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使用量",[1254,1306,1307],{},"ナレッジインデックス記録DBの容量。ベクトルDBの容量は含みません。",[1239,1309,1310,1313],{},[1254,1311,1312],{},"インデックスモード",[1254,1314,1315],{},"AI検索データを作成するファイルの範囲（例：フルインデックス）",[1239,1317,1318,1321],{},[1254,1319,1320],{},"埋め込みプロバイダー",[1254,1322,1323],{},"使用中のAI埋め込みモデル（例：Ollama bge-m3:latest / 1024d）",[1239,1325,1326,1329],{},[1254,1327,1328],{},"実行対象ルート",[1254,1330,1331],{},"インデックス作成の対象パス",[1239,1333,1334,1337],{},[1254,1335,1336],{},"チームチェックポイント",[1254,1338,1339],{},"チームフォルダを最後に確認した時刻",[1239,1341,1342,1345],{},[1254,1343,1344],{},"クライアントチェックポイント",[1254,1346,1347],{},"個人フォルダを最後に確認した時刻",[1239,1349,1350,1353],{},[1254,1351,1352],{},"最終更新",[1254,1354,1355],{},"状態を最後に更新した時刻",[1166,1357,1358,1361,1362,1365],{},[1187,1359,1360],{},"自動更新","をオンにしておくか、",[1187,1363,1364],{},"更新","ボタンで状況を手動で更新できます。",[1166,1367,1368,1369,1371,1372,1375,1376,1378],{},"ベクトルDBの状態を確認中の場合、または確認に失敗した場合、",[1187,1370,1288],{},"は",[1187,1373,1374],{},"-","と表示されます。同じスペースでサーバーが一時的に以前の時点のスナップショットを返した場合、画面は最後に確認したより新しい値を維持し、スナップショットが古いことを示す警告を表示します。チームスペースと個人スペースを切り替えると、以前のスペースの値はすぐに消去され、新しいスペースの照会が終わるまで、不明な値は",[1187,1377,1374],{},"と表示されます。",[1170,1380,1381],{"id":1381},"開始前の設定確認",[1166,1383,1384],{},"インデックス作成を開始する前に、次の必須設定を確認してください。各ボタンを押すと、該当する設定に移動します。",[1233,1386,1387,1400],{},[1236,1388,1389],{},[1239,1390,1391,1394,1397],{},[1242,1392,1393],{},"必須設定",[1242,1395,1396],{},"確認する内容",[1242,1398,1399],{},"移動",[1249,1401,1402,1420,1436,1452],{},[1239,1403,1404,1410,1413],{},[1254,1405,1406],{},[1407,1408,1409],"strong",{},"AIランタイム",[1254,1411,1412],{},"Ollama、LM Studioなど、インデックス作成に使用するAIランタイムを選択し、接続状態を確認します。",[1254,1414,1415],{},[1416,1417,1419],"a",{"href":1418},"../ai/models#%E3%83%AD%E3%83%BC%E3%82%AB%E3%83%ABai%E3%83%A9%E3%83%B3%E3%82%BF%E3%82%A4%E3%83%A0","AIランタイムを設定する",[1239,1421,1422,1427,1430],{},[1254,1423,1424],{},[1407,1425,1426],{},"ビジョンモデル（VL）",[1254,1428,1429],{},"PDFと画像の内容を読み取るビジョンモデルを、AIモデルの中から選択します。",[1254,1431,1432],{},[1416,1433,1435],{"href":1434},"../ai/models#%E3%83%93%E3%82%B8%E3%83%A7%E3%83%B3%E3%83%A2%E3%83%87%E3%83%AB%E5%84%AA%E5%85%88%E9%A0%86%E4%BD%8D","ビジョンモデルを設定する",[1239,1437,1438,