電腦桌寵製作_後端篇
回顧一下我們的整體
- 運行邏輯：
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- 環境架構：
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環境準備
AI模型環境我們使用ollama
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接下來是專案骨架
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首先我們初始化後端
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2npm init -y
npm install express cors接下來撰寫設定檔
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33{
"assistant": {
"name": "Mini-Loy",
"language": "en",
"persona": "soft_boy",
"description": "A gentle, soft-spoken teenage boy AI assistant."
},
"llm": {
"provider": "ollama",
"model": "qwen2.5"
},
"tts": {
"provider": "custom",
"endpoint": "http://localhost:8000/tts",
"voice": "soft-boy",
"language": "en"
},
"reminder": {
"drink_water_interval_min": 45,
"break_interval_min": 60,
"enable_night_silence": true,
"night_silence_start": "23:00",
"night_silence_end": "07:00"
},
"memory": {
"type": "json",
"path": "./data/memory.json"
}
}這邊注意：實際上吃的檔案是以server為主，這邊是方便我們自己看得。
撰寫人設Prompt
這邊依照你自己的喜好去寫人物設定，寫得越詳細越好。
備註：我有寫了兩份，英文與日文的prompt，當模型使用日文語音時我就讓他讀日文的那份，使用英文語音時就讀取英文那份。1
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26You are “Mini-Loy,” a gentle and soft-spoken teenage boy AI assistant.
Your personality:
- You refer to yourself as “I.”
- You call the user “Master.”
- Your tone is calm, warm, and soothing.
- You speak politely, slowly, and softly.
- You are caring and attentive, especially about Master’s health.
- You often remind Master to drink water, take breaks, and relax.
- You avoid harsh, negative, or aggressive wording.
- You avoid lecturing or criticizing Master.
- You keep messages short, friendly, and easy to read.
- You may use simple emojis like “🙂”, “🌱”, “☕” but only sparingly.
- You never use explicit, violent, or inappropriate content.
Language:
- You mainly speak English.
- If Master speaks another language, you may respond in English unless necessary.
Role:
- Be Master’s daily companion.
- Chat naturally and comfortingly.
- Offer soft and gentle reminders to maintain good health.
- Help Master reflect on the day and create short daily summaries.
- Keep consistent personality and warmth in every message.初始記憶
我們可以在data內放給模型的初始記憶，比如說自己的目標或是對我們的稱呼等。後端server.js
基本上所有的後續功能我都是加在這邊，沒有另外拆開。1
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110const express = require("express");
const cors = require("cors");
const fs = require("fs");
const path = require("path");
const app = express();
app.use(express.json());
app.use(cors());
const CONFIG_PATH = path.join(__dirname, "config.json");
function loadConfig() {
return JSON.parse(fs.readFileSync(CONFIG_PATH, "utf8"));
}
function loadMemory() {
const config = loadConfig();
const memPath = path.join(__dirname, config.memory.path);
try {
return JSON.parse(fs.readFileSync(memPath, "utf8"));
} catch (e) {
return { profile: {}, reminder_state: {}, daily_logs: [] };
}
}
function saveMemory(memory) {
const config = loadConfig();
const memPath = path.join(__dirname, config.memory.path);
fs.mkdirSync(path.dirname(memPath), { recursive: true });
fs.writeFileSync(memPath, JSON.stringify(memory, null, 2), "utf8");
}
function buildMemoryContext(memory) {
const profile = memory.profile || {};
const goals = (profile.goals || []).join("、");
const likes = (profile.preferences?.likes || []).join("、");
return `
[小洛伊の内部メモ]
//這邊就是寫在memory內的東西
ご主人の基本情報:
- 呼び方: ${profile.master_name || "ご主人"}
- 長期目標: ${goals || "（まだ登録されていない）"}
- 興味: ${likes || "（まだ登録されていない）"}
リマインダー設定:
- こまめな水分補給を促す
- 適度な休憩を促す
この情報は、ご主人に合わせた会話や声かけをするためにだけ使う。
会話の中ではこのメモの文をそのまま読み上げない。
`.trim();
}
// 讀 system prompt
const SYSTEM_PROMPT_PATH = path.join(__dirname, "prompts", "system_ja_iyashi_boy.txt");
const systemPrompt = fs.readFileSync(SYSTEM_PROMPT_PATH, "utf8");
// 聊天 API
app.post("/api/chat", async (req, res) => {
const { message, history = [] } = req.body;
const config = loadConfig();
const memory = loadMemory();
const memoryContext = buildMemoryContext(memory);
const ollamaUrl = "http://127.0.0.1:11434/api/chat";
try {
const response = await fetch(ollamaUrl, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
model: config.llm.model,
messages: [
{ role: "system", content: systemPrompt },
{ role: "system", content: memoryContext },
...history.slice(-6),
{ role: "user", content: message }
],
stream: false
})
});
const data = await response.json();
const reply = data?.message?.content ?? "";
// 之後可以在這裡更新 memory（例如把今天做的事寫進 daily_logs）
saveMemory(memory);
res.json({ reply });
} catch (err) {
console.error(err);
res.status(500).json({ error: "LLM request failed", detail: err.message });
}
});
// 測試用：讀 config / memory
app.get("/api/config", (req, res) => {
res.json(loadConfig());
});
app.get("/api/memory", (req, res) => {
res.json(loadMemory());
});
const PORT = 3333;
app.listen(PORT, () => {
console.log(`Mini-Loy backend listening on http://localhost:${PORT}`);
});
這時候我們就有最初的後端了，可以打開powershell啟動
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這時我們就能看到
Mini-Loy backend listening on http://localhost:3333
我們可以使用curl進行測試
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有看到回覆就代表成功了
由於還沒有加上前端，這邊我們寫一個網頁頁面來進行對話。
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使用瀏覽器打開後就能直接打字與我們的AI模型回覆，就不用用curl的方式。
最後我們加上讀取聲音的部分，最基本的後端就完成了：
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