diff --git a/images/metavision2_advanced_event_search2.png b/images/metavision2_advanced_event_search2.png index 681ff27..42b301f 100644 Binary files a/images/metavision2_advanced_event_search2.png and b/images/metavision2_advanced_event_search2.png differ diff --git a/images/metavision2_semantic_search2.png b/images/metavision2_semantic_search2.png index 68fb617..1a2c353 100644 Binary files a/images/metavision2_semantic_search2.png and b/images/metavision2_semantic_search2.png differ diff --git a/images/metavision2_semantic_search3.png b/images/metavision2_semantic_search3.png index 7acc149..7778631 100644 Binary files a/images/metavision2_semantic_search3.png and b/images/metavision2_semantic_search3.png differ diff --git a/site/404.html b/site/404.html new file mode 100644 index 0000000..a8d1a08 --- /dev/null +++ b/site/404.html @@ -0,0 +1,157 @@ + + + + + + + + MetaVision Docs + + + + + + + + + + + +
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1. AI Device Status

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AI Device StatusData Agent에서 원격으로 모니터링하는 장비의 상태를 체크하고, 현황을 보여줍니다.

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상태 모니터링을 하고 있는 장비는 다음과 같습니다.

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  • 돌봄센터 서버
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  • NVR(Network Video Recorder)
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  • AI Box(Device)
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  • Camera
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MetaVision2 Workflow +

< AI Device 상태 모니터링 >

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dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block} diff --git a/site/css/theme_extra.css b/site/css/theme_extra.css new file mode 100644 index 0000000..ab0631a --- /dev/null +++ b/site/css/theme_extra.css @@ -0,0 +1,197 @@ +/* + * Wrap inline code samples otherwise they shoot of the side and + * can't be read at all. + * + * https://github.com/mkdocs/mkdocs/issues/313 + * https://github.com/mkdocs/mkdocs/issues/233 + * https://github.com/mkdocs/mkdocs/issues/834 + */ +.rst-content code { + white-space: pre-wrap; + word-wrap: break-word; + padding: 2px 5px; +} + +/** + * Make code blocks display as blocks and give them the appropriate + * font size and padding. + * + * https://github.com/mkdocs/mkdocs/issues/855 + * https://github.com/mkdocs/mkdocs/issues/834 + * https://github.com/mkdocs/mkdocs/issues/233 + */ +.rst-content pre code { + white-space: pre; + word-wrap: normal; + display: block; + padding: 12px; + font-size: 12px; +} + +/** + * Fix code colors + * + * https://github.com/mkdocs/mkdocs/issues/2027 + */ +.rst-content code { + color: #E74C3C; +} + +.rst-content pre code { + color: #000; + background: #f8f8f8; +} + +/* + * Fix link colors when the link text is inline code. + * + * https://github.com/mkdocs/mkdocs/issues/718 + */ +a code { + color: #2980B9; +} +a:hover code { + color: #3091d1; +} +a:visited code { + color: #9B59B6; +} + +/* + * The CSS classes from highlight.js seem to clash with the + * ReadTheDocs theme causing some code to be incorrectly made + * bold and italic. + * + * https://github.com/mkdocs/mkdocs/issues/411 + */ +pre .cs, pre .c { + font-weight: inherit; + font-style: inherit; +} + +/* + * Fix some issues with the theme and non-highlighted code + * samples. Without and highlighting styles attached the + * formatting is broken. + * + * https://github.com/mkdocs/mkdocs/issues/319 + */ +.rst-content .no-highlight { + display: block; + padding: 0.5em; + color: #333; +} + + +/* + * Additions specific to the search functionality provided by MkDocs + */ + +.search-results { + margin-top: 23px; +} + +.search-results article { + border-top: 1px solid #E1E4E5; + padding-top: 24px; +} + +.search-results article:first-child { + border-top: none; +} + +form .search-query { + width: 100%; + border-radius: 50px; + padding: 6px 12px; + border-color: #D1D4D5; +} + +/* + * Improve inline code blocks within admonitions. + * + * https://github.com/mkdocs/mkdocs/issues/656 + */ + .rst-content .admonition code { + color: #404040; + border: 1px solid #c7c9cb; + border: 1px solid rgba(0, 0, 0, 0.2); + background: #f8fbfd; + background: rgba(255, 255, 255, 0.7); +} + +/* + * Account for wide tables which go off the side. + * Override borders to avoid weirdness on narrow tables. + * + * https://github.com/mkdocs/mkdocs/issues/834 + * https://github.com/mkdocs/mkdocs/pull/1034 + */ +.rst-content .section .docutils { + width: 100%; + overflow: auto; + display: block; + border: none; +} + +td, th { + border: 1px solid #e1e4e5 !important; + border-collapse: collapse; +} + +/* + * Without the following amendments, the navigation in the theme will be + * slightly cut off. This is due to the fact that the .wy-nav-side has a + * padding-bottom of 2em, which must not necessarily align with the font-size of + * 90 % on the .rst-current-version container, combined with the padding of 12px + * above and below. These amendments fix this in two steps: First, make sure the + * .rst-current-version container has a fixed height of 40px, achieved using + * line-height, and then applying a padding-bottom of 40px to this container. In + * a second step, the items within that container are re-aligned using flexbox. + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ + .wy-nav-side { + padding-bottom: 40px; +} + +/* For section-index only */ +.wy-menu-vertical .current-section p { + background-color: #e3e3e3; + color: #404040; +} + +/* + * The second step of above amendment: Here we make sure the items are aligned + * correctly within the .rst-current-version container. Using flexbox, we + * achieve it in such a way that it will look like the following: + * + * [No repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * [With repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-versions .rst-current-version { + padding: 0 12px; + display: flex; + font-size: initial; + justify-content: space-between; + align-items: center; + line-height: 40px; +} + +/* + * Please note that this amendment also involves removing certain inline-styles + * from the file ./mkdocs/themes/readthedocs/versions.html. + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-current-version span { + flex: 1; + text-align: center; +} diff --git a/site/dashboard/index.html b/site/dashboard/index.html new file mode 100644 index 0000000..a72709c --- /dev/null +++ b/site/dashboard/index.html @@ -0,0 +1,171 @@ + + + + + + + + Dashboard - MetaVision Docs + + + + + + + + + + + + + +
+ + +
+ +
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+
+
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+ +

Dashboard

+

Dashboard에서는 프로젝트 정보, 보유한 데이터 현황 그리고 데이터를 공유할 사용자 정보를 제공합니다.

+

제공하는 차트는 다음과 같습니다.

+
    +
  • Storage Size: object storage에 저장된 파일에 대해 유형별 총 사이즈
  • +
  • File Count: object storage에 저장된 파일에 대해 유형별 총 개수
  • +
  • Object Count: ML Workflow로 추출된 메타 정보에 대해 유형별 총 개수
  • +
  • Workflow Count: 등록된 Workflow의 진행 현황
  • +
+

각 프로젝트별로 사용자 추가 및 삭제를 통해 데이터를 공유할 수 있다.

+

MetaVision2 Dashboard

+ +
+
+ +
+
+ +
+ +
+ +
+ + + + + +
+ + + + + + + + + diff --git a/site/data_lake/index.html b/site/data_lake/index.html new file mode 100644 index 0000000..f8847fc --- /dev/null +++ b/site/data_lake/index.html @@ -0,0 +1,176 @@ + + + + + + + + File explore - MetaVision Docs + + + + + + + + + + + + + +
+ + +
+ +
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    +
  • + +
  • +
  • +
+
+
+
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+ +

File explore

+

File explore는 object storage에 저장된 파일들을 File browser 형태로 볼 수 있도록 환경을 제공합니다.

+

Object storage는 구조화되지 않은 대량의 데이터를 처리하도록 설계된 data storage architecture입니다.

+

Object storage의 파일들은 파일 자체와 메타 정보를 가지고 있는 객체(object) 형태로 관리되고 있으며, 수평 확장이 가능하여 확장성에 제한이 거의 없어 대용량의 데이터를 저장하고 메타 정보를 활용하여 쉽게 검색 및 활용할 수 있습니다.

+

사진과 동영상 파일의 경우, 미리보기를 제공하고 있어 실시간으로 데이터를 확인할 수 있습니다.

+

모든 데이터는 프로젝트별로 접근 권한 관리가 되고 있으며, 사용자는 권한이 있는 프로젝트에서만 데이터를 업로드 및 다운로드를 할 수 있습니다.

+

MetaVision2 File Explore +

< File explore 조회 >

+

File search

+

File explore의 경우, 직접 object storage에 데이터를 순차적으로 조회하여 결과를 받는 방식을 사용하여 대용량의 데이터가 있는 경우 수행 시간이 지연될 수 있습니다.

+

File search는 object storage에 업로드된 모든 데이터에 대해 메타 정보를 별도로 저장하여 파일 경로, 파일 이름뿐만 아니라 문자 기반 파일의 경우 문서 내 키워드도 태그로 저장하여 키워드 기반 검색을 실시간으로 제공하고 있습니다.

+

MetaVision2 File Search +

< File search 조회 >

+

이를 기반으로 원하는 데이터를 태그 기반으로 쉽게 찾을 수 있으며, 찾은 데이터가 있는 위치로 이동하여 관련 데이터를 전체 확인할 수 있는 기능을 제공하고 있습니다.

+

MetaVision2 File Search +

< 검색한 파일이 있는 Directory로 이동 >

+ +
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+ +
+ +
+ +
+ + + + + +
+ + + + + + + + + diff --git a/site/data_warehouse/index.html b/site/data_warehouse/index.html new file mode 100644 index 0000000..92ad9ba --- /dev/null +++ b/site/data_warehouse/index.html @@ -0,0 +1,178 @@ + + + + + + + + Metadata explore - MetaVision Docs + + + + + + + + + + + + + +
+ + +
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+
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+ +

Metadata explore

+

Metadata explore는 object storage에 있는 데이터에 대해 ML Workflow를 수행한 결과를 File browser 형태로 제공합니다.

+

datasets에는 기본 dataset(프로젝트 이름과 동일)이 제공되어 ML Workflow를 통해 추출된 파일 및 객체에 대한 메타 정보가 저장됩니다.

+

MetaVision2 Metadata Explore +

< Metadata explore 조회 >

+

그 외에 create dataset 기능을 사용하여, 검색을 통해 수집한 파일들을 별도의 dataset으로 저장하여 관리할 수 있습니다.

+

MetaVision2 Create Dataset +

< 신규 dataset 생성 >

+

Metadata search

+

ML Workflow를 통해 추출된 메타 정보는 LightningDB에 저장되는데, 추출된 각 객체는 update_date, class, tags, confidence_score 등의 정보를 가지고 있어 이를 기반한 조회를 제공합니다.

+

MetaVision2 Metadata Search +

< Metadata search 조회 결과 >

+

각 객체는 feature extraction 모델을 사용하여 객체에 대한 feature 정보를 추출하고 저장하고 있습니다.

+

KNN search 기능을 사용하면, 선택한 이미지와 유사한 이미지를 검색할 수 있습니다.

+

MetaVision2 Metadata Search +

< KNN search 조회 >

+

MetaVision2 Metadata Search +

< KNN search 조회 결과 >

+ +
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+ +
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+ +
+ +
+ +
+ + + + + +
+ + + + + + + + + diff --git a/site/datasets/index.html b/site/datasets/index.html new file mode 100644 index 0000000..6be71a4 --- /dev/null +++ b/site/datasets/index.html @@ -0,0 +1,195 @@ + + + + + + + + Datasets - MetaVision Docs + + + + + + + + + + + + + +
+ + +
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+
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+ +

1. Datasets List

+

Datasets는 object storage에 있는 데이터에 대해 ML Workflow를 수행한 결과를 File browser 형태로 제공합니다.

+

datasets에는 기본 데이터셋('All files')이 제공되어 ML Workflow를 통해 추출된 파일 및 객체에 대한 메타 정보가 저장됩니다.

+

사용자는 기본 데이터셋 외에 데이터셋을 생성할 수 있으며, 검색 등을 이용하여 선택적으로 파일을 데이터셋에 추가할 수 있습니다.

+

MetaVision2 Datasets List +

< 데이터셋 조회 >

+

2. Add to Dataset

+

기본 데이터셋에서 또는 검색을 통해 확인한 파일 목록 중에서 선택적으로 원하는 파일을 데이터셋에 추가할 수 있습니다.

+

추가할 데이터셋은 기존 데이터셋에서 고를 수도 있고, 새로 생성하여 추가할 수도 있습니다.

+

MetaVision2 Datasets add

+

MetaVision2 Datasets create

+

< 데이터셋에 파일 추가 >

+

3. Detail View

+

데이터셋에 있는 파일을 선택하면 '상세 보기' 화면이 제공됩니다.

+

'상세 보기' 화면에서 제공되는 기능은 아래와 같습니다.

+
    +
  • 이미지 및 영상 재생
  • +
  • 파일 다운로드
  • +
  • 파일 삭제
  • +
  • 메타데이터(파일 경로, 유형, 파일 사이즈, 파일 업로드 날짜 및 시간) 제공
  • +
  • 데이터셋에 추가
  • +
+

MetaVision2 Metadata Search +

< 영상/이미지 상세 보기 >

+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/site/event_search/index.html b/site/event_search/index.html new file mode 100644 index 0000000..2c8c870 --- /dev/null +++ b/site/event_search/index.html @@ -0,0 +1,204 @@ + + + + + + + + Event Search - MetaVision Docs + + + + + + + + + + + + + +
+ + +
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+
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+
+ +

1. Event Search

+

Event Search는 AI가 인지한 행동 또는 이벤트를 검색할 수 있는 플랫폼입니다.

+

아울러 Semantic Search 기능을 제공하여 검색을 더욱 정교하게 할 수 있습니다.

+

검색 가능한 이벤트 종류는 다음과 같습니다.

+
    +
  • Intrusion
  • +
  • Loitering
  • +
  • Motion
  • +
  • Line-crossing
  • +
  • Fall-down
  • +
  • Fire
  • +
  • LPR(License Plate Recognition)
  • +
  • Etc
  • +
+

MetaVision2 Dashboard +

< Event Search 검색 옵션 >

+

MetaVision2 Dashboard +

< Event Search 검색 결과 >

+

2. 검색 결과 상세 보기

+

검색 결과는 3 가지 형태로 제공됩니다.

+
    +
  • 이벤트에 대한 요약 영상
  • +
+

MetaVision2 Dashboard

+
    +
  • 이벤트 발생 시점 이미지
  • +
+

MetaVision2 Dashboard

+
    +
  • 이벤트 발생 시점 원본 영상
  • +
+

MetaVision2 Dashboard

+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/site/files/index.html b/site/files/index.html new file mode 100644 index 0000000..a38626d --- /dev/null +++ b/site/files/index.html @@ -0,0 +1,198 @@ + + + + + + + + Files - MetaVision Docs + + + + + + + + + + + + + +
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1. File Browser

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Files는 Object Storage에 저장된 파일들을 File browser 형태로 볼 수 있도록 환경을 제공합니다.

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Object storage는 구조화되지 않은 대량의 데이터를 처리하도록 설계된 Data Storage Architecture입니다.

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Object Storage의 파일들은 파일 자체와 메타 정보를 가지고 있는 객체(object) 형태로 관리되고 있으며, 수평 확장이 가능하여 확장성에 제한이 거의 없어 대용량의 데이터를 저장하고 메타 정보를 활용하여 쉽게 검색 및 활용할 수 있습니다.

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사진과 동영상 파일의 경우, 미리보기를 제공하고 있어 실시간으로 데이터를 확인할 수 있습니다.

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MetaVision2 File Explore +

< 디렉토리 및 파일 목록 조회 >

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2. File Search

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우측 상단의 검색 창을 통해 특정 디렉토리나 파일을 검색할 수 있습니다.

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MetaVision2 File Search +

< 디렉토리 및 파일 검색 >

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3. Show Hidden Files

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MetaVision은 기본적으로 영상 또는 이미지 파일을 관리하고 제공합니다.

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영상이나 이미지가 아닌 파일들을 조회하고 다운로드하려면 우측 상단의 'Show Hidden Files' 버튼을 활성화하면 됩니다.

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MetaVision2 File Hidden +

< 영상이나 이미지가 아닌 파일 조회 >

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4. Detail View

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Files에서 제공된 목록을 선택하면 '상세 보기' 화면이 제공됩니다.

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'상세 보기' 화면에서 제공되는 기능은 아래와 같습니다.

