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训练）","/plugin/fine-tuning","50.plugin/40.fine-tuning",[1020,1025,1030,1035,1040,1045],{"title":1021,"path":1022,"stem":1023,"tags":6,"slug":1024,"sourcePath":1022},"微调简介","/plugin/fine-tuning/overview","50.plugin/40.fine-tuning/00.overview","/plugin/fine-tuning/overview/",{"title":1026,"path":1027,"stem":1028,"tags":6,"slug":1029,"sourcePath":1027},"训练数据预处理","/plugin/fine-tuning/pre-processing","50.plugin/40.fine-tuning/10.pre-processing","/plugin/fine-tuning/pre-processing/",{"title":1031,"path":1032,"stem":1033,"tags":6,"slug":1034,"sourcePath":1032},"训练","/plugin/fine-tuning/training","50.plugin/40.fine-tuning/20.training","/plugin/fine-tuning/training/",{"title":1036,"path":1037,"stem":1038,"tags":6,"slug":1039,"sourcePath":1037},"评估","/plugin/fine-tuning/evaluation","50.plugin/40.fine-tuning/30.evaluation","/plugin/fine-tuning/evaluation/",{"title":1041,"path":1042,"stem":1043,"tags":6,"slug":1044,"sourcePath":1042},"聊天测试","/plugin/fine-tuning/chat-test","50.plugin/40.fine-tuning/40.chat-test","/plugin/fine-tuning/chat-test/",{"title":1046,"path":1047,"stem":1048,"tags":6,"slug":1049,"sourcePath":1047},"导出","/plugin/fine-tuning/export","50.plugin/40.fine-tuning/50.export","/plugin/fine-tuning/export/",{"title":1051,"tags":6,"slug":6,"path":1052,"stem":1053,"children":1054,"page":66,"sourcePath":1052},"EDC（数据收集）","/plugin/edc","50.plugin/50.edc",[1055,1060,1065,1070],{"title":1056,"path":1057,"stem":1058,"tags":6,"slug":1059,"sourcePath":1057},"EDC 简介","/plugin/edc/overview","50.plugin/50.edc/00.overview","/plugin/edc/overview/",{"title":1061,"path":1062,"stem":1063,"tags":6,"slug":1064,"sourcePath":1062},"数据录入、Audit Trail","/plugin/edc/data-entry","50.plugin/50.edc/10.data-entry","/plugin/edc/data-entry/",{"title":1066,"path":1067,"stem":1068,"tags":6,"slug":1069,"sourcePath":1067},"构建器","/plugin/edc/builder","50.plugin/50.edc/20.builder","/plugin/edc/builder/",{"title":1071,"path":1072,"stem":1073,"tags":6,"slug":1074,"sourcePath":1072},"用户/机构设置","/plugin/edc/account","50.plugin/50.edc/30.account","/plugin/edc/account/",{"title":1076,"tags":6,"slug":6,"path":1077,"stem":1078,"children":1079,"page":66,"sourcePath":1077},"命令行工具","/cli","90.cli",[1080],{"title":1081,"path":1082,"stem":1083,"tags":6,"slug":1084,"sourcePath":1082},"使用命令管理服务器","/cli/commands","90.cli/10.commands","/cli/commands/",{"title":1086,"tags":6,"slug":6,"path":1087,"stem":1088,"children":1089,"page":66,"sourcePath":1087},"安全更新","/security-updates","95.security-updates",[1090,1095,1100,1105],{"title":1091,"path":1092,"stem":1093,"tags":6,"slug":1094,"sourcePath":