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RAG는 저장된 문서에서 관련 내용을 찾아 AI가 답변에 참고하는 방식입니다.",[999,1000,1002],"h2",{"id":1001},"시작하기-전에","시작하기 전에",[1004,1005,1006,1010,1013],"ul",{},[1007,1008,1009],"li",{},"필요 권한: Workspace 관리자",[1007,1011,1012],{},"사용할 임베딩 모델의 출력 차원을 확인합니다.",[1007,1014,1015],{},"외부 저장소를 선택하면 SpaceBuilder 서버에서 해당 엔드포인트에 접근할 수 있어야 합니다.",[999,1017,1019],{"id":1018},"지식-저장소-열기","지식 저장소 열기",[1021,1022,1023,1026,1033],"ol",{},[1007,1024,1025],{},"관리자 페이지에 접속합니다.",[1007,1027,1028,1029,1032],{},"왼쪽 메뉴에서 ",[1030,1031,164],"code",{},"를 선택합니다.",[1007,1034,1035,1032],{},[1030,1036,183],{},[995,1038,1039],{},[1040,1041],"img",{"alt":1042,"src":1043},"지식 저장소 화면","./_screenshots/admin-knowledge-storage.png",[1045,1046,1047],"note",{},[995,1048,1049],{},"화면에 표시된 수치는 기능을 설명하기 위한 예시입니다.",[1051,1052],"hr",{},[999,1054,1056],{"id":1055},"벡터-db-운영-현황","벡터 DB 운영 현황",[995,1058,1059],{},"현재 연결된 벡터 DB의 상태를 확인합니다.",[1061,1062,1063,1076],"table",{},[1064,1065,1066],"thead",{},[1067,1068,1069,1073],"tr",{},[1070,1071,1072],"th",{},"항목",[1070,1074,1075],{},"설명",[1077,1078,1079,1088,1096,1104,1112,1120],"tbody",{},[1067,1080,1081,1085],{},[1082,1083,1084],"td",{},"연결 상태",[1082,1086,1087],{},"벡터 DB 연결 여부",[1067,1089,1090,1093],{},[1082,1091,1092],{},"벡터 스토어",[1082,1094,1095],{},"AI 검색 데이터를 저장하는 방식과 유형 (예: Qdrant 서비스형)",[1067,1097,1098,1101],{},[1082,1099,1100],{},"데이터(엔티티) 수",[1082,1102,1103],{},"이 인스턴스(현재 SpaceBuilder)가 저장한 데이터 항목 수",[1067,1105,1106,1109],{},[1082,1107,1108],{},"컬렉션 수",[1082,1110,1111],{},"저장소의 컬렉션 수. Qdrant·Milvus는 해당 서버의 전체 컬렉션 수입니다.",[1067,1113,1114,1117],{},[1082,1115,1116],{},"컬렉션 구성",[1082,1118,1119],{},"저장소의 차원과 거리 계산 방식이 설정과 맞는지 여부",[1067,1121,1122,1125],{},[1082,1123,1124],{},"저장 경로",[1082,1126,1127],{},"데이터 저장 위치 (Local·Automatic만 표시, 예: vectordb_local)",[995,1129,1130,1131,1133,1134,1139],{},"각 항목의 상세한 의미와 ",[1030,1132,1116],{}," 표시별 조치는 ",[1135,1136,1138],"a",{"href":1137},"/ko/docs/admin/knowledge/storage-operations/#%EC%9A%B4%EC%98%81-%EC%83%81%ED%83%9C-%ED%99%95%EC%9D%B8","벡터 DB 운영 가이드","를 참고하세요.",[999,1141,1143],{"id":1142},"벡터-db-구성","벡터 DB 구성",[995,1145,1146],{},"AI 검색 데이터를 저장할 방식과 위치를 설정합니다.",[1148,1149,1151],"h3",{"id":1150},"임베딩-모델-선택","임베딩 모델 선택",[995,1153,1154,1158,1159,1162],{},[1135,1155,1157],{"href":1156},"/ko/docs/admin/ai/models/#%EC%84%A4%EC%B9%98%EB%90%9C-%EB%AA%A8%EB%8D%B8","AI 모델 설정","에서 다운로드한 모델 중 문서를 검색용 벡터로 변환할 임베딩 모델을 선택합니다. 