1443,1446],{},[1254,1439,1440],{},[1407,1441,1442],{},"AIベクトルDB",[1254,1444,1445],{},"ベクトルストレージをLocal（SQLite）、Milvus、Qdrantなどから選択し、接続情報を設定します。",[1254,1447,1448],{},[1416,1449,1451],{"href":1450},"/ja/docs/admin/knowledge/storage/","AIベクトルDBを設定する",[1239,1453,1454,1459,1466],{},[1254,1455,1456],{},[1407,1457,1458],{},"埋め込みモデル",[1254,1460,1461,1462,1465],{},"文書を検索用のベクトルに変換するモデルを選択します。（例：",[1187,1463,1464],{},"bge-m3","）",[1254,1467,1468],{},[1416,1469,1471],{"href":1470},"#ai%E5%9F%8B%E3%82%81%E8%BE%BC%E3%81%BF%E3%83%A2%E3%83%87%E3%83%AB%E8%A8%AD%E5%AE%9A","埋め込みモデルを設定する",[1473,1474,1475],"blockquote",{},[1166,1476,1477,1478,1482],{},"ここでは、使用するモデルを選択するだけです。ビジョンモデルと埋め込みモデルは、先に",[1416,1479,1481],{"href":1480},"/ja/docs/admin/ai/models/","AIモデル設定","でダウンロードしておかないと、インデックス作成に使用できません。",[1166,1484,1485,1487,1488,1491,1492,1495,1496,1499,1500,1503,1504,1507,1508,1512],{},[1187,1486,1442],{},"の項目では、設定の有無だけでなく、実際の接続状態も確認します。ベクトルDBに接続できない場合は",[1187,1489,1490],{},"DB接続エラー・設定を確認してください","、コレクションの次元や距離の計算方式が一致しない場合は",[1187,1493,1494],{},"コレクション設定の不一致・確認が必要","、Milvusにコレクションをまだ作成していない場合は",[1187,1497,1498],{},"コレクションなし・先に作成が必要","が警告色で表示されます。接続エラーとコレクション設定の不一致は、カードにマウスポインターを合わせると詳細なエラーを確認できます。Milvusのコレクションなしの場合は、別途表示されるエラー内容がないため、カードを押してベクトルDBの設定に移動し、",[1416,1501,240],{"href":1502},"./milvus#%E3%82%B3%E3%83%AC%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%82%92%E4%BA%8B%E5%89%8D%E3%81%AB%E4%BD%9C%E6%88%90%E3%81%99%E3%82%8B","を参照してコレクションを作成してください。Qdrantのコレクションがまだない場合はエラーではないため、",[1187,1505,1506],{},"設定完了・コレクションは初回インデックス作成時に生成","と表示されます。詳しい対処方法は、",[1416,1509,1511],{"href":1510},"./storage-operations#%E3%83%88%E3%83%A9%E3%83%96%E3%83%AB%E3%82%B7%E3%83%A5%E3%83%BC%E3%83%86%E3%82%A3%E3%83%B3%E3%82%B0","ベクトルDB運用ガイド","を参照してください。",[1170,1514,1515],{"id":1515},"収集および処理ルール",[1166,1517,1518],{},"検索データをいつ作成するか、どのフォルダの文書をAI検索に使用するかを決めます。",[1520,1521,1522],"h3",{"id":1522},"知識インデックスを有効化",[1166,1524,1525],{},"ナレッジインデックス作成の機能全体をオンまたはオフにします。",[1520,1527,1528],{"id":1528},"インデックスモード設定",[1233,1530,1531,1542],{},[1236,1532,1533],{},[1239,1534,1535,1537,1539],{},[1242,1536,1244],{},[1242,1538,1247],{},[1242,1540,1541],{},"デフォルト値",[1249,1543,1544,1554,1565