] [--sourceType <type>] [--storage json|sqlite] query --q <text> [--limit <n>] [--embedding keyword|openai] status [--storage json|sqlite] reset [--storage json|sqlite] help Options: --storage <adapter> Storage: json (default) or sqlite --embedding <provider> Embedding: keyword (default) or openai --db-path <path> Custom db path Examples: node dist/index.js ingest --file examples/sample-note.md --title "Sample" node dist/index.js query --q "knowledge connector" node dist/index.js status --storage sqlite node dist/index.js reset --storage sqlite`); } async function main() { const [, , command, ...rest] = process.argv; const args = parseArgs(rest); const storageType = args.storage || 'json'; const embeddingType = args.embedding || 'keyword'; const dbPathArg = args['db-path'] || undefined; let adapter = null; try { if (!command || command === 'help' || args.help) { printHelp(); return; } try { const adaptersDir = IS_DIST ? 'storage-adapters' : 'src/storage-adapters'; if (storageType === 'sqlite') { const { SqliteStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'SqliteStorageAdapter.js')); adapter = new SqliteStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } else { const { JsonStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'JsonStorageAdapter.js')); adapter = new JsonStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } } catch (e) { if (e.message.includes('sqlite3 not installed')) { console.error('[LinkMind] sqlite3 not installed. Run: npm install sqlite3'); } else { console.error(`[LinkMind] Adapter load error: ${e.message}`); } process.exitCode = 1; return; } let embeddingProvider = null; try { const providersDir = IS_DIST ? 'embedding-providers' : 'src/embedding-providers'; const { MockProvider, OpenAICompatibleProvider } = require(path.join(THIS_DIR, providersDir, 'index.js')); embeddingProvider = embeddingType === 'openai' ? new OpenAICompatibleProvider({ baseURL: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1', apiKey: process.env.OPENAI_API_KEY || '', model: process.env.OPENAI_MODEL || 'text-embedding-3-small' }) : new MockProvider(); } catch { embeddingProvider = null; } const now = () => new Date().toISOString(); if (command === 'ingest') { if (!args.file) throw new Error('--file is required for ingest'); const full = path.resolve(process.cwd(), args.file); if (!fs.existsSync(full)) throw new Error(`File not found: ${full}`); const text = fs.readFileSync(full, 'utf8'); const db = await adapter.load(); const doc = { id: stableId('doc', `${full}:${args.title || path.basename(full)}`), type: 'document', title: args.title || path.basename(full), sourceType: args.sourceType || 'file', sourceUri: full, importedAt: now(), status: 'active', text }; upsertDocument(db, doc); const stats = buildForDocument(db, doc); await adapter.save(db); console.log(JSON.stringify({ documentId: doc.id, title: doc.title, ...stats }, null, 2)); return; } if (command === 'query') { if (!args.q && !args.query) throw new Error('--q is required for query'); const result = await queryWithEmbedding(adapter, embeddingProvider, args.q || args.query, { limit: args.limit }); console.log(JSON.stringify(result, null, 2)); return; } if (command === 'status') { const db = await adapter.load(); console.log(JSON.stringify({ documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, adapter: storageType, embedding: embeddingType }, null, 2)); return; } if (command === 'reset') { await adapter.clear(); console.log(JSON.stringify({ ok: true, adapter: storageType }, null, 2)); return; } throw new Error(`Unknown command: ${command}`); } catch (error) { console.error(`[LinkMind] ${error.message}`); process.exitCode = 1; } finally { if (adapter) await adapter.close(); } } if (require.main === module) { main(); } module.exports = { buildForDocument, extractConcepts, splitParagraphs, normalizeConcept, stableId, queryWithEmbedding, createEmptyDb }; FILE:dist/retriever.js /** * retriever.js * 关键词召回 + 向量相似度召回双层检索。 * ranker：余弦相似度 * 最终结果 = 关键词召回 ∪ 向量召回 → 去重排序 */ const { normalizeConcept, STOPWORDS } = require('./utils/nlp'); /** * @typedef {Object} RetrievalResult * @property {string} fragmentId * @property {string} documentId * @property {string} documentTitle * @property {number} score * @property {string} text * @property {'keyword'|'vector'|'hybrid'} source */ /** * 计算余弦相似度 * @param {number[]} a * @param {number[]} b */ function cosineSimilarity(a, b) { if (a.length !== b.length) return 0; let dot = 0, na = 0, nb = 0; for (let i = 0; i < a.length; i += 1) { dot += a[i] * b[i]; na += a[i] * a[i]; nb += b[i] * b[i]; } const denom = Math.sqrt(na) * Math.sqrt(nb); return denom === 0 ? 0 : dot / denom; } /** * 关键词召回（从 fragment 列表） */ function keywordSearch(fragments, query, limit = 20) { const terms = normalizeConcept(query) .split(' ') .filter(Boolean) .filter((t) => !STOPWORDS.has(t)); if (terms.length === 0) return []; return fragments .map((frag) => { const textNorm = normalizeConcept(frag.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if ((frag.conceptNames || []).includes(term)) score += 5; } return { frag, score }; }) .filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.frag.index - b.frag.index) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.score, text: item.frag.text, source: 'keyword' })); } /** * 向量相似度召回 * @param {object[]} fragments - 带 documentTitle * @param {string} query * @param {import('./embedding-providers/EmbeddingProvider')} embeddingProvider * @param {number} limit */ async function vectorSearch(fragments, query, embeddingProvider, limit = 20) { if (!embeddingProvider || fragments.length === 0) return []; const texts = fragments.map((f) => f.text); const [queryVec, fragVecs] = await Promise.all([ embeddingProvider.embed([query]), embeddingProvider.embed(texts) ]); const qv = queryVec[0]; return fragments .map((frag, i) => ({ frag, sim: cosineSimilarity(qv, fragVecs[i] || []) })) .filter((item) => item.sim > 0) .sort((a, b) => b.sim - a.sim) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.sim, text: item.frag.text, source: 'vector' })); } /** * 合并关键词召回 + 向量召回，去重并排序 * @param {RetrievalResult[]} keywordResults * @param {RetrievalResult[]} vectorResults * @param {number} limit */ function mergeResults(keywordResults, vectorResults, limit = 10) { const seen = new Map(); for (const r of [...keywordResults, ...vectorResults]) { if (!seen.has(r.fragmentId)) { seen.set(r.fragmentId, { ...r }); } else { // 取最高分 const existing = seen.get(r.fragmentId); existing.score = Math.max(existing.score, r.score); existing.source = existing.source === r.source ? existing.source : 'hybrid'; } } return [...seen.values()] .sort((a, b) => b.score - a.score) .slice(0, limit); } /** * 主检索入口 * @param {object} options * @param {object[]} options.fragments - fragment 列表，需含 documentTitle * @param {string} options.query * @param {import('./embedding-providers/EmbeddingProvider')} [options.embeddingProvider] * @param {number} [options.limit] */ async function retrieve({ fragments, query, embeddingProvider = null, limit = 10 }) { const kw = keywordSearch(fragments, query, limit * 2); const vec = embeddingProvider ? await vectorSearch(fragments, query, embeddingProvider, limit * 2) : []; return mergeResults(kw, vec, limit); } module.exports = { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve }; FILE:dist/storage-adapters/JsonStorageAdapter.js /** * JsonStorageAdapter * 基于本地 JSON 文件的存储实现，保持向后兼容。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./StorageAdapter'); const ROOT = path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } class JsonStorageAdapter extends StorageAdapter { constructor() { super(); this._db = null; } _loadDb() { ensureDir(DATA_DIR); if (!fs.existsSync(DB_PATH)) { const empty = this._emptyDb(); this._saveDb(empty); return empty; } return JSON.parse(fs.readFileSync(DB_PATH, 'utf8')); } _saveDb(db) { db.updatedAt = now(); ensureDir(DATA_DIR); fs.writeFileSync(DB_PATH, JSON.stringify(db, null, 2), 'utf8'); } _emptyDb() { return { version: 1, createdAt: now(), updatedAt: now(), documents: [], fragments: [], concepts: [], links: [] }; } async init() { this._db = this._loadDb(); } // --- Document --- async saveDocument(doc) { const db = this._loadDb(); const idx = db.documents.findIndex((d) => d.id === doc.id); if (idx >= 0) db.documents[idx] = doc; else db.documents.push(doc); this._saveDb(db); return doc; } async getDocument(id) { const db = this._loadDb(); return db.documents.find((d) => d.id === id) || null; } async listDocuments() { const db = this._loadDb(); return db.documents; } async deleteDocument(id) { const db = this._loadDb(); db.documents = db.documents.filter((d) => d.id !== id); this._saveDb(db); } // --- Fragment --- async saveFragment(frag) { const db = this._loadDb(); const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); this._saveDb(db); return frag; } async getFragment(id) { const db = this._loadDb(); return db.fragments.find((f) => f.id === id) || null; } async listFragments(documentId) { const db = this._loadDb(); return db.fragments.filter((f) => f.documentId === documentId); } async deleteFragmentsByDocument(documentId) { const db = this._loadDb(); db.fragments = db.fragments.filter((f) => f.documentId !== documentId); this._saveDb(db); } async saveFragments(fragments) { const db = this._loadDb(); for (const frag of fragments) { const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); } this._saveDb(db); } // --- Concept --- async saveConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async getConcept(id) { const db = this._loadDb(); return db.concepts.find((c) => c.id === id) || null; } async listConcepts() { const db = this._loadDb(); return db.concepts; } async upsertConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async saveConcepts(concepts) { const db = this._loadDb(); for (const c of concepts) { const idx = db.concepts.findIndex((x) => x.id === c.id); if (idx >= 0) db.concepts[idx] = c; else db.concepts.push(c); } this._saveDb(db); } // --- Link --- async saveLink(link) { const db = this._loadDb(); const idx = db.links.findIndex((l) => l.id === link.id); if (idx >= 0) db.links[idx] = link; else db.links.push(link); this._saveDb(db); return link; } async getLinks(fromId, toId) { const db = this._loadDb(); return db.links.filter( (l) => (fromId ? l.fromId === fromId : true) && (toId ? l.toId === toId : true) ); } async deleteLinksByDocument(documentId) { const db = this._loadDb(); db.links = db.links.filter((l) => l.documentId !== documentId); this._saveDb(db); } async saveLinks(links) { const db = this._loadDb(); for (const l of links) { const idx = db.links.findIndex((x) => x.id === l.id); if (idx >= 0) db.links[idx] = l; else db.links.push(l); } this._saveDb(db); } // --- Full DB operations (for CLI compat) --- async load() { return this._loadDb(); } async save(db) { this._saveDb(db); } async close() { // no-op for JSON } // --- Workspace --- async clear() { this._saveDb(this._emptyDb()); } // --- Stats --- getStats() { const db = this._loadDb(); return { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, dbPath: DB_PATH }; } } module.exports = { JsonStorageAdapter }; FILE:dist/storage-adapters/SqliteStorageAdapter.js /** * SqliteStorageAdapter * 基于 better-sqlite3 的 SQLite 存储实现。 * 建表语句 + 所有 StorageAdapter 方法的完整实现。 */ const { StorageAdapter } = require('./StorageAdapter'); let sqlite3 = null; try { sqlite3 = require('better-sqlite3'); } catch { // better-sqlite3 not installed — adapter will throw on init() } const ROOT = path => require('path').resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'db.sqlite'); function ensureDir(dir) { require('fs').mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, sourceType TEXT, sourceUri TEXT, importedAt TEXT, status TEXT DEFAULT 'active', text TEXT, createdAt TEXT, updatedAt TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', documentId TEXT, "index" INTEGER, text TEXT, summary TEXT, conceptNames TEXT, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalizedName TEXT UNIQUE, salience REAL, createdAt TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, fromId TEXT, fromType TEXT, toId TEXT, toType TEXT, documentId TEXT, score REAL, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS fragment_vectors ( fragmentId TEXT PRIMARY KEY, vector BLOB, updatedAt TEXT, FOREIGN KEY (fragmentId) REFERENCES fragments(id) ); CREATE INDEX IF NOT EXISTS idx_fragments_documentId ON fragments(documentId); CREATE INDEX IF NOT EXISTS idx_links_fromId ON links(fromId); CREATE INDEX IF NOT EXISTS idx_links_toId ON links(toId); CREATE INDEX IF NOT EXISTS idx_links_documentId ON links(documentId); CREATE INDEX IF NOT EXISTS idx_concepts_normalizedName ON concepts(normalizedName); `; class SqliteStorageAdapter extends StorageAdapter { constructor({ dbPath } = {}) { super(); this._dbPath = dbPath || DB_PATH; this._db = null; } async init() { if (!sqlite3) { throw new Error('better-sqlite3 is not installed. Run: npm install better-sqlite3'); } ensureDir(require('path').dirname(this._dbPath)); this._db = sqlite3(this._dbPath); this._db.pragma('journal_mode = WAL'); this._db.exec(SCHEMA); } _run(sql, params = []) { try { return this._db.prepare(sql).run(...params); } catch (e) { throw new Error(`SQLite run error: ${e.message} | sql: ${sql}`); } } _all(sql, params = []) { try { return this._db.prepare(sql).all(...params); } catch (e) { throw new Error(`SQLite all error: ${e.message} | sql: ${sql}`); } } _get(sql, params = []) { try { return this._db.prepare(sql).get(...params); } catch (e) { throw new Error(`SQLite get error: ${e.message} | sql: ${sql}`); } } // --- Document --- async saveDocument(doc) { this._run( `INSERT OR REPLACE INTO documents (id,type,title,sourceType,sourceUri,importedAt,status,text,createdAt,updatedAt) VALUES (?,?,?,?,?,?,?,?,?,?)`, [doc.id, doc.type || 'document', doc.title || '', doc.sourceType || '', doc.sourceUri || '', doc.importedAt || now(), doc.status || 'active', doc.text || '', doc.createdAt || now(), now()] ); return doc; } async getDocument(id) { const row = this._get('SELECT * FROM documents WHERE id = ?', [id]); return row ? this._mapDoc(row) : null; } async listDocuments() { return this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); } async deleteDocument(id) { this._run('DELETE FROM documents WHERE id = ?', [id]); } // --- Fragment --- async saveFragment(frag) { this._run( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)`, [frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()] ); return frag; } async getFragment(id) { const row = this._get('SELECT * FROM fragments WHERE id = ?', [id]); return row ? this._mapFrag(row) : null; } async listFragments(documentId) { return this._all('SELECT * FROM fragments WHERE documentId = ? ORDER BY "index" ASC', [documentId]) .map(this._mapFrag); } async deleteFragmentsByDocument(documentId) { this._run('DELETE FROM fragments WHERE documentId = ?', [documentId]); } async saveFragments(fragments) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const frag of items) { stmt.run(frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()); } }); insertMany(fragments); return fragments; } // --- Concept --- async saveConcept(concept) { this._run( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)`, [concept.id, 'concept', concept.name, concept.normalizedName, concept.salience || 0, concept.createdAt || now()] ); return concept; } async getConcept(id) { const row = this._get('SELECT * FROM concepts WHERE id = ?', [id]); return row ? this._mapConcept(row) : null; } async listConcepts() { return this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); } async upsertConcept(concept) { return this.saveConcept(concept); } async saveConcepts(concepts) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const c of items) { stmt.run(c.id, 'concept', c.name, c.normalizedName, c.salience || 0, c.createdAt || now()); } }); insertMany(concepts); return concepts; } // --- Link --- async saveLink(link) { this._run( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)`, [link.id, link.type, link.fromId, link.fromType, link.toId, link.toType, link.documentId, link.score || 0, link.createdAt || now()] ); return link; } async getLinks(fromId, toId) { let sql = 'SELECT * FROM links WHERE 1=1'; const params = []; if (fromId) { sql += ' AND fromId = ?'; params.push(fromId); } if (toId) { sql += ' AND toId = ?'; params.push(toId); } return this._all(sql, params); } async deleteLinksByDocument(documentId) { this._run('DELETE FROM links WHERE documentId = ?', [documentId]); } async saveLinks(links) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const l of items) { stmt.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score || 0, l.createdAt || now()); } }); insertMany(links); return links; } // --- Vector --- async saveFragmentVector(fragmentId, vector) { const buf = Buffer.from(JSON.stringify(vector)); this._run( `INSERT OR REPLACE INTO fragment_vectors (fragmentId,vector,updatedAt) VALUES (?,?,?)`, [fragmentId, buf, now()] ); } async getFragmentVector(fragmentId) { const row = this._get('SELECT vector FROM fragment_vectors WHERE fragmentId = ?', [fragmentId]); if (!row) return null; return JSON.parse(row.vector); } async listFragmentVectors() { return this._all('SELECT fragmentId, vector FROM fragment_vectors').map((r) => ({ fragmentId: r.fragmentId, vector: JSON.parse(r.vector) })); } // --- Full DB operations (for CLI compat) --- async load() { const docs = this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); const frags = this._all('SELECT * FROM fragments ORDER BY "index" ASC').map(this._mapFrag); const concepts = this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); const links = this._all('SELECT * FROM links'); const updatedAt = this._get('SELECT MAX(updatedAt) as t FROM documents UNION ALL SELECT MAX(updatedAt) as t FROM fragments')?.t || new Date().toISOString(); return { version: 1, documents: docs, fragments: frags, concepts, links, updatedAt }; } async save(db) { await this.clear(); if (db.documents) for (const d of db.documents) await this.saveDocument(d); if (db.fragments) for (const f of db.fragments) await this.saveFragment(f); if (db.concepts) for (const c of db.concepts) await this.saveConcept(c); if (db.links) for (const l of db.links) await this.saveLink(l); } async close() { if (this._db) { this._db.close(); this._db = null; } } // --- Workspace --- async clear() { this._run('DELETE FROM fragment_vectors'); this._run('DELETE FROM links'); this._run('DELETE FROM fragments'); this._run('DELETE FROM concepts'); this._run('DELETE FROM documents'); } getStats() { return { documents: this._get('SELECT COUNT(*) as n FROM documents')?.n || 0, fragments: this._get('SELECT COUNT(*) as n FROM fragments')?.n || 0, concepts: this._get('SELECT COUNT(*) as n FROM concepts')?.n || 0, links: this._get('SELECT COUNT(*) as n FROM links')?.n || 0, dbPath: this._dbPath }; } // --- Mappers --- _mapDoc(row) { return { ...row }; } _mapFrag(row) { return { ...row, conceptNames: JSON.parse(row.conceptNames || '[]') }; } _mapConcept(row) { return { ...row }; } } module.exports = { SqliteStorageAdapter }; FILE:dist/storage-adapters/StorageAdapter.js /** * StorageAdapter Interface * 所有 storage adapter 必须实现此接口契约。 */ class StorageAdapter { /** * @returns {Promise<void>} */ async init() { throw new Error('Not implemented'); } // --- Document --- async saveDocument(doc) { throw new Error('Not implemented'); } async getDocument(id) { throw new Error('Not implemented'); } async listDocuments() { throw new Error('Not implemented'); } async deleteDocument(id) { throw new Error('Not implemented'); } // --- Fragment --- async saveFragment(frag) { throw new Error('Not implemented'); } async getFragment(id) { throw new Error('Not implemented'); } async listFragments(documentId) { throw new Error('Not implemented'); } async deleteFragmentsByDocument(documentId) { throw new Error('Not implemented'); } // --- Concept --- async saveConcept(concept) { throw new Error('Not implemented'); } async getConcept(id) { throw new Error('Not implemented'); } async listConcepts() { throw new Error('Not implemented'); } async upsertConcept(concept) { throw new Error('Not implemented'); } // --- Link --- async saveLink(link) { throw new Error('Not implemented'); } async getLinks(fromId, toId) { throw new Error('Not implemented'); } async deleteLinksByDocument(documentId) { throw new Error('Not implemented'); } // --- Bulk --- async saveFragments(fragments) { throw new Error('Not implemented'); } async saveConcepts(concepts) { throw new Error('Not implemented'); } async saveLinks(links) { throw new Error('Not implemented'); } // --- Workspace --- async clear() { throw new Error('Not implemented'); } } module.exports = { StorageAdapter }; FILE:dist/storage-adapters/index.js /** * storage-adapters/index.js * Factory: 根据配置返回对应的 storage adapter 实例。 */ const { JsonStorageAdapter } = require('./JsonStorageAdapter'); const { SqliteStorageAdapter } = require('./SqliteStorageAdapter'); function createStorageAdapter(type = 'json', options = {}) { switch (type) { case 'json': return new JsonStorageAdapter(options); case 'sqlite': return new SqliteStorageAdapter(options); default: throw new Error(`Unknown storage adapter type: ${type}. Use 'json' or 'sqlite'.`); } } module.exports = { createStorageAdapter, JsonStorageAdapter, SqliteStorageAdapter }; FILE:dist/utils/nlp.js /** * nlp.js - 文本规范化工具 */ const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were','can','could','should','about','what','when','where','which','while','than','then','them','they','their','there','here','also','more','most','some','such','using','used','use','make','made','over','under','very','just','only','each','been','being','does','did','done','how','why','our','you','its','his','her','she','him','who','has','had','but','too','via','per','one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及','进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有','对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为','实现','相关','用于' ]); function normalizeConcept(text) { if (!text) return ''; return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } module.exports = { normalizeConcept, STOPWORDS }; FILE:examples/sample-note.md # LinkMind sample note LinkMind is a connected knowledge system. It ingests notes and documents, splits them into fragments, extracts concepts, and builds explainable links. The MVP focuses on a local JSON workspace. This keeps the implementation simple while preserving a future path toward vector retrieval, graph storage, and richer evidence assembly. Knowledge Connector is the capability layer. LinkMind is the user-facing product layer built on top of it. Good retrieval should return answer, evidence, and related concepts instead of a single opaque paragraph. FILE:package.json { "name": "linkmind", "version": "0.2.0", "description": "LinkMind MVP skeleton - local knowledge connector with ingest/build/query CLI", "main": "dist/index.js", "bin": { "linkmind": "dist/index.js" }, "scripts": { "build": "cp -r src/storage-adapters dist/ && cp -r src/embedding-providers dist/ && cp -r src/utils dist/ && cp src/index.js dist/ && cp src/retriever.js dist/ && chmod +x dist/index.js", "test": "node tests/smoke-test.js" }, "keywords": ["knowledge", "graph", "rag", "linkmind"], "author": "Golden Bean", "license": "MIT", "optionalDependencies": { "sqlite3": "^5.1.7" } } FILE:skill.json { "name": "linkmind", "version": "1.0.0", "description": "知识连接引擎 - 本地化知识中枢 CLI 工具，支持 storage adapter 抽象层和 OpenAI-compatible embedding provider", "keywords": ["linkmind", "knowledge-management", "embedding", "local-knowledge", "cli", "vector-search"], "author": "harrylabsj", "license": "MIT", "repository": "https://github.com/harrylabsj/linkmind", "language": ["en", "zh"], "tags": ["knowledge-management", "embedding", "local-knowledge", "cli"], "createdAt": "2026-04-04", "updatedAt": "2026-04-05" } FILE:src/adapters/base.js /** * StorageAdapter 接口契约 * 所有 adapter 必须实现以下方法： * load() -> Promise<WorkspaceDb> * save(db) -> Promise<void> * clear() -> Promise<void> * query(fn) -> Promise<any> (事务查询) * close() -> Promise<void> */ class StorageAdapter { async load() { throw new Error('Not implemented: load()'); } async save(db) { throw new Error('Not implemented: save(db)'); } async clear() { throw new Error('Not implemented: clear()'); } async query(fn) { throw new Error('Not implemented: query(fn)'); } async close() { throw new Error('Not implemented: close()'); } } module.exports = { StorageAdapter }; FILE:src/adapters/json.js /** * JsonStorageAdapter * 保持现有 JSON 文件行为，完全兼容旧数据格式。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./base'); const ROOT = __dirname.includes(`${path.sep}dist`) ? path.resolve(__dirname, '../..') : path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } class JsonStorageAdapter extends StorageAdapter { constructor(opts = {}) { super(); this.dbPath = opts.dbPath || DB_PATH; this.dataDir = opts.dataDir || DATA_DIR; } _createEmpty() { return { version: 1, createdAt: new Date().toISOString(), updatedAt: new Date().toISOString(), documents: [], fragments: [], concepts: [], links: [] }; } async load() { ensureDir(this.dataDir); if (!fs.existsSync(this.dbPath)) { const empty = this._createEmpty(); await this.save(empty); return empty; } return JSON.parse(fs.readFileSync(this.dbPath, 'utf8')); } async save(db) { db.updatedAt = new Date().toISOString(); ensureDir(this.dataDir); fs.writeFileSync(this.dbPath, JSON.stringify(db, null, 2), 'utf8'); } async clear() { await this.save(this._createEmpty()); } async query(fn) { // JSON adapter 不需要事务，直接执行 const db = await this.load(); return fn(db); } async close() { // 无需关闭 } dbPath() { return this.dbPath; } } module.exports = { JsonStorageAdapter }; FILE:src/adapters/sqlite.js /** * SqliteStorageAdapter * SQLite 存储适配器骨架，表结构与 JSON 格式等效。 * 事务支持、批量操作能力。 */ const path = require('path'); const { StorageAdapter } = require('./base'); // sqlite3 为可选依赖，运行时检测 let sqlite3 = null; try { sqlite3 = require('sqlite3'); } catch { // sqlite3 不可用，稍后通过 init() 检测并给出明确错误 } const ROOT = __dirname.includes(`${path.sep}dist`) ? path.resolve(__dirname, '../..') : path.resolve(__dirname, '../..'); const DATA_DIR = path.resolve(ROOT, 'data'); const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, source_type TEXT, source_uri TEXT, imported_at TEXT, status TEXT DEFAULT 'active', text TEXT, metadata TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', document_id TEXT, frag_index INTEGER, text TEXT, summary TEXT, concept_names TEXT, metadata TEXT, FOREIGN KEY (document_id) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalized_name TEXT UNIQUE, salience REAL, metadata TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, from_id TEXT, from_type TEXT, to_id TEXT, to_type TEXT, document_id TEXT, score REAL, metadata TEXT ); CREATE INDEX IF NOT EXISTS idx_fragments_document ON fragments(document_id); CREATE INDEX IF NOT EXISTS idx_links_from ON links(from_id); CREATE INDEX IF NOT EXISTS idx_links_to ON links(to_id); CREATE INDEX IF NOT EXISTS idx_concepts_normalized ON concepts(normalized_name); `; class SqliteStorageAdapter extends StorageAdapter { constructor(opts = {}) { super(); if (!sqlite3) { throw new Error('sqlite3 not installed. Run: npm install sqlite3'); } this.dbPath = opts.dbPath || path.join(DATA_DIR, 'workspace.db'); this._db = null; } async init() { if (this._db) return; return new Promise((resolve, reject) => { this._db = new sqlite3.Database(this.dbPath, (err) => { if (err) return reject(err); this._db.exec(SCHEMA, (execErr) => { if (execErr) return reject(execErr); resolve(); }); }); }); } async load() { await this.init(); return new Promise((resolve, reject) => { const docStmt = this._db.prepare('SELECT * FROM documents'); const fragStmt = this._db.prepare('SELECT * FROM fragments'); const conceptStmt = this._db.prepare('SELECT * FROM concepts'); const linkStmt = this._db.prepare('SELECT * FROM links'); const rows = { documents: [], fragments: [], concepts: [], links: [] }; const finish = () => { // 反序列化 JSON 字段 rows.fragments = rows.fragments.map((f) => ({ ...f, id: f.id, type: 'fragment', documentId: f.document_id, index: f.frag_index, conceptNames: f.concept_names ? JSON.parse(f.concept_names) : [] })); rows.concepts = rows.concepts.map((c) => ({ ...c, type: 'concept', normalizedName: c.normalized_name })); rows.links = rows.links.map((l) => ({ ...l, fromId: l.from_id, fromType: l.from_type, toId: l.to_id, toType: l.to_type, documentId: l.document_id })); resolve(rows); }; let pending = 4; const done = () => { if (--pending === 0) finish(); }; docStmt.all((err, docs) => { if (err) { docStmt.close(); return reject(err); } rows.documents = docs.map((d) => ({ ...d, type: 'document' })); docStmt.close(); done(); }); fragStmt.all((err, frags) => { if (err) { fragStmt.close(); return reject(err); } rows.fragments = frags; fragStmt.close(); done(); }); conceptStmt.all((err, concepts) => { if (err) { conceptStmt.close(); return reject(err); } rows.concepts = concepts; conceptStmt.close(); done(); }); linkStmt.all((err, links) => { if (err) { linkStmt.close(); return reject(err); } rows.links = links; linkStmt.close(); done(); }); }); } async save(rows) { await this.init(); return new Promise((resolve, reject) => { this._db.serialize(() => { const insertDoc = this._db.prepare( 'INSERT OR REPLACE INTO documents (id,type,title,source_type,source_uri,imported_at,status,text,metadata) VALUES (?,?,?,?,?,?,?,?,?)' ); const insertFrag = this._db.prepare( 'INSERT OR REPLACE INTO fragments (id,type,document_id,frag_index,text,summary,concept_names,metadata) VALUES (?,?,?,?,?,?,?,?)' ); const insertConcept = this._db.prepare( 'INSERT OR REPLACE INTO concepts (id,type,name,normalized_name,salience,metadata) VALUES (?,?,?,?,?,?)' ); const insertLink = this._db.prepare( 'INSERT OR REPLACE INTO links (id,type,from_id,from_type,to_id,to_type,document_id,score,metadata) VALUES (?,?,?,?,?,?,?,?,?)' ); try { for (const d of rows.documents) { insertDoc.run(d.id, d.type, d.title, d.sourceType, d.sourceUri, d.importedAt, d.status, d.text, d.metadata ? JSON.stringify(d.metadata) : null); } for (const f of rows.fragments) { insertFrag.run(f.id, f.type, f.documentId, f.index, f.text, f.summary, JSON.stringify(f.conceptNames || []), f.metadata ? JSON.stringify(f.metadata) : null); } for (const c of rows.concepts) { insertConcept.run(c.id, c.type, c.name, c.normalizedName, c.salience, c.metadata ? JSON.stringify(c.metadata) : null); } for (const l of rows.links) { insertLink.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score, l.metadata ? JSON.stringify(l.metadata) : null); } resolve(); } catch (err) { reject(err); } finally { insertDoc.close(); insertFrag.close(); insertConcept.close(); insertLink.close(); } }); }); } async clear() { await this.init(); return new Promise((resolve, reject) => { this._db.run('DELETE FROM links; DELETE FROM fragments; DELETE FROM concepts; DELETE FROM documents;', function (err) { if (err) return reject(err); resolve(); }); }); } async query(fn) { // 供未来事务查询使用，当前与 load 行为相同 const db = await this.load(); return fn(db); } async close() { if (this._db) { return new Promise((resolve) => { this._db.close(() => resolve()); this._db = null; }); } } } module.exports = { SqliteStorageAdapter }; FILE:src/embedding-providers/EmbeddingProvider.js /** * EmbeddingProvider Interface * 所有 embedding provider 必须实现此接口契约。 */ class EmbeddingProvider { /** * 将文本列表转为向量 * @param {string[]} texts * @returns {Promise<number[][]>} */ async embed(texts) { throw new Error('Not implemented'); } /** * 返回 provider 名称 */ get name() { return 'unknown'; } /** * 返回向量维度 */ get dimension() { throw new Error('Not implemented'); } } module.exports = { EmbeddingProvider }; FILE:src/embedding-providers/MockProvider.js /** * MockProvider * 返回随机向量，用于测试和离线开发。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class MockProvider extends EmbeddingProvider { constructor({ dimension = DEFAULT_DIM, seed = 42 } = {}) { super(); this._dim = dimension; this._seed = seed; this._cache = new Map(); } get name() { return 'mock'; } get dimension() { return this._dim; } _pseudoRandom(text) { let hash = 0; for (let i = 0; i < text.length; i += 1) { hash = ((hash << 5) - hash + text.charCodeAt(i)) | 0; } return Math.abs(hash) / 0x7fffffff; } async embed(texts) { return texts.map((text) => { if (this._cache.has(text)) return this._cache.get(text); const vec = []; for (let i = 0; i < this._dim; i += 1) { // deterministic random based on text + index const base = this._pseudoRandom(text + i); vec.push(base); } // L2 normalize const norm = Math.sqrt(vec.reduce((s, v) => s + v * v, 0)); const normalized = norm > 0 ? vec.map((v) => v / norm) : vec; this._cache.set(text, normalized); return normalized; }); } } module.exports = { MockProvider }; FILE:src/embedding-providers/OpenAICompatibleProvider.js /** * OpenAICompatibleProvider * 调用 OpenAI-compatible API endpoint（如 vLLM、Ollama、Azure OpenAI 等）。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class OpenAICompatibleProvider extends EmbeddingProvider { constructor({ baseURL = 'https://api.openai.com/v1', apiKey = '', model = 'text-embedding-3-small', dimension = DEFAULT_DIM, batchSize = 100 } = {}) { super(); this._baseURL = baseURL.replace(/\/$/, ''); this._apiKey = apiKey; this._model = model; this._dim = dimension; this._batchSize = batchSize; } get name() { return `openai-compatible:${this._model}`; } get dimension() { return this._dim; } async embed(texts) { if (!texts || texts.length === 0) return []; const results = []; for (let i = 0; i < texts.length; i += this._batchSize) { const batch = texts.slice(i, i + this._batchSize); const vectors = await this._fetchBatch(batch); results.push(...vectors); } return results; } async _fetchBatch(batch) { const url = `${this._baseURL}/embeddings`; const body = { model: this._model, input: batch }; const headers = { 'Content-Type': 'application/json' }; if (this._apiKey) { headers['Authorization'] = `Bearer ${this._apiKey}`; } const resp = await fetch(url, { method: 'POST', headers, body: JSON.stringify(body) }); if (!resp.ok) { const text = await resp.text().catch(() => ''); throw new Error(`Embedding API error ${resp.status}: ${text}`); } const json = await resp.json(); if (!json.data || !Array.isArray(json.data)) { throw new Error(`Unexpected embedding response format`); } // sort by index to maintain order const sorted = json.data.slice().sort((a, b) => a.index - b.index); return sorted.map((item) => { if (!item.embedding || !Array.isArray(item.embedding)) { throw new Error(`Invalid embedding vector in response`); } return item.embedding; }); } } module.exports = { OpenAICompatibleProvider }; FILE:src/embedding-providers/index.js /** * embedding-providers/index.js * Factory: 根据配置返回对应的 embedding provider 实例。 */ const { MockProvider } = require('./MockProvider'); const { OpenAICompatibleProvider } = require('./OpenAICompatibleProvider'); function createEmbeddingProvider(type = 'mock', options = {}) { switch (type) { case 'mock': return new MockProvider(options); case 'openai': return new OpenAICompatibleProvider(options); default: throw new Error(`Unknown embedding provider type: ${type}. Use 'mock' or 'openai'.`); } } module.exports = { createEmbeddingProvider, MockProvider, OpenAICompatibleProvider }; FILE:src/index.js #!/usr/bin/env node /** * LinkMind CLI - 知识连接引擎 * 支持 adapter 切换（json/sqlite）和 embedding 切换（keyword/openai） */ const fs = require('fs'); const path = require('path'); const THIS_DIR = __dirname.includes(`${path.sep}dist`) ? __dirname : __dirname; const IS_DIST = THIS_DIR.includes(`${path.sep}dist`); const ROOT = IS_DIST ? path.resolve(THIS_DIR, '..') : path.resolve(THIS_DIR, '..'); const DATA_DIR = path.join(ROOT, 'data'); function createEmptyDb() { return { version: 1, createdAt: new Date().toISOString(), updatedAt: new Date().toISOString(), documents: [], fragments: [], concepts: [], links: [] }; } function stableId(prefix, input) { let hash = 0; for (let i = 0; i < input.length; i += 1) { hash = ((hash << 5) - hash + input.charCodeAt(i)) | 0; } return `${prefix}_${Math.abs(hash).toString(36)}`; } function normalizeConcept(text) { return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were', 'can','could','should','about','what','when','where','which','while','than','then','them','they', 'their','there','here','also','more','most','some','such','using','used','use','make','made', 'over','under','very','just','only','each','been','being','does','did','done','how','why', 'our','you','its','his','her','she','him','who','has','had','but','too','via','per', 'one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及', '进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有', '对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为' ]); function extractConcepts(text) { const zhMatches = text.match(/[\u4e00-\u9fa5]{2,8}/g) || []; const enMatches = text.match(/[A-Za-z][A-Za-z0-9_-]{2,}/g) || []; const tokens = [...zhMatches, ...enMatches] .map((item) => item.trim()) .map((item) => ({ raw: item, normalized: normalizeConcept(item) })) .filter((item) => item.normalized && !STOPWORDS.has(item.normalized)); const counts = new Map(); for (const token of tokens) { counts.set(token.normalized, (counts.get(token.normalized) || 0) + 1); } return [...counts.entries()] .map(([normalizedName, count]) => ({ normalizedName, name: tokens.find((item) => item.normalized === normalizedName)?.raw || normalizedName, count })) .sort((a, b) => b.count - a.count || a.normalizedName.localeCompare(b.normalizedName)) .slice(0, 12); } function splitParagraphs(text) { return text .split(/\n\s*\n+/) .map((part) => part.trim()) .filter(Boolean); } function upsertDocument(db, doc) { const existingIndex = db.documents.findIndex((item) => item.id === doc.id); if (existingIndex >= 0) db.documents[existingIndex] = doc; else db.documents.push(doc); } function buildForDocument(db, document) { const fragmentIds = db.fragments.filter((f) => f.documentId === document.id).map((f) => f.id); db.fragments = db.fragments.filter((f) => f.documentId !== document.id); db.links = db.links.filter((l) => !fragmentIds.includes(l.fromId) && !fragmentIds.includes(l.toId) && l.documentId !== document.id); const usedConceptIds = new Set(); for (const link of db.links) { if (link.toType === 'concept') usedConceptIds.add(link.toId); if (link.fromType === 'concept') usedConceptIds.add(link.fromId); } db.concepts = db.concepts.filter((c) => usedConceptIds.has(c.id)); const fragments = splitParagraphs(document.text).map((text, index) => ({ id: stableId('frag', `${document.id}:${index}:${text.slice(0, 80)}`), type: 'fragment', documentId: document.id, index, text, summary: text.slice(0, 100), conceptNames: [] })); const conceptMap = new Map(db.concepts.map((c) => [c.normalizedName, c])); const links = []; for (const fragment of fragments) { const fragmentConcepts = extractConcepts(fragment.text).slice(0, 6); fragment.conceptNames = fragmentConcepts.map((item) => item.normalizedName); for (const concept of fragmentConcepts) { if (!conceptMap.has(concept.normalizedName)) { conceptMap.set(concept.normalizedName, { id: stableId('concept', concept.normalizedName), type: 'concept', name: concept.name, normalizedName: concept.normalizedName, salience: Math.min(1, concept.count / 3) }); } const conceptNode = conceptMap.get(concept.normalizedName); links.push({ id: stableId('link', `${fragment.id}->${conceptNode.id}`), type: 'mentions', fromId: fragment.id, fromType: 'fragment', toId: conceptNode.id, toType: 'concept', documentId: document.id, score: concept.count }); } } for (let i = 0; i < fragments.length - 1; i += 1) { links.push({ id: stableId('link', `${fragments[i].id}->${fragments[i + 1].id}`), type: 'adjacent', fromId: fragments[i].id, fromType: 'fragment', toId: fragments[i + 1].id, toType: 'fragment', documentId: document.id, score: 0.4 }); } db.fragments.push(...fragments); db.links.push(...links); db.concepts = [...conceptMap.values()].sort((a, b) => a.normalizedName.localeCompare(b.normalizedName)); return { fragmentsCreated: fragments.length, linksCreated: links.length, conceptsTotal: db.concepts.length }; } async function queryWithEmbedding(adapter, embeddingProvider, q, options = {}) { const db = await adapter.load(); const query = normalizeConcept(q || ''); if (!query) throw new Error('Query is required'); const terms = query.split(' ').filter(Boolean); let scored = db.fragments.map((fragment) => { const textNorm = normalizeConcept(fragment.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if (fragment.conceptNames.includes(term)) score += 5; } return { fragment, score }; }).filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.fragment.index - b.fragment.index) .slice(0, Number(options.limit || 3)); // Vector re-rank if real embedding provider (not KeywordEmbeddingProvider) if (embeddingProvider && embeddingProvider.constructor.name !== 'KeywordEmbeddingProvider') { try { const queryVec = await embeddingProvider.embed(q); const fragTexts = scored.length > 0 ? scored.map((s) => s.fragment.text) : db.fragments.slice(0, 20).map((f) => f.text); const fragVecs = await embeddingProvider.embedBatch(fragTexts); if (fragVecs.length > 0) { const scoredWithVec = scored.length > 0 ? scored.map((s, i) => ({ ...s, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })) : db.fragments.slice(0, 20).map((f, i) => ({ fragment: f, score: 0, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })); scoredWithVec.forEach((item) => { item.blendedScore = item.score * 0.6 + item.vecScore * 10 * 0.4; }); scored = scoredWithVec.sort((a, b) => b.blendedScore - a.blendedScore).slice(0, Number(options.limit || 3)); } } catch { // No API key or vector search failed, keep keyword results } } const evidence = scored.map((item) => { const doc = db.documents.find((d) => d.id === item.fragment.documentId); return { fragmentId: item.fragment.id, documentId: item.fragment.documentId, documentTitle: doc?.title || 'unknown', score: item.score || item.vecScore, text: item.fragment.text }; }); const relatedConcepts = [...new Set(scored.flatMap((item) => item.fragment.conceptNames))] .map((name) => db.concepts.find((c) => c.normalizedName === name)) .filter(Boolean) .slice(0, 8) .map((concept) => ({ id: concept.id, name: concept.name, normalizedName: concept.normalizedName })); const answer = evidence.length ? `Found ${evidence.length} relevant fragments for "${q}". Top evidence comes from ${[...new Set(evidence.map((item) => item.documentTitle))].join(', ')}.` : `No strong match found for "${q}". Try a broader concept or ingest more documents.`; return { query: q, answer, evidence, relatedConcepts, stats: { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length } }; } function parseArgs(argv) { const result = { _: [] }; for (let i = 0; i < argv.length; i += 1) { const token = argv[i]; if (!token.startsWith('--')) { result._.push(token); continue; } const [key, inline] = token.slice(2).split('='); if (inline !== undefined) { result[key] = inline; continue; } const next = argv[i + 1]; if (!next || next.startsWith('--')) { result[key] = true; } else { result[key] = next; i += 1; } } return result; } function printHelp() { console.log(`LinkMind MVP CLI v0.2.0 Commands: ingest --file <path> [--title <title>] [--sourceType <type>] [--storage json|sqlite] query --q <text> [--limit <n>] [--embedding keyword|openai] status [--storage json|sqlite] reset [--storage json|sqlite] help Options: --storage <adapter> Storage: json (default) or sqlite --embedding <provider> Embedding: keyword (default) or openai --db-path <path> Custom db path Examples: node dist/index.js ingest --file examples/sample-note.md --title "Sample" node dist/index.js query --q "knowledge connector" node dist/index.js status --storage sqlite node dist/index.js reset --storage sqlite`); } async function main() { const [, , command, ...rest] = process.argv; const args = parseArgs(rest); const storageType = args.storage || 'json'; const embeddingType = args.embedding || 'keyword'; const dbPathArg = args['db-path'] || undefined; let adapter = null; try { if (!command || command === 'help' || args.help) { printHelp(); return; } try { const adaptersDir = IS_DIST ? 'storage-adapters' : 'src/storage-adapters'; if (storageType === 'sqlite') { const { SqliteStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'SqliteStorageAdapter.js')); adapter = new SqliteStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } else { const { JsonStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'JsonStorageAdapter.js')); adapter = new JsonStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } } catch (e) { if (e.message.includes('sqlite3 not installed')) { console.error('[LinkMind] sqlite3 not installed. Run: npm install sqlite3'); } else { console.error(`[LinkMind] Adapter load error: ${e.message}`); } process.exitCode = 1; return; } let embeddingProvider = null; try { const providersDir = IS_DIST ? 'embedding-providers' : 'src/embedding-providers'; const { MockProvider, OpenAICompatibleProvider } = require(path.join(THIS_DIR, providersDir, 'index.js')); embeddingProvider = embeddingType === 'openai' ? new OpenAICompatibleProvider({ baseURL: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1', apiKey: process.env.OPENAI_API_KEY || '', model: process.env.OPENAI_MODEL || 'text-embedding-3-small' }) : new MockProvider(); } catch { embeddingProvider = null; } const now = () => new Date().toISOString(); if (command === 'ingest') { if (!args.file) throw new Error('--file is required for ingest'); const full = path.resolve(process.cwd(), args.file); if (!fs.existsSync(full)) throw new Error(`File not found: ${full}`); const text = fs.readFileSync(full, 'utf8'); const db = await adapter.load(); const doc = { id: stableId('doc', `${full}:${args.title || path.basename(full)}`), type: 'document', title: args.title || path.basename(full), sourceType: args.sourceType || 'file', sourceUri: full, importedAt: now(), status: 'active', text }; upsertDocument(db, doc); const stats = buildForDocument(db, doc); await adapter.save(db); console.log(JSON.stringify({ documentId: doc.id, title: doc.title, ...stats }, null, 2)); return; } if (command === 'query') { if (!args.q && !args.query) throw new Error('--q is required for query'); const result = await queryWithEmbedding(adapter, embeddingProvider, args.q || args.query, { limit: args.limit }); console.log(JSON.stringify(result, null, 2)); return; } if (command === 'status') { const db = await adapter.load(); console.log(JSON.stringify({ documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, adapter: storageType, embedding: embeddingType }, null, 2)); return; } if (command === 'reset') { await adapter.clear(); console.log(JSON.stringify({ ok: true, adapter: storageType }, null, 2)); return; } throw new Error(`Unknown command: ${command}`); } catch (error) { console.error(`[LinkMind] ${error.message}`); process.exitCode = 1; } finally { if (adapter) await adapter.close(); } } if (require.main === module) { main(); } module.exports = { buildForDocument, extractConcepts, splitParagraphs, normalizeConcept, stableId, queryWithEmbedding, createEmptyDb }; FILE:src/providers/base.js /** * EmbeddingProvider 接口契约 * 所有 provider 必须实现以下方法： * embed(text) -> Promise<number[]> 单文本向量 * embedBatch(texts) -> Promise<number[][]> 批量向量 * similarity(a, b) -> number 余弦相似度 * dimensions() -> number 向量维度 */ class EmbeddingProvider { async embed(text) { throw new Error('Not implemented: embed(text)'); } async embedBatch(texts) { throw new Error('Not implemented: embedBatch(texts)'); } similarity(a, b) { if (a.length !== b.length) throw new Error('Vector dimensions mismatch'); let dot = 0, normA = 0, normB = 0; for (let i = 0; i < a.length; i++) { dot += a[i] * b[i]; normA += a[i] * a[i]; normB += b[i] * b[i]; } return dot / (Math.sqrt(normA) * Math.sqrt(normB) + 1e-10); } dimensions() { throw new Error('Not implemented: dimensions()'); } } module.exports = { EmbeddingProvider }; FILE:src/providers/openai.js /** * KeywordEmbeddingProvider * MVP 版本：基于词项匹配的伪嵌入，用于无外部 API 依赖场景。 * 后续可替换为真正的 OpenAI/text-embedding-3* provider。 */ const { EmbeddingProvider } = require('./base'); class KeywordEmbeddingProvider extends EmbeddingProvider { constructor(opts = {}) { super(); this._dims = opts.dimensions || 384; // 与 text-embedding-3-small 常用维度兼容 } dimensions() { return this._dims; } /** * 简单词袋伪嵌入：基于 term frequency 构建稀疏向量 * 兼容性接口，实际向量召回走 retriever 的关键词逻辑 */ async embed(text) { const terms = (text || '') .toLowerCase() .replace(/[^\w\s\u4e00-\u9fa5]/g, ' ') .split(/\s+/) .filter(Boolean); // 生成确定性伪向量（基于 term hash） const vec = new Array(this._dims).fill(0); for (const term of terms) { const hash = this._hash(term); for (let i = 0; i < terms.length; i++) { const idx = (hash + i * 31) % this._dims; vec[idx] += 1; } } // L2 normalize const norm = Math.sqrt(vec.reduce((s, v) => s + v * v, 0)); return norm > 0 ? vec.map((v) => v / norm) : vec; } async embedBatch(texts) { return Promise.all(texts.map((t) => this.embed(t))); } _hash(s) { let h = 0; for (let i = 0; i < s.length; i++) { h = ((h << 5) - h + s.charCodeAt(i)) | 0; } return Math.abs(h); } } /** * OpenAiEmbeddingProvider * OpenAI-compatible API 调用桩，后续填入真实 API key 和 endpoint 即可启用。 */ class OpenAiEmbeddingProvider extends EmbeddingProvider { constructor(opts = {}) { super(); this.apiKey = opts.apiKey || process.env.OPENAI_API_KEY; this.endpoint = opts.endpoint || 'https://api.openai.com/v1/embeddings'; this.model = opts.model || 'text-embedding-3-small'; this.dims = opts.dimensions || 1536; } dimensions() { return this.dims; } async embed(text) { if (!this.apiKey) { throw new Error('OPENAI_API_KEY not set. Use --embedding=keyword or set OPENAI_API_KEY.'); } const res = await fetch(this.endpoint, { method: 'POST', headers: { 'Authorization': `Bearer ${this.apiKey}`, 'Content-Type': 'application/json' }, body: JSON.stringify({ model: this.model, input: text, dimensions: this.dims }) }); if (!res.ok) { const err = await res.text(); throw new Error(`OpenAI embedding error ${res.status}: ${err}`); } const json = await res.json(); return json.data[0].embedding; } async embedBatch(texts) { if (!this.apiKey) { throw new Error('OPENAI_API_KEY not set. Use --embedding=keyword or set OPENAI_API_KEY.'); } const res = await fetch(this.endpoint, { method: 'POST', headers: { 'Authorization': `Bearer ${this.apiKey}`, 'Content-Type': 'application/json' }, body: JSON.stringify({ model: this.model, input: texts, dimensions: this.dims }) }); if (!res.ok) { const err = await res.text(); throw new Error(`OpenAI embedding error ${res.status}: ${err}`); } const json = await res.json(); return json.data.map((item) => item.embedding); } } module.exports = { KeywordEmbeddingProvider, OpenAiEmbeddingProvider }; FILE:src/retriever.js /** * retriever.js * 关键词召回 + 向量相似度召回双层检索。 * ranker：余弦相似度 * 最终结果 = 关键词召回 ∪ 向量召回 → 去重排序 */ const { normalizeConcept, STOPWORDS } = require('./utils/nlp'); /** * @typedef {Object} RetrievalResult * @property {string} fragmentId * @property {string} documentId * @property {string} documentTitle * @property {number} score * @property {string} text * @property {'keyword'|'vector'|'hybrid'} source */ /** * 计算余弦相似度 * @param {number[]} a * @param {number[]} b */ function cosineSimilarity(a, b) { if (a.length !== b.length) return 0; let dot = 0, na = 0, nb = 0; for (let i = 0; i < a.length; i += 1) { dot += a[i] * b[i]; na += a[i] * a[i]; nb += b[i] * b[i]; } const denom = Math.sqrt(na) * Math.sqrt(nb); return denom === 0 ? 0 : dot / denom; } /** * 关键词召回（从 fragment 列表） */ function keywordSearch(fragments, query, limit = 20) { const terms = normalizeConcept(query) .split(' ') .filter(Boolean) .filter((t) => !STOPWORDS.has(t)); if (terms.length === 0) return []; return fragments .map((frag) => { const textNorm = normalizeConcept(frag.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if ((frag.conceptNames || []).includes(term)) score += 5; } return { frag, score }; }) .filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.frag.index - b.frag.index) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.score, text: item.frag.text, source: 'keyword' })); } /** * 向量相似度召回 * @param {object[]} fragments - 带 documentTitle * @param {string} query * @param {import('./embedding-providers/EmbeddingProvider')} embeddingProvider * @param {number} limit */ async function vectorSearch(fragments, query, embeddingProvider, limit = 20) { if (!embeddingProvider || fragments.length === 0) return []; const texts = fragments.map((f) => f.text); const [queryVec, fragVecs] = await Promise.all([ embeddingProvider.embed([query]), embeddingProvider.embed(texts) ]); const qv = queryVec[0]; return fragments .map((frag, i) => ({ frag, sim: cosineSimilarity(qv, fragVecs[i] || []) })) .filter((item) => item.sim > 0) .sort((a, b) => b.sim - a.sim) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.sim, text: item.frag.text, source: 'vector' })); } /** * 合并关键词召回 + 向量召回，去重并排序 * @param {RetrievalResult[]} keywordResults * @param {RetrievalResult[]} vectorResults * @param {number} limit */ function mergeResults(keywordResults, vectorResults, limit = 10) { const seen = new Map(); for (const r of [...keywordResults, ...vectorResults]) { if (!seen.has(r.fragmentId)) { seen.set(r.fragmentId, { ...r }); } else { // 取最高分 const existing = seen.get(r.fragmentId); existing.score = Math.max(existing.score, r.score); existing.source = existing.source === r.source ? existing.source : 'hybrid'; } } return [...seen.values()] .sort((a, b) => b.score - a.score) .slice(0, limit); } /** * 主检索入口 * @param {object} options * @param {object[]} options.fragments - fragment 列表，需含 documentTitle * @param {string} options.query * @param {import('./embedding-providers/EmbeddingProvider')} [options.embeddingProvider] * @param {number} [options.limit] */ async function retrieve({ fragments, query, embeddingProvider = null, limit = 10 }) { const kw = keywordSearch(fragments, query, limit * 2); const vec = embeddingProvider ? await vectorSearch(fragments, query, embeddingProvider, limit * 2) : []; return mergeResults(kw, vec, limit); } module.exports = { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve }; FILE:src/storage-adapters/JsonStorageAdapter.js /** * JsonStorageAdapter * 基于本地 JSON 文件的存储实现，保持向后兼容。