다운로드된 모델이 없으면 먼저 AI 모델 설정에서 임베딩 모델을 다운로드하세요. (예: ",[1030,1160,1161],{},"bge-m3",")",[1061,1164,1165,1176],{},[1064,1166,1167],{},[1067,1168,1169,1171,1173],{},[1070,1170,1072],{},[1070,1172,1075],{},[1070,1174,1175],{},"기본값",[1077,1177,1178,1189],{},[1067,1179,1180,1183,1186],{},[1082,1181,1182],{},"임베딩 모델",[1082,1184,1185],{},"AI 모델 설정에서 다운로드한 임베딩 모델을 선택합니다.",[1082,1187,1188],{},"-",[1067,1190,1191,1194,1197],{},[1082,1192,1193],{},"벡터 차원",[1082,1195,1196],{},"선택한 모델이 생성하는 벡터의 차원 수입니다.",[1082,1198,1199],{},"1024",[995,1201,1202,1204,1205,1208],{},[1030,1203,1161],{},", ",[1030,1206,1207],{},"nomic-embed-text"," 등 알려진 모델은 선택하면 벡터 차원이 자동으로 입력됩니다. 다른 모델을 사용할 때는 해당 모델의 출력 차원과 같은 값을 입력하세요.",[1045,1210,1211],{},[995,1212,1213],{},"임베딩 모델이나 벡터 차원을 바꾸면 기존 문서를 모두 다시 인덱싱합니다. 저장하면 자동으로 시작됩니다.",[1148,1215,1216],{"id":1216},"드라이버",[995,1218,1219],{},"AI 검색 데이터를 저장할 방식을 선택합니다.",[1061,1221,1222,1230],{},[1064,1223,1224],{},[1067,1225,1226,1228],{},[1070,1227,1216],{},[1070,1229,1075],{},[1077,1231,1232,1240,1252,1263],{},[1067,1233,1234,1237],{},[1082,1235,1236],{},"Automatic (SQLite)",[1082,1238,1239],{},"기본값입니다. 임베딩 모델을 선택하면 SpaceBuilder에 내장된 SQLite 저장소를 사용합니다.",[1067,1241,1242,1245],{},[1082,1243,1244],{},"Local (SQLite)",[1082,1246,1247,1248,1251],{},"AI 검색 데이터를 지정한 경로의 파일로 저장합니다. 별도 DB 서버가 필요 없으며 벡터 약 5만 개 이하 환경에 적합합니다. 기본 경로는 ",[1030,1249,1250],{},"vectordb_local","입니다.",[1067,1253,1254,1257],{},[1082,1255,1256],{},"Milvus",[1082,1258,1259,1260],{},"대용량 환경에서 사용하는 외부 벡터 DB입니다. 컬렉션을 미리 만들어야 합니다. ",[1135,1261,199],{"href":1262},"/ko/docs/admin/knowledge/milvus/",[1067,1264,1265,1268],{},[1082,1266,1267],{},"Qdrant",[1082,1269,1270,1271],{},"외부 벡터 DB입니다. 컬렉션은 첫 인덱싱 때 자동으로 만들어집니다. ",[1135,1272,191],{"href":1273},"/ko/docs/admin/knowledge/qdrant/",[995,1275,1276,1277,1139],{},"어떤 드라이버를 고를지는 ",[1135,1278,1138],{"href":1279},"/ko/docs/admin/knowledge/storage-operations/#%EB%B2%A1%ED%84%B0-%EC%A0%80%EC%9E%A5%EC%86%8C-%EC%84%A0%ED%83%9D",[1148,1281,1283],{"id":1282},"데이터-경로","데이터 경로",[995,1285,1286],{},"Local·Automatic 드라이버에서 데이터를 저장할 경로를 지정합니다. 