,1576,1587,1597,1608],{},[1239,1545,1546,1548,1551],{},[1254,1547,1312],{},[1254,1549,1550],{},"インデックス作成の範囲と方式を選択",[1254,1552,1553],{},"フルインデックス",[1239,1555,1556,1559,1562],{},[1254,1557,1558],{},"待機時間(分)",[1254,1560,1561],{},"ファイルの変更を検知してから、インデックス作成を開始するまで待機する時間",[1254,1563,1564],{},"20",[1239,1566,1567,1570,1573],{},[1254,1568,1569],{},"最大同時ワーカー数",[1254,1571,1572],{},"同時に処理できるインデックス作成の作業数",[1254,1574,1575],{},"1",[1239,1577,1578,1581,1584],{},[1254,1579,1580],{},"ワーカー実行を許可",[1254,1582,1583],{},"オフにすると、変更されたファイルの検出は続けますが、AI検索データは作成しません。",[1254,1585,1586],{},"有効",[1239,1588,1589,1592,1595],{},[1254,1590,1591],{},"高負荷時に自動一時停止",[1254,1593,1594],{},"サーバーの使用率が高いときはインデックス作成を一時停止し、サーバーが安定すると再開します。",[1254,1596,1586],{},[1239,1598,1599,1602,1605],{},[1254,1600,1601],{},"ブーストモード",[1254,1603,1604],{},"ファイル変更の5秒後にインデックス作成を開始し、たまっている作業もすぐ処理対象にします。処理速度は上がらず、たまっている作業が終わると自動でオフになります。",[1254,1606,1607],{},"無効",[1239,1609,1610,1613,1616],{},[1254,1611,1612],{},"許可時間帯",[1254,1614,1615],{},"インデックス作成を許可する時間帯を指定します。空欄にすると、いつでもインデックスを作成します。（例：01:00-06:00）",[1254,1617,1618],{},"制限なし",[1520,1620,1621],{"id":1621},"対象ルート",[1166,1623,1624],{},"インデックスを作成するフォルダのパスを指定します。空欄にすると、すべてのパスが対象になります。",[1166,1626,1627],{},"例：",[1629,1630,1635],"pre",{"className":1631,"code":1633,"language":1634},[1632],"language-text","/@team/docs\n/@client/projects\n","text",[1187,1636,1633],{"__ignoreMap":1637},"",[1520,1639,1641],{"id":1640},"ai埋め込みモデル設定","AI埋め込みモデル設定",[1166,1643,1644],{},"文書を検索データに変換するAIモデルを選択します。",[1233,1646,1647,1656],{},[1236,1648,1649],{},[1239,1650,1651,1654],{},[1242,1652,1653],{},"プロバイダー",[1242,1655,1247],{},[1249,1657,1658,1666,1674,1682],{},[1239,1659,1660,1663],{},[1254,1661,1662],{},"Ollama",[1254,1664,1665],{},"ローカルにインストールされたOllamaモデルを使用します。（例：bge-m3:latest）",[1239,1667,1668,1671],{},[1254,1669,1670],{},"ONNX Local CPU",[1254,1672,1673],{},"CPUベースでローカル実行されるONNXモデルを使用します。",[1239,1675,1676,1679],{},[1254,1677,1678],{},"ONNX Local GPU",[1254,1680,1681],{},"GPUアクセラレーションを使用するローカルのONNXモデルを使用します。",[1239,1683,1684,1687],{},[1254,1685,1686],{},"Remote API",[1254,1688,1689],{},"外部APIを通じて埋め込みモデルを使用します。",[1166,1691,1692,1693,1378],{},"モデルがインストールされていないプロバイダーは、",[1187,1694,1695],{},"利用可能なモデルはありません",[1233,1697,1698,1706],{},[1236,1699,1700],{},[1239,1701,1702,1704],{},[1242,1703,1244],{},[1242,1705,1247],{},[1249,1707,1708,1716],{},[1239,1709,1710,1713],{},[1254,1711,1712],{},"ベクトル次元数",[1254,1714,1715],{},"AIモデルが使用する検索データのサイズです。