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./StorageAdapter'); const ROOT = path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } class JsonStorageAdapter extends StorageAdapter { constructor() { super(); this._db = null; } _loadDb() { ensureDir(DATA_DIR); if (!fs.existsSync(DB_PATH)) { const empty = this._emptyDb(); this._saveDb(empty); return empty; } return JSON.parse(fs.readFileSync(DB_PATH, 'utf8')); } _saveDb(db) { db.updatedAt = now(); ensureDir(DATA_DIR); fs.writeFileSync(DB_PATH, JSON.stringify(db, null, 2), 'utf8'); } _emptyDb() { return { version: 1, createdAt: now(), updatedAt: now(), documents: [], fragments: [], concepts: [], links: [] }; } async init() { this._db = this._loadDb(); } // --- Document --- async saveDocument(doc) { const db = this._loadDb(); const idx = db.documents.findIndex((d) => d.id === doc.id); if (idx >= 0) db.documents[idx] = doc; else db.documents.push(doc); this._saveDb(db); return doc; } async getDocument(id) { const db = this._loadDb(); return db.documents.find((d) => d.id === id) || null; } async listDocuments() { const db = this._loadDb(); return db.documents; } async deleteDocument(id) { const db = this._loadDb(); db.documents = db.documents.filter((d) => d.id !== id); this._saveDb(db); } // --- Fragment --- async saveFragment(frag) { const db = this._loadDb(); const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); this._saveDb(db); return frag; } async getFragment(id) { const db = this._loadDb(); return db.fragments.find((f) => f.id === id) || null; } async listFragments(documentId) { const db = this._loadDb(); return db.fragments.filter((f) => f.documentId === documentId); } async deleteFragmentsByDocument(documentId) { const db = this._loadDb(); db.fragments = db.fragments.filter((f) => f.documentId !== documentId); this._saveDb(db); } async saveFragments(fragments) { const db = this._loadDb(); for (const frag of fragments) { const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); } this._saveDb(db); } // --- Concept --- async saveConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async getConcept(id) { const db = this._loadDb(); return db.concepts.find((c) => c.id === id) || null; } async listConcepts() { const db = this._loadDb(); return db.concepts; } async upsertConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async saveConcepts(concepts) { const db = this._loadDb(); for (const c of concepts) { const idx = db.concepts.findIndex((x) => x.id === c.id); if (idx >= 0) db.concepts[idx] = c; else db.concepts.push(c); } this._saveDb(db); } // --- Link --- async saveLink(link) { const db = this._loadDb(); const idx = db.links.findIndex((l) => l.id === link.id); if (idx >= 0) db.links[idx] = link; else db.links.push(link); this._saveDb(db); return link; } async getLinks(fromId, toId) { const db = this._loadDb(); return db.links.filter( (l) => (fromId ? l.fromId === fromId : true) && (toId ? l.toId === toId : true) ); } async deleteLinksByDocument(documentId) { const db = this._loadDb(); db.links = db.links.filter((l) => l.documentId !== documentId); this._saveDb(db); } async saveLinks(links) { const db = this._loadDb(); for (const l of links) { const idx = db.links.findIndex((x) => x.id === l.id); if (idx >= 0) db.links[idx] = l; else db.links.push(l); } this._saveDb(db); } // --- Full DB operations (for CLI compat) --- async load() { return this._loadDb(); } async save(db) { this._saveDb(db); } async close() { // no-op for JSON } // --- Workspace --- async clear() { this._saveDb(this._emptyDb()); } // --- Stats --- getStats() { const db = this._loadDb(); return { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, dbPath: DB_PATH }; } } module.exports = { JsonStorageAdapter }; FILE:src/storage-adapters/SqliteStorageAdapter.js /** * SqliteStorageAdapter * 基于 better-sqlite3 的 SQLite 存储实现。 * 建表语句 + 所有 StorageAdapter 方法的完整实现。 */ const { StorageAdapter } = require('./StorageAdapter'); let sqlite3 = null; try { sqlite3 = require('better-sqlite3'); } catch { // better-sqlite3 not installed — adapter will throw on init() } const ROOT = path => require('path').resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'db.sqlite'); function ensureDir(dir) { require('fs').mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, sourceType TEXT, sourceUri TEXT, importedAt TEXT, status TEXT DEFAULT 'active', text TEXT, createdAt TEXT, updatedAt TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', documentId TEXT, "index" INTEGER, text TEXT, summary TEXT, conceptNames TEXT, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalizedName TEXT UNIQUE, salience REAL, createdAt TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, fromId TEXT, fromType TEXT, toId TEXT, toType TEXT, documentId TEXT, score REAL, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS fragment_vectors ( fragmentId TEXT PRIMARY KEY, vector BLOB, updatedAt TEXT, FOREIGN KEY (fragmentId) REFERENCES fragments(id) ); CREATE INDEX IF NOT EXISTS idx_fragments_documentId ON fragments(documentId); CREATE INDEX IF NOT EXISTS idx_links_fromId ON links(fromId); CREATE INDEX IF NOT EXISTS idx_links_toId ON links(toId); CREATE INDEX IF NOT EXISTS idx_links_documentId ON links(documentId); CREATE INDEX IF NOT EXISTS idx_concepts_normalizedName ON concepts(normalizedName); `; class SqliteStorageAdapter extends StorageAdapter { constructor({ dbPath } = {}) { super(); this._dbPath = dbPath || DB_PATH; this._db = null; } async init() { if (!sqlite3) { throw new Error('better-sqlite3 is not installed. Run: npm install better-sqlite3'); } ensureDir(require('path').dirname(this._dbPath)); this._db = sqlite3(this._dbPath); this._db.pragma('journal_mode = WAL'); this._db.exec(SCHEMA); } _run(sql, params = []) { try { return this._db.prepare(sql).run(...params); } catch (e) { throw new Error(`SQLite run error: ${e.message} | sql: ${sql}`); } } _all(sql, params = []) { try { return this._db.prepare(sql).all(...params); } catch (e) { throw new Error(`SQLite all error: ${e.message} | sql: ${sql}`); } } _get(sql, params = []) { try { return this._db.prepare(sql).get(...params); } catch (e) { throw new Error(`SQLite get error: ${e.message} | sql: ${sql}`); } } // --- Document --- async saveDocument(doc) { this._run( `INSERT OR REPLACE INTO documents (id,type,title,sourceType,sourceUri,importedAt,status,text,createdAt,updatedAt) VALUES (?,?,?,?,?,?,?,?,?,?)`, [doc.id, doc.type || 'document', doc.title || '', doc.sourceType || '', doc.sourceUri || '', doc.importedAt || now(), doc.status || 'active', doc.text || '', doc.createdAt || now(), now()] ); return doc; } async getDocument(id) { const row = this._get('SELECT * FROM documents WHERE id = ?', [id]); return row ? this._mapDoc(row) : null; } async listDocuments() { return this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); } async deleteDocument(id) { this._run('DELETE FROM documents WHERE id = ?', [id]); } // --- Fragment --- async saveFragment(frag) { this._run( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)`, [frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()] ); return frag; } async getFragment(id) { const row = this._get('SELECT * FROM fragments WHERE id = ?', [id]); return row ? this._mapFrag(row) : null; } async listFragments(documentId) { return this._all('SELECT * FROM fragments WHERE documentId = ? ORDER BY "index" ASC', [documentId]) .map(this._mapFrag); } async deleteFragmentsByDocument(documentId) { this._run('DELETE FROM fragments WHERE documentId = ?', [documentId]); } async saveFragments(fragments) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const frag of items) { stmt.run(frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()); } }); insertMany(fragments); return fragments; } // --- Concept --- async saveConcept(concept) { this._run( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)`, [concept.id, 'concept', concept.name, concept.normalizedName, concept.salience || 0, concept.createdAt || now()] ); return concept; } async getConcept(id) { const row = this._get('SELECT * FROM concepts WHERE id = ?', [id]); return row ? this._mapConcept(row) : null; } async listConcepts() { return this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); } async upsertConcept(concept) { return this.saveConcept(concept); } async saveConcepts(concepts) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const c of items) { stmt.run(c.id, 'concept', c.name, c.normalizedName, c.salience || 0, c.createdAt || now()); } }); insertMany(concepts); return concepts; } // --- Link --- async saveLink(link) { this._run( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)`, [link.id, link.type, link.fromId, link.fromType, link.toId, link.toType, link.documentId, link.score || 0, link.createdAt || now()] ); return link; } async getLinks(fromId, toId) { let sql = 'SELECT * FROM links WHERE 1=1'; const params = []; if (fromId) { sql += ' AND fromId = ?'; params.push(fromId); } if (toId) { sql += ' AND toId = ?'; params.push(toId); } return this._all(sql, params); } async deleteLinksByDocument(documentId) { this._run('DELETE FROM links WHERE documentId = ?', [documentId]); } async saveLinks(links) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const l of items) { stmt.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score || 0, l.createdAt || now()); } }); insertMany(links); return links; } // --- Vector --- async saveFragmentVector(fragmentId, vector) { const buf = Buffer.from(JSON.stringify(vector)); this._run( `INSERT OR REPLACE INTO fragment_vectors (fragmentId,vector,updatedAt) VALUES (?,?,?)`, [fragmentId, buf, now()] ); } async getFragmentVector(fragmentId) { const row = this._get('SELECT vector FROM fragment_vectors WHERE fragmentId = ?', [fragmentId]); if (!row) return null; return JSON.parse(row.vector); } async listFragmentVectors() { return this._all('SELECT fragmentId, vector FROM fragment_vectors').map((r) => ({ fragmentId: r.fragmentId, vector: JSON.parse(r.vector) })); } // --- Full DB operations (for CLI compat) --- async load() { const docs = this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); const frags = this._all('SELECT * FROM fragments ORDER BY "index" ASC').map(this._mapFrag); const concepts = this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); const links = this._all('SELECT * FROM links'); const updatedAt = this._get('SELECT MAX(updatedAt) as t FROM documents UNION ALL SELECT MAX(updatedAt) as t FROM fragments')?.t || new Date().toISOString(); return { version: 1, documents: docs, fragments: frags, concepts, links, updatedAt }; } async save(db) { await this.clear(); if (db.documents) for (const d of db.documents) await this.saveDocument(d); if (db.fragments) for (const f of db.fragments) await this.saveFragment(f); if (db.concepts) for (const c of db.concepts) await this.saveConcept(c); if (db.links) for (const l of db.links) await this.saveLink(l); } async close() { if (this._db) { this._db.close(); this._db = null; } } // --- Workspace --- async clear() { this._run('DELETE FROM fragment_vectors'); this._run('DELETE FROM links'); this._run('DELETE FROM fragments'); this._run('DELETE FROM concepts'); this._run('DELETE FROM documents'); } getStats() { return { documents: this._get('SELECT COUNT(*) as n FROM documents')?.n || 0, fragments: this._get('SELECT COUNT(*) as n FROM fragments')?.n || 0, concepts: this._get('SELECT COUNT(*) as n FROM concepts')?.n || 0, links: this._get('SELECT COUNT(*) as n FROM links')?.n || 0, dbPath: this._dbPath }; } // --- Mappers --- _mapDoc(row) { return { ...row }; } _mapFrag(row) { return { ...row, conceptNames: JSON.parse(row.conceptNames || '[]') }; } _mapConcept(row) { return { ...row }; } } module.exports = { SqliteStorageAdapter }; FILE:src/storage-adapters/StorageAdapter.js /** * StorageAdapter Interface * 所有 storage adapter 必须实现此接口契约。 */ class StorageAdapter { /** * @returns {Promise<void>} */ async init() { throw new Error('Not implemented'); } // --- Document --- async saveDocument(doc) { throw new Error('Not implemented'); } async getDocument(id) { throw new Error('Not implemented'); } async listDocuments() { throw new Error('Not implemented'); } async deleteDocument(id) { throw new Error('Not implemented'); } // --- Fragment --- async saveFragment(frag) { throw new Error('Not implemented'); } async getFragment(id) { throw new Error('Not implemented'); } async listFragments(documentId) { throw new Error('Not implemented'); } async deleteFragmentsByDocument(documentId) { throw new Error('Not implemented'); } // --- Concept --- async saveConcept(concept) { throw new Error('Not implemented'); } async getConcept(id) { throw new Error('Not implemented'); } async listConcepts() { throw new Error('Not implemented'); } async upsertConcept(concept) { throw new Error('Not implemented'); } // --- Link --- async saveLink(link) { throw new Error('Not implemented'); } async getLinks(fromId, toId) { throw new Error('Not implemented'); } async deleteLinksByDocument(documentId) { throw new Error('Not implemented'); } // --- Bulk --- async saveFragments(fragments) { throw new Error('Not implemented'); } async saveConcepts(concepts) { throw new Error('Not implemented'); } async saveLinks(links) { throw new Error('Not implemented'); } // --- Workspace --- async clear() { throw new Error('Not implemented'); } } module.exports = { StorageAdapter }; FILE:src/storage-adapters/index.js /** * storage-adapters/index.js * Factory: 根据配置返回对应的 storage adapter 实例。 */ const { JsonStorageAdapter } = require('./JsonStorageAdapter'); const { SqliteStorageAdapter } = require('./SqliteStorageAdapter'); function createStorageAdapter(type = 'json', options = {}) { switch (type) { case 'json': return new JsonStorageAdapter(options); case 'sqlite': return new SqliteStorageAdapter(options); default: throw new Error(`Unknown storage adapter type: ${type}. Use 'json' or 'sqlite'.`); } } module.exports = { createStorageAdapter, JsonStorageAdapter, SqliteStorageAdapter }; FILE:src/utils/nlp.js /** * nlp.js - 文本规范化工具 */ const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were','can','could','should','about','what','when','where','which','while','than','then','them','they','their','there','here','also','more','most','some','such','using','used','use','make','made','over','under','very','just','only','each','been','being','does','did','done','how','why','our','you','its','his','her','she','him','who','has','had','but','too','via','per','one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及','进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有','对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为','实现','相关','用于' ]); function normalizeConcept(text) { if (!text) return ''; return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } module.exports = { normalizeConcept, STOPWORDS }; FILE:tests/smoke-test.js #!/usr/bin/env node /** * LinkMind Smoke Test - Phase 2 * 覆盖: storage adapters + embedding providers + retriever */ import path from 'path'; import { fileURLToPath } from 'url'; import { execSync } from 'child_process'; import { pathToFileURL } from 'url'; const __filename = fileURLToPath(import.meta.url); const __dirname = path.dirname(__filename); const SKILL_ROOT = path.resolve(__dirname, '..'); process.chdir(SKILL_ROOT); function run(cmd) { console.log(`\n$ ${cmd}`); return JSON.parse(execSync(cmd, { encoding: 'utf8' })); } function check(label, cond) { if (cond) { console.log(` ✓ ${label}`); } else { console.error(` ✗ FAIL: ${label}`); process.exitCode = 1; } } // Import phase-2 modules via dynamic import const srcDir = path.join(SKILL_ROOT, 'src'); async function runTests() { console.log('=== LinkMind Smoke Test (Phase 2) ===\n'); // Phase 1: CLI commands console.log('--- Phase 1: CLI Commands ---'); const reset = run('node dist/index.js reset'); check('reset returns ok', reset.ok === true); const empty = run('node dist/index.js status'); check('empty workspace has 0 documents', empty.documents === 0); const ingest = run('node dist/index.js ingest --file examples/sample-note.md --title "Sample Note"'); check('ingest returns documentId', !!ingest.documentId); check('ingest creates fragments', ingest.fragmentsCreated >= 1); const after = run('node dist/index.js status'); check('has 1 document', after.documents === 1); check('has fragments', after.fragments >= 1); const q1 = run('node dist/index.js query --q "knowledge"'); check('query "knowledge" finds evidence', q1.evidence && q1.evidence.length >= 1); check('query "knowledge" returns answer', !!q1.answer); const q2 = run('node dist/index.js query --q "xyznonexistentterm12345"'); check('query no-match returns empty evidence', q2.evidence && q2.evidence.length === 0); const q3 = run('node dist/index.js query --q "knowledge" --limit 1'); check('query with limit respects limit', q3.evidence && q3.evidence.length <= 1); // Phase 2: Storage Adapters console.log('\n--- Phase 2: Storage Adapters ---'); const JsonStorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/JsonStorageAdapter.js')).href); const StorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/StorageAdapter.js')).href); const { JsonStorageAdapter } = JsonStorageAdapterMod; const { StorageAdapter } = StorageAdapterMod; const jsAdapter = new JsonStorageAdapter(); await jsAdapter.init(); await jsAdapter.clear(); check('JsonStorageAdapter.init() ok', true); const testDoc = { id: 'doc_test1', type: 'document', title: 'Test Doc', sourceType: 'note', sourceUri: '/test/path.md', importedAt: new Date().toISOString(), status: 'active', text: 'This is a test document for storage adapter testing.' }; await jsAdapter.saveDocument(testDoc); const retrieved = await jsAdapter.getDocument('doc_test1'); check('JsonStorageAdapter.saveDocument + getDocument', retrieved && retrieved.title === 'Test Doc'); const docs = await jsAdapter.listDocuments(); check('JsonStorageAdapter.listDocuments', docs && docs.length >= 1); const testFrag = { id: 'frag_test1', type: 'fragment', documentId: 'doc_test1', index: 0, text: 'This is a test fragment.', summary: 'This is a test', conceptNames: ['test'], createdAt: new Date().toISOString() }; await jsAdapter.saveFragment(testFrag); const frag = await jsAdapter.getFragment('frag_test1'); check('JsonStorageAdapter.saveFragment + getFragment', frag && frag.text.includes('test fragment')); const frags = await jsAdapter.listFragments('doc_test1'); check('JsonStorageAdapter.listFragments', frags && frags.length >= 1); const testConcept = { id: 'concept_test1', type: 'concept', name: 'TestConcept', normalizedName: 'testconcept', salience: 0.5, createdAt: new Date().toISOString() }; await jsAdapter.saveConcept(testConcept); const concepts = await jsAdapter.listConcepts(); check('JsonStorageAdapter.listConcepts', concepts && concepts.some(c => c.normalizedName === 'testconcept')); const testLink = { id: 'link_test1', type: 'mentions', fromId: 'frag_test1', fromType: 'fragment', toId: 'concept_test1', toType: 'concept', documentId: 'doc_test1', score: 1, createdAt: new Date().toISOString() }; await jsAdapter.saveLink(testLink); const links = await jsAdapter.getLinks('frag_test1', null); check('JsonStorageAdapter.saveLink + getLinks', links && links.some(l => l.id === 'link_test1')); // test bulk save await jsAdapter.clear(); await jsAdapter.saveDocument(testDoc); await jsAdapter.saveFragments([testFrag]); await jsAdapter.saveConcepts([testConcept]); await jsAdapter.saveLinks([testLink]); const afterBulk = await jsAdapter.listFragments('doc_test1'); check('JsonStorageAdapter.saveFragments bulk', afterBulk && afterBulk.length >= 1); await jsAdapter.clear(); const emptyAfterClear = await jsAdapter.listDocuments(); check('JsonStorageAdapter.clear()', emptyAfterClear.length === 0); // StorageAdapter interface check('StorageAdapter is a class', typeof StorageAdapter === 'function'); // Phase 2: Embedding Providers console.log('\n--- Phase 2: Embedding Providers ---'); const MockProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/MockProvider.js')).href); const OpenAICompatibleProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/OpenAICompatibleProvider.js')).href); const EmbeddingProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/EmbeddingProvider.js')).href); const { MockProvider } = MockProviderMod; const { OpenAICompatibleProvider } = OpenAICompatibleProviderMod; const { EmbeddingProvider } = EmbeddingProviderMod; const mock = new MockProvider({ dimension: 128 }); check('MockProvider.dimension', mock.dimension === 128); check('MockProvider.name', mock.name === 'mock'); const [vec] = await mock.embed(['hello world']); check('MockProvider.embed returns vector', Array.isArray(vec) && vec.length === 128); check('MockProvider.vector is L2-normalized', Math.abs(vec.reduce((s, v) => s + v * v, 0) - 1) < 0.01); const [vec2] = await mock.embed(['hello world']); check('MockProvider caches results', vec2 === vec); const differentText = await mock.embed(['different text']); check('MockProvider different text yields different vector', differentText[0] !== vec); const openai = new OpenAICompatibleProvider({ baseURL: 'https://api.example.com/v1', apiKey: 'test-key', model: 'test-model', dimension: 256 }); check('OpenAICompatibleProvider.dimension', openai.dimension === 256); check('OpenAICompatibleProvider.name includes model', openai.name.includes('test-model')); check('OpenAICompatibleProvider.name includes baseURL hint', openai.name.includes('openai-compatible')); check('EmbeddingProvider is a class', typeof EmbeddingProvider === 'function'); // Phase 2: Retriever console.log('\n--- Phase 2: Retriever ---'); const retrieverMod = await import(pathToFileURL(path.join(srcDir, 'retriever.js')).href); const { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve } = retrieverMod; check('cosineSimilarity identical vectors = 1', Math.abs(cosineSimilarity([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]) - 1) < 0.0001); check('cosineSimilarity orthogonal = 0', Math.abs(cosineSimilarity([1, 0, 0], [0, 1, 0])) < 0.0001); const testFrags = [ { id: 'f1', documentId: 'd1', documentTitle: 'Doc 1', index: 0, text: 'knowledge graph is useful', conceptNames: ['knowledge', 'graph'] }, { id: 'f2', documentId: 'd1', documentTitle: 'Doc 1', index: 1, text: 'machine learning and AI', conceptNames: ['machine', 'learning'] }, { id: 'f3', documentId: 'd2', documentTitle: 'Doc 2', index: 0, text: 'knowledge base systems', conceptNames: ['knowledge', 'base'] } ]; const kw = keywordSearch(testFrags, 'knowledge'); check('keywordSearch finds knowledge fragments', kw.length >= 2); check('keywordSearch assigns score > 0', kw.every(r => r.score > 0)); check('keywordSearch source=keyword', kw.every(r => r.source === 'keyword')); const vecResults = await vectorSearch(testFrags, 'knowledge graph', mock); check('vectorSearch returns results', Array.isArray(vecResults)); check('vectorSearch source=vector', vecResults.every(r => r.source === 'vector')); const merged = mergeResults(kw, vecResults, 5); check('mergeResults deduplicates', merged.length <= kw.length + vecResults.length); check('mergeResults returns array', Array.isArray(merged)); check('mergeResults items have score', merged.every(r => typeof r.score === 'number')); const kwOnly = await retrieve({ fragments: testFrags, query: 'knowledge', limit: 5 }); check('retrieve keyword-only works', kwOnly.length >= 1); const hybrid = await retrieve({ fragments: testFrags, query: 'knowledge', embeddingProvider: mock, limit: 5 }); check('retrieve hybrid works', hybrid.length >= 1); check('retrieve hybrid may include vector source', hybrid.some(r => r.source === 'vector' || r.source === 'hybrid')); console.log('\n=== Smoke Test Complete ==='); if (process.exitCode === 1) { console.log('RESULT: FAILED'); process.exit(1); } else { console.log('RESULT: PASSED'); } } runTests().catch((err) => { console.error('Test error:', err); process.exit(1); }); </article></body></html>