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  • 이미지 및 영상 재생
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  • 파일 다운로드
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  • 파일 삭제
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  • 메타데이터(파일 경로, 유형, 파일 사이즈, 파일 업로드 날짜 및 시간) 제공
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MetaVision2 File Detail +

< 영상/이미지 상세 보기 >

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1. Golden-Data Events

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Golden-Data Events돌봄센터에서 수집한 메타데이터와 영상 중 AI 개발에 필요한 부분을 쉽게 찾기 위해 통계 차트와 이벤트 목록을 제공합니다.

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데이터 조회에 사용되는 조건은 다음과 같습니다.

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  • 돌봄센터
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  • AI Box(Device)
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  • Camera(설치 장소)
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  • AI가 인지한 이벤트
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  • 사람이 확인한 실제 이벤트
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  • AI 이벤트의 정답 여부
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  • 영상 다운로드 여부
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MetaVision2 golden-data +

< Golden-Data Events 목록 및 통계 차트 >

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2. Video Download

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Data Agent의 메타데이터로부터 사전에 정의한 규칙에 의해 영상을 자동 다운로드할 수 있습니다.

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아울러 로그 확인 후, 다운로드 요청을 할 수 있습니다.

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다운로드가 완료되면 재생 버튼이 활성화됩니다. (다운로드 요청 후, 완료까지 1~2일이 소요될 수 있습니다.)

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MetaVision2 golden-data +MetaVision2 golden-data +

< 이벤트 영상 다운로드 및 재생 >

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1. Group Monitoring

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Group Monitoring에서는 돌봄센터별, 발생한 이벤트별로 통계 데이터를 제공합니다.

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제공하는 기능 다음과 같습니다.

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  • 최근 1개월 간 돌봄센터 현황
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  • 통계 기간
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    • 일별, 주별, 월별 설정 가능
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  • 돌봄센터/이벤트별 수집 통계
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    • Total Events: AI가 인지한 전체 이벤트
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    • AI-Assist Add: AI가 인지한 이벤트가 맞은 경우
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    • AI-Human Edit: AI가 인지한 이벤트가 틀린 경우
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    • Human Add: 이벤트가 발생하였으나 AI가 놓친 경우
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    • Dev. Event: 오탐, 미탐 등 위에 해당하지 않는 경우
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MetaVision2 Dashboard +MetaVision2 Dashboard +

< Group Monitoring >

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1. MetaVision is ...

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MetaVision은 대용량의 Vision Data를 효율적으로 관리하고 관련 응용 서비스를 쉽게 사용할 수 있는 환경을 제공하는 플랫폼입니다.

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  • 기존에 관리되지 않고 있는 데이터에 대한 관리 커버리지 및 재활용성을 제고
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  • CVPaaS Cloud 서비스에서의 확장성 있고, 비용 효율적인 AI Ops 서비스의 기반을 제공
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  • Golden Data Discovery & AI 모델 개선 Cycle 완성을 위한 환경 제공
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  • Vision AI가 적용된 실 서비스의 데이터를 자동으로 수집하고 데이터셋 형태로 관리할 수 있는 기능 제공
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2. Architecture

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MetaVision의 모든 서비스는 Kubernetes 환경에서 동작하고 있습니다.

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AWS S3와 호환되는 Ceph를 사용하여 object storage를 구성하였고, Apache KAFKA, ElasticSearch 등을 사용하여 object storage에 업로드한 파일들에 대한 메타데이터를 관리하고 있습니다.

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Apache KAFKA 및 자체 개발한 ML Workflow 서비스를 사용하여 보유한 데이터에 대해 데이터 가공 및 Machine Learning(ML) inference 작업을 수행할 수 있습니다.

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ML inference의 결과는 자체 개발한 LightningDB에 저장되어 메타데이터 기반으로 지연없는 데이터 검색 및 객체로부터 추출한 feature value 기반으로 유사 이미지 검색을 지원하고 있습니다.

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MetaVision2 archi +

< MetaVision 아키텍처 >

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3. Features

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    +
  • AWS S3와 호환되는 object storage인 Ceph 제공 및 확장가능한 스토리지 제공
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  • 사용자 인증 및 이를 기반으로 사용자별 데이터 접근 권한 관리 및 공유 기능 제공
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  • Object storage에 저장된 모든 파일에 대해 키워드 검색 지원
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  • Object storage에 저장된 미디어 파일에 대한 썸네일 제공
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  • ML inference를 위한 기본 ML Workflow 이미지(Motion Detection/Object Detection/Feature Extraction) 제공
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  • 다양한 model 사용 및 데이터 처리 그리고 inference 코드를 운영할 수 있는 ML Workflow Image Repository 운영(사용자가 이미지를 생성하여 업로드한 후 사용 가능)
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  • Object storage에 업로드한 파일에 대해 자동으로 ML Workflow를 수행할 수 있는 Event 등록 서비스 제공
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  • ML Inference를 통해 추출한 메타데이터를 효율적으로 관리하기 위한 LightningDB 사용(데이터 조회 및 유사 이미지 검색 지원)
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  • 프로젝트별 데이터 현황을 모니터링할 수 있는 대시보드 제공
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  • 서비스 데이터를 실시간으로 수집하여 통계 및 메타데이터를 조회할 수 있는 대시보드를 제공하고, 규칙 기반 또는 사용자가 선택한 목록 기반으로 데이터를 다운받을 수 있는 기능 제공
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1. 발달장애 AI 돌봄센터

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MetaVisionData Agent는 Vision AI가 적용된 서비스로부터 생성된 영상/이미지 데이터와 이로부터 추출된 메타데이터를 수집하고 관리하는 플랫폼입니다.

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아래는 '발달장애 AI 돌봄센터'에 구축된 AI 서비스의 로그 및 영상 데이터를 수집하고 관리하는 플랫폼의 아키텍처입니다.

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주요 기능은 아래와 같습니다.

+
    +
  • 돌봄센터에 설치된 AI Camera 또는 AI Box로 부터 생성되는 영상 및 메타데이터 저장
  • +
  • 주기적으로 센터로부터 메타데이터를 수집하여 MetaVisionData Agent에 저장
  • +
  • 수집한 메타데이터를 기반으로 사전 정의된 규칙에 따라 영상을 수집
  • +
  • 사용자가 임의로 지정한 메타데이터에 대해서도 영상 저장
  • +
  • 수집한 메타데이터와 영상은 ML Workflow를 수행하여 지정된 DatasetsFiles에 저장
  • +
  • 저장된 영상에 대해 검색 서비스 제공
  • +
+

MetaVision2 System Architecture +

< 발달장애 AI 돌봄센터 서비스 데이터 수집 아키텍처 >

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+ + + + + + + + + diff --git a/site/search/lunr.js b/site/search/lunr.js new file mode 100644 index 0000000..aca0a16 --- /dev/null +++ b/site/search/lunr.js @@ -0,0 +1,3475 @@ +/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */ + +;(function(){ + +/** + * A convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.3.9" +/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + * @namespace lunr.utils + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf lunr.utils + * @function + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf lunr.utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} + +/** + * Clones an object. + * + * Will create a copy of an existing object such that any mutations + * on the copy cannot affect the original. + * + * Only shallow objects are supported, passing a nested object to this + * function will cause a TypeError. + * + * Objects with primitives, and arrays of primitives are supported. + * + * @param {Object} obj The object to clone. + * @return {Object} a clone of the passed object. + * @throws {TypeError} when a nested object is passed. + * @memberOf Utils + */ +lunr.utils.clone = function (obj) { + if (obj === null || obj === undefined) { + return obj + } + + var clone = Object.create(null), + keys = Object.keys(obj) + + for (var i = 0; i < keys.length; i++) { + var key = keys[i], + val = obj[key] + + if (Array.isArray(val)) { + clone[key] = val.slice() + continue + } + + if (typeof val === 'string' || + typeof val === 'number' || + typeof val === 'boolean') { + clone[key] = val + continue + } + + throw new TypeError("clone is not deep and does not support nested objects") + } + + return clone +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/*! + * lunr.Set + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A lunr set. + * + * @constructor + */ +lunr.Set = function (elements) { + this.elements = Object.create(null) + + if (elements) { + this.length = elements.length + + for (var i = 0; i < this.length; i++) { + this.elements[elements[i]] = true + } + } else { + this.length = 0 + } +} + +/** + * A complete set that contains all elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.complete = { + intersect: function (other) { + return other + }, + + union: function () { + return this + }, + + contains: function () { + return true + } +} + +/** + * An empty set that contains no elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.empty = { + intersect: function () { + return this + }, + + union: function (other) { + return other + }, + + contains: function () { + return false + } +} + +/** + * Returns true if this set contains the specified object. + * + * @param {object} object - Object whose presence in this set is to be tested. + * @returns {boolean} - True if this set contains the specified object. + */ +lunr.Set.prototype.contains = function (object) { + return !!this.elements[object] +} + +/** + * Returns a new set containing only the elements that are present in both + * this set and the specified set. + * + * @param {lunr.Set} other - set to intersect with this set. + * @returns {lunr.Set} a new set that is the intersection of this and the specified set. + */ + +lunr.Set.prototype.intersect = function (other) { + var a, b, elements, intersection = [] + + if (other === lunr.Set.complete) { + return this + } + + if (other === lunr.Set.empty) { + return other + } + + if (this.length < other.length) { + a = this + b = other + } else { + a = other + b = this + } + + elements = Object.keys(a.elements) + + for (var i = 0; i < elements.length; i++) { + var element = elements[i] + if (element in b.elements) { + intersection.push(element) + } + } + + return new lunr.Set (intersection) +} + +/** + * Returns a new set combining the elements of this and the specified set. + * + * @param {lunr.Set} other - set to union with this set. + * @return {lunr.Set} a new set that is the union of this and the specified set. + */ + +lunr.Set.prototype.union = function (other) { + if (other === lunr.Set.complete) { + return lunr.Set.complete + } + + if (other === lunr.Set.empty) { + return this + } + + return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * Optional metadata can be passed to the tokenizer, this metadata will be cloned and + * added as metadata to every token that is created from the object to be tokenized. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @param {?object} metadata - Optional metadata to associate with every token + * @returns {lunr.Token[]} + * @see {@link lunr.Pipeline} + */ +lunr.tokenizer = function (obj, metadata) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token( + lunr.utils.asString(t).toLowerCase(), + lunr.utils.clone(metadata) + ) + }) + } + + var str = obj.toString().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + var tokenMetadata = lunr.utils.clone(metadata) || {} + tokenMetadata["position"] = [sliceStart, sliceLength] + tokenMetadata["index"] = tokens.length + + tokens.push( + new lunr.Token ( + str.slice(sliceStart, sliceEnd), + tokenMetadata + ) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null, undefined or an empty string. This token will not be passed to any downstream pipeline + * functions and will not be added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === null || result === void 0 || result === '') continue + + if (Array.isArray(result)) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @param {?object} metadata - Optional metadata to associate with the token + * passed to the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str, metadata) { + var token = new lunr.Token (str, metadata) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the similarity between this vector and another vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / this.magnitude() || 0 +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2020 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + * @function + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @function + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @function + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } + + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + + if (frame.editsRemaining == 0) { + continue + } + + // insertion + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } + + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.str.length > 1) { + stack.push({ + node: frame.node, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // deletion + // just removing the last character from the str + if (frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } + + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } + + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * When a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * This is not intended to be used on a TokenSet that + * contains wildcards, in these cases the results are + * undefined and are likely to cause an infinite loop. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + /* In Safari, at this point the prefix is sometimes corrupted, see: + * https://github.com/olivernn/lunr.js/issues/279 Calling any + * String.prototype method forces Safari to "cast" this string to what + * it's supposed to be, fixing the bug. */ + frame.prefix.charAt(0) + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.fieldVectors - Field vectors + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * Each term also supports a presence modifier. By default a term's presence in document is optional, however + * this can be changed to either required or prohibited. For a term's presence to be required in a document the + * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and + * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not + * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + * @example terms with presence modifiers + * -foo +bar baz + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. For details on how the score is calculated, please see + * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null), + requiredMatches = Object.create(null), + prohibitedMatches = Object.create(null) + + /* + * To support field level boosts a query vector is created per + * field. An empty vector is eagerly created to support negated + * queries. + */ + for (var i = 0; i < this.fields.length; i++) { + queryVectors[this.fields[i]] = new lunr.Vector + } + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null, + clauseMatches = lunr.Set.empty + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term, { + fields: clause.fields + }) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + /* + * If a term marked as required does not exist in the tokenSet it is + * impossible for the search to return any matches. We set all the field + * scoped required matches set to empty and stop examining any further + * clauses. + */ + if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = lunr.Set.empty + } + + break + } + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field, + matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) + + /* + * if the presence of this term is required ensure that the matching + * documents are added to the set of required matches for this clause. + * + */ + if (clause.presence == lunr.Query.presence.REQUIRED) { + clauseMatches = clauseMatches.union(matchingDocumentsSet) + + if (requiredMatches[field] === undefined) { + requiredMatches[field] = lunr.Set.complete + } + } + + /* + * if the presence of this term is prohibited ensure that the matching + * documents are added to the set of prohibited matches for this field, + * creating that set if it does not yet exist. + */ + if (clause.presence == lunr.Query.presence.PROHIBITED) { + if (prohibitedMatches[field] === undefined) { + prohibitedMatches[field] = lunr.Set.empty + } + + prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) + + /* + * Prohibited matches should not be part of the query vector used for + * similarity scoring and no metadata should be extracted so we continue + * to the next field + */ + continue + } + + /* + * The query field vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + + /** + * If the presence was required we need to update the requiredMatches field sets. + * We do this after all fields for the term have collected their matches because + * the clause terms presence is required in _any_ of the fields not _all_ of the + * fields. + */ + if (clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) + } + } + } + + /** + * Need to combine the field scoped required and prohibited + * matching documents into a global set of required and prohibited + * matches + */ + var allRequiredMatches = lunr.Set.complete, + allProhibitedMatches = lunr.Set.empty + + for (var i = 0; i < this.fields.length; i++) { + var field = this.fields[i] + + if (requiredMatches[field]) { + allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) + } + + if (prohibitedMatches[field]) { + allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + /* + * If the query is negated (contains only prohibited terms) + * we need to get _all_ fieldRefs currently existing in the + * index. This is only done when we know that the query is + * entirely prohibited terms to avoid any cost of getting all + * fieldRefs unnecessarily. + * + * Additionally, blank MatchData must be created to correctly + * populate the results. + */ + if (query.isNegated()) { + matchingFieldRefs = Object.keys(this.fieldVectors) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + var matchingFieldRef = matchingFieldRefs[i] + var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) + matchingFields[matchingFieldRef] = new lunr.MatchData + } + } + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef + + if (!allRequiredMatches.contains(docRef)) { + continue + } + + if (allProhibitedMatches.contains(docRef)) { + continue + } + + var fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = Object.create(null), + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = Object.create(null) + this._documents = Object.create(null) + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * A function that is used to extract a field from a document. + * + * Lunr expects a field to be at the top level of a document, if however the field + * is deeply nested within a document an extractor function can be used to extract + * the right field for indexing. + * + * @callback fieldExtractor + * @param {object} doc - The document being added to the index. + * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. + * @example Extracting a nested field + * function (doc) { return doc.nested.field } + */ + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * Fields can be boosted at build time. This allows terms within that field to have more + * importance when ranking search results. Use a field boost to specify that matches within + * one field are more important than other fields. + * + * @param {string} fieldName - The name of a field to index in all documents. + * @param {object} attributes - Optional attributes associated with this field. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. + * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. + * @throws {RangeError} fieldName cannot contain unsupported characters '/' + */ +lunr.Builder.prototype.field = function (fieldName, attributes) { + if (/\//.test(fieldName)) { + throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") + } + + this._fields[fieldName] = attributes || {} +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * Entire documents can be boosted at build time. Applying a boost to a document indicates that + * this document should rank higher in search results than other documents. + * + * @param {object} doc - The document to add to the index. + * @param {object} attributes - Optional attributes associated with this document. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. + */ +lunr.Builder.prototype.add = function (doc, attributes) { + var docRef = doc[this._ref], + fields = Object.keys(this._fields) + + this._documents[docRef] = attributes || {} + this.documentCount += 1 + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i], + extractor = this._fields[fieldName].extractor, + field = extractor ? extractor(doc) : doc[fieldName], + tokens = this.tokenizer(field, { + fields: [fieldName] + }), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < fields.length; k++) { + posting[fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + var fields = Object.keys(this._fields) + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i] + accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + fieldName = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + + var fieldBoost = this._fields[fieldName].boost || 1, + docBoost = this._documents[fieldRef.docRef].boost || 1 + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) + score *= fieldBoost + score *= docBoost + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: Object.keys(this._fields), + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata || {}) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + + if (term !== undefined) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata + } +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ + +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * Constants for indicating what kind of presence a term must have in matching documents. + * + * @constant + * @enum {number} + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with required presence + * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) + */ +lunr.Query.presence = { + /** + * Term's presence in a document is optional, this is the default value. + */ + OPTIONAL: 1, + + /** + * Term's presence in a document is required, documents that do not contain + * this term will not be returned. + */ + REQUIRED: 2, + + /** + * Term's presence in a document is prohibited, documents that do contain + * this term will not be returned. + */ + PROHIBITED: 3 +} + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. + * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + if (!('presence' in clause)) { + clause.presence = lunr.Query.presence.OPTIONAL + } + + this.clauses.push(clause) + + return this +} + +/** + * A negated query is one in which every clause has a presence of + * prohibited. These queries require some special processing to return + * the expected results. + * + * @returns boolean + */ +lunr.Query.prototype.isNegated = function () { + for (var i = 0; i < this.clauses.length; i++) { + if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { + return false + } + } + + return true +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion + * to a token or token-like string should be done before calling this method. + * + * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an + * array, each term in the array will share the same options. + * + * @param {object|object[]} term - The term(s) to add to the query. + * @param {object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + * @example using lunr.tokenizer to convert a string to tokens before using them as terms + * query.term(lunr.tokenizer("foo bar")) + */ +lunr.Query.prototype.term = function (term, options) { + if (Array.isArray(term)) { + term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) + return this + } + + var clause = options || {} + clause.term = term.toString() + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' +lunr.QueryLexer.PRESENCE = 'PRESENCE' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + // "+" indicates term presence is required + // checking for length to ensure that only + // leading "+" are considered + if (char == "+" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + // "-" indicates term presence is prohibited + // checking for length to ensure that only + // leading "-" are considered + if (char == "-" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseClause + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseClause = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.PRESENCE: + return lunr.QueryParser.parsePresence + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parsePresence = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.str) { + case "-": + parser.currentClause.presence = lunr.Query.presence.PROHIBITED + break + case "+": + parser.currentClause.presence = lunr.Query.presence.REQUIRED + break + default: + var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term or field, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/site/search/main.js b/site/search/main.js new file mode 100644 index 0000000..a5e469d --- /dev/null +++ b/site/search/main.js @@ -0,0 +1,109 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function escapeHtml (value) { + return value.replace(/&/g, '&') + .replace(/"/g, '"') + .replace(//g, '>'); +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + var noResultsText = search_results.getAttribute('data-no-results-text'); + if (!noResultsText) { + noResultsText = "No results found"; + } + search_results.insertAdjacentHTML('beforeend', '

