feat: 凭证生成、成本分析、AI问答、前端页面、集成测试与E2E测试
- 后端: 凭证生成引擎、金蝶导出器、凭证模板服务 - 后端: 成本分析服务、AI问答服务 - 后端: 科目映射CRUD API、分析API、QA API - 后端: 集成测试(认证/任务/凭证) 49个测试全部通过 - 前端: 凭证管理、成本分析、导出中心、知识库、系统设置页面 - 前端: AuthGuard认证守卫、Dashboard AI聊天功能 - 前端: Playwright E2E测试 16 passed, 1 skipped - 基础设施: Docker Compose、Nginx反向代理、.env.example - 文档: 用户手册、管理员手册、发布检查清单
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"""
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AI 成本变化分析服务
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使用 LLM 生成成本变化原因摘要和建议追问问题
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"""
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import json
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import logging
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from typing import Any, Dict, List, Optional
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from app.core.config import get_settings
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logger = logging.getLogger(__name__)
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settings = get_settings()
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# Prompt 模板
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COST_ANALYSIS_PROMPT = """你是一个专业的财务分析师。请根据以下成本对比数据，生成简洁的成本变化分析摘要。
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## 当前月数据
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- 工资成本: {curr_salary}
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- 社保成本: {curr_social}
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- 公积金成本: {curr_fund}
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- 总成本: {curr_total}
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- 员工人数: {curr_count}
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## 上月数据
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- 工资成本: {prev_salary}
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- 社保成本: {prev_social}
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- 公积金成本: {prev_fund}
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- 总成本: {prev_total}
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- 员工人数: {prev_count}
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## 变化分析
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- 新增员工: {new_count} 人，新增成本 {new_cost}
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- 离职员工: {left_count} 人，减少成本 {left_cost}
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- 薪资调整: {adjustment_count} 人，净变化 {adjustment_cost}
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## 要求
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1. 用2-3句话概括成本变化的主要原因
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2. 包含具体金额和比例
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3. 指出top变化项
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4. 输出格式：纯文本，不要Markdown
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请直接输出分析摘要：
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"""
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SUGGESTED_QUESTIONS_PROMPT = """基于以下成本分析数据，生成3-5个用户可能追问的问题。
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## 成本变化数据
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{change_data}
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## 要求
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1. 问题要具体、有针对性
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2. 关注关键变化项
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3. 输出JSON数组格式：["问题1", "问题2", ...]
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请直接输出JSON：
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"""
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class CostAnalyzerService:
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"""AI 成本变化分析服务"""
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async def analyze_cost_changes(
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self,
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current_data: Dict[str, Any],
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previous_data: Dict[str, Any],
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changes: Dict[str, Any],
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) -> str:
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"""
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使用 LLM 生成成本变化原因摘要
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Args:
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current_data: 当前月成本数据
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previous_data: 上月成本数据
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changes: 变化分析数据
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Returns:
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AI 生成的分析摘要文本
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"""
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prompt = COST_ANALYSIS_PROMPT.format(
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curr_salary=current_data.get("salary_cost", 0),
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curr_social=current_data.get("social_security_cost", 0),
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curr_fund=current_data.get("fund_cost", 0),
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curr_total=current_data.get("total_cost", 0),
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curr_count=current_data.get("employee_count", 0),
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prev_salary=previous_data.get("salary_cost", 0),
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prev_social=previous_data.get("social_security_cost", 0),
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prev_fund=previous_data.get("fund_cost", 0),
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prev_total=previous_data.get("total_cost", 0),
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prev_count=previous_data.get("employee_count", 0),
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new_count=len(changes.get("new_employees", [])),
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new_cost=changes.get("new_employee_cost", 0),
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left_count=len(changes.get("left_employees", [])),
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left_cost=changes.get("left_employee_saving", 0),
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adjustment_count=len(changes.get("salary_adjustments", [])),
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adjustment_cost=changes.get("adjustment_cost", 0),
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)
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try:
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result = await self._call_llm(prompt)
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return result.strip()
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except Exception as e:
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logger.error(f"AI 成本分析失败: {e}")
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# 兜底：生成规则化摘要
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return self._generate_fallback_summary(current_data, previous_data, changes)
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async def generate_suggested_questions(
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self, analysis: Dict[str, Any]
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) -> List[str]:
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"""
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基于变化分析生成可追问的问题
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Args:
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analysis: 变化分析数据
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Returns:
