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/*#__PURE__*/_jsx(LayoutGroup,{id:layoutId??defaultLayoutId,children:/*#__PURE__*/_jsx(Variants,{animate:variants,initial:false,children:/*#__PURE__*/_jsx(Transition,{value:transition1,children:/*#__PURE__*/_jsx(motion.div,{...restProps,...gestureHandlers,className:cx(scopingClassNames,\"framer-18ak2hv\",className,classNames),\"data-framer-name\":\"tech-blog\",layoutDependency:layoutDependency,layoutId:\"I727:3109;954:127\",ref:refBinding,style:{...style},...addPropertyOverrides({\"cAJBxMbNi-hover\":{\"data-framer-name\":undefined},\"I727:3109;954:127-hover\":{\"data-framer-name\":undefined},\"Jno4SFetp-hover\":{\"data-framer-name\":undefined},\"Pqmh_atWo-hover\":{\"data-framer-name\":undefined},cAJBxMbNi:{\"data-framer-name\":\"(en)tech-blog\"},gAzUinc9n:{\"data-framer-name\":\"(en/selected)release-note\"},Jno4SFetp:{\"data-framer-name\":\"release-note\"},jX5LForHY:{\"data-framer-name\":\"(selected)tech-blog\"},Pqmh_atWo:{\"data-framer-name\":\"(en)release-note\"},wQm_st2gK:{\"data-framer-name\":\"(selected)release-note\"},Y0O0eHjRm:{\"data-framer-name\":\"(en/selected)tech-blog\"}},baseVariant,gestureVariant),children:/*#__PURE__*/_jsx(RichText,{__fromCanvasComponent:true,children:/*#__PURE__*/_jsx(React.Fragment,{children:/*#__PURE__*/_jsx(motion.p,{className:\"framer-styles-preset-1d2clcy\",\"data-styles-preset\":\"Wkw0oBFqY\",style:{\"--framer-text-color\":\"var(--extracted-r6o4lv, 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white-space: pre; width: auto; }\",\".framer-wuiRT.framer-v-1r08kju.framer-18ak2hv, .framer-wuiRT.framer-v-38v7hk.framer-18ak2hv, .framer-wuiRT.framer-v-em1b4x.framer-18ak2hv, .framer-wuiRT.framer-v-u1g2ju.framer-18ak2hv { cursor: unset; }\",...sharedStyle.css];/**\n * This is a generated Framer component.\n * @framerIntrinsicHeight 48\n * @framerIntrinsicWidth 92\n * @framerCanvasComponentVariantDetails 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framer-1yaiscd\",\"data-framer-name\":\"tabMenu\",layoutDependency:layoutDependency,layoutId:\"WOsIl3F29\",children:[/*#__PURE__*/_jsx(ComponentViewportProvider,{height:48,width:`max(${componentViewport?.width||\"100vw\"} / 2, 1px)`,y:(componentViewport?.y||0)+0+(((componentViewport?.height||50)-0-50)/2+0+0)+0+0+0,children:/*#__PURE__*/_jsx(SmartComponentScopedContainer,{className:\"framer-152tya2-container\",layoutDependency:layoutDependency,layoutId:\"LPbKkgnRP-container\",nodeId:\"LPbKkgnRP\",rendersWithMotion:true,scopeId:\"WfFy8x3c2\",children:/*#__PURE__*/_jsx(BlogTabs,{height:\"100%\",id:\"LPbKkgnRP\",layoutId:\"LPbKkgnRP\",style:{height:\"100%\",width:\"100%\"},variant:\"jX5LForHY\",width:\"100%\",...addPropertyOverrides({d_3CXvZlj:{variant:\"I727:3109;954:127\"},GUV_Ool9i:{variant:\"cAJBxMbNi\"},TFVWDhja3:{variant:\"Y0O0eHjRm\"}},baseVariant,gestureVariant)})})}),/*#__PURE__*/_jsx(motion.div,{className:\"framer-4sx6mh\",\"data-framer-name\":\"Rectangle 3056\",layoutDependency:layoutDependency,layoutId:\"I727:3109;954:129\",style:{backgroundColor:\"var(--token-3f6d0e2e-bc08-41da-80d4-e85922a60dd8, rgb(40, 40, 40))\"},variants:{d_3CXvZlj:{backgroundColor:\"rgba(0, 0, 0, 0)\"},GUV_Ool9i:{backgroundColor:\"rgba(0, 0, 0, 0)\"}}})]})}),/*#__PURE__*/_jsx(Link,{href:{webPageId:\"WIDfGB_hN\"},motionChild:true,nodeId:\"qxWjcDaNo\",openInNewTab:false,scopeId:\"WfFy8x3c2\",smoothScroll:true,...addPropertyOverrides({d_3CXvZlj:{href:undefined},GUV_Ool9i:{href:undefined},TFVWDhja3:{href:{webPageId:\"kPhBvzGhZ\"}}},baseVariant,gestureVariant),children:/*#__PURE__*/_jsxs(motion.a,{className:\"framer-1yygmqb framer-1yaiscd\",\"data-framer-name\":\"tabMenu\",layoutDependency:layoutDependency,layoutId:\"qxWjcDaNo\",children:[/*#__PURE__*/_jsx(ComponentViewportProvider,{height:48,width:`max(${componentViewport?.width||\"100vw\"} / 2, 1px)`,y:(componentViewport?.y||0)+0+(((componentViewport?.height||50)-0-50)/2+0+0)+0+0+0,children:/*#__PURE__*/_jsx(SmartComponentScopedContainer,{className:\"framer-oy4gc6-container\",layoutDependency:layoutDependency,layoutId:\"atNaa54eM-container\",nodeId:\"atNaa54eM\",rendersWithMotion:true,scopeId:\"WfFy8x3c2\",children:/*#__PURE__*/_jsx(BlogTabs,{height:\"100%\",id:\"atNaa54eM\",layoutId:\"atNaa54eM\",style:{height:\"100%\",width:\"100%\"},variant:\"Jno4SFetp\",width:\"100%\",...addPropertyOverrides({d_3CXvZlj:{variant:\"wQm_st2gK\"},GUV_Ool9i:{variant:\"gAzUinc9n\"},TFVWDhja3:{variant:\"Pqmh_atWo\"}},baseVariant,gestureVariant)})})}),/*#__PURE__*/_jsx(motion.div,{className:\"framer-1x7s3f2\",\"data-framer-name\":\"Rectangle 3056\",layoutDependency:layoutDependency,layoutId:\"I727:3113;954:137\",style:{backgroundColor:\"rgba(0, 0, 0, 0)\"},variants:{d_3CXvZlj:{backgroundColor:\"var(--token-3f6d0e2e-bc08-41da-80d4-e85922a60dd8, rgb(40, 40, 40))\"},GUV_Ool9i:{backgroundColor:\"var(--token-3f6d0e2e-bc08-41da-80d4-e85922a60dd8, rgb(40, 40, 40))\"},TFVWDhja3:{backgroundColor:\"rgba(0, 0, 0, 0)\"}}})]})})]})})})})});});const css=[\"@supports (aspect-ratio: 1) { body { --framer-aspect-ratio-supported: auto; } }\",\".framer-gDUMI.framer-1yaiscd, .framer-gDUMI .framer-1yaiscd { display: block; }\",\".framer-gDUMI.framer-13xd2a1 { align-content: flex-start; align-items: flex-start; display: flex; flex-direction: column; flex-wrap: nowrap; gap: 10px; height: min-content; justify-content: center; padding: 0px; position: relative; width: 586px; }\",\".framer-gDUMI .framer-1a87bus { align-content: center; align-items: center; display: flex; flex: none; flex-direction: row; flex-wrap: nowrap; gap: 0px; height: 50px; justify-content: flex-start; overflow: visible; padding: 0px; position: relative; width: 100%; z-index: 5; }\",\".framer-gDUMI .framer-v9ufc5 { align-content: center; align-items: center; display: flex; flex: 1 0 0px; flex-direction: column; flex-wrap: nowrap; gap: 0px; height: 100%; justify-content: flex-start; overflow: visible; padding: 0px; position: relative; width: 1px; }\",\".framer-gDUMI .framer-152tya2-container, .framer-gDUMI .framer-oy4gc6-container { flex: 1 0 0px; height: 1px; position: relative; width: 100%; }\",\".framer-gDUMI .framer-4sx6mh, .framer-gDUMI .framer-1x7s3f2 { flex: none; height: 2px; position: relative; width: 100%; }\",\".framer-gDUMI .framer-1yygmqb { align-content: center; align-items: center; display: flex; flex: 1 0 0px; flex-direction: column; flex-wrap: nowrap; gap: 0px; height: 100%; justify-content: flex-start; overflow: visible; padding: 0px; position: relative; text-decoration: none; width: 1px; }\",\".framer-gDUMI.framer-v-16cfart .framer-v9ufc5, .framer-gDUMI.framer-v-1u7jpkl .framer-v9ufc5 { text-decoration: none; }\",'.framer-gDUMI[data-border=\"true\"]::after, .framer-gDUMI [data-border=\"true\"]::after { content: \"\"; border-width: var(--border-top-width, 0) var(--border-right-width, 0) var(--border-bottom-width, 0) var(--border-left-width, 0); border-color: var(--border-color, none); border-style: var(--border-style, none); width: 100%; height: 100%; position: absolute; box-sizing: border-box; left: 0; top: 0; border-radius: inherit; pointer-events: none; }'];/**\n * This is a generated Framer component.\n * @framerIntrinsicHeight 50\n * @framerIntrinsicWidth 586\n * @framerCanvasComponentVariantDetails {\"propertyName\":\"variant\",\"data\":{\"default\":{\"layout\":[\"fixed\",\"auto\"]},\"d_3CXvZlj\":{\"layout\":[\"fixed\",\"auto\"]},\"TFVWDhja3\":{\"layout\":[\"fixed\",\"auto\"]},\"GUV_Ool9i\":{\"layout\":[\"fixed\",\"auto\"]}}}\n * @framerImmutableVariables true\n * @framerDisplayContentsDiv false\n * @framerAutoSizeImages true\n * @framerComponentViewportWidth true\n * @framerColorSyntax true\n */const FramerWfFy8x3c2=withCSS(Component,css,\"framer-gDUMI\");export default FramerWfFy8x3c2;FramerWfFy8x3c2.displayName=\"blog-tabmenu\";FramerWfFy8x3c2.defaultProps={height:50,width:586};addPropertyControls(FramerWfFy8x3c2,{variant:{options:[\"EDv7viTBC\",\"d_3CXvZlj\",\"TFVWDhja3\",\"GUV_Ool9i\"],optionTitles:[\"tech-blog\",\"release-note\",\"(en)tech-blog\",\"(en)release-note\"],title:\"Variant\",type:ControlType.Enum}});addFonts(FramerWfFy8x3c2,[{explicitInter:true,fonts:[]},...BlogTabsFonts],{supportsExplicitInterCodegen:true});\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"FramerWfFy8x3c2\",\"slots\":[],\"annotations\":{\"framerIntrinsicWidth\":\"586\",\"framerComponentViewportWidth\":\"true\",\"framerAutoSizeImages\":\"true\",\"framerIntrinsicHeight\":\"50\",\"framerCanvasComponentVariantDetails\":\"{\\\"propertyName\\\":\\\"variant\\\",\\\"data\\\":{\\\"default\\\":{\\\"layout\\\":[\\\"fixed\\\",\\\"auto\\\"]},\\\"d_3CXvZlj\\\":{\\\"layout\\\":[\\\"fixed\\\",\\\"auto\\\"]},\\\"TFVWDhja3\\\":{\\\"layout\\\":[\\\"fixed\\\",\\\"auto\\\"]},\\\"GUV_Ool9i\\\":{\\\"layout\\\":[\\\"fixed\\\",\\\"auto\\\"]}}}\",\"framerImmutableVariables\":\"true\",\"framerDisplayContentsDiv\":\"false\",\"framerContractVersion\":\"1\",\"framerColorSyntax\":\"true\"}},\"Props\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./WfFy8x3c2.map","// 플러그인에서 JSON 검색 -> CMS-JSON 변환 및 Copy\n// BlogPosts 뒤의 내용을 모두 지우고 복사한 내용을 붙여넣기하시면 됩니다.\nexport const BlogPosts=[{Slug:\"v-0-5-0-en\",id:0,Title:\"0.5.0\",Date:\"2025-11-03T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"eng\",\"preview-contents\":\"\",Content:'

New Features

Added Chat History Feature

Added Qurify Files MCP and Advanced Document Management

Category

Description

Collection Management

  • Changed from table view to card UI and added collection search

  • Added selectable indexing options during collection creation/editing (Vector, Full-Text Search, Summary, Vision)

Document Management

  • Added ability to check indexing progress per document and rebuild index

  • Added document search, column visibility, and items-per-page options

  • Added document detail view page (supports PDF/Markdown)

Question Answering Evaluation

  • Added feature to evaluate the system\\'s responses using registered question sets per collection