--- name: linkmind description: LinkMind 知识连接引擎 Phase 2 - 本地化知识中枢 CLI 工具，支持 storage adapter 抽象层和 OpenAI-compatible embedding provider。 --- # LinkMind 知识连接引擎 (Phase 2) LinkMind 是一个本地化知识连接引擎，将异构内容沉淀为统一知识单元，建立可解释的节点连接网络，并支持带证据的查询回答。 ## Phase 2 新增功能 - **Storage Adapter 抽象层**：`JsonStorageAdapter`（默认，保持兼容）+ `SqliteStorageAdapter`（基于 better-sqlite3） - **Embedding Provider 抽象层**：`MockProvider`（离线测试）+ `OpenAICompatibleProvider`（接入 OpenAI/vLLM/Ollama） - **向量相似度召回**：余弦相似度 ranker，与关键词召回结果合并去重 - **渐进升级**：Phase 1 JSON 方案完全兼容，无需迁移 ## 核心功能 - **摄入 (ingest)**: 读取本地文本文件，切分为语义友好的段落片段，抽取概念节点，建立片段-概念、片段-片段邻接连接 - **查询 (query)**: 支持关键词/概念查询 + 向量相似度召回，返回答案摘要、证据片段列表、相关概念节点、统计概览 - **状态 (status)**: 查看当前工作空间统计信息 - **重置 (reset)**: 清空工作空间 ## 架构模块 | 模块 | 职责 | 状态 | |------|------|------| | `storage-adapters/` | StorageAdapter 接口 + JSON/SQLite 双实现 | ✅ Phase 2 | | `embedding-providers/` | EmbeddingProvider 接口 + Mock/OpenAI 双实现 | ✅ Phase 2 | | `retriever` | 关键词召回 + 向量召回双层检索，余弦相似度 ranker | ✅ Phase 2 | | `unit-builder` | 文档切分为 fragment，抽取 fragment.conceptNames | ✅ MVP | | `link-builder` | fragment↔concept、fragment↔fragment 邻接连接 | ✅ MVP | | `answer-composer` | 组装 answer + evidence + relatedConcepts | ✅ MVP | | `ingest-normalizer` | 原始文件标准化为 Document | ✅ MVP | | `guardrails` | 空查询、空结果边界处理 | ✅ MVP | ## 技术栈 - 零外部依赖（pure Node.js built-ins） - 本地 JSON 文件存储（`data/workspace.json`） - 可选 SQLite 存储（`better-sqlite3`） - 数据模型: Document → Fragment → Concept + LinkEdge ## 安装与运行 ```bash cd skills/linkmind # 构建 npm run build # 运行 CLI node dist/index.js --help ``` ## 使用方法 ### 摄入文档 ```bash node dist/index.js ingest --file examples/sample-note.md --title "我的笔记" --sourceType note ``` ### 查询知识 ```bash node dist/index.js query --q "知识连接" node dist/index.js query --q "knowledge" --limit 5 ``` ### 查看状态 ```bash node dist/index.js status ``` ### 重置工作空间 ```bash node dist/index.js reset ``` ## 存储适配器 (Storage Adapter) ### JsonStorageAdapter（默认） - 数据存储在 `data/workspace.json` - 完全向后兼容 Phase 1 - 零配置开箱即用 ### SqliteStorageAdapter - 需要安装：`npm install better-sqlite3` - 数据存储在 `data/db.sqlite` - 支持事务批量写入，性能更高 ```js import { createStorageAdapter } from './src/storage-adapters/index.js'; const adapter = createStorageAdapter('sqlite'); await adapter.init(); await adapter.saveDocument({ id: 'doc_1', title: 'Test', ... }); ``` ## Embedding Provider ### MockProvider（默认，离线可用） ```js import { createEmbeddingProvider } from './src/embedding-providers/index.js'; const provider = createEmbeddingProvider('mock', { dimension: 1536 }); const [vec] = await provider.embed(['hello world']); ``` ### OpenAICompatibleProvider ```js const provider = createEmbeddingProvider('openai', { baseURL: 'https://api.openai.com/v1', // 或 vLLM/Ollama 地址 apiKey: 'sk-xxx', model: 'text-embedding-3-small', dimension: 1536 }); const vectors = await provider.embed(['hello', 'world']); ``` ## 检索流程 (Retriever) 查询时使用双层召回： 1. **关键词召回**（keyword）：基于 `normalizeConcept` + concept 名称匹配 2. **向量召回**（vector）：余弦相似度（可选，需配置 embedding provider） 3. **合并去重**：取最高分，结果标记 `source: 'keyword' | 'vector' | 'hybrid'` ```js import { retrieve } from './src/retriever.js'; const results = await retrieve({ fragments, query: 'knowledge graph', embeddingProvider: mockProvider, // 传 null 则仅关键词召回 limit: 10 }); // results: [{ fragmentId, documentId, documentTitle, score, text, source }] ``` ## 数据模型 ### Document ```json { "id": "doc_xxxxx", "type": "document", "title": "我的笔记", "sourceType": "note", "sourceUri": "/path/to/file.md", "importedAt": "2026-04-04T...", "status": "active" } ``` ### Fragment ```json { "id": "frag_xxxxx", "type": "fragment", "documentId": "doc_xxxxx", "index": 0, "text": "LinkMind 是...", "summary": "LinkMind 是...", "conceptNames": ["linkmind", "知识连接", "知识中枢"] } ``` ### Concept ```json { "id": "concept_xxxxx", "type": "concept", "name": "LinkMind", "normalizedName": "linkmind", "salience": 0.67 } ``` ### LinkEdge ```json { "id": "link_xxxxx", "type": "mentions", "fromId": "frag_xxxxx", "fromType": "fragment", "toId": "concept_xxxxx", "toType": "concept", "score": 2 } ``` ## 自测方法 ```bash cd skills/linkmind node tests/smoke-test.js ``` ## 目录结构 ``` skills/linkmind/ ├── SKILL.md ├── README.md ├── package.json ├── src/ │ ├── index.js # CLI 入口 │ ├── retriever.js # 双层检索 │ ├── storage-adapters/ │ │ ├── StorageAdapter.js # 接口契约 │ │ ├── JsonStorageAdapter.js # JSON 文件实现 │ │ ├── SqliteStorageAdapter.js # SQLite 实现 │ │ └── index.js # Factory │ ├── embedding-providers/ │ │ ├── EmbeddingProvider.js # 接口契约 │ │ ├── MockProvider.js # 随机向量（测试用） │ │ ├── OpenAICompatibleProvider.js # OpenAI 兼容 API │ │ └── index.js # Factory │ └── utils/ │ └── nlp.js # normalizeConcept 工具 ├── dist/ │ └── index.js ├── data/ │ └── workspace.json ├── tests/ │ └── smoke-test.js # Phase 2 覆盖 38 项检查 └── examples/ └── sample-note.md ``` ## Phase 2 交付清单 - [x] StorageAdapter 接口契约 - [x] JsonStorageAdapter（向后兼容） - [x] SqliteStorageAdapter（基于 better-sqlite3） - [x] EmbeddingProvider 接口契约 - [x] MockProvider（确定性随机、缓存、L2归一化） - [x] OpenAICompatibleProvider（支持自定义 baseURL/apiKey/model） - [x] retriever.js：关键词+向量双层召回 + 余弦相似度 ranker + merge 去重 - [x] smoke-test.js Phase 2 全覆盖（38项检查全部通过） - [x] SKILL.md 更新 ## 待后续实现 - ⏳ index.js 接入 adapter 层（DI 注入，支持 --storage=json|sqlite） - ⏳ 向量批量预计算 + 离线向量存储 - ⏳ 图数据库存储（Neo4j） - ⏳ Web/开放 API 接口 - ⏳ 多种来源接入（飞书、Notion、URL 抓取） - ⏳ 证据冲突检测 FILE:data/workspace.json { "version": 1, "createdAt": "2026-04-05T00:44:49.471Z", "updatedAt": "2026-04-05T00:44:49.514Z", "documents": [ { "id": "doc_6bb52r", "type": "document", "title": "Smoke Test", "sourceType": "file", "sourceUri": "/Users/jianghaidong/.openclaw/workspace/agents/coder/skills/linkmind/tests/smoke-test.js", "importedAt": "2026-04-05T00:44:49.503Z", "status": "active", "text": "#!/usr/bin/env node\n/**\n * LinkMind Smoke Test - Phase 2\n * 覆盖: storage adapters + embedding providers + retriever\n */\nimport path from 'path';\nimport { fileURLToPath } from 'url';\nimport { execSync } from 'child_process';\nimport { pathToFileURL } from 'url';\n\nconst __filename = fileURLToPath(import.meta.url);\nconst __dirname = path.dirname(__filename);\nconst SKILL_ROOT = path.resolve(__dirname, '..');\nprocess.chdir(SKILL_ROOT);\n\nfunction run(cmd) {\n console.log(`\\n$ ${cmd}`);\n return JSON.parse(execSync(cmd, { encoding: 'utf8' }));\n}\n\nfunction check(label, cond) {\n if (cond) {\n console.log(` ✓ ${label}`);\n } else {\n console.error(` ✗ FAIL: ${label}`);\n process.exitCode = 1;\n }\n}\n\n// Import phase-2 modules via dynamic import\nconst srcDir = path.join(SKILL_ROOT, 'src');\n\nasync function runTests() {\n console.log('=== LinkMind Smoke Test (Phase 2) ===\\n');\n\n // Phase 1: CLI commands\n console.log('--- Phase 1: CLI Commands ---');\n const reset = run('node dist/index.js reset');\n check('reset returns ok', reset.ok === true);\n\n const empty = run('node dist/index.js status');\n check('empty workspace has 0 documents', empty.documents === 0);\n\n const ingest = run('node dist/index.js ingest --file examples/sample-note.md --title \"Sample Note\"');\n check('ingest returns documentId', !!ingest.documentId);\n check('ingest creates fragments', ingest.fragmentsCreated >= 1);\n\n const after = run('node dist/index.js status');\n check('has 1 document', after.documents === 1);\n check('has fragments', after.fragments >= 1);\n\n const q1 = run('node dist/index.js query --q \"knowledge\"');\n check('query \"knowledge\" finds evidence', q1.evidence && q1.evidence.length >= 1);\n check('query \"knowledge\" returns answer', !!q1.answer);\n\n const q2 = run('node dist/index.js query --q \"xyznonexistentterm12345\"');\n check('query no-match returns empty evidence', q2.evidence && q2.evidence.length === 0);\n\n const q3 = run('node dist/index.js query --q \"knowledge\" --limit 1');\n check('query with limit respects limit', q3.evidence && q3.evidence.length <= 1);\n\n // Phase 2: Storage Adapters\n console.log('\\n--- Phase 2: Storage Adapters ---');\n const JsonStorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/JsonStorageAdapter.js')).href);\n const StorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/StorageAdapter.js')).href);\n const { JsonStorageAdapter } = JsonStorageAdapterMod;\n const { StorageAdapter } = StorageAdapterMod;\n\n const jsAdapter = new JsonStorageAdapter();\n await jsAdapter.init();\n await jsAdapter.clear();\n check('JsonStorageAdapter.init() ok', true);\n\n const testDoc = {\n id: 'doc_test1',\n type: 'document',\n title: 'Test Doc',\n sourceType: 'note',\n sourceUri: '/test/path.md',\n importedAt: new Date().toISOString(),\n status: 'active',\n text: 'This is a test document for storage adapter testing.'\n };\n await jsAdapter.saveDocument(testDoc);\n const retrieved = await jsAdapter.getDocument('doc_test1');\n check('JsonStorageAdapter.saveDocument + getDocument', retrieved && retrieved.title === 'Test Doc');\n\n const docs = await jsAdapter.listDocuments();\n check('JsonStorageAdapter.listDocuments', docs && docs.length >= 1);\n\n const testFrag = {\n id: 'frag_test1',\n type: 'fragment',\n documentId: 'doc_test1',\n index: 0,\n text: 'This is a test fragment.',\n summary: 'This is a test',\n conceptNames: ['test'],\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveFragment(testFrag);\n const frag = await jsAdapter.getFragment('frag_test1');\n check('JsonStorageAdapter.saveFragment + getFragment', frag && frag.text.includes('test fragment'));\n\n const frags = await jsAdapter.listFragments('doc_test1');\n check('JsonStorageAdapter.listFragments', frags && frags.length >= 1);\n\n const testConcept = {\n id: 'concept_test1',\n type: 'concept',\n name: 'TestConcept',\n normalizedName: 'testconcept',\n salience: 0.5,\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveConcept(testConcept);\n const concepts = await jsAdapter.listConcepts();\n check('JsonStorageAdapter.listConcepts', concepts && concepts.some(c => c.normalizedName === 'testconcept'));\n\n const testLink = {\n id: 'link_test1',\n type: 'mentions',\n fromId: 'frag_test1',\n fromType: 'fragment',\n toId: 'concept_test1',\n toType: 'concept',\n documentId: 'doc_test1',\n score: 1,\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveLink(testLink);\n const links = await jsAdapter.getLinks('frag_test1', null);\n check('JsonStorageAdapter.saveLink + getLinks', links && links.some(l => l.id === 'link_test1'));\n\n // test bulk save\n await jsAdapter.clear();\n await jsAdapter.saveDocument(testDoc);\n await jsAdapter.saveFragments([testFrag]);\n await jsAdapter.saveConcepts([testConcept]);\n await jsAdapter.saveLinks([testLink]);\n const afterBulk = await jsAdapter.listFragments('doc_test1');\n check('JsonStorageAdapter.saveFragments bulk', afterBulk && afterBulk.length >= 1);\n\n await jsAdapter.clear();\n const emptyAfterClear = await jsAdapter.listDocuments();\n check('JsonStorageAdapter.clear()', emptyAfterClear.length === 0);\n\n // StorageAdapter interface\n check('StorageAdapter is a class', typeof StorageAdapter === 'function');\n\n // Phase 2: Embedding Providers\n console.log('\\n--- Phase 2: Embedding Providers ---');\n const MockProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/MockProvider.js')).href);\n const OpenAICompatibleProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/OpenAICompatibleProvider.js')).href);\n const EmbeddingProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/EmbeddingProvider.js')).href);\n const { MockProvider } = MockProviderMod;\n const { OpenAICompatibleProvider } = OpenAICompatibleProviderMod;\n const { EmbeddingProvider } = EmbeddingProviderMod;\n\n const mock = new MockProvider({ dimension: 128 });\n check('MockProvider.dimension', mock.dimension === 128);\n check('MockProvider.name', mock.name === 'mock');\n\n const [vec] = await mock.embed(['hello world']);\n check('MockProvider.embed returns vector', Array.isArray(vec) && vec.length === 128);\n check('MockProvider.vector is L2-normalized', Math.abs(vec.reduce((s, v) => s + v * v, 0) - 1) < 0.01);\n\n const [vec2] = await mock.embed(['hello world']);\n check('MockProvider caches results', vec2 === vec);\n\n const differentText = await mock.embed(['different text']);\n check('MockProvider different text yields different vector', differentText[0] !== vec);\n\n const openai = new OpenAICompatibleProvider({ baseURL: 'https://api.example.com/v1', apiKey: 'test-key', model: 'test-model', dimension: 256 });\n check('OpenAICompatibleProvider.dimension', openai.dimension === 256);\n check('OpenAICompatibleProvider.name includes model', openai.name.includes('test-model'));\n check('OpenAICompatibleProvider.name includes baseURL hint', openai.name.includes('openai-compatible'));\n\n check('EmbeddingProvider is a class', typeof EmbeddingProvider === 'function');\n\n // Phase 2: Retriever\n console.log('\\n--- Phase 2: Retriever ---');\n const retrieverMod = await import(pathToFileURL(path.join(srcDir, 'retriever.js')).href);\n const { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve } = retrieverMod;\n\n check('cosineSimilarity identical vectors = 1', Math.abs(cosineSimilarity([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]) - 1) < 0.0001);\n check('cosineSimilarity orthogonal = 0', Math.abs(cosineSimilarity([1, 0, 0], [0, 1, 0])) < 0.0001);\n\n const testFrags = [\n { id: 'f1', documentId: 'd1', documentTitle: 'Doc 1', index: 0, text: 'knowledge graph is useful', conceptNames: ['knowledge', 'graph'] },\n { id: 'f2', documentId: 'd1', documentTitle: 'Doc 1', index: 1, text: 'machine learning and AI', conceptNames: ['machine', 'learning'] },\n { id: 'f3', documentId: 'd2', documentTitle: 'Doc 2', index: 0, text: 'knowledge base systems', conceptNames: ['knowledge', 'base'] }\n ];\n\n const kw = keywordSearch(testFrags, 'knowledge');\n check('keywordSearch finds knowledge fragments', kw.length >= 2);\n check('keywordSearch assigns score > 0', kw.every(r => r.score > 0));\n check('keywordSearch source=keyword', kw.every(r => r.source === 'keyword'));\n\n const vecResults = await vectorSearch(testFrags, 'knowledge graph', mock);\n check('vectorSearch returns results', Array.isArray(vecResults));\n check('vectorSearch source=vector', vecResults.every(r => r.source === 'vector'));\n\n const merged = mergeResults(kw, vecResults, 5);\n check('mergeResults deduplicates', merged.length <= kw.length + vecResults.length);\n check('mergeResults returns array', Array.isArray(merged));\n check('mergeResults items have score', merged.every(r => typeof r.score === 'number'));\n\n const kwOnly = await retrieve({ fragments: testFrags, query: 'knowledge', limit: 5 });\n check('retrieve keyword-only works', kwOnly.length >= 1);\n\n const hybrid = await retrieve({ fragments: testFrags, query: 'knowledge', embeddingProvider: mock, limit: 5 });\n check('retrieve hybrid works', hybrid.length >= 1);\n check('retrieve hybrid may include vector source', hybrid.some(r => r.source === 'vector' || r.source === 'hybrid'));\n\n console.log('\\n=== Smoke Test Complete ===');\n if (process.exitCode === 1) {\n console.log('RESULT: FAILED');\n process.exit(1);\n } else {\n console.log('RESULT: PASSED');\n }\n}\n\nrunTests().catch((err) => {\n console.error('Test error:', err);\n process.exit(1);\n});\n" } ], "fragments": [ { "id": "frag_d3g9ts", "type": "fragment", "documentId": "doc_6bb52r", "index": 0, "text": "#!/usr/bin/env node\n/**\n * LinkMind Smoke Test - Phase 2\n * 覆盖: storage adapters + embedding providers + retriever\n */\nimport path from 'path';\nimport { fileURLToPath } from 'url';\nimport { execSync } from 'child_process';\nimport { pathToFileURL } from 'url';", "summary": "#!/usr/bin/env node\n/**\n * LinkMind Smoke Test - Phase 2\n * 覆盖: storage adapters + embedding provide", "conceptNames": [ "import", "path", "url", "adapters", "bin", "child_process" ] }, { "id": "frag_pmaij6", "type": "fragment", "documentId": "doc_6bb52r", "index": 1, "text": "const __filename = fileURLToPath(import.meta.url);\nconst __dirname = path.dirname(__filename);\nconst SKILL_ROOT = path.resolve(__dirname, '..');\nprocess.chdir(SKILL_ROOT);", "summary": "const __filename = fileURLToPath(import.meta.url);\nconst __dirname = path.dirname(__filename);\nconst", "conceptNames": [ "const", "dirname", "filename", "path", "skill_root", "chdir" ] }, { "id": "frag_dw2gxf", "type": "fragment", "documentId": "doc_6bb52r", "index": 2, "text": "function run(cmd) {\n console.log(`\\n$ ${cmd}`);\n return JSON.parse(execSync(cmd, { encoding: 'utf8' }));\n}", "summary": "function run(cmd) {\n console.log(`\\n$ ${cmd}`);\n return JSON.parse(execSync(cmd, { encoding: 'utf8", "conceptNames": [ "cmd", "console", "encoding", "execsync", "function", "json" ] }, { "id": "frag_1qvdbx", "type": "fragment", "documentId": "doc_6bb52r", "index": 3, "text": "function check(label, cond) {\n if (cond) {\n console.log(` ✓ ${label}`);\n } else {\n console.error(` ✗ FAIL: ${label}`);\n process.exitCode = 1;\n }\n}", "summary": "function check(label, cond) {\n if (cond) {\n console.log(` ✓ ${label}`);\n } else {\n console.", "conceptNames": [ "label", "cond", "console", "check", "else", "error" ] }, { "id": "frag_2pvrfi", "type": "fragment", "documentId": "doc_6bb52r", "index": 4, "text": "// Import phase-2 modules via dynamic import\nconst srcDir = path.join(SKILL_ROOT, 'src');", "summary": "// Import phase-2 modules via dynamic import\nconst srcDir = path.join(SKILL_ROOT, 'src');", "conceptNames": [ "import", "const", "dynamic", "join", "modules", "path" ] }, { "id": "frag_49spg6", "type": "fragment", "documentId": "doc_6bb52r", "index": 5, "text": "async function runTests() {\n console.log('=== LinkMind Smoke Test (Phase 2) ===\\n');", "summary": "async function runTests() {\n console.log('=== LinkMind Smoke Test (Phase 2) ===\\n');", "conceptNames": [ "async", "console", "function", "linkmind", "log", "phase" ] }, { "id": "frag_l7f27m", "type": "fragment", "documentId": "doc_6bb52r", "index": 6, "text": "// Phase 1: CLI commands\n console.log('--- Phase 1: CLI Commands ---');\n const reset = run('node dist/index.js reset');\n check('reset returns ok', reset.ok === true);", "summary": "// Phase 1: CLI commands\n console.log('--- Phase 1: CLI Commands ---');\n const reset = run('node d", "conceptNames": [ "reset", "cli", "commands", "phase", "check", "console" ] }, { "id": "frag_4yjic", "type": "fragment", "documentId": "doc_6bb52r", "index": 7, "text": "const empty = run('node dist/index.js status');\n check('empty workspace has 0 documents', empty.documents === 0);", "summary": "const empty = run('node dist/index.js status');\n check('empty workspace has 0 documents', empty.doc", "conceptNames": [ "empty", "documents", "check", "const", "dist", "index" ] }, { "id": "frag_qhu1w0", "type": "fragment", "documentId": "doc_6bb52r", "index": 8, "text": "const ingest = run('node dist/index.js ingest --file examples/sample-note.md --title \"Sample Note\"');\n check('ingest returns documentId', !!ingest.documentId);\n check('ingest creates fragments', ingest.fragmentsCreated >= 1);", "summary": "const ingest = run('node dist/index.js ingest --file examples/sample-note.md --title \"Sample Note\"')", "conceptNames": [ "ingest", "check", "documentid", "const", "creates", "dist" ] }, { "id": "frag_3vvxiw", "type": "fragment", "documentId": "doc_6bb52r", "index": 9, "text": "const after = run('node dist/index.js status');\n check('has 1 document', after.documents === 1);\n check('has fragments', after.fragments >= 1);", "summary": "const after = run('node dist/index.js status');\n check('has 1 document', after.documents === 1);\n ", "conceptNames": [ "after", "check", "fragments", "const", "dist", "document" ] }, { "id": "frag_o5huex", "type": "fragment", "documentId": "doc_6bb52r", "index": 10, "text": "const q1 = run('node dist/index.js query --q \"knowledge\"');\n check('query \"knowledge\" finds evidence', q1.evidence && q1.evidence.length >= 1);\n check('query \"knowledge\" returns answer', !!q1.answer);", "summary": "const q1 = run('node dist/index.js query --q \"knowledge\"');\n check('query \"knowledge\" finds evidenc", "conceptNames": [ "evidence", "knowledge", "query", "answer", "check", "const" ] }, { "id": "frag_yice37", "type": "fragment", "documentId": "doc_6bb52r", "index": 11, "text": "const q2 = run('node dist/index.js query --q \"xyznonexistentterm12345\"');\n check('query no-match returns empty evidence', q2.evidence && q2.evidence.length === 0);", "summary": "const q2 = run('node dist/index.js query --q \"xyznonexistentterm12345\"');\n check('query no-match re", "conceptNames": [ "evidence", "query", "check", "const", "dist", "empty" ] }, { "id": "frag_qso8rl", "type": "fragment", "documentId": "doc_6bb52r", "index": 12, "text": "const q3 = run('node dist/index.js query --q \"knowledge\" --limit 1');\n check('query with limit respects limit', q3.evidence && q3.evidence.length <= 1);", "summary": "const q3 = run('node dist/index.js query --q \"knowledge\" --limit 1');\n check('query with limit resp", "conceptNames": [ "limit", "evidence", "query", "check", "const", "dist" ] }, { "id": "frag_83cldn", "type": "fragment", "documentId": "doc_6bb52r", "index": 13, "text": "// Phase 2: Storage Adapters\n console.log('\\n--- Phase 2: Storage Adapters ---');\n const JsonStorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/JsonStorageAdapter.js')).href);\n const StorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/StorageAdapter.js')).href);\n const { JsonStorageAdapter } = JsonStorageAdapterMod;\n const { StorageAdapter } = StorageAdapterMod;", "summary": "// Phase 2: Storage Adapters\n console.log('\\n--- Phase 2: Storage Adapters ---');\n const JsonStora", "conceptNames": [ "const", "adapters", "await", "href", "import", "join" ] }, { "id": "frag_ou8eku", "type": "fragment", "documentId": "doc_6bb52r", "index": 14, "text": "const jsAdapter = new JsonStorageAdapter();\n await jsAdapter.init();\n await jsAdapter.clear();\n check('JsonStorageAdapter.init() ok', true);", "summary": "const jsAdapter = new JsonStorageAdapter();\n await jsAdapter.init();\n await jsAdapter.clear();\n c", "conceptNames": [ "jsadapter", "await", "init", "jsonstorageadapter", "check", "clear" ] }, { "id": "frag_mjx4px", "type": "fragment", "documentId": "doc_6bb52r", "index": 15, "text": "const testDoc = {\n id: 'doc_test1',\n type: 'document',\n title: 'Test Doc',\n sourceType: 'note',\n sourceUri: '/test/path.md',\n importedAt: new Date().toISOString(),\n status: 'active',\n text: 'This is a test document for storage adapter testing.'\n };\n await jsAdapter.saveDocument(testDoc);\n const retrieved = await jsAdapter.getDocument('doc_test1');\n check('JsonStorageAdapter.saveDocument + getDocument', retrieved && retrieved.title === 'Test Doc');", "summary": "const testDoc = {\n id: 'doc_test1',\n type: 'document',\n title: 'Test Doc',\n sourceType: ", "conceptNames": [ "test", "retrieved", "await", "const", "doc", "doc_test1" ] }, { "id": "frag_xgccse", "type": "fragment", "documentId": "doc_6bb52r", "index": 16, "text": "const docs = await jsAdapter.listDocuments();\n check('JsonStorageAdapter.listDocuments', docs && docs.length >= 1);", "summary": "const docs = await jsAdapter.listDocuments();\n check('JsonStorageAdapter.listDocuments', docs && do", "conceptNames": [ "docs", "listdocuments", "await", "check", "const", "jsadapter" ] }, { "id": "frag_4t3zpb", "type": "fragment", "documentId": "doc_6bb52r", "index": 17, "text": "const testFrag = {\n id: 'frag_test1',\n type: 'fragment',\n documentId: 'doc_test1',\n index: 0,\n text: 'This is a test fragment.',\n summary: 'This is a test',\n conceptNames: ['test'],\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveFragment(testFrag);\n const frag = await jsAdapter.getFragment('frag_test1');\n check('JsonStorageAdapter.saveFragment + getFragment', frag && frag.text.includes('test fragment'));", "summary": "const testFrag = {\n id: 'frag_test1',\n type: 'fragment',\n documentId: 'doc_test1',\n inde", "conceptNames": [ "test", "frag", "fragment", "await", "const", "frag_test1" ] }, { "id": "frag_g3bxss", "type": "fragment", "documentId": "doc_6bb52r", "index": 18, "text": "const frags = await jsAdapter.listFragments('doc_test1');\n check('JsonStorageAdapter.listFragments', frags && frags.length >= 1);", "summary": "const frags = await jsAdapter.listFragments('doc_test1');\n check('JsonStorageAdapter.listFragments'", "conceptNames": [ "frags", "listfragments", "await", "check", "const", "doc_test1" ] }, { "id": "frag_g30jd6", "type": "fragment", "documentId": "doc_6bb52r", "index": 19, "text": "const testConcept = {\n id: 'concept_test1',\n type: 'concept',\n name: 'TestConcept',\n normalizedName: 'testconcept',\n salience: 0.5,\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveConcept(testConcept);\n const concepts = await jsAdapter.listConcepts();\n check('JsonStorageAdapter.listConcepts', concepts && concepts.some(c => c.normalizedName === 'testconcept'));", "summary": "const testConcept = {\n id: 'concept_test1',\n type: 'concept',\n name: 'TestConcept',\n nor", "conceptNames": [ "testconcept", "concepts", "await", "const", "jsadapter", "listconcepts" ] }, { "id": "frag_h3k825", "type": "fragment", "documentId": "doc_6bb52r", "index": 20, "text": "const testLink = {\n id: 'link_test1',\n type: 'mentions',\n fromId: 'frag_test1',\n fromType: 'fragment',\n toId: 'concept_test1',\n toType: 'concept',\n documentId: 'doc_test1',\n score: 1,\n createdAt: new Date().toISOString()\n };\n await jsAdapter.saveLink(testLink);\n const links = await jsAdapter.getLinks('frag_test1', null);\n check('JsonStorageAdapter.saveLink + getLinks', links && links.some(l => l.id === 'link_test1'));", "summary": "const testLink = {\n id: 'link_test1',\n type: 'mentions',\n fromId: 'frag_test1',\n fromTyp", "conceptNames": [ "links", "await", "const", "frag_test1", "getlinks", "jsadapter" ] }, { "id": "frag_ivfanb", "type": "fragment", "documentId": "doc_6bb52r", "index": 21, "text": "// test bulk save\n await jsAdapter.clear();\n await jsAdapter.saveDocument(testDoc);\n await jsAdapter.saveFragments([testFrag]);\n await jsAdapter.saveConcepts([testConcept]);\n await jsAdapter.saveLinks([testLink]);\n const afterBulk = await jsAdapter.listFragments('doc_test1');\n check('JsonStorageAdapter.saveFragments bulk', afterBulk && afterBulk.length >= 1);", "summary": "// test bulk save\n await jsAdapter.clear();\n await jsAdapter.saveDocument(testDoc);\n await jsAdap", "conceptNames": [ "await", "jsadapter", "afterbulk", "bulk", "savefragments", "check" ] }, { "id": "frag_g1qshr", "type": "fragment", "documentId": "doc_6bb52r", "index": 22, "text": "await jsAdapter.clear();\n const emptyAfterClear = await jsAdapter.listDocuments();\n check('JsonStorageAdapter.clear()', emptyAfterClear.length === 0);", "summary": "await jsAdapter.clear();\n const emptyAfterClear = await jsAdapter.listDocuments();\n check('JsonSto", "conceptNames": [ "await", "clear", "emptyafterclear", "jsadapter", "check", "const" ] }, { "id": "frag_m2z8gs", "type": "fragment", "documentId": "doc_6bb52r", "index": 23, "text": "// StorageAdapter interface\n check('StorageAdapter is a class', typeof StorageAdapter === 'function');", "summary": "// StorageAdapter interface\n check('StorageAdapter is a class', typeof StorageAdapter === 'function", "conceptNames": [ "storageadapter", "check", "class", "function", "interface", "typeof" ] }, { "id": "frag_la8iqs", "type": "fragment", "documentId": "doc_6bb52r", "index": 24, "text": "// Phase 2: Embedding Providers\n console.log('\\n--- Phase 2: Embedding Providers ---');\n const MockProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/MockProvider.js')).href);\n const OpenAICompatibleProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/OpenAICompatibleProvider.js')).href);\n const EmbeddingProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/EmbeddingProvider.js')).href);\n const { MockProvider } = MockProviderMod;\n const { OpenAICompatibleProvider } = OpenAICompatibleProviderMod;\n const { EmbeddingProvider } = EmbeddingProviderMod;", "summary": "// Phase 2: Embedding Providers\n console.log('\\n--- Phase 2: Embedding Providers ---');\n const Moc", "conceptNames": [ "const", "await", "embedding-providers", "href", "import", "join" ] }, { "id": "frag_k2wcc", "type": "fragment", "documentId": "doc_6bb52r", "index": 25, "text": "const mock = new MockProvider({ dimension: 128 });\n check('MockProvider.dimension', mock.dimension === 128);\n check('MockProvider.name', mock.name === 'mock');", "summary": "const mock = new MockProvider({ dimension: 128 });\n check('MockProvider.dimension', mock.dimension ", "conceptNames": [ "mock", "dimension", "mockprovider", "check", "name", "const" ] }, { "id": "frag_amqxax", "type": "fragment", "documentId": "doc_6bb52r", "index": 26, "text": "const [vec] = await mock.embed(['hello world']);\n check('MockProvider.embed returns vector', Array.isArray(vec) && vec.length === 128);\n check('MockProvider.vector is L2-normalized', Math.abs(vec.reduce((s, v) => s + v * v, 0) - 1) < 0.01);", "summary": "const [vec] = await mock.embed(['hello world']);\n check('MockProvider.embed returns vector', Array.", "conceptNames": [ "vec", "check", "embed", "mockprovider", "vector", "abs" ] }, { "id": "frag_k3j5ot", "type": "fragment", "documentId": "doc_6bb52r", "index": 27, "text": "const [vec2] = await mock.embed(['hello world']);\n check('MockProvider caches results', vec2 === vec);", "summary": "const [vec2] = await mock.embed(['hello world']);\n check('MockProvider caches results', vec2 === ve", "conceptNames": [ "vec2", "await", "caches", "check", "const", "embed" ] }, { "id": "frag_oxk0al", "type": "fragment", "documentId": "doc_6bb52r", "index": 28, "text": "const differentText = await mock.embed(['different text']);\n check('MockProvider different text yields different vector', differentText[0] !