feat(AI服务)：lpt-ai 独立服务初始版本
- Fastify + TypeScript，OpenAI 兼容接口对接 SiliconFlow - POST /ai/aggregate-report：残片聚合为学习报告（支持预期对比） - POST /ai/generate-mind-map：生成思维导图缩进大纲（与后端解析格式一致） - Key 从环境变量读取且日志脱敏，未配置时返回 503 由调用方降级 - prompt 模板集中管理，vitest 单元测试
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/**
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* LLM 客户端 — SiliconFlow OpenAI 兼容接口
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*
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* 设计要点：
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* - Key 从环境变量读取，永不写入代码或日志
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* - 未配置 Key 时 isAvailable() 为 false，调用方返回 503，由 Java 端降级
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* - 超时保护，避免长时间挂起
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*/
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export interface ChatMessage {
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role: "system" | "user" | "assistant";
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content: string;
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}
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export interface LlmConfig {
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apiUrl: string;
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apiKey: string;
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model: string;
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timeoutMs: number;
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}
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export function loadConfig(): LlmConfig {
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return {
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apiUrl: process.env.LLM_API_URL ?? "https://api.siliconflow.cn/v1/chat/completions",
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apiKey: process.env.LLM_API_KEY ?? "",
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model: process.env.LLM_MODEL ?? "Qwen/Qwen2.5-32B-Instruct",
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timeoutMs: Number(process.env.LLM_TIMEOUT_MS ?? 60000),
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};
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}
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export function isAvailable(config: LlmConfig): boolean {
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return config.apiKey.length > 0;
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}
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export class LlmError extends Error {
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constructor(
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message: string,
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public readonly statusCode: number,
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) {
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super(message);
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}
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}
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/**
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* 非流式调用，返回完整文本
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*/
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export async function chat(
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config: LlmConfig,
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messages: ChatMessage[],
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options?: { temperature?: number; maxTokens?: number },
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): Promise<string> {
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if (!isAvailable(config)) {
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throw new LlmError("LLM API Key 未配置", 503);
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}
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const controller = new AbortController();
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const timer = setTimeout(() => controller.abort(), config.timeoutMs);
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try {
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const response = await fetch(config.apiUrl, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify({
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model: config.model,
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messages,
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temperature: options?.temperature ?? 0.3,
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max_tokens: options?.maxTokens ?? 2048,
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stream: false,
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}),
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signal: controller.signal,
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});
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if (!response.ok) {
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const body = await response.text().catch(() => "");
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throw new LlmError(`LLM 请求失败 (${response.status}): ${body.slice(0, 200)}`, 502);
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}
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const data = (await response.json()) as {
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choices?: { message?: { content?: string } }[];
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};
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const content = data.choices?.[0]?.message?.content;
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if (!content) {
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throw new LlmError("LLM 返回内容为空", 502);
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}
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return content;
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} catch (err) {
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if (err instanceof LlmError) throw err;
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if (err instanceof Error && err.name === "AbortError") {
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throw new LlmError("LLM 请求超时", 504);
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}
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throw new LlmError(`LLM 请求异常: ${err instanceof Error ? err.message : String(err)}`, 502);
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} finally {
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clearTimeout(timer);
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}
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}
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import { describe, expect, it } from "vitest";
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import { aggregateReportPrompt, generateMindMapPrompt } from "./prompts.js";
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describe("aggregateReportPrompt", () => {
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it("包含全部残片和任务名", () => {
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const messages = aggregateReportPrompt("Java 并发", [
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{ content: "corePoolSize 是核心线程数" },
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{ content: "workQueue 满后创建新线程", createdTime: "2026-07-01" },
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]);
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expect(messages).toHaveLength(2);
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expect(messages[0].role).toBe("system");
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expect(messages[1].content).toContain("Java 并发");
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expect(messages[1].content).toContain("corePoolSize");
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expect(messages[1].content).toContain("workQueue");
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expect(messages[1].content).toContain("2026-07-01");
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});
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it("有预期时包含预期内容和对比要求", () => {
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const messages = aggregateReportPrompt(