  • Question sets (Q&A pairs) can be added manually or auto-generated

  • Compares system responses with question sets to derive an evaluation score

Added In-Conversation Collection & Prompt Selection

Category

Description

Collection

Collections created in \\'Collection Management\\' can be selected and referenced during a conversation

Prompt

Create and apply prompts belonging to each MCP tool to improve conversation quality

Changes

UI & Layout Improvement

'},{Slug:\"v-0-5-0\",id:0,Title:\"0.5.0\",Date:\"2025-11-03T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"kor\",\"preview-contents\":\"\",Content:'

New Features

챗 히스토리 기능 추가

Qurify Files MCP 추가 및 문서 관리 기능 고도화

구분

설명

컬렉션 관리

  • 기존 테이블 뷰에서 카드 UI로 변경 및 컬렉션 검색 기능 추가

  • 컬렉션 생성/수정 시 인덱싱 기능 선택 가능
    (벡터, 전문 검색, 그래프, 요약, 비전)

문서 관리

  • 문서별 인덱싱 진행 상황 확인 및 인덱스 재구축 기능 추가

  • 문서 검색, 표시할 컬럼 선택, 페이지 당 문서 수 선택 기능 추가

  • 문서 상세 뷰 페이지 추가 (PDF, Markdown 지원)

질문 평가

  • 컬렉션별 질문 세트를 등록하여 RAG 답변을 평가하는 기능 추가

  • 질문 세트(질문/답변 세트)를 수동 추가/자동 생성 가능

  • 질문 세트와 시스템 답변을 비교하여 평가 점수 도출

대화 중 컬렉션 \\xb7 프롬프트 선택 기능 추가

구분

설명

컬렉션

\\'컬렉션 관리\\'에서 생성한 컬렉션을 대화 중 선택하여 참조 가능

프롬프트

각 MCP 도구에 속한 프롬프트를 생성 및 적용하여 대화 품질 향상

Changes 

UI 및 레이아웃 개선

'},{Slug:\"v-0-4-6-en\",id:0,Title:\"0.4.6\",Date:\"2025-09-22T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"eng\",\"preview-contents\":\"\",Content:'

New Features

Added Settings for Auto-Generation, Conversation, and Tool Calls

Category

Details

Auto-Generation for Descriptions

  • Settings for generating table and column descriptions.

  • e.g., Columns processed per run, DB analysis scope, number of reference data, retry attempts, etc.

Auto-Generation for Examples

  • Settings for generating Question-SQL examples.

  • e.g., Number of generation iterations, SQL-to-Question count, Question-to-SQL count, etc.

Response Scale

  • Settings to optimize response quality and token usage.

  • e.g., Response length, tokens for SQL examples, etc.

Tool Call

  • Settings to optimize data scope and processing for tool usage.

  • e.g., Number of similar documents to retrieve, chunk size, chunk overlap, etc.

Advanced Management for Question-SQL Examples

Added RAG Folder & Document Management

Category

Details

Folders Management

  • Create, edit, and delete folders.

  • Assign names and metadata (JSON) per folder.

Documents Management

  • Upload documents (pdf, txt, md, docx) to each folder.

  • Set detailed options like metadata and chunk size before upload.

  • View details and delete documents and chunks.

Documents Search

  • Search for documents within each folder.

  • Apply filters such as number of results, search type (semantic/keyword/hybrid), etc.


Changes

Improved Side Navigation Bar UI

'},{Slug:\"v-0-4-6\",id:0,Title:\"0.4.6\",Date:\"2025-09-22T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"kor\",\"preview-contents\":\"\",Content:'

New Features

설명 \\xb7 예제 생성, 대화 \\xb7 도구 호출 옵션 설정 기능 추가

구분

주요 설정 항목

자동 생성 설명 옵션

  • 테이블, 칼럼 설명 생성을 위한 설정

  • 회당 칼럼 처리 규모, 데이터베이스 분석 범위, 참고 데이터 수, 재시도 횟수, 재시도 대기 시간 등

자동 설명 예제 옵션

  • 질문-SQL 예제 생성을 위한 설정

  • 예제 생성 반복 횟수, SQL-질문 생성 수, 질문-SQL 생성 수, 기존 예제 참고 수 등

답변 규모 옵션

  • 답변 품질과 토큰 사용량 최적화를 위한 설정

  • 답변 길이, SQL 예제 포함 토큰 등

도구 호출 옵션

  • 도구 사용 시 데이터 범위와 처리 방식 최적화를 위한 설정

  • 유사 문서 검색 수, 청크 크기, 청크 오버랩, 유사 SQL 예제 수 등

질문-SQL 예제 관리 기능 추가

RAG 폴더 \\xb7 문서 관리 기능 추가

구분

설명

폴더 관리

  • 폴더 생성, 수정, 삭제 기능

  • 폴더별 명칭 및 메타데이터(JSON) 지정 가능

문서 관리

  • 각 폴더에 pdf, txt, md, docx 형식의 문서 등록 가능

  • 문서 등록 시 메타데이터, 단위 문맥 크기 등 상세 옵션 설정 가능

  • 문서 \\xb7 청크 상세 정보 확인 및 삭제 기능

문서 검색

  • 폴더별 문서 검색 기능

  • 검색 결과 수, 검색 유형(시맨틱/키워드/하이브리드) 등 필터 적용 가능


Changes

사이드 네비게이션 바 UI 개선

'},{Slug:\"v-0-4-5-en\",id:0,Title:\"0.4.5\",Date:\"2025-09-04T04:35:43.740Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"eng\",\"preview-contents\":\"\",Content:'

New Features

Added Detailed View for Tables & Columns

Category

Details

Common

Database, Schema, Description, Alias, Memo

Column

Parent table name, Sample data, PK/FK status, Type, Default value, Nullable status

Added Editing Feature for Table & Column Information

Category

Editable Items

Table

Description, Alias

Column

Description, Alias, Sample data

Added Search Functionality for Tables & Columns

Category

Search Location

Table

Sidebar or \\'Table & Column Information Management\\'

Column

\\'Table & Column Information Management\\' menu → More options for each table


Changes

Improved UI for Table & Column Information Management

Other Usability Improvements

'},{Slug:\"(블로그)-테이블-칼럼-업데이트\",\":draft\":true,id:19,Title:\"(블로그) 테이블/칼럼 업데이트\",Date:\"2025-08-25T08:38:05.301Z\",Image:null,Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"ㄹㅁㅇ\",Content:\"

데이터베이스로부터 특정한 정보나 유용한 인사이트를 도출하려 해도, 해당 데이터베이스에 어떤 테이블과 칼럼이 포함되어 있는지 잘 모른다면 데이터 추출 과정이 힘들어질 수 있습니다. 반대로 말하면, 데이터베이스 내 존재하는 테이블과 칼럼, 각자의 역할과 관계를 파악할 경우 원하는 정보를 더욱 빠르고 첨예하게 얻을 수 있습니다.

이러한 과정을 사용자가 편리하게 수행할 수 있도록, Qurify가 제공하는 기능은 크게 x가지가 있습니다.

\\b테이블/칼럼 정보 관리



테이블/칼럼 설명 생성



ERD 보기



이 포스트에서는 테이블/칼럼 정보 관리를 중심적으로 다루며, 데이터베이스 내의 테이블/칼럼을 열람하는 법부터 효과적으로 활용할 수 있는 팁까지 소개해보도록 하겠습니다.

팁: 메모..

설명생성: 이전 블로그 포스트 링크 걸기

릴리즈 노트 참고해서 작성해보기

\"},{Slug:\"v-0-4-5\",id:0,Title:\"0.4.5\",Date:\"2025-09-04T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"kor\",\"preview-contents\":\"\",Content:'

New Features

테이블 \\xb7 칼럼 상세 정보 드로어 추가

구분

항목

공통

데이터베이스, 스키마, 설명, 별칭, 메모

칼럼

소속 테이블명, 예시 데이터, PK/FK 여부, 유형, 기본값, 결측치 허용 여부

테이블 \\xb7 칼럼 정보 편집 기능 추가

구분

수정 가능 항목

테이블

설명, 별칭

칼럼

설명, 별칭, 예시

테이블 \\xb7 칼럼 검색 기능 추가

구분

검색 위치

테이블

사이드바 또는 \\'테이블 \\xb7 칼럼 정보 관리\\'

칼럼

\\'테이블 \\xb7 칼럼 정보 관리\\' 메뉴 → 각 테이블의 더 보기


Changes

테이블 \\xb7 칼럼 정보 관리 UI 개선

기타 사용성 개선

'},{Slug:\"인자\",\":draft\":true,id:0,Title:\"(블로그)인자\",Date:\"2025-08-13T04:17:00.920Z\",Image:null,Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

사용자의 프롬프트에 응하는 답변을 생성하기 위해, Qurify는 여러 가지 도구들의 협업을 통해 결과를 정제해나갑니다. 즉, 사용자가 원하는 응답을 생성하는 과정에 일련의 도구 실행 과정이 수반됩니다.
Qurify는 이러한 도구 실행의 흐름과 결과를 사용자가 확인하고 활용할 수 있도록 돕습니다.

\"\"

사용자의 각 입력마다 도구 실행 결과가 제공되며 이는 언제든지 열고 닫을 수 있습니다.
도구 실행 결과를 살펴 보면, 어떤 MCP 서버가 사용되었고 어떤 도구를 활용해 질의를 수행하였는지 순차적으로 확인할 수 있습니다.

또한 단순히 실행 결과를 확인하는 수준을 넘어, 생성된 SQL 쿼리를 직접 수정 및 실행하거나 CSV 형식으로 다운로드하는 등 사용자가 더욱 능동적으로 Qurify를 활용할 수 있도록 여러 가지의 유용한 기능을 제공합니다.

\"\"'},{Slug:\"튜토리얼-에이전트-02-qurify와-함께-데이터를-활용하고-인사이트-얻기\",id:7,Title:\"튜토리얼 에이전트 – 02 Qurify와 함께 데이터를 활용하고 인사이트 얻기\",Date:\"2025-08-07T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/TSr3zxa6ZRhBPIqqKZg7mLhiNXY.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"간단한 데이터 분석부터 복잡한 인사이트 추출까지, Qurify는 사용자가 데이터베이스와 대화할 수 있도록 돕습니다. 이러한 기능들을 사용자가 쉽게 인지하고 이용할 수 있도록 Qurify 튜토리얼 에이전트를 도입했습니다. 이번 포스트에서는 큐리파이 배우기의 후반 단계를 직접 진행해보며, Qurify에서 진행할 수 있는 데이터 분석 방법을 알아봅니다.\",Content:'

지난 게시물에서는 Qurify 튜토리얼 에이전트와 함께 Qurify의 기본적인 사용법을 익히고, 테이블과 칼럼의 목록을 가져오거나 테이블 간의 관계, 그리고 샘플 데이터를 가져오는 방법을 알아보았습니다.

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이번 게시물에서는 튜토리얼을 9단계부터 이어서 진행하면서, 지금까지 살펴본 기능들을 응용하여 보다 복잡한 요청을 처리할 수 있는 가이드라인을 제시하도록 하겠습니다.


  1. 결과 정렬하기 (ORDER BY)

특정한 칼럼만 조회하거나, 조건을 걸어 특정한 데이터만 탐색할 수 있다고 해도 그 결과가 정렬되어 있지 않으면 데이터를 한 눈에 알아보기 어려울 수 있습니다. 이러한 문제점을 해소하기 위해, Qurify와 함께 결괏값을 정렬해보도록 하겠습니다.

generate_sql_query를 이용해서 orders 테이블에서 freight이 높은 순으로 정렬해서 5개 보여줘

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SQL에서는 결괏값을 정렬하기 위해 ORDER BY 컬럼명을 사용합니다. 이 구문은 해당 컬럼을 기준으로 데이터를 정렬하는 기능을 수행합니다. 기본적으로는 오름차순 정렬(1, 2, 3, …)을 수행하며, 내림차순 정렬(…, 3, 2, 1)이 필요하다면 컬럼명의 뒤에 DESC를 추가하면 됩니다.

사용자의 요청은 freight이 높은 순으로 정렬하는 것입니다. 내림차순 정렬, 즉 큰 값을 가진 데이터부터 정렬해야 사용자의 요구사항을 만족시킬 수 있습니다. 그 결과로 생성된 SQL 쿼리는 다음과 같습니다.

SELECT\\n  order_id,\\n  freight\\nFROM\\n  orders\\nORDER BY\\n  freight DESC\\nLIMIT\\n  5;

ORDER BY freight DESC를 사용하여 ①freight 컬럼을 기준으로 ②높은 수부터 정렬, 즉 내림차순 정렬을 수행했습니다. 이처럼 정렬된 결괏값이 필요할 경우, 프롬프트에 \\'컬럼 A를 기준으로 정렬\\'과 같은 키워드를 입력하기만 하면 Qurify가 정렬 작업을 수행하여 적절한 결과를 제공합니다.


  1. 전체 개수 계산 (COUNT)

10단계에서는 데이터를 직접 집계함으로써 그 개수를 알아보는 시간을 갖겠습니다.
데이터의 개수를 파악하는 것은 데이터의 전체 규모를 파악하는 데 있어 가장 기본적이면서 유용한 방법입니다. 데이터 분석을 시작하기 전에 데이터 개수를 먼저 계산하면 앞으로의 분석 방향을 정하는 데 도움이 되기도 합니다.

orders에 데이터 총 몇 개야?