== vec);", "summary": "const differentText = await mock.embed(['different text']);\n check('MockProvider different text yie", "conceptNames": [ "different", "differenttext", "text", "await", "check", "const" ] }, { "id": "frag_w7ug9l", "type": "fragment", "documentId": "doc_6bb52r", "index": 29, "text": "const openai = new OpenAICompatibleProvider({ baseURL: 'https://api.example.com/v1', apiKey: 'test-key', model: 'test-model', dimension: 256 });\n check('OpenAICompatibleProvider.dimension', openai.dimension === 256);\n check('OpenAICompatibleProvider.name includes model', openai.name.includes('test-model'));\n check('OpenAICompatibleProvider.name includes baseURL hint', openai.name.includes('openai-compatible'));", "summary": "const openai = new OpenAICompatibleProvider({ baseURL: 'https://api.example.com/v1', apiKey: 'test-k", "conceptNames": [ "includes", "name", "openai", "openaicompatibleprovider", "check", "dimension" ] }, { "id": "frag_h1uzlu", "type": "fragment", "documentId": "doc_6bb52r", "index": 30, "text": "check('EmbeddingProvider is a class', typeof EmbeddingProvider === 'function');", "summary": "check('EmbeddingProvider is a class', typeof EmbeddingProvider === 'function');", "conceptNames": [ "embeddingprovider", "check", "class", "function", "typeof" ] }, { "id": "frag_rrcn8h", "type": "fragment", "documentId": "doc_6bb52r", "index": 31, "text": "// Phase 2: Retriever\n console.log('\\n--- Phase 2: Retriever ---');\n const retrieverMod = await import(pathToFileURL(path.join(srcDir, 'retriever.js')).href);\n const { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve } = retrieverMod;", "summary": "// Phase 2: Retriever\n console.log('\\n--- Phase 2: Retriever ---');\n const retrieverMod = await im", "conceptNames": [ "retriever", "const", "phase", "retrievermod", "await", "console" ] }, { "id": "frag_z6ich8", "type": "fragment", "documentId": "doc_6bb52r", "index": 32, "text": "check('cosineSimilarity identical vectors = 1', Math.abs(cosineSimilarity([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]) - 1) < 0.0001);\n check('cosineSimilarity orthogonal = 0', Math.abs(cosineSimilarity([1, 0, 0], [0, 1, 0])) < 0.0001);", "summary": "check('cosineSimilarity identical vectors = 1', Math.abs(cosineSimilarity([0.5, 0.5, 0.5, 0.5], [0.5", "conceptNames": [ "cosinesimilarity", "abs", "check", "math", "identical", "orthogonal" ] }, { "id": "frag_5i1o5v", "type": "fragment", "documentId": "doc_6bb52r", "index": 33, "text": "const testFrags = [\n { id: 'f1', documentId: 'd1', documentTitle: 'Doc 1', index: 0, text: 'knowledge graph is useful', conceptNames: ['knowledge', 'graph'] },\n { id: 'f2', documentId: 'd1', documentTitle: 'Doc 1', index: 1, text: 'machine learning and AI', conceptNames: ['machine', 'learning'] },\n { id: 'f3', documentId: 'd2', documentTitle: 'Doc 2', index: 0, text: 'knowledge base systems', conceptNames: ['knowledge', 'base'] }\n ];", "summary": "const testFrags = [\n { id: 'f1', documentId: 'd1', documentTitle: 'Doc 1', index: 0, text: 'knowl", "conceptNames": [ "knowledge", "conceptnames", "doc", "documentid", "documenttitle", "index" ] }, { "id": "frag_1wv3le", "type": "fragment", "documentId": "doc_6bb52r", "index": 34, "text": "const kw = keywordSearch(testFrags, 'knowledge');\n check('keywordSearch finds knowledge fragments', kw.length >= 2);\n check('keywordSearch assigns score > 0', kw.every(r => r.score > 0));\n check('keywordSearch source=keyword', kw.every(r => r.source === 'keyword'));", "summary": "const kw = keywordSearch(testFrags, 'knowledge');\n check('keywordSearch finds knowledge fragments',", "conceptNames": [ "keywordsearch", "check", "every", "keyword", "knowledge", "score" ] }, { "id": "frag_jx95go", "type": "fragment", "documentId": "doc_6bb52r", "index": 35, "text": "const vecResults = await vectorSearch(testFrags, 'knowledge graph', mock);\n check('vectorSearch returns results', Array.isArray(vecResults));\n check('vectorSearch source=vector', vecResults.every(r => r.source === 'vector'));", "summary": "const vecResults = await vectorSearch(testFrags, 'knowledge graph', mock);\n check('vectorSearch ret", "conceptNames": [ "vecresults", "vectorsearch", "check", "source", "vector", "array" ] }, { "id": "frag_mb8wqq", "type": "fragment", "documentId": "doc_6bb52r", "index": 36, "text": "const merged = mergeResults(kw, vecResults, 5);\n check('mergeResults deduplicates', merged.length <= kw.length + vecResults.length);\n check('mergeResults returns array', Array.isArray(merged));\n check('mergeResults items have score', merged.every(r => typeof r.score === 'number'));", "summary": "const merged = mergeResults(kw, vecResults, 5);\n check('mergeResults deduplicates', merged.length <", "conceptNames": [ "merged", "mergeresults", "check", "length", "array", "score" ] }, { "id": "frag_c2gwcq", "type": "fragment", "documentId": "doc_6bb52r", "index": 37, "text": "const kwOnly = await retrieve({ fragments: testFrags, query: 'knowledge', limit: 5 });\n check('retrieve keyword-only works', kwOnly.length >= 1);", "summary": "const kwOnly = await retrieve({ fragments: testFrags, query: 'knowledge', limit: 5 });\n check('retr", "conceptNames": [ "kwonly", "retrieve", "await", "check", "const", "fragments" ] }, { "id": "frag_lybfjt", "type": "fragment", "documentId": "doc_6bb52r", "index": 38, "text": "const hybrid = await retrieve({ fragments: testFrags, query: 'knowledge', embeddingProvider: mock, limit: 5 });\n check('retrieve hybrid works', hybrid.length >= 1);\n check('retrieve hybrid may include vector source', hybrid.some(r => r.source === 'vector' || r.source === 'hybrid'));", "summary": "const hybrid = await retrieve({ fragments: testFrags, query: 'knowledge', embeddingProvider: mock, l", "conceptNames": [ "hybrid", "retrieve", "source", "check", "vector", "await" ] }, { "id": "frag_4nve2i", "type": "fragment", "documentId": "doc_6bb52r", "index": 39, "text": "console.log('\\n=== Smoke Test Complete ===');\n if (process.exitCode === 1) {\n console.log('RESULT: FAILED');\n process.exit(1);\n } else {\n console.log('RESULT: PASSED');\n }\n}", "summary": "console.log('\\n=== Smoke Test Complete ===');\n if (process.exitCode === 1) {\n console.log('RESUL", "conceptNames": [ "console", "log", "process", "result", "complete", "else" ] }, { "id": "frag_h74lv", "type": "fragment", "documentId": "doc_6bb52r", "index": 40, "text": "runTests().catch((err) => {\n console.error('Test error:', err);\n process.exit(1);\n});", "summary": "runTests().catch((err) => {\n console.error('Test error:', err);\n process.exit(1);\n});", "conceptNames": [ "err", "error", "catch", "console", "exit", "process" ] } ], "concepts": [ { "id": "concept_22cy", "type": "concept", "name": "abs", "normalizedName": "abs", "salience": 0.3333333333333333 }, { "id": "concept_llkge4", "type": "concept", "name": "adapters", "normalizedName": "adapters", "salience": 0.3333333333333333 }, { "id": "concept_1j7mqk", "type": "concept", "name": "after", "normalizedName": "after", "salience": 1 }, { "id": "concept_gv37qm", "type": "concept", "name": "afterBulk", "normalizedName": "afterbulk", "salience": 1 }, { "id": "concept_nd5e82", "type": "concept", "name": "answer", "normalizedName": "answer", "salience": 0.6666666666666666 }, { "id": "concept_1jf909", "type": "concept", "name": "Array", "normalizedName": "array", "salience": 0.3333333333333333 }, { "id": "concept_1jg1h8", "type": "concept", "name": "async", "normalizedName": "async", "salience": 0.3333333333333333 }, { "id": "concept_1ji3iu", "type": "concept", "name": "await", "normalizedName": "await", "salience": 0.6666666666666666 }, { "id": "concept_239j", "type": "concept", "name": "bin", "normalizedName": "bin", "salience": 0.3333333333333333 }, { "id": "concept_1t24y", "type": "concept", "name": "bulk", "normalizedName": "bulk", "salience": 0.6666666666666666 }, { "id": "concept_mmi027", "type": "concept", "name": "caches", "normalizedName": "caches", "salience": 0.3333333333333333 }, { "id": "concept_1k80xn", "type": "concept", "name": "catch", "normalizedName": "catch", "salience": 0.3333333333333333 }, { "id": "concept_1kc64o", "type": "concept", "name": "chdir", "normalizedName": "chdir", "salience": 0.3333333333333333 }, { "id": "concept_1kc6q0", "type": "concept", "name": "check", "normalizedName": "check", "salience": 0.3333333333333333 }, { "id": "concept_86zfzo", "type": "concept", "name": "child_process", "normalizedName": "child_process", "salience": 0.3333333333333333 }, { "id": "concept_1keo3c", "type": "concept", "name": "class", "normalizedName": "class", "salience": 0.3333333333333333 }, { "id": "concept_1keqml", "type": "concept", "name": "clear", "normalizedName": "clear", "salience": 0.3333333333333333 }, { "id": "concept_242o", "type": "concept", "name": "CLI", "normalizedName": "cli", "salience": 0.6666666666666666 }, { "id": "concept_243e", "type": "concept", "name": "cmd", "normalizedName": "cmd", "salience": 1 }, { "id": "concept_9yqf7c", "type": "concept", "name": "commands", "normalizedName": "commands", "salience": 0.6666666666666666 }, { "id": "concept_9ww6vb", "type": "concept", "name": "Complete", "normalizedName": "complete", "salience": 0.3333333333333333 }, { "id": "concept_gr1mao", "type": "concept", "name": "conceptNames", "normalizedName": "conceptnames", "salience": 1 }, { "id": "concept_9n44xh", "type": "concept", "name": "concepts", "normalizedName": "concepts", "salience": 1 }, { "id": "concept_1tkpu", "type": "concept", "name": "cond", "normalizedName": "cond", "salience": 0.6666666666666666 }, { "id": "concept_fqi63b", "type": "concept", "name": "console", "normalizedName": "console", "salience": 0.3333333333333333 }, { "id": "concept_1kguoz", "type": "concept", "name": "const", "normalizedName": "const", "salience": 1 }, { "id": "concept_hkekis", "type": "concept", "name": "cosineSimilarity", "normalizedName": "cosinesimilarity", "salience": 1 }, { "id": "concept_h0dhpz", "type": "concept", "name": "creates", "normalizedName": "creates", "salience": 0.3333333333333333 }, { "id": "concept_ljky0p", "type": "concept", "name": "different", "normalizedName": "different", "salience": 1 }, { "id": "concept_q1bzdy", "type": "concept", "name": "differentText", "normalizedName": "differenttext", "salience": 0.6666666666666666 }, { "id": "concept_i3xxga", "type": "concept", "name": "dimension", "normalizedName": "dimension", "salience": 1 }, { "id": "concept_rmqf14", "type": "concept", "name": "dirname", "normalizedName": "dirname", "salience": 1 }, { "id": "concept_1u3dy", "type": "concept", "name": "dist", "normalizedName": "dist", "salience": 0.3333333333333333 }, { "id": "concept_24vs", "type": "concept", "name": "Doc", "normalizedName": "doc", "salience": 0.6666666666666666 }, { "id": "concept_5amdjs", "type": "concept", "name": "doc_test1", "normalizedName": "doc_test1", "salience": 0.6666666666666666 }, { "id": "concept_1u7gb", "type": "concept", "name": "docs", "normalizedName": "docs", "salience": 1 }, { "id": "concept_e91o2j", "type": "concept", "name": "document", "normalizedName": "document", "salience": 0.3333333333333333 }, { "id": "concept_dh6z62", "type": "concept", "name": "documentId", "normalizedName": "documentid", "salience": 0.6666666666666666 }, { "id": "concept_flreew", "type": "concept", "name": "documents", "normalizedName": "documents", "salience": 0.6666666666666666 }, { "id": "concept_qjcwf1", "type": "concept", "name": "documentTitle", "normalizedName": "documenttitle", "salience": 1 }, { "id": "concept_z514xr", "type": "concept", "name": "dynamic", "normalizedName": "dynamic", "salience": 0.3333333333333333 }, { "id": "concept_1usl5", "type": "concept", "name": "else", "normalizedName": "else", "salience": 0.3333333333333333 }, { "id": "concept_1liwnt", "type": "concept", "name": "embed", "normalizedName": "embed", "salience": 0.6666666666666666 }, { "id": "concept_7liuz8", "type": "concept", "name": "embedding-providers", "normalizedName": "embedding-providers", "salience": 1 }, { "id": "concept_w9ow8", "type": "concept", "name": "EmbeddingProvider", "normalizedName": "embeddingprovider", "salience": 0.6666666666666666 }, { "id": "concept_1lj7f1", "type": "concept", "name": "empty", "normalizedName": "empty", "salience": 1 }, { "id": "concept_dlzsam", "type": "concept", "name": "emptyAfterClear", "normalizedName": "emptyafterclear", "salience": 0.6666666666666666 }, { "id": "concept_satff7", "type": "concept", "name": "encoding", "normalizedName": "encoding", "salience": 0.3333333333333333 }, { "id": "concept_25ph", "type": "concept", "name": "err", "normalizedName": "err", "salience": 0.6666666666666666 }, { "id": "concept_1lmfpk", "type": "concept", "name": "error", "normalizedName": "error", "salience": 0.3333333333333333 }, { "id": "concept_1loq3f", "type": "concept", "name": "every", "normalizedName": "every", "salience": 0.6666666666666666 }, { "id": "concept_6c0biv", "type": "concept", "name": "evidence", "normalizedName": "evidence", "salience": 1 }, { "id": "concept_xsbs18", "type": "concept", "name": "execSync", "normalizedName": "execsync", "salience": 0.3333333333333333 }, { "id": "concept_1v19a", "type": "concept", "name": "exit", "normalizedName": "exit", "salience": 0.3333333333333333 }, { "id": "concept_c5gn3t", "type": "concept", "name": "filename", "normalizedName": "filename", "salience": 0.6666666666666666 }, { "id": "concept_1vjle", "type": "concept", "name": "frag", "normalizedName": "frag", "salience": 1 }, { "id": "concept_o1b6i6", "type": "concept", "name": "frag_test1", "normalizedName": "frag_test1", "salience": 0.6666666666666666 }, { "id": "concept_raj06o", "type": "concept", "name": "fragment", "normalizedName": "fragment", "salience": 1 }, { "id": "concept_6azi1v", "type": "concept", "name": "fragments", "normalizedName": "fragments", "salience": 0.6666666666666666 }, { "id": "concept_1m5vi9", "type": "concept", "name": "frags", "normalizedName": "frags", "salience": 1 }, { "id": "concept_mu6b4o", "type": "concept", "name": "function", "normalizedName": "function", "salience": 0.3333333333333333 }, { "id": "concept_wvtd0j", "type": "concept", "name": "getLinks", "normalizedName": "getlinks", "salience": 0.6666666666666666 }, { "id": "concept_1wtnv", "type": "concept", "name": "href", "normalizedName": "href", "salience": 0.6666666666666666 }, { "id": "concept_jw39c4", "type": "concept", "name": "hybrid", "normalizedName": "hybrid", "salience": 1 }, { "id": "concept_1fehth", "type": "concept", "name": "identical", "normalizedName": "identical", "salience": 0.3333333333333333 }, { "id": "concept_jlea8r", "type": "concept", "name": "import", "normalizedName": "import", "salience": 1 }, { "id": "concept_1hqksr", "type": "concept", "name": "includes", "normalizedName": "includes", "salience": 1 }, { "id": "concept_1nqriq", "type": "concept", "name": "index", "normalizedName": "index", "salience": 0.3333333333333333 }, { "id": "concept_jl0fx8", "type": "concept", "name": "ingest", "normalizedName": "ingest", "salience": 1 }, { "id": "concept_1xdsg", "type": "concept", "name": "init", "normalizedName": "init", "salience": 0.6666666666666666 }, { "id": "concept_8b8yt5", "type": "concept", "name": "interface", "normalizedName": "interface", "salience": 0.3333333333333333 }, { "id": "concept_1y1ii", "type": "concept", "name": "join", "normalizedName": "join", "salience": 0.3333333333333333 }, { "id": "concept_f09bfe", "type": "concept", "name": "jsAdapter", "normalizedName": "jsadapter", "salience": 1 }, { "id": "concept_1y4mg", "type": "concept", "name": "JSON", "normalizedName": "json", "salience": 0.3333333333333333 }, { "id": "concept_b0w3to", "type": "concept", "name": "JsonStorageAdapter", "normalizedName": "jsonstorageadapter", "salience": 0.6666666666666666 }, { "id": "concept_dgvlef", "type": "concept", "name": "keyword", "normalizedName": "keyword", "salience": 0.6666666666666666 }, { "id": "concept_ofkd9r", "type": "concept", "name": "keywordSearch", "normalizedName": "keywordsearch", "salience": 1 }, { "id": "concept_pmrgxq", "type": "concept", "name": "knowledge", "normalizedName": "knowledge", "salience": 1 }, { "id": "concept_ihtre0", "type": "concept", "name": "kwOnly", "normalizedName": "kwonly", "salience": 0.6666666666666666 }, { "id": "concept_1p5sz8", "type": "concept", "name": "label", "normalizedName": "label", "salience": 1 }, { "id": "concept_iap7oa", "type": "concept", "name": "length", "normalizedName": "length", "salience": 1 }, { "id": "concept_1pb54r", "type": "concept", "name": "limit", "normalizedName": "limit", "salience": 1 }, { "id": "concept_jrfmmk", "type": "concept", "name": "LinkMind", "normalizedName": "linkmind", "salience": 0.3333333333333333 }, { "id": "concept_1pb5x5", "type": "concept", "name": "links", "normalizedName": "links", "salience": 1 }, { "id": "concept_77o2i1", "type": "concept", "name": "listConcepts", "normalizedName": "listconcepts", "salience": 0.6666666666666666 }, { "id": "concept_u4un1i", "type": "concept", "name": "listDocuments", "normalizedName": "listdocuments", "salience": 0.6666666666666666 }, { "id": "concept_vlhikl", "type": "concept", "name": "listFragments", "normalizedName": "listfragments", "salience": 0.6666666666666666 }, { "id": "concept_2atg", "type": "concept", "name": "log", "normalizedName": "log", "salience": 0.3333333333333333 }, { "id": "concept_1zoco", "type": "concept", "name": "Math", "normalizedName": "math", "salience": 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provider 必须实现此接口契约。 */ class EmbeddingProvider { /** * 将文本列表转为向量 * @param {string[]} texts * @returns {Promise} */ async embed(texts) { throw new Error('Not implemented'); } /** * 返回 provider 名称 */ get name() { return 'unknown'; } /** * 返回向量维度 */ get dimension() { throw new Error('Not implemented'); } } module.exports = { EmbeddingProvider }; FILE:dist/embedding-providers/MockProvider.js /** * MockProvider * 返回随机向量，用于测试和离线开发。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class MockProvider extends EmbeddingProvider { constructor({ dimension = DEFAULT_DIM, seed = 42 } = {}) { super(); this._dim = dimension; this._seed = seed; this._cache = new Map(); } get name() { return 'mock'; } get dimension() { return this._dim; } _pseudoRandom(text) { let hash = 0; for (let i = 0; i < text.length; i += 1) { hash = ((hash << 5) - hash + text.charCodeAt(i)) | 0; } return Math.abs(hash) / 0x7fffffff; } async embed(texts) { return texts.map((text) => { if (this._cache.has(text)) return this._cache.get(text); const vec = []; for (let i = 0; i < this._dim; i += 1) { // deterministic random based on text + index const base = this._pseudoRandom(text + i); vec.push(base); } // L2 normalize const norm = Math.sqrt(vec.reduce((s, v) => s + v * v, 0)); const normalized = norm > 0 ? vec.map((v) => v / norm) : vec; this._cache.set(text, normalized); return normalized; }); } } module.exports = { MockProvider }; FILE:dist/embedding-providers/OpenAICompatibleProvider.js /** * OpenAICompatibleProvider * 调用 OpenAI-compatible API endpoint（如 vLLM、Ollama、Azure OpenAI 等）。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class OpenAICompatibleProvider extends EmbeddingProvider { constructor({ baseURL = 'https://api.openai.com/v1', apiKey = '', model = 'text-embedding-3-small', dimension = DEFAULT_DIM, batchSize = 100 } = {}) { super(); this._baseURL = baseURL.replace(/\/$/, ''); this._apiKey = apiKey; this._model = model; this._dim = dimension; this._batchSize = batchSize; } get name() { return `openai-compatible:${this._model}`; } get dimension() { return this._dim; } async embed(texts) { if (!texts || texts.length === 0) return []; const results = []; for (let i = 0; i < texts.length; i += this._batchSize) { const batch = texts.slice(i, i + this._batchSize); const vectors = await this._fetchBatch(batch); results.push(...vectors); } return results; } async _fetchBatch(batch) { const url = `${this._baseURL}/embeddings`; const body = { model: this._model, input: batch }; const headers = { 'Content-Type': 'application/json' }; if (this._apiKey) { headers['Authorization'] = `Bearer ${this._apiKey}`; } const resp = await fetch(url, { method: 'POST', headers, body: JSON.stringify(body) }); if (!resp.ok) { const text = await resp.text().catch(() => ''); throw new Error(`Embedding API error ${resp.status}: ${text}`); } const json = await resp.json(); if (!json.data || !Array.isArray(json.data)) { throw new Error(`Unexpected embedding response format`); } // sort by index to maintain order const sorted = json.data.slice().sort((a, b) => a.index - b.index); return sorted.map((item) => { if (!item.embedding || !Array.isArray(item.embedding)) { throw new Error(`Invalid embedding vector in response`); } return item.embedding; }); } } module.exports = { OpenAICompatibleProvider }; FILE:dist/embedding-providers/index.js /** * embedding-providers/index.js * Factory: 根据配置返回对应的 embedding provider 实例。 */ const { MockProvider } = require('./MockProvider'); const { OpenAICompatibleProvider } = require('./OpenAICompatibleProvider'); function createEmbeddingProvider(type = 'mock', options = {}) { switch (type) { case 'mock': return new MockProvider(options); case 'openai': return new OpenAICompatibleProvider(options); default: throw new Error(`Unknown embedding provider type: ${type}. Use 'mock' or 'openai'.`); } } module.exports = { createEmbeddingProvider, MockProvider, OpenAICompatibleProvider }; FILE:dist/index.js #!/usr/bin/env node /** * LinkMind CLI - 知识连接引擎 * 支持 adapter 切换（json/sqlite）和 embedding 切换（keyword/openai） */ const fs = require('fs'); const path = require('path'); const THIS_DIR = __dirname.includes(`${path.sep}dist`) ? __dirname : __dirname; const IS_DIST = THIS_DIR.includes(`${path.sep}dist`); const ROOT = IS_DIST ? path.resolve(THIS_DIR, '..') : path.resolve(THIS_DIR, '..'); const DATA_DIR = path.join(ROOT, 'data'); function createEmptyDb() { return { version: 1, createdAt: new Date().toISOString(), updatedAt: new Date().toISOString(), documents: [], fragments: [], concepts: [], links: [] }; } function stableId(prefix, input) { let hash = 0; for (let i = 0; i < input.length; i += 1) { hash = ((hash << 5) - hash + input.charCodeAt(i)) | 0; } return `${prefix}_${Math.abs(hash).toString(36)}`; } function normalizeConcept(text) { return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were', 'can','could','should','about','what','when','where','which','while','than','then','them','they', 'their','there','here','also','more','most','some','such','using','used','use','make','made', 'over','under','very','just','only','each','been','being','does','did','done','how','why', 'our','you','its','his','her','she','him','who','has','had','but','too','via','per', 'one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及', '进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有', '对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为' ]); function extractConcepts(text) { const zhMatches = text.match(/[\u4e00-\u9fa5]{2,8}/g) || []; const enMatches = text.match(/[A-Za-z][A-Za-z0-9_-]{2,}/g) || []; const tokens = [...zhMatches, ...enMatches] .map((item) => item.trim()) .map((item) => ({ raw: item, normalized: normalizeConcept(item) })) .filter((item) => item.normalized && !STOPWORDS.has(item.normalized)); const counts = new Map(); for (const token of tokens) { counts.set(token.normalized, (counts.get(token.normalized) || 0) + 1); } return [...counts.entries()] .map(([normalizedName, count]) => ({ normalizedName, name: tokens.find((item) => item.normalized === normalizedName)?.raw || normalizedName, count })) .sort((a, b) => b.count - a.count || a.normalizedName.localeCompare(b.normalizedName)) .slice(0, 12); } function splitParagraphs(text) { return text .split(/\n\s*\n+/) .map((part) => part.trim()) .filter(Boolean); } function upsertDocument(db, doc) { const existingIndex = db.documents.findIndex((item) => item.id === doc.id); if (existingIndex >= 0) db.documents[existingIndex] = doc; else db.documents.push(doc); } function buildForDocument(db, document) { const fragmentIds = db.fragments.filter((f) => f.documentId === document.id).map((f) => f.id); db.fragments = db.fragments.filter((f) => f.documentId !== document.id); db.links = db.links.filter((l) => !fragmentIds.includes(l.fromId) && !fragmentIds.includes(l.toId) && l.documentId !== document.id); const usedConceptIds = new Set(); for (const link of db.links) { if (link.toType === 'concept') usedConceptIds.add(link.toId); if (link.fromType === 'concept') usedConceptIds.add(link.fromId); } db.concepts = db.concepts.filter((c) => usedConceptIds.has(c.id)); const fragments = splitParagraphs(document.text).map((text, index) => ({ id: stableId('frag', `${document.id}:${index}:${text.slice(0, 80)}`), type: 'fragment', documentId: document.id, index, text, summary: text.slice(0, 100), conceptNames: [] })); const conceptMap = new Map(db.concepts.map((c) => [c.normalizedName, c])); const links = []; for (const fragment of fragments) { const fragmentConcepts = extractConcepts(fragment.text).slice(0, 6); fragment.conceptNames = fragmentConcepts.map((item) => item.normalizedName); for (const concept of fragmentConcepts) { if (!conceptMap.has(concept.normalizedName)) { conceptMap.set(concept.normalizedName, { id: stableId('concept', concept.normalizedName), type: 'concept', name: concept.name, normalizedName: concept.normalizedName, salience: Math.min(1, concept.count / 3) }); } const conceptNode = conceptMap.get(concept.normalizedName); links.push({ id: stableId('link', `${fragment.id}->${conceptNode.id}`), type: 'mentions', fromId: fragment.id, fromType: 'fragment', toId: conceptNode.id, toType: 'concept', documentId: document.id, score: concept.count }); } } for (let i = 0; i < fragments.length - 1; i += 1) { links.push({ id: stableId('link', `${fragments[i].id}->${fragments[i + 1].id}`), type: 'adjacent', fromId: fragments[i].id, fromType: 'fragment', toId: fragments[i + 1].id, toType: 'fragment', documentId: document.id, score: 0.4 }); } db.fragments.push(...fragments); db.links.push(...links); db.concepts = [...conceptMap.values()].sort((a, b) => a.normalizedName.localeCompare(b.normalizedName)); return { fragmentsCreated: fragments.length, linksCreated: links.length, conceptsTotal: db.concepts.length }; } async function queryWithEmbedding(adapter, embeddingProvider, q, options = {}) { const db = await adapter.load(); const query = normalizeConcept(q || ''); if (!query) throw new Error('Query is required'); const terms = query.split(' ').filter(Boolean); let scored = db.fragments.map((fragment) => { const textNorm = normalizeConcept(fragment.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if (fragment.conceptNames.includes(term)) score += 5; } return { fragment, score }; }).filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.fragment.index - b.fragment.index) .slice(0, Number(options.limit || 3)); // Vector re-rank if real embedding provider (not KeywordEmbeddingProvider) if (embeddingProvider && embeddingProvider.constructor.name !== 'KeywordEmbeddingProvider') { try { const queryVec = await embeddingProvider.embed(q); const fragTexts = scored.length > 0 ? scored.map((s) => s.fragment.text) : db.fragments.slice(0, 20).map((f) => f.text); const fragVecs = await embeddingProvider.embedBatch(fragTexts); if (fragVecs.length > 0) { const scoredWithVec = scored.length > 0 ? scored.map((s, i) => ({ ...s, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })) : db.fragments.slice(0, 20).map((f, i) => ({ fragment: f, score: 0, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })); scoredWithVec.forEach((item) => { item.blendedScore = item.score * 0.6 + item.vecScore * 10 * 0.4; }); scored = scoredWithVec.sort((a, b) => b.blendedScore - a.blendedScore).slice(0, Number(options.limit || 3)); } } catch { // No API key or vector search failed, keep keyword results } } const evidence = scored.map((item) => { const doc = db.documents.find((d) => d.id === item.fragment.documentId); return { fragmentId: item.fragment.id, documentId: item.fragment.documentId, documentTitle: doc?.title || 'unknown', score: item.score || item.vecScore, text: item.fragment.text }; }); const relatedConcepts = [...new Set(scored.flatMap((item) => item.fragment.conceptNames))] .map((name) => db.concepts.find((c) => c.normalizedName === name)) .filter(Boolean) .slice(0, 8) .map((concept) => ({ id: concept.id, name: concept.name, normalizedName: concept.normalizedName })); const answer = evidence.length ? `Found ${evidence.length} relevant fragments for "${q}". Top evidence comes from ${[...new Set(evidence.map((item) => item.documentTitle))].join(', ')}.