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waggle-os/packages/hive-mind-core/tests/mind/search-hive-mind.test.ts
Oleg Maslov 0c3e2ead3b
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TypeScript

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