상대 경로는 SpaceBuilder 설정 파일이 있는 폴더를 기준으로 합니다.",[1148,1288,1290],{"id":1289},"외부-벡터-db-연결-정보","외부 벡터 DB 연결 정보",[995,1292,1293],{},"Qdrant·Milvus를 선택하면 데이터 경로 대신 외부 벡터 DB 연결 정보를 입력합니다. Qdrant는 REST와 gRPC 엔드포인트를 각각 입력합니다.",[995,1295,1296],{},[1040,1297],{"alt":1298,"src":1299},"드라이버로 Qdrant를 선택했을 때의 연결 정보 입력 화면","./_screenshots/admin-ai-db-qdrant.png",[1061,1301,1302,1310],{},[1064,1303,1304],{},[1067,1305,1306,1308],{},[1070,1307,1072],{},[1070,1309,1075],{},[1077,1311,1312,1320,1335,1355,1363],{},[1067,1313,1314,1317],{},[1082,1315,1316],{},"컬렉션",[1082,1318,1319],{},"데이터를 저장할 컬렉션 이름입니다. 인스턴스마다 다른 이름을 권장하며, 화면에 이 인스턴스의 권장 이름이 표시됩니다.",[1067,1321,1322,1325],{},[1082,1323,1324],{},"REST 엔드포인트",[1082,1326,1327,1328,1331,1332,1162],{},"외부 벡터 DB 서버의 접속 주소입니다. (예: Qdrant ",[1030,1329,1330],{},"http://localhost:6333",", Milvus ",[1030,1333,1334],{},"http://localhost:19530",[1067,1336,1337,1340],{},[1082,1338,1339],{},"gRPC 엔드포인트 (Qdrant, 권장)",[1082,1341,1342,1343,1346,1347,1350,1351,1354],{},"REST와 동일한 Qdrant 인스턴스의 gRPC 주소 (기본값: ",[1030,1344,1345],{},"http://localhost:6334","). 검색과 벡터 저장에 gRPC를 우선 사용합니다. 비워 두면 REST 주소가 ",[1030,1348,1349],{},"6333"," 포트일 때 ",[1030,1352,1353],{},"6334","를 자동으로 사용하며, 연결할 수 없으면 REST로 전환합니다.",[1067,1356,1357,1360],{},[1082,1358,1359],{},"API 키",[1082,1361,1362],{},"외부 벡터 DB에 인증이 켜져 있는 경우에만 입력합니다. 저장된 값은 화면에 마스킹되어 표시되며, 바꾸지 않으면 그대로 유지됩니다. 비워 두고 저장하면 삭제됩니다.",[1067,1364,1365,1368],{},[1082,1366,1367],{},"사용자 이름 / 비밀번호",[1082,1369,1370],{},"Milvus 계정 인증에 사용합니다. 비밀번호는 바꿀 때만 입력하세요.",[999,1372,1374],{"id":1373},"벡터-db-초기화-및-삭제","벡터 DB 초기화 및 삭제",[1376,1377,1378,1381],"warning",{},[995,1379,1380],{},"초기화해도 워크스페이스의 원본 파일은 삭제되지 않습니다. AI 검색용 데이터만 다시 만들어집니다.",[1004,1382,1383,1386],{},[1007,1384,1385],{},"전체 데이터 다시 인덱싱: 원본 문서를 다시 읽어 AI 검색 데이터를 새로 만듭니다.",[1007,1387,1388],{},"초기화(벡터 삭제 후 재인덱싱): 현재 저장소에서 이 인스턴스의 AI 검색 데이터를 삭제하고, 원본 전체 재인덱싱을 요청합니다. 새 인덱스가 준비될 때까지 벡터 검색이 제한됩니다.",[995,1390,1391,1392,1139],{},"두 작업의 차이와 사용 시점은 ",[1135,1393,1138],{"href":1394},"/ko/docs/admin/knowledge/storage-operations/#%EC%9E%AC%EC%9D%B8%EB%8D%B1%EC%8B%B1%EA%B3%BC-%EC%B4%88%EA%B8%B0%ED%99%94",[999,1396,1398],{"id":1397},"관련-문서","관련 문서",[1004,1400,1401,1406,1410,1415,1419],{},[1007,1402,1403],{},[1135,1404,1138],{"href":1405},"/ko/docs/admin/knowledge/storage-operations/",[1007,1407,1408],{},[1135,1409,191],{"href":1273},[1007,1411,1412],{},[1135,1413,195],{"href":1414},"/ko/docs/admin/knowledge/qdrant-server-setup/",[1007,1416,1417],{},[1135,1418,199],{"href":1262},[1007,1420,1421],{},[1135,1422,1424],{"href":1423},"/ko/docs/admin/knowledge/indexing/","지식 