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access","/webspace/admin/workspace-access","40.webspace/50.admin/20.workspace-access",{"title":679,"path":680,"stem":681,"children":-1},"API & site security","/webspace/admin/api-site-security","40.webspace/50.admin/30.api-site-security",{"title":683,"path":684,"stem":685,"children":-1},"Services and capacity","/webspace/admin/service-capacity","40.webspace/50.admin/40.service-capacity",{"title":687,"path":688,"stem":689,"children":-1},"Deployment management and frontend releases","/webspace/admin/deployments","40.webspace/50.admin/50.deployments",{"title":691,"path":692,"stem":693,"children":694,"page":55},"Try WebSpace","/webspace/examples","40.webspace/60.examples",[695,699,703,708,713],{"title":696,"path":697,"stem":698,"children":-1},"WebSpace examples","/webspace/examples/overview","40.webspace/60.examples/00.overview",{"title":700,"path":701,"stem":702,"children":-1},"Build a real-time Gomoku game","/webspace/examples/realtime-gomoku","40.webspace/60.examples/20.realtime-gomoku",{"title":704,"path":705,"stem":706,"tags":707,"children":-1},"Build a resource board from folder files","/webspace/examples/folder-board","40.webspace/60.examples/30.folder-board",[91],{"title":709,"path":710,"stem":711,"tags":712,"children":-1},"Build a shared monster raid","/webspace/examples/monster-raid","40.webspace/60.examples/40.monster-raid",[91],{"title":714,"path":715,"stem":716,"tags":717,"children":-1},"Deploy an AI support chat sample","/webspace/examples/ai-support-chat","40.webspace/60.examples/50.ai-support-chat",[91],{"title":719,"path":720,"stem":721,"children":722,"page":55},"Using plugin","/plugin","50.plugin",[723,727,744,788,806,836],{"title":724,"path":725,"stem":726,"children":-1},"Plugin Overview","/plugin/overview","50.plugin/00.overview",{"title":728,"path":729,"stem":730,"children":731,"page":55},"Calendar","/plugin/calendar","50.plugin/10.calendar",[732,736,740],{"title":733,"path":734,"stem":735,"children":-1},"Calendar Introduction","/plugin/calendar/overview","50.plugin/10.calendar/00.overview",{"title":737,"path":738,"stem":739,"children":-1},"Creating, Editing, and Deleting Events","/plugin/calendar/create-event","50.plugin/10.calendar/10.create-event",{"title":741,"path":742,"stem":743,"children":-1},"Checking Activity History","/plugin/calendar/file-activity","50.plugin/10.calendar/40.file-activity",{"title":745,"path":746,"stem":747,"children":748,"page":55},"Email","/plugin/email","50.plugin/20.email",[749,753,756,760,764,768,772,776,780,784],{"title":750,"path":751,"stem":752,"children":-1},"Email Introduction","/plugin/email/overview","50.plugin/20.email/00.overview",{"title":157,"path":754,"stem":755,"children":-1},"/plugin/email/setfn-email-setting","50.plugin/20.email/05.setfn-email-setting",{"title":757,"path":758,"stem":759,"children":-1},"Connect an External Email Account","/plugin/email/external-email","50.plugin/20.email/07.external-email",{"title":761,"path":762,"stem":763,"children":-1},"Basic Email Functionality","/plugin/email/email-basic","50.plugin/20.email/10.email-basic",{"title":765,"path":766,"stem":767,"children":-1},"Composing and Sending Email","/plugin/email/email-compose","50.plugin/20.email/20.email-compose",{"title":769,"path":770,"stem":771,"children":-1},"Attaching Files","/plugin/email/email-attachment","50.plugin/20.email/30.email-attachment",{"title":773,"path":774,"stem":775,"children":-1},"Mail Folder