' + noResultsText + '

'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/site/search/search_index.json b/site/search/search_index.json new file mode 100644 index 0000000..260619f --- /dev/null +++ b/site/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"1. MetaVision is ... \u00b6 MetaVision\uc740 \ub300\uc6a9\ub7c9\uc758 Vision Data\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uace0 \uad00\ub828 \uc751\uc6a9 \uc11c\ube44\uc2a4\ub97c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud658\uacbd\uc744 \uc81c\uacf5\ud558\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uae30\uc874\uc5d0 \uad00\ub9ac\ub418\uc9c0 \uc54a\uace0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uad00\ub9ac \ucee4\ubc84\ub9ac\uc9c0 \ubc0f \uc7ac\ud65c\uc6a9\uc131\uc744 \uc81c\uace0 CVPaaS Cloud \uc11c\ube44\uc2a4\uc5d0\uc11c\uc758 \ud655\uc7a5\uc131 \uc788\uace0, \ube44\uc6a9 \ud6a8\uc728\uc801\uc778 AI Ops \uc11c\ube44\uc2a4\uc758 \uae30\ubc18\uc744 \uc81c\uacf5 Golden Data Discovery & AI \ubaa8\ub378 \uac1c\uc120 Cycle \uc644\uc131\uc744 \uc704\ud55c \ud658\uacbd \uc81c\uacf5 Vision AI\uac00 \uc801\uc6a9\ub41c \uc2e4 \uc11c\ube44\uc2a4\uc758 \ub370\uc774\ud130\ub97c \uc790\ub3d9\uc73c\ub85c \uc218\uc9d1\ud558\uace0 \ub370\uc774\ud130\uc14b \ud615\ud0dc\ub85c \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uae30\ub2a5 \uc81c\uacf5 2. Architecture \u00b6 MetaVision\uc758 \ubaa8\ub4e0 \uc11c\ube44\uc2a4\ub294 Kubernetes \ud658\uacbd\uc5d0\uc11c \ub3d9\uc791\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. AWS S3\uc640 \ud638\ud658\ub418\ub294 Ceph\ub97c \uc0ac\uc6a9\ud558\uc5ec object storage\ub97c \uad6c\uc131\ud558\uc600\uace0, Apache KAFKA, ElasticSearch \ub4f1\uc744 \uc0ac\uc6a9\ud558\uc5ec object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud55c \ud30c\uc77c\ub4e4\uc5d0 \ub300\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uad00\ub9ac\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. Apache KAFKA \ubc0f \uc790\uccb4 \uac1c\ubc1c\ud55c ML Workflow \uc11c\ube44\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubcf4\uc720\ud55c \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ub370\uc774\ud130 \uac00\uacf5 \ubc0f Machine Learning(ML) inference \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. ML inference\uc758 \uacb0\uacfc\ub294 \uc790\uccb4 \uac1c\ubc1c\ud55c LightningDB\uc5d0 \uc800\uc7a5\ub418\uc5b4 \uba54\ud0c0\ub370\uc774\ud130 \uae30\ubc18\uc73c\ub85c \uc9c0\uc5f0\uc5c6\ub294 \ub370\uc774\ud130 \uac80\uc0c9 \ubc0f \uac1d\uccb4\ub85c\ubd80\ud130 \ucd94\ucd9c\ud55c feature value \uae30\ubc18\uc73c\ub85c \uc720\uc0ac \uc774\ubbf8\uc9c0 \uac80\uc0c9\uc744 \uc9c0\uc6d0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < MetaVision \uc544\ud0a4\ud14d\ucc98 > 3. Features \u00b6 AWS S3\uc640 \ud638\ud658\ub418\ub294 object storage\uc778 Ceph \uc81c\uacf5 \ubc0f \ud655\uc7a5\uac00\ub2a5\ud55c \uc2a4\ud1a0\ub9ac\uc9c0 \uc81c\uacf5 \uc0ac\uc6a9\uc790 \uc778\uc99d \ubc0f \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc0ac\uc6a9\uc790\ubcc4 \ub370\uc774\ud130 \uc811\uadfc \uad8c\ud55c \uad00\ub9ac \ubc0f \uacf5\uc720 \uae30\ub2a5 \uc81c\uacf5 Object storage\uc5d0 \uc800\uc7a5\ub41c \ubaa8\ub4e0 \ud30c\uc77c\uc5d0 \ub300\ud574 \ud0a4\uc6cc\ub4dc \uac80\uc0c9 \uc9c0\uc6d0 Object storage\uc5d0 \uc800\uc7a5\ub41c \ubbf8\ub514\uc5b4 \ud30c\uc77c\uc5d0 \ub300\ud55c \uc378\ub124\uc77c \uc81c\uacf5 ML inference\ub97c \uc704\ud55c \uae30\ubcf8 ML Workflow \uc774\ubbf8\uc9c0(Motion Detection/Object Detection/Feature Extraction) \uc81c\uacf5 \ub2e4\uc591\ud55c model \uc0ac\uc6a9 \ubc0f \ub370\uc774\ud130 \ucc98\ub9ac \uadf8\ub9ac\uace0 inference \ucf54\ub4dc\ub97c \uc6b4\uc601\ud560 \uc218 \uc788\ub294 ML Workflow Image Repository \uc6b4\uc601(\uc0ac\uc6a9\uc790\uac00 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uc5ec \uc5c5\ub85c\ub4dc\ud55c \ud6c4 \uc0ac\uc6a9 \uac00\ub2a5) Object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc790\ub3d9\uc73c\ub85c ML Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub294 Event \ub4f1\ub85d \uc11c\ube44\uc2a4 \uc81c\uacf5 ML Inference\ub97c \ud1b5\ud574 \ucd94\ucd9c\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uae30 \uc704\ud55c LightningDB \uc0ac\uc6a9(\ub370\uc774\ud130 \uc870\ud68c \ubc0f \uc720\uc0ac \uc774\ubbf8\uc9c0 \uac80\uc0c9 \uc9c0\uc6d0) \ud504\ub85c\uc81d\ud2b8\ubcc4 \ub370\uc774\ud130 \ud604\ud669\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud560 \uc218 \uc788\ub294 \ub300\uc2dc\ubcf4\ub4dc \uc81c\uacf5 \uc11c\ube44\uc2a4 \ub370\uc774\ud130\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \uc218\uc9d1\ud558\uc5ec \ud1b5\uacc4 \ubc0f \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc870\ud68c\ud560 \uc218 \uc788\ub294 \ub300\uc2dc\ubcf4\ub4dc\ub97c \uc81c\uacf5\ud558\uace0, \uaddc\uce59 \uae30\ubc18 \ub610\ub294 \uc0ac\uc6a9\uc790\uac00 \uc120\ud0dd\ud55c \ubaa9\ub85d \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ubc1b\uc744 \uc218 \uc788\ub294 \uae30\ub2a5 \uc81c\uacf5","title":"Overview"},{"location":"#1-metavision-is","text":"MetaVision\uc740 \ub300\uc6a9\ub7c9\uc758 Vision Data\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uace0 \uad00\ub828 \uc751\uc6a9 \uc11c\ube44\uc2a4\ub97c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud658\uacbd\uc744 \uc81c\uacf5\ud558\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uae30\uc874\uc5d0 \uad00\ub9ac\ub418\uc9c0 \uc54a\uace0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uad00\ub9ac \ucee4\ubc84\ub9ac\uc9c0 \ubc0f \uc7ac\ud65c\uc6a9\uc131\uc744 \uc81c\uace0 CVPaaS Cloud \uc11c\ube44\uc2a4\uc5d0\uc11c\uc758 \ud655\uc7a5\uc131 \uc788\uace0, \ube44\uc6a9 \ud6a8\uc728\uc801\uc778 AI Ops \uc11c\ube44\uc2a4\uc758 \uae30\ubc18\uc744 \uc81c\uacf5 Golden Data Discovery & AI \ubaa8\ub378 \uac1c\uc120 Cycle \uc644\uc131\uc744 \uc704\ud55c \ud658\uacbd \uc81c\uacf5 Vision AI\uac00 \uc801\uc6a9\ub41c \uc2e4 \uc11c\ube44\uc2a4\uc758 \ub370\uc774\ud130\ub97c \uc790\ub3d9\uc73c\ub85c \uc218\uc9d1\ud558\uace0 \ub370\uc774\ud130\uc14b \ud615\ud0dc\ub85c \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uae30\ub2a5 \uc81c\uacf5","title":"1. MetaVision is ..."},{"location":"#2-architecture","text":"MetaVision\uc758 \ubaa8\ub4e0 \uc11c\ube44\uc2a4\ub294 Kubernetes \ud658\uacbd\uc5d0\uc11c \ub3d9\uc791\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. AWS S3\uc640 \ud638\ud658\ub418\ub294 Ceph\ub97c \uc0ac\uc6a9\ud558\uc5ec object storage\ub97c \uad6c\uc131\ud558\uc600\uace0, Apache KAFKA, ElasticSearch \ub4f1\uc744 \uc0ac\uc6a9\ud558\uc5ec object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud55c \ud30c\uc77c\ub4e4\uc5d0 \ub300\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uad00\ub9ac\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. Apache KAFKA \ubc0f \uc790\uccb4 \uac1c\ubc1c\ud55c ML Workflow \uc11c\ube44\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubcf4\uc720\ud55c \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ub370\uc774\ud130 \uac00\uacf5 \ubc0f Machine Learning(ML) inference \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. ML inference\uc758 \uacb0\uacfc\ub294 \uc790\uccb4 \uac1c\ubc1c\ud55c LightningDB\uc5d0 \uc800\uc7a5\ub418\uc5b4 \uba54\ud0c0\ub370\uc774\ud130 \uae30\ubc18\uc73c\ub85c \uc9c0\uc5f0\uc5c6\ub294 \ub370\uc774\ud130 \uac80\uc0c9 \ubc0f \uac1d\uccb4\ub85c\ubd80\ud130 \ucd94\ucd9c\ud55c feature value \uae30\ubc18\uc73c\ub85c \uc720\uc0ac \uc774\ubbf8\uc9c0 \uac80\uc0c9\uc744 \uc9c0\uc6d0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < MetaVision \uc544\ud0a4\ud14d\ucc98 >","title":"2. Architecture"},{"location":"#3-features","text":"AWS S3\uc640 \ud638\ud658\ub418\ub294 object storage\uc778 Ceph \uc81c\uacf5 \ubc0f \ud655\uc7a5\uac00\ub2a5\ud55c \uc2a4\ud1a0\ub9ac\uc9c0 \uc81c\uacf5 \uc0ac\uc6a9\uc790 \uc778\uc99d \ubc0f \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc0ac\uc6a9\uc790\ubcc4 \ub370\uc774\ud130 \uc811\uadfc \uad8c\ud55c \uad00\ub9ac \ubc0f \uacf5\uc720 \uae30\ub2a5 \uc81c\uacf5 Object storage\uc5d0 \uc800\uc7a5\ub41c \ubaa8\ub4e0 \ud30c\uc77c\uc5d0 \ub300\ud574 \ud0a4\uc6cc\ub4dc \uac80\uc0c9 \uc9c0\uc6d0 Object storage\uc5d0 \uc800\uc7a5\ub41c \ubbf8\ub514\uc5b4 \ud30c\uc77c\uc5d0 \ub300\ud55c \uc378\ub124\uc77c \uc81c\uacf5 ML inference\ub97c \uc704\ud55c \uae30\ubcf8 ML Workflow \uc774\ubbf8\uc9c0(Motion Detection/Object Detection/Feature Extraction) \uc81c\uacf5 \ub2e4\uc591\ud55c model \uc0ac\uc6a9 \ubc0f \ub370\uc774\ud130 \ucc98\ub9ac \uadf8\ub9ac\uace0 inference \ucf54\ub4dc\ub97c \uc6b4\uc601\ud560 \uc218 \uc788\ub294 ML Workflow Image Repository \uc6b4\uc601(\uc0ac\uc6a9\uc790\uac00 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uc5ec \uc5c5\ub85c\ub4dc\ud55c \ud6c4 \uc0ac\uc6a9 \uac00\ub2a5) Object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc790\ub3d9\uc73c\ub85c ML Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub294 Event \ub4f1\ub85d \uc11c\ube44\uc2a4 \uc81c\uacf5 ML Inference\ub97c \ud1b5\ud574 \ucd94\ucd9c\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uae30 \uc704\ud55c LightningDB \uc0ac\uc6a9(\ub370\uc774\ud130 \uc870\ud68c \ubc0f \uc720\uc0ac \uc774\ubbf8\uc9c0 \uac80\uc0c9 \uc9c0\uc6d0) \ud504\ub85c\uc81d\ud2b8\ubcc4 \ub370\uc774\ud130 \ud604\ud669\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud560 \uc218 \uc788\ub294 \ub300\uc2dc\ubcf4\ub4dc \uc81c\uacf5 \uc11c\ube44\uc2a4 \ub370\uc774\ud130\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \uc218\uc9d1\ud558\uc5ec \ud1b5\uacc4 \ubc0f \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc870\ud68c\ud560 \uc218 \uc788\ub294 \ub300\uc2dc\ubcf4\ub4dc\ub97c \uc81c\uacf5\ud558\uace0, \uaddc\uce59 \uae30\ubc18 \ub610\ub294 \uc0ac\uc6a9\uc790\uac00 \uc120\ud0dd\ud55c \ubaa9\ub85d \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ubc1b\uc744 \uc218 \uc788\ub294 \uae30\ub2a5 \uc81c\uacf5","title":"3. Features"},{"location":"ai_device_status/","text":"1. AI Device Status \u00b6 AI Device Status \ub294 Data Agent \uc5d0\uc11c \uc6d0\uaca9\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uc7a5\ube44\uc758 \uc0c1\ud0dc\ub97c \uccb4\ud06c\ud558\uace0, \ud604\ud669\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc0c1\ud0dc \ubaa8\ub2c8\ud130\ub9c1\uc744 \ud558\uace0 \uc788\ub294 \uc7a5\ube44\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130 \uc11c\ubc84 NVR(Network Video Recorder) AI Box(Device) Camera < AI Device \uc0c1\ud0dc \ubaa8\ub2c8\ud130\ub9c1 >","title":"AI Device Status"},{"location":"ai_device_status/#1-ai-device-status","text":"AI Device Status \ub294 Data Agent \uc5d0\uc11c \uc6d0\uaca9\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uc7a5\ube44\uc758 \uc0c1\ud0dc\ub97c \uccb4\ud06c\ud558\uace0, \ud604\ud669\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc0c1\ud0dc \ubaa8\ub2c8\ud130\ub9c1\uc744 \ud558\uace0 \uc788\ub294 \uc7a5\ube44\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130 \uc11c\ubc84 NVR(Network Video Recorder) AI Box(Device) Camera < AI Device \uc0c1\ud0dc \ubaa8\ub2c8\ud130\ub9c1 >","title":"1. AI Device Status"},{"location":"dashboard/","text":"Dashboard \u00b6 Dashboard \uc5d0\uc11c\ub294 \ud504\ub85c\uc81d\ud2b8 \uc815\ubcf4, \ubcf4\uc720\ud55c \ub370\uc774\ud130 \ud604\ud669 \uadf8\ub9ac\uace0 \ub370\uc774\ud130\ub97c \uacf5\uc720\ud560 \uc0ac\uc6a9\uc790 \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc81c\uacf5\ud558\ub294 \ucc28\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. Storage Size: object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uc0ac\uc774\uc988 File Count: object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uac1c\uc218 Object Count: ML Workflow\ub85c \ucd94\ucd9c\ub41c \uba54\ud0c0 \uc815\ubcf4\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uac1c\uc218 Workflow Count: \ub4f1\ub85d\ub41c Workflow\uc758 \uc9c4\ud589 \ud604\ud669 \uac01 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc0ac\uc6a9\uc790 \ucd94\uac00 \ubc0f \uc0ad\uc81c\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uacf5\uc720\ud560 \uc218 \uc788\ub2e4.","title":"Dashboard"},{"location":"dashboard/#dashboard","text":"Dashboard \uc5d0\uc11c\ub294 \ud504\ub85c\uc81d\ud2b8 \uc815\ubcf4, \ubcf4\uc720\ud55c \ub370\uc774\ud130 \ud604\ud669 \uadf8\ub9ac\uace0 \ub370\uc774\ud130\ub97c \uacf5\uc720\ud560 \uc0ac\uc6a9\uc790 \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc81c\uacf5\ud558\ub294 \ucc28\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. Storage Size: object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uc0ac\uc774\uc988 File Count: object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uac1c\uc218 Object Count: ML Workflow\ub85c \ucd94\ucd9c\ub41c \uba54\ud0c0 \uc815\ubcf4\uc5d0 \ub300\ud574 \uc720\ud615\ubcc4 \ucd1d \uac1c\uc218 Workflow Count: \ub4f1\ub85d\ub41c Workflow\uc758 \uc9c4\ud589 \ud604\ud669 \uac01 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc0ac\uc6a9\uc790 \ucd94\uac00 \ubc0f \uc0ad\uc81c\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uacf5\uc720\ud560 \uc218 \uc788\ub2e4.","title":"Dashboard"},{"location":"data_lake/","text":"File explore \u00b6 File explore \ub294 object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\ub4e4\uc744 File browser \ud615\ud0dc\ub85c \ubcfc \uc218 \uc788\ub3c4\ub85d \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. Object storage\ub294 \uad6c\uc870\ud654\ub418\uc9c0 \uc54a\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub3c4\ub85d \uc124\uacc4\ub41c