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
273 lines
9.3 KiB
Python
273 lines
9.3 KiB
Python
"""
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能力评估服务
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用于分析用户对话数据，生成能力评估报告和课程推荐
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使用 Python 原生实现
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"""
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import json
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import logging
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from typing import Dict, Any, List, Literal
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from app.models.ability import AbilityAssessment
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from app.services.ai import ability_analysis_service
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logger = logging.getLogger(__name__)
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class AbilityAssessmentService:
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"""能力评估服务类"""
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async def analyze_yanji_conversations(
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self,
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user_id: int,
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phone: str,
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db: AsyncSession,
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yanji_service,
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engine: Literal["v2"] = "v2"
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) -> Dict[str, Any]:
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"""
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分析言迹对话并生成能力评估及课程推荐
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Args:
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user_id: 用户ID
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phone: 用户手机号（用于获取言迹数据）
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db: 数据库会话
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yanji_service: 言迹服务实例
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engine: 引擎类型（v2=Python原生）
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Returns:
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评估结果字典，包含：
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- assessment_id: 评估记录ID
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- total_score: 综合评分
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- dimensions: 能力维度列表
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- recommended_courses: 推荐课程列表
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- conversation_count: 分析的对话数量
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Raises:
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ValueError: 未找到员工的录音记录
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Exception: API调用失败或其他错误
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"""
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logger.info(f"开始分析言迹对话: user_id={user_id}, phone={phone}, engine={engine}")
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# 1. 获取员工对话数据（最多10条录音）
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conversations = await yanji_service.get_employee_conversations_for_analysis(
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phone=phone,
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limit=10
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)
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if not conversations:
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logger.warning(f"未找到员工的录音记录: user_id={user_id}, phone={phone}")
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raise ValueError("未找到该员工的录音记录")
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# 2. 合并所有对话历史
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all_dialogues = []
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for conv in conversations:
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all_dialogues.extend(conv['dialogue_history'])
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logger.info(
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f"准备分析: user_id={user_id}, "
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f"对话数={len(conversations)}, "
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f"总轮次={len(all_dialogues)}"
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)
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used_engine = "v2"
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# Python 原生实现
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logger.info(f"调用原生能力分析服务")
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# 将对话历史格式化为文本
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dialogue_text = self._format_dialogues_for_analysis(all_dialogues)
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# 调用原生服务
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result = await ability_analysis_service.analyze(
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db=db,
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user_id=user_id,
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dialogue_history=dialogue_text
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)
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if not result.success:
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raise Exception(f"能力分析失败: {result.error}")
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# 转换为兼容格式
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analysis_result = {
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"analysis": {
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"total_score": result.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 result.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 result.course_recommendations
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]
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}
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}
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logger.info(
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f"能力分析完成 - total_score: {result.total_score}, "
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f"provider: {result.ai_provider}, latency: {result.ai_latency_ms}ms"
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)
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# 4. 提取结果
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analysis = analysis_result.get('analysis', {})
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ability_dims = analysis.get('ability_dimensions', [])
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course_recs = analysis.get('course_recommendations', [])
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total_score = analysis.get('total_score')
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logger.info(
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f"分析完成 (engine={used_engine}): total_score={total_score}, "
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f"dimensions={len(ability_dims)}, courses={len(course_recs)}"
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)
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# 5. 保存能力评估记录到数据库
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assessment = AbilityAssessment(
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user_id=user_id,
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source_type='yanji_badge',
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source_id=','.join([str(c['audio_id']) for c in conversations]),
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total_score=total_score,
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ability_dimensions=ability_dims,
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recommended_courses=course_recs,
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conversation_count=len(conversations)
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)
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db.add(assessment)
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await db.commit()
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await db.refresh(assessment)
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logger.info(
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f"评估记录已保存: assessment_id={assessment.id}, "
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f"user_id={user_id}, total_score={total_score}"
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)
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# 6. 返回评估结果
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return {
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"assessment_id": assessment.id,
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"total_score": total_score,
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"dimensions": ability_dims,
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"recommended_courses": course_recs,
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"conversation_count": len(conversations),
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"analyzed_at": assessment.analyzed_at,
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"engine": used_engine,
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}
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def _format_dialogues_for_analysis(self, dialogues: List[Dict[str, Any]]) -> str:
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"""
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将对话历史列表格式化为文本
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Args:
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dialogues: 对话历史列表，每项包含 speaker, content 等字段
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Returns:
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格式化后的对话文本
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"""
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lines = []
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for i, d in enumerate(dialogues, 1):
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speaker = d.get('speaker', 'unknown')
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content = d.get('content', '')
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# 统一说话者标识
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if speaker in ['consultant', 'employee', 'user', '员工']:
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speaker_label = '员工'
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elif speaker in ['customer', 'client', '顾客', '客户']:
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speaker_label = '顾客'
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else:
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speaker_label = speaker
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lines.append(f"[{i}] {speaker_label}: {content}")
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return '\n'.join(lines)
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async def get_user_assessment_history(
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self,
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user_id: int,
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db: AsyncSession,
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limit: int = 10
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) -> List[Dict[str, Any]]:
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"""
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获取用户的能力评估历史记录
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Args:
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user_id: 用户ID
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db: 数据库会话
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limit: 返回记录数量限制
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Returns:
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评估历史记录列表
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"""
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stmt = (
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select(AbilityAssessment)
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.where(AbilityAssessment.user_id == user_id)
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.order_by(AbilityAssessment.analyzed_at.desc())
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.limit(limit)
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)
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result = await db.execute(stmt)
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assessments = result.scalars().all()
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history = []
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for assessment in assessments:
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history.append({
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"id": assessment.id,
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"source_type": assessment.source_type,
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"total_score": assessment.total_score,
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"ability_dimensions": assessment.ability_dimensions,
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"recommended_courses": assessment.recommended_courses,
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"conversation_count": assessment.conversation_count,
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"analyzed_at": assessment.analyzed_at.isoformat() if assessment.analyzed_at else None,
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"created_at": assessment.created_at.isoformat() if assessment.created_at else None
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})
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logger.info(f"获取评估历史: user_id={user_id}, count={len(history)}")
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return history
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async def get_assessment_detail(
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self,
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assessment_id: int,
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db: AsyncSession
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) -> Dict[str, Any]:
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"""
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获取单个评估记录的详细信息
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Args:
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assessment_id: 评估记录ID
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db: 数据库会话
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Returns:
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评估详细信息
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Raises:
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ValueError: 评估记录不存在
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"""
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stmt = select(AbilityAssessment).where(AbilityAssessment.id == assessment_id)
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result = await db.execute(stmt)
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assessment = result.scalar_one_or_none()
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if not assessment:
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raise ValueError(f"评估记录不存在: assessment_id={assessment_id}")
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return {
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"id": assessment.id,
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"user_id": assessment.user_id,
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"source_type": assessment.source_type,
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"source_id": assessment.source_id,
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"total_score": assessment.total_score,
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"ability_dimensions": assessment.ability_dimensions,
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"recommended_courses": assessment.recommended_courses,
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"conversation_count": assessment.conversation_count,
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"analyzed_at": assessment.analyzed_at.isoformat() if assessment.analyzed_at else None,
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"created_at": assessment.created_at.isoformat() if assessment.created_at else None
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}
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def get_ability_assessment_service() -> AbilityAssessmentService:
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"""获取能力评估服务实例（依赖注入）"""
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return AbilityAssessmentService()
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