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"Java 并发",
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[{ content: "x" }],
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"理解线程池核心参数",
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);
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expect(messages[1].content).toContain("理解线程池核心参数");
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expect(messages[0].content).toContain("预期");
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});
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it("无预期时不包含预期块", () => {
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const messages = aggregateReportPrompt("Java 并发", [{ content: "x" }]);
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expect(messages[1].content).not.toContain("预期目标");
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});
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});
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describe("generateMindMapPrompt", () => {
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it("要求输出缩进大纲且包含全部输入", () => {
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const messages = generateMindMapPrompt(
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"Java 并发",
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"并发编程学习",
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["报告一"],
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["残片一", "残片二"],
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);
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expect(messages[0].content).toContain("缩进");
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expect(messages[1].content).toContain("报告一");
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expect(messages[1].content).toContain("残片二");
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});
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});
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/**
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* Prompt 模板集中管理。
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* 修改提示词只动这个文件，方便版本对照与回滚。
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*/
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import type { ChatMessage } from "./client.js";
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export interface FragmentInput {
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content: string;
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createdTime?: string;
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}
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/**
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* 残片聚合为学习报告。
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* 原则：报告是写给"未来复习的自己"的，保留具体知识点，剔除口语碎屑；
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* 不虚构残片中不存在的内容。
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*/
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export function aggregateReportPrompt(
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taskName: string,
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fragments: FragmentInput[],
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expectation?: string,
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): ChatMessage[] {
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const fragmentLines = fragments
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.map((f, i) => `${i + 1}. ${f.content}${f.createdTime ? `（${f.createdTime}）` : ""}`)
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.join("\n");
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const expectationBlock = expectation
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? `\n本次学习开始前的预期目标：\n${expectation}\n`
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: "";
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return [
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{
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role: "system",
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content: [
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"你是一名学习记录整理助手。用户在一次学习会话中随手记录了若干学习残片，",
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"请将它们整理为一份简洁的学习报告。要求：",
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"1. 只基于残片内容，不虚构、不扩写残片中没有的知识；",
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"2. 保留具体的知识点、概念、结论，去掉口语化的碎屑；",
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"3. 按逻辑而非时间组织内容，相关的点合并；",
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"4. 报告以要点形式呈现，控制在 300 字以内；",
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"5. 如果提供了学习预期，在报告末尾用一句话说明本次学习与预期的差距（完成/部分完成/未涉及）；",
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"6. 直接输出报告正文，不要输出「报告如下」之类的引导语。",
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].join(""),
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},
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{
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role: "user",
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content: `学习任务：${taskName}\n${expectationBlock}\n本次学习的残片记录：\n${fragmentLines}`,
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},
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];
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}
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/**
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* 从学习报告和残片生成标准思维导图（预留，用于替代 Java 端 BuiltinMindMapGenerator）。
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* 输出为缩进大纲文本，与后端 MindMapTreeTool.parseOutline 格式一致。
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*/
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export function generateMindMapPrompt(
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taskName: string,
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taskDescription: string,
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reports: string[],
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fragments: string[],
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): ChatMessage[] {
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return [
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{
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role: "system",
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content: [
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"你是一名知识结构整理助手。请阅读某个学习任务的全部学习报告和残片，",
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"将其中的知识点整理成一份思维导图大纲。要求：",
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"1. 第一行是导图根标题（任务主题）；",
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"2. 之后每行一个节点，用两个空格的缩进表示层级；",
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"3. 按知识的内在结构组织（概念→细分→要点），不按时间顺序；",
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"4. 合并重复的知识点；",
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"5. 只基于给定内容，不补充外部知识；",
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"6. 直接输出大纲，不要任何解释。",
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].join(""),
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},
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{
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role: "user",
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content: [
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`学习任务：${taskName}`,
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taskDescription ? `任务描述：${taskDescription}` : "",
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"",
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"学习报告：",
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...reports.map((r, i) => `${i + 1}. ${r}`),
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"",
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"学习残片：",
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...fragments.map((f, i) => `${i + 1}. ${f}`),
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]
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.filter(Boolean)
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.join("\n"),
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},
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];
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}
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