data storage architecture\uc785\ub2c8\ub2e4. Object storage\uc758 \ud30c\uc77c\ub4e4\uc740 \ud30c\uc77c \uc790\uccb4\uc640 \uba54\ud0c0 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\ub294 \uac1d\uccb4(object) \ud615\ud0dc\ub85c \uad00\ub9ac\ub418\uace0 \uc788\uc73c\uba70, \uc218\ud3c9 \ud655\uc7a5\uc774 \uac00\ub2a5\ud558\uc5ec \ud655\uc7a5\uc131\uc5d0 \uc81c\ud55c\uc774 \uac70\uc758 \uc5c6\uc5b4 \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uace0 \uba54\ud0c0 \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc27d\uac8c \uac80\uc0c9 \ubc0f \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc9c4\uacfc \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc758 \uacbd\uc6b0, \ubbf8\ub9ac\ubcf4\uae30\ub97c \uc81c\uacf5\ud558\uace0 \uc788\uc5b4 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c \uad00\ub9ac\uac00 \ub418\uace0 \uc788\uc73c\uba70, \uc0ac\uc6a9\uc790\ub294 \uad8c\ud55c\uc774 \uc788\ub294 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub9cc \ub370\uc774\ud130\ub97c \uc5c5\ub85c\ub4dc \ubc0f \ub2e4\uc6b4\ub85c\ub4dc\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < File explore \uc870\ud68c > File search \u00b6 File explore\uc758 \uacbd\uc6b0, \uc9c1\uc811 object storage\uc5d0 \ub370\uc774\ud130\ub97c \uc21c\ucc28\uc801\uc73c\ub85c \uc870\ud68c\ud558\uc5ec \uacb0\uacfc\ub97c \ubc1b\ub294 \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\uac00 \uc788\ub294 \uacbd\uc6b0 \uc218\ud589 \uc2dc\uac04\uc774 \uc9c0\uc5f0\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. File search \ub294 object storage\uc5d0 \uc5c5\ub85c\ub4dc\ub41c \ubaa8\ub4e0 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uba54\ud0c0 \uc815\ubcf4\ub97c \ubcc4\ub3c4\ub85c \uc800\uc7a5\ud558\uc5ec \ud30c\uc77c \uacbd\ub85c, \ud30c\uc77c \uc774\ub984\ubfd0\ub9cc \uc544\ub2c8\ub77c \ubb38\uc790 \uae30\ubc18 \ud30c\uc77c\uc758 \uacbd\uc6b0 \ubb38\uc11c \ub0b4 \ud0a4\uc6cc\ub4dc\ub3c4 \ud0dc\uadf8\ub85c \uc800\uc7a5\ud558\uc5ec \ud0a4\uc6cc\ub4dc \uae30\ubc18 \uac80\uc0c9 \uc744 \uc2e4\uc2dc\uac04\uc73c\ub85c \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < File search \uc870\ud68c > \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc6d0\ud558\ub294 \ub370\uc774\ud130\ub97c \ud0dc\uadf8 \uae30\ubc18\uc73c\ub85c \uc27d\uac8c \ucc3e\uc744 \uc218 \uc788\uc73c\uba70, \ucc3e\uc740 \ub370\uc774\ud130\uac00 \uc788\ub294 \uc704\uce58\ub85c \uc774\ub3d9\ud558\uc5ec \uad00\ub828 \ub370\uc774\ud130\ub97c \uc804\uccb4 \ud655\uc778\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < \uac80\uc0c9\ud55c \ud30c\uc77c\uc774 \uc788\ub294 Directory\ub85c \uc774\ub3d9 >","title":"File explore"},{"location":"data_lake/#file-explore","text":"File explore \ub294 object storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\ub4e4\uc744 File browser \ud615\ud0dc\ub85c \ubcfc \uc218 \uc788\ub3c4\ub85d \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. Object storage\ub294 \uad6c\uc870\ud654\ub418\uc9c0 \uc54a\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub3c4\ub85d \uc124\uacc4\ub41c data storage architecture\uc785\ub2c8\ub2e4. Object storage\uc758 \ud30c\uc77c\ub4e4\uc740 \ud30c\uc77c \uc790\uccb4\uc640 \uba54\ud0c0 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\ub294 \uac1d\uccb4(object) \ud615\ud0dc\ub85c \uad00\ub9ac\ub418\uace0 \uc788\uc73c\uba70, \uc218\ud3c9 \ud655\uc7a5\uc774 \uac00\ub2a5\ud558\uc5ec \ud655\uc7a5\uc131\uc5d0 \uc81c\ud55c\uc774 \uac70\uc758 \uc5c6\uc5b4 \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uace0 \uba54\ud0c0 \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc27d\uac8c \uac80\uc0c9 \ubc0f \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc9c4\uacfc \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc758 \uacbd\uc6b0, \ubbf8\ub9ac\ubcf4\uae30\ub97c \uc81c\uacf5\ud558\uace0 \uc788\uc5b4 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c \uad00\ub9ac\uac00 \ub418\uace0 \uc788\uc73c\uba70, \uc0ac\uc6a9\uc790\ub294 \uad8c\ud55c\uc774 \uc788\ub294 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub9cc \ub370\uc774\ud130\ub97c \uc5c5\ub85c\ub4dc \ubc0f \ub2e4\uc6b4\ub85c\ub4dc\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < File explore \uc870\ud68c >","title":"File explore"},{"location":"data_lake/#file-search","text":"File explore\uc758 \uacbd\uc6b0, \uc9c1\uc811 object storage\uc5d0 \ub370\uc774\ud130\ub97c \uc21c\ucc28\uc801\uc73c\ub85c \uc870\ud68c\ud558\uc5ec \uacb0\uacfc\ub97c \ubc1b\ub294 \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\uac00 \uc788\ub294 \uacbd\uc6b0 \uc218\ud589 \uc2dc\uac04\uc774 \uc9c0\uc5f0\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. File search \ub294 object storage\uc5d0 \uc5c5\ub85c\ub4dc\ub41c \ubaa8\ub4e0 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uba54\ud0c0 \uc815\ubcf4\ub97c \ubcc4\ub3c4\ub85c \uc800\uc7a5\ud558\uc5ec \ud30c\uc77c \uacbd\ub85c, \ud30c\uc77c \uc774\ub984\ubfd0\ub9cc \uc544\ub2c8\ub77c \ubb38\uc790 \uae30\ubc18 \ud30c\uc77c\uc758 \uacbd\uc6b0 \ubb38\uc11c \ub0b4 \ud0a4\uc6cc\ub4dc\ub3c4 \ud0dc\uadf8\ub85c \uc800\uc7a5\ud558\uc5ec \ud0a4\uc6cc\ub4dc \uae30\ubc18 \uac80\uc0c9 \uc744 \uc2e4\uc2dc\uac04\uc73c\ub85c \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < File search \uc870\ud68c > \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc6d0\ud558\ub294 \ub370\uc774\ud130\ub97c \ud0dc\uadf8 \uae30\ubc18\uc73c\ub85c \uc27d\uac8c \ucc3e\uc744 \uc218 \uc788\uc73c\uba70, \ucc3e\uc740 \ub370\uc774\ud130\uac00 \uc788\ub294 \uc704\uce58\ub85c \uc774\ub3d9\ud558\uc5ec \uad00\ub828 \ub370\uc774\ud130\ub97c \uc804\uccb4 \ud655\uc778\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. < \uac80\uc0c9\ud55c \ud30c\uc77c\uc774 \uc788\ub294 Directory\ub85c \uc774\ub3d9 >","title":"File search"},{"location":"data_warehouse/","text":"Metadata explore \u00b6 Metadata explore \ub294 object storage\uc5d0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud574 ML Workflow\ub97c \uc218\ud589\ud55c \uacb0\uacfc\ub97c File browser \ud615\ud0dc\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. datasets \uc5d0\ub294 \uae30\ubcf8 dataset(\ud504\ub85c\uc81d\ud2b8 \uc774\ub984\uacfc \ub3d9\uc77c)\uc774 \uc81c\uacf5\ub418\uc5b4 ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \ud30c\uc77c \ubc0f \uac1d\uccb4\uc5d0 \ub300\ud55c \uba54\ud0c0 \uc815\ubcf4\uac00 \uc800\uc7a5\ub429\ub2c8\ub2e4. < Metadata explore \uc870\ud68c > \uadf8 \uc678\uc5d0 create dataset \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uc5ec, \uac80\uc0c9\uc744 \ud1b5\ud574 \uc218\uc9d1\ud55c \ud30c\uc77c\ub4e4\uc744 \ubcc4\ub3c4\uc758 dataset\uc73c\ub85c \uc800\uc7a5\ud558\uc5ec \uad00\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uc2e0\uaddc dataset \uc0dd\uc131 > Metadata search \u00b6 ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \uba54\ud0c0 \uc815\ubcf4\ub294 LightningDB\uc5d0 \uc800\uc7a5\ub418\ub294\ub370, \ucd94\ucd9c\ub41c \uac01 \uac1d\uccb4\ub294 update_date, class, tags, confidence_score \ub4f1\uc758 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\uc5b4 \uc774\ub97c \uae30\ubc18\ud55c \uc870\ud68c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. < Metadata search \uc870\ud68c \uacb0\uacfc > \uac01 \uac1d\uccb4\ub294 feature extraction \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\uc5d0 \ub300\ud55c feature \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uace0 \uc800\uc7a5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. KNN search \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uba74, \uc120\ud0dd\ud55c \uc774\ubbf8\uc9c0\uc640 \uc720\uc0ac\ud55c \uc774\ubbf8\uc9c0\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < KNN search \uc870\ud68c > < KNN search \uc870\ud68c \uacb0\uacfc >","title":"Metadata explore"},{"location":"data_warehouse/#metadata-explore","text":"Metadata explore \ub294 object storage\uc5d0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud574 ML Workflow\ub97c \uc218\ud589\ud55c \uacb0\uacfc\ub97c File browser \ud615\ud0dc\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. datasets \uc5d0\ub294 \uae30\ubcf8 dataset(\ud504\ub85c\uc81d\ud2b8 \uc774\ub984\uacfc \ub3d9\uc77c)\uc774 \uc81c\uacf5\ub418\uc5b4 ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \ud30c\uc77c \ubc0f \uac1d\uccb4\uc5d0 \ub300\ud55c \uba54\ud0c0 \uc815\ubcf4\uac00 \uc800\uc7a5\ub429\ub2c8\ub2e4. < Metadata explore \uc870\ud68c > \uadf8 \uc678\uc5d0 create dataset \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uc5ec, \uac80\uc0c9\uc744 \ud1b5\ud574 \uc218\uc9d1\ud55c \ud30c\uc77c\ub4e4\uc744 \ubcc4\ub3c4\uc758 dataset\uc73c\ub85c \uc800\uc7a5\ud558\uc5ec \uad00\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uc2e0\uaddc dataset \uc0dd\uc131 >","title":"Metadata explore"},{"location":"data_warehouse/#metadata-search","text":"ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \uba54\ud0c0 \uc815\ubcf4\ub294 LightningDB\uc5d0 \uc800\uc7a5\ub418\ub294\ub370, \ucd94\ucd9c\ub41c \uac01 \uac1d\uccb4\ub294 update_date, class, tags, confidence_score \ub4f1\uc758 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\uc5b4 \uc774\ub97c \uae30\ubc18\ud55c \uc870\ud68c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. < Metadata search \uc870\ud68c \uacb0\uacfc > \uac01 \uac1d\uccb4\ub294 feature extraction \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\uc5d0 \ub300\ud55c feature \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uace0 \uc800\uc7a5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. KNN search \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uba74, \uc120\ud0dd\ud55c \uc774\ubbf8\uc9c0\uc640 \uc720\uc0ac\ud55c \uc774\ubbf8\uc9c0\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < KNN search \uc870\ud68c > < KNN search \uc870\ud68c \uacb0\uacfc >","title":"Metadata search"},{"location":"datasets/","text":"1. Datasets List \u00b6 Datasets \ub294 object storage\uc5d0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud574 ML Workflow\ub97c \uc218\ud589\ud55c \uacb0\uacfc\ub97c File browser \ud615\ud0dc\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. datasets \uc5d0\ub294 \uae30\ubcf8 \ub370\uc774\ud130\uc14b('All files')\uc774 \uc81c\uacf5\ub418\uc5b4 ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \ud30c\uc77c \ubc0f \uac1d\uccb4\uc5d0 \ub300\ud55c \uba54\ud0c0 \uc815\ubcf4\uac00 \uc800\uc7a5\ub429\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\ub294 \uae30\ubcf8 \ub370\uc774\ud130\uc14b \uc678\uc5d0 \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc73c\uba70, \uac80\uc0c9 \ub4f1\uc744 \uc774\uc6a9\ud558\uc5ec \uc120\ud0dd\uc801\uc73c\ub85c \ud30c\uc77c\uc744 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub370\uc774\ud130\uc14b \uc870\ud68c > 2. Add to Dataset \u00b6 \uae30\ubcf8 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ub610\ub294 \uac80\uc0c9\uc744 \ud1b5\ud574 \ud655\uc778\ud55c \ud30c\uc77c \ubaa9\ub85d \uc911\uc5d0\uc11c \uc120\ud0dd\uc801\uc73c\ub85c \uc6d0\ud558\ub294 \ud30c\uc77c\uc744 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\ud560 \ub370\uc774\ud130\uc14b\uc740 \uae30\uc874 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uace0\ub97c \uc218\ub3c4 \uc788\uace0, \uc0c8\ub85c \uc0dd\uc131\ud558\uc5ec \ucd94\uac00\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. < \ub370\uc774\ud130\uc14b\uc5d0 \ud30c\uc77c \ucd94\uac00 > 3. Detail View \u00b6 \ub370\uc774\ud130\uc14b\uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc120\ud0dd\ud558\uba74 '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc774 \uc81c\uacf5\ub429\ub2c8\ub2e4. '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ubc0f \uc601\uc0c1 \uc7ac\uc0dd \ud30c\uc77c \ub2e4\uc6b4\ub85c\ub4dc \ud30c\uc77c \uc0ad\uc81c \uba54\ud0c0\ub370\uc774\ud130(\ud30c\uc77c \uacbd\ub85c, \uc720\ud615, \ud30c\uc77c \uc0ac\uc774\uc988, \ud30c\uc77c \uc5c5\ub85c\ub4dc \ub0a0\uc9dc \ubc0f \uc2dc\uac04) \uc81c\uacf5 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00 < \uc601\uc0c1/\uc774\ubbf8\uc9c0 \uc0c1\uc138 \ubcf4\uae30 >","title":"Datasets"},{"location":"datasets/#1-datasets-list","text":"Datasets \ub294 object storage\uc5d0 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \ub300\ud574 ML Workflow\ub97c \uc218\ud589\ud55c \uacb0\uacfc\ub97c File browser \ud615\ud0dc\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. datasets \uc5d0\ub294 \uae30\ubcf8 \ub370\uc774\ud130\uc14b('All files')\uc774 \uc81c\uacf5\ub418\uc5b4 ML Workflow\ub97c \ud1b5\ud574 \ucd94\ucd9c\ub41c \ud30c\uc77c \ubc0f \uac1d\uccb4\uc5d0 \ub300\ud55c \uba54\ud0c0 \uc815\ubcf4\uac00 \uc800\uc7a5\ub429\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\ub294 \uae30\ubcf8 \ub370\uc774\ud130\uc14b \uc678\uc5d0 \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc73c\uba70, \uac80\uc0c9 \ub4f1\uc744 \uc774\uc6a9\ud558\uc5ec \uc120\ud0dd\uc801\uc73c\ub85c \ud30c\uc77c\uc744 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub370\uc774\ud130\uc14b \uc870\ud68c >","title":"1. Datasets List"},{"location":"datasets/#2-add-to-dataset","text":"\uae30\ubcf8 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ub610\ub294 \uac80\uc0c9\uc744 \ud1b5\ud574 \ud655\uc778\ud55c \ud30c\uc77c \ubaa9\ub85d \uc911\uc5d0\uc11c \uc120\ud0dd\uc801\uc73c\ub85c \uc6d0\ud558\ub294 \ud30c\uc77c\uc744 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\ud560 \ub370\uc774\ud130\uc14b\uc740 \uae30\uc874 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uace0\ub97c \uc218\ub3c4 \uc788\uace0, \uc0c8\ub85c \uc0dd\uc131\ud558\uc5ec \ucd94\uac00\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. < \ub370\uc774\ud130\uc14b\uc5d0 \ud30c\uc77c \ucd94\uac00 >","title":"2. Add to Dataset"},{"location":"datasets/#3-detail-view","text":"\ub370\uc774\ud130\uc14b\uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc120\ud0dd\ud558\uba74 '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc774 \uc81c\uacf5\ub429\ub2c8\ub2e4. '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ubc0f \uc601\uc0c1 \uc7ac\uc0dd \ud30c\uc77c \ub2e4\uc6b4\ub85c\ub4dc \ud30c\uc77c \uc0ad\uc81c \uba54\ud0c0\ub370\uc774\ud130(\ud30c\uc77c \uacbd\ub85c, \uc720\ud615, \ud30c\uc77c \uc0ac\uc774\uc988, \ud30c\uc77c \uc5c5\ub85c\ub4dc \ub0a0\uc9dc \ubc0f \uc2dc\uac04) \uc81c\uacf5 \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00 < \uc601\uc0c1/\uc774\ubbf8\uc9c0 \uc0c1\uc138 \ubcf4\uae30 >","title":"3. Detail View"},{"location":"event_search/","text":"1. Event Search \u00b6 Event Search \ub294 AI\uac00 \uc778\uc9c0\ud55c \ud589\ub3d9 \ub610\ub294 \uc774\ubca4\ud2b8\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\uc6b8\ub7ec Semantic Search \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \uac80\uc0c9\uc744 \ub354\uc6b1 \uc815\uad50\ud558\uac8c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac80\uc0c9 \uac00\ub2a5\ud55c \uc774\ubca4\ud2b8 \uc885\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. Intrusion Loitering Motion Line-crossing Fall-down Fire LPR(License Plate Recognition) Etc < Event Search \uac80\uc0c9 \uc635\uc158 > < Event Search \uac80\uc0c9 \uacb0\uacfc > 2. \uac80\uc0c9 \uacb0\uacfc \uc0c1\uc138 \ubcf4\uae30 \u00b6 \uac80\uc0c9 \uacb0\uacfc\ub294 3 \uac00\uc9c0 \ud615\ud0dc\ub85c \uc81c\uacf5\ub429\ub2c8\ub2e4. \uc774\ubca4\ud2b8\uc5d0 \ub300\ud55c \uc694\uc57d \uc601\uc0c1 \uc774\ubca4\ud2b8 \ubc1c\uc0dd \uc2dc\uc810 \uc774\ubbf8\uc9c0 \uc774\ubca4\ud2b8 \ubc1c\uc0dd \uc2dc\uc810 \uc6d0\ubcf8 \uc601\uc0c1","title":"Event Search"},{"location":"event_search/#1-event-search","text":"Event Search \ub294 AI\uac00 \uc778\uc9c0\ud55c \ud589\ub3d9 \ub610\ub294 \uc774\ubca4\ud2b8\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\uc6b8\ub7ec Semantic Search \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \uac80\uc0c9\uc744 \ub354\uc6b1 \uc815\uad50\ud558\uac8c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac80\uc0c9 \uac00\ub2a5\ud55c \uc774\ubca4\ud2b8 \uc885\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. Intrusion Loitering Motion Line-crossing Fall-down Fire LPR(License Plate Recognition) Etc < Event Search \uac80\uc0c9 \uc635\uc158 > < Event Search \uac80\uc0c9 \uacb0\uacfc >","title":"1. Event Search"},{"location":"event_search/#2","text":"\uac80\uc0c9 \uacb0\uacfc\ub294 3 \uac00\uc9c0 \ud615\ud0dc\ub85c \uc81c\uacf5\ub429\ub2c8\ub2e4. \uc774\ubca4\ud2b8\uc5d0 \ub300\ud55c \uc694\uc57d \uc601\uc0c1 \uc774\ubca4\ud2b8 \ubc1c\uc0dd \uc2dc\uc810 \uc774\ubbf8\uc9c0 \uc774\ubca4\ud2b8 \ubc1c\uc0dd \uc2dc\uc810 \uc6d0\ubcf8 \uc601\uc0c1","title":"2. \uac80\uc0c9 \uacb0\uacfc \uc0c1\uc138 \ubcf4\uae30"},{"location":"files/","text":"1. File Browser \u00b6 Files \ub294 Object Storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\ub4e4\uc744 File browser \ud615\ud0dc\ub85c \ubcfc \uc218 \uc788\ub3c4\ub85d \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. Object storage\ub294 \uad6c\uc870\ud654\ub418\uc9c0 \uc54a\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub3c4\ub85d \uc124\uacc4\ub41c Data Storage Architecture\uc785\ub2c8\ub2e4. Object Storage\uc758 \ud30c\uc77c\ub4e4\uc740 \ud30c\uc77c \uc790\uccb4\uc640 \uba54\ud0c0 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\ub294 \uac1d\uccb4(object) \ud615\ud0dc\ub85c \uad00\ub9ac\ub418\uace0 \uc788\uc73c\uba70, \uc218\ud3c9 \ud655\uc7a5\uc774 \uac00\ub2a5\ud558\uc5ec \ud655\uc7a5\uc131\uc5d0 \uc81c\ud55c\uc774 \uac70\uc758 \uc5c6\uc5b4 \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uace0 \uba54\ud0c0 \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc27d\uac8c \uac80\uc0c9 \ubc0f \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc9c4\uacfc \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc758 \uacbd\uc6b0, \ubbf8\ub9ac\ubcf4\uae30\ub97c \uc81c\uacf5\ud558\uace0 \uc788\uc5b4 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub514\ub809\ud1a0\ub9ac \ubc0f \ud30c\uc77c \ubaa9\ub85d \uc870\ud68c > 2. File Search \u00b6 \uc6b0\uce21 \uc0c1\ub2e8\uc758 \uac80\uc0c9 \ucc3d\uc744 \ud1b5\ud574 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub098 \ud30c\uc77c\uc744 \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub514\ub809\ud1a0\ub9ac \ubc0f \ud30c\uc77c \uac80\uc0c9 > 3. Show Hidden Files \u00b6 MetaVision\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \uc601\uc0c1 \ub610\ub294 \uc774\ubbf8\uc9c0 \ud30c\uc77c\uc744 \uad00\ub9ac\ud558\uace0 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc601\uc0c1\uc774\ub098 \uc774\ubbf8\uc9c0\uac00 \uc544\ub2cc \ud30c\uc77c\ub4e4\uc744 \uc870\ud68c\ud558\uace0 \ub2e4\uc6b4\ub85c\ub4dc\ud558\ub824\uba74 \uc6b0\uce21 \uc0c1\ub2e8\uc758 'Show Hidden Files' \ubc84\ud2bc\uc744 \ud65c\uc131\ud654\ud558\uba74 \ub429\ub2c8\ub2e4. < \uc601\uc0c1\uc774\ub098 \uc774\ubbf8\uc9c0\uac00 \uc544\ub2cc \ud30c\uc77c \uc870\ud68c > 4. Detail View \u00b6 Files \uc5d0\uc11c \uc81c\uacf5\ub41c \ubaa9\ub85d\uc744 \uc120\ud0dd\ud558\uba74 '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc774 \uc81c\uacf5\ub429\ub2c8\ub2e4. '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ubc0f \uc601\uc0c1 \uc7ac\uc0dd \ud30c\uc77c \ub2e4\uc6b4\ub85c\ub4dc \ud30c\uc77c \uc0ad\uc81c \uba54\ud0c0\ub370\uc774\ud130(\ud30c\uc77c \uacbd\ub85c, \uc720\ud615, \ud30c\uc77c \uc0ac\uc774\uc988, \ud30c\uc77c \uc5c5\ub85c\ub4dc \ub0a0\uc9dc \ubc0f \uc2dc\uac04) \uc81c\uacf5 < \uc601\uc0c1/\uc774\ubbf8\uc9c0 \uc0c1\uc138 \ubcf4\uae30 >","title":"Files"},{"location":"files/#1-file-browser","text":"Files \ub294 Object Storage\uc5d0 \uc800\uc7a5\ub41c \ud30c\uc77c\ub4e4\uc744 File browser \ud615\ud0dc\ub85c \ubcfc \uc218 \uc788\ub3c4\ub85d \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. Object storage\ub294 \uad6c\uc870\ud654\ub418\uc9c0 \uc54a\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub3c4\ub85d \uc124\uacc4\ub41c Data Storage Architecture\uc785\ub2c8\ub2e4. Object Storage\uc758 \ud30c\uc77c\ub4e4\uc740 \ud30c\uc77c \uc790\uccb4\uc640 \uba54\ud0c0 \uc815\ubcf4\ub97c \uac00\uc9c0\uace0 \uc788\ub294 \uac1d\uccb4(object) \ud615\ud0dc\ub85c \uad00\ub9ac\ub418\uace0 \uc788\uc73c\uba70, \uc218\ud3c9 \ud655\uc7a5\uc774 \uac00\ub2a5\ud558\uc5ec \ud655\uc7a5\uc131\uc5d0 \uc81c\ud55c\uc774 \uac70\uc758 \uc5c6\uc5b4 \ub300\uc6a9\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uace0 \uba54\ud0c0 \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc27d\uac8c \uac80\uc0c9 \ubc0f \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc9c4\uacfc \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc758 \uacbd\uc6b0, \ubbf8\ub9ac\ubcf4\uae30\ub97c \uc81c\uacf5\ud558\uace0 \uc788\uc5b4 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub514\ub809\ud1a0\ub9ac \ubc0f \ud30c\uc77c \ubaa9\ub85d \uc870\ud68c >","title":"1. File Browser"},{"location":"files/#2-file-search","text":"\uc6b0\uce21 \uc0c1\ub2e8\uc758 \uac80\uc0c9 \ucc3d\uc744 \ud1b5\ud574 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub098 \ud30c\uc77c\uc744 \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub514\ub809\ud1a0\ub9ac \ubc0f \ud30c\uc77c \uac80\uc0c9 >","title":"2. File Search"},{"location":"files/#3-show-hidden-files","text":"MetaVision\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \uc601\uc0c1 \ub610\ub294 \uc774\ubbf8\uc9c0 \ud30c\uc77c\uc744 \uad00\ub9ac\ud558\uace0 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc601\uc0c1\uc774\ub098 \uc774\ubbf8\uc9c0\uac00 \uc544\ub2cc \ud30c\uc77c\ub4e4\uc744 \uc870\ud68c\ud558\uace0 \ub2e4\uc6b4\ub85c\ub4dc\ud558\ub824\uba74 \uc6b0\uce21 \uc0c1\ub2e8\uc758 'Show Hidden Files' \ubc84\ud2bc\uc744 \ud65c\uc131\ud654\ud558\uba74 \ub429\ub2c8\ub2e4. < \uc601\uc0c1\uc774\ub098 \uc774\ubbf8\uc9c0\uac00 \uc544\ub2cc \ud30c\uc77c \uc870\ud68c >","title":"3. Show Hidden Files"},{"location":"files/#4-detail-view","text":"Files \uc5d0\uc11c \uc81c\uacf5\ub41c \ubaa9\ub85d\uc744 \uc120\ud0dd\ud558\uba74 '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc774 \uc81c\uacf5\ub429\ub2c8\ub2e4. '\uc0c1\uc138 \ubcf4\uae30' \ud654\uba74\uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ubc0f \uc601\uc0c1 \uc7ac\uc0dd \ud30c\uc77c \ub2e4\uc6b4\ub85c\ub4dc \ud30c\uc77c \uc0ad\uc81c \uba54\ud0c0\ub370\uc774\ud130(\ud30c\uc77c \uacbd\ub85c, \uc720\ud615, \ud30c\uc77c \uc0ac\uc774\uc988, \ud30c\uc77c \uc5c5\ub85c\ub4dc \ub0a0\uc9dc \ubc0f \uc2dc\uac04) \uc81c\uacf5 < \uc601\uc0c1/\uc774\ubbf8\uc9c0 \uc0c1\uc138 \ubcf4\uae30 >","title":"4. Detail View"},{"location":"golden_data_events/","text":"1. Golden-Data Events \u00b6 Golden-Data Events \ub294 \ub3cc\ubd04\uc13c\ud130 \uc5d0\uc11c \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc640 \uc601\uc0c1 \uc911 AI \uac1c\ubc1c\uc5d0 \ud544\uc694\ud55c \ubd80\ubd84\uc744 \uc27d\uac8c \ucc3e\uae30 \uc704\ud574 \ud1b5\uacc4 \ucc28\ud2b8\uc640 \uc774\ubca4\ud2b8 \ubaa9\ub85d\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc870\ud68c\uc5d0 \uc0ac\uc6a9\ub418\ub294 \uc870\uac74\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130 AI Box(Device) Camera(\uc124\uce58 \uc7a5\uc18c) AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8 \uc0ac\ub78c\uc774 \ud655\uc778\ud55c \uc2e4\uc81c \uc774\ubca4\ud2b8 AI \uc774\ubca4\ud2b8\uc758 \uc815\ub2f5 \uc5ec\ubd80 \uc601\uc0c1 \ub2e4\uc6b4\ub85c\ub4dc \uc5ec\ubd80 < Golden-Data Events \ubaa9\ub85d \ubc0f \ud1b5\uacc4 \ucc28\ud2b8 > 2. Video Download \u00b6 Data Agent \uc758 \uba54\ud0c0\ub370\uc774\ud130\ub85c\ubd80\ud130 \uc0ac\uc804\uc5d0 \uc815\uc758\ud55c \uaddc\uce59\uc5d0 \uc758\ud574 \uc601\uc0c1\uc744 \uc790\ub3d9 \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\uc6b8\ub7ec \ub85c\uadf8 \ud655\uc778 \ud6c4, \ub2e4\uc6b4\ub85c\ub4dc \uc694\uccad\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc6b4\ub85c\ub4dc\uac00 \uc644\ub8cc\ub418\uba74 \uc7ac\uc0dd \ubc84\ud2bc\uc774 \ud65c\uc131\ud654\ub429\ub2c8\ub2e4. (\ub2e4\uc6b4\ub85c\ub4dc \uc694\uccad \ud6c4, \uc644\ub8cc\uae4c\uc9c0 1~2\uc77c\uc774 \uc18c\uc694\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.) < \uc774\ubca4\ud2b8 \uc601\uc0c1 \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \uc7ac\uc0dd >","title":"Golden-Data Events"},{"location":"golden_data_events/#1-golden-data-events","text":"Golden-Data Events \ub294 \ub3cc\ubd04\uc13c\ud130 \uc5d0\uc11c \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc640 \uc601\uc0c1 \uc911 AI \uac1c\ubc1c\uc5d0 \ud544\uc694\ud55c \ubd80\ubd84\uc744 \uc27d\uac8c \ucc3e\uae30 \uc704\ud574 \ud1b5\uacc4 \ucc28\ud2b8\uc640 \uc774\ubca4\ud2b8 \ubaa9\ub85d\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc870\ud68c\uc5d0 \uc0ac\uc6a9\ub418\ub294 \uc870\uac74\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130 AI Box(Device) Camera(\uc124\uce58 \uc7a5\uc18c) AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8 \uc0ac\ub78c\uc774 \ud655\uc778\ud55c \uc2e4\uc81c \uc774\ubca4\ud2b8 AI \uc774\ubca4\ud2b8\uc758 \uc815\ub2f5 \uc5ec\ubd80 \uc601\uc0c1 \ub2e4\uc6b4\ub85c\ub4dc \uc5ec\ubd80 < Golden-Data Events \ubaa9\ub85d \ubc0f \ud1b5\uacc4 \ucc28\ud2b8 >","title":"1. Golden-Data Events"},{"location":"golden_data_events/#2-video-download","text":"Data Agent \uc758 \uba54\ud0c0\ub370\uc774\ud130\ub85c\ubd80\ud130 \uc0ac\uc804\uc5d0 \uc815\uc758\ud55c \uaddc\uce59\uc5d0 \uc758\ud574 \uc601\uc0c1\uc744 \uc790\ub3d9 \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\uc6b8\ub7ec \ub85c\uadf8 \ud655\uc778 \ud6c4, \ub2e4\uc6b4\ub85c\ub4dc \uc694\uccad\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc6b4\ub85c\ub4dc\uac00 \uc644\ub8cc\ub418\uba74 \uc7ac\uc0dd \ubc84\ud2bc\uc774 \ud65c\uc131\ud654\ub429\ub2c8\ub2e4. (\ub2e4\uc6b4\ub85c\ub4dc \uc694\uccad \ud6c4, \uc644\ub8cc\uae4c\uc9c0 1~2\uc77c\uc774 \uc18c\uc694\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.) < \uc774\ubca4\ud2b8 \uc601\uc0c1 \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \uc7ac\uc0dd >","title":"2. Video Download"},{"location":"group_monitoring/","text":"1. Group Monitoring \u00b6 Group Monitoring \uc5d0\uc11c\ub294 \ub3cc\ubd04\uc13c\ud130\ubcc4, \ubc1c\uc0dd\ud55c \uc774\ubca4\ud2b8\ubcc4\ub85c \ud1b5\uacc4 \ub370\uc774\ud130\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc81c\uacf5\ud558\ub294 \uae30\ub2a5 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ucd5c\uadfc 1\uac1c\uc6d4 \uac04 \ub3cc\ubd04\uc13c\ud130 \ud604\ud669 \ud1b5\uacc4 \uae30\uac04 \uc77c\ubcc4, \uc8fc\ubcc4, \uc6d4\ubcc4 \uc124\uc815 \uac00\ub2a5 \ub3cc\ubd04\uc13c\ud130/\uc774\ubca4\ud2b8\ubcc4 \uc218\uc9d1 \ud1b5\uacc4 Total Events: AI\uac00 \uc778\uc9c0\ud55c \uc804\uccb4 \uc774\ubca4\ud2b8 AI-Assist Add: AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8\uac00 \ub9de\uc740 \uacbd\uc6b0 AI-Human Edit: AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8\uac00 \ud2c0\ub9b0 \uacbd\uc6b0 Human Add: \uc774\ubca4\ud2b8\uac00 \ubc1c\uc0dd\ud558\uc600\uc73c\ub098 AI\uac00 \ub193\uce5c \uacbd\uc6b0 Dev. Event: \uc624\ud0d0, \ubbf8\ud0d0 \ub4f1 \uc704\uc5d0 \ud574\ub2f9\ud558\uc9c0 \uc54a\ub294 \uacbd\uc6b0 < Group Monitoring >","title":"Group