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SQL에서는 데이터의 개수, 즉 테이블의 행(row) 개수를 세기 위하여 COUNT를 사용합니다. 별다른 조건 없이 테이블 상에 존재하는 모든 데이터를 집계하려면 COUNT(*) 구문을 사용하면 됩니다. 생성된 쿼리 역시 orders 테이블의 모든 데이터를 세고 있습니다.

SELECT\\n  count(*)\\nFROM\\n  orders;

DBHub의 execute_sql 실행 결과를 보면 orders 테이블 내에는 총 830개의 데이터, 즉 830행이 존재함을 알 수 있습니다.


  1. 그룹별 집계 (GROUP BY)

테이블 내 모든 데이터의 개수를 세어보았으니, 이제 데이터를 그룹화하여 그 개수를 집계해보겠습니다.
그룹별 집계를 쉽게 설명하자면, 특정 컬럼의 값이 같은 데이터끼리 묶어 그룹을 만드는 것과 같습니다. 예를 들어 고객 테이블의 거주 국가 를 기준으로 그룹별 집계를 진행한다면, 각 국가 별로 몇 명의 고객이 존재하는지 알 수 있습니다.

generate_sql_query를 이용해서 orders 테이블에서 customer_id 별로 개수 알려줘

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SQL에서는 GROUP BY를 통해 특정 컬럼의 값별로 데이터를 분류할 수 있습니다. 특히 지금의 경우에는 customer_id, 즉 고객 별로 주문한 개수를 세기 위해 그룹별 집계를 사용했습니다.
생성된 SQL 쿼리는 다음과 같습니다.

SELECT\\n  customer_id,\\n  count(order_id) AS order_count\\nFROM\\n  orders\\nGROUP BY\\n  customer_id;

위 SQL문은 GROUP BY customer_id를 사용함으로써 \\'customer_id 별로 분류\\'해야 한다는 조건을 만족하고 있습니다. 또한 COUNT(order_id)를 사용하고 있는데, GROUP BY와 함께 사용함으로써 그룹 별로 묶인 customer_id의 각 주문 개수를 세고 있습니다.
한편 AS order_count 구문을 통해 컬럼의 이름이 변한 모습을 볼 수 있습니다. execute_sql의 결과로 생성된 테이블 또한 order_count라는 컬럼으로 각 고객 ID의 주문 개수를 나타냅니다. 이처럼 Qurify는 컬럼명에 별칭을 붙이는 등, 데이터베이스 질의 결과의 가독성과 직관성을 향상시킴으로써 개선된 사용자 경험을 제공하고 있습니다.


  1. 두 테이블 조인 (JOIN)

관계형 데이터베이스는 데이터를 여러 테이블에 나누어 저장합니다. 아래 ERD는 데이터를 나누어 보관하는 테이블들의 모습을 잘 보여주고 있습니다.

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단일 테이블만으로 데이터를 조회할 수 있는 경우도 존재하지만, 여러 테이블로 분리된 데이터를 다시 조합하여 정보를 탐색해야 하는 경우도 있습니다.
조인은 이러한 데이터 조합 과정을 돕습니다. 여러 테이블에 흩어져 있는 관련 데이터를 한데 모을 수 있는 조인을 Qurify와 함께 살펴보겠습니다.

generate_sql_query를 이용해서 orders와 order_details를 조인해서 order_details 별 order_count 합계를 알려줘

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orders 테이블과 order_details 테이블 모두 order_id 컬럼을 가지고 있습니다. Qurify가 이를 파악하고 order_id 컬럼을 중심으로 두 테이블을 조인했습니다. 생성된 SQL 쿼리는 다음과 같습니다.

SELECT\\n  od.order_id,\\n  count(o.order_id) AS order_count\\nFROM\\n  order_details od\\n  JOIN orders o ON od.order_id = o.order_id\\nGROUP BY\\n  od.order_id;

이 중 JOIN 부분을 나타내는 구문 order_details od JOIN orders o ON od.order_id = o.order_id 은 크게 2가지로 구분하여 볼 수 있습니다.

  1. order_details od JOIN orders o: 두 테이블을 조인합니다. 이 때 order_details 테이블은 od, orders 테이블은 o라는 별칭으로 부릅니다.

  2. ON od.order_id = o.order_id: od 테이블의 order_id와 o 테이블의 order_id가 일치하는 행(row)를 기준으로 연결합니다.

이 쿼리를 실행하면 od 테이블의 주문 ID를 그룹화하여 각 주문에 대한 세부 항목 개수를 셀 수 있습니다.


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만약 데이터베이스 질의 결과를 이해하기 어려울 경우, 위 사진처럼 Qurify의 실행 결과 기반 질문을 활용할 수 있습니다. \\'대화에 #tool-qmC8A37E 참조\\' 버튼을 누르면 대화창 위에 해당 툴 번호가 나타나게 됩니다. 모든 도구는 이러한 참조 기능을 제공하며, 대화창 상단의 툴 번호 목록을 통해 참조 목록을 확인 및 관리할 수 있습니다.
DBHub의 execute_sql 실행 결과로 생성된 테이블을 이해하기 위해, 각 컬럼이 의미하는 바를 물어보겠습니다.

각 컬럼이 의미하는 것을 알려줘

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  1. order_id: 각 주문을 식별하는 고유한 식별자

  2. order_count: 해당 order_id에 속하는 주문의 개수

Qurify는 사용자의 프롬프트를 바탕으로 질의를 수행할 뿐만 아니라, 질의 결과에 대한 의문을 해소해 주기도 합니다. 대화 과정에서 실행 결과를 이해하기 어렵거나 궁금한 점이 생길 경우, 언제든지 실행 결과 기반 질문을 사용해 궁금증을 해결해 보세요.


  1. 2단계 이상 조인을 통한 숨은 인사이트 발굴

13단계에서는 이전 단계에서보다 조금 더 복잡한 미션을 제시합니다. 지금까지의 튜토리얼 단게에서 익혔던 기능들을 조합하여 미션을 해결해야 합니다.
사용자가 단순한 데이터 탐색을 넘어 보다 복잡한 질의를 통해 숨은 인사이트를 발굴할 수 있도록 준비한 단계입니다. 이를 통해 테이블 간의 숨은 관계를 파악하고, 알아낸 인사이트를 정해진 양식으로 작성하는 능력을 기를 수 있습니다.

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사용자는 직접 연결된 관계가 아닌, 2단계 이상으로 간접 연결된 테이블을 찾아 아래의 7단계 프로세스를 통해 인사이트를 추출하는 것을 목표로 합니다. 또한 발굴한 인사이트는 지정된 형식으로 정리해야 합니다. 이 프로세스와 형식을 참고하여, 데이터베이스로부터 숨겨진 정보를 이끌어낼 수 있는 프롬프트를 직접 작성해 보도록 하겠습니다.

프로세스

  1. 연결 관계를 확인합니다.

  2. 컬럼 정보를 확인합니다.

  3. 적절한 질문을 생성합니다.

  4. find_similar_examples를 사용해 유사한 예제를 찾습니다.

  5. find_relevant_schema를 사용해 관련 테이블의 스키마를 확인합니다.

  6. 적절한 쿼리를 생성합니다.

  7. 결과를 확인하고 분석합니다

답변 양식

  1. 질문: (자신이 생성한 질문)

  2. 최종 쿼리: (실행한 SQL 쿼리)

  3. 사용한 테이블, 컬럼, 연결관계: (도구로부터 얻은 정보의 종합)

  4. 답변: (쿼리 결과에 대한 설명)

  5. 사용 도구 요약: (이 분석을 위해 사용한 도구와 각 도구의 역할을 시간 순서대로 요약)

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사용자가 제시한 양식을 따라 Qurify가 응답을 생성한 모습입니다. 사용자의 요청에 따라 ①인사이트 추출 프로세스를 준수하였고 ②2단계 이상으로 간접 연결된 테이블들을 탐색했으며 ③최종 보고 형식을 준수했습니다. 답변의 마지막 부분인 사용 도구 요약이 정확한지 확인하기 위해 도구 실행 결과를 분석하겠습니다.

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사용자가 입력한 프로세스대로 알맞은 도구를 호출 및 실행한 모습을 보여주고 있습니다. 또한 사용한 도구들을 적절히 요약하여 답변의 마지막 부분에 제시한 것을 확인할 수 있습니다.


  1. 최종 미션: AI 주도 전체 분석 워크플로우

튜토리얼의 마지막 단계입니다. 이전 단계까지 수행했던 미션을 토대로 AI가 데이터 탐색부터 질문 생성, 쿼리 실행, 최종 분석으로 이루어진 전체 워크플로우를 자율적으로 수행하도록 지시하는 미션을 수행합니다.

최종 미션의 경우 Qurify에서 튜토리얼 에이전트와 함께 차근차근 큐리파이 배우기를 진행하며 직접 수행해 보세요.


마치며

Qurify 튜토리얼 에이전트가 제시하는 단계를 따라 튜토리얼을 모두 진행해보았습니다. 테이블과 컬럼 목록을 확인하는 기초적인 기능부터 AI를 이용한 워크플로우 자동화까지 직접 실행하며, Qurify와 함께 데이터 탐색 및 분석과 인사이트 발굴을 모두 성공적으로 수행했습니다.

튜토리얼을 모두 마친 후에도 Qurify의 기능을 다시 알아보고 싶다면 언제든지 튜토리얼을 재시작할 수 있습니다. 튜토리얼 에이전트와 함께 Qurify를 사용하는 법을 익히며 데이터베이스를 최대한으로, 그리고 효율적으로 활용해 보세요.

'},{Slug:\"v-0-4-4-en\",id:0,Title:\"0.4.4\",Date:\"2025-08-06T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"eng\",\"preview-contents\":\"\",Content:'

New Features

Qurify Tutorial Agent

Tool Execution Result Enhancement

SQL Editor

Direct editing, execution, and copying of Qurify-generated SQL queries

Result-based Follow-up Questions

Reference Qurify execution results for subsequent inquiries

CSV-based Result Download

Export table-format responses as CSV files

Fuction-calling Arguments Display

Table-format visualization of arguments passed during tool calls

Database Connection Management


Changes

UI Improvements & Convenience Features

Personal Settings Menu Relocation

Moved personal customization options below table & column selection

  • Prompt Configuration Settings

  • Table/Column Information Management

  • Question-SQL Example Management

Side Menu Configuration Updates

Added quick access shortcut buttons

  • Landing Page

  • Tech Blog

  • Release Notes

  • Bug Report

Main Layout Modifications

Added tutorial-related features

  • Chat ↔ Learn Qurify toggle switch button

  • Learn Qurify quick access shortcut

Chatbot Tool Response Improvements

'},{Slug:\"v-0-4-4\",id:0,Title:\"0.4.4\",Date:\"2025-08-06T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"kor\",\"preview-contents\":\"\",Content:'

New Features

Qurify 튜토리얼 에이전트 업데이트

✎ 관련 포스트
튜토리얼 에이전트 – 01 Qurify로 데이터베이스와 대화 시작하기
튜토리얼 에이전트 – 02 Qurify와 함께 데이터를 활용하고 인사이트 얻기

도구 실행 결과 활용 기능 업데이트

SQL 에디터 도입

Qurify가 생성한 SQL 쿼리를 직접 편집, 실행, 복사 가능

실행 결과 기반 질문

Qurify 실행 결과를 인용하여 후속 질문 진행 가능

실행 결과 CSV 다운로드

테이블 형식의 답변을 CSV 형식으로 다운로드하는 기능 제공

도구 호출 인자 확인

각 도구 호출 시 전달된 인자를 테이블 형식으로 표기

데이터베이스 연결 관리


Changes

기존 UI 개선 및 편의 기능 추가

개인 맞춤 설정 메뉴 위치 변경

테이블 \\xb7 컬럼 선택 하단으로 위치 변경

  • 프롬프트(지침) 설정

  • 테이블/칼럼 정보 관리

  • 질문-SQL 예제 관리

좌측 사이드 메뉴 구성 변경

바로가기 버튼 추가

  • 랜딩 페이지

  • 기술 블로그

  • 릴리즈 노트

  • 버그 리포트

메인 화면 구성 변경


튜토리얼 관련 편의 기능 추가

  • 대화 ↔ 큐리파이 배우기 스위치 버튼 추가

  • 큐리파이 배우기 바로가기 추가

챗봇 도구 응답 개선

'},{Slug:\"튜토리얼-에이전트-01-qurify로-데이터베이스와-대화-시작하기\",id:6,Title:\"튜토리얼 에이전트 – 01 Qurify로 데이터베이스와 대화 시작하기\",Date:\"2025-08-06T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/v924fPbPjcONIA4ur8AxhUMi7g.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

Qurify 튜토리얼 모드가 0.4.4 업데이트로 새롭게 추가되었습니다. ✎ 0.4.4 릴리즈 노트 바로가기

데이터베이스와의 대화가 낯선 사용자부터, Qurify의 유용한 기능들을 샅샅이 알아보고 싶은 사용자들을 위해 \\'큐리파이 배우기\\' 모드를 도입했습니다. Qurify를 처음 접한 사용자들은 튜토리얼 에이전트와 함께 언제든지 Qurify의 기능을 차근차근 익힐 수 있습니다.
또한 Qurify 튜토리얼 에이전트는 SQL 학습 흐름을 제공합니다. SQL의 기본 조작법부터 그룹별 집계, 조인 등을 통한 효과적인 데이터 검색 방법까지 튜토리얼을 통해 익힐 수 있습니다. 따라서 SQL을 모르는 사용자라도 튜토리얼 에이전트와 함께 데이터를 분석하며 SQL에 익숙해지는 시간을 가질 수 있습니다.