Management","/plugin/email/email-folders","50.plugin/20.email/40.email-folders",{"title":777,"path":778,"stem":779,"children":-1},"Automatic Email Classification","/plugin/email/email-auto-classify","50.plugin/20.email/50.email-auto-classify",{"title":781,"path":782,"stem":783,"children":-1},"Email File Archiving and Sharing","/plugin/email/email-open-in-space","50.plugin/20.email/60.email-open-in-space",{"title":785,"path":786,"stem":787,"children":-1},"Email Security","/plugin/email/email-security","50.plugin/20.email/70.email-security",{"title":789,"path":790,"stem":791,"children":792,"page":55},"Workflow","/plugin/workflow","50.plugin/30.workflow",[793,797,802],{"title":794,"path":795,"stem":796,"children":-1},"Workflow Introduction","/plugin/workflow/overview","50.plugin/30.workflow/00.overview",{"title":798,"path":799,"stem":800,"tags":801,"children":-1},"Creating a New Workflow","/plugin/workflow/new-workflow","50.plugin/30.workflow/10.new-workflow",[368],{"title":803,"path":804,"stem":805,"children":-1},"Checking Execution History","/plugin/workflow/execution-history","50.plugin/30.workflow/20.execution-history",{"title":807,"path":808,"stem":809,"children":810,"page":55},"Pinetuning (AI learning)","/plugin/fine-tuning","50.plugin/40.fine-tuning",[811,816,820,824,828,832],{"title":812,"path":813,"stem":814,"tags":815,"children":-1},"Fine-tuning Introduction","/plugin/fine-tuning/overview","50.plugin/40.fine-tuning/00.overview",[368],{"title":817,"path":818,"stem":819,"children":-1},"Preprocessing Training Data","/plugin/fine-tuning/pre-processing","50.plugin/40.fine-tuning/10.pre-processing",{"title":821,"path":822,"stem":823,"children":-1},"Training","/plugin/fine-tuning/training","50.plugin/40.fine-tuning/20.training",{"title":825,"path":826,"stem":827,"children":-1},"Evaluation","/plugin/fine-tuning/evaluation","50.plugin/40.fine-tuning/30.evaluation",{"title":829,"path":830,"stem":831,"children":-1},"Chat Test","/plugin/fine-tuning/chat-test","50.plugin/40.fine-tuning/40.chat-test",{"title":833,"path":834,"stem":835,"children":-1},"Export","/plugin/fine-tuning/export","50.plugin/40.fine-tuning/50.export",{"title":837,"path":838,"stem":839,"children":840,"page":55},"EDC (data collection)","/plugin/edc","50.plugin/50.edc",[841,845,849,853],{"title":842,"path":843,"stem":844,"children":-1},"EDC Introduction","/plugin/edc/overview","50.plugin/50.edc/00.overview",{"title":846,"path":847,"stem":848,"children":-1},"Data Entry, Audit Trail","/plugin/edc/data-entry","50.plugin/50.edc/10.data-entry",{"title":850,"path":851,"stem":852,"children":-1},"Builder","/plugin/edc/builder","50.plugin/50.edc/20.builder",{"title":854,"path":855,"stem":856,"children":-1},"User/Site Settings","/plugin/edc/account","50.plugin/50.edc/30.account",{"title":858,"path":859,"stem":860,"children":861,"page":55},"FAQ","/faq","99.FAQ",[862,866,870,874,878,882,886],{"title":863,"path":864,"stem":865,"children":-1},"SetFN vs Cloud Services Comparison","/faq/comparison-cloud","99.FAQ/10.comparison-cloud",{"title":867,"path":868,"stem":869,"children":-1},"SetFN vs NAS Comparison","/faq/comparison-nas","99.FAQ/20.comparison-nas",{"title":871,"path":872,"stem":873,"children":-1},"Security Policy","/faq/security-policy","99.FAQ/30.security-policy",{"title":875,"path":876,"stem":877,"children":-1},"Why Is SetFN Fast","/faq/why-fast","99.FAQ/40.why-fast",{"title":879,"path":880,"stem":881,"children":-1},"License FAQ","/faq/license-faq","99.FAQ/50.license-faq",{"title":883,"path":884,"stem":885,"children":-1},"AI