Monitoring"},{"location":"group_monitoring/#1-group-monitoring","text":"Group Monitoring \uc5d0\uc11c\ub294 \ub3cc\ubd04\uc13c\ud130\ubcc4, \ubc1c\uc0dd\ud55c \uc774\ubca4\ud2b8\ubcc4\ub85c \ud1b5\uacc4 \ub370\uc774\ud130\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc81c\uacf5\ud558\ub294 \uae30\ub2a5 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ucd5c\uadfc 1\uac1c\uc6d4 \uac04 \ub3cc\ubd04\uc13c\ud130 \ud604\ud669 \ud1b5\uacc4 \uae30\uac04 \uc77c\ubcc4, \uc8fc\ubcc4, \uc6d4\ubcc4 \uc124\uc815 \uac00\ub2a5 \ub3cc\ubd04\uc13c\ud130/\uc774\ubca4\ud2b8\ubcc4 \uc218\uc9d1 \ud1b5\uacc4 Total Events: AI\uac00 \uc778\uc9c0\ud55c \uc804\uccb4 \uc774\ubca4\ud2b8 AI-Assist Add: AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8\uac00 \ub9de\uc740 \uacbd\uc6b0 AI-Human Edit: AI\uac00 \uc778\uc9c0\ud55c \uc774\ubca4\ud2b8\uac00 \ud2c0\ub9b0 \uacbd\uc6b0 Human Add: \uc774\ubca4\ud2b8\uac00 \ubc1c\uc0dd\ud558\uc600\uc73c\ub098 AI\uac00 \ub193\uce5c \uacbd\uc6b0 Dev. Event: \uc624\ud0d0, \ubbf8\ud0d0 \ub4f1 \uc704\uc5d0 \ud574\ub2f9\ud558\uc9c0 \uc54a\ub294 \uacbd\uc6b0 < Group Monitoring >","title":"1. Group Monitoring"},{"location":"intro_developmental_disorder/","text":"1. \ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130 \u00b6 MetaVision \uc758 Data Agent \ub294 Vision AI\uac00 \uc801\uc6a9\ub41c \uc11c\ube44\uc2a4\ub85c\ubd80\ud130 \uc0dd\uc131\ub41c \uc601\uc0c1/\uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc640 \uc774\ub85c\ubd80\ud130 \ucd94\ucd9c\ub41c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uad00\ub9ac\ud558\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\ub798\ub294 '\ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130'\uc5d0 \uad6c\ucd95\ub41c AI \uc11c\ube44\uc2a4\uc758 \ub85c\uadf8 \ubc0f \uc601\uc0c1 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uad00\ub9ac\ud558\ub294 \ud50c\ub7ab\ud3fc\uc758 \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. \uc8fc\uc694 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130\uc5d0 \uc124\uce58\ub41c AI Camera \ub610\ub294 AI Box\ub85c \ubd80\ud130 \uc0dd\uc131\ub418\ub294 \uc601\uc0c1 \ubc0f \uba54\ud0c0\ub370\uc774\ud130 \uc800\uc7a5 \uc8fc\uae30\uc801\uc73c\ub85c \uc13c\ud130\ub85c\ubd80\ud130 \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec MetaVision \uc758 Data Agent \uc5d0 \uc800\uc7a5 \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc0ac\uc804 \uc815\uc758\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \uc601\uc0c1\uc744 \uc218\uc9d1 \uc0ac\uc6a9\uc790\uac00 \uc784\uc758\ub85c \uc9c0\uc815\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc5d0 \ub300\ud574\uc11c\ub3c4 \uc601\uc0c1 \uc800\uc7a5 \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc640 \uc601\uc0c1\uc740 ML Workflow\ub97c \uc218\ud589\ud558\uc5ec \uc9c0\uc815\ub41c Datasets \uc640 Files \uc5d0 \uc800\uc7a5 \uc800\uc7a5\ub41c \uc601\uc0c1\uc5d0 \ub300\ud574 \uac80\uc0c9 \uc11c\ube44\uc2a4 \uc81c\uacf5 < \ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130 \uc11c\ube44\uc2a4 \ub370\uc774\ud130 \uc218\uc9d1 \uc544\ud0a4\ud14d\ucc98 >","title":"Introduction"},{"location":"intro_developmental_disorder/#1-ai","text":"MetaVision \uc758 Data Agent \ub294 Vision AI\uac00 \uc801\uc6a9\ub41c \uc11c\ube44\uc2a4\ub85c\ubd80\ud130 \uc0dd\uc131\ub41c \uc601\uc0c1/\uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc640 \uc774\ub85c\ubd80\ud130 \ucd94\ucd9c\ub41c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uad00\ub9ac\ud558\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\ub798\ub294 '\ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130'\uc5d0 \uad6c\ucd95\ub41c AI \uc11c\ube44\uc2a4\uc758 \ub85c\uadf8 \ubc0f \uc601\uc0c1 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uad00\ub9ac\ud558\ub294 \ud50c\ub7ab\ud3fc\uc758 \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. \uc8fc\uc694 \uae30\ub2a5\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. \ub3cc\ubd04\uc13c\ud130\uc5d0 \uc124\uce58\ub41c AI Camera \ub610\ub294 AI Box\ub85c \ubd80\ud130 \uc0dd\uc131\ub418\ub294 \uc601\uc0c1 \ubc0f \uba54\ud0c0\ub370\uc774\ud130 \uc800\uc7a5 \uc8fc\uae30\uc801\uc73c\ub85c \uc13c\ud130\ub85c\ubd80\ud130 \uba54\ud0c0\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec MetaVision \uc758 Data Agent \uc5d0 \uc800\uc7a5 \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc0ac\uc804 \uc815\uc758\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \uc601\uc0c1\uc744 \uc218\uc9d1 \uc0ac\uc6a9\uc790\uac00 \uc784\uc758\ub85c \uc9c0\uc815\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc5d0 \ub300\ud574\uc11c\ub3c4 \uc601\uc0c1 \uc800\uc7a5 \uc218\uc9d1\ud55c \uba54\ud0c0\ub370\uc774\ud130\uc640 \uc601\uc0c1\uc740 ML Workflow\ub97c \uc218\ud589\ud558\uc5ec \uc9c0\uc815\ub41c Datasets \uc640 Files \uc5d0 \uc800\uc7a5 \uc800\uc7a5\ub41c \uc601\uc0c1\uc5d0 \ub300\ud574 \uac80\uc0c9 \uc11c\ube44\uc2a4 \uc81c\uacf5 < \ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130 \uc11c\ube44\uc2a4 \ub370\uc774\ud130 \uc218\uc9d1 \uc544\ud0a4\ud14d\ucc98 >","title":"1. \ubc1c\ub2ec\uc7a5\uc560 AI \ub3cc\ubd04\uc13c\ud130"},{"location":"login/","text":"Register & Sign in \u00b6 MetaVision2\uc758 \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c\uc774 \uad00\ub9ac\ub418\uba70, \ud574\ub2f9 \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\uc790\ub97c \ucd94\uac00 \ubc0f \uc0ad\uc81c\ub97c \ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc811\uadfc\uc744 \uc81c\ud55c\ud569\ub2c8\ub2e4. \ucd5c\ucd08 \uc0ac\uc6a9 \uc2dc Sign in \ud654\uba74\uc5d0\uc11c 'register'\ub97c \uc120\ud0dd\ud558\uc5ec \uc0ac\uc6a9\uc790\ub97c \ub4f1\ub85d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uc0ac\uc6a9\uc790 \ub4f1\ub85d > \uc0ac\uc6a9\uc790 \ub4f1\ub85d \ud6c4 \ub85c\uadf8\uc778 \ud558\uc5ec \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131 \ubc0f \uacf5\uc720\ubc1b\uc740 \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub85c\uadf8\uc778 >","title":"Register & Sign in"},{"location":"login/#register-sign-in","text":"MetaVision2\uc758 \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c\uc774 \uad00\ub9ac\ub418\uba70, \ud574\ub2f9 \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\uc790\ub97c \ucd94\uac00 \ubc0f \uc0ad\uc81c\ub97c \ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc811\uadfc\uc744 \uc81c\ud55c\ud569\ub2c8\ub2e4. \ucd5c\ucd08 \uc0ac\uc6a9 \uc2dc Sign in \ud654\uba74\uc5d0\uc11c 'register'\ub97c \uc120\ud0dd\ud558\uc5ec \uc0ac\uc6a9\uc790\ub97c \ub4f1\ub85d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uc0ac\uc6a9\uc790 \ub4f1\ub85d > \uc0ac\uc6a9\uc790 \ub4f1\ub85d \ud6c4 \ub85c\uadf8\uc778 \ud558\uc5ec \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131 \ubc0f \uacf5\uc720\ubc1b\uc740 \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub85c\uadf8\uc778 >","title":"Register & Sign in"},{"location":"object_search/","text":"1. Object Search \u00b6 Object Search \ub294 AI\uac00 \uc778\uc9c0\ud55c \uc0ac\ub78c \ubc0f \uc790\ub3d9\ucc28\uc758 \ud2b9\uc131\uc744 \uae30\ubc18\uc73c\ub85c \uac80\uc0c9\uc744 \ud560 \uc218 \uc788\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\uc6b8\ub7ec Semantic Search \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \uac80\uc0c9\uc744 \ub354\uc6b1 \uc815\uad50\ud558\uac8c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac80\uc0c9 \uac00\ub2a5\ud55c \uac1d\uccb4\uc758 \ud2b9\uc131\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc0ac\ub78c \uc131\ubcc4 \ub098\uc774 \uc18c\ub9e4 \uae38\uc774 \ubc14\uc9c0 \uae38\uc774 \uba38\ub9ac \uae38\uc774 \ubaa8\uc790 \ucc29\uc6a9 \uc5ec\ubd80 \uac00\ubc29 \uc18c\uc9c0 \uc5ec\ubd80 \ubc31\ud329 \uc18c\uc9c0 \uc5ec\ubd80 \uc0c1\uc758 \uc0c9\uc0c1 \ud558\uc758 \uc0c9\uc0c1 \uc790\ub3d9\ucc28 \uc0c9\uc0c1 \ucc28\uc885 \uc774\ub95c\ucc28 \ucc28\uc885 < Object Search \uac80\uc0c9 \uc635\uc158 > < Object Search \uac80\uc0c9 \uacb0\uacfc > 2. \uac80\uc0c9 \uacb0\uacfc \uc0c1\uc138 \ubcf4\uae30 \u00b6 \uac80\uc0c9 \uacb0\uacfc\ub294 3 \uac00\uc9c0 \ud615\ud0dc\ub85c \uc81c\uacf5\ub429\ub2c8\ub2e4. \uac1d\uccb4\uac00 \ud3ec\ud568\ub41c \uc601\uc0c1 \ud504\ub808\uc784 \uc774\ubbf8\uc9c0 \uac1d\uccb4\uac00 \ud3ec\ud568\ub41c \uc6d0\ubcf8 \uc601\uc0c1","title":"Object Search"},{"location":"object_search/#1-object-search","text":"Object Search \ub294 AI\uac00 \uc778\uc9c0\ud55c \uc0ac\ub78c \ubc0f \uc790\ub3d9\ucc28\uc758 \ud2b9\uc131\uc744 \uae30\ubc18\uc73c\ub85c \uac80\uc0c9\uc744 \ud560 \uc218 \uc788\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc544\uc6b8\ub7ec Semantic Search \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \uac80\uc0c9\uc744 \ub354\uc6b1 \uc815\uad50\ud558\uac8c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac80\uc0c9 \uac00\ub2a5\ud55c \uac1d\uccb4\uc758 \ud2b9\uc131\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc0ac\ub78c \uc131\ubcc4 \ub098\uc774 \uc18c\ub9e4 \uae38\uc774 \ubc14\uc9c0 \uae38\uc774 \uba38\ub9ac \uae38\uc774 \ubaa8\uc790 \ucc29\uc6a9 \uc5ec\ubd80 \uac00\ubc29 \uc18c\uc9c0 \uc5ec\ubd80 \ubc31\ud329 \uc18c\uc9c0 \uc5ec\ubd80 \uc0c1\uc758 \uc0c9\uc0c1 \ud558\uc758 \uc0c9\uc0c1 \uc790\ub3d9\ucc28 \uc0c9\uc0c1 \ucc28\uc885 \uc774\ub95c\ucc28 \ucc28\uc885 < Object Search \uac80\uc0c9 \uc635\uc158 > < Object Search \uac80\uc0c9 \uacb0\uacfc >","title":"1. Object Search"},{"location":"object_search/#2","text":"\uac80\uc0c9 \uacb0\uacfc\ub294 3 \uac00\uc9c0 \ud615\ud0dc\ub85c \uc81c\uacf5\ub429\ub2c8\ub2e4. \uac1d\uccb4\uac00 \ud3ec\ud568\ub41c \uc601\uc0c1 \ud504\ub808\uc784 \uc774\ubbf8\uc9c0 \uac1d\uccb4\uac00 \ud3ec\ud568\ub41c \uc6d0\ubcf8 \uc601\uc0c1","title":"2. \uac80\uc0c9 \uacb0\uacfc \uc0c1\uc138 \ubcf4\uae30"},{"location":"project/","text":"Create Project \u00b6 \ub85c\uadf8\uc778 \ud6c4 \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131\uc744 \ud1b5\ud574 Object Storage\uc5d0 \uc800\uc7a5 \uacf5\uac04\uacfc \uba54\ud0c0\ub370\uc774\ud130\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\ub294 Database\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc88c\uce21 'PROJECT' \uba54\ub274\ub97c \ud1b5\ud574 \uc0dd\uc131\ud55c \ud504\ub85c\uc81d\ud2b8 \ubaa9\ub85d\uc744 \ud655\uc778\ud560 \uc218 \uc788\uace0, \ud558\ub2e8 New project \ub97c \ud1b5\ud574 \uc2e0\uaddc \ud504\ub85c\uc81d\ud2b8\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ud504\ub85c\uc81d\ud2b8 \ubaa9\ub85d \ubc0f \uc0dd\uc131 >","title":"Projects"},{"location":"project/#create-project","text":"\ub85c\uadf8\uc778 \ud6c4 \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131\uc744 \ud1b5\ud574 Object Storage\uc5d0 \uc800\uc7a5 \uacf5\uac04\uacfc \uba54\ud0c0\ub370\uc774\ud130\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\ub294 Database\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc88c\uce21 'PROJECT' \uba54\ub274\ub97c \ud1b5\ud574 \uc0dd\uc131\ud55c \ud504\ub85c\uc81d\ud2b8 \ubaa9\ub85d\uc744 \ud655\uc778\ud560 \uc218 \uc788\uace0, \ud558\ub2e8 New project \ub97c \ud1b5\ud574 \uc2e0\uaddc \ud504\ub85c\uc81d\ud2b8\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ud504\ub85c\uc81d\ud2b8 \ubaa9\ub85d \ubc0f \uc0dd\uc131 >","title":"Create Project"},{"location":"semantic_search/","text":"1. Semantic Search \u00b6 Semantic Search \uc5d0\uc11c \ud0a4\uc6cc\ub4dc \ub610\ub294 \ubb38\uc7a5\uc744 \uc785\ub825\ubc1b\uc73c\uba74, \ud574\ub2f9 \ud14d\uc2a4\ud2b8\uc640 \uc758\ubbf8\uac00 \uc720\uc0ac\ud55c \uc774\ubbf8\uc9c0\ub97c \uac80\uc0c9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \uac80\uc0c9\ub41c \uc774\ubbf8\uc9c0\ub098 \uc601\uc0c1\uc740 \uc7ac\uc0dd \ub610\ub294 \ub2e4\uc6b4\ub85c\ub4dc\ub97c \ud560 \uc218 \uc788\uc73c\uba70, \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud0a4\uc6cc\ub4dc/\ubb38\uc7a5 \uc678\uc5d0 \uc81c\uacf5\ub418\ub294 \uac80\uc0c9 \uc635\uc158\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. - Target DB(\uc800\uc7a5\uc18c) - \ub0a0\uc9dc - \uc774\ubbf8\uc9c0 \uc18d \uac1d\uccb4 Class - \uc720\uc0ac \uc774\ubbf8\uc9c0 \uc81c\uac70 \uc635\uc158 - \uac80\uc0c9 \uacb0\uacfc \uc218(\uac00\uc7a5 \uc720\uc0ac\ud55c k \uac1c \ubc18\ud658) < Semantic Search \uac80\uc0c9 \ud654\uba74 > < Semantic Search \uac80\uc0c9 \uacb0\uacfc > < \uc774\ubbf8\uc9c0/\uc601\uc0c1 \uc0c1\uc138 \ubcf4\uae30 >","title":"UI Scenario"},{"location":"semantic_search/#1-semantic-search","text":"Semantic Search \uc5d0\uc11c \ud0a4\uc6cc\ub4dc \ub610\ub294 \ubb38\uc7a5\uc744 \uc785\ub825\ubc1b\uc73c\uba74, \ud574\ub2f9 \ud14d\uc2a4\ud2b8\uc640 \uc758\ubbf8\uac00 \uc720\uc0ac\ud55c \uc774\ubbf8\uc9c0\ub97c \uac80\uc0c9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \uac80\uc0c9\ub41c \uc774\ubbf8\uc9c0\ub098 \uc601\uc0c1\uc740 \uc7ac\uc0dd \ub610\ub294 \ub2e4\uc6b4\ub85c\ub4dc\ub97c \ud560 \uc218 \uc788\uc73c\uba70, \ub370\uc774\ud130\uc14b\uc5d0 \ucd94\uac00\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud0a4\uc6cc\ub4dc/\ubb38\uc7a5 \uc678\uc5d0 \uc81c\uacf5\ub418\ub294 \uac80\uc0c9 \uc635\uc158\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. - Target DB(\uc800\uc7a5\uc18c) - \ub0a0\uc9dc - \uc774\ubbf8\uc9c0 \uc18d \uac1d\uccb4 Class - \uc720\uc0ac \uc774\ubbf8\uc9c0 \uc81c\uac70 \uc635\uc158 - \uac80\uc0c9 \uacb0\uacfc \uc218(\uac00\uc7a5 \uc720\uc0ac\ud55c k \uac1c \ubc18\ud658) < Semantic Search \uac80\uc0c9 \ud654\uba74 > < Semantic Search \uac80\uc0c9 \uacb0\uacfc > < \uc774\ubbf8\uc9c0/\uc601\uc0c1 \uc0c1\uc138 \ubcf4\uae30 >","title":"1. Semantic Search"},{"location":"semantic_search_archi/","text":"1. Main Features for