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Qurify는 언제든지 사용자가 큐리파이 배우기를 시작할 수 있도록 지원합니다. 예제 질문이나 입력 창을 통해 Qurify와 바로 대화를 시작할 수 있지만, 튜토리얼 에이전트를 실행하면 Qurify가 제공하는 데이터베이스 대화에 필요한 기능들을 모두 사용해볼 수 있습니다.

대화 화면의 좌측 상단에서도 언제든지 대화-큐리파이 배우기 전환이 가능합니다.
대화 도중 Qurify를 깊이 있게 알아보기 위해 튜토리얼을 시작하고 싶거나, 반대로 튜토리얼 도중에 Qurify와 자유롭게 대화하고 싶은 사용자들은 모드를 전환하여 원하는 기능을 이용할 수 있습니다.
또한 Qurify는 사용자의 튜토리얼 진행 상황을 저장합니다. 즉 튜토리얼을 중단하고 대화를 시작하여도, 후에 튜토리얼을 중단한 부분부터 이어서 다시 진행할 수 있습니다.


이제 본격적으로 튜토리얼 에이전트와 함께 Qurify가 제공하는 기능을 직접 사용해보며 데이터베이스와 대화하는 법을 익혀 보도록 하겠습니다.

\"\"

튜토리얼은 총 14단계로 이루어져 있습니다. 이 여정 동안 우리는 튜토리얼 에이전트와 함께 Qurify와 대화하는 법부터 데이터베이스로부터 정보를 얻어내고 이를 분석하는 법까지 익히게 됩니다.

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\\'큐리파이 배우기 시작\\' 버튼을 눌러 튜토리얼 에이전트를 실행해보겠습니다.
(대화 창의 ⬆️ 버튼을 눌러 실행할 수도 있습니다.)


  1. 큐리파이 배우기 시작 - 테이블 목록 확인하기

튜토리얼은 Qurify 대화 과정에서 실제로 사용하는 환경에서 진행하게 됩니다.
튜토리얼 과정에서의 Qurify 대화 화면은 총 3가지의 요소로 구분할 수 있습니다.

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내비게이션 바
& 사이드 바

- Qurify 개인 맞춤 설정과 환경설정, 그리고 데이터베이스 \\xb7 스키마 \\xb7 테이블/컬럼 선택 기능이 있습니다.

관련 포스트: 프롬프트 설정으로 정밀하게 데이터 분석하기

대화 화면
(대화 패널 좌측)

- 입력창을 통해 Qurify와 대화할 수 있으며, 좌측 상단에서 대화-튜토리얼 모드 전환이 가능합니다.
- 사용자가 입력한 프롬프트와 그에 따른 도구 실행 결과, 그리고 Qurify의 답변을 확인할 수 있습니다.
- (튜토리얼 한정) 현재 단계와 미션을 확인 가능합니다.

- 처음으로 돌아가기: 튜토리얼을 1단계부터 다시 시작합니다.
- 다음 단계로 이동: 튜토리얼의 다음 단계로 이동합니다.
- 힌트 실행: 큐리파이가 제안하는 힌트 프롬프트를 직접 실행할 수 있습니다.

도구 실행 결과
(대화 패널 우측)

- Qurify 질의 과정에서 도구가 실행된 결과를 순차적으로 확인할 수 있습니다.
- 예를 들어, tutorial 도구 실행 결과는 현재 진행 단계와 현재 데이터 등을 표시합니다.

튜토리얼 에이전트는 다음 항목들로 이루어진 답변을 사용자에게 제공합니다.

  1. 사용자가 수행해야 할 미션 제공

  2. 미션을 완료할 수 있는 힌트 제공

  3. 현재 단계 및 진행 방법 안내

  4. 처음으로 돌아가기 & 다음 단계로 이동 & 힌트 실행

튜토리얼 에이전트의 답변에 포함되어 있는 힌트는 튜토리얼의 현재 단계를 완료하기 위한 해결책과도 같습니다. 대화 하단의 힌트 실행 버튼을 누르거나 직접 chat input에 입력 및 전송하면, 힌트를 직접 실행하여 그 결과를 살펴 보고 튜토리얼의 다음 단계로 이동할 수 있습니다.
\\'데이터베이스의 테이블 목록 확인하기\\' 태스크를 완료하기 위해, 힌트 실행 버튼을 통해 Qurify가 제안한 힌트를 직접 실행해보도록 하겠습니다.

get_table_info를 사용하여 데이터베이스에 있는 모든 테이블 목록 보여줘


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Qurify가 데이터베이스의 전체 테이블 목록을 가져오는 데 성공했습니다. Qurify MCP Server가 get_table_info를 실행하여 사용자의 요청을 처리한 것입니다.
튜토리얼의 첫 단계를 성공적으로 마쳤으니 다음 단계로 이동 버튼을 눌러 이후 단계를 진행하도록 하겠습니다.

(MCP에 대해 더 알아보고 싶다면 이전 게시물을 참고하세요.)

  1. 주요 테이블의 컬럼 확인하기

\\'다음 단계로 이동\\' 프롬프트와 함께 다음 단계의 튜토리얼을 진행하게 되었습니다.
대화 화면 상단, 또는 도구 실행 결과의 tutorial_tracker를 참고하면 현재 단계와 실행해야 할 태스크를 언제든지 확인 가능합니다. 플레이스 홀더는 AI를 통해 자동으로 생성되어, 사용자가 해당 단계에서 어떤 태스크를 수행해야 하는지 안내합니다.

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2단계는 특정 테이블의 컬럼 목록을 확인하는 것을 목표로 합니다.
이번에는 프롬프트를 직접 입력 및 전송함으로써 목표를 달성해보도록 하겠습니다.

get_table_columns를 이용하여 orders 테이블의 모든 컬럼 정보를 보여줘


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1단계와 달리 Qurify MCP Server가 이번에는 get_table_columns를 사용하여 orders 테이블의 모든 컬럼 정보를 가져왔습니다. 사이드 바의 Tables 목록에서 orders 테이블과 이에 속한 컬럼들을 보면, get_table_columns가 올바른 컬럼 정보를 추출했다는 점을 알 수 있습니다.

또한 답변 하단에 배치된 버튼을 이용하는 대신 직접 프롬프트를 입력하여도 튜토리얼을 정상적으로 진행하는 모습을 보입니다. 프롬프트를 직접 입력하는 방법으로 프롬프트를 진행하다 보면, 튜토리얼의 의도에 벗어나는 프롬프트를 입력하여 해당 단계의 목표를 완수하지 못할 수 있습니다. 미션을 이해하기 힘들거나 튜토리얼의 이전 단계부터 차근차근 다시 진행하고 싶다면, 언제든지 처음으로 돌아가기 버튼을 눌러 튜토리얼의 첫 단계부터 다시 시작할 수 있습니다.

(이후 모든 단계는 답변 하단의 힌트 실행 버튼을 이용하여 진행하겠습니다.)


  1. 전체 테이블 관계도 확인

테이블 목록, 칼럼 목록을 확인하는 방법을 알았으니 이번에는 테이블 간의 관계를 알아보겠습니다.

find_join_path를 이용하여 모든 테이블의 조인 관계를 보여줘

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Qurify MCP Server가 find_join_path를 실행하여 테이블 간의 모든 조인 관계를 성공적으로 탐색했습니다.
해당 조인 관계가 실존하는지 검증하기 위해, Qurify의 답변 중 5, 6번(products<—>categories, products<—>suppliers) 관계를 ERD에서 직접 찾아보겠습니다.
(ERD 보기 기능은 사이드 바 - Schema - 관계도 버튼을 통해 활성화할 수 있습니다.)

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products 테이블과 categories 테이블, suppliers 테이블이 조인 관계에 있음을 ERD에서도 확인 가능합니다.
이로써 find_join_path를 이용하면 테이블 간의 조인 관계를 ERD 없이도 쉽게 찾을 수 있다는 점을 알 수 있었습니다.


  1. 특정 테이블 중심 관계 확인

이번에는 모든 테이블이 아닌 특정한 하나의 테이블을 중심으로 다른 테이블과의 관계를 찾아보겠습니다.

find_join_path를 이용하여 orders 테이블과 직접 연결된 테이블 알려줘

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3단계와 똑같이 Qurify MCP Server가 find_join_path를 실행했지만, 이번에는 orders 테이블과 직접 연결된 테이블만 탐색했습니다. 어떤 원리로 각자 다른 결과를 낳았을까요?
도구 실행 결과에서 확인할 수 있는 \\'도구 호출 시 전달된 인자\\'를 통해 힌트를 얻을 수 있습니다.

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좌측은 모든 테이블을 중심으로, 우측은 orders 테이블을 중심으로 다른 테이블과의 관계를 탐색한 결과입니다.
도구 호출 시 전달된 인자 중 table_a를 살펴 보면, 좌측은 아무 값도 전달하지 않았으나 우측은 orders를 전달하였습니다. 즉 table_a 인자가 아무 값도 전달하지 않으면 모든 테이블의 조인 관계를, 특정한 테이블명을 전달하면 해당 테이블의 조인 관계를 찾는 것입니다.

도구 호출 및 실행 결과의 메커니즘이 궁금하다면, 이처럼 도구 호출 시 전달된 인자를 살펴봄으로써 실행 과정과 원리를 유추할 수 있습니다.


  1. 테이블의 샘플 데이터 확인하기

지금까지 데이터베이스와 테이블의 구조를 살펴보았습니다. 이제는 테이블의 내부에 존재하는 데이터를 본격적으로 활용할 시간입니다.
데이터를 직접적으로 활용하기에 앞서, 테이블에 어떤 데이터가 존재하는지 알면 더욱 원활한 질의 과정을 경험할 수 있습니다. 5단계에서는 이를 실현할 수 있는 \\'샘플 데이터 확인\\'을 미션으로 제시합니다.

orders 테이블에서 데이터 3개만 보여줘


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이번에는 DBHub가 execute_sql을 호출하여 orders 테이블로부터 샘플 데이터 3개를 추출했습니다. execute_sql 호출 시 전달된 인자를 보면, 다음과 같은 SQL 쿼리가 전달됐음을 확인 가능합니다.

SELECT * FROM orders LIMIT 3;

이를 통해 orders 테이블에서 3개의 데이터를 보여 달라는 사용자의 프롬프트를 잘 이해하고 실행했음을 알 수 있습니다.
또한 Qurify는 테이블 형식의 응답을 CSV로 추출하는 기능을 지원합니다. 만약 실행 결과를 다른 응용 프로그램에 이용하거나 외부와 공유하고 싶다면, CSV 다운로드 기능을 이용할 수 있습니다.


  1. 특정 컬럼만 선택 조회하기

테이블 내 데이터를 조회할 때, 모든 컬럼을 대상으로 쿼리를 진행한다면 결과가 너무 방대해지거나 알고 싶은 정보를 찾기 어려울 수 있습니다. 이러한 문제를 해결하기 위해 특정 컬럼만 골라서 조회하는 방법을 알아보겠습니다.

orders 테이블에서 customer_id와 freight 컬럼만 5개 보여줘

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5단계와 같이 DBHub가 execute_sql을 실행했으나, 이번에는 2개의 컬럼과 5개의 데이터를 조회했다는 점이 다릅니다.
우리는 앞서 \\'도구 호출 인자\\'를 통해 이러한 변화의 원인을 유추할 수 있다는 점을 알아보았습니다. 특정 컬럼만 조회하라는 사용자의 요청에 맞추어 전달한 SQL 구문은 다음과 같습니다.

SELECT customer_id, freight FROM orders LIMIT 5;

5단계의 SQL 쿼리와 비교하면, SELECT와 LIMIT의 범위가 달라졌음을 알 수 있습니다.
이처럼 Qurify는 사용자의 자연어 질문으로부터 요구 사항을 이해하고, 이를 만족시킬 수 있도록 적절한 인자 전달을 통해 질의를 수행합니다.


