378 lines
14 KiB
TypeScript
378 lines
14 KiB
TypeScript
import axios from 'axios';
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import * as PerformanceDAO from '../dao/PerformanceDAO';
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import * as AIResultDAO from '../dao/AIResultDAO';
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// ─── Types ────────────────────────────────────────────────────────────────────
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export interface AIScoreData {
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aiScoreDetail: AIResultDAO.AIScoreItem[];
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aiTotalScore: number;
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aiProblems: string[];
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aiSuggestions: string[];
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}
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// ─── Prompt Builder ───────────────────────────────────────────────────────────
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/**
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* Build the prompt sent to FastGPT.
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* Includes employee position, month, performance items, attendance, and scoring rules.
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* Requirements: 13.2
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*/
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export function buildPrompt(
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performance: PerformanceDAO.PerformanceRow,
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items: PerformanceDAO.PerfItemRow[],
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attendance: PerformanceDAO.AttendanceRow | null,
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position: string
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): string {
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const itemsText = items
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.map(
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(item, idx) =>
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`${idx + 1}. 【${item.item_name}】(${item.item_category === 'business' ? '业务素质' : '综合素质'}，权重${item.weight}分)\n` +
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` 员工填写内容：${item.user_content ?? '（未填写）'}\n` +
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` 员工自评分：${item.self_score ?? '（未填写）'}`
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)
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.join('\n');
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const attendanceText = attendance
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? `事假${attendance.leave_days}天，迟到${attendance.late_times}次，缺卡${attendance.lack_card_times}次，旷工${attendance.absent_days}天`
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: '无考勤数据';
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return `你是一名专业的HR绩效评估专家，请根据以下员工绩效填报内容进行客观评分。
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## 员工信息
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- 岗位：${position}
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- 考核月份：${performance.month}
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## 考核项目及填报内容
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${itemsText}
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## 考勤情况
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${attendanceText}
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## 工作汇总
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${performance.work_summary ?? '（未填写）'}
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## 评分规则
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- 业务素质考评占总分70%，综合素质考评占总分30%
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- 每个考核项按0-100分评分，最终加权计算总分
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- 请根据员工填写内容的质量、完整性和实际工作表现进行评分
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## 输出要求
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请严格按照以下JSON格式输出，不要包含任何其他内容：
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{
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"ai_score_detail": [
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{
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"itemName": "考核项名称",
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"weight": 权重分值,
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"aiScore": 评分(0-100),
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"scoreExplanation": "评分说明"
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}
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],
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"ai_total_score": 加权总分(0-100),
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"ai_problems": ["问题1", "问题2", "问题3"],
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"ai_suggestions": ["建议1", "建议2", "建议3"]
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}
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注意：
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- ai_problems 和 ai_suggestions 各需要3到5条
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- ai_total_score 为各项加权分数之和，业务素质项权重之和为70，综合素质项权重之和为30
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- 所有字段必须存在且格式正确`;
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}
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// ─── Response Parser ──────────────────────────────────────────────────────────
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/**
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* Parse and validate the AI JSON response.
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* Extracts ai_score_detail, ai_total_score, ai_problems, ai_suggestions.
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* Throws on invalid format, recording the raw response.
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* Requirements: 3.2, 3.3, 3.4, 13.3, 13.4
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*/
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export function parseAIResponse(rawResponse: string): AIScoreData {
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let parsed: any;
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// Extract JSON from the response (model may wrap it in markdown code blocks)
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const jsonMatch = rawResponse.match(/```(?:json)?\s*([\s\S]*?)```/) ||
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rawResponse.match(/(\{[\s\S]*\})/);
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const jsonStr = jsonMatch ? jsonMatch[1].trim() : rawResponse.trim();
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try {
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parsed = JSON.parse(jsonStr);
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} catch (e) {
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throw new Error(`AI响应JSON解析失败，原始响应：${rawResponse}`);
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}
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// Validate required fields
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if (!Array.isArray(parsed.ai_score_detail)) {
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throw new Error(`AI响应缺少 ai_score_detail 字段，原始响应：${rawResponse}`);
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}
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if (typeof parsed.ai_total_score !== 'number') {
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throw new Error(`AI响应缺少有效的 ai_total_score 字段，原始响应：${rawResponse}`);
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}
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if (!Array.isArray(parsed.ai_problems) || parsed.ai_problems.length < 1) {
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throw new Error(`AI响应缺少 ai_problems 字段，原始响应：${rawResponse}`);
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}
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if (!Array.isArray(parsed.ai_suggestions) || parsed.ai_suggestions.length < 1) {
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throw new Error(`AI响应缺少 ai_suggestions 字段，原始响应：${rawResponse}`);
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}
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// Validate and map score detail items
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const aiScoreDetail: AIResultDAO.AIScoreItem[] = parsed.ai_score_detail.map((item: any, idx: number) => {
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if (typeof item.itemName !== 'string' || typeof item.weight !== 'number' ||
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typeof item.aiScore !== 'number' || typeof item.scoreExplanation !== 'string') {
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throw new Error(`AI响应 ai_score_detail[${idx}] 格式无效，原始响应：${rawResponse}`);
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}
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return {
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itemName: item.itemName,
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weight: item.weight,
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aiScore: item.aiScore,
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scoreExplanation: item.scoreExplanation,
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};
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});