Semantic Search \u00b6 1) Contrastive Language-Image Pre-training(CLIP) Model \u00b6 \u201821 OpenAI, \u2018\uc790\uc5f0\uc5b4 \uc9c0\ub3c4\ub97c \ud1b5\ud574 \uc2dc\uac01\uc801 \uac1c\ub150\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\u2019 \uc774\ubbf8\uc9c0\uc640 \ud14d\uc2a4\ud2b8 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uac19\uc740 vector space\uc5d0 \ud45c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ud559\uc2b5 OpenCLIP Model LAION, \uc218\uc2ed\uc5b5 \uc30d\uc758 \ub370\uc774\ud130\uc14b\uc73c\ub85c CLIP model\uc744 \ud559\uc2b5 Procedure Image Embedding \uc0dd\uc131 \ubc0f DB \uc800\uc7a5 Text Embedding \uc2e4\uc2dc\uac04 \uc0dd\uc131 Similarity Search(Image Embedding, Text Embedding) < Contrastive pre-training (from the CLIP blog post) > 2) Vector Similarity Search \u00b6 Exact k-Nearest Neighbors(kNN) \uc804\uccb4 \ub370\uc774\ud130\uc758 Embedding\uacfc distance\ub97c \uacc4\uc0b0\ud558\uc5ec \uac00\uc7a5 \uadfc\uc811\ud55c top k \uac1c\ub97c \uac80\uc0c9 \uf0e8 \ub370\uc774\ud130\uac00 \ub9ce\uc544\uc9c8\uc218\ub85d \uc131\ub2a5 \uc800\ud558 Approximate Nearest Neighbor(ANN) \uc720\uc0ac\ub3c4 \uc778\ub371\uc2f1 \uc0dd\uc131 \ud6c4, \uac00\uc7a5 \uadfc\uc811\ud55c \uc774\uc6c3\uc744 \ub2e8\uacc4\uc801\uc73c\ub85c \ud0d0\uc0c9\ud558\uba74\uc11c \ube60\ub974\uac8c top k \uac1c\ub97c \uac80\uc0c9 kNN \ub300\ube44 \uc0c1\ub300\uc801\uc73c\ub85c \uc815\ud655\ub3c4\uac00 \uc57d\uac04 \ub0ae\uc9c0\ub9cc, \uc131\ub2a5\uc774 \ub354 \ub6f0\uc5b4\ub0a8 \uf0e8 \ub300\uc6a9\ub7c9 \ub370\uc774\ud130 \uac80\uc0c9\uc5d0 \uc801\ud569 3) Lightning DB \u00b6 SKT\uc5d0\uc11c \uc790\uccb4 \uac1c\ubc1c\ud55c \uc778\uba54\ubaa8\ub9ac \ube45\ub370\uc774\ud130 \ub370\uc774\ud130\ubca0\uc774\uc2a4 KNN/ANN Search\ub97c DB\uc5d0\uc11c \ubd84\uc0b0 \ubc0f \uac00\uc18d \ucc98\ub9ac https://docs.lightningdb.io < Lightning DB > 2. Semantic Search \u00b6 MetaVision \uc5d0 \uc218\uc9d1\ub41c \uc601\uc0c1/\uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c image embedding \ucd94\ucd9c \uc0ac\uc6a9\uc790\uac00 \ud0a4\uc6cc\ub4dc \ub610\ub294 \ubb38\uc7a5\uc744 \uc785\ub825\ud558\uba74 \uc774\uc5d0 \ub300\ud55c text embedding \ucd94\ucd9c Lightning DB\uc5d0\uc11c \ucd94\ucd9c\ud55c text embedding\uacfc \uac00\uc7a5 \uc720\uc0ac\ud55c image embedding \ucd94\ucd9c (KNN/ANN Search \uc0ac\uc6a9) < System Architecture of Semantic Search >","title":"System Architecture"},{"location":"semantic_search_archi/#1-main-features-for-semantic-search","text":"","title":"1. Main Features for Semantic Search"},{"location":"semantic_search_archi/#1-contrastive-language-image-pre-trainingclip-model","text":"\u201821 OpenAI, \u2018\uc790\uc5f0\uc5b4 \uc9c0\ub3c4\ub97c \ud1b5\ud574 \uc2dc\uac01\uc801 \uac1c\ub150\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\u2019 \uc774\ubbf8\uc9c0\uc640 \ud14d\uc2a4\ud2b8 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uac19\uc740 vector space\uc5d0 \ud45c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ud559\uc2b5 OpenCLIP Model LAION, \uc218\uc2ed\uc5b5 \uc30d\uc758 \ub370\uc774\ud130\uc14b\uc73c\ub85c CLIP model\uc744 \ud559\uc2b5 Procedure Image Embedding \uc0dd\uc131 \ubc0f DB \uc800\uc7a5 Text Embedding \uc2e4\uc2dc\uac04 \uc0dd\uc131 Similarity Search(Image Embedding, Text Embedding) < Contrastive pre-training (from the CLIP blog post) >","title":"1) Contrastive Language-Image Pre-training(CLIP) Model"},{"location":"semantic_search_archi/#2-vector-similarity-search","text":"Exact k-Nearest Neighbors(kNN) \uc804\uccb4 \ub370\uc774\ud130\uc758 Embedding\uacfc distance\ub97c \uacc4\uc0b0\ud558\uc5ec \uac00\uc7a5 \uadfc\uc811\ud55c top k \uac1c\ub97c \uac80\uc0c9 \uf0e8 \ub370\uc774\ud130\uac00 \ub9ce\uc544\uc9c8\uc218\ub85d \uc131\ub2a5 \uc800\ud558 Approximate Nearest Neighbor(ANN) \uc720\uc0ac\ub3c4 \uc778\ub371\uc2f1 \uc0dd\uc131 \ud6c4, \uac00\uc7a5 \uadfc\uc811\ud55c \uc774\uc6c3\uc744 \ub2e8\uacc4\uc801\uc73c\ub85c \ud0d0\uc0c9\ud558\uba74\uc11c \ube60\ub974\uac8c top k \uac1c\ub97c \uac80\uc0c9 kNN \ub300\ube44 \uc0c1\ub300\uc801\uc73c\ub85c \uc815\ud655\ub3c4\uac00 \uc57d\uac04 \ub0ae\uc9c0\ub9cc, \uc131\ub2a5\uc774 \ub354 \ub6f0\uc5b4\ub0a8 \uf0e8 \ub300\uc6a9\ub7c9 \ub370\uc774\ud130 \uac80\uc0c9\uc5d0 \uc801\ud569","title":"2) Vector Similarity Search"},{"location":"semantic_search_archi/#3-lightning-db","text":"SKT\uc5d0\uc11c \uc790\uccb4 \uac1c\ubc1c\ud55c \uc778\uba54\ubaa8\ub9ac \ube45\ub370\uc774\ud130 \ub370\uc774\ud130\ubca0\uc774\uc2a4 KNN/ANN Search\ub97c DB\uc5d0\uc11c \ubd84\uc0b0 \ubc0f \uac00\uc18d \ucc98\ub9ac https://docs.lightningdb.io < Lightning DB >","title":"3) Lightning DB"},{"location":"semantic_search_archi/#2-semantic-search","text":"MetaVision \uc5d0 \uc218\uc9d1\ub41c \uc601\uc0c1/\uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c image embedding \ucd94\ucd9c \uc0ac\uc6a9\uc790\uac00 \ud0a4\uc6cc\ub4dc \ub610\ub294 \ubb38\uc7a5\uc744 \uc785\ub825\ud558\uba74 \uc774\uc5d0 \ub300\ud55c text embedding \ucd94\ucd9c Lightning DB\uc5d0\uc11c \ucd94\ucd9c\ud55c text embedding\uacfc \uac00\uc7a5 \uc720\uc0ac\ud55c image embedding \ucd94\ucd9c (KNN/ANN Search \uc0ac\uc6a9) < System Architecture of Semantic Search >","title":"2. Semantic Search"},{"location":"signin/","text":"\uc0ac\uc6a9\uc790 \ub4f1\ub85d \uc0ac\uc6a9\uc790 \ub4f1\ub85d\uc740 \uad00\ub9ac\uc790\uc5d0\uac8c \uc5f0\ub77d\ubc14\ub78d\ub2c8\ub2e4. \uad00\ub9ac\uc790: \uae40\uc131\ud638(sungho2.kim@sk.com) 1. Sign in \u00b6 MetaVision\uc758 \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c\uc774 \uad00\ub9ac\ub418\uba70, \uc0ac\uc6a9\uc790 \uc778\uc99d\uc744 \ud1b5\ud574 \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc6a9\uc790 \ub4f1\ub85d \ud6c4 \ub85c\uadf8\uc778 \ud558\uc5ec \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131 \ubc0f \uacf5\uc720\ubc1b\uc740 \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub85c\uadf8\uc778 >","title":"Register & Sign in"},{"location":"signin/#1-sign-in","text":"MetaVision\uc758 \ubaa8\ub4e0 \ub370\uc774\ud130\ub294 \ud504\ub85c\uc81d\ud2b8\ubcc4\ub85c \uc811\uadfc \uad8c\ud55c\uc774 \uad00\ub9ac\ub418\uba70, \uc0ac\uc6a9\uc790 \uc778\uc99d\uc744 \ud1b5\ud574 \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc6a9\uc790 \ub4f1\ub85d \ud6c4 \ub85c\uadf8\uc778 \ud558\uc5ec \ud504\ub85c\uc81d\ud2b8 \uc0dd\uc131 \ubc0f \uacf5\uc720\ubc1b\uc740 \ub370\uc774\ud130\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ub85c\uadf8\uc778 >","title":"1. Sign in"},{"location":"similar_search/","text":"1. Similar Search \u00b6 Event Search \uc640 Object Search \uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uc0c1\uc138 \ubcf4\uae30 \uc758 Original \uc5d0\uc11c\ub294 \uc6d0\ubcf8 \uc601\uc0c1\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc601\uc0c1 \uc7ac\uc0dd \uc911 \ud654\uba74\uc5d0 \ub9c8\uc6b0\uc2a4\ub97c \ub4dc\ub798\uadf8(Drag)\ud558\uc5ec \uac1d\uccb4\ub97c \uc120\ud0dd\ud558\uba74 \uc774\uc640 \uc720\uc0ac\ud55c \uac1d\uccb4\ub97c \uac00\uc9c4 \uc774\ubbf8\uc9c0 \ub610\ub294 \uc601\uc0c1\uc744 \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < Similar Search \uac80\uc0c9 \ud654\uba74 > < Similar Search \uac80\uc0c9 \uacb0\uacfc > \uac80\uc0c9 \uacb0\uacfc\ub85c \ub098\uc628 \uac1d\uccb4 \uc774\ubbf8\uc9c0\uc758 \uc6b0\uce21 \uc0c1\ub2e8 \uc544\uc774\ucf58\uc744 \uc120\ud0dd\ud558\uba74 \uc774\uc640 \uc720\uc0ac\ud55c \uac1d\uccb4\ub97c \ucd94\uac00\ub85c \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ucd94\uac00 \uc720\uc0ac \uac1d\uccb4 \uac80\uc0c9 \uacb0\uacfc > \uac1d\uccb4 \uac80\uc0c9 \uacb0\uacfc \uc774\ubbf8\uc9c0\ub97c \uc120\ud0dd\ud558\uba74 \ud574\ub2f9 \uac1d\uccb4\uac00 \uc788\ub294 \uc774\ubbf8\uc9c0\uc640 \uc6d0\ubcf8 \uc601\uc0c1\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uac80\uc0c9\ud55c \uac1d\uccb4\uc758 \uc0c1\uc138 \ubcf4\uae30 >","title":"Similar Search"},{"location":"similar_search/#1-similar-search","text":"Event Search \uc640 Object Search \uc5d0\uc11c \uc81c\uacf5\ub418\ub294 \uc0c1\uc138 \ubcf4\uae30 \uc758 Original \uc5d0\uc11c\ub294 \uc6d0\ubcf8 \uc601\uc0c1\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc601\uc0c1 \uc7ac\uc0dd \uc911 \ud654\uba74\uc5d0 \ub9c8\uc6b0\uc2a4\ub97c \ub4dc\ub798\uadf8(Drag)\ud558\uc5ec \uac1d\uccb4\ub97c \uc120\ud0dd\ud558\uba74 \uc774\uc640 \uc720\uc0ac\ud55c \uac1d\uccb4\ub97c \uac00\uc9c4 \uc774\ubbf8\uc9c0 \ub610\ub294 \uc601\uc0c1\uc744 \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < Similar Search \uac80\uc0c9 \ud654\uba74 > < Similar Search \uac80\uc0c9 \uacb0\uacfc > \uac80\uc0c9 \uacb0\uacfc\ub85c \ub098\uc628 \uac1d\uccb4 \uc774\ubbf8\uc9c0\uc758 \uc6b0\uce21 \uc0c1\ub2e8 \uc544\uc774\ucf58\uc744 \uc120\ud0dd\ud558\uba74 \uc774\uc640 \uc720\uc0ac\ud55c \uac1d\uccb4\ub97c \ucd94\uac00\ub85c \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \ucd94\uac00 \uc720\uc0ac \uac1d\uccb4 \uac80\uc0c9 \uacb0\uacfc > \uac1d\uccb4 \uac80\uc0c9 \uacb0\uacfc \uc774\ubbf8\uc9c0\ub97c \uc120\ud0dd\ud558\uba74 \ud574\ub2f9 \uac1d\uccb4\uac00 \uc788\ub294 \uc774\ubbf8\uc9c0\uc640 \uc6d0\ubcf8 \uc601\uc0c1\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < \uac80\uc0c9\ud55c \uac1d\uccb4\uc758 \uc0c1\uc138 \ubcf4\uae30 >","title":"1. Similar Search"},{"location":"workflow_event/","text":"Workflow \u00b6 Workflow \ub294 ML inference\ub97c \uc218\ud589\ud560 \ucf54\ub4dc\ub97c Docker \uc774\ubbf8\uc9c0\ub85c \uc0dd\uc131\ud558\uace0 \ub4f1\ub85d\ud558\uc5ec, \uc774\ud6c4 \ub4f1\ub85d\ud55c event\uc5d0 \uc758\ud574 \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. ML Workflow\uc5d0 \uc0ac\uc6a9\ud560 \uc774\ubbf8\uc9c0 \uc0dd\uc131\ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uac00\uacf5 \ubc0f ML \uc218\ud589 \ucf54\ub4dc \uc791\uc131 Docker \uc774\ubbf8\uc9c0\ub85c \ube4c\ub4dc \ube4c\ub4dc\ud55c \uc774\ubbf8\uc9c0\ub97c \uac00\uc9c0\uace0 Workflow \ub4f1\ub85d < Workflow \ub4f1\ub85d > Event \u00b6 Event \ub294 \ub4f1\ub85d\ud55c Workflow\ub97c \uc218\ud589\ud560 \uc870\uac74\uc744 \ucd94\uac00\ud558\uc5ec \uc6d0\ud558\ub294 \ud30c\uc77c\uc5d0 \ub300\ud574 Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. Object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ud30c\uc77c\uc758 'Prefix'(\ud30c\uc77c \uacbd\ub85c)\uc640 'Suffix'(\ud655\uc7a5\uc790) \uc815\ubcf4\ub97c \uc774\uc6a9\ud558\uc5ec \uc870\uac74\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < Event \ub4f1\ub85d > Run Workflow \u00b6 \ub4f1\ub85d\ud55c Event\ub97c \uc774\uc6a9\ud55c \ubc29\ubc95 \uc678\uc5d0\ub3c4, \uc774\ubbf8 \ubcf4\uc720\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc120\ud0dd\uc801\uc73c\ub85c \ub4f1\ub85d\ud55c Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. File explore\uc5d0\uc11c \ud30c\uc77c \uc120\ud0dd \ud6c4 'Run workflow'\ub97c \uc120\ud0dd\ud558\uace0 \uc6d0\ud558\ub294 Workflow \uc774\ubbf8\uc9c0\ub97c \uc120\ud0dd\ud558\uba74 \ub429\ub2c8\ub2e4. < \uc120\ud0dd\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 Workflow \uc2e4\ud589 >","title":"Workflow"},{"location":"workflow_event/#workflow","text":"Workflow \ub294 ML inference\ub97c \uc218\ud589\ud560 \ucf54\ub4dc\ub97c Docker \uc774\ubbf8\uc9c0\ub85c \uc0dd\uc131\ud558\uace0 \ub4f1\ub85d\ud558\uc5ec, \uc774\ud6c4 \ub4f1\ub85d\ud55c event\uc5d0 \uc758\ud574 \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. ML Workflow\uc5d0 \uc0ac\uc6a9\ud560 \uc774\ubbf8\uc9c0 \uc0dd\uc131\ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uac00\uacf5 \ubc0f ML \uc218\ud589 \ucf54\ub4dc \uc791\uc131 Docker \uc774\ubbf8\uc9c0\ub85c \ube4c\ub4dc \ube4c\ub4dc\ud55c \uc774\ubbf8\uc9c0\ub97c \uac00\uc9c0\uace0 Workflow \ub4f1\ub85d < Workflow \ub4f1\ub85d >","title":"Workflow"},{"location":"workflow_event/#event","text":"Event \ub294 \ub4f1\ub85d\ud55c Workflow\ub97c \uc218\ud589\ud560 \uc870\uac74\uc744 \ucd94\uac00\ud558\uc5ec \uc6d0\ud558\ub294 \ud30c\uc77c\uc5d0 \ub300\ud574 Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. Object storage\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ud30c\uc77c\uc758 'Prefix'(\ud30c\uc77c \uacbd\ub85c)\uc640 'Suffix'(\ud655\uc7a5\uc790) \uc815\ubcf4\ub97c \uc774\uc6a9\ud558\uc5ec \uc870\uac74\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. < Event \ub4f1\ub85d >","title":"Event"},{"location":"workflow_event/#run-workflow","text":"\ub4f1\ub85d\ud55c Event\ub97c \uc774\uc6a9\ud55c \ubc29\ubc95 \uc678\uc5d0\ub3c4, \uc774\ubbf8 \ubcf4\uc720\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 \uc120\ud0dd\uc801\uc73c\ub85c \ub4f1\ub85d\ud55c Workflow\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. File explore\uc5d0\uc11c \ud30c\uc77c \uc120\ud0dd \ud6c4 'Run workflow'\ub97c \uc120\ud0dd\ud558\uace0 \uc6d0\ud558\ub294 Workflow \uc774\ubbf8\uc9c0\ub97c \uc120\ud0dd\ud558\uba74 \ub429\ub2c8\ub2e4. < \uc120\ud0dd\ud55c \ud30c\uc77c\uc5d0 \ub300\ud574 Workflow \uc2e4\ud589 >","title":"Run Workflow"}]} \ No newline at end of file diff --git a/site/search/worker.js b/site/search/worker.js new file mode 100644 index 0000000..8628dbc --- /dev/null +++ b/site/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/site/semantic_search/index.html b/site/semantic_search/index.html new file mode 100644 index 0000000..62503bb --- /dev/null +++ b/site/semantic_search/index.html @@ -0,0 +1,187 @@ + + + + + + + + UI Scenario - MetaVision Docs + + + + + + + + + + + + + +
+ + +
+ +
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+
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+ +