  1. 문자열 기반 단일 조건 필터링

특정 컬럼에 이어, 이번에는 특정 조건에 해당하는 데이터만을 조회해보겠습니다.
데이터 검색 과정에서 적용할 수 있는 조건의 종류는 다양합니다. \\'1998년 이전의 주문 일자\\', \\'운송 비용이 50000 이상인 주문\\', \\'김씨 또는 이씨인 주문자\\'와 같은 조건을 탐색 과정에 적용할 수 있습니다.
이 중에서도 customer_id가 특정 문자열과 일치해야 한다는 조건을 추가하여 데이터를 검색해보겠습니다.

generate_sql_query를 이용해서 orders 테이블에서 customer_id이(가) \\'VINET\\'인 데이터 5개만 보여줘

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도구 실행 결과의 일부를 살펴 보면, Qurify MCP Server가 generate_sql_query를 통해 생성한 SQL문을 DBHub의 execute_sql이 인자로 사용하고 있습니다. 해당 SQL 쿼리는 다음과 같습니다.

SELECT \\n  * \\nFROM \\n  orders \\nWHERE \\n  customer_id = \\'VINET\\' \\nLIMIT \\n  5;

앞서 살펴보았던 SQL문과 달리, 이번에는 WHERE customer_id = \\'VINET\\'이라는 조건문이 추가되었습니다. 그 결과로 execute_sql의 실행 결과에는 모두 customer_id가 VINET인 데이터만 표시되고 있습니다.
지금까지 진행했던 튜토리얼 단계들과는 달리, 7단계에서는 여러 가지 도구를 사용하여 질의를 수행하였습니다. 이처럼 Qurify는 각자의 고유한 기능을 가진 MCP 서버들이 모여 사용자의 태스크를 완수하기 위해 긴밀한 협업 과정을 거칩니다.

(MCP에 대한 자세한 내용은 이전 포스트에서 확인할 수 있습니다.)


  1. 숫자 기반 조건 필터링

7단계와 유사하지만, 이번에는 문자열 대신 숫자를 기준으로 하는 조건을 추가해보도록 하겠습니다. 숫자 기반 조건은 \\'x보다 큰 수\\', \\'y보다 작은 수\\', 혹은 \\'z와 같은 수\\'와 같은 방식으로 작성할 수 있습니다.

generate_sql_query를 이용해서 orders 테이블에서 freight이(가) 50000보다 작은 데이터 5개만 찾아줘

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7단계와 마찬가지로 Qurify MCP Server가 generate_sql_query를 이용하여 SQL 쿼리를 생성하고, 이는 DBHub의 execute_sql 호출 시 인자로 전달됩니다. 해당 SQL 쿼리는 다음과 같습니다.

SELECT\\n  order_id,\\n  freight\\nFROM\\n  orders\\nWHERE\\n  freight < 50000\\nLIMIT\\n  5;

이번에는 freight < 50000이라는 조건이 포함되어 있습니다. 사용자의 입력으로부터 사용자가 요구하는 조건문을 알아내어 SQL 쿼리 생성과 실행에 반영한 것입니다. 그 결과로 freight이 50000보다 작은 데이터 5개를 성공적으로 확인할 수 있었습니다.
이처럼 Qurify는 자연어 입력을 분석하여 테이블과 컬럼, 필요한 조건 등을 알아내고 이를 질의 과정에 반영하여 사용자의 요구사항을 만족시킬 수 있는 응답을 제공합니다. 즉 데이터베이스를 다루는 법을 몰라도 Qurify를 사용한다면 자연어만으로 충분히 원하는 데이터를 탐색하고 분석할 수 있습니다.


마치며

지금까지 튜토리얼의 처음부터 8단계까지 순서대로 진행하며 Qurify에서 사용할 수 있는 기본적인 기능부터 도구 호출 인자와 같은 유용한 추가 기능까지 알아보았습니다.
다음 포스트에서는 9단계부터 끝까지 튜토리얼을 이어서 진행하며, 아직 알아보지 못한 Qurify의 기능을 소개하도록 하겠습니다.

'},{Slug:\"프롬프트-설정으로-정밀하게-데이터-분석하기\",id:5,Title:\"프롬프트 설정으로 정밀하게 데이터 분석하기\",Date:\"2025-08-04T08:15:28.698Z\",Image:{url:\"https://framerusercontent.com/images/rFnPs6hgukY15kzCmh09DcG7Gc.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"최근 대화형 AI 활용에 있어 프롬프트의 중요성이 높아지고 있습니다. 보다 정확하고 가치 있는 답변을 이끌어내기 위해서, 사용자가 입력하는 프롬프트 또한 유의미한 정보를 담고 있어야 할 필요성이 커진 것입니다. Qurify는 이처럼 사용자가 적절한 프롬프트를 간단하게 설정하고 활용할 수 있도록 돕습니다. 데이터 분석 과정에서 정확하고 세밀한 결과를 얻을 수 있도록, Qurify가 지원하는 4가지의 프롬프트를 적용 예시와 함께 소개합니다.\",Content:'

2025년 현재, 수많은 생성형 AI 서비스가 출시되었고 이를 실생활에 활용하는 인구의 수 또한 가시적으로 증가하고 있습니다.

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(출처: https://www.journalist.or.kr/news/article.html?no=58480)

한국언론진흥재단에 따르면, 2024년 이후 생성형 AI를 사용하기 시작한 이용자의 수가 그 전에 비해 폭발적으로 증가했음을 알 수 있습니다. 특히 ChatGPT, Claude, Gemini와 같은 대화형 인공지능 서비스의 경우 ‘대화’라는 간단한 수단으로 AI를 이용할 수 있고, 직관성과 접근성이 높습니다. 이는 서비스 사용자 유입을 유도하고, 결론적으로 높은 사용자 수를 유치하게 된 것입니다.

하지만 대화형 AI는 사용자가 원하는 답을 항상 내놓지는 않습니다. 사용자의 질문이나 의도를 이해하지 못해 엉뚱하거나 불충분한 답변을 내놓는 경우도 존재합니다. 또한 AI가 특정한 조건 내에서 답변을 생성하길 원하는 사용자도 존재합니다. 예를 들어, 답변을 특정한 언어로 생성하거나 정해진 분량 내에서 생성하고 싶을 수 있습니다.

이러한 사용자의 요구를 만족시키기 위해 프롬프트 엔지니어링을 적용할 수 있습니다. 프롬프트 엔지니어링은 프롬프트를 효과적으로 설계하고 최적화함으로써 AI 모델이 적절한 응답을 생성하도록 유도하는 것을 목표로 합니다. 이 과정에서 AI 모델이 답변하는 방식 등을 조절함으로써 모델 성능과 답변 품질을 높일 수 있습니다.

특히 Qurify의 경우, 데이터 탐색 \\xb7 분석 과정에서 프롬프트 설정을 통해 더 정확하고 세밀한 결과를 얻을 수 있습니다. Qurify에서 프롬프트를 어떻게 설정할 수 있는지, 각 프롬프트 설정은 어떤 이점을 제공하는지 예시와 함께 알아보도록 하겠습니다.


Qurify에서 개인 맞춤형 프롬프트 설정하기

Qurify는 총 4가지의 프롬프트 설정을 지원합니다.

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프롬프트 종류

설명

시스템 프롬프트

AI 모델의 역할이나 행동 기준, 답변 형식 등을 지정할 수 있는 프롬프트

SQL 생성 프롬프트

SQL 쿼리를 정확하게 생성하기 위한 프롬프트

DB 가이드라인 프롬프트

‘테이블/칼럼 정보 관리’에서 이용할 수 있는 ‘설명 생성하기\\' 기능의 규칙과 형식 등을 지정할 수 있는 프롬프트

Text-to-SQL 예제 생성 프롬프트

‘질문-SQL 예제 관리’에서 이용할 수 있는 ‘예제 생성하기’ 기능의 지침을 지정할 수 있는 프롬프트

이 프롬프트 설정들을 하나씩 살펴 보고 직접 프롬프트를 작성하며 적용 전후 결과를 분석해보도록 하겠습니다.


1. 시스템 프롬프트

시스템 프롬프트는 AI 모델의 응답에 맥락과 행동, 또는 규칙을 부여합니다. 즉 AI 모델이 사용자에게 응답하는 방법을 정의한다고 볼 수 있습니다. 문체나 글의 길이 등의 측면에서 AI가 일관성 있는 답변을 생성하길 원한다면 시스템 프롬프트 적용을 고려해볼 수 있습니다.

대표적으로 적용할 수 있는 시스템 프롬프트는 다음과 같습니다.

종류

예시

응답 형식

모든 응답을 200자 내로 생성하세요.

맥락 제공

당신은 구직 컨설팅 업체에서 일하는 전문 컨설턴트이며, 이에 따라 구직 방면에서 전문적이고 신빙성 있는 답변을 제공해야 합니다.

윤리 지침

사용자에게 개인 정보, 관심사 등 예민한 정보를 묻지 마세요.


이제 Qurify에서 직접 시스템 프롬프트를 작성하고 프롬프트 적용 전후 결과를 비교해보도록 하겠습니다.

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답변에 생성된 쿼리를 포함하지 마세요.
모든 답변은 7줄 이내로 요약해서 보여주세요.

Qurify의 답변 형식과 길이를 조절하기 위해 위와 같은 시스템 프롬프트를 적용하였습니다.
그리고 “employees 테이블의 hire_data를 기준으로 각 연도별 직원 채용 수를 확인할 수 있나요?”라는 질문을 시스템 프롬프트 설정 전후, 총 2번에 걸쳐 Qurify에 입력했습니다.
해당 시스템 프롬프트를 적용하기 전과 후의 응답은 아래와 같습니다.

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도구 실행 결과를 참조하면, 시스템 프롬프트 적용 전후 모두 질의 과정은 동일합니다.
하지만 아무 시스템 프롬프트도 적용하지 않았을 때와 달리, 우측의 답변은 ‘생성된 쿼리’를 포함하고 있지 않고 답변의 길이 또한 7줄 이내로 간소화되었습니다.

이처럼 시스템 프롬프트를 적절히 활용하면 AI 모델의 답변을 사용자의 요구사항에 맞추어 조절할 수 있습니다.


2. SQL 생성 프롬프트

더 정확한 SQL 쿼리를 생성할 수 있도록 Qurify는 SQL 생성 프롬프트 설정을 지원합니다.

그 예시로 특정 기간을 나타내는 데이터만 처리할 수 있도록 예제 프롬프트를 생성 후, 프롬프트 적용 전후 결과를 비교해보겠습니다.

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주문 날짜가 1998년인 데이터만 처리

고객의 주문 내역과 운송 업체에 관한 정보를 갖고 있는 테이블(순서대로 orders, shippers 테이블)을 이용하여, 각 운송 업체가 배송한 주문의 건수를 알아보려고 합니다.
특히 1998년에 배송을 완료한 주문의 건수를 알아보기 위해 SQL 생성 프롬프트를 위와 같이 작성했습니다.

“orders 테이블의 ship_via와 shippers 테이블을 기준으로 각 운송 회사의 총 배송 건수를 확인할 수 있나요?”라는 질문을 SQL 생성 프롬프트 설정 전후에 각각 입력한 결과는 다음과 같습니다.

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SQL 생성 프롬프트를 적용한 후의 응답은 ‘1998년 동안’의 데이터만 처리했음을 명시하고 있습니다. 프롬프트 적용 전후의 응답을 비교하면 총 배송 건수 또한 차이를 보입니다.
또한 Qurify MCP Server가 생성한 SQL 쿼리를 보면, 주문 일자가 1998년인 데이터를 추출하는 부분이 추가되었음을 알 수 있습니다.

WHERE extract(YEAR\\n              FROM o.order_date) = 1998


SQL 생성 프롬프트를 적용한 질의 결과가 정확한지 확인하기 위해, graph_generate MCP 서버를 활용해 ‘각 연도 별로 주문이 접수된 횟수’를 시각화하였습니다.
MCP에 대해 더 알아보고 싶다면 이전 게시물을 참조하세요.

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연도별로 접수된 주문 횟수를 시각화한 그래프를 참조하면, 1998년에 접수된 주문은 총 270건임을 볼 수 있습니다.
앞서 알아본 ‘1998년 동안 각 운송 회사의 총 배송 건수’는 각각 75회, 78회, 117회로 해당 질의 결과와 일치합니다.

이로써 SQL 생성 프롬프트를 적용하여 더 정밀한 SQL 쿼리를 생성했음을 알 수 있습니다.


3. DB 가이드라인 프롬프트

Qurify는 사용자의 데이터베이스 이해를 돕기 위해 DB 가이드라인을 제공합니다.
데이터베이스의 테이블과 칼럼에 대한 설명을 자연어 형태로 제공함으로써, 테이블과 칼럼이 어떤 데이터를 저장하고 있는지 개략적으로 확인할 수 있습니다.

좌측의 테이블 선택 메뉴에서 테이블과 칼럼에 마우스 오버 시, 해당 테이블 또는 칼럼의 정보를 개략적으로 확인 가능합니다.

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또한 앞서 언급했듯이 ‘테이블/칼럼 정보 관리’ 메뉴를 통해 데이터베이스의 테이블과 칼럼을 관리하거나, ‘설명 생성하기’ 기능을 통해 데이터베이스 설명을 자동으로 생성 및 변경할 수 있습니다

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앞서 마우스 오버를 통해 확인한 테이블/칼럼 설명을 포함해 각 테이블과 칼럼의 정보를 표 형태로 확인 가능합니다.
특히 table_description, column_description은 각각 테이블, 그리고 칼럼에 대한 설명을 담고 있습니다.