選択したモデルに合わせて自動的に設定されます。（例：1024）",[1239,1717,1718,1721],{},[1254,1719,1720],{},"PDF/画像抽出戦略",[1254,1722,1723,1724,1727],{},"PDFと画像からテキストを読み取る方法を選択します。",[1187,1725,1726],{},"VL 優先、失敗時は OCR","は、先に画像理解AIを使用し、失敗した場合に文字認識を使用するという意味です。",[1520,1729,1731],{"id":1730},"pdf画像の対象パターン","PDF/画像の対象パターン",[1166,1733,1734],{},"テキストを読み取るPDFと画像ファイルの拡張子、またはパスのルールを入力します。",[1166,1736,1627],{},[1629,1738,1741],{"className":1739,"code":1740,"language":1634},[1632],"*.pdf, *.png, image/**/*\n",[1187,1742,1740],{"__ignoreMap":1637},[1520,1744,1746],{"id":1745},"pdf画像の除外パターン","PDF/画像の除外パターン",[1166,1748,1749],{},"処理しないPDFと画像ファイルの拡張子、またはパスのルールを入力します。空欄にすると、上で対象に含めたファイルをすべて処理します。",[1166,1751,1627],{},[1629,1753,1756],{"className":1754,"code":1755,"language":1634},[1632],"thumbs.db, *.gif, cache/**\n",[1187,1757,1755],{"__ignoreMap":1637},[1170,1759,1760],{"id":1760},"保存スペース別の進行状況",[1166,1762,1763],{},"チームや個人の保存スペースごとに、資料がAIに渡されるまでどれだけ待機しているかを確認します。",[1166,1765,1766],{},"スペース（team/client）とユーザーを条件に検索でき、更新待ちの件数で並べ替えることができます。",[1233,1768,1769,1777],{},[1236,1770,1771],{},[1239,1772,1773,1775],{},[1242,1774,1244],{},[1242,1776,1247],{},[1249,1778,1779,1787,1795,1801,1807,1813],{},[1239,1780,1781,1784],{},[1254,1782,1783],{},"スペース",[1254,1785,1786],{},"チームスペース（team）または個人スペース（client）の区分",[1239,1788,1789,1792],{},[1254,1790,1791],{},"ユーザー",[1254,1793,1794],{},"個人スペースの場合、該当するユーザー",[1239,1796,1797,1799],{},[1254,1798,1256],{},[1254,1800,1259],{},[1239,1802,1803,1805],{},[1254,1804,1264],{},[1254,1806,1267],{},[1239,1808,1809,1811],{},[1254,1810,1280],{},[1254,1812,1283],{},[1239,1814,1815,1818],{},[1254,1816,1817],{},"埋め込み数",[1254,1819,1820],{},"完了した埋め込みの総数",[1170,1822,1824],{"id":1823},"ai学習データ状況","AI学習データ状況",[1166,1826,1827],{},"AI検索データがモデルごとにどれだけ作成されたかを確認します。