: {ai_response.latency_ms}ms"
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)
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# 5. 解析 JSON 结果
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analysis_data = self._parse_analysis_result(ai_response.content, courses)
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# 6. 构建返回结果
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result = AbilityAnalysisResult(
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success=True,
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total_score=analysis_data.get("total_score", 0),
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ability_dimensions=[
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AbilityDimension(
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name=d.get("name", ""),
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score=d.get("score", 0),
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feedback=d.get("feedback", "")
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)
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for d in analysis_data.get("ability_dimensions", [])
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],
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course_recommendations=[
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CourseRecommendation(
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course_id=c.get("course_id", 0),
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course_name=c.get("course_name", ""),
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recommendation_reason=c.get("recommendation_reason", ""),
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priority=c.get("priority", "medium"),
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match_score=c.get("match_score", 0)
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)
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for c in analysis_data.get("course_recommendations", [])
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],
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ai_provider=ai_response.provider,
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ai_model=ai_response.model,
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ai_tokens=ai_response.total_tokens,
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ai_latency_ms=ai_response.latency_ms,
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)
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logger.info(
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f"能力分析完成 - user_id: {user_id}, total_score: {result.total_score}, "
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f"recommendations: {len(result.course_recommendations)}"
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)
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return result
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except Exception as e:
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logger.error(
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f"能力分析失败 - user_id: {user_id}, error: {e}",
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exc_info=True
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)
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return AbilityAnalysisResult(
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success=False,
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error=str(e)
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)
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async def _get_user_positions(
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self,
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db: AsyncSession,
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user_id: int
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) -> List[UserPositionInfo]:
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"""
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查询用户的岗位信息
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获取用户基本信息
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"""
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query = text("""
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SELECT
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p.id as position_id,
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p.name as position_name,
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p.code,
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p.description,
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p.skills,
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p.level,
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p.status
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FROM positions p
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INNER JOIN position_members pm ON p.id = pm.position_id
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WHERE pm.user_id = :user_id
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AND pm.is_deleted = 0
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AND p.is_deleted = 0
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""")
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result = await db.execute(query, {"user_id": user_id})
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rows = result.fetchall()
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positions = []
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for row in rows:
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# 解析 skills JSON
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skills = None
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if row.skills:
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if isinstance(row.skills, str):
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try:
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skills = json.loads(row.skills)
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except json.JSONDecodeError:
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skills = None
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else:
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skills = row.skills
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positions.append(UserPositionInfo(
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position_id=row.position_id,
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position_name=row.position_name,
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code=row.code or "",
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description=row.description or "",
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skills=skills,
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level=row.level or "",
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status=row.status or ""
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))
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return positions
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async def _get_published_courses(self, db: AsyncSession) -> List[CourseInfo]:
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"""
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查询所有已发布的课程
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获取所有课程列表
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"""
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query = text("""
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SELECT
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id,
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name,
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description,
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category,
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tags,
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difficulty_level,
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duration_hours
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FROM courses
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WHERE status = 'published'
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AND is_deleted = FALSE
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ORDER BY sort_order
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""")
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result = await db.execute(query)
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rows = result.fetchall()
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courses = []
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for row in rows:
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# 解析 tags JSON
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tags = None
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if row.tags:
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if isinstance(row.tags, str):
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try:
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tags = json.loads(row.tags)
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except json.JSONDecodeError:
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tags = None
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else:
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tags = row.tags
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courses.append(CourseInfo(
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id=row.id,
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name=row.name,
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description=row.description or "",
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category=row.category or "",
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tags=tags,
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difficulty_level=row.difficulty_level or 3,
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duration_hours=row.duration_hours or 0
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))
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return courses
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def _format_user_info(self, positions: List[UserPositionInfo]) -> str:
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"""格式化用户岗位信息为文本"""
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if not positions:
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return "暂无岗位信息"
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lines = []
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for p in positions:
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info = f"- 岗位：{p.position_name}（{p.code}）"
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if p.level:
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info += f"，级别：{p.level}"
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if p.description:
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info += f"\n 描述：{p.description}"
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if p.skills:
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skills_str = json.dumps(p.skills, ensure_ascii=False)
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info += f"\n 核心技能：{skills_str}"
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lines.append(info)
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return "\n".join(lines)
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def _format_courses(self, courses: List[CourseInfo]) -> str:
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"""格式化课程列表为文本"""
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if not courses:
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return "暂无可选课程"
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lines = []
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for c in courses:
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info = f"- ID: {c.id}, 课程名称: {c.name}"
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if c.category:
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info += f", 分类: {c.category}"
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if c.difficulty_level:
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info += f", 难度: {c.difficulty_level}"
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if c.duration_hours:
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info += f", 时长: {c.duration_hours}小时"
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if c.description:
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# 截断过长的描述
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desc = c.description[:100] + "..." if len(c.description) > 100 else c.description
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info += f"\n 描述: {desc}"
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lines.append(info)
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return "\n".join(lines)
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async def _call_ai_analysis(
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self,
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dialogue_history: str,
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user_info: str,
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courses: str
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) -> AIResponse:
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"""调用 AI 进行能力分析"""
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# 构建用户消息
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user_message = USER_PROMPT.format(
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dialogue_history=dialogue_history,
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user_info=user_info,
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courses=courses
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)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_message}
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]
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# 调用 AI（自动支持 4sapi → OpenRouter 降级）
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response = await self.ai_service.chat(
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messages=messages,
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temperature=0.7, # 保持一定创意性
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prompt_name="ability_analysis"
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)
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return response
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def _parse_analysis_result(
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self,
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ai_output: str,
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courses: List[CourseInfo]
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) -> Dict[str, Any]:
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"""
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解析 AI 输出的分析结果 JSON
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使用 LLM JSON Parser 进行多层兜底解析
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"""
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# 先清洗输出
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cleaned_output, rules = clean_llm_output(ai_output)
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if rules:
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logger.debug(f"AI 输出已清洗: {rules}")
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# 使用带 Schema 校验的解析
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parsed = parse_with_fallback(
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cleaned_output,
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schema=ABILITY_ANALYSIS_SCHEMA,
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default={"analysis": {}},
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validate_schema=True,
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on_error="default"
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)
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# 提取 analysis 部分
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analysis = parsed.get("analysis", {})
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# 后处理：验证课程推荐的有效性
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valid_course_ids = {c.id for c in courses}
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valid_recommendations = []
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for rec in analysis.get("course_recommendations", []):
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course_id = rec.get("course_id")
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if course_id in valid_course_ids:
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valid_recommendations.append(rec)
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else:
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logger.warning(f"推荐的课程ID不存在: {course_id}")
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analysis["course_recommendations"] = valid_recommendations
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# 确保能力维度完整
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existing_dims = {d.get("name") for d in analysis.get("ability_dimensions", [])}
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for dim_name in ABILITY_DIMENSIONS:
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if dim_name not in existing_dims:
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logger.warning(f"缺少能力维度: {dim_name}，使用默认值")
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analysis.setdefault("ability_dimensions", []).append({
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"name": dim_name,
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"score": 70,
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"feedback": "暂无具体评价"
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})
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return analysis
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# ==================== 全局实例 ====================
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ability_analysis_service = AbilityAnalysisService()
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