1092},"了解安全检查与更新方式","/security-updates/overview","95.security-updates/00.overview","/security-updates/overview/",{"title":1096,"path":1097,"stem":1098,"tags":6,"slug":1099,"sourcePath":1097},"安全检查记录","/security-updates/review-log","95.security-updates/10.review-log","/security-updates/review-log/",{"title":1101,"path":1102,"stem":1103,"tags":6,"slug":1104,"sourcePath":1102},"主要漏洞检查状态","/security-updates/vulnerability-status","95.security-updates/20.vulnerability-status","/security-updates/vulnerability-status/",{"title":1106,"path":1107,"stem":1108,"tags":6,"slug":1109,"sourcePath":1107},"检查服务器安全更新","/security-updates/server-checklist","95.security-updates/30.server-checklist","/security-updates/server-checklist/",{"title":1111,"tags":6,"slug":6,"path":1112,"stem":1113,"children":1114,"page":66,"sourcePath":1112},"FAQ","/faq","99.FAQ",[1115,1121,1127,1133,1139,1145,1151],{"title":1116,"path":1117,"stem":1118,"tags":6,"slug":1119,"sourcePath":1120},"SetFN 与云服务对比","/FAQ/comparison-cloud","99.FAQ/10.comparison-cloud","/FAQ/comparison-cloud/","/faq/comparison-cloud",{"title":1122,"path":1123,"stem":1124,"tags":6,"slug":1125,"sourcePath":1126},"SetFN 与 NAS 对比","/FAQ/comparison-nas","99.FAQ/20.comparison-nas","/FAQ/comparison-nas/","/faq/comparison-nas",{"title":1128,"path":1129,"stem":1130,"tags":6,"slug":1131,"sourcePath":1132},"安全政策","/FAQ/security-policy","99.FAQ/30.security-policy","/FAQ/security-policy/","/faq/security-policy",{"title":1134,"path":1135,"stem":1136,"tags":6,"slug":1137,"sourcePath":1138},"SetFN 为什么这么快","/FAQ/why-fast","99.FAQ/40.why-fast","/FAQ/why-fast/","/faq/why-fast",{"title":1140,"path":1141,"stem":1142,"tags":6,"slug":1143,"sourcePath":1144},"许可证 FAQ","/FAQ/license-faq","99.FAQ/50.license-faq","/FAQ/license-faq/","/faq/license-faq",{"title":1146,"path":1147,"stem":1148,"tags":6,"slug":1149,"sourcePath":1150},"AI 功能 FAQ","/FAQ/ai-faq","99.FAQ/60.ai-faq","/FAQ/ai-faq/","/faq/ai-faq",{"title":1152,"path":1153,"stem":1154,"tags":6,"slug":1155,"sourcePath":1156},"问题排查指南","/FAQ/troubleshooting","99.FAQ/70.troubleshooting","/FAQ/troubleshooting/","/faq/troubleshooting",{"id":1158,"title":220,"body":1159,"cover":6,"description":1617,"extension":1618,"image":6,"meta":1619,"navigation":1620,"path":221,"reviewSummary":6,"reviewedAt":6,"seo":1621,"slug":223,"stem":222,"tags":6,"thumbnail":6,"thumbnailPosition":6,"thumbnailScale":6,"__hash__":1628},"docs_zh/10.admin/30.knowledge/30.storage.md",{"type":1160,"value":1161,"toc":1601},"minimal",[1162,1165,1169,1173,1186,1189,1207,1214,1220,1223,1226,1229,1297,1308,1311,1314,1318,1330,1367,1376,1381,1384,1387,1440,1446,1449,1452,1455,1458,1464,1543,1546,1553,1567,1573,1576],[1163,1164,220],"h1",{"id":220},[1166,1167,1168],"p",{},"管理文档搜索和 RAG 回答所需的 AI 搜索数据。