` : `No strong match found for "${q}". Try a broader concept or ingest more documents.`; return { query: q, answer, evidence, relatedConcepts, stats: { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length } }; } function parseArgs(argv) { const result = { _: [] }; for (let i = 0; i < argv.length; i += 1) { const token = argv[i]; if (!token.startsWith('--')) { result._.push(token); continue; } const [key, inline] = token.slice(2).split('='); if (inline !== undefined) { result[key] = inline; continue; } const next = argv[i + 1]; if (!next || next.startsWith('--')) { result[key] = true; } else { result[key] = next; i += 1; } } return result; } function printHelp() { console.log(`LinkMind MVP CLI v0.2.0 Commands: ingest --file [--title ] [--sourceType <type>] [--storage json|sqlite] query --q <text> [--limit <n>] [--embedding keyword|openai] status [--storage json|sqlite] reset [--storage json|sqlite] help Options: --storage <adapter> Storage: json (default) or sqlite --embedding <provider> Embedding: keyword (default) or openai --db-path <path> Custom db path Examples: node dist/index.js ingest --file examples/sample-note.md --title "Sample" node dist/index.js query --q "knowledge connector" node dist/index.js status --storage sqlite node dist/index.js reset --storage sqlite`); } async function main() { const [, , command, ...rest] = process.argv; const args = parseArgs(rest); const storageType = args.storage || 'json'; const embeddingType = args.embedding || 'keyword'; const dbPathArg = args['db-path'] || undefined; let adapter = null; try { if (!command || command === 'help' || args.help) { printHelp(); return; } try { const adaptersDir = IS_DIST ? 'storage-adapters' : 'src/storage-adapters'; if (storageType === 'sqlite') { const { SqliteStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'SqliteStorageAdapter.js')); adapter = new SqliteStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } else { const { JsonStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'JsonStorageAdapter.js')); adapter = new JsonStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } } catch (e) { if (e.message.includes('sqlite3 not installed')) { console.error('[LinkMind] sqlite3 not installed. Run: npm install sqlite3'); } else { console.error(`[LinkMind] Adapter load error: ${e.message}`); } process.exitCode = 1; return; } let embeddingProvider = null; try { const providersDir = IS_DIST ? 'embedding-providers' : 'src/embedding-providers'; const { MockProvider, OpenAICompatibleProvider } = require(path.join(THIS_DIR, providersDir, 'index.js')); embeddingProvider = embeddingType === 'openai' ? new OpenAICompatibleProvider({ baseURL: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1', apiKey: process.env.OPENAI_API_KEY || '', model: process.env.OPENAI_MODEL || 'text-embedding-3-small' }) : new MockProvider(); } catch { embeddingProvider = null; } const now = () => new Date().toISOString(); if (command === 'ingest') { if (!args.file) throw new Error('--file is required for ingest'); const full = path.resolve(process.cwd(), args.file); if (!fs.existsSync(full)) throw new Error(`File not found: ${full}`); const text = fs.readFileSync(full, 'utf8'); const db = await adapter.load(); const doc = { id: stableId('doc', `${full}:${args.title || path.basename(full)}`), type: 'document', title: args.title || path.basename(full), sourceType: args.sourceType || 'file', sourceUri: full, importedAt: now(), status: 'active', text }; upsertDocument(db, doc); const stats = buildForDocument(db, doc); await adapter.save(db); console.log(JSON.stringify({ documentId: doc.id, title: doc.title, ...stats }, null, 2)); return; } if (command === 'query') { if (!args.q && !args.query) throw new Error('--q is required for query'); const result = await queryWithEmbedding(adapter, embeddingProvider, args.q || args.query, { limit: args.limit }); console.log(JSON.stringify(result, null, 2)); return; } if (command === 'status') { const db = await adapter.load(); console.log(JSON.stringify({ documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, adapter: storageType, embedding: embeddingType }, null, 2)); return; } if (command === 'reset') { await adapter.clear(); console.log(JSON.stringify({ ok: true, adapter: storageType }, null, 2)); return; } throw new Error(`Unknown command: ${command}`); } catch (error) { console.error(`[LinkMind] ${error.message}`); process.exitCode = 1; } finally { if (adapter) await adapter.close(); } } if (require.main === module) { main(); } module.exports = { buildForDocument, extractConcepts, splitParagraphs, normalizeConcept, stableId, queryWithEmbedding, createEmptyDb }; FILE:dist/retriever.js /** * retriever.js * 关键词召回 + 向量相似度召回双层检索。 * ranker：余弦相似度 * 最终结果 = 关键词召回 ∪ 向量召回 → 去重排序 */ const { normalizeConcept, STOPWORDS } = require('./utils/nlp'); /** * @typedef {Object} RetrievalResult * @property {string} fragmentId * @property {string} documentId * @property {string} documentTitle * @property {number} score * @property {string} text * @property {'keyword'|'vector'|'hybrid'} source */ /** * 计算余弦相似度 * @param {number[]} a * @param {number[]} b */ function cosineSimilarity(a, b) { if (a.length !== b.length) return 0; let dot = 0, na = 0, nb = 0; for (let i = 0; i < a.length; i += 1) { dot += a[i] * b[i]; na += a[i] * a[i]; nb += b[i] * b[i]; } const denom = Math.sqrt(na) * Math.sqrt(nb); return denom === 0 ? 0 : dot / denom; } /** * 关键词召回（从 fragment 列表） */ function keywordSearch(fragments, query, limit = 20) { const terms = normalizeConcept(query) .split(' ') .filter(Boolean) .filter((t) => !STOPWORDS.has(t)); if (terms.length === 0) return []; return fragments .map((frag) => { const textNorm = normalizeConcept(frag.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if ((frag.conceptNames || []).includes(term)) score += 5; } return { frag, score }; }) .filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.frag.index - b.frag.index) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.score, text: item.frag.text, source: 'keyword' })); } /** * 向量相似度召回 * @param {object[]} fragments - 带 documentTitle * @param {string} query * @param {import('./embedding-providers/EmbeddingProvider')} embeddingProvider * @param {number} limit */ async function vectorSearch(fragments, query, embeddingProvider, limit = 20) { if (!embeddingProvider || fragments.length === 0) return []; const texts = fragments.map((f) => f.text); const [queryVec, fragVecs] = await Promise.all([ embeddingProvider.embed([query]), embeddingProvider.embed(texts) ]); const qv = queryVec[0]; return fragments .map((frag, i) => ({ frag, sim: cosineSimilarity(qv, fragVecs[i] || []) })) .filter((item) => item.sim > 0) .sort((a, b) => b.sim - a.sim) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.sim, text: item.frag.text, source: 'vector' })); } /** * 合并关键词召回 + 向量召回，去重并排序 * @param {RetrievalResult[]} keywordResults * @param {RetrievalResult[]} vectorResults * @param {number} limit */ function mergeResults(keywordResults, vectorResults, limit = 10) { const seen = new Map(); for (const r of [...keywordResults, ...vectorResults]) { if (!seen.has(r.fragmentId)) { seen.set(r.fragmentId, { ...r }); } else { // 取最高分 const existing = seen.get(r.fragmentId); existing.score = Math.max(existing.score, r.score); existing.source = existing.source === r.source ? existing.source : 'hybrid'; } } return [...seen.values()] .sort((a, b) => b.score - a.score) .slice(0, limit); } /** * 主检索入口 * @param {object} options * @param {object[]} options.fragments - fragment 列表，需含 documentTitle * @param {string} options.query * @param {import('./embedding-providers/EmbeddingProvider')} [options.embeddingProvider] * @param {number} [options.limit] */ async function retrieve({ fragments, query, embeddingProvider = null, limit = 10 }) { const kw = keywordSearch(fragments, query, limit * 2); const vec = embeddingProvider ? await vectorSearch(fragments, query, embeddingProvider, limit * 2) : []; return mergeResults(kw, vec, limit); } module.exports = { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve }; FILE:dist/storage-adapters/JsonStorageAdapter.js /** * JsonStorageAdapter * 基于本地 JSON 文件的存储实现，保持向后兼容。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./StorageAdapter'); const ROOT = path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } class JsonStorageAdapter extends StorageAdapter { constructor() { super(); this._db = null; } _loadDb() { ensureDir(DATA_DIR); if (!fs.existsSync(DB_PATH)) { const empty = this._emptyDb(); this._saveDb(empty); return empty; } return JSON.parse(fs.readFileSync(DB_PATH, 'utf8')); } _saveDb(db) { db.updatedAt = now(); ensureDir(DATA_DIR); fs.writeFileSync(DB_PATH, JSON.stringify(db, null, 2), 'utf8'); } _emptyDb() { return { version: 1, createdAt: now(), updatedAt: now(), documents: [], fragments: [], concepts: [], links: [] }; } async init() { this._db = this._loadDb(); } // --- Document --- async saveDocument(doc) { const db = this._loadDb(); const idx = db.documents.findIndex((d) => d.id === doc.id); if (idx >= 0) db.documents[idx] = doc; else db.documents.push(doc); this._saveDb(db); return doc; } async getDocument(id) { const db = this._loadDb(); return db.documents.find((d) => d.id === id) || null; } async listDocuments() { const db = this._loadDb(); return db.documents; } async deleteDocument(id) { const db = this._loadDb(); db.documents = db.documents.filter((d) => d.id !== id); this._saveDb(db); } // --- Fragment --- async saveFragment(frag) { const db = this._loadDb(); const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); this._saveDb(db); return frag; } async getFragment(id) { const db = this._loadDb(); return db.fragments.find((f) => f.id === id) || null; } async listFragments(documentId) { const db = this._loadDb(); return db.fragments.filter((f) => f.documentId === documentId); } async deleteFragmentsByDocument(documentId) { const db = this._loadDb(); db.fragments = db.fragments.filter((f) => f.documentId !== documentId); this._saveDb(db); } async saveFragments(fragments) { const db = this._loadDb(); for (const frag of fragments) { const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); } this._saveDb(db); } // --- Concept --- async saveConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async getConcept(id) { const db = this._loadDb(); return db.concepts.find((c) => c.id === id) || null; } async listConcepts() { const db = this._loadDb(); return db.concepts; } async upsertConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async saveConcepts(concepts) { const db = this._loadDb(); for (const c of concepts) { const idx = db.concepts.findIndex((x) => x.id === c.id); if (idx >= 0) db.concepts[idx] = c; else db.concepts.push(c); } this._saveDb(db); } // --- Link --- async saveLink(link) { const db = this._loadDb(); const idx = db.links.findIndex((l) => l.id === link.id); if (idx >= 0) db.links[idx] = link; else db.links.push(link); this._saveDb(db); return link; } async getLinks(fromId, toId) { const db = this._loadDb(); return db.links.filter( (l) => (fromId ? l.fromId === fromId : true) && (toId ? l.toId === toId : true) ); } async deleteLinksByDocument(documentId) { const db = this._loadDb(); db.links = db.links.filter((l) => l.documentId !== documentId); this._saveDb(db); } async saveLinks(links) { const db = this._loadDb(); for (const l of links) { const idx = db.links.findIndex((x) => x.id === l.id); if (idx >= 0) db.links[idx] = l; else db.links.push(l); } this._saveDb(db); } // --- Full DB operations (for CLI compat) --- async load() { return this._loadDb(); } async save(db) { this._saveDb(db); } async close() { // no-op for JSON } // --- Workspace --- async clear() { this._saveDb(this._emptyDb()); } // --- Stats --- getStats() { const db = this._loadDb(); return { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, dbPath: DB_PATH }; } } module.exports = { JsonStorageAdapter }; FILE:dist/storage-adapters/SqliteStorageAdapter.js /** * SqliteStorageAdapter * 基于 better-sqlite3 的 SQLite 存储实现。 * 建表语句 + 所有 StorageAdapter 方法的完整实现。 */ const { StorageAdapter } = require('./StorageAdapter'); let sqlite3 = null; try { sqlite3 = require('better-sqlite3'); } catch { // better-sqlite3 not installed — adapter will throw on init() } const ROOT = path => require('path').resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'db.sqlite'); function ensureDir(dir) { require('fs').mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, sourceType TEXT, sourceUri TEXT, importedAt TEXT, status TEXT DEFAULT 'active', text TEXT, createdAt TEXT, updatedAt TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', documentId TEXT, "index" INTEGER, text TEXT, summary TEXT, conceptNames TEXT, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalizedName TEXT UNIQUE, salience REAL, createdAt TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, fromId TEXT, fromType TEXT, toId TEXT, toType TEXT, documentId TEXT, score REAL, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS fragment_vectors ( fragmentId TEXT PRIMARY KEY, vector BLOB, updatedAt TEXT, FOREIGN KEY (fragmentId) REFERENCES fragments(id) ); CREATE INDEX IF NOT EXISTS idx_fragments_documentId ON fragments(documentId); CREATE INDEX IF NOT EXISTS idx_links_fromId ON links(fromId); CREATE INDEX IF NOT EXISTS idx_links_toId ON links(toId); CREATE INDEX IF NOT EXISTS idx_links_documentId ON links(documentId); CREATE INDEX IF NOT EXISTS idx_concepts_normalizedName ON concepts(normalizedName); `; class SqliteStorageAdapter extends StorageAdapter { constructor({ dbPath } = {}) { super(); this._dbPath = dbPath || DB_PATH; this._db = null; } async init() { if (!sqlite3) { throw new Error('better-sqlite3 is not installed. Run: npm install better-sqlite3'); } ensureDir(require('path').dirname(this._dbPath)); this._db = sqlite3(this._dbPath); this._db.pragma('journal_mode = WAL'); this._db.exec(SCHEMA); } _run(sql, params = []) { try { return this._db.prepare(sql).run(...params); } catch (e) { throw new Error(`SQLite run error: ${e.message} | sql: ${sql}`); } } _all(sql, params = []) { try { return this._db.prepare(sql).all(...params); } catch (e) { throw new Error(`SQLite all error: ${e.message} | sql: ${sql}`); } } _get(sql, params = []) { try { return this._db.prepare(sql).get(...params); } catch (e) { throw new Error(`SQLite get error: ${e.message} | sql: ${sql}`); } } // --- Document --- async saveDocument(doc) { this._run( `INSERT OR REPLACE INTO documents (id,type,title,sourceType,sourceUri,importedAt,status,text,createdAt,updatedAt) VALUES (?,?,?,?,?,?,?,?,?,?)`, [doc.id, doc.type || 'document', doc.title || '', doc.sourceType || '', doc.sourceUri || '', doc.importedAt || now(), doc.status || 'active', doc.text || '', doc.createdAt || now(), now()] ); return doc; } async getDocument(id) { const row = this._get('SELECT * FROM documents WHERE id = ?', [id]); return row ? this._mapDoc(row) : null; } async listDocuments() { return this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); } async deleteDocument(id) { this._run('DELETE FROM documents WHERE id = ?', [id]); } // --- Fragment --- async saveFragment(frag) { this._run( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)`, [frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()] ); return frag; } async getFragment(id) { const row = this._get('SELECT * FROM fragments WHERE id = ?', [id]); return row ? this._mapFrag(row) : null; } async listFragments(documentId) { return this._all('SELECT * FROM fragments WHERE documentId = ? ORDER BY "index" ASC', [documentId]) .map(this._mapFrag); } async deleteFragmentsByDocument(documentId) { this._run('DELETE FROM fragments WHERE documentId = ?', [documentId]); } async saveFragments(fragments) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const frag of items) { stmt.run(frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()); } }); insertMany(fragments); return fragments; } // --- Concept --- async saveConcept(concept) { this._run( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)`, [concept.id, 'concept', concept.name, concept.normalizedName, concept.salience || 0, concept.createdAt || now()] ); return concept; } async getConcept(id) { const row = this._get('SELECT * FROM concepts WHERE id = ?', [id]); return row ? this._mapConcept(row) : null; } async listConcepts() { return this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); } async upsertConcept(concept) { return this.saveConcept(concept); } async saveConcepts(concepts) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const c of items) { stmt.run(c.id, 'concept', c.name, c.normalizedName, c.salience || 0, c.createdAt || now()); } }); insertMany(concepts); return concepts; } // --- Link --- async saveLink(link) { this._run( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)`, [link.id, link.type, link.fromId, link.fromType, link.toId, link.toType, link.documentId, link.score || 0, link.createdAt || now()] ); return link; } async getLinks(fromId, toId) { let sql = 'SELECT * FROM links WHERE 1=1'; const params = []; if (fromId) { sql += ' AND fromId = ?'; params.push(fromId); } if (toId) { sql += ' AND toId = ?'; params.push(toId); } return this._all(sql, params); } async deleteLinksByDocument(documentId) { this._run('DELETE FROM links WHERE documentId = ?', [documentId]); } async saveLinks(links) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const l of items) { stmt.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score || 0, l.createdAt || now()); } }); insertMany(links); return links; } // --- Vector --- async saveFragmentVector(fragmentId, vector) { const buf = Buffer.from(JSON.stringify(vector)); this._run( `INSERT OR REPLACE INTO fragment_vectors (fragmentId,vector,updatedAt) VALUES (?,?,?)`, [fragmentId, buf, now()] ); } async getFragmentVector(fragmentId) { const row = this._get('SELECT vector FROM fragment_vectors WHERE fragmentId = ?', [fragmentId]); if (!row) return null; return JSON.parse(row.vector); } async listFragmentVectors() { return this._all('SELECT fragmentId, vector FROM fragment_vectors').map((r) => ({ fragmentId: r.fragmentId, vector: JSON.parse(r.vector) })); } // --- Full DB operations (for CLI compat) --- async load() { const docs = this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); const frags = this._all('SELECT * FROM fragments ORDER BY "index" ASC').map(this._mapFrag); const concepts = this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); const links = this._all('SELECT * FROM links'); const updatedAt = this._get('SELECT MAX(updatedAt) as t FROM documents UNION ALL SELECT MAX(updatedAt) as t FROM fragments')?.t || new Date().toISOString(); return { version: 1, documents: docs, fragments: frags, concepts, links, updatedAt }; } async save(db) { await this.clear(); if (db.documents) for (const d of db.documents) await this.saveDocument(d); if (db.fragments) for (const f of db.fragments) await this.saveFragment(f); if (db.concepts) for (const c of db.concepts) await this.saveConcept(c); if (db.links) for (const l of db.links) await this.saveLink(l); } async close() { if (this._db) { this._db.close(); this._db = null; } } // --- Workspace --- async clear() { this._run('DELETE FROM fragment_vectors'); this._run('DELETE FROM links'); this._run('DELETE FROM fragments'); this._run('DELETE FROM concepts'); this._run('DELETE FROM documents'); } getStats() { return { documents: this._get('SELECT COUNT(*) as n FROM documents')?.n || 0, fragments: this._get('SELECT COUNT(*) as n FROM fragments')?.n || 0, concepts: this._get('SELECT COUNT(*) as n FROM concepts')?.n || 0, links: this._get('SELECT COUNT(*) as n FROM links')?.n || 0, dbPath: this._dbPath }; } // --- Mappers --- _mapDoc(row) { return { ...row }; } _mapFrag(row) { return { ...row, conceptNames: JSON.parse(row.conceptNames || '[]') }; } _mapConcept(row) { return { ...row }; } } module.exports = { SqliteStorageAdapter }; FILE:dist/storage-adapters/StorageAdapter.js /** * StorageAdapter Interface * 所有 storage adapter 必须实现此接口契约。 */ class StorageAdapter { /** * @returns {Promise<void>} */ async init() { throw new Error('Not implemented'); } // --- Document --- async saveDocument(doc) { throw new Error('Not implemented'); } async getDocument(id) { throw new Error('Not implemented'); } async listDocuments() { throw new Error('Not implemented'); } async deleteDocument(id) { throw new Error('Not implemented'); } // --- Fragment --- async saveFragment(frag) { throw new Error('Not implemented'); } async getFragment(id) { throw new Error('Not implemented'); } async listFragments(documentId) { throw new Error('Not implemented'); } async deleteFragmentsByDocument(documentId) { throw new Error('Not implemented'); } // --- Concept --- async saveConcept(concept) { throw new Error('Not implemented'); } async getConcept(id) { throw new Error('Not implemented'); } async listConcepts() { throw new Error('Not implemented'); } async upsertConcept(concept) { throw new Error('Not implemented'); } // --- Link --- async saveLink(link) { throw new Error('Not implemented'); } async getLinks(fromId, toId) { throw new Error('Not implemented'); } async deleteLinksByDocument(documentId) { throw new Error('Not implemented'); } // --- Bulk --- async saveFragments(fragments) { throw new Error('Not implemented'); } async saveConcepts(concepts) { throw new Error('Not implemented'); } async saveLinks(links) { throw new Error('Not implemented'); } // --- Workspace --- async clear() { throw new Error('Not implemented'); } } module.exports = { StorageAdapter }; FILE:dist/storage-adapters/index.js /** * storage-adapters/index.js * Factory: 根据配置返回对应的 storage adapter 实例。 */ const { JsonStorageAdapter } = require('./JsonStorageAdapter'); const { SqliteStorageAdapter } = require('./SqliteStorageAdapter'); function createStorageAdapter(type = 'json', options = {}) { switch (type) { case 'json': return new JsonStorageAdapter(options); case 'sqlite': return new SqliteStorageAdapter(options); default: throw new Error(`Unknown storage adapter type: ${type}. Use 'json' or 'sqlite'.`); } } module.exports = { createStorageAdapter, JsonStorageAdapter, SqliteStorageAdapter }; FILE:dist/utils/nlp.js /** * nlp.js - 文本规范化工具 */ const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were','can','could','should','about','what','when','where','which','while','than','then','them','they','their','there','here','also','more','most','some','such','using','used','use','make','made','over','under','very','just','only','each','been','being','does','did','done','how','why','our','you','its','his','her','she','him','who','has','had','but','too','via','per','one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及','进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有','对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为','实现','相关','用于' ]); function normalizeConcept(text) { if (!text) return ''; return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } module.exports = { normalizeConcept, STOPWORDS }; FILE:examples/sample-note.md # LinkMind sample note LinkMind is a connected knowledge system. It ingests notes and documents, splits them into fragments, extracts concepts, and builds explainable links. The MVP focuses on a local JSON workspace. This keeps the implementation simple while preserving a future path toward vector retrieval, graph storage, and richer evidence assembly. Knowledge Connector is the capability layer. LinkMind is the user-facing product layer built on top of it. Good retrieval should return answer, evidence, and related concepts instead of a single opaque paragraph. FILE:package.json { "name": "linkmind", "version": "0.2.0", "description": "LinkMind MVP skeleton - local knowledge connector with ingest/build/query CLI", "main": "dist/index.js", "bin": { "linkmind": "dist/index.js" }, "scripts": { "build": "cp -r src/storage-adapters dist/ && cp -r src/embedding-providers dist/ && cp -r src/utils dist/ && cp src/index.js dist/ && cp src/retriever.js dist/ && chmod +x dist/index.js", "test": "node tests/smoke-test.js" }, "keywords": ["knowledge", "graph", "rag", "linkmind"], "author": "Golden Bean", "license": "MIT", "optionalDependencies": { "sqlite3": "^5.1.7" } } FILE:skill.json { "name": "linkmind", "version": "1.0.0", "description": "知识连接引擎 - 本地化知识中枢 CLI 工具，支持 storage adapter 抽象层和 OpenAI-compatible embedding provider", "keywords": ["linkmind", "knowledge-management", "embedding", "local-knowledge", "cli", "vector-search"], "author": "harrylabsj", "license": "MIT", "repository": "https://github.com/harrylabsj/linkmind", "language": ["en", "zh"], "tags": ["knowledge-management", "embedding", "local-knowledge", "cli"], "createdAt": "2026-04-04", "updatedAt": "2026-04-05" } FILE:src/adapters/base.js /** * StorageAdapter 接口契约 * 所有 adapter 必须实现以下方法： * load() -> Promise<WorkspaceDb> * save(db) -> Promise<void> * clear() -> Promise<void> * query(fn) -> Promise<any> (事务查询) * close() -> Promise<void> */ class StorageAdapter { async load() { throw new Error('Not implemented: load()'); } async save(db) { throw new Error('Not implemented: save(db)'); } async clear() { throw new Error('Not implemented: clear()'); } async query(fn) { throw new Error('Not implemented: query(fn)'); } async close() { throw new Error('Not implemented: close()'); } } module.exports = { StorageAdapter }; FILE:src/adapters/json.js /** * JsonStorageAdapter * 保持现有 JSON 文件行为，完全兼容旧数据格式。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./base'); const ROOT = __dirname.includes(`${path.sep}dist`) ? path.resolve(__dirname, '../..') : path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } class JsonStorageAdapter extends StorageAdapter { constructor(opts = {}) { super(); this.dbPath = opts.dbPath || DB_PATH; this.dataDir = opts.dataDir || DATA_DIR; } _createEmpty() { return { version: 1, createdAt: new Date().toISOString(), updatedAt: new Date().toISOString(), documents: [], fragments: [], concepts: [], links: [] }; } async load() { ensureDir(this.dataDir); if (!fs.existsSync(this.dbPath)) { const empty = this._createEmpty(); await this.save(empty); return empty; } return JSON.parse(fs.readFileSync(this.dbPath, 'utf8')); } async save(db) { db.updatedAt = new Date().toISOString(); ensureDir(this.dataDir); fs.writeFileSync(this.dbPath, JSON.stringify(db, null, 2), 'utf8'); } async clear() { await this.save(this._createEmpty()); } async query(fn) { // JSON adapter 不需要事务，直接执行 const db = await this.load(); return fn(db); } async close() { // 无需关闭 } dbPath() { return this.dbPath; } } module.exports = { JsonStorageAdapter }; FILE:src/adapters/sqlite.js /** * SqliteStorageAdapter * SQLite 存储适配器骨架，表结构与 JSON 格式等效。 * 事务支持、批量操作能力。 */ const path = require('path'); const { StorageAdapter } = require('./base'); // sqlite3 为可选依赖，运行时检测 let sqlite3 = null; try { sqlite3 = require('sqlite3'); } catch { // sqlite3 不可用，稍后通过 init() 检测并给出明确错误 } const ROOT = __dirname.includes(`${path.sep}dist`) ? path.resolve(__dirname, '../..') : path.resolve(__dirname, '../..'); const DATA_DIR = path.resolve(ROOT, 'data'); const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, source_type TEXT, source_uri TEXT, imported_at TEXT, status TEXT DEFAULT 'active', text TEXT, metadata TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', document_id TEXT, frag_index INTEGER, text TEXT, summary TEXT, concept_names TEXT, metadata TEXT, FOREIGN KEY (document_id) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalized_name TEXT UNIQUE, salience REAL, metadata TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, from_id TEXT, from_type TEXT, to_id TEXT, to_type TEXT, document_id TEXT, score REAL, metadata TEXT ); CREATE INDEX IF NOT EXISTS idx_fragments_document ON fragments(document_id); CREATE INDEX IF NOT EXISTS idx_links_from ON links(from_id); CREATE INDEX IF NOT EXISTS idx_links_to ON links(to_id); CREATE INDEX IF NOT EXISTS idx_concepts_normalized ON concepts(normalized_name); `; class SqliteStorageAdapter extends StorageAdapter { constructor(opts = {}) { super(); if (!sqlite3) { throw new Error('sqlite3 not installed. Run: npm install sqlite3'); } this.dbPath = opts.dbPath || path.join(DATA_DIR, 'workspace.db'); this._db = null; } async init() { if (this._db) return; return new Promise((resolve, reject) => { this._db = new sqlite3.Database(this.dbPath, (err) => { if (err) return reject(err); this._db.exec(SCHEMA, (execErr) => { if (execErr) return reject(execErr); resolve(); }); }); }); } async load() { await this.init(); return new Promise((resolve, reject) => { const docStmt = this._db.prepare('SELECT * FROM documents'); const fragStmt = this._db.prepare('SELECT * FROM fragments'); const conceptStmt = this._db.prepare('SELECT * FROM concepts'); const linkStmt = this._db.prepare('SELECT * FROM links'); const rows = { documents: [], fragments: [], concepts: [], links: [] }; const finish = () => { // 反序列化 JSON 字段 rows.fragments = rows.fragments.map((f) => ({ ...f, id: f.id, type: 'fragment', documentId: f.document_id, index: f.frag_index, conceptNames: f.concept_names ? JSON.parse(f.concept_names) : [] })); rows.concepts = rows.concepts.map((c) => ({ ...c, type: 'concept', normalizedName: c.normalized_name })); rows.links = rows.links.map((l) => ({ ...l, fromId: l.from_id, fromType: l.from_type, toId: l.to_id, toType: l.to_type, documentId: l.document_id })); resolve(rows); }; let pending = 4; const done = () => { if (--pending === 0) finish(); }; docStmt.all((err, docs) => { if (err) { docStmt.close(); return reject(err); } rows.documents = docs.map((d) => ({ ...d, type: 'document' })); docStmt.close(); done(); }); fragStmt.all((err, frags) => { if (err) { fragStmt.close(); return reject(err); } rows.fragments = frags; fragStmt.close(); done(); }); conceptStmt.all((err, concepts) => { if (err) { conceptStmt.close(); return reject(err); } rows.concepts = concepts; conceptStmt.close(); done(); }); linkStmt.all((err, links) => { if (err) { linkStmt.close(); return reject(err); } rows.links = links; linkStmt.close(); done(); }); }); } async save(rows) { await this.init(); return new Promise((resolve, reject) => { this._db.serialize(() => { const insertDoc = this._db.prepare( 'INSERT OR REPLACE INTO documents (id,type,title,source_type,source_uri,imported_at,status,text,metadata) VALUES (?,?,?,?,?,?,?,?,?)' ); const insertFrag = this._db.prepare( 'INSERT OR REPLACE INTO fragments (id,type,document_id,frag_index,text,summary,concept_names,metadata) VALUES (?,?,?,?,?,?,?,?)' ); const insertConcept = this._db.prepare( 'INSERT OR REPLACE INTO concepts (id,type,name,normalized_name,salience,metadata) VALUES (?,?,?,?,?,?)' ); const insertLink = this._db.prepare( 'INSERT OR REPLACE INTO links (id,type,from_id,from_type,to_id,to_type,document_id,score,metadata) VALUES (?,?,?,?,?,?,?,?,?)' ); try { for (const d of rows.documents) { insertDoc.run(d.id, d.type, d.title, d.sourceType, d.sourceUri, d.importedAt, d.status, d.text, d.metadata ? JSON.stringify(d.metadata) : null); } for (const f of rows.fragments) { insertFrag.run(f.id, f.type, f.documentId, f.index, f.text, f.summary, JSON.stringify(f.conceptNames || []), f.metadata ? JSON.stringify(f.metadata) : null); } for (const c of rows.concepts) { insertConcept.run(c.id, c.type, c.name, c.normalizedName, c.salience, c.metadata ? JSON.stringify(c.metadata) : null); } for (const l of rows.links) { insertLink.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score, l.metadata ? JSON.stringify(l.metadata) : null); } resolve(); } catch (err) { reject(err); } finally { insertDoc.close(); insertFrag.close(); insertConcept.close(); insertLink.close(); } }); }); } async clear() { await this.init(); return new Promise((resolve, reject) => { this._db.run('DELETE FROM links; DELETE FROM fragments; DELETE FROM concepts; DELETE FROM documents;', function (err) { if (err) return reject(err); resolve(); }); }); } async query(fn) { // 供未来事务查询使用，当前与 load 行为相同 const db = await this.load(); return fn(db); } async close() { if (this._db) { return new Promise((resolve) => { this._db.close(() => resolve()); this._db = null; }); } } } module.exports = { SqliteStorageAdapter }; FILE:src/embedding-providers/EmbeddingProvider.js /** * EmbeddingProvider Interface * 所有 embedding provider 必须实现此接口契约。 */ class EmbeddingProvider { /** * 将文本列表转为向量 * @param {string[]} texts * @returns {Promise<number[][]>} */ async embed(texts) { throw new Error('Not implemented'); } /** * 返回 provider 名称 */ get name() { return 'unknown'; } /** * 返回向量维度 */ get dimension() { throw new Error('Not implemented'); } } module.exports = { EmbeddingProvider }; FILE:src/embedding-providers/MockProvider.js /** * MockProvider * 返回随机向量，用于测试和离线开发。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class MockProvider extends EmbeddingProvider { constructor({ dimension = DEFAULT_DIM, seed = 42 } = {}) { super(); this._dim = dimension; this._seed = seed; this._cache = new Map(); } get name() { return 'mock'; } get dimension() { return this._dim; } _pseudoRandom(text) { let hash = 0; for (let i = 0; i < text.length; i += 1) { hash = ((hash << 5) - hash + text.charCodeAt(i)) | 0; } return Math.abs(hash) / 0x7fffffff; } async embed(texts) { return texts.map((text) => { if (this._cache.has(text)) return this._cache.get(text); const vec = []; for (let i = 0; i < this._dim; i += 1) { // deterministic random based on text + index const base = this._pseudoRandom(text + i); vec.push(base); } // L2 normalize const norm = Math.sqrt(vec.reduce((s, v) => s + v * v, 0)); const normalized = norm > 0 ? vec.map((v) => v / norm) : vec; this._cache.set(text, normalized); return normalized; }); } } module.exports = { MockProvider }; FILE:src/embedding-providers/OpenAICompatibleProvider.js /** * OpenAICompatibleProvider * 调用 OpenAI-compatible API endpoint（如 vLLM、Ollama、Azure OpenAI 等）。 */ const { EmbeddingProvider } = require('./EmbeddingProvider'); const DEFAULT_DIM = 1536; class OpenAICompatibleProvider extends EmbeddingProvider { constructor({ baseURL = 'https://api.openai.com/v1', apiKey = '', model = 'text-embedding-3-small', dimension = DEFAULT_DIM, batchSize = 100 } = {}) { super(); this._baseURL = baseURL.replace(/\/$/, ''); this._apiKey = apiKey; this._model = model; this._dim = dimension; this._batchSize = batchSize; } get name() { return `openai-compatible:${this._model}`; } get dimension() { return this._dim; } async embed(texts) { if (!texts || texts.length === 0) return []; const results = []; for (let i = 0; i < texts.length; i += this._batchSize) { const batch = texts.slice(i, i + this._batchSize); const vectors = await this._fetchBatch(batch); results.push(...vectors); } return results; } async _fetchBatch(batch) { const url = `${this._baseURL}/embeddings`; const body = { model: this._model, input: batch }; const headers = { 'Content-Type': 'application/json' }; if (this._apiKey) { headers['Authorization'] = `Bearer ${this._apiKey}`; } const resp = await fetch(url, { method: 'POST', headers, body: JSON.stringify(body) }); if (!resp.ok) { const text = await resp.text().catch(() => ''); throw new Error(`Embedding API error ${resp.status}: ${text}`); } const json = await resp.json(); if (!json.data || !Array.isArray(json.data)) { throw new Error(`Unexpected embedding response format`); } // sort by index to maintain order const sorted = json.data.slice().sort((a, b) => a.index - b.index); return sorted.map((item) => { if (!item.embedding || !Array.isArray(item.embedding)) { throw new Error(`Invalid embedding vector in response`); } return item.embedding; }); } } module.exports = { OpenAICompatibleProvider }; FILE:src/embedding-providers/index.js /** * embedding-providers/index.js * Factory: 根据配置返回对应的 embedding provider 实例。 */ const { MockProvider } = require('./MockProvider'); const { OpenAICompatibleProvider } = require('./OpenAICompatibleProvider'); function createEmbeddingProvider(type = 'mock', options = {}) { switch (type) { case 'mock': return new MockProvider(options); case 'openai': return new OpenAICompatibleProvider(options); default: throw new Error(`Unknown embedding provider type: ${type}. Use 'mock' or 'openai'.`); } } module.exports = { createEmbeddingProvider, MockProvider, OpenAICompatibleProvider }; FILE:src/index.js #!/usr/bin/env node /** * LinkMind CLI - 知识连接引擎 * 支持 adapter 切换（json/sqlite）和 embedding 切换（keyword/openai） */ const fs = require('fs'); const path = require('path'); const THIS_DIR = __dirname.includes(`${path.sep}dist`) ? __dirname : __dirname; const IS_DIST = THIS_DIR.includes(`${path.sep}dist`); const ROOT = IS_DIST ? path.resolve(THIS_DIR, '..') : path.resolve(THIS_DIR, '..'); const DATA_DIR = path.join(ROOT, 'data'); function createEmptyDb() { return { version: 1, createdAt: new Date().toISOString(), updatedAt: new Date().toISOString(), documents: [], fragments: [], concepts: [], links: [] }; } function stableId(prefix, input) { let hash = 0; for (let i = 0; i < input.length; i += 1) { hash = ((hash << 5) - hash + input.charCodeAt(i)) | 0; } return `${prefix}_${Math.abs(hash).toString(36)}`; } function normalizeConcept(text) { return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were', 'can','could','should','about','what','when','where','which','while','than','then','them','they', 'their','there','here','also','more','most','some','such','using','used','use','make','made', 'over','under','very','just','only','each','been','being','does','did','done','how','why', 'our','you','its','his','her','she','him','who','has','had','but','too','via','per', 'one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及', '进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有', '对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为' ]); function extractConcepts(text) { const zhMatches = text.match(/[\u4e00-\u9fa5]{2,8}/g) || []; const enMatches = text.match(/[A-Za-z][A-Za-z0-9_-]{2,}/g) || []; const tokens = [...zhMatches, ...enMatches] .map((item) => item.trim()) .map((item) => ({ raw: item, normalized: normalizeConcept(item) })) .filter((item) => item.normalized && !STOPWORDS.has(item.normalized)); const counts = new Map(); for (const token of tokens) { counts.set(token.normalized, (counts.get(token.normalized) || 0) + 1); } return [...counts.entries()] .map(([normalizedName, count]) => ({ normalizedName, name: tokens.find((item) => item.normalized === normalizedName)?.raw || normalizedName, count })) .sort((a, b) => b.count - a.count || a.normalizedName.localeCompare(b.normalizedName)) .slice(0, 12); } function splitParagraphs(text) { return text .split(/\n\s*\n+/) .map((part) => part.trim()) .filter(Boolean); } function upsertDocument(db, doc) { const existingIndex = db.documents.findIndex((item) => item.id === doc.id); if (existingIndex >= 0) db.documents[existingIndex] = doc; else db.documents.push(doc); } function buildForDocument(db, document) { const fragmentIds = db.fragments.filter((f) => f.documentId === document.id).map((f) => f.id); db.fragments = db.fragments.filter((f) => f.documentId !== document.id); db.links = db.links.filter((l) => !fragmentIds.includes(l.fromId) && !fragmentIds.includes(l.toId) && l.documentId !== document.id); const usedConceptIds = new Set(); for (const link of db.links) { if (link.toType === 'concept') usedConceptIds.add(link.toId); if (link.fromType === 'concept') usedConceptIds.add(link.fromId); } db.concepts = db.concepts.filter((c) => usedConceptIds.has(c.id)); const fragments = splitParagraphs(document.text).map((text, index) => ({ id: stableId('frag', `${document.id}:${index}:${text.slice(0, 80)}`), type: 'fragment', documentId: document.id, index, text, summary: text.slice(0, 100), conceptNames: [] })); const conceptMap = new Map(db.concepts.map((c) => [c.normalizedName, c])); const links = []; for (const fragment of fragments) { const fragmentConcepts = extractConcepts(fragment.text).slice(0, 6); fragment.conceptNames = fragmentConcepts.map((item) => item.normalizedName); for (const concept of fragmentConcepts) { if (!conceptMap.has(concept.normalizedName)) { conceptMap.set(concept.normalizedName, { id: stableId('concept', concept.normalizedName), type: 'concept', name: concept.name, normalizedName: concept.normalizedName, salience: Math.min(1, concept.count / 3) }); } const conceptNode = conceptMap.get(concept.normalizedName); links.push({ id: stableId('link', `${fragment.id}->${conceptNode.id}`), type: 'mentions', fromId: fragment.id, fromType: 'fragment', toId: conceptNode.id, toType: 'concept', documentId: document.id, score: concept.count }); } } for (let i = 0; i < fragments.length - 1; i += 1) { links.push({ id: stableId('link', `${fragments[i].id}->${fragments[i + 1].id}`), type: 'adjacent', fromId: fragments[i].id, fromType: 'fragment', toId: fragments[i + 1].id, toType: 'fragment', documentId: document.id, score: 0.4 }); } db.fragments.push(...fragments); db.links.push(...links); db.concepts = [...conceptMap.values()].sort((a, b) => a.normalizedName.localeCompare(b.normalizedName)); return { fragmentsCreated: fragments.length, linksCreated: links.length, conceptsTotal: db.concepts.length }; } async function queryWithEmbedding(adapter, embeddingProvider, q, options = {}) { const db = await adapter.load(); const query = normalizeConcept(q || ''); if (!query) throw new Error('Query is required'); const terms = query.split(' ').filter(Boolean); let scored = db.fragments.map((fragment) => { const textNorm = normalizeConcept(fragment.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if (fragment.conceptNames.includes(term)) score += 5; } return { fragment, score }; }).filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.fragment.index - b.fragment.index) .slice(0, Number(options.limit || 3)); // Vector re-rank if real embedding provider (not KeywordEmbeddingProvider) if (embeddingProvider && embeddingProvider.constructor.name !== 'KeywordEmbeddingProvider') { try { const queryVec = await embeddingProvider.embed(q); const fragTexts = scored.length > 0 ? scored.map((s) => s.fragment.text) : db.fragments.slice(0, 20).map((f) => f.text); const fragVecs = await embeddingProvider.embedBatch(fragTexts); if (fragVecs.length > 0) { const scoredWithVec = scored.length > 0 ? scored.map((s, i) => ({ ...s, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })) : db.fragments.slice(0, 20).map((f, i) => ({ fragment: f, score: 0, vecScore: embeddingProvider.similarity(queryVec, fragVecs[i]) })); scoredWithVec.forEach((item) => { item.blendedScore = item.score * 0.6 + item.vecScore * 10 * 0.4; }); scored = scoredWithVec.sort((a, b) => b.blendedScore - a.blendedScore).slice(0, Number(options.limit || 3)); } } catch { // No API key or vector search failed, keep keyword results } } const evidence = scored.map((item) => { const doc = db.documents.find((d) => d.id === item.fragment.documentId); return { fragmentId: item.fragment.id, documentId: item.fragment.documentId, documentTitle: doc?.title || 'unknown', score: item.score || item.vecScore, text: item.fragment.text }; }); const relatedConcepts = [...new Set(scored.flatMap((item) => item.fragment.conceptNames))] .map((name) => db.concepts.find((c) => c.normalizedName === name)) .filter(Boolean) .slice(0, 8) .map((concept) => ({ id: concept.id, name: concept.name, normalizedName: concept.normalizedName })); const answer = evidence.length ? `Found ${evidence.length} relevant fragments for "${q}". Top evidence comes from ${[...new Set(evidence.map((item) => item.documentTitle))].join(', ')}.` : `No strong match found for "${q}". Try a broader concept or ingest more documents.`; return { query: q, answer, evidence, relatedConcepts, stats: { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length } }; } function parseArgs(argv) { const result = { _: [] }; for (let i = 0; i < argv.length; i += 1) { const token = argv[i]; if (!token.startsWith('--')) { result._.push(token); continue; } const [key, inline] = token.slice(2).split('='); if (inline !== undefined) { result[key] = inline; continue; } const next = argv[i + 1]; if (!next || next.startsWith('--')) { result[key] = true; } else { result[key] = next; i += 1; } } return result; } function printHelp() { console.log(`LinkMind MVP CLI v0.2.0 Commands: ingest --file <path> [--title <title>] [--sourceType <type>] [--storage json|sqlite] query --q <text> [--limit <n>] [--embedding keyword|openai] status [--storage json|sqlite] reset [--storage json|sqlite] help Options: --storage <adapter> Storage: json (default) or sqlite --embedding <provider> Embedding: keyword (default) or openai --db-path <path> Custom db path Examples: node dist/index.js ingest --file examples/sample-note.md --title "Sample" node dist/index.js query --q "knowledge connector" node dist/index.js status --storage sqlite node dist/index.js reset --storage sqlite`); } async function main() { const [, , command, ...rest] = process.argv; const args = parseArgs(rest); const storageType = args.storage || 'json'; const embeddingType = args.embedding || 'keyword'; const dbPathArg = args['db-path'] || undefined; let adapter = null; try { if (!command || command === 'help' || args.help) { printHelp(); return; } try { const adaptersDir = IS_DIST ? 'storage-adapters' : 'src/storage-adapters'; if (storageType === 'sqlite') { const { SqliteStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'SqliteStorageAdapter.js')); adapter = new SqliteStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } else { const { JsonStorageAdapter } = require(path.join(THIS_DIR, adaptersDir, 'JsonStorageAdapter.js')); adapter = new JsonStorageAdapter(dbPathArg ? { dbPath: dbPathArg } : {}); } } catch (e) { if (e.message.includes('sqlite3 not installed')) { console.error('[LinkMind] sqlite3 not installed. Run: npm install sqlite3'); } else { console.error(`[LinkMind] Adapter load error: ${e.message}`); } process.exitCode = 1; return; } let embeddingProvider = null; try { const providersDir = IS_DIST ? 'embedding-providers' : 'src/embedding-providers'; const { MockProvider, OpenAICompatibleProvider } = require(path.join(THIS_DIR, providersDir, 'index.js')); embeddingProvider = embeddingType === 'openai' ? new OpenAICompatibleProvider({ baseURL: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1', apiKey: process.env.OPENAI_API_KEY || '', model: process.env.OPENAI_MODEL || 'text-embedding-3-small' }) : new MockProvider(); } catch { embeddingProvider = null; } const now = () => new Date().toISOString(); if (command === 'ingest') { if (!args.file) throw new Error('--file is required for ingest'); const full = path.resolve(process.cwd(), args.file); if (!fs.existsSync(full)) throw new Error(`File not found: ${full}`); const text = fs.readFileSync(full, 'utf8'); const db = await adapter.load(); const doc = { id: stableId('doc', `${full}:${args.title || path.basename(full)}`), type: 'document', title: args.title || path.basename(full), sourceType: args.sourceType || 'file', sourceUri: full, importedAt: now(), status: 'active', text }; upsertDocument(db, doc); const stats = buildForDocument(db, doc); await adapter.save(db); console.log(JSON.stringify({ documentId: doc.id, title: doc.title, ...stats }, null, 2)); return; } if (command === 'query') { if (!args.q && !args.query) throw new Error('--q is required for query'); const result = await queryWithEmbedding(adapter, embeddingProvider, args.q || args.query, { limit: args.limit }); console.log(JSON.stringify(result, null, 2)); return; } if (command === 'status') { const db = await adapter.load(); console.log(JSON.stringify({ documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, adapter: storageType, embedding: embeddingType }, null, 2)); return; } if (command === 'reset') { await adapter.clear(); console.log(JSON.stringify({ ok: true, adapter: storageType }, null, 2)); return; } throw new Error(`Unknown command: ${command}`); } catch (error) { console.error(`[LinkMind] ${error.message}`); process.exitCode = 1; } finally { if (adapter) await adapter.close(); } } if (require.main === module) { main(); } module.exports = { buildForDocument, extractConcepts, splitParagraphs, normalizeConcept, stableId, queryWithEmbedding, createEmptyDb }; FILE:src/providers/base.js /** * EmbeddingProvider 接口契约 * 所有 provider 必须实现以下方法： * embed(text) -> Promise<number[]> 单文本向量 * embedBatch(texts) -> Promise<number[][]> 批量向量 * similarity(a, b) -> number 余弦相似度 * dimensions() -> number 向量维度 */ class EmbeddingProvider { async embed(text) { throw new Error('Not implemented: embed(text)'); } async embedBatch(texts) { throw new Error('Not implemented: embedBatch(texts)'); } similarity(a, b) { if (a.length !== b.length) throw new Error('Vector dimensions mismatch'); let dot = 0, normA = 0, normB = 0; for (let i = 0; i < a.length; i++) { dot += a[i] * b[i]; normA += a[i] * a[i]; normB += b[i] * b[i]; } return dot / (Math.sqrt(normA) * Math.sqrt(normB) + 1e-10); } dimensions() { throw new Error('Not implemented: dimensions()'); } } module.exports = { EmbeddingProvider }; FILE:src/providers/openai.js /** * KeywordEmbeddingProvider * MVP 版本：基于词项匹配的伪嵌入，用于无外部 API 依赖场景。 * 后续可替换为真正的 OpenAI/text-embedding-3* provider。 */ const { EmbeddingProvider } = require('./base'); class KeywordEmbeddingProvider extends EmbeddingProvider { constructor(opts = {}) { super(); this._dims = opts.dimensions || 384; // 与 text-embedding-3-small 常用维度兼容 } dimensions() { return this._dims; } /** * 简单词袋伪嵌入：基于 term frequency 构建稀疏向量 * 兼容性接口，实际向量召回走 retriever 的关键词逻辑 */ async embed(text) { const terms = (text || '') .toLowerCase() .replace(/[^\w\s\u4e00-\u9fa5]/g, ' ') .split(/\s+/) .filter(Boolean); // 生成确定性伪向量（基于 term hash） const vec = new Array(this._dims).fill(0); for (const term of terms) { const hash = this._hash(term); for (let i = 0; i < terms.length; i++) { const idx = (hash + i * 31) % this._dims; vec[idx] += 1; } } // L2 normalize const norm = Math.sqrt(vec.reduce((s, v) => s + v * v, 0)); return norm > 0 ? vec.map((v) => v / norm) : vec; } async embedBatch(texts) { return Promise.all(texts.map((t) => this.embed(t))); } _hash(s) { let h = 0; for (let i = 0; i < s.length; i++) { h = ((h << 5) - h + s.charCodeAt(i)) | 0; } return Math.abs(h); } } /** * OpenAiEmbeddingProvider * OpenAI-compatible API 调用桩，后续填入真实 API key 和 endpoint 即可启用。 */ class OpenAiEmbeddingProvider extends EmbeddingProvider { constructor(opts = {}) { super(); this.apiKey = opts.apiKey || process.env.OPENAI_API_KEY; this.endpoint = opts.endpoint || 'https://api.openai.com/v1/embeddings'; this.model = opts.model || 'text-embedding-3-small'; this.dims = opts.dimensions || 1536; } dimensions() { return this.dims; } async embed(text) { if (!this.apiKey) { throw new Error('OPENAI_API_KEY not set. Use --embedding=keyword or set OPENAI_API_KEY.'); } const res = await fetch(this.endpoint, { method: 'POST', headers: { 'Authorization': `Bearer ${this.apiKey}`, 'Content-Type': 'application/json' }, body: JSON.stringify({ model: this.model, input: text, dimensions: this.dims }) }); if (!res.ok) { const err = await res.text(); throw new Error(`OpenAI embedding error ${res.status}: ${err}`); } const json = await res.json(); return json.data[0].embedding; } async embedBatch(texts) { if (!this.apiKey) { throw new Error('OPENAI_API_KEY not set. Use --embedding=keyword or set OPENAI_API_KEY.'); } const res = await fetch(this.endpoint, { method: 'POST', headers: { 'Authorization': `Bearer ${this.apiKey}`, 'Content-Type': 'application/json' }, body: JSON.stringify({ model: this.model, input: texts, dimensions: this.dims }) }); if (!res.ok) { const err = await res.text(); throw new Error(`OpenAI embedding error ${res.status}: ${err}`); } const json = await res.json(); return json.data.map((item) => item.embedding); } } module.exports = { KeywordEmbeddingProvider, OpenAiEmbeddingProvider }; FILE:src/retriever.js /** * retriever.js * 关键词召回 + 向量相似度召回双层检索。 * ranker：余弦相似度 * 最终结果 = 关键词召回 ∪ 向量召回 → 去重排序 */ const { normalizeConcept, STOPWORDS } = require('./utils/nlp'); /** * @typedef {Object} RetrievalResult * @property {string} fragmentId * @property {string} documentId * @property {string} documentTitle * @property {number} score * @property {string} text * @property {'keyword'|'vector'|'hybrid'} source */ /** * 计算余弦相似度 * @param {number[]} a * @param {number[]} b */ function cosineSimilarity(a, b) { if (a.length !== b.length) return 0; let dot = 0, na = 0, nb = 0; for (let i = 0; i < a.length; i += 1) { dot += a[i] * b[i]; na += a[i] * a[i]; nb += b[i] * b[i]; } const denom = Math.sqrt(na) * Math.sqrt(nb); return denom === 0 ? 