1. Semantic Search

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Semantic Search에서 키워드 또는 문장을 입력받으면, 해당 텍스트와 의미가 유사한 이미지를 검색하여 제공합니다.

+

이렇게 검색된 이미지나 영상은 재생 또는 다운로드를 할 수 있으며, 데이터셋에 추가를 할 수 있습니다.

+

키워드/문장 외에 제공되는 검색 옵션은 다음과 같습니다. +- Target DB(저장소) +- 날짜 +- 이미지 속 객체 Class +- 유사 이미지 제거 옵션 +- 검색 결과 수(가장 유사한 k 개 반환)

+

MetaVision2 Dashboard +

< Semantic Search 검색 화면 >

+

MetaVision2 Dashboard +

< Semantic Search 검색 결과 >

+

MetaVision2 Dashboard +

< 이미지/영상 상세 보기 >

+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/site/semantic_search_archi/index.html b/site/semantic_search_archi/index.html new file mode 100644 index 0000000..6f01b19 --- /dev/null +++ b/site/semantic_search_archi/index.html @@ -0,0 +1,224 @@ + + + + + + + + System Architecture - MetaVision Docs + + + + + + + + + + + + + +
+ + +
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1. Main Features for Semantic Search

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1) Contrastive Language-Image Pre-training(CLIP) Model

+
    +
  • ‘21 OpenAI, ‘자연어 지도를 통해 시각적 개념을 효율적으로 학습할 수 있는 모델’
  • +
  • 이미지와 텍스트 사이의 관계를 같은 vector space에 표현할 수 있도록 학습
  • +
  • OpenCLIP Model
      +
    • LAION, 수십억 쌍의 데이터셋으로 CLIP model을 학습
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    +
  • +
  • Procedure
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    • Image Embedding 생성 및 DB 저장
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    • Text Embedding 실시간 생성
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    • Similarity Search(Image Embedding, Text Embedding)
    • +
    +
  • +
+

MetaVision2 Dashboard +

< Contrastive pre-training (from the CLIP blog post) >

+ +
    +
  • Exact k-Nearest Neighbors(kNN)
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    • 전체 데이터의 Embedding과 distance를 계산하여 가장 근접한 top k 개를 검색  데이터가 많아질수록 성능 저하
    • +
    +
  • +
  • Approximate Nearest Neighbor(ANN)
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    • 유사도 인덱싱 생성 후, 가장 근접한 이웃을 단계적으로 탐색하면서 빠르게 top k 개를 검색
    • +
    • kNN 대비 상대적으로 정확도가 약간 낮지만, 성능이 더 뛰어남  대용량 데이터 검색에 적합
    • +
    +
  • +
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3) Lightning DB

+
    +
  • SKT에서 자체 개발한 인메모리 빅데이터 데이터베이스
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  • KNN/ANN Search를 DB에서 분산 및 가속 처리
  • +
  • https://docs.lightningdb.io
  • +
+

MetaVision2 Dashboard +

< Lightning DB >

+

2. Semantic Search

+
    +
  • MetaVision에 수집된 영상/이미지에 대한 image embedding 추출
  • +
  • 사용자가 키워드 또는 문장을 입력하면 이에 대한 text embedding 추출
  • +
  • Lightning DB에서 추출한 text embedding과 가장 유사한 image embedding 추출 (KNN/ANN Search 사용)
  • +
+

MetaVision2 Dashboard +

< System Architecture of Semantic Search >

+ +
+
+ +
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+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/site/signin/index.html b/site/signin/index.html new file mode 100644 index 0000000..df9c48e --- /dev/null +++ b/site/signin/index.html @@ -0,0 +1,183 @@ + + + + + + + + Register & Sign in - MetaVision Docs + + + + + + + + + + + + + +
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사용자 등록

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사용자 등록은 관리자에게 연락바랍니다.

+
    +
  • 관리자: 김성호(sungho2.kim@sk.com)
  • +
+
+

1. Sign in

+

MetaVision의 모든 데이터는 프로젝트별로 접근 권한이 관리되며, 사용자 인증을 통해 접근할 수 있습니다.

+

사용자 등록 후 로그인 하여 프로젝트 생성 및 공유받은 데이터를 확인할 수 있습니다.

+

MetaVision2 signin +

< 로그인 >

+ +
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+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/site/similar_search/index.html b/site/similar_search/index.html new file mode 100644 index 0000000..a447911 --- /dev/null +++ b/site/similar_search/index.html @@ -0,0 +1,182 @@ + + + + + + + + Similar Search - MetaVision Docs + + + + + + + + + + + + + +
+ + +
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+
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+ +

1. Similar Search

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Event SearchObject Search에서 제공되는 상세 보기Original에서는 원본 영상을 확인할 수 있습니다.

+

여기서 영상 재생 중 화면에 마우스를 드래그(Drag)하여 객체를 선택하면 이와 유사한 객체를 가진 이미지 또는 영상을 검색할 수 있습니다.

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MetaVision2 Similar Search +

< Similar Search 검색 화면 >

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MetaVision2 Similar Search +

< Similar Search 검색 결과 >

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검색 결과로 나온 객체 이미지의 우측 상단 아이콘을 선택하면 이와 유사한 객체를 추가로 검색할 수 있습니다.

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MetaVision2 Similar Search +

< 추가 유사 객체 검색 결과 >

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객체 검색 결과 이미지를 선택하면 해당 객체가 있는 이미지와 원본 영상을 확인할 수 있습니다.

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MetaVision2 Similar Search +

< 검색한 객체의 상세 보기 >

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Workflow

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Workflow는 ML inference를 수행할 코드를 Docker 이미지로 생성하고 등록하여, 이후 등록한 event에 의해 실행할 수 있도록 합니다.

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ML Workflow에 사용할 이미지 생성방법은 다음과 같습니다.

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  • 데이터 가공 및 ML 수행 코드 작성
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  • Docker 이미지로 빌드
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  • 빌드한 이미지를 가지고 Workflow 등록
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MetaVision2 Workflow +

< Workflow 등록 >

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Event

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Event는 등록한 Workflow를 수행할 조건을 추가하여 원하는 파일에 대해 Workflow를 수행할 수 있도록 합니다.

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Object storage에 업로드하는 파일의 'Prefix'(파일 경로)와 'Suffix'(확장자) 정보를 이용하여 조건을 생성할 수 있습니다.

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MetaVision2 Event +

< Event 등록 >

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Run Workflow

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등록한 Event를 이용한 방법 외에도, 이미 보유한 파일에 대해 선택적으로 등록한 Workflow를 수행할 수 있습니다.

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File explore에서 파일 선택 후 'Run workflow'를 선택하고 원하는 Workflow 이미지를 선택하면 됩니다.

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MetaVision2 Event +

< 선택한 파일에 대해 Workflow 실행 >

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