RAG 是一种从已存储的文档中查找相关内容，供 AI 在回答时参考的方式。",[1170,1171,1172],"h2",{"id":1172},"开始之前",[1174,1175,1176,1180,1183],"ul",{},[1177,1178,1179],"li",{},"所需权限：Workspace 管理员",[1177,1181,1182],{},"确认要使用的嵌入模型的输出维度。",[1177,1184,1185],{},"如果选择外部存储，SpaceBuilder 服务器必须能够访问相应的端点。",[1170,1187,1188],{"id":1188},"打开知识存储",[1190,1191,1192,1195,1202],"ol",{},[1177,1193,1194],{},"访问管理员页面。",[1177,1196,1197,1198,1201],{},"在左侧菜单中选择 ",[1199,1200,198],"code",{},"。",[1177,1203,1204,1205,1201],{},"选择 ",[1199,1206,220],{},[1166,1208,1209],{},[1210,1211],"img",{"alt":1212,"src":1213},"知识存储页面","./_screenshots/admin-knowledge-storage_zh.png",[1215,1216,1217],"note",{},[1166,1218,1219],{},"页面上显示的数值仅为说明功能的示例。",[1221,1222],"hr",{},[1170,1224,1225],{"id":1225},"向量数据库概览",[1166,1227,1228],{},"查看当前连接的向量数据库的状态。",[1230,1231,1232,1245],"table",{},[1233,1234,1235],"thead",{},[1236,1237,1238,1242],"tr",{},[1239,1240,1241],"th",{},"项目",[1239,1243,1244],{},"说明",[1246,1247,1248,1257,1265,1273,1281,1289],"tbody",{},[1236,1249,1250,1254],{},[1251,1252,1253],"td",{},"连接状态",[1251,1255,1256],{},"向量数据库是否已连接",[1236,1258,1259,1262],{},[1251,1260,1261],{},"向量存储",[1251,1263,1264],{},"存储 AI 搜索数据的方式和类型（例如：Qdrant 服务型）",[1236,1266,1267,1270],{},[1251,1268,1269],{},"已存储的向量数",[1251,1271,1272],{},"此实例（当前 SpaceBuilder）存储的数据条目数",[1236,1274,1275,1278],{},[1251,1276,1277],{},"集合数",[1251,1279,1280],{},"存储中的集合数。Qdrant·Milvus 显示的是相应服务器上的全部集合数。",[1236,1282,1283,1286],{},[1251,1284,1285],{},"集合配置",[1251,1287,1288],{},"存储的维度和距离计算方式是否与设置一致",[1236,1290,1291,1294],{},[1251,1292,1293],{},"存储路径",[1251,1295,1296],{},"数据的存储位置（仅 Local·Automatic 显示，例如：vectordb_local）",[1166,1298,1299,1300,1302,1303,1201],{},"各项目的详细含义以及 ",[1199,1301,1285],{}," 各种显示对应的处理方法，请参阅",[1304,1305,1307],"a",{"href":1306},"/zh/docs/admin/knowledge/storage-operations/#%E6%A3%80%E6%9F%A5%E8%BF%90%E8%A1%8C%E7%8A%B6%E6%80%81","向量数据库运维指南",[1170,1309,1310],{"id":1310},"向量数据库设置",[1166,1312,1313],{},"设置 AI 搜索数据的存储方式和位置。",[1315,1316,1317],"h3",{"id":1317},"选择嵌入模型",[1166,1319,1320,1321,1325,1326,1329],{},"从在 ",[1304,1322,1324],{"href":1323},"/zh/docs/admin/ai/models/#%E5%B7%B2%E5%AE%89%E8%A3%85%E6%A8%A1%E5%9E%8B","AI 模型设置","中下载的模型里，选择用于将文档转换为搜索向量的嵌入模型。如果还没有已下载的模型，请先在 AI 模型设置中下载嵌入模型。（例如：",[1199,1327,1328],{},"bge-m3","）",[1230,1331,1332,1343],{},[1233,1333,1334],{},[1236,1335,1336,1338,1340],{},[1239,1337,1241],{},[1239,1339,1244],{},[1239,1341,1342],{},"默认值",[1246,1344,1345,1356],{},[1236,1346,1347,1350,1353],{},[1251,1348,1349],{},"嵌入模型",[1251,1351,1352],{},"选择在 AI 模型设置中下载的嵌入模型。",[1251,1354,1355],{},"-",[1236,1357,1358,1361,1364],{},[1251,1359,1360],{},"向量维度",[1251,1362,1363],{},"所选模型生成的向量的维度数。",[1251,1365,1366],{},"1024",[1166,1368,1204,1369,1371,1372,1375],{},[1199,1370,1328],{},"、",[1199,1373,1374],{},"nomic-embed-text"," 等已知模型时，向量维度会自动填入。