LLM

llm/index.ts

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+import OpenAI from "openai";
+import storage from 'node-persist'
+import { sendMsg } from "../lib/qq";
+import prompt from './prompt.txt'
+
+await storage.init();
+storage.clear();
+
+const client = new OpenAI({
+    baseURL: process.env.OPENAI_BASE_URL,
+    apiKey: process.env.OPENAI_API_KEY,
+    // logLevel: "debug"
+})
+
+const tools = [{
+    type: "function" as const,
+    name: "send_msg",
+    description: "Send a message to the user. Always use this to respond to the user.",
+    parameters: {
+        type: "object",
+        properties: {
+            text: { type: "string", description: "The message content sent to the user through QQ." }
+        },
+        required: ["text"],
+        additionalProperties: false
+    },
+    strict: true
+}]
+
+/**
+ * 
+ * @param input 提问
+ * @param target_id 用户 QQ 号
+ */
+export async function chat(input: string, target_id: string) {
+    const chatHistoryKey = `chat_history_${target_id}`;
+    let chatHistory: OpenAI.Responses.ResponseInput = await storage.getItem(chatHistoryKey) || [];
+
+    // 添加新输入到对话历史
+    chatHistory.push({ role: "user", content: input });
+
+    // 保存更新后的对话历史
+    console.log(`[LLM] 使用对话, 历史：`, chatHistory);
+
+    await storage.setItem(chatHistoryKey, chatHistory);
+
+    const response = await client.responses.create({
+        model: process.env.CHAT_MODEL || "gpt-5-nano",
+        instructions: prompt,
+        reasoning: { effort: 'minimal' },
+        input: chatHistory,
+        tools
+    });
+
+    await storage.setItem(chatHistoryKey, chatHistory);
+
+    // 继续调用工具，直到没有工具调用为止
+
+    await toolUseCycle(response.output);
+
+    async function toolUseCycle(outputArr: OpenAI.Responses.ResponseOutputItem[]) {
+        chatHistory.push(...outputArr);
+        const functionCalls = (outputArr ?? []).filter(item => item.type === 'function_call');
+        console.log("进入 toolUseCycle, with functionCalls", functionCalls.length, "个");
+        console.log(JSON.stringify(chatHistory, null, 2));
+
+        if (functionCalls.length == 0) {
+            let lastMessage = outputArr.at(-1);
+            if (!lastMessage) return
+            if (lastMessage.type != 'message') return
+            if (lastMessage.role != 'assistant') return
+
+            const msg = lastMessage.content.map(c => c.type == 'output_text' ? c.text : '').join('');
+            if (msg.trim().length > 0) {
+                // 结束，发送最后的消息
+                sendMsg(msg, target_id);
+            }
+            return
+        }
+        for (const item of functionCalls ?? []) {
+            if (item.name === "send_msg") {
+                console.log(item.arguments);
+
+                const { text } = JSON.parse(item.arguments);
+                sendMsg(text, target_id);
+
+                chatHistory.push({ type: "function_call_output", call_id: item.call_id, output: "OK" });
+            }
+        }
+        await storage.setItem(chatHistoryKey, chatHistory);
+
+        const response = await client.responses.create({
+            model: process.env.CHAT_MODEL || "gpt-5-nano",
+            instructions: prompt,
+            reasoning: { effort: 'minimal' },
+            input: chatHistory,
+            tools
+        });
+
+        toolUseCycle(response.output);
+    }
+}
+
+export async function resetChat(target_id: string) {
+    const chatHistoryKey = `chat_history_${target_id}`;
+    await storage.removeItem(chatHistoryKey);
+    sendMsg("已为你重置对话历史。", target_id);
+}

llm/prompt.txt

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+# Role  
+You are a “humorous, gentle, yet professional” Chinese chat and knowledge assistant working in a QQ-like instant messaging environment.  
+
+# Goals  
+1) Engage in natural casual chat;  
+2) Provide accurate answers;  
+3) Explain complex topics clearly over multiple turns.  
+
+# Style  
+- Friendly but not cheesy; witty but not snarky; professional but not stiff.  
+- Use short sentences and bullet points; send 1–3 sentences per message.  
+- Use one emoji appropriately; avoid excessive emoji or long paragraphs.  
+
+# Tool Rules (Important)  
+- **Never** output text directly in the assistant channel. All user-visible content **must** be sent via `tools.send_msg`.  
+- Each message must not exceed 50 characters. If it’s longer, split it into multiple `tools.send_msg` messages to simulate natural chat flow.  
+- Keep lists/code snippets short; if long, split them into multiple messages.  
+
+# Clarification  
+- If a user’s question is **unclear / ambiguous / missing details**, **immediately ask for clarification** and provide **specific options**.  
+- After asking, briefly explain **why** that information is needed.