元の文書でAIモデルを新たに学習させる機能ではありません。",[1233,1829,1830,1838],{},[1236,1831,1832],{},[1239,1833,1834,1836],{},[1242,1835,1244],{},[1242,1837,1247],{},[1249,1839,1840,1847,1855],{},[1239,1841,1842,1844],{},[1254,1843,1653],{},[1254,1845,1846],{},"埋め込みに使用されたモデルのプロバイダー（例：ollama）",[1239,1848,1849,1852],{},[1254,1850,1851],{},"モデル",[1254,1853,1854],{},"使用された埋め込みモデル名（例：bge-m3:latest）",[1239,1856,1857,1860],{},[1254,1858,1859],{},"件数",[1254,1861,1862],{},"そのモデルで生成された埋め込みの総数",[1166,1864,1865],{},"件数で並べ替えて、モデルごとの検索データの規模を比較できます。",{"title":1637,"searchDepth":1867,"depth":1868,"links":1869},2,3,[1870,1871,1872,1873,1874,1882,1883],{"id":1172,"depth":1867,"text":1172},{"id":1192,"depth":1867,"text":1193},{"id":1228,"depth":1867,"text":1228},{"id":1381,"depth":1867,"text":1381},{"id":1515,"depth":1867,"text":1515,"children":1875},[1876,1877,1878,1879,1880,1881],{"id":1522,"depth":1868,"text":1522},{"id":1528,"depth":1868,"text":1528},{"id":1621,"depth":1868,"text":1621},{"id":1640,"depth":1868,"text":1641},{"id":1730,"depth":1868,"text":1731},{"id":1745,"depth":1868,"text":1746},{"id":1760,"depth":1867,"text":1760},{"id":1823,"depth":1867,"text":1824},"管理者ページでナレッジの収集範囲と処理ルールを設定し、インデックス作成の状態を確認する方法を説明します。","md",{},true,{"title":1889,"description":1890,"ogType":1891,"twitterCard":1892,"keywords":1893},"収集・インデックス作成 | Admin Docs","SetFNナレッジサービスで収集範囲、処理ルール、インデックス作成の進行状況を管理する方法を説明します。","article","summary_large_image",[1894,1895,1896,755,1228,1515,1760],"ナレッジインデックス作成","API","PDF","kK0nKxl-ya0IH_VX2yS5fVwqHtUy-jWfKN2PiXlF9u4",[1899,1900],{"title":203,"path":206,"stem":204,"slug":205,"children":-1},{"title":215,"path":216,"stem":217,"slug":218,"children":-1},{"title":208,"filename":1902,"content":1903},"indexing","# 収集・インデックス作成\n\nユーザーがアップロードした文書がAI検索で使用できる状態かどうかを確認します。ここで作成した検索データは、AIが関連文書を探して回答するRAGに使用されます。\n\n## 始める前に\n\n- 必要な権限：Workspace管理者\n- 埋め込みを実行するAIランタイムとモデルを準備しておく必要があります。\n- ベクトル検索を使用するには、`ナレッジストレージ`の接続と埋め込みモデルの設定を完了しておく必要があります。\n\n## 収集・インデックス作成を開く\n\n1. 管理者ページにアクセスします。\n2. 左側のメニューで`ナレッジサービス`を選択します。\n3. `収集・インデックス作成`を選択します。\n\n![収集・インデックス作成画面](./_screenshots/admin-knowledge-indexing_ja.png)\n\n::note\n画面に表示されている数値は、機能を説明するための例です。\n::\n\n---\n\n## インデックス要約\n\n現在のインデックス作成の状態をひと目で確認します。