使用其他模型时，请输入与该模型输出维度相同的值。",[1215,1377,1378],{},[1166,1379,1380],{},"更改嵌入模型或向量维度后，会对所有现有文档重新索引。保存后会自动开始。",[1315,1382,1383],{"id":1383},"驱动程序",[1166,1385,1386],{},"选择 AI 搜索数据的存储方式。",[1230,1388,1389,1397],{},[1233,1390,1391],{},[1236,1392,1393,1395],{},[1239,1394,1383],{},[1239,1396,1244],{},[1246,1398,1399,1407,1418,1429],{},[1236,1400,1401,1404],{},[1251,1402,1403],{},"Automatic (SQLite)",[1251,1405,1406],{},"默认值。选择嵌入模型后，使用 SpaceBuilder 内置的 SQLite 存储。",[1236,1408,1409,1412],{},[1251,1410,1411],{},"Local (SQLite)",[1251,1413,1414,1415,1201],{},"将 AI 搜索数据保存为指定路径下的文件。无需单独的数据库服务器，适合向量数量约 5 万个以下的环境。默认路径为 ",[1199,1416,1417],{},"vectordb_local",[1236,1419,1420,1423],{},[1251,1421,1422],{},"Milvus",[1251,1424,1425,1426],{},"用于大容量环境的外部向量数据库。需要预先创建集合。",[1304,1427,240],{"href":1428},"/zh/docs/admin/knowledge/milvus/",[1236,1430,1431,1434],{},[1251,1432,1433],{},"Qdrant",[1251,1435,1436,1437],{},"外部向量数据库。集合会在首次索引时自动创建。",[1304,1438,230],{"href":1439},"/zh/docs/admin/knowledge/qdrant/",[1166,1441,1442,1443,1201],{},"关于如何选择驱动程序，请参阅",[1304,1444,1307],{"href":1445},"/zh/docs/admin/knowledge/storage-operations/#%E9%80%89%E6%8B%A9%E5%90%91%E9%87%8F%E5%AD%98%E5%82%A8",[1315,1447,1448],{"id":1448},"数据路径",[1166,1450,1451],{},"指定 Local·Automatic 驱动程序保存数据的路径。相对路径以 SpaceBuilder 配置文件所在的文件夹为基准。",[1315,1453,1454],{"id":1454},"外部向量数据库连接信息",[1166,1456,1457],{},"选择 Qdrant 或 Milvus 时，需要输入外部向量数据库的连接信息，而不是数据路径。Qdrant 需要分别输入 REST 和 gRPC 端点。",[1166,1459,1460],{},[1210,1461],{"alt":1462,"src":1463},"选择 Qdrant 作为驱动程序时的连接信息输入页面","./_screenshots/admin-ai-db-qdrant_en.png",[1230,1465,1466,1474],{},[1233,1467,1468],{},[1236,1469,1470,1472],{},[1239,1471,1241],{},[1239,1473,1244],{},[1246,1475,1476,1484,1507,1527,1535],{},[1236,1477,1478,1481],{},[1251,1479,1480],{},"集合",[1251,1482,1483],{},"用于存储数据的集合名称。建议每个实例使用不同的名称，页面上会显示此实例的建议名称。",[1236,1485,1486,1489],{},[1251,1487,1488],{},"REST 端点 / 端点",[1251,1490,1491,1492,1495,1496,1499,1500,1495,1503,1506],{},"外部向量数据库服务器的访问地址。Qdrant 显示为 ",[1199,1493,1494],{},"REST 端点","（例如：",[1199,1497,1498],{},"http://localhost:6333","），Milvus 显示为 ",[1199,1501,1502],{},"端点",[1199,1504,1505],{},"http://localhost:19530","）。",[1236,1508,1509,1512],{},[1251,1510,1511],{},"gRPC 端点（Qdrant，推荐）",[1251,1513,1514,1515,1518,1519,1522,1523,1526],{},"与 REST 相同的 Qdrant 实例的 gRPC 地址（默认值：",[1199,1516,1517],{},"http://localhost:6334","）。搜索和向量存储优先使用 gRPC。