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建议问题列表
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"""
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change_summary = json.dumps(analysis, ensure_ascii=False, default=str)
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prompt = SUGGESTED_QUESTIONS_PROMPT.format(change_data=change_summary)
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try:
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result = await self._call_llm(prompt)
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questions = json.loads(result.strip())
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if isinstance(questions, list):
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return questions[:5]
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except Exception as e:
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logger.error(f"生成建议问题失败: {e}")
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# 兜底问题
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return self._generate_fallback_questions(analysis)
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def _generate_fallback_summary(
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self,
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current_data: Dict[str, Any],
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previous_data: Dict[str, Any],
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changes: Dict[str, Any],
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) -> str:
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"""规则兜底：生成摘要"""
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curr_total = current_data.get("total_cost", 0)
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prev_total = previous_data.get("total_cost", 0)
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change = curr_total - prev_total
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ratio = (change / prev_total * 100) if prev_total > 0 else 0
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parts = []
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if change > 0:
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parts.append(f"本月人工成本较上月增加 {change:.2f} 元（{ratio:.1f}%）")
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elif change < 0:
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parts.append(f"本月人工成本较上月减少 {abs(change):.2f} 元（{abs(ratio):.1f}%）")
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else:
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parts.append("本月人工成本与上月持平")
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new_count = len(changes.get("new_employees", []))
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left_count = len(changes.get("left_employees", []))
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if new_count > 0:
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parts.append(f"新增 {new_count} 名员工增加成本 {changes.get('new_employee_cost', 0):.2f} 元")
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if left_count > 0:
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parts.append(f"离职 {left_count} 名员工减少成本 {changes.get('left_employee_saving', 0):.2f} 元")
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adj_count = len(changes.get("salary_adjustments", []))
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if adj_count > 0:
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parts.append(f"{adj_count} 名员工薪资调整净变化 {changes.get('adjustment_cost', 0):.2f} 元")
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return "，".join(parts) + "。"
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def _generate_fallback_questions(self, analysis: Dict[str, Any]) -> List[str]:
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"""规则兜底：生成问题"""
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questions = []
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new_emps = analysis.get("new_employees", [])
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left_emps = analysis.get("left_employees", [])
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adjustments = analysis.get("salary_adjustments", [])
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if new_emps:
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questions.append(f"新增的 {len(new_emps)} 名员工分布在哪些部门？")
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if left_emps:
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questions.append(f"离职的 {len(left_emps)} 名员工减少了多少成本？")
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if adjustments:
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top = max(adjustments, key=lambda x: abs(x.get("change", 0)))
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questions.append(f"薪资调整幅度最大的是谁？变化了多少？")
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questions.append("哪个部门成本变化最大？")
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questions.append("社保和公积金成本占比如何？")
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return questions[:5]
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async def _call_llm(self, prompt: str) -> str:
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"""调用 LLM API"""
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provider = settings.ai_provider
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if provider == "zhipu" and settings.zhipu_api_key:
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return await self._call_zhipu(prompt)
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elif settings.openai_api_key:
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return await self._call_openai(prompt)
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else:
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raise ValueError("未配置 AI API Key")
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async def _call_openai(self, prompt: str) -> str:
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"""调用 OpenAI API"""
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from openai import AsyncOpenAI
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client = AsyncOpenAI(api_key=settings.openai_api_key)
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response = await client.chat.completions.create(
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model=settings.openai_model,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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max_tokens=500,
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)
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return response.choices[0].message.content or ""
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async def _call_zhipu(self, prompt: str) -> str:
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"""调用智谱 AI API"""
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import httpx
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url = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
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headers = {
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"Authorization": f"Bearer {settings.zhipu_api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": "glm-4-flash",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3,
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"max_tokens": 500,
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}
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async with httpx.AsyncClient(timeout=30) as client:
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response = await client.post(url, json=payload, headers=headers)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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