하지만 테이블/칼럼 설명이 조금 더 간결하거나, 반대로 더 구체적이기를 원하는 사용자가 존재할 수 있습니다.
그런 사용자를 위해 Qurify는 DB 가이드라인 프롬프트를 제공합니다. DB 가이드라인 프롬프트 설정을 통해 테이블/칼럼 설명 방식을 조절함으로써 데이터베이스를 더욱 정확히 파악할 수 있습니다.


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‘설명 생성하기’ 기능을 이용하면 Qurify가 데이터베이스(테이블과 칼럼)의 설명을 자동으로 생성합니다. 이 과정에서 DB 가이드라인 프롬프트를 설정하여 사용자가 설명 생성 방식을 직접 조절할 수 있습니다.
(DB 가이드라인 프롬프트는 ‘프롬프트(지침) 설정’에서도 변경 가능합니다.)

DB 가이드라인의 길이와 서술 방식을 변경하기 위하여 다음과 같은 프롬프트를 사용하였습니다.

20자 내외로 간단하게 설명하세요.
\\'~습니다\\'와 같이 서술하지 말고, 간단한 단어 위주로 묘사하세요.

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테이블과 칼럼의 설명이 프롬프트를 반영하여 20자 내외로, 그리고 단어 위주로 간단하게 변경된 점을 확인할 수 있습니다.

이와 같이 DB 가이드라인 프롬프트를 통해 테이블/칼럼의 설명을 생성 \\xb7 변경함으로써 데이터베이스에 관한 이해도를 제고할 수 있습니다.


4. Text-to-SQL 예제 생성 프롬프트

Qurify는 사용자의 편리한 쿼리와 인사이트 획득을 돕기 위해 질문-SQL 예제를 지원합니다.
이 예제들은 Qurify의 메인 화면에서 질문으로 사용할 수 있고, 질의 과정에서 Qurify MCP 서버에 의해 사용됩니다.

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질문-SQL 예제 또한 ‘예제 생성하기’ 기능을 통해 생성하거나 내용을 조절할 수 있습니다.

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‘예제 생성하기’ 기능에서는 Text-to-SQL 예제 생성 프롬프트를 포함하여 반복 횟수, 질문-SQL 생성 개수, 기존 예제 참조 개수를 설정할 수 있습니다.
해당 요소들을 입력함으로써 Qurify가 질문을 이해하고 SQL을 생성하는 방식을 사용자의 요구사항대로 조절 가능합니다.
(Text-to-SQL 프롬프트는 ‘프롬프트(지침) 설정’에서도 변경 가능합니다.)

이번에는 매장별 거래 내역, 특히 상품 정보(스타일, 색상 외)를 저장하고 있는 salestransactions 테이블을 위주로 질문-SQL 예제를 생성하겠습니다. 특히 판매 \\xb7 거래된 상품의 색상과 스타일을 알아보기 위해 color_code와 style_code 칼럼을 포함하는 질문을 생성하려고 합니다.
질문-SQL 예제 생성 방식을 변경하고 데이터의 범위를 제한하기 위해 다음과 같은 프롬프트를 입력했습니다.

모든 테이블명과 칼럼명을 소문자로 작성하세요.
salestransactions 테이블, 그리고 color_code 또는 style_code 칼럼이 포함된 질문을 생성해 주세요.
2025년 5월에 발생한 데이터만을 처리해 주세요.

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생성된 예제와 SQL 쿼리를 살펴 보면 테이블과 칼럼의 이름이 모두 소문자로 표기되어 있습니다. 또한 생성된 질문들은 매장 판매 \\xb7 거래 내역, 그리고 상품의 색상과 스타일을 다루고 있습니다. 사용자의 Text-to-SQL 예제 생성 프롬프트를 반영하여 예제와 SQL 쿼리를 생성했다는 점을 알 수 있습니다.

결론적으로, Text-to-SQL 예제 생성 프롬프트와 생성 설정을 이용함으로써 효과적인 질의 과정을 돕는 예제를 생성할 수 있습니다. 해당 예제를 사용하여 유의미한 분석 결과를 얻는 효과를 기대할 수 있습니다.


마치며

대화형 AI로부터 정확하고 가치 있는 답변을 유도하는 과정에서, 사용자의 적절한 프롬프트 또한 중요한 요소로써 작용합니다. 프롬프트 작성 능력, 특히 사용자의 요구 사항이 잘 드러나 있고 AI가 잘 이해하는 프롬프트를 쓰는 능력은 이제 유용한 역량으로 자리매김하고 있습니다.

Qurify는 이처럼 ‘잘 쓰여진’ 프롬프트를 사용자가 쉽게 만들고 활용할 수 있도록 돕습니다. 서비스에 적용할 수 있는 프롬프트를 4가지 종류로 나누어, 일반적인 대화에 적용하는 프롬프트부터 테이블/칼럼 설명 등 Qurify의 유용한 기능에 적용하는 프롬프트까지 사용자가 자유롭게 설정할 수 있도록 세분화하였습니다.

프롬프트 설정과 함께 Qurify로 데이터를 활용하고 정밀한 SQL 쿼리를 생성해 보세요.

'},{Slug:\"mcp를-통해-다양한-ai-에이전트와-협업하기\",id:4,Title:\"MCP를 통해 다양한 AI Agent와 협업하기\",Date:\"2025-07-31T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/lWxoHK0qUao5jJjhczW68gzPokM.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"MCP의 등장으로 인해 AI 에이전트를 다른 서비스와 연동하여 AI를 보다 유연하게 활용할 수 있게 되었습니다. MCP의 개념부터 이점, 그리고 Qurify에서 MCP를 활용한 방식을 다룹니다. \",Content:'

질의를 통해 AI 챗봇으로부터 결과물을 도출할 뿐만 아니라, 이제는 AI 에이전트와 시스템을 연결하거나 에이전트끼리의 소통을 통하여 AI가 스스로 행동하고 사용자의 목표를 달성할 수 있는 수준에 이르렀습니다. 단순히 답변을 얻어내는 행위에서 그치지 않고 태스크를 완수하는 과정까지 AI가 함께하게 된 것입니다.

여기서 에이전트, 특히 LLM Agent가 다른 시스템과 협업하기 위해서는 MCP라는 프로토콜이 필요합니다. MCP란 무엇인지, 왜 MCP를 사용했으며 어떤 이점을 얻을 수 있는지, Qurify에서는 어떤 방식으로 활용되었는지 알아보도록 하겠습니다.


1. MCP란?

MCP(Model Context Protocol)는 애플리케이션이 LLM에 컨텍스트를 제공하는 방식을 표준화한 프로토콜로, Claude를 개발한 Anthropic(앤트로픽)이 2024년 11월 공개했습니다. MCP는 AI 모델을 다양한 데이터 소스 및 도구에 연결할 수 있도록 표준화된 방식을 제공하는데, 이를 사용함으로써 LLM 기반의 에이전트나 복잡한 워크플로를 구축할 수 있습니다.

즉, MCP는 AI 시스템과 데이터 소스를 연결하는 과정을 더 쉽고 보편적으로 만들어 주는 기술이라고 할 수 있습니다.


2. MCP를 적용하는 이유

AI 에이전트는 사용자를 대신하여 스스로 계획하고 행동함으로써 목표를 완수해야 하는데, 학습된 데이터를 이용해 단순히 답을 생성하기만 하는 수준으로는 에이전트의 임무를 달성하기 어렵습니다. 그러나 MCP를 이용하면 여러 AI 툴과 협업하고 LLM 연결 컨텍스트를 유지할 수 있기 때문에, AI 에이전트가 작업을 스스로 수행하고 목표를 달성하기까지의 과정을 서포트할 수 있습니다.

또한 MCP는 LLM이 다른 시스템을 이해하고 연동하는 데 있어 어댑터와 같은 역할을 합니다. MCP를 적용하면 LLM이 다른 프로그램을 이용할 때마다 맞춤형 인터페이스를 구현하는 등의 상이한 연동 과정을 거칠 필요가 사라집니다. 표준화된 연결 방식으로써 MCP가 작용하기 때문에, AI 모델을 다양한 도구에 연결하는 과정이 한층 간편해진 것입니다.

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(출처: https://norahsakal.com/blog/mcp-vs-api-model-context-protocol-explained/)

결론적으로 MCP를 이용하면 각 서비스의 개별적인 API를 관리할 필요 없이, 표준화된 방식으로 AI 모델이 서비스와 협업하는 과정을 간소화하며 AI 에이전트의 태스크 수행을 편리하게 도울 수 있습니다.


3. MCP 작동 원리

MCP는 클라이언트-서버 구조를 가집니다. MCP 호스트는 하나 이상의 MCP 서버와 연결을 설정하며, MCP 서버마다 하나의 MCP 클라이언트를 생성합니다. 즉, MCP 클라이언트와 MCP 서버는 1:1 대응 관계를 가집니다.

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MCP 아키텍처의 주요 구성 요소들은 다음과 같습니다.

  1. MCP 호스트: 하나 또는 그 이상의 MCP 클라이언트를 관리하는 AI 애플리케이션입니다.

  2. MCP 클라이언트: MCP 서버와의 연결을 유지하고 컨텍스트를 가져와 MCP 호스트가 사용하도록 합니다.

  3. MCP 서버: MCP 클라이언트에 컨텍스트를 제공하는 프로그램입니다.

MCP 호스트는 사용자에게 어떠한 요청을 받으면 이를 처리할 수 있는 서버에게 지시를 보냅니다. 클라이언트는 서버와 1:1로 소통하며 호스트로부터 받은 지시를 서버에 전달하고, 서버가 요청 처리를 완료하면 결과물을 다시 호스트에게 전달합니다. 서버는 로컬 또는 원격 데이터를 사용하여 요청을 처리합니다.

(출처: https://modelcontextprotocol.io/docs/learn/architecture)


4. Qurify에서의 MCP 서버 살펴보기

이번에는 Qurify에서 MCP 서버를 어떻게 적용했는지 직접 알아보도록 하겠습니다.

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Chat input의 좌측 하단에 위치한 ‘MCP 서버 설정’ 버튼을 클릭하면 MCP 서버를 직접 관리할 수 있습니다. 좌측에서는 적용된 MCP 서버 목록을 확인 가능하며, 각 서버를 켜거나 끌 수 있습니다. 우측에서는 MCP 서버를 편집하거나 새로운 MCP 서버를 등록 가능합니다.

Qurify에 기본적으로 탑재된 MCP 서버로는 총 3가지가 있습니다.

  1. Qurify MCP Server: 자연어 처리 기반의 데이터베이스 쿼리 및 분석 도구를 제공합니다.

  2. PostgreSQL: ORDBMS(객체 관계형 데이터베이스 관리 시스템)의 일종으로, 오픈 소스 기반이기 때문에 자유롭게 사용이 가능하며 우수한 확장성을 가지고 있습니다. 자세한 내용은 여기를 참조하세요.

  3. Perplexity: LLM과 웹 검색 기능을 결합한 대화형 AI 검색 엔진 서비스입니다.


4-1. Qurify에서 MCP 서버를 직접 활용해보기

3가지의 기본 MCP 서버를 활성화하여 Qurify를 직접 이용해보겠습니다.

  1. 데이터베이스 쿼리 요청

“주문한 고객들의 국적을 분석하여, 주문량이 가장 많은 국가 3곳을 알 수 있나요?”라는 질문을 Qurify에 입력했습니다.

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우측의 ‘도구 실행 결과’를 참조하면 Qurify MCP Server와 PostgreSQL를 실행한 결과를 확인할 수 있습니다. 데이터베이스 쿼리를 원하는 사용자의 요청에 따라 적절한 MCP 서버를 활용하여 요청을 처리한 것입니다. PostgreSQL의 처리 결과와 Qurify의 답변을 보면 주문량이 가장 많은 국가 3곳은 독일과 미국, 그리고 브라질임을 알 수 있습니다.


  1. 웹 검색 요청

이번에는 “금일을 기준으로 미국과 한국의 증시를 검색하여 각각 분석한 후, 주요 이슈를 요약할 수 있나요?”라는 프롬프트를 Qurify에 입력했습니다.

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우측의 ‘도구 실행 결과’를 참조하면 Perplexity를 실행하여 답변을 생성했다는 점을 확인할 수 있습니다. 직전의 경우와는 달리, 데이터베이스 쿼리 대신 웹 탐색이 필요한 질문이라고 판단하여 이를 수행할 수 있는 Perplexity가 요청을 처리했다는 것을 알 수 있습니다.

또한 사용자가 요청한 내용으로 검색을 진행한 뒤, 프롬프트에 따라 각각 분석을 거치고 주요 이슈를 포함하여 답변을 생성하였습니다. 즉 Perplexity를 활용하여 단순 검색뿐만 아니라 정보의 분석 및 가공 또한 Qurify를 통해 수행할 수 있습니다.