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return {
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aiScoreDetail,
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aiTotalScore: parsed.ai_total_score,
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aiProblems: parsed.ai_problems as string[],
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aiSuggestions: parsed.ai_suggestions as string[],
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};
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}
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// ─── FastGPT API Call ─────────────────────────────────────────────────────────
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const FASTGPT_API_URL = process.env.FASTGPT_API_URL || 'https://cloud.fastgpt.cn/api/v1/chat/completions';
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const FASTGPT_API_KEY = process.env.FASTGPT_API_KEY || '';
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const AI_TIMEOUT_MS = 60_000; // 60秒超时
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// 读取系统代理配置（支持 HTTP_PROXY / HTTPS_PROXY 环境变量）
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function getProxyConfig() {
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const proxyUrl = process.env.HTTPS_PROXY || process.env.HTTP_PROXY || process.env.https_proxy || process.env.http_proxy;
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if (proxyUrl) {
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try {
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const url = new URL(proxyUrl);
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return {
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host: url.hostname,
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port: parseInt(url.port, 10),
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protocol: url.protocol.replace(':', '') as 'http' | 'https',
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};
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} catch {
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return undefined;
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}
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}
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return undefined;
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}
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/**
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* Call FastGPT workflow API with structured variables.
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* FastGPT workflow expects: variables (position, month) + userChatInput (绩效内容)
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*/
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async function callFastGPT(
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position: string,
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month: string,
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contentText: string
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): Promise<AIScoreData> {
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console.log(`[AI] 调用 FastGPT API: ${FASTGPT_API_URL}`);
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const proxyConfig = getProxyConfig();
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if (proxyConfig) {
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console.log(`[AI] 使用代理: ${proxyConfig.host}:${proxyConfig.port}`);
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}
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const response = await axios.post(
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FASTGPT_API_URL,
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{
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messages: [{ role: 'user', content: contentText }],
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variables: {
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zslh34AG: position, // 员工岗位
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month: month, // 考核月份
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},
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stream: false,
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},
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{
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headers: {
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Authorization: `Bearer ${FASTGPT_API_KEY}`,
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'Content-Type': 'application/json',
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},
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timeout: AI_TIMEOUT_MS,
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proxy: proxyConfig,
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}
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);
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console.log(`[AI] FastGPT 响应状态: ${response.status}`);
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// FastGPT workflow 返回的最终结果在 choices[0].message.content 里
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// 内容是 system_rawResponse 对象（JSON字符串或对象）
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const content = response.data?.choices?.[0]?.message?.content;
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if (!content) {
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throw new Error(`FastGPT API 返回内容为空，响应: ${JSON.stringify(response.data).substring(0, 300)}`);
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}
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console.log(`[AI] FastGPT 原始返回:`, typeof content === 'string' ? content.substring(0, 300) : JSON.stringify(content).substring(0, 300));
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// content 可能是带 markdown 代码块的字符串，或纯 JSON 字符串，或对象
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let parsed: any;
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if (typeof content === 'object') {
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parsed = content;
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} else {
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// 先尝试去掉 markdown 代码块 ```json ... ```
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let jsonStr = content.trim();
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const codeBlockMatch = jsonStr.match(/```(?:json)?\s*([\s\S]*?)```/);
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if (codeBlockMatch) {
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jsonStr = codeBlockMatch[1].trim();
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} else {
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// 直接提取第一个完整 JSON 对象
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const objMatch = jsonStr.match(/\{[\s\S]*\}/);
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if (objMatch) {
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jsonStr = objMatch[0];
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}
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}
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try {
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parsed = JSON.parse(jsonStr);
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} catch (e: any) {
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throw new Error(`FastGPT 返回内容无法解析为 JSON: ${e.message}，内容片段: ${jsonStr.substring(0, 200)}`);
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}
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}
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// 校验必要字段
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if (!Array.isArray(parsed.ai_score_detail)) {
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throw new Error(`响应缺少 ai_score_detail，内容: ${JSON.stringify(parsed).substring(0, 300)}`);
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}
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if (typeof parsed.ai_total_score !== 'number') {
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throw new Error(`响应缺少有效的 ai_total_score，内容: ${JSON.stringify(parsed).substring(0, 300)}`);
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}
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return {
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aiScoreDetail: parsed.ai_score_detail.map((item: any) => ({
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itemName: item.itemName,
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weight: item.weight,
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aiScore: item.aiScore,
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scoreExplanation: item.scoreExplanation,
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})),
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aiTotalScore: parsed.ai_total_score,
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aiProblems: Array.isArray(parsed.ai_problems) ? parsed.ai_problems : [],
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aiSuggestions: Array.isArray(parsed.ai_suggestions) ? parsed.ai_suggestions : [],
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};
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}
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// ─── Core Evaluation ──────────────────────────────────────────────────────────
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/**
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* Perform a single AI evaluation attempt for the given performance record.