Features FAQ","/faq/ai-faq","99.FAQ/60.ai-faq",{"title":887,"path":888,"stem":889,"children":-1},"Troubleshooting Guide","/faq/troubleshooting","99.FAQ/70.troubleshooting",{"title":891,"path":892,"stem":893,"children":894,"page":55},"Applied (draft)","/draft","draft",[895,899,903,907,911,915,919,923,927,931,935,939,943],{"title":896,"path":897,"stem":898,"children":-1},"Example 1. Building an Introduction Page","/draft/static-intro","draft/05.static-intro",{"title":900,"path":901,"stem":902,"children":-1},"Example 2. Building a Notice Board","/draft/public-notice-board","draft/07.public-notice-board",{"title":904,"path":905,"stem":906,"children":-1},"Example 3. Building a Coffee Chat Booking System","/draft/coffee-chat-booking","draft/08.coffee-chat-booking",{"title":908,"path":909,"stem":910,"children":-1},"Example 4. Building a Leave Request System","/draft/leave-request","draft/09.leave-request",{"title":912,"path":913,"stem":914,"children":-1},"Example 5. Building Real-Time Team Chat","/draft/team-chat","draft/10.team-chat",{"title":916,"path":917,"stem":918,"children":-1},"Example 6. Building a Job Application Intake","/draft/client-submission","draft/11.client-submission",{"title":920,"path":921,"stem":922,"children":-1},"Example 7. Building an Issue Tracker with Team and Level Conditions","/draft/crm-basic","draft/12.crm-basic",{"title":924,"path":925,"stem":926,"children":-1},"Example 8. Building a Status Filter and Stats Summary Screen","/draft/crm-search-filter","draft/13.crm-search-filter",{"title":928,"path":929,"stem":930,"children":-1},"Example 9. Building Real-Time Community Chat with Moderation","/draft/community-moderation","draft/14.community-moderation",{"title":932,"path":933,"stem":934,"children":-1},"Example 10. Building a Real-Time Attendance Check / Poll","/draft/attendance-poll","draft/15.attendance-poll",{"title":936,"path":937,"stem":938,"children":-1},"Example 11. Building a Debate Battle","/draft/debate-battle","draft/16.debate-battle",{"title":940,"path":941,"stem":942,"children":-1},"Example 13. Building an Announcement Read Receipt","/draft/announcement-read","draft/18.announcement-read",{"title":944,"path":945,"stem":946,"children":-1},"Example 14. Building an Inventory Management System","/draft/inventory-management","draft/19.inventory-management",{"title":948,"path":949,"stem":950,"children":951,"page":55},"SJAUTO","/sjauto","sjauto",[952,956,960],{"title":953,"path":954,"stem":955,"children":-1},"Access the Administrator Page","/sjauto/admin-page-access","sjauto/00.admin-page-access",{"title":957,"path":958,"stem":959,"children":-1},"Sign Up and Sign In","/sjauto/signup","sjauto/10.signup",{"title":961,"path":962,"stem":963,"children":-1},"AI Chat Files","/sjauto/ai-chat-file","sjauto/ai-chat-file",{"id":965,"title":182,"body":966,"cover":1382,"description":1383,"extension":1384,"image":1382,"meta":1385,"navigation":1387,"path":183,"seo":1388,"stem":184,"tags":1382,"thumbnail":1382,"__hash__":1399},"docs_en/10.admin/30.knowledge/30.storage.md",{"type":967,"value":968,"toc":1366},"minimal",[969,973,977,982,995,999,1017,1024,1030,1033,1037,1040,1100,1104,1111,1116,1129,1166,1172,1176,1179,1222,1226,1229,1233,1236,1242,1309,1313,1327,1335,1339],[970,971,182],"h1",{"id":972},"knowledge-storage",[974,975,976],"p",{},"Manage the AI search data needed for document search and RAG responses. RAG is a method in which the AI finds relevant content from stored