4-2. Qurify에서 새로운 MCP 서버 추가하기

앞서 언급했듯, Qurify는 기본 MCP 서버뿐만 아니라 사용자가 직접 추가한 MCP 서버를 이용할 수도 있습니다.

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Qurify에서 클라이언트-서버 통신을 위해 적용 가능한 연결 방식은 3가지가 있습니다.

연결 방식

요약

Streamable HTTP

일반적인 웹 API처럼 요청-응답 구조이지만, 응답을 스트리밍 형태로 받기 때문에 더욱 유연한 활용이 가능함.
서버리스 환경에 적합함.

SSE

장시간 걸리는 작업의 실시간 상태 업데이트를 단방향으로, 그리고 지속적으로 받을 때 효과적임.

STDIO

로컬에서 도구를 가장 빠르고 간단하게 연동하는 방식.
프로세스를 직접 실행하여 표준 입출력으로 통신함.

  1. Streamable HTTP: 클라이언트(MCP 호스트)가 원격 MCP 서버에 단일 HTTP 요청을 보내면, 서버는 그 요청에 대한 응답을 스트리밍 형태로 조각(chunked) 전송합니다. SSE와 달리 지속적인 연결을 유지하지 않기 때문에, RESTful API 기반 도구나 서버리스 환경에서도 쉽게 적용할 수 있는 현대적인 통신 방식입니다.

  2. SSE (Server-Sent Events): 원격 MCP 서버와 한 번 연결을 맺으면, 서버에서 클라이언트(MCP 호스트)로 단방향 스트리밍이 가능한 방식입니다. 예를 들어, 시간이 오래 걸리는 작업을 수행하면서 \\'분석 시작\\', \\'데이터 처리 중\\', \\'완료\\'와 같은 진행 상태를 실시간으로 전송할 수 있어 사용자 경험을 크게 향상시킵니다.

  3. STDIO: MCP 호스트(Qurify)가 사용자의 로컬 컴퓨터에 설치된 도구(MCP 서버)를 직접 프로세스로 생성하고, 표준 입출력을 통해 통신하는 방식입니다. 네트워크 설정이 필요 없고 실행 환경이 동일하기 때문에 오버헤드가 거의 없어 응답 속도가 빠릅니다. 주로 로컬에서 스크립트나 CLI 기반 도구를 연동할 때 사용됩니다.

MCP 서버를 추가하기에 앞서, “제품별 평균 할인율을 분석하여 시각화한 내용을 리포트로 작성할 수 있나요?”라는 질문을 Qurify에게 해보겠습니다.

\"\"

Qurify MCP Server와 PostgreSQL를 활용하여 제품별 평균 할인율을 성공적으로 분석하였지만, 사용자의 요청대로 분석 내용을 그래프 등으로 시각화하지는 않았습니다.

시각화 과정까지 만족할 수 있도록 ‘graph_generate’라는 이름의 MCP 서버를 직접 추가해보도록 하겠습니다.

\"\"\"\"

Streamable HTTP 방식으로 ‘graph_generate’라는 이름의 MCP 서버를 추가 및 활성화했습니다. 이제 앞서 입력했던 프롬프트를 그대로 활용하여 Qurify에게 분석부터 시각화 리포트 작성까지 요청하도록 하겠습니다.

\"\"

이번에는 사용자의 프롬프트에 따라 평균 할인율 분석부터 이를 시각화한 내용까지 모두 포함한 답변을 생성했습니다. 직전에 추가한 ‘graph_generate’ MCP 서버가 그래프를 생성한 사실을 확인할 수 있습니다.


5. 마치며

MCP의 등장으로 AI 에이전트를 활용하고 다른 서비스와 접목할 수 있는 기회가 많아졌습니다.

특히 Qurify의 경우에는 사용자의 기호에 따라 MCP 서버를 직접 추가 및 활성화할 수 있기 때문에, AI를 보다 유연하게 활용 가능합니다. 여러 종류의 MCP 서버와 함께 요청을 처리하며 데이터를 다방면으로 분석하거나 생각지 못한 인사이트 획득을 기대해볼 수 있습니다.

'},{Slug:\"v-0-4-3-en\",id:0,Title:\"0.4.3\",Date:\"2025-07-04T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"eng\",\"preview-contents\":\"\",Content:'

New Features

Prompt Configuration Setting


MCP Server Integration & Configuration


Changes

Management Button Separation

'},{Slug:\"v-0-4-3\",id:0,Title:\"0.4.3\",Date:\"2025-07-04T00:00:00.000Z\",Image:null,Author:\"smartmind\",category:\"release-note\",language:\"kor\",\"preview-contents\":\"\",Content:'

New Features

프롬프트(지침) 설정 기능 업데이트

✎ 관련 포스트: 프롬프트 설정으로 정밀하게 데이터 분석하기

MCP 서버 연동 및 설정 업데이트

✎ 관련 포스트: MCP를 통해 다양한 AI 에이전트와 협업하기


Changes

개인 맞춤 설정 - 관리 버튼 구분

'},{Slug:\"a-faster-and-more-accurate-method-for-rag-implementation-case\",id:3,Title:\"A Faster and More Accurate Method for RAG Implementation Case\",Date:\"2024-11-17T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/SveOVA3nMQ74p6cfujHbHry3BQY.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"We address the practical challenges enterprises encounter when implementing RAG systems—specifically tackling hallucination issues and declining search accuracy. By classifying documents based on metadata and organizing collections according to specific scenarios, we deliver a structure that improves both search speed and precision. We also provide guidance on utilizing vector databases to extract similar information with accuracy.\",Content:'

1. The Limitation of Generative AI and LLM Utilization

As you may know, over the past one to two years, generative AI models based on various LLMs, such as OpenAI\\'s ChatGPT, Google\\'s Gemini, and Meta\\'s LLaMA, have become widely popular. Naturally, businesses are increasingly adopting generative AI to enhance productivity.

Generative AI operates on LLMs pre-trained with vast datasets, allowing them to provide accurate answers to ordinary questions. However, when it comes to domain-specific expertise or queries requiring additional datasets, they may generate irrelevant or incorrect responses without traceable sources.

This phenomenon, known as hallucination, occurs when a model outputs false information as if it were factual, representing a significant limitation of general-purpose generative AI models.

For businesses, relying on incorrect information for decision-making is unacceptable, making the issue of hallucination a significant barrier to adopting generative AI in corporate environments.

To address this issue, companies typically employ prompt engineering, where specific instructions are tailored for the LLM, or fine-tuning, where model parameters are adjusted. However, prompt engineering has limitations, such as constraints on input length and the degree of performance improvement achievable. On the other hand, fine-tuning is technically complex, requires high-performance computing resources, and incurs significant costs.

2. Increasing Demand for LLM+RAG Services Targeting Corporate Internal Data

Retrieval-Augmented Generation (RAG) is one of several methods designed to overcome the limitations of generative AI. Simply put, it combines the information retrieval component (referred to as the Retriever) with the answer generation component to improve the accuracy of the final output. RAG involves converting a company\\'s data into vectors and storing them in a database. When a user submits a query, it is transformed into a search query, and similar data is retrieved from the database to assist in generating the answer.

The advantage of RAG is that it allows user data to be incorporated quickly and cost-effectively without requiring additional model training, simply by adding a retriever. Moreover, since the embedded data is indexed and managed, it is possible to identify which data was referenced in generating the answer and even determine whether hallucination has occurred.

\"\"

By using RAG, the accuracy of answers improves significantly compared to using only LLMs. As a result, many companies are increasingly adopting RAG to build Q&A systems targeting their internal data.

3. RAG Implementation Process

The complete process of implementing RAG, from connecting data sources to receiving answers, is outlined in the diagram below.

\"\"

▲ RAG Implementation Process

Each step can utilize tools provided by LangChain or various external tools as needed.

Key decisions, such as selecting the embedding model for documents or choosing a retrieval technique for implementing the retriever, offer multiple options and play a crucial role in improving the accuracy of RAG, along with prompt engineering.

4. High-Quality RAG Use Case

Let us examine a recent case where SmartMind implemented RAG using Qurify for a client.

The project focused on developing an RAG chatbot to process the client’s internal business documents. How can faster and more accurate responses be achieved compared to conventional RAG chatbots?

The general steps for implementing RAG mentioned earlier are well-documented online, with many excellent reference materials available. Therefore, we will not go into detailed explanations here.

In this post, we will discuss what specific steps were taken to enhance response quality when applying RAG in real-world corporate settings, focusing particularly on improving speed and accuracy during the \\'retrieval\\' phase.

In most cases, when companies adopt RAG-based chatbots, the data targeted by RAG typically consists of the company\\'s internal \\'documents.\\'

However, not all documents are created equally and can vary in several aspects. Key differences include (1) the content of the document (e.g., related to specific tasks), (2) access permissions (e.g., confidential documents or those restricted to specific departments), and (3) whether the document can be disclosed (e.g., cases where copyright issues prevent revealing certain materials). To address these differences, such information must be appended to the documents as metadata.

\"\"

▲ Document and Metadata

For example, consider a document titled \\'S/W Development Methodology.\\' This document is related to development (document topic), accessible only to members of the development team (access permissions), and classified as confidential (disclosure status). Metadata reflecting these attributes would be added to the document. Another example is a document titled \\'Company Life Guide,\\' which pertains to general company information, is accessible to all employees, and is publicly shareable. Metadata would also be added to represent these characteristics.

While it is possible to assign unique metadata to every document, this approach can become burdensome to manage. Instead, documents with the same metadata are grouped together and labeled as a \\'collection.\\' Metadata is then assigned at the collection level, meaning all documents within a specific collection share the same metadata.

\"\"

▲ Metadata Based Document Collection

Corporate chatbots can serve a variety of purposes. For instance, there may be chatbots designed for specific departments or roles (e.g., a chatbot for security monitoring staff in the IT security department) and chatbots focused on specific areas for all employees (e.g., a chatbot supporting company welfare-related tasks). In Qurify, such diverse use cases are referred to as \\'scenarios.\\' Since each scenario requires different RAG data, effectively mapping the document collections defined earlier to these scenarios can save time and resources by preventing the retriever from searching through unnecessary data. Moreover, using only the relevant data enhances the accuracy of the chatbot\\'s responses.

\"\"

▲ Collection and Scenario Mapping

For example, if a legal officer inquiry about the subcontracting terms of a specific project, the chatbot selects the relevant collections, such as the project execution collection and the laws/regulations collection and performs searches only within those collections. By avoiding unrelated collections, the chatbot not only minimizes the risk of generating false information but also reduces response time.

5. Conclusion

Depending on the types and characteristics of a company’s documents and the variety of scenarios to be applied, additional metadata beyond the three mentioned earlier may be required. While tagging metadata for collection configuration can be a labor-intensive process, certain aspects can be automated with AI. This step is essential to improve the speed and accuracy of RAG searches.

By implementing RAG through the mapping of collections and scenarios using Qurify, companies can deliver high-quality services efficiently across various domains.

'},{Slug:\"how-much-does-it-cost-to-build-an-ai-service-based-on-langchain\",\":draft\":true,id:0,Title:\"How Much Does It Cost to Build an AI Service Based on LangChain?\",Date:\"2024-10-26T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/tjJOm6zQkogUcrVk2c0N1LIY0E8.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

With the recent surge in LLM services, many companies find that existing products do not fully meet their needs, and those that do often come at excessive costs. This has led some to consider developing an AI application tailored to their requirements.

Today, we will estimate the cost for a company to build an AI application based on LangChain.

1. LangChain-Based AI Applications

LangChain is a framework that allows you to build applications powered by large language models (LLMs). LangChain provides a standard interface for connecting various components and includes features such as chaining, data awareness, and agents, which allow applications to be built more easily and quickly.

The diagram below shows how LangChain and agents operate.

\"\"

▲ Operation Mechanism of LangChain and Agents

In short, agents work by calling a Python agent in the background, which executes Python code generated by the LLM. User questions or prompts are interpreted by the LLM and converted into Python code, which is then passed to a Python engine for execution. The Python engine can access and read data necessary for the process.

2. LangChain (LLM & AI Agent) Ecosystem

The diagram below illustrates the LangChain ecosystem.

\"\"

▲ LangChain Market Map (Source: CBInsights)

As shown in the diagram, there are numerous solutions in each area of LangChain\\'s ecosystem (and even this does not cover everything), placing the challenging task of choosing and orchestrating the right solutions entirely on the company.

Since it is difficult to select a solution in every area and estimate each of their costs, we will instead focus on a few specific components as outlined below.

\"\"

▲ Key Cost Factors within the LangChain Ecosystem

Let us look at each component below.

3. Solutions and Estimated Costs for Each Component

As seen in the diagram above, the components that make up the LangChain ecosystem are highly diverse, but for convenience, we have simplified them to a few key elements here.

These mainly include the LLM, vector DB, in-memory data store, and data platform.

To get a rough sense of costs, we have chosen representative solutions and estimated expenses based on hypothetical criteria.

Keep in mind that the actual costs will vary depending on the scale of datasets and the performance or quality requirements of each organization. The figures in this post are for reference only.