留空时，如果 REST 地址使用 ",[1199,1520,1521],{},"6333"," 端口，则自动使用 ",[1199,1524,1525],{},"6334","；无法连接时切换为 REST。",[1236,1528,1529,1532],{},[1251,1530,1531],{},"API 密钥",[1251,1533,1534],{},"仅在外部向量数据库启用了认证时输入。已保存的值在页面上以掩码显示，不更改则保持原样。清空后保存即删除。",[1236,1536,1537,1540],{},[1251,1538,1539],{},"用户名 / 密码",[1251,1541,1542],{},"用于 Milvus 账户认证。密码仅在需要更改时输入。",[1170,1544,1545],{"id":1545},"向量同步与重建",[1166,1547,1548,1549,1552],{},"在 ",[1199,1550,1551],{},"仅限管理员"," 区域重新填充或重新构建向量数据库的数据。两项操作都不会删除工作空间中的原始文件，并且需要 Team 及以上的许可证。",[1174,1554,1555,1561],{},[1177,1556,1557,1560],{},[1199,1558,1559],{},"同步已有嵌入","：将已有的嵌入复制到所选的向量数据库，仅重新生成无法恢复的向量。",[1177,1562,1563,1566],{},[1199,1564,1565],{},"重建向量生成","：在保留旧向量的同时创建新的向量世代，验证完成后清理旧向量。",[1166,1568,1569,1570,1201],{},"关于两项操作的区别和使用时机，请参阅",[1304,1571,1307],{"href":1572},"/zh/docs/admin/knowledge/storage-operations/#%E5%90%91%E9%87%8F%E5%90%8C%E6%AD%A5%E4%B8%8E%E9%87%8D%E5%BB%BA",[1170,1574,1575],{"id":1575},"相关文档",[1174,1577,1578,1583,1587,1592,1596],{},[1177,1579,1580],{},[1304,1581,225],{"href":1582},"/zh/docs/admin/knowledge/storage-operations/",[1177,1584,1585],{},[1304,1586,230],{"href":1439},[1177,1588,1589],{},[1304,1590,235],{"href":1591},"/zh/docs/admin/knowledge/qdrant-server-setup/",[1177,1593,1594],{},[1304,1595,240],{"href":1428},[1177,1597,1598],{},[1304,1599,208],{"href":1600},"/zh/docs/admin/knowledge/indexing/",{"title":1602,"searchDepth":1603,"depth":1604,"links":1605},"",2,3,[1606,1607,1608,1609,1615,1616],{"id":1172,"depth":1603,"text":1172},{"id":1188,"depth":1603,"text":1188},{"id":1225,"depth":1603,"text":1225},{"id":1310,"depth":1603,"text":1310,"children":1610},[1611,1612,1613,1614],{"id":1317,"depth":1604,"text":1317},{"id":1383,"depth":1604,"text":1383},{"id":1448,"depth":1604,"text":1448},{"id":1454,"depth":1604,"text":1454},{"id":1545,"depth":1603,"text":1545},{"id":1575,"depth":1603,"text":1575},"介绍用于知识索引、文档搜索和 RAG 的向量数据库的设置方法。","md",{},true,{"title":1622,"description":1617,"ogType":1623,"twitterCard":1624,"keywords":1625},"知识存储 | Admin Docs","article","summary_large_image",[1310,1626,755,1627,1225,1310,1545],"向量数据库","DB","jTK8heYGzSpew2aUq3s6NSU0QzHdee1834Xv1ML-GzI",[1630,1631],{"title":215,"path":216,"stem":217,"slug":218,"children":-1},{"title":225,"path":226,"stem":227,"slug":228,"children":-1},{"title":220,"filename":1633,"content":1634},"storage","# 知识存储\n\n管理文档搜索和 RAG 回答所需的 AI 搜索数据。RAG 是一种从已存储的文档中查找相关内容，供 AI 在回答时参考的方式。\n\n## 开始之前\n\n- 所需权限：Workspace 管理员\n- 确认要使用的嵌入模型的输出维度。\n- 如果选择外部存储，SpaceBuilder 服务器必须能够访问相应的端点。\n\n## 打开知识存储\n\n1. 访问管理员页面。\n2. 在左侧菜单中选择 `知识服务`。\n3. 选择 `知识存储`。\n\n![知识存储页面](./_screenshots/admin-knowledge-storage_zh.png)\n\n::note\n页面上显示的数值仅为说明功能的示例。