documents to reference when answering.",[978,979,981],"h2",{"id":980},"before-you-begin","Before you begin",[983,984,985,989,992],"ul",{},[986,987,988],"li",{},"Required role: Workspace administrator",[986,990,991],{},"Confirm the output dimension of the embedding model you will use.",[986,993,994],{},"For an external store, the SpaceBuilder server must be able to reach its endpoint.",[978,996,998],{"id":997},"open-knowledge-storage","Open Knowledge Storage",[1000,1001,1002,1005,1012],"ol",{},[986,1003,1004],{},"Open the administrator console.",[986,1006,1007,1008,1011],{},"Select ",[1009,1010,165],"code",{}," in the left menu.",[986,1013,1007,1014,1016],{},[1009,1015,182],{},".",[974,1018,1019],{},[1020,1021],"img",{"alt":1022,"src":1023},"Knowledge Storage page","./_screenshots/admin-knowledge-storage_en.png",[1025,1026,1027],"note",{},[974,1028,1029],{},"Values shown in the screenshot are illustrative.",[1031,1032],"hr",{},[978,1034,1036],{"id":1035},"vector-db-operating-status","Vector DB Operating Status",[974,1038,1039],{},"Check the status of the currently connected Vector DB.",[1041,1042,1043,1056],"table",{},[1044,1045,1046],"thead",{},[1047,1048,1049,1053],"tr",{},[1050,1051,1052],"th",{},"Item",[1050,1054,1055],{},"Description",[1057,1058,1059,1068,1076,1084,1092],"tbody",{},[1047,1060,1061,1065],{},[1062,1063,1064],"td",{},"Connection status",[1062,1066,1067],{},"Whether the Vector DB is connected",[1047,1069,1070,1073],{},[1062,1071,1072],{},"Vector store",[1062,1074,1075],{},"The method used to store AI search data (e.g., embedded local SQLite)",[1047,1077,1078,1081],{},[1062,1079,1080],{},"Data (entity) count",[1062,1082,1083],{},"Number of stored data items",[1047,1085,1086,1089],{},[1062,1087,1088],{},"Collection count",[1062,1090,1091],{},"Number of created collections",[1047,1093,1094,1097],{},[1062,1095,1096],{},"Storage path",[1062,1098,1099],{},"The storage location for the data (e.g., vectordb_local)",[978,1101,1103],{"id":1102},"vector-db-setup","Vector DB Setup",[974,1105,1106,1107,1110],{},"Configure how and where AI search data is stored. On a fresh installation, this is set to ",[1009,1108,1109],{},"Disabled",". To use AI document search, select a storage method below.",[1112,1113,1115],"h3",{"id":1114},"selecting-an-embedding-model","Selecting an Embedding Model",[974,1117,1118,1119,1124,1125,1128],{},"Select the model that converts documents into search vectors from the models downloaded under ",[1120,1121,1123],"a",{"href":1122},"/docs/admin/ai/models/#installed-models","AI Model Settings",". If no downloaded models are available, download an embedding model under AI Model Settings first. (e.g., ",[1009,1126,1127],{},"bge-m3",")",[1041,1130,1131,1142],{},[1044,1132,1133],{},[1047,1134,1135,1137,1139],{},[1050,1136,1052],{},[1050,1138,1055],{},[1050,1140,1141],{},"Default",[1057,1143,1144,1155],{},[1047,1145,1146,1149,1152],{},[1062,1147,1148],{},"Embedding model",[1062,1150,1151],{},"Select an embedding model downloaded under AI Model Settings.",[1062,1153,1154],{},"-",[1047,1156,1157,1160,1163],{},[1062,1158,1159],{},"Dimension size",[1062,1161,1162],{},"The number of dimensions in the vectors generated by the selected model.",[1062,1164,1165],{},"1024",[974,1167,1168,1169,1171],{},"The default dimension size is ",[1009,1170,1165],{},". Because supported dimensions can differ by model, set this value to match the output dimension of the selected model.",[1112,1173,1175],{"id":1174},"driver","Driver",[974,1177,1178],{},"Select