refactor: Business logic and dependencies updates
- 核心服务代码更新 (db, server, auth, proxy) - Agent 相关模块更新 (mindspace, experience) - 前端组件和 hooks 更新 - 数据库 schema 更新 - 依赖版本更新
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import crypto from 'node:crypto';
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/**
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* Shared experience store (etat C of the goose scale plan).
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*
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* All goosed Worker instances read/write the same learned experience so that
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* capability is not siloed per instance. This first implementation is backed by
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* MySQL (`h5_experience`) with a keyword + recency ranking. The retrieval scoring
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* is deliberately isolated in `rankRows` so that swapping to PostgreSQL + pgvector
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* later only replaces the query/scoring layer, not the calling contract
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* (record / search / reflect).
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*
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* @param {import('mysql2/promise').Pool} pool
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* @param {object} [options]
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* @param {() => number} [options.now] - injectable clock for tests
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*/
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export function createExperienceService(pool, options = {}) {
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const now = options.now ?? (() => Date.now());
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// Recency half-life: a record this old contributes half its keyword score.
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const halfLifeMs = Math.max(
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60_000,
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Number(options.recencyHalfLifeMs ?? 30 * 24 * 60 * 60 * 1000),
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);
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function normalizeTags(tags) {
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if (!Array.isArray(tags)) return [];
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return [...new Set(tags.map((t) => String(t).trim()).filter(Boolean))];
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}
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function rowToExperience(row) {
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return {
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id: row.id,
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scope: row.scope,
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kind: row.kind,
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title: row.title,
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body: row.body,
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tags: row.tags_json ? safeJsonArray(row.tags_json) : [],
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sourceSessionId: row.source_session_id ?? null,
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sourceUserId: row.source_user_id ?? null,
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useCount: Number(row.use_count ?? 0),
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createdAt: Number(row.created_at),
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updatedAt: Number(row.updated_at),
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};
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}
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function safeJsonArray(value) {
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if (Array.isArray(value)) return value;
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try {
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const parsed = JSON.parse(value);
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return Array.isArray(parsed) ? parsed : [];
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} catch {
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return [];
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}
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}
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// Keyword overlap × recency decay. Kept separate so the pgvector backend can
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// replace this with cosine similarity without touching search()'s shape.
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function rankRows(rows, queryTerms, nowMs) {
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const terms = queryTerms;
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return rows
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.map((row) => {
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const haystack = `${row.title}\n${row.body}`.toLowerCase();
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let keywordScore = 0;
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for (const term of terms) {
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if (haystack.includes(term)) keywordScore += 1;
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}
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const ageMs = Math.max(0, nowMs - Number(row.updated_at));
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const recency = Math.pow(0.5, ageMs / halfLifeMs);
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const score = keywordScore * recency;
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return { row, score };
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})
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.filter((entry) => entry.score > 0)
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.sort((a, b) => b.score - a.score);
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}
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function tokenize(query) {
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return [
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...new Set(
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String(query ?? '')
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.toLowerCase()
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.split(/[^\p{L}\p{N}]+/u)
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.map((t) => t.trim())
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.filter((t) => t.length >= 2),
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),
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];
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}
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/**
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* Persist a piece of experience. Returns the stored record.
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*/
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async function record(input) {
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const title = String(input?.title ?? '').trim();
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const body = String(input?.body ?? '').trim();
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if (!title) throw experienceError('经验标题不能为空', 'invalid_experience_input');
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if (!body) throw experienceError('经验内容不能为空', 'invalid_experience_input');
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const id = crypto.randomUUID();
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const ts = now();
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const scope = String(input?.scope ?? 'global').trim() || 'global';
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const kind = String(input?.kind ?? 'lesson').trim() || 'lesson';
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const tags = normalizeTags(input?.tags);
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await pool.query(
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`INSERT INTO h5_experience
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(id, scope, kind, title, body, tags_json, source_session_id, source_user_id,
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use_count, created_at, updated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?)`,
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[
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id,
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scope,
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kind,
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title,
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body,
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JSON.stringify(tags),
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input?.sourceSessionId ?? null,
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input?.sourceUserId ?? null,
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ts,
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ts,
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],
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);
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return rowToExperience({
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id,
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scope,
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kind,
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title,
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body,
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tags_json: tags,
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source_session_id: input?.sourceSessionId ?? null,
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source_user_id: input?.sourceUserId ?? null,
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use_count: 0,
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created_at: ts,
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updated_at: ts,
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});
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}
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/**
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* Retrieve the most relevant experience for a query within a scope.
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* @returns {Promise<Array>} ranked experiences (best first), max `limit`.
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*/
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async function search(query, { scope = 'global', limit = 5 } = {}) {
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const terms = tokenize(query);
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if (terms.length === 0) return [];
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// Pull a bounded candidate set by scope+recency, then rank in JS. The
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// candidate cap keeps this cheap on MySQL; pgvector would push ranking into SQL.
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const [rows] = await pool.query(
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`SELECT id, scope, kind, title, body, tags_json, source_session_id,
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source_user_id, use_count, created_at, updated_at
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FROM h5_experience
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WHERE scope = ?
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ORDER BY updated_at DESC
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LIMIT 500`,
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[scope],
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);
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const ranked = rankRows(rows, terms, now()).slice(0, Math.max(1, limit));
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if (ranked.length > 0) {
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// Best-effort usage bump so popular experience can be surfaced/weighted later.
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const ids = ranked.map((entry) => entry.row.id);
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const placeholders = ids.map(() => '?').join(',');
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await pool
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.query(
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`UPDATE h5_experience SET use_count = use_count + 1 WHERE id IN (${placeholders})`,
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ids,
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)
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.catch(() => {});
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}
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return ranked.map((entry) => rowToExperience(entry.row));
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}
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/**
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* Placeholder for the reflection pass that distills raw session traces into
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* durable lessons. Wired later once the record() trigger points are defined;
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* kept here so the calling contract is stable.
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*/
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async function reflect() {
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return { ok: false, reason: 'not_implemented' };
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}
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return { record, search, reflect };
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}
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function experienceError(message, code) {
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return Object.assign(new Error(message), { code });
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}
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