인덱싱",{"title":1426,"searchDepth":1427,"depth":1428,"links":1429},"",2,3,[1430,1431,1432,1433,1439,1440],{"id":1001,"depth":1427,"text":1002},{"id":1018,"depth":1427,"text":1019},{"id":1055,"depth":1427,"text":1056},{"id":1142,"depth":1427,"text":1143,"children":1434},[1435,1436,1437,1438],{"id":1150,"depth":1428,"text":1151},{"id":1216,"depth":1428,"text":1216},{"id":1282,"depth":1428,"text":1283},{"id":1289,"depth":1428,"text":1290},{"id":1373,"depth":1427,"text":1374},{"id":1397,"depth":1427,"text":1398},null,"지식 인덱싱, 문서 검색 및 RAG에 사용하는 벡터 DB 설정 방법을 설명합니다.","md",{"slug":1445},"/admin/knowledge/storage/",true,{"title":1448,"description":1442,"ogType":1449,"twitterCard":1450,"keywords":1451},"지식 저장소 | Admin Docs","article","summary_large_image",[1452,1453,610,1454,1056,1143,1374],"벡터 DB 설정","벡터 DB","DB","XJG2jnBSKgnj4Kwkq-n6-J_1hCqrNvgyl0r2t5B0ox0",[1457,1458],{"title":179,"path":180,"stem":181,"children":-1},{"title":187,"path":188,"stem":189,"children":-1},{"title":183,"filename":1460,"content":1461},"storage","# 지식 저장소\r\n\r\n문서 검색과 RAG 답변에 필요한 AI 검색 데이터를 관리합니다. RAG는 저장된 문서에서 관련 내용을 찾아 AI가 답변에 참고하는 방식입니다.\r\n\r\n## 시작하기 전에\r\n\r\n- 필요 권한: Workspace 관리자\r\n- 사용할 임베딩 모델의 출력 차원을 확인합니다.\r\n- 외부 저장소를 선택하면 SpaceBuilder 서버에서 해당 엔드포인트에 접근할 수 있어야 합니다.\r\n\r\n## 지식 저장소 열기\r\n\r\n1. 관리자 페이지에 접속합니다.\r\n2. 왼쪽 메뉴에서 `지식 서비스`를 선택합니다.\r\n3. `지식 저장소`를 선택합니다.\r\n\r\n![지식 저장소 화면](./_screenshots/admin-knowledge-storage.png)\r\n\r\n::note\r\n화면에 표시된 수치는 기능을 설명하기 위한 예시입니다.\r\n::\r\n\r\n---\r\n\r\n## 벡터 DB 운영 현황\r\n\r\n현재 연결된 벡터 DB의 상태를 확인합니다.\r\n\r\n| 항목 | 설명 |\r\n|------|------|\r\n| 연결 상태 | 벡터 DB 연결 여부 |\r\n| 벡터 스토어 | AI 검색 데이터를 저장하는 방식과 유형 (예: Qdrant 서비스형) |\r\n| 데이터(엔티티) 수 | 이 인스턴스(현재 SpaceBuilder)가 저장한 데이터 항목 수 |\r\n| 컬렉션 수 | 저장소의 컬렉션 수. Qdrant·Milvus는 해당 서버의 전체 컬렉션 수입니다. |\r\n| 컬렉션 구성 | 저장소의 차원과 거리 계산 방식이 설정과 맞는지 여부 |\r\n| 저장 경로 | 데이터 저장 위치 (Local·Automatic만 표시, 예: vectordb_local) |\r\n\r\n각 항목의 상세한 의미와 `컬렉션 구성` 표시별 조치는 [벡터 DB 운영 가이드](/ko/docs/admin/knowledge/storage-operations/#운영-상태-확인)를 참고하세요.\r\n\r\n## 벡터 DB 구성\r\n\r\nAI 검색 데이터를 저장할 방식과 위치를 설정합니다.\r\n\r\n### 임베딩 모델 선택\r\n\r\n[AI 모델 설정](/ko/docs/admin/ai/models/#설치된-모델)에서 다운로드한 모델 중 문서를 검색용 벡터로 변환할 임베딩 모델을 선택합니다. 다운로드된 모델이 없으면 먼저 AI 모델 설정에서 임베딩 모델을 다운로드하세요. (예: `bge-m3`)\r\n\r\n| 항목 | 설명 | 기본값 |\r\n|------|------|--------|\r\n| 임베딩 모델 | AI 모델 설정에서 다운로드한 임베딩 모델을 선택합니다. | - |\r\n| 벡터 차원 | 선택한 모델이 생성하는 벡터의 차원 수입니다. | 1024 |\r\n\r\n`bge-m3`, `nomic-embed-text` 등 알려진 모델은 선택하면 벡터 차원이 자동으로 입력됩니다. 