the method for storing AI search data.",[1041,1180,1181,1189],{},[1044,1182,1183],{},[1047,1184,1185,1187],{},[1050,1186,1175],{},[1050,1188,1055],{},[1057,1190,1191,1202,1210],{},[1047,1192,1193,1196],{},[1062,1194,1195],{},"Local (SQLite)",[1062,1197,1198,1199,1016],{},"Stores AI search data as a file on the current server. No separate DB server is required, making it suitable for typical installations. The default path is ",[1009,1200,1201],{},"vectordb_local",[1047,1203,1204,1207],{},[1062,1205,1206],{},"Milvus",[1062,1208,1209],{},"An external vector DB used in environments with large amounts of data.",[1047,1211,1212,1215],{},[1062,1213,1214],{},"Qdrant",[1062,1216,1217,1218,1221],{},"An external vector DB for fast vector search. You need to set up a reachable server yourself — see ",[1120,1219,194],{"href":1220},"/docs/admin/knowledge/qdrant-server-setup/"," for details.",[1112,1223,1225],{"id":1224},"data-path","Data Path",[974,1227,1228],{},"When using the local (SQLite) driver, specify the path where data will be stored.",[1112,1230,1232],{"id":1231},"external-vector-db-connection-info","External Vector DB Connection Info",[974,1234,1235],{},"When you select an external vector DB driver like Milvus or Qdrant, fill in these additional fields. For Qdrant, you enter separate REST and gRPC endpoints.",[974,1237,1238],{},[1020,1239],{"alt":1240,"src":1241},"Connection info fields shown when Qdrant is selected as the driver","./_screenshots/admin-ai-db-qdrant_en.png",[1041,1243,1244,1252],{},[1044,1245,1246],{},[1047,1247,1248,1250],{},[1050,1249,1052],{},[1050,1251,1055],{},[1057,1253,1254,1262,1273,1293,1301],{},[1047,1255,1256,1259],{},[1062,1257,1258],{},"Collection",[1062,1260,1261],{},"The collection name to use, distinct per instance.",[1047,1263,1264,1267],{},[1062,1265,1266],{},"REST endpoint",[1062,1268,1269,1270,1128],{},"The external vector DB server's REST API address (Qdrant default: ",[1009,1271,1272],{},"http://localhost:6333",[1047,1274,1275,1278],{},[1062,1276,1277],{},"gRPC endpoint (Qdrant, recommended)",[1062,1279,1280,1281,1284,1285,1288,1289,1292],{},"The gRPC address of the same Qdrant instance as REST (default: ",[1009,1282,1283],{},"http://localhost:6334","). Search and vector storage use gRPC first. If left blank, ",[1009,1286,1287],{},"6334"," is used automatically when the REST address is on port ",[1009,1290,1291],{},"6333","; it falls back to REST if it can't connect.",[1047,1294,1295,1298],{},[1062,1296,1297],{},"API key",[1062,1299,1300],{},"Only fill this in if authentication is enabled on the external vector DB. The stored value shows masked on screen; saving it blank deletes it.",[1047,1302,1303,1306],{},[1062,1304,1305],{},"Username / password",[1062,1307,1308],{},"Credentials for a Milvus account. Enter the password only when changing it.",[978,1310,1312],{"id":1311},"initializing-and-deleting-the-vector-db","Initializing and Deleting the Vector DB",[1314,1315,1316,1319],"warning",{},[974,1317,1318],{},"Initializing does not delete the original files in the workspace. Only the AI search data is deleted; to use it again, all data must be re-indexed.",[983,1320,1321,1324],{},[986,1322,1323],{},"Re-index all data: Re-reads the original documents and rebuilds the AI search data.",[986,1325,1326],{},"Initialize (delete vectors and re-index): Deletes this instance's AI search vectors from the current store and requests a full re-index. Vector search is limited