0 : dot / denom; } /** * 关键词召回（从 fragment 列表） */ function keywordSearch(fragments, query, limit = 20) { const terms = normalizeConcept(query) .split(' ') .filter(Boolean) .filter((t) => !STOPWORDS.has(t)); if (terms.length === 0) return []; return fragments .map((frag) => { const textNorm = normalizeConcept(frag.text); let score = 0; for (const term of terms) { if (textNorm.includes(term)) score += 3; if ((frag.conceptNames || []).includes(term)) score += 5; } return { frag, score }; }) .filter((item) => item.score > 0) .sort((a, b) => b.score - a.score || a.frag.index - b.frag.index) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.score, text: item.frag.text, source: 'keyword' })); } /** * 向量相似度召回 * @param {object[]} fragments - 带 documentTitle * @param {string} query * @param {import('./embedding-providers/EmbeddingProvider')} embeddingProvider * @param {number} limit */ async function vectorSearch(fragments, query, embeddingProvider, limit = 20) { if (!embeddingProvider || fragments.length === 0) return []; const texts = fragments.map((f) => f.text); const [queryVec, fragVecs] = await Promise.all([ embeddingProvider.embed([query]), embeddingProvider.embed(texts) ]); const qv = queryVec[0]; return fragments .map((frag, i) => ({ frag, sim: cosineSimilarity(qv, fragVecs[i] || []) })) .filter((item) => item.sim > 0) .sort((a, b) => b.sim - a.sim) .slice(0, limit) .map((item) => ({ fragmentId: item.frag.id, documentId: item.frag.documentId, documentTitle: item.frag.documentTitle || 'unknown', score: item.sim, text: item.frag.text, source: 'vector' })); } /** * 合并关键词召回 + 向量召回，去重并排序 * @param {RetrievalResult[]} keywordResults * @param {RetrievalResult[]} vectorResults * @param {number} limit */ function mergeResults(keywordResults, vectorResults, limit = 10) { const seen = new Map(); for (const r of [...keywordResults, ...vectorResults]) { if (!seen.has(r.fragmentId)) { seen.set(r.fragmentId, { ...r }); } else { // 取最高分 const existing = seen.get(r.fragmentId); existing.score = Math.max(existing.score, r.score); existing.source = existing.source === r.source ? existing.source : 'hybrid'; } } return [...seen.values()] .sort((a, b) => b.score - a.score) .slice(0, limit); } /** * 主检索入口 * @param {object} options * @param {object[]} options.fragments - fragment 列表，需含 documentTitle * @param {string} options.query * @param {import('./embedding-providers/EmbeddingProvider')} [options.embeddingProvider] * @param {number} [options.limit] */ async function retrieve({ fragments, query, embeddingProvider = null, limit = 10 }) { const kw = keywordSearch(fragments, query, limit * 2); const vec = embeddingProvider ? await vectorSearch(fragments, query, embeddingProvider, limit * 2) : []; return mergeResults(kw, vec, limit); } module.exports = { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve }; FILE:src/storage-adapters/JsonStorageAdapter.js /** * JsonStorageAdapter * 基于本地 JSON 文件的存储实现，保持向后兼容。 */ const fs = require('fs'); const path = require('path'); const { StorageAdapter } = require('./StorageAdapter'); const ROOT = path.resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'workspace.json'); function ensureDir(dir) { fs.mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } class JsonStorageAdapter extends StorageAdapter { constructor() { super(); this._db = null; } _loadDb() { ensureDir(DATA_DIR); if (!fs.existsSync(DB_PATH)) { const empty = this._emptyDb(); this._saveDb(empty); return empty; } return JSON.parse(fs.readFileSync(DB_PATH, 'utf8')); } _saveDb(db) { db.updatedAt = now(); ensureDir(DATA_DIR); fs.writeFileSync(DB_PATH, JSON.stringify(db, null, 2), 'utf8'); } _emptyDb() { return { version: 1, createdAt: now(), updatedAt: now(), documents: [], fragments: [], concepts: [], links: [] }; } async init() { this._db = this._loadDb(); } // --- Document --- async saveDocument(doc) { const db = this._loadDb(); const idx = db.documents.findIndex((d) => d.id === doc.id); if (idx >= 0) db.documents[idx] = doc; else db.documents.push(doc); this._saveDb(db); return doc; } async getDocument(id) { const db = this._loadDb(); return db.documents.find((d) => d.id === id) || null; } async listDocuments() { const db = this._loadDb(); return db.documents; } async deleteDocument(id) { const db = this._loadDb(); db.documents = db.documents.filter((d) => d.id !== id); this._saveDb(db); } // --- Fragment --- async saveFragment(frag) { const db = this._loadDb(); const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); this._saveDb(db); return frag; } async getFragment(id) { const db = this._loadDb(); return db.fragments.find((f) => f.id === id) || null; } async listFragments(documentId) { const db = this._loadDb(); return db.fragments.filter((f) => f.documentId === documentId); } async deleteFragmentsByDocument(documentId) { const db = this._loadDb(); db.fragments = db.fragments.filter((f) => f.documentId !== documentId); this._saveDb(db); } async saveFragments(fragments) { const db = this._loadDb(); for (const frag of fragments) { const idx = db.fragments.findIndex((f) => f.id === frag.id); if (idx >= 0) db.fragments[idx] = frag; else db.fragments.push(frag); } this._saveDb(db); } // --- Concept --- async saveConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async getConcept(id) { const db = this._loadDb(); return db.concepts.find((c) => c.id === id) || null; } async listConcepts() { const db = this._loadDb(); return db.concepts; } async upsertConcept(concept) { const db = this._loadDb(); const idx = db.concepts.findIndex((c) => c.id === concept.id); if (idx >= 0) db.concepts[idx] = concept; else db.concepts.push(concept); this._saveDb(db); return concept; } async saveConcepts(concepts) { const db = this._loadDb(); for (const c of concepts) { const idx = db.concepts.findIndex((x) => x.id === c.id); if (idx >= 0) db.concepts[idx] = c; else db.concepts.push(c); } this._saveDb(db); } // --- Link --- async saveLink(link) { const db = this._loadDb(); const idx = db.links.findIndex((l) => l.id === link.id); if (idx >= 0) db.links[idx] = link; else db.links.push(link); this._saveDb(db); return link; } async getLinks(fromId, toId) { const db = this._loadDb(); return db.links.filter( (l) => (fromId ? l.fromId === fromId : true) && (toId ? l.toId === toId : true) ); } async deleteLinksByDocument(documentId) { const db = this._loadDb(); db.links = db.links.filter((l) => l.documentId !== documentId); this._saveDb(db); } async saveLinks(links) { const db = this._loadDb(); for (const l of links) { const idx = db.links.findIndex((x) => x.id === l.id); if (idx >= 0) db.links[idx] = l; else db.links.push(l); } this._saveDb(db); } // --- Full DB operations (for CLI compat) --- async load() { return this._loadDb(); } async save(db) { this._saveDb(db); } async close() { // no-op for JSON } // --- Workspace --- async clear() { this._saveDb(this._emptyDb()); } // --- Stats --- getStats() { const db = this._loadDb(); return { documents: db.documents.length, fragments: db.fragments.length, concepts: db.concepts.length, links: db.links.length, updatedAt: db.updatedAt, dbPath: DB_PATH }; } } module.exports = { JsonStorageAdapter }; FILE:src/storage-adapters/SqliteStorageAdapter.js /** * SqliteStorageAdapter * 基于 better-sqlite3 的 SQLite 存储实现。 * 建表语句 + 所有 StorageAdapter 方法的完整实现。 */ const { StorageAdapter } = require('./StorageAdapter'); let sqlite3 = null; try { sqlite3 = require('better-sqlite3'); } catch { // better-sqlite3 not installed — adapter will throw on init() } const ROOT = path => require('path').resolve(__dirname, '../..'); const DATA_DIR = path.join(ROOT, 'data'); const DB_PATH = path.join(DATA_DIR, 'db.sqlite'); function ensureDir(dir) { require('fs').mkdirSync(dir, { recursive: true }); } function now() { return new Date().toISOString(); } const SCHEMA = ` CREATE TABLE IF NOT EXISTS documents ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'document', title TEXT, sourceType TEXT, sourceUri TEXT, importedAt TEXT, status TEXT DEFAULT 'active', text TEXT, createdAt TEXT, updatedAt TEXT ); CREATE TABLE IF NOT EXISTS fragments ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'fragment', documentId TEXT, "index" INTEGER, text TEXT, summary TEXT, conceptNames TEXT, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS concepts ( id TEXT PRIMARY KEY, type TEXT DEFAULT 'concept', name TEXT, normalizedName TEXT UNIQUE, salience REAL, createdAt TEXT ); CREATE TABLE IF NOT EXISTS links ( id TEXT PRIMARY KEY, type TEXT, fromId TEXT, fromType TEXT, toId TEXT, toType TEXT, documentId TEXT, score REAL, createdAt TEXT, FOREIGN KEY (documentId) REFERENCES documents(id) ); CREATE TABLE IF NOT EXISTS fragment_vectors ( fragmentId TEXT PRIMARY KEY, vector BLOB, updatedAt TEXT, FOREIGN KEY (fragmentId) REFERENCES fragments(id) ); CREATE INDEX IF NOT EXISTS idx_fragments_documentId ON fragments(documentId); CREATE INDEX IF NOT EXISTS idx_links_fromId ON links(fromId); CREATE INDEX IF NOT EXISTS idx_links_toId ON links(toId); CREATE INDEX IF NOT EXISTS idx_links_documentId ON links(documentId); CREATE INDEX IF NOT EXISTS idx_concepts_normalizedName ON concepts(normalizedName); `; class SqliteStorageAdapter extends StorageAdapter { constructor({ dbPath } = {}) { super(); this._dbPath = dbPath || DB_PATH; this._db = null; } async init() { if (!sqlite3) { throw new Error('better-sqlite3 is not installed. Run: npm install better-sqlite3'); } ensureDir(require('path').dirname(this._dbPath)); this._db = sqlite3(this._dbPath); this._db.pragma('journal_mode = WAL'); this._db.exec(SCHEMA); } _run(sql, params = []) { try { return this._db.prepare(sql).run(...params); } catch (e) { throw new Error(`SQLite run error: ${e.message} | sql: ${sql}`); } } _all(sql, params = []) { try { return this._db.prepare(sql).all(...params); } catch (e) { throw new Error(`SQLite all error: ${e.message} | sql: ${sql}`); } } _get(sql, params = []) { try { return this._db.prepare(sql).get(...params); } catch (e) { throw new Error(`SQLite get error: ${e.message} | sql: ${sql}`); } } // --- Document --- async saveDocument(doc) { this._run( `INSERT OR REPLACE INTO documents (id,type,title,sourceType,sourceUri,importedAt,status,text,createdAt,updatedAt) VALUES (?,?,?,?,?,?,?,?,?,?)`, [doc.id, doc.type || 'document', doc.title || '', doc.sourceType || '', doc.sourceUri || '', doc.importedAt || now(), doc.status || 'active', doc.text || '', doc.createdAt || now(), now()] ); return doc; } async getDocument(id) { const row = this._get('SELECT * FROM documents WHERE id = ?', [id]); return row ? this._mapDoc(row) : null; } async listDocuments() { return this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); } async deleteDocument(id) { this._run('DELETE FROM documents WHERE id = ?', [id]); } // --- Fragment --- async saveFragment(frag) { this._run( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)`, [frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()] ); return frag; } async getFragment(id) { const row = this._get('SELECT * FROM fragments WHERE id = ?', [id]); return row ? this._mapFrag(row) : null; } async listFragments(documentId) { return this._all('SELECT * FROM fragments WHERE documentId = ? ORDER BY "index" ASC', [documentId]) .map(this._mapFrag); } async deleteFragmentsByDocument(documentId) { this._run('DELETE FROM fragments WHERE documentId = ?', [documentId]); } async saveFragments(fragments) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO fragments (id,type,documentId,"index",text,summary,conceptNames,createdAt) VALUES (?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const frag of items) { stmt.run(frag.id, 'fragment', frag.documentId, frag.index, frag.text, frag.summary || frag.text.slice(0, 100), JSON.stringify(frag.conceptNames || []), frag.createdAt || now()); } }); insertMany(fragments); return fragments; } // --- Concept --- async saveConcept(concept) { this._run( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)`, [concept.id, 'concept', concept.name, concept.normalizedName, concept.salience || 0, concept.createdAt || now()] ); return concept; } async getConcept(id) { const row = this._get('SELECT * FROM concepts WHERE id = ?', [id]); return row ? this._mapConcept(row) : null; } async listConcepts() { return this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); } async upsertConcept(concept) { return this.saveConcept(concept); } async saveConcepts(concepts) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO concepts (id,type,name,normalizedName,salience,createdAt) VALUES (?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const c of items) { stmt.run(c.id, 'concept', c.name, c.normalizedName, c.salience || 0, c.createdAt || now()); } }); insertMany(concepts); return concepts; } // --- Link --- async saveLink(link) { this._run( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)`, [link.id, link.type, link.fromId, link.fromType, link.toId, link.toType, link.documentId, link.score || 0, link.createdAt || now()] ); return link; } async getLinks(fromId, toId) { let sql = 'SELECT * FROM links WHERE 1=1'; const params = []; if (fromId) { sql += ' AND fromId = ?'; params.push(fromId); } if (toId) { sql += ' AND toId = ?'; params.push(toId); } return this._all(sql, params); } async deleteLinksByDocument(documentId) { this._run('DELETE FROM links WHERE documentId = ?', [documentId]); } async saveLinks(links) { const stmt = this._db.prepare( `INSERT OR REPLACE INTO links (id,type,fromId,fromType,toId,toType,documentId,score,createdAt) VALUES (?,?,?,?,?,?,?,?,?)` ); const insertMany = this._db.transaction((items) => { for (const l of items) { stmt.run(l.id, l.type, l.fromId, l.fromType, l.toId, l.toType, l.documentId, l.score || 0, l.createdAt || now()); } }); insertMany(links); return links; } // --- Vector --- async saveFragmentVector(fragmentId, vector) { const buf = Buffer.from(JSON.stringify(vector)); this._run( `INSERT OR REPLACE INTO fragment_vectors (fragmentId,vector,updatedAt) VALUES (?,?,?)`, [fragmentId, buf, now()] ); } async getFragmentVector(fragmentId) { const row = this._get('SELECT vector FROM fragment_vectors WHERE fragmentId = ?', [fragmentId]); if (!row) return null; return JSON.parse(row.vector); } async listFragmentVectors() { return this._all('SELECT fragmentId, vector FROM fragment_vectors').map((r) => ({ fragmentId: r.fragmentId, vector: JSON.parse(r.vector) })); } // --- Full DB operations (for CLI compat) --- async load() { const docs = this._all('SELECT * FROM documents ORDER BY importedAt DESC').map(this._mapDoc); const frags = this._all('SELECT * FROM fragments ORDER BY "index" ASC').map(this._mapFrag); const concepts = this._all('SELECT * FROM concepts ORDER BY normalizedName ASC').map(this._mapConcept); const links = this._all('SELECT * FROM links'); const updatedAt = this._get('SELECT MAX(updatedAt) as t FROM documents UNION ALL SELECT MAX(updatedAt) as t FROM fragments')?.t || new Date().toISOString(); return { version: 1, documents: docs, fragments: frags, concepts, links, updatedAt }; } async save(db) { await this.clear(); if (db.documents) for (const d of db.documents) await this.saveDocument(d); if (db.fragments) for (const f of db.fragments) await this.saveFragment(f); if (db.concepts) for (const c of db.concepts) await this.saveConcept(c); if (db.links) for (const l of db.links) await this.saveLink(l); } async close() { if (this._db) { this._db.close(); this._db = null; } } // --- Workspace --- async clear() { this._run('DELETE FROM fragment_vectors'); this._run('DELETE FROM links'); this._run('DELETE FROM fragments'); this._run('DELETE FROM concepts'); this._run('DELETE FROM documents'); } getStats() { return { documents: this._get('SELECT COUNT(*) as n FROM documents')?.n || 0, fragments: this._get('SELECT COUNT(*) as n FROM fragments')?.n || 0, concepts: this._get('SELECT COUNT(*) as n FROM concepts')?.n || 0, links: this._get('SELECT COUNT(*) as n FROM links')?.n || 0, dbPath: this._dbPath }; } // --- Mappers --- _mapDoc(row) { return { ...row }; } _mapFrag(row) { return { ...row, conceptNames: JSON.parse(row.conceptNames || '[]') }; } _mapConcept(row) { return { ...row }; } } module.exports = { SqliteStorageAdapter }; FILE:src/storage-adapters/StorageAdapter.js /** * StorageAdapter Interface * 所有 storage adapter 必须实现此接口契约。 */ class StorageAdapter { /** * @returns {Promise<void>} */ async init() { throw new Error('Not implemented'); } // --- Document --- async saveDocument(doc) { throw new Error('Not implemented'); } async getDocument(id) { throw new Error('Not implemented'); } async listDocuments() { throw new Error('Not implemented'); } async deleteDocument(id) { throw new Error('Not implemented'); } // --- Fragment --- async saveFragment(frag) { throw new Error('Not implemented'); } async getFragment(id) { throw new Error('Not implemented'); } async listFragments(documentId) { throw new Error('Not implemented'); } async deleteFragmentsByDocument(documentId) { throw new Error('Not implemented'); } // --- Concept --- async saveConcept(concept) { throw new Error('Not implemented'); } async getConcept(id) { throw new Error('Not implemented'); } async listConcepts() { throw new Error('Not implemented'); } async upsertConcept(concept) { throw new Error('Not implemented'); } // --- Link --- async saveLink(link) { throw new Error('Not implemented'); } async getLinks(fromId, toId) { throw new Error('Not implemented'); } async deleteLinksByDocument(documentId) { throw new Error('Not implemented'); } // --- Bulk --- async saveFragments(fragments) { throw new Error('Not implemented'); } async saveConcepts(concepts) { throw new Error('Not implemented'); } async saveLinks(links) { throw new Error('Not implemented'); } // --- Workspace --- async clear() { throw new Error('Not implemented'); } } module.exports = { StorageAdapter }; FILE:src/storage-adapters/index.js /** * storage-adapters/index.js * Factory: 根据配置返回对应的 storage adapter 实例。 */ const { JsonStorageAdapter } = require('./JsonStorageAdapter'); const { SqliteStorageAdapter } = require('./SqliteStorageAdapter'); function createStorageAdapter(type = 'json', options = {}) { switch (type) { case 'json': return new JsonStorageAdapter(options); case 'sqlite': return new SqliteStorageAdapter(options); default: throw new Error(`Unknown storage adapter type: ${type}. Use 'json' or 'sqlite'.`); } } module.exports = { createStorageAdapter, JsonStorageAdapter, SqliteStorageAdapter }; FILE:src/utils/nlp.js /** * nlp.js - 文本规范化工具 */ const STOPWORDS = new Set([ 'the','and','for','that','this','with','from','into','your','have','will','not','are','was','were','can','could','should','about','what','when','where','which','while','than','then','them','they','their','there','here','also','more','most','some','such','using','used','use','make','made','over','under','very','just','only','each','been','being','does','did','done','how','why','our','you','its','his','her','she','him','who','has','had','but','too','via','per','one','two','three', '我们','你们','他们','这个','那个','一个','一种','可以','需要','如果','因为','所以','以及','进行','通过','没有','不是','就是','如何','什么','为什么','时候','今天','已经','还有','对于','一些','这种','那些','并且','或者','主要','用户','系统','功能','能力','作为','实现','相关','用于' ]); function normalizeConcept(text) { if (!text) return ''; return text .toLowerCase() .replace(/[\u2018\u2019'"""''()（）【】\[\],.:;!?/\\]+/g, ' ') .replace(/\s+/g, ' ') .trim(); } module.exports = { normalizeConcept, STOPWORDS }; FILE:tests/smoke-test.js #!/usr/bin/env node /** * LinkMind Smoke Test - Phase 2 * 覆盖: storage adapters + embedding providers + retriever */ import path from 'path'; import { fileURLToPath } from 'url'; import { execSync } from 'child_process'; import { pathToFileURL } from 'url'; const __filename = fileURLToPath(import.meta.url); const __dirname = path.dirname(__filename); const SKILL_ROOT = path.resolve(__dirname, '..'); process.chdir(SKILL_ROOT); function run(cmd) { console.log(`\n$ ${cmd}`); return JSON.parse(execSync(cmd, { encoding: 'utf8' })); } function check(label, cond) { if (cond) { console.log(` ✓ ${label}`); } else { console.error(` ✗ FAIL: ${label}`); process.exitCode = 1; } } // Import phase-2 modules via dynamic import const srcDir = path.join(SKILL_ROOT, 'src'); async function runTests() { console.log('=== LinkMind Smoke Test (Phase 2) ===\n'); // Phase 1: CLI commands console.log('--- Phase 1: CLI Commands ---'); const reset = run('node dist/index.js reset'); check('reset returns ok', reset.ok === true); const empty = run('node dist/index.js status'); check('empty workspace has 0 documents', empty.documents === 0); const ingest = run('node dist/index.js ingest --file examples/sample-note.md --title "Sample Note"'); check('ingest returns documentId', !!ingest.documentId); check('ingest creates fragments', ingest.fragmentsCreated >= 1); const after = run('node dist/index.js status'); check('has 1 document', after.documents === 1); check('has fragments', after.fragments >= 1); const q1 = run('node dist/index.js query --q "knowledge"'); check('query "knowledge" finds evidence', q1.evidence && q1.evidence.length >= 1); check('query "knowledge" returns answer', !!q1.answer); const q2 = run('node dist/index.js query --q "xyznonexistentterm12345"'); check('query no-match returns empty evidence', q2.evidence && q2.evidence.length === 0); const q3 = run('node dist/index.js query --q "knowledge" --limit 1'); check('query with limit respects limit', q3.evidence && q3.evidence.length <= 1); // Phase 2: Storage Adapters console.log('\n--- Phase 2: Storage Adapters ---'); const JsonStorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/JsonStorageAdapter.js')).href); const StorageAdapterMod = await import(pathToFileURL(path.join(srcDir, 'storage-adapters/StorageAdapter.js')).href); const { JsonStorageAdapter } = JsonStorageAdapterMod; const { StorageAdapter } = StorageAdapterMod; const jsAdapter = new JsonStorageAdapter(); await jsAdapter.init(); await jsAdapter.clear(); check('JsonStorageAdapter.init() ok', true); const testDoc = { id: 'doc_test1', type: 'document', title: 'Test Doc', sourceType: 'note', sourceUri: '/test/path.md', importedAt: new Date().toISOString(), status: 'active', text: 'This is a test document for storage adapter testing.' }; await jsAdapter.saveDocument(testDoc); const retrieved = await jsAdapter.getDocument('doc_test1'); check('JsonStorageAdapter.saveDocument + getDocument', retrieved && retrieved.title === 'Test Doc'); const docs = await jsAdapter.listDocuments(); check('JsonStorageAdapter.listDocuments', docs && docs.length >= 1); const testFrag = { id: 'frag_test1', type: 'fragment', documentId: 'doc_test1', index: 0, text: 'This is a test fragment.', summary: 'This is a test', conceptNames: ['test'], createdAt: new Date().toISOString() }; await jsAdapter.saveFragment(testFrag); const frag = await jsAdapter.getFragment('frag_test1'); check('JsonStorageAdapter.saveFragment + getFragment', frag && frag.text.includes('test fragment')); const frags = await jsAdapter.listFragments('doc_test1'); check('JsonStorageAdapter.listFragments', frags && frags.length >= 1); const testConcept = { id: 'concept_test1', type: 'concept', name: 'TestConcept', normalizedName: 'testconcept', salience: 0.5, createdAt: new Date().toISOString() }; await jsAdapter.saveConcept(testConcept); const concepts = await jsAdapter.listConcepts(); check('JsonStorageAdapter.listConcepts', concepts && concepts.some(c => c.normalizedName === 'testconcept')); const testLink = { id: 'link_test1', type: 'mentions', fromId: 'frag_test1', fromType: 'fragment', toId: 'concept_test1', toType: 'concept', documentId: 'doc_test1', score: 1, createdAt: new Date().toISOString() }; await jsAdapter.saveLink(testLink); const links = await jsAdapter.getLinks('frag_test1', null); check('JsonStorageAdapter.saveLink + getLinks', links && links.some(l => l.id === 'link_test1')); // test bulk save await jsAdapter.clear(); await jsAdapter.saveDocument(testDoc); await jsAdapter.saveFragments([testFrag]); await jsAdapter.saveConcepts([testConcept]); await jsAdapter.saveLinks([testLink]); const afterBulk = await jsAdapter.listFragments('doc_test1'); check('JsonStorageAdapter.saveFragments bulk', afterBulk && afterBulk.length >= 1); await jsAdapter.clear(); const emptyAfterClear = await jsAdapter.listDocuments(); check('JsonStorageAdapter.clear()', emptyAfterClear.length === 0); // StorageAdapter interface check('StorageAdapter is a class', typeof StorageAdapter === 'function'); // Phase 2: Embedding Providers console.log('\n--- Phase 2: Embedding Providers ---'); const MockProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/MockProvider.js')).href); const OpenAICompatibleProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/OpenAICompatibleProvider.js')).href); const EmbeddingProviderMod = await import(pathToFileURL(path.join(srcDir, 'embedding-providers/EmbeddingProvider.js')).href); const { MockProvider } = MockProviderMod; const { OpenAICompatibleProvider } = OpenAICompatibleProviderMod; const { EmbeddingProvider } = EmbeddingProviderMod; const mock = new MockProvider({ dimension: 128 }); check('MockProvider.dimension', mock.dimension === 128); check('MockProvider.name', mock.name === 'mock'); const [vec] = await mock.embed(['hello world']); check('MockProvider.embed returns vector', Array.isArray(vec) && vec.length === 128); check('MockProvider.vector is L2-normalized', Math.abs(vec.reduce((s, v) => s + v * v, 0) - 1) < 0.01); const [vec2] = await mock.embed(['hello world']); check('MockProvider caches results', vec2 === vec); const differentText = await mock.embed(['different text']); check('MockProvider different text yields different vector', differentText[0] !== vec); const openai = new OpenAICompatibleProvider({ baseURL: 'https://api.example.com/v1', apiKey: 'test-key', model: 'test-model', dimension: 256 }); check('OpenAICompatibleProvider.dimension', openai.dimension === 256); check('OpenAICompatibleProvider.name includes model', openai.name.includes('test-model')); check('OpenAICompatibleProvider.name includes baseURL hint', openai.name.includes('openai-compatible')); check('EmbeddingProvider is a class', typeof EmbeddingProvider === 'function'); // Phase 2: Retriever console.log('\n--- Phase 2: Retriever ---'); const retrieverMod = await import(pathToFileURL(path.join(srcDir, 'retriever.js')).href); const { keywordSearch, vectorSearch, mergeResults, cosineSimilarity, retrieve } = retrieverMod; check('cosineSimilarity identical vectors = 1', Math.abs(cosineSimilarity([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]) - 1) < 0.0001); check('cosineSimilarity orthogonal = 0', Math.abs(cosineSimilarity([1, 0, 0], [0, 1, 0])) < 0.0001); const testFrags = [ { id: 'f1', documentId: 'd1', documentTitle: 'Doc 1', index: 0, text: 'knowledge graph is useful', conceptNames: ['knowledge', 'graph'] }, { id: 'f2', documentId: 'd1', documentTitle: 'Doc 1', index: 1, text: 'machine learning and AI', conceptNames: ['machine', 'learning'] }, { id: 'f3', documentId: 'd2', documentTitle: 'Doc 2', index: 0, text: 'knowledge base systems', conceptNames: ['knowledge', 'base'] } ]; const kw = keywordSearch(testFrags, 'knowledge'); check('keywordSearch finds knowledge fragments', kw.length >= 2); check('keywordSearch assigns score > 0', kw.every(r => r.score > 0)); check('keywordSearch source=keyword', kw.every(r => r.source === 'keyword')); const vecResults = await vectorSearch(testFrags, 'knowledge graph', mock); check('vectorSearch returns results', Array.isArray(vecResults)); check('vectorSearch source=vector', vecResults.every(r => r.source === 'vector')); const merged = mergeResults(kw, vecResults, 5); check('mergeResults deduplicates', merged.length <= kw.length + vecResults.length); check('mergeResults returns array', Array.isArray(merged)); check('mergeResults items have score', merged.every(r => typeof r.score === 'number')); const kwOnly = await retrieve({ fragments: testFrags, query: 'knowledge', limit: 5 }); check('retrieve keyword-only works', kwOnly.length >= 1); const hybrid = await retrieve({ fragments: testFrags, query: 'knowledge', embeddingProvider: mock, limit: 5 }); check('retrieve hybrid works', hybrid.length >= 1); check('retrieve hybrid may include vector source', hybrid.some(r => r.source === 'vector' || r.source === 'hybrid')); console.log('\n=== Smoke Test Complete ==='); if (process.exitCode === 1) { console.log('RESULT: FAILED'); process.exit(1); } else { console.log('RESULT: PASSED'); } } runTests().catch((err) => { console.error('Test error:', err); process.exit(1); }); </article></body></html>

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