161 lines
6.0 KiB
TypeScript
161 lines
6.0 KiB
TypeScript
/**
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* HybridSearch tests — full-file port from
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* hive-mind/packages/core/src/mind/search.test.ts.
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*
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* Memory Sync Repair Step 2. Source: hive-mind file at HEAD c363257.
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*
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* Filename suffix `-hive-mind` keeps this distinct from waggle-os's own
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* `search.test.ts` (which focuses on scoring profiles, temporal decay,
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* NaN/Infinity guards). Hive-mind's file focuses on the search-pipeline
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* smoke contract: keywordSearch + scoping + stop-word handling, vector
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* indexing + retrieval, indexFramesBatch atomicity, search() fusion +
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* scoping end-to-end.
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*
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* Adapted imports: `./db.js`, `./frames.js`, `./search.js`,
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* `./embedding-provider.js` → `../../src/mind/...`.
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*/
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import { describe, it, expect, beforeEach, afterEach } from 'vitest';
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import { tmpdir } from 'node:os';
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import { join } from 'node:path';
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import { rmSync, existsSync } from 'node:fs';
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import { MindDB } from '../../src/mind/db.js';
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import { FrameStore } from '../../src/mind/frames.js';
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import { HybridSearch } from '../../src/mind/search.js';
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import { createEmbeddingProvider, type EmbeddingProviderInstance } from '../../src/mind/embedding-provider.js';
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describe('HybridSearch (hive-mind port)', () => {
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let dbPath: string;
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let db: MindDB;
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let frames: FrameStore;
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let embedder: EmbeddingProviderInstance;
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let search: HybridSearch;
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beforeEach(async () => {
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dbPath = join(tmpdir(), `waggle-mind-search-test-${Date.now()}-${Math.random()}.mind`);
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db = new MindDB(dbPath);
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db.getDatabase()
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.prepare(
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"INSERT INTO sessions (gop_id, status, started_at) VALUES ('gop-a', 'active', datetime('now'))",
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)
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.run();
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db.getDatabase()
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.prepare(
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"INSERT INTO sessions (gop_id, status, started_at) VALUES ('gop-b', 'active', datetime('now'))",
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)
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.run();
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frames = new FrameStore(db);
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embedder = await createEmbeddingProvider({ provider: 'mock' });
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search = new HybridSearch(db, embedder);
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});
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afterEach(() => {
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db.close();
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if (existsSync(dbPath)) rmSync(dbPath);
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for (const suffix of ['-shm', '-wal']) {
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if (existsSync(dbPath + suffix)) rmSync(dbPath + suffix);
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}
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});
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it('keywordSearch finds frames containing the query terms', async () => {
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const a = frames.createIFrame('gop-a', 'user prefers TypeScript over JavaScript');
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frames.createIFrame('gop-a', 'user likes weekend hiking trips in the Alps');
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const ids = await search.keywordSearch('TypeScript preferences', 10);
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expect(ids).toContain(a.id);
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});
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it('keywordSearch scopes results to gopId when provided', async () => {
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const inA = frames.createIFrame('gop-a', 'deployment blueprint alpha');
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const inB = frames.createIFrame('gop-b', 'deployment blueprint bravo');
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const scopedToA = await search.keywordSearch('deployment blueprint', 10, 'gop-a');
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expect(scopedToA).toContain(inA.id);
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expect(scopedToA).not.toContain(inB.id);
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const scopedToB = await search.keywordSearch('deployment blueprint', 10, 'gop-b');
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expect(scopedToB).toContain(inB.id);
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expect(scopedToB).not.toContain(inA.id);
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});
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it('keywordSearch returns [] for queries containing only stop words', async () => {
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frames.createIFrame('gop-a', 'content that will not match a stop-word query');
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const ids = await search.keywordSearch('the a an of to in for on with', 10);
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expect(ids).toEqual([]);
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});
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it('indexFrame inserts into memory_frames_vec and vectorSearch retrieves it', async () => {
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const frame = frames.createIFrame('gop-a', 'quantum annealing implementation notes');
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await search.indexFrame(frame.id, frame.content);
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const count = db
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.getDatabase()
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.prepare('SELECT COUNT(*) as n FROM memory_frames_vec')
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.get() as { n: number };
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expect(count.n).toBe(1);
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const ids = await search.vectorSearch('quantum annealing implementation notes', 10);
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expect(ids).toContain(frame.id);
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});
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it('indexFramesBatch inserts multiple rows atomically', async () => {
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const a = frames.createIFrame('gop-a', 'alpha content');
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const b = frames.createIFrame('gop-a', 'bravo content');
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const c = frames.createIFrame('gop-a', 'charlie content');
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await search.indexFramesBatch([
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{ id: a.id, content: a.content },
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{ id: b.id, content: b.content },
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{ id: c.id, content: c.content },
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]);
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const count = db
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.getDatabase()
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.prepare('SELECT COUNT(*) as n FROM memory_frames_vec')
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.get() as { n: number };
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expect(count.n).toBe(3);
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});
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it('search() fuses keyword + vector ranks and returns sorted SearchResults', async () => {
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const a = frames.createIFrame('gop-a', 'roadmap for Q2 launch', 'important');
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const b = frames.createIFrame('gop-a', 'Q2 launch success criteria', 'critical');
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const c = frames.createIFrame('gop-a', 'unrelated conversation about coffee');
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await search.indexFramesBatch([
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{ id: a.id, content: a.content },
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{ id: b.id, content: b.content },
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{ id: c.id, content: c.content },
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]);
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const results = await search.search('Q2 launch', { limit: 3 });
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expect(results.length).toBeGreaterThan(0);
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for (const r of results) {
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expect(r.rrfScore).toBeGreaterThan(0);
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expect(r.relevanceScore).toBeGreaterThan(0);
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expect(r.finalScore).toBe(r.rrfScore * r.relevanceScore);
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}
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for (let i = 1; i < results.length; i++) {
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expect(results[i - 1].finalScore).toBeGreaterThanOrEqual(results[i].finalScore);
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}
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const topIds = results.map((r) => r.frame.id);
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expect(topIds).toContain(a.id);
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expect(topIds).toContain(b.id);
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});
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it('search() honours gopId scoping end-to-end', async () => {
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const inA = frames.createIFrame('gop-a', 'report for project apollo alpha');
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const inB = frames.createIFrame('gop-b', 'report for project apollo bravo');
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await search.indexFramesBatch([
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{ id: inA.id, content: inA.content },
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{ id: inB.id, content: inB.content },
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]);
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const results = await search.search('report apollo', { gopId: 'gop-a', limit: 10 });
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const ids = results.map((r) => r.frame.id);
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expect(ids).toContain(inA.id);
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expect(ids).not.toContain(inB.id);
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});
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});
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