until the replacement index is ready.",[974,1328,1329,1330,1334],{},"See ",[1120,1331,1333],{"href":1332},"/docs/admin/knowledge/storage-operations/","Vector Store Operations"," for when to use each recovery action.",[978,1336,1338],{"id":1337},"related-documents","Related Documents",[983,1340,1341,1345,1351,1355,1361],{},[986,1342,1343],{},[1120,1344,1333],{"href":1332},[986,1346,1347],{},[1120,1348,1350],{"href":1349},"/docs/admin/knowledge/qdrant/","Connecting Qdrant",[986,1352,1353],{},[1120,1354,194],{"href":1220},[986,1356,1357],{},[1120,1358,1360],{"href":1359},"/docs/admin/knowledge/milvus/","Connecting Milvus",[986,1362,1363],{},[1120,1364,172],{"href":1365},"/docs/admin/knowledge/indexing/",{"title":1367,"searchDepth":1368,"depth":1369,"links":1370},"",2,3,[1371,1372,1373,1374,1380,1381],{"id":980,"depth":1368,"text":981},{"id":997,"depth":1368,"text":998},{"id":1035,"depth":1368,"text":1036},{"id":1102,"depth":1368,"text":1103,"children":1375},[1376,1377,1378,1379],{"id":1114,"depth":1369,"text":1115},{"id":1174,"depth":1369,"text":1175},{"id":1224,"depth":1369,"text":1225},{"id":1231,"depth":1369,"text":1232},{"id":1311,"depth":1368,"text":1312},{"id":1337,"depth":1368,"text":1338},null,"Explains how to configure the vector DB used for knowledge indexing, document search, and RAG.","md",{"slug":1386},"/admin/knowledge/storage/",true,{"title":1389,"description":1383,"ogType":1390,"twitterCard":1391,"keywords":1392},"Knowledge Storage | Admin Docs","article","summary_large_image",[1393,1394,598,1395,1396,1397,1398],"Vector DB settings","SQL","DB","Vector DB operating status","Vector DB configuration","Initializing and deleting the Vector DB","ZbdnE_DVbpJOiQHDn7Geb0F8nV44FPi2SJ_X5V03yh0",[1401,1402],{"title":178,"path":179,"stem":180,"children":-1},{"title":186,"path":187,"stem":188,"children":-1},{"title":182,"filename":1404,"content":1405},"storage","# Knowledge Storage\r\n\r\nManage the AI search data needed for document search and RAG responses. RAG is a method in which the AI finds relevant content from stored documents to reference when answering.\r\n\r\n## Before you begin\r\n\r\n- Required role: Workspace administrator\r\n- Confirm the output dimension of the embedding model you will use.\r\n- For an external store, the SpaceBuilder server must be able to reach its endpoint.\r\n\r\n## Open Knowledge Storage\r\n\r\n1. Open the administrator console.\r\n2. Select `Knowledge Services` in the left menu.\r\n3. Select `Knowledge Storage`.\r\n\r\n![Knowledge Storage page](./_screenshots/admin-knowledge-storage_en.png)\r\n\r\n::note\r\nValues shown in the screenshot are illustrative.\r\n::\r\n\r\n---\r\n\r\n## Vector DB Operating Status\r\n\r\nCheck the status of the currently connected Vector DB.\r\n\r\n| Item | Description |\r\n|------|------|\r\n| Connection status | Whether the Vector DB is connected |\r\n| Vector store | The method used to store AI search data (e.g., embedded local SQLite) |\r\n| Data (entity) count | Number of stored data items |\r\n| Collection count | Number of created collections |\r\n| Storage path | The storage location for the data (e.g., vectordb_local) |\r\n\r\n## Vector DB Setup\r\n\r\nConfigure how and where AI search data is stored. On a fresh installation, this is set to `Disabled`. To use AI document search, select a storage method below.