\n\n| 項目 | 説明 |\n|------|------|\n| 更新待ち | 新しく作成または変更したファイルが、AI検索への反映を待っている状態 |\n| 削除待ち | 削除したファイルの検索データが、整理されるのを待っている状態 |\n| ベクトル削除待ち | ファイルの削除やストレージの切り替えの後、以前のベクトルが元の保存場所から削除されるのを待っている数 |\n| 埋め込み待ち | 文書からテキストを読み取り、AI検索データへの変換を待っている状態 |\n| ベクトルDB保存数 | 選択したベクトルDBに、現在のスペース用として実際に保存されているベクトル数。カードの下の`ローカル埋め込み`は、現在の文書バージョンについてローカルの`knowledge.db`に保管されているモデル別の埋め込み行数であり、2つの値は異なる場合があります。 |\n| SQLite 使用量 | ナレッジインデックス記録DBの容量。ベクトルDBの容量は含みません。 |\n| インデックスモード | AI検索データを作成するファイルの範囲（例：フルインデックス） |\n| 埋め込みプロバイダー | 使用中のAI埋め込みモデル（例：Ollama bge-m3:latest / 1024d） |\n| 実行対象ルート | インデックス作成の対象パス |\n| チームチェックポイント | チームフォルダを最後に確認した時刻 |\n| クライアントチェックポイント | 個人フォルダを最後に確認した時刻 |\n| 最終更新 | 状態を最後に更新した時刻 |\n\n`自動更新`をオンにしておくか、`更新`ボタンで状況を手動で更新できます。\n\nベクトルDBの状態を確認中の場合、または確認に失敗した場合、`ベクトルDB保存数`は`-`と表示されます。同じスペースでサーバーが一時的に以前の時点のスナップショットを返した場合、画面は最後に確認したより新しい値を維持し、スナップショットが古いことを示す警告を表示します。チームスペースと個人スペースを切り替えると、以前のスペースの値はすぐに消去され、新しいスペースの照会が終わるまで、不明な値は`-`と表示されます。\n\n## 開始前の設定確認\n\nインデックス作成を開始する前に、次の必須設定を確認してください。各ボタンを押すと、該当する設定に移動します。\n\n| 必須設定 | 確認する内容 | 移動 |\n|----------|------------|------|\n| **AIランタイム** | Ollama、LM Studioなど、インデックス作成に使用するAIランタイムを選択し、接続状態を確認します。 | [AIランタイムを設定する](../35.ai/10.models.md#ローカルaiランタイム) |\n| **ビジョンモデル（VL）** | PDFと画像の内容を読み取るビジョンモデルを、AIモデルの中から選択します。 | [ビジョンモデルを設定する](../35.ai/10.models.md#ビジョンモデル優先順位) |\n| **AIベクトルDB** | ベクトルストレージをLocal（SQLite）、Milvus、Qdrantなどから選択し、接続情報を設定します。 | [AIベクトルDBを設定する](/ja/docs/admin/knowledge/storage/) |\n| **埋め込みモデル** | 文書を検索用のベクトルに変換するモデルを選択します。（例：`bge-m3`） | [埋め込みモデルを設定する](#ai埋め込みモデル設定) |\n\n> ここでは、使用するモデルを選択するだけです。ビジョンモデルと埋め込みモデルは、先に[AIモデル設定](/ja/docs/admin/ai/models/)でダウンロードしておかないと、インデックス作成に使用できません。\n\n`AIベクトルDB`の項目では、設定の有無だけでなく、実際の接続状態も確認します。ベクトルDBに接続できない場合は`DB接続エラー・設定を確認してください`、コレクションの次元や距離の計算方式が一致しない場合は`コレクション設定の不一致・確認が必要`、Milvusにコレクションをまだ作成していない場合は`コレクションなし・先に作成が必要`が警告色で表示されます。接続エラーとコレクション設定の不一致は、カードにマウスポインターを合わせると詳細なエラーを確認できます。Milvusのコレクションなしの場合は、別途表示されるエラー内容がないため、カードを押してベクトルDBの設定に移動し、[Milvusを接続する](./36.milvus.md#コレクションを事前に作成する)を参照してコレクションを作成してください。Qdrantのコレクションがまだない場合はエラーではないため、`設定完了・コレクションは初回インデックス作成時に生成`と表示されます。詳しい対処方法は、[ベクトルDB運用ガイド](./32.storage-operations.md#トラブルシューティング)を参照してください。\n\n## 収集および処理ルール\n\n検索データをいつ作成するか、どのフォルダの文書をAI検索に使用するかを決めます。