add scheduler api

functions/api/scheduler.ts

@@ -0,0 +1,126 @@
+import { modelConfigs, shedulerAICharacter } from '../../src/config/aiCharacters';
+import OpenAI from 'openai';
+
+interface AICharacter {
+  id: string;
+  name: string;
+  tags?: string[];
+}
+
+interface MessageHistory {
+  role: string;
+  content: string;
+  name: string;
+}
+
+export async function onRequestPost({ env, request }) {
+  try {
+    const { message, history, availableAIs } = await request.json();
+    const selectedAIs = await scheduleAIResponses(message, history, availableAIs);
+
+    return Response.json({
+      selectedAIs: selectedAIs
+    });
+  } catch (error) {
+    console.error(error);
+    return Response.json(
+      { error: error.message },
+      { status: 500 }
+    );
+  }
+}
+
+async function analyzeMessageWithAI(message: string, allTags: string[]): Promise<string[]> {
+    const shedulerAI = shedulerAICharacter(message, allTags);
+    const modelConfig = modelConfigs.find(config => config.model === shedulerAI.model);
+    const openai = new OpenAI({
+    apiKey: modelConfig.apiKey,
+    baseURL: modelConfig.baseURL, // DeepSeek API 的基础URL
+  });
+
+  const prompt = shedulerAI.custom_prompt;
+
+  try {
+    const completion = await openai.chat.completions.create({
+      model: shedulerAI.model,
+      messages: [
+        { role: "user", content: prompt }
+      ],
+    });
+
+    const matchedTags = completion.choices[0].message.content?.split(',').map(tag => tag.trim()) || [];
+    return matchedTags;
+  } catch (error) {
+    console.error('AI分析失败:', error);
+    return [];
+  }
+}
+
+async function scheduleAIResponses(
+  message: string, 
+  history: MessageHistory[], 
+  availableAIs: AICharacter[]
+): Promise<string[]> {
+  // 1. 收集所有可用的标签
+  const allTags = new Set<string>();
+  availableAIs.forEach(ai => {
+    ai.tags?.forEach(tag => allTags.add(tag));
+  });
+
+  // 2. 使用AI模型分析消息并匹配标签
+  const matchedTags = await analyzeMessageWithAI(message, Array.from(allTags));
+
+  // 3. 计算每个AI的匹配分数
+  const aiScores = new Map<string, number>();
+  const messageLC = message.toLowerCase();
+
+  for (const ai of availableAIs) {
+    if (!ai.tags) continue;
+
+    let score = 0;
+    // 标签匹配分数
+    matchedTags.forEach(tag => {
+      if (ai.tags?.includes(tag)) {
+        score += 2; // 每个匹配的标签得2分
+      }
+    });
+
+    // 直接提到AI名字额外加分
+    if (messageLC.includes(ai.name.toLowerCase())) {
+      score += 5;
+    }
+
+    // 历史对话相关性加分
+    const recentHistory = history.slice(-5); // 只看最近5条消息
+    recentHistory.forEach(hist => {
+      if (hist.name === ai.name && hist.content.length > 0) {
+        score += 1; // 最近有参与对话的AI加分
+      }
+    });
+
+    if (score > 0) {
+      aiScores.set(ai.id, score);
+    }
+  }
+
+  // 4. 根据分数排序选择AI
+  const sortedAIs = Array.from(aiScores.entries())
+    .sort((a, b) => b[1] - a[1])
+    .map(([id]) => id);
+
+  // 5. 如果没有匹配到任何AI，随机选择1-2个
+  if (sortedAIs.length === 0) {
+    const maxResponders = Math.min(2, availableAIs.length);
+    const numResponders = Math.floor(Math.random() * maxResponders) + 1;
+    
+    const shuffledAIs = [...availableAIs]
+      .sort(() => Math.random() - 0.5)
+      .slice(0, numResponders);
+    
+    return shuffledAIs.map(ai => ai.id);
+  }
+
+  // 6. 限制最大回复数量
+  const MAX_RESPONDERS = 3;
+  return sortedAIs.slice(0, MAX_RESPONDERS);
+}