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*/
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async function evaluateOnce(
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perfId: number,
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performance: PerformanceDAO.PerformanceRow,
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items: PerformanceDAO.PerfItemRow[],
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attendance: PerformanceDAO.AttendanceRow | null,
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position: string
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): Promise<AIResultDAO.AIResult> {
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// 构建员工绩效内容文本（作为 userChatInput 传入 workflow）
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const itemsText = items
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.map(
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(item, idx) =>
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`${idx + 1}. 【${item.item_name}】(权重${item.weight}分)\n` +
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` 员工填写：${item.user_content ?? '（未填写）'}\n` +
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` 自评分：${item.self_score ?? '（未填写）'}`
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)
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.join('\n');
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const attendanceText = attendance
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? `事假${attendance.leave_days}天，迟到${attendance.late_times}次，缺卡${attendance.lack_card_times}次，旷工${attendance.absent_days}天`
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: '无考勤数据';
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const contentText = `考勤情况：${attendanceText}\n\n工作汇总：${performance.work_summary ?? '（未填写）'}\n\n考核项目：\n${itemsText}`;
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const scoreData = await callFastGPT(position, performance.month, contentText);
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const aiId = await AIResultDAO.save({
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perfId,
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aiScoreDetail: scoreData.aiScoreDetail,
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aiTotalScore: scoreData.aiTotalScore,
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aiProblems: scoreData.aiProblems,
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aiSuggestions: scoreData.aiSuggestions,
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apiResponse: JSON.stringify(scoreData),
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});
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return {
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aiId,
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perfId,
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aiScoreDetail: scoreData.aiScoreDetail,
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aiTotalScore: scoreData.aiTotalScore,
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aiProblems: scoreData.aiProblems,
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aiSuggestions: scoreData.aiSuggestions,
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createTime: new Date(),
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};
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}
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// ─── Retry + Degradation ──────────────────────────────────────────────────────
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const MAX_RETRIES = 3;
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/**
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* Evaluate performance with up to MAX_RETRIES attempts.
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* On all failures, logs the error and updates the performance status to 'ai_failed' (degradation).
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* Requirements: 3.5
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*/
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export async function evaluatePerformance(perfId: number): Promise<AIResultDAO.AIResult> {
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// Load full performance detail
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const detail = await PerformanceDAO.findDetailByPerfId(perfId);
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if (!detail) {
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throw new Error(`绩效记录不存在: perfId=${perfId}`);
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}
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// Load employee position from user table
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const pool = (await import('../config/database')).default;
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const [userRows] = await pool.query<any[]>(
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'SELECT position FROM user WHERE user_id = ? LIMIT 1',
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[detail.performance.user_id]
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);
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const position: string = userRows[0]?.position ?? '未知岗位';
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let lastError: Error = new Error('未知错误');
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for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
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try {
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const result = await evaluateOnce(
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perfId,
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detail.performance,
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detail.items,
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detail.attendance,
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position
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);
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// Update ai_score on performance_month
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await pool.query(
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'UPDATE performance_month SET ai_score = ? WHERE perf_id = ?',
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[result.aiTotalScore, perfId]
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);
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// 回写 AI 评分到 perf_item 各考核项
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for (const scoreItem of result.aiScoreDetail) {
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await pool.query(
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`UPDATE perf_item SET ai_score = ?, ai_explanation = ?
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WHERE perf_id = ? AND item_name = ?`,
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[scoreItem.aiScore, scoreItem.scoreExplanation, perfId, scoreItem.itemName]
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);
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}
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console.log(`[AI] 评分完成 perfId=${perfId}，总分=${result.aiTotalScore}（第${attempt}次尝试）`);
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return result;
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} catch (err: any) {
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lastError = err;
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console.error(`[AI] 第${attempt}次评分失败 perfId=${perfId}:`, err.message);
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if (attempt < MAX_RETRIES) {
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// Brief delay before retry (exponential backoff: 1s, 2s)
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await new Promise((resolve) => setTimeout(resolve, attempt * 1000));
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}
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}
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}
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// All retries exhausted — degradation: log error, notify admin via console
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console.error(`[AI] 评分最终失败 perfId=${perfId}，已重试${MAX_RETRIES}次。错误：${lastError.message}`);
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// Note: We don't log to operation_log here because there's no user_id context
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// Admin can check console logs for AI evaluation failures
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throw lastError;
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}
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