\n\n### 知識インデックスを有効化\n\nナレッジインデックス作成の機能全体をオンまたはオフにします。\n\n### インデックスモード設定\n\n| 項目 | 説明 | デフォルト値 |\n|------|------|--------|\n| インデックスモード | インデックス作成の範囲と方式を選択 | フルインデックス |\n| 待機時間(分) | ファイルの変更を検知してから、インデックス作成を開始するまで待機する時間 | 20 |\n| 最大同時ワーカー数 | 同時に処理できるインデックス作成の作業数 | 1 |\n| ワーカー実行を許可 | オフにすると、変更されたファイルの検出は続けますが、AI検索データは作成しません。 | 有効 |\n| 高負荷時に自動一時停止 | サーバーの使用率が高いときはインデックス作成を一時停止し、サーバーが安定すると再開します。 | 有効 |\n| ブーストモード | ファイル変更の5秒後にインデックス作成を開始し、たまっている作業もすぐ処理対象にします。処理速度は上がらず、たまっている作業が終わると自動でオフになります。 | 無効 |\n| 許可時間帯 | インデックス作成を許可する時間帯を指定します。空欄にすると、いつでもインデックスを作成します。（例：01:00-06:00） | 制限なし |\n\n### 対象ルート\n\nインデックスを作成するフォルダのパスを指定します。空欄にすると、すべてのパスが対象になります。\n\n例：\n\n```\n/@team/docs\n/@client/projects\n```\n\n### AI埋め込みモデル設定\n\n文書を検索データに変換するAIモデルを選択します。\n\n| プロバイダー | 説明 |\n|--------|------|\n| Ollama | ローカルにインストールされたOllamaモデルを使用します。（例：bge-m3:latest） |\n| ONNX Local CPU | CPUベースでローカル実行されるONNXモデルを使用します。 |\n| ONNX Local GPU | GPUアクセラレーションを使用するローカルのONNXモデルを使用します。 |\n| Remote API | 外部APIを通じて埋め込みモデルを使用します。 |\n\nモデルがインストールされていないプロバイダーは、`利用可能なモデルはありません`と表示されます。\n\n| 項目 | 説明 |\n|------|------|\n| ベクトル次元数 | AIモデルが使用する検索データのサイズです。選択したモデルに合わせて自動的に設定されます。（例：1024） |\n| PDF/画像抽出戦略 | PDFと画像からテキストを読み取る方法を選択します。`VL 優先、失敗時は OCR`は、先に画像理解AIを使用し、失敗した場合に文字認識を使用するという意味です。 |\n\n### PDF/画像の対象パターン\n\nテキストを読み取るPDFと画像ファイルの拡張子、またはパスのルールを入力します。\n\n例：\n\n```\n*.pdf, *.png, image/**/*\n```\n\n### PDF/画像の除外パターン\n\n処理しないPDFと画像ファイルの拡張子、またはパスのルールを入力します。空欄にすると、上で対象に含めたファイルをすべて処理します。\n\n例：\n\n```\nthumbs.db, *.gif, cache/**\n```\n\n## 保存スペース別の進行状況\n\nチームや個人の保存スペースごとに、資料がAIに渡されるまでどれだけ待機しているかを確認します。\n\nスペース（team/client）とユーザーを条件に検索でき、更新待ちの件数で並べ替えることができます。\n\n| 項目 | 説明 |\n|------|------|\n| スペース | チームスペース（team）または個人スペース（client）の区分 |\n| ユーザー | 個人スペースの場合、該当するユーザー |\n| 更新待ち | 新しく作成または変更したファイルが、AI検索への反映を待っている状態 |\n| 削除待ち | 削除したファイルの検索データが、整理されるのを待っている状態 |\n| 埋め込み待ち | 文書からテキストを読み取り、AI検索データへの変換を待っている状態 |\n| 埋め込み数 | 完了した埋め込みの総数 |\n\n## AI学習データ状況\n\nAI検索データがモデルごとにどれだけ作成されたかを確認します。元の文書でAIモデルを新たに学習させる機能ではありません。\n\n| 項目 | 説明 |\n|------|------|\n| プロバイダー | 埋め込みに使用されたモデルのプロバイダー（例：ollama） |\n| モデル | 使用された埋め込みモデル名（例：bge-m3:latest） |\n| 件数 | そのモデルで生成された埋め込みの総数 |\n\n件数で並べ替えて、モデルごとの検索データの規模を比較できます。\n",1793056606259]