Initial commit: HR Performance System

- Database schema with 31 tables for 4-card system
- LLM API integration (Gemini, DeepSeek, OpenAI)
- Error handling system with modal component
- Connection test UI for LLM services
- Environment configuration files
- Complete database documentation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

routes/llm.routes.js

@@ -0,0 +1,171 @@
+/**
+ * LLM API Routes
+ * 處理 LLM 相關的 API 請求
+ */
+
+const express = require('express');
+const router = express.Router();
+const llmService = require('../services/llm.service');
+const { asyncHandler, createError } = require('../utils/errorHandler');
+
+/**
+ * POST /api/llm/test/gemini
+ * 測試 Gemini API 連線
+ */
+router.post('/test/gemini', asyncHandler(async (req, res) => {
+  const result = await llmService.testGeminiConnection();
+  res.json(result);
+}));
+
+/**
+ * POST /api/llm/test/deepseek
+ * 測試 DeepSeek API 連線
+ */
+router.post('/test/deepseek', asyncHandler(async (req, res) => {
+  const result = await llmService.testDeepSeekConnection();
+  res.json(result);
+}));
+
+/**
+ * POST /api/llm/test/openai
+ * 測試 OpenAI API 連線
+ */
+router.post('/test/openai', asyncHandler(async (req, res) => {
+  const result = await llmService.testOpenAIConnection();
+  res.json(result);
+}));
+
+/**
+ * POST /api/llm/test/all
+ * 測試所有 LLM API 連線
+ */
+router.post('/test/all', asyncHandler(async (req, res) => {
+  const results = await llmService.testAllConnections();
+  res.json(results);
+}));
+
+/**
+ * POST /api/llm/generate
+ * 使用 LLM 生成內容
+ *
+ * Body:
+ * {
+ *   "prompt": "你的提示內容",
+ *   "provider": "gemini|deepseek|openai", (可選，預設使用 gemini)
+ *   "options": {
+ *     "temperature": 0.7,
+ *     "maxTokens": 2000
+ *   }
+ * }
+ */
+router.post('/generate', asyncHandler(async (req, res) => {
+  const { prompt, provider, options } = req.body;
+
+  if (!prompt) {
+    throw createError('BAD_REQUEST', '缺少必要參數: prompt');
+  }
+
+  const result = await llmService.generate(prompt, provider, options);
+  res.json(result);
+}));
+
+/**
+ * POST /api/llm/help-me-fill
+ * Help Me AI 智能填寫功能
+ *
+ * Body:
+ * {
+ *   "cardType": "role|competency|performance|growth",
+ *   "cardId": "卡片ID",
+ *   "filledFields": { ... },
+ *   "emptyFields": [ ... ],
+ *   "context": { ... }
+ * }
+ */
+router.post('/help-me-fill', asyncHandler(async (req, res) => {
+  const { cardType, cardId, filledFields, emptyFields, context } = req.body;
+
+  if (!cardType || !emptyFields || emptyFields.length === 0) {
+    throw createError('BAD_REQUEST', '缺少必要參數');
+  }
+
+  // 建立提示詞
+  const prompt = buildHelpMeFillPrompt(cardType, filledFields, emptyFields, context);
+
+  // 使用 LLM 生成建議內容
+  const result = await llmService.generate(prompt, 'gemini', {
+    temperature: 0.7,
+    maxTokens: 2000,
+  });
+
+  if (!result.success) {
+    throw createError('LLM_API_ERROR', '生成內容失敗');
+  }
+
+  // 解析生成的內容
+  const suggestions = parseHelpMeFillResponse(result.content, emptyFields);
+
+  res.json({
+    success: true,
+    filledCount: Object.keys(suggestions).length,
+    suggestions,
+  });
+}));
+
+/**
+ * 建立 Help Me Fill 提示詞
+ */
+function buildHelpMeFillPrompt(cardType, filledFields, emptyFields, context) {
+  let prompt = `你是一個 HR 績效評核系統的智能助手。請根據以下資訊，為空白欄位生成合適的內容。\n\n`;
+
+  prompt += `卡片類型: ${cardType}\n\n`;
+
+  if (Object.keys(filledFields).length > 0) {
+    prompt += `已填寫的欄位:\n`;
+    for (const [key, value] of Object.entries(filledFields)) {
+      prompt += `- ${key}: ${value}\n`;
+    }
+    prompt += `\n`;
+  }
+
+  if (context) {
+    prompt += `上下文資訊:\n`;
+    prompt += JSON.stringify(context, null, 2);
+    prompt += `\n\n`;
+  }
+
+  prompt += `請為以下空白欄位生成內容:\n`;
+  emptyFields.forEach(field => {
+    prompt += `- ${field}\n`;
+  });
+
+  prompt += `\n請以 JSON 格式回覆，格式如下:\n`;
+  prompt += `{\n`;
+  emptyFields.forEach(field => {
+    prompt += `  "${field}": "生成的內容",\n`;
+  });
+  prompt += `}\n`;
+
+  return prompt;
+}
+
+/**
+ * 解析 Help Me Fill 回應
+ */
+function parseHelpMeFillResponse(content, emptyFields) {
+  try {
+    // 嘗試直接解析 JSON
+    const jsonMatch = content.match(/\{[\s\S]*\}/);
+    if (jsonMatch) {
+      return JSON.parse(jsonMatch[0]);
+    }
+
+    // 如果無法解析 JSON，則返回空物件
+    return {};
+  } catch (error) {
+    console.error('解析 LLM 回應失敗:', error);
+    return {};
+  }
+}
+
+module.exports = router;