다른 모델을 사용할 때는 해당 모델의 출력 차원과 같은 값을 입력하세요.\r\n\r\n::note\r\n임베딩 모델이나 벡터 차원을 바꾸면 기존 문서를 모두 다시 인덱싱합니다. 저장하면 자동으로 시작됩니다.\r\n::\r\n\r\n### 드라이버\r\n\r\nAI 검색 데이터를 저장할 방식을 선택합니다.\r\n\r\n| 드라이버 | 설명 |\r\n|----------|------|\r\n| Automatic (SQLite) | 기본값입니다. 임베딩 모델을 선택하면 SpaceBuilder에 내장된 SQLite 저장소를 사용합니다. |\r\n| Local (SQLite) | AI 검색 데이터를 지정한 경로의 파일로 저장합니다. 별도 DB 서버가 필요 없으며 벡터 약 5만 개 이하 환경에 적합합니다. 기본 경로는 `vectordb_local`입니다. |\r\n| Milvus | 대용량 환경에서 사용하는 외부 벡터 DB입니다. 컬렉션을 미리 만들어야 합니다. [Milvus 연결](/ko/docs/admin/knowledge/milvus/) |\r\n| Qdrant | 외부 벡터 DB입니다. 컬렉션은 첫 인덱싱 때 자동으로 만들어집니다. [Qdrant 연결](/ko/docs/admin/knowledge/qdrant/) |\r\n\r\n어떤 드라이버를 고를지는 [벡터 DB 운영 가이드](/ko/docs/admin/knowledge/storage-operations/#벡터-저장소-선택)를 참고하세요.\r\n\r\n### 데이터 경로\r\n\r\nLocal·Automatic 드라이버에서 데이터를 저장할 경로를 지정합니다. 상대 경로는 SpaceBuilder 설정 파일이 있는 폴더를 기준으로 합니다.\r\n\r\n### 외부 벡터 DB 연결 정보\r\n\r\nQdrant·Milvus를 선택하면 데이터 경로 대신 외부 벡터 DB 연결 정보를 입력합니다. Qdrant는 REST와 gRPC 엔드포인트를 각각 입력합니다.\r\n\r\n![드라이버로 Qdrant를 선택했을 때의 연결 정보 입력 화면](./_screenshots/admin-ai-db-qdrant.png)\r\n\r\n| 항목 | 설명 |\r\n|------|------|\r\n| 컬렉션 | 데이터를 저장할 컬렉션 이름입니다. 인스턴스마다 다른 이름을 권장하며, 화면에 이 인스턴스의 권장 이름이 표시됩니다. |\r\n| REST 엔드포인트 | 외부 벡터 DB 서버의 접속 주소입니다. (예: Qdrant `http://localhost:6333`, Milvus `http://localhost:19530`) |\r\n| gRPC 엔드포인트 (Qdrant, 권장) | REST와 동일한 Qdrant 인스턴스의 gRPC 주소 (기본값: `http://localhost:6334`). 검색과 벡터 저장에 gRPC를 우선 사용합니다. 비워 두면 REST 주소가 `6333` 포트일 때 `6334`를 자동으로 사용하며, 연결할 수 없으면 REST로 전환합니다. |\r\n| API 키 | 외부 벡터 DB에 인증이 켜져 있는 경우에만 입력합니다. 저장된 값은 화면에 마스킹되어 표시되며, 바꾸지 않으면 그대로 유지됩니다. 비워 두고 저장하면 삭제됩니다. |\r\n| 사용자 이름 / 비밀번호 | Milvus 계정 인증에 사용합니다. 비밀번호는 바꿀 때만 입력하세요. |\r\n\r\n## 벡터 DB 초기화 및 삭제\r\n\r\n::warning\r\n\r\n초기화해도 워크스페이스의 원본 파일은 삭제되지 않습니다. AI 검색용 데이터만 다시 만들어집니다.\r\n\r\n- 전체 데이터 다시 인덱싱: 원본 문서를 다시 읽어 AI 검색 데이터를 새로 만듭니다.\r\n- 초기화(벡터 삭제 후 재인덱싱): 현재 저장소에서 이 인스턴스의 AI 검색 데이터를 삭제하고, 원본 전체 재인덱싱을 요청합니다. 새 인덱스가 준비될 때까지 벡터 검색이 제한됩니다.\r\n::\r\n\r\n두 작업의 차이와 사용 시점은 [벡터 DB 운영 가이드](/ko/docs/admin/knowledge/storage-operations/#재인덱싱과-초기화)를 참고하세요.\r\n\r\n## 관련 문서\r\n\r\n- [벡터 DB 운영 가이드](/ko/docs/admin/knowledge/storage-operations/)\r\n- [Qdrant 연결](/ko/docs/admin/knowledge/qdrant/)\r\n- [Qdrant 서버 준비하기](/ko/docs/admin/knowledge/qdrant-server-setup/)\r\n- [Milvus 연결](/ko/docs/admin/knowledge/milvus/)\r\n- [지식 인덱싱](/ko/docs/admin/knowledge/indexing/)\n",1791199137700]