\r\n\r\n### Selecting an Embedding Model\r\n\r\nSelect the model that converts documents into search vectors from the models downloaded under [AI Model Settings](/docs/admin/ai/models/#installed-models). If no downloaded models are available, download an embedding model under AI Model Settings first. (e.g., `bge-m3`)\r\n\r\n| Item | Description | Default |\r\n|------|-------------|---------|\r\n| Embedding model | Select an embedding model downloaded under AI Model Settings. | - |\r\n| Dimension size | The number of dimensions in the vectors generated by the selected model. | 1024 |\r\n\r\nThe default dimension size is `1024`. Because supported dimensions can differ by model, set this value to match the output dimension of the selected model.\r\n\r\n### Driver\r\n\r\nSelect the method for storing AI search data.\r\n\r\n| Driver | Description |\r\n|----------|------|\r\n| Local (SQLite) | Stores AI search data as a file on the current server. No separate DB server is required, making it suitable for typical installations. The default path is `vectordb_local`. |\r\n| Milvus | An external vector DB used in environments with large amounts of data. |\r\n| Qdrant | An external vector DB for fast vector search. You need to set up a reachable server yourself — see [Setting Up a Qdrant Server](/docs/admin/knowledge/qdrant-server-setup/) for details. |\r\n\r\n### Data Path\r\n\r\nWhen using the local (SQLite) driver, specify the path where data will be stored.\r\n\r\n### External Vector DB Connection Info\r\n\r\nWhen you select an external vector DB driver like Milvus or Qdrant, fill in these additional fields. For Qdrant, you enter separate REST and gRPC endpoints.\r\n\r\n![Connection info fields shown when Qdrant is selected as the driver](./_screenshots/admin-ai-db-qdrant_en.png)\r\n\r\n| Item | Description |\r\n|------|-------------|\r\n| Collection | The collection name to use, distinct per instance. |\r\n| REST endpoint | The external vector DB server's REST API address (Qdrant default: `http://localhost:6333`) |\r\n| gRPC endpoint (Qdrant, recommended) | The gRPC address of the same Qdrant instance as REST (default: `http://localhost:6334`). Search and vector storage use gRPC first. If left blank, `6334` is used automatically when the REST address is on port `6333`; it falls back to REST if it can't connect. |\r\n| API key | Only fill this in if authentication is enabled on the external vector DB. The stored value shows masked on screen; saving it blank deletes it. |\r\n| Username / password | Credentials for a Milvus account. Enter the password only when changing it. |\r\n\r\n## Initializing and Deleting the Vector DB\r\n\r\n::warning\r\n\r\nInitializing does not delete the original files in the workspace. Only the AI search data is deleted; to use it again, all data must be re-indexed.\r\n\r\n- Re-index all data: Re-reads the original documents and rebuilds the AI search data.\r\n- Initialize (delete vectors and re-index): Deletes this instance's AI search vectors from the current store and requests a full re-index. Vector search is limited until the replacement index is ready.\r\n::\r\n\r\nSee [Vector Store Operations](/docs/admin/knowledge/storage-operations/) for when to use each recovery action.\r\n\r\n## Related Documents\r\n\r\n- [Vector Store Operations](/docs/admin/knowledge/storage-operations/)\r\n- [Connecting Qdrant](/docs/admin/knowledge/qdrant/)\r\n- [Setting Up a Qdrant Server](/docs/admin/knowledge/qdrant-server-setup/)\r\n- [Connecting Milvus](/docs/admin/knowledge/milvus/)\r\n- [Ingestion & Indexing](/docs/admin/knowledge/indexing/)\n",1791199125007]