\n::\n\n---\n\n## 向量数据库概览\n\n查看当前连接的向量数据库的状态。\n\n| 项目 | 说明 |\n|------|------|\n| 连接状态 | 向量数据库是否已连接 |\n| 向量存储 | 存储 AI 搜索数据的方式和类型（例如：Qdrant 服务型） |\n| 已存储的向量数 | 此实例（当前 SpaceBuilder）存储的数据条目数 |\n| 集合数 | 存储中的集合数。Qdrant·Milvus 显示的是相应服务器上的全部集合数。 |\n| 集合配置 | 存储的维度和距离计算方式是否与设置一致 |\n| 存储路径 | 数据的存储位置（仅 Local·Automatic 显示，例如：vectordb_local） |\n\n各项目的详细含义以及 `集合配置` 各种显示对应的处理方法，请参阅[向量数据库运维指南](/zh/docs/admin/knowledge/storage-operations/#检查运行状态)。\n\n## 向量数据库设置\n\n设置 AI 搜索数据的存储方式和位置。\n\n### 选择嵌入模型\n\n从在 [AI 模型设置](/zh/docs/admin/ai/models/#已安装模型)中下载的模型里，选择用于将文档转换为搜索向量的嵌入模型。如果还没有已下载的模型，请先在 AI 模型设置中下载嵌入模型。（例如：`bge-m3`）\n\n| 项目 | 说明 | 默认值 |\n|------|------|--------|\n| 嵌入模型 | 选择在 AI 模型设置中下载的嵌入模型。 | - |\n| 向量维度 | 所选模型生成的向量的维度数。 | 1024 |\n\n选择 `bge-m3`、`nomic-embed-text` 等已知模型时，向量维度会自动填入。使用其他模型时，请输入与该模型输出维度相同的值。\n\n::note\n更改嵌入模型或向量维度后，会对所有现有文档重新索引。保存后会自动开始。\n::\n\n### 驱动程序\n\n选择 AI 搜索数据的存储方式。\n\n| 驱动程序 | 说明 |\n|----------|------|\n| Automatic (SQLite) | 默认值。选择嵌入模型后，使用 SpaceBuilder 内置的 SQLite 存储。 |\n| Local (SQLite) | 将 AI 搜索数据保存为指定路径下的文件。无需单独的数据库服务器，适合向量数量约 5 万个以下的环境。默认路径为 `vectordb_local`。 |\n| Milvus | 用于大容量环境的外部向量数据库。需要预先创建集合。[连接 Milvus](/zh/docs/admin/knowledge/milvus/) |\n| Qdrant | 外部向量数据库。集合会在首次索引时自动创建。[连接 Qdrant](/zh/docs/admin/knowledge/qdrant/) |\n\n关于如何选择驱动程序，请参阅[向量数据库运维指南](/zh/docs/admin/knowledge/storage-operations/#选择向量存储)。\n\n### 数据路径\n\n指定 Local·Automatic 驱动程序保存数据的路径。相对路径以 SpaceBuilder 配置文件所在的文件夹为基准。\n\n### 外部向量数据库连接信息\n\n选择 Qdrant 或 Milvus 时，需要输入外部向量数据库的连接信息，而不是数据路径。Qdrant 需要分别输入 REST 和 gRPC 端点。\n\n![选择 Qdrant 作为驱动程序时的连接信息输入页面](./_screenshots/admin-ai-db-qdrant_en.png)\n\n| 项目 | 说明 |\n|------|------|\n| 集合 | 用于存储数据的集合名称。建议每个实例使用不同的名称，页面上会显示此实例的建议名称。 |\n| REST 端点 / 端点 | 外部向量数据库服务器的访问地址。Qdrant 显示为 `REST 端点`（例如：`http://localhost:6333`），Milvus 显示为 `端点`（例如：`http://localhost:19530`）。 |\n| gRPC 端点（Qdrant，推荐） | 与 REST 相同的 Qdrant 实例的 gRPC 地址（默认值：`http://localhost:6334`）。搜索和向量存储优先使用 gRPC。留空时，如果 REST 地址使用 `6333` 端口，则自动使用 `6334`；无法连接时切换为 REST。 |\n| API 密钥 | 仅在外部向量数据库启用了认证时输入。已保存的值在页面上以掩码显示，不更改则保持原样。清空后保存即删除。 |\n| 用户名 / 密码 | 用于 Milvus 账户认证。密码仅在需要更改时输入。 |\n\n## 向量同步与重建\n\n在 `仅限管理员` 区域重新填充或重新构建向量数据库的数据。两项操作都不会删除工作空间中的原始文件，并且需要 Team 及以上的许可证。\n\n- `同步已有嵌入`：将已有的嵌入复制到所选的向量数据库，仅重新生成无法恢复的向量。\n- `重建向量生成`：在保留旧向量的同时创建新的向量世代，验证完成后清理旧向量。\n\n关于两项操作的区别和使用时机，请参阅[向量数据库运维指南](/zh/docs/admin/knowledge/storage-operations/#向量同步与重建)。\n\n## 相关文档\n\n- [知识存储运维](/zh/docs/admin/knowledge/storage-operations/)\n- [连接 Qdrant](/zh/docs/admin/knowledge/qdrant/)\n- [准备 Qdrant 服务器](/zh/docs/admin/knowledge/qdrant-server-setup/)\n- [连接 Milvus](/zh/docs/admin/knowledge/milvus/)\n- [采集与索引](/zh/docs/admin/knowledge/indexing/)\n",1793056630078]