Method searchHubChunks

packages/memos-core/src/storage/sqlite.ts:2412–2432

(query: string, options?: { userId?: string; maxResults?: number })

  }

  searchHubChunks(query: string, options?: { userId?: string; maxResults?: number }): Array<{ hit: HubSearchRow; rank: number }> {
    const limit = options?.maxResults ?? 10;
    const userId = options?.userId ?? "";
    const rows = this.db.prepare(`
      SELECT hc.id, hc.content, hc.summary, hc.role, hc.created_at, ht.title as task_title, ht.visibility,
             COALESCE(hg.name, '') as group_name, hu.username as owner_name,
             bm25(hub_chunks_fts) as rank
      FROM hub_chunks_fts f
      JOIN hub_chunks hc ON hc.rowid = f.rowid
      JOIN hub_tasks ht ON ht.id = hc.hub_task_id
      LEFT JOIN hub_users hu ON hu.id = ht.source_user_id
      LEFT JOIN hub_groups hg ON hg.id = ht.group_id
      WHERE hub_chunks_fts MATCH ?
        AND (ht.visibility = 'public'
             OR ht.source_user_id = ?
             OR EXISTS (SELECT 1 FROM hub_group_members gm WHERE gm.group_id = ht.group_id AND gm.user_id = ?))
      ORDER BY rank
      LIMIT ?
    `).all(sanitizeFtsQuery(query), userId, userId, limit) as HubSearchRow[];
    return rows.map((row, idx) => ({ hit: row, rank: idx + 1 }));
  }

  upsertHubEmbedding(chunkId: string, vector: Float32Array): void {
    const buf = Buffer.from(vector.buffer, vector.byteOffset, vector.byteLength);