1) LLM

The cost of LLMs varies depending on context. For popular models, prices based on one million tokens are as follows:

\"\"

▲ Price per 1 Million Tokens by Major Model


Assuming daily usage of 1,000 tokens and 1 million tokens, the annual cost calculation results in the following estimates:

\"\"

▲ Annual Costs Based on Usage and OpenAI/Cloud-Hosted Models (Source: Skanda Vivek)

For OpenAI, low usage (1,000 tokens per day) costs around $1,000 to $50,000 annually, while high usage (1 million tokens per day) costs range from $1 million to $5.6 million annually.

Open-source models like Falcon-7B, LLaMa2-7B, and Vicuna-33B, on the other hand, have significantly lower hosting costs for higher usage than OpenAI.

2) Vector DB

We selected Pinecone as a representative vector DB solution and set hypothetical criteria as follows:

Assuming +a is zero, if we multiply the above amount by 12, the annual cost would be approximately 164 million KRW.
(As mentioned previously, please keep in mind that these figures are not absolute and are provided solely as a reference.)

It is important to note that this setup is for a relatively small dataset and is not configured for high-performance runtime. This setup supports a relatively small dataset with a non-optimized runtime configuration, handling only 30 queries per second. For context, initializing 10 million vectors would take about four days at this rate. If higher performance or speed is required, additional replicas become necessary, increasing costs significantly. Companies with large corpora for RAG applications might expect costs 10 to 100 times higher than this baseline estimate.

3) In-Memory Data Store

In-memory data store is an in-memory database that provides sub-millisecond data access, scalability, and high availability.
We selected Redis as a representative solution for our in-memory data store and set an arbitrary standard for cost calculation as shown below.

Using the above specifications in a managed service form would cost approximately $6 per hour, totaling $4,300 per month. Multiplying this by 12 for an annual estimate, the cost comes to around 71 million KRW.

4) Data Platform

Leading data platform solutions in the market include Databricks, Snowflake, and Dataiku.

Since all data platform costs fluctuate based on usage, it is difficult to provide a definitive amount.

For mid-sized U.S. companies, Databricks costs typically range from $100,000 to $1 million annually, depending on average data usage. (Source)

Thus, the estimated annual expense for a company might range from about 138 million KRW to 1.38 billion KRW ($115,000 to $1.15 million).

5) Pipeline Construction and Integration

Finally, there are the costs of constructing and integrating a pipeline that links all components of the LangChain ecosystem. This part is challenging to standardize, and since it can vary from case to case, we will not be estimating the costs for it.

It is important to consider that pipeline construction and integration require a prominent level of expertise and considerable time investment from multiple professionals, potentially resulting in substantial (or even the largest) costs.

Additionally, there will be hidden extra costs related to Machine Learning Operations (MLOps) or Large Language Model Operations (LLMOps).

So, what is the total when we add up all five components? That is right: building an AI application based on LangChain is extremely expensive. Not only are the costs high, but it also requires harmonizing numerous elements, as well as managing ongoing operations and maintenance. This is precisely why many companies find AI adoption challenging, and even when they start, they often face failure.

4. Is a Data Platform Necessary?

Apart from LLMs, data platforms make up the largest cost. In most cases, developing an AI application based on LangChain necessitates using a data platform. While it is possible to build an AI service without a data platform solution, there would be a significant increase in custom (or outsourced) development requirements and difficulty.

Below is a brief comparison of characteristics, costs, and difficulty levels for each approach with or without a data platform.

\"\"

The mentioned solution costs and labor expenses are approximate amounts under specific conditions and are intended only for comparative reference.

5. Conclusion

LangChain is an open-source toolkit prepared to use LLM, prompt engineering, RAG, text-to-SQL, other ML services, and external APIs.

RAG and prompt engineering require vector DB and in-memory data store, and other machine learning models require a data platform.

LangChain, as a protocol, allows any solution to integrate into its functionality as long as it aligns with LangChain standards. From a customer perspective, however, using LangChain still requires individually selecting and paying for necessary solutions, such as vector DB, in-memory data store, and data platform.

Meanwhile, data platforms used for data analysis and machine learning development were designed before LLM and GPT models; therefore, they do not process data in a vector DB format. This means unstructured data like images, videos, and text must be stored in costly data storage within the data platform.

If a company opts to develop an AI application using ThanoSQL instead of the LangChain approach, these complex issues can be resolved all at once.

Additionally, hanoSQL offers significantly higher accuracy and execution speed compared to the LangChain approach, all at a lower cost. This is because, along with LLMs, ThanoSQL leverages Transformer models for other machine learning developments, making full use of its built-in vector DB. In practice, raw data is stored in inexpensive distributed storage, like S3, while the data used for analysis is converted into much smaller embedding vectors, which are then stored and processed in the vector DB.

ThanoSQL enables the TAG framework (using LLM, prompt engineering, RAG, text-to-SQL, additional ML models, and external APIs within SQL) to function by integrating transformer models for LLM and other machine learning (analysis).


ThanoSQL Implementation Inquiry

'},{Slug:\"thanosql-vs-ai-agents-analyzing-complex-business-queries-with-efficiency-and-accuracy\",\":draft\":true,id:13,Title:\"ThanoSQL vs. AI Agents: Analyzing Complex Business Queries with Efficiency and Accuracy\",Date:\"2024-10-12T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/iqdpvUVHfvF817a7rnJHvX14dIo.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"Can AI Agents Solve or Analyze Complex Problems?\",Content:'

Can AI Agents Solve or Analyze Complex Problems?

Recently, the term \"AI Agent\" has become as prevalent as \"LLM\" (Large Language Model). In August, more than ten companies at home and abroad unveiled products they claim to be AI agents.

Today, let us explore what AI agents are and whether they can solve or analyze complex problems arising in real-world business scenarios.

1. About AI Agents

From the perspective of LLMs, an agent is a system that uses an LLM as a fundamental component to formulate plans through appropriate reasoning and solve problems using available tools and resources. It is like how humans contemplate strategies and utilize necessary tools when faced with a problem. In an agent, the LLM functions much like the human brain. The ideal way to solve a problem is to divide it into one or more smaller tasks that can be performed sequentially or independently. Agents perform this same function.

Typically, the LLM plans how to solve the problem, and the agent uses one or more tools. In addition to tools, agents have other components like prompts or memory to perform intended functions properly. The diagram below illustrates the configuration of a single-agent system.

\"\"

A single-agent system consists of a specific AI agent capable of using multiple tools to solve a problem. Such systems are designed to autonomously handle tasks by leveraging the combined capabilities of tools and LLMs\\' reasoning abilities. 

Due to memory constraints and processing limitations of single-agent systems, multi-agent systems are needed when broader tasks or more complex problems must be addressed. A multi-agent system comprises multiple single agents, each capable of performing tasks autonomously but designed to collaborate toward a common goal.

\"\"

The diagram above shows the configuration of a multi-agent system. Even though multi-agent systems may seem all-powerful, they still have limitations. (Refer to our previous post on Limitations of LLMs and Langchain, Problems of Agents, and TAG)

2. Methods for Solving Users\\' Complex Natural Language Queries

When a user poses a lengthy and complex question in natural language, there are three ways to solve the problem, as shown in the diagram below:

\"\"

▲ Handling Complex Natural Language Queries Using LLMs


LLMs 1) Convert the user\\'s input into agent language and process it using the agent approach. 2) Use Text-to-SQL to transform it into SQL and handle it with TAG. 3) Process it using ThanoSQL.

Before we proceed, let us briefly touch on what Text-to-SQL is.

\"\"

Returning to the main topic, although agent systems are currently mainstream, they have clear limitations for enterprise use due to slow speed and low accuracy. While TAG (TableAugmented Generation), a paper published last month, is expected to alleviate some of the problems of agents, it is still in the research stage and not ready for immediate enterprise adoption.

SmartMind\\'s ThanoSQL is a solution that implements the concept of TAG, expanding it not only to LLMs but also to RAG, external APIs, and all other actions.

3. ThanoSQL: Commercial Implementation and Expansion of TAG

When a user poses a lengthy and complex question in natural language, there are three ways to solve the problem, as shown in the diagram below:

\"\"

▲ ThanoSQL as an LLM-based Analytical Tool

ThanoSQL is an LLM-based analysis tool designed for non-IT personnel, accessing those non-IT personnel to analyze complicated data quickly. Traditionally, various such as SQL, RAG, AI/ML, and external APIs have been required to analyze data. An integrated solution is essential to connect these tools effectively; technologies like LangChain make this possible. LangChain helps connect multiple tools in a chain format using programming languages like Python or JavaScript.

However, connecting these tools and appropriately utilizing them for specific problems is not simple. You must rely on AI to select tools and determine their usage sequence. Since there is no guarantee that the AI\\'s answers are always correct, incorrect results can occur. Users find it difficult to verify whether the final output is generated correctly, leading to inefficient situations where they have to explain the required modifications again in natural language.

SQL was developed for precise communication with databases, allowing the execution of a single task and easy modification when necessary. Using nested SQL minimizes the risk of repeated or incorrect tool application orders.

ThanoSQL was designed to solve these issues. While it shares the same goals as LangChain, it primarily targets non-IT personnel. It takes a different approach to solving complex problems. Instead of the agent-based sequential approach, ThanoSQL solves complex problems using nested SQL. In the current market, various products offer individual functionalities; however, an all-in-one solution like ThanoSQL that can solve complex queries simultaneously does not exist.

4. Case Studies of Analyzing Complex Queries

Let us look at how ThanoSQL processes the following two questions:

Question 1) \"Extract reviews with ratings of 2 or below within the last three days, summarize the content related to side effects, create a summary, and find similar posts on the web.\"

First, we create the necessary functions: ① Summarize the review content. ② Search reviews where the content is about \\'side effects.\\' ③ Perform a web search (using external APIs).

Next, we generate and execute SQL queries one by one through Text-to-SQL in order. Select reviews with ratings of 2 or below within the last three days -> Summarize those reviews -> Select reviews related to side effects -> Output the selected results -> Output web search results

The SQL statements processed in ThanoSQL are shown in the diagram below.

\"\"

Now, let us move on to the second question.

Question 2) \"Analyze changes in average sales over the past week, identify changes in customer review keywords, extract reviews written with those keywords, compare them with recent trending search terms, and propose a new marketing strategy based on this.\"

As in the previous case, we create the necessary functions and generate and execute SQL queries.

The actual SQL statements processed in ThanoSQL are shown in the diagram below.

\"\"

ThanoSQL automatically generates nested SQL statements that utilize SQL, RAG, AI/ML, and external APIs to handle complex questions like the ones above. This approach ensures consistent answers by sequentially executing each tool.

You can review the automatically generated queries in ThanoSQL to verify whether the transformations occurred as intended and, if necessary, immediately modify or supplement them through queries. Additionally, you can ask further questions based on the answers and verify whether those answers are correct.

Such \\'process verification\\' and \\'result verification\\' are impossible with agent-based methods relying on LangChain; they are only possible with ThanoSQL.

5. Conclusion

As seen in the cases above, when you compose a result table by writing the user\\'s complex natural language queries into nested SQL queries and then make natural language queries based on that table, you can not only overcome the problems and limitations of the agent approach but also gain the following advantages:

■ You can ask further questions and perform analyses based on the table created.
■ The SQL generated in this way can be used repeatedly, stored, shared, and utilized. This improves efficiency in repetitive tasks by reusing frequently used or recently used SQL to extract insights.
■ Improving analysis results is very straightforward because it is based on SQL, letting users swiftly identify and solve issues.
■ Well-refined SQL generated from natural language queries can be used as standalone applications.
■ Combining several such SQL statements allows you to create more advanced applications easily and quickly.

We look forward to your continued interest in ThanoSQL.

'},{Slug:\"limitations-of-llms-and-langchain-problems-of-agent-and-tag\",\":draft\":true,id:12,Title:\"Limitations of LLMs and Langchain, Problems of Agent, and TAG\",Date:\"2024-09-27T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/OOGQ1UWzWPnDm51PFbyUVAOLFkU.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Today, many AI developers use LangChain to create most of the recent LLM-based AI services. LangChain is a framework that simplifies application development by using LLMs.

\"\"

▲ LangChain Diagram (출처: LangChain 101: Build Your Own GPT-Powered Applications)

Developers use LangChain as a great starting point and educational tool. However, they do not design it for real-world operational use. It is like going to Yongsan Electronics Market (Micro Center) to assemble a server yourself—building and running your pipelines and chains at a production level for business use is like purchasing and assembling every computer part by hand.

We will analyze the limitations of LLMs and LangChain, ways to overcome them, and the recently introduced Table Augmented Generation (TAG).

1. Limitations of LLMs

LLMs are AI models designed to \\'generate\\' the most appropriate answer that \\'pairs\\' with the question. LLMs are trained in many sample documents and produce plausible answers to a wide range of questions, which has garnered significant attention; however, a few drawbacks have soon arisen on the surface.

The first con is the hallucination phenomenon, where they provide plausible-sounding answers to things they do not know.

The second con is that many problems (such as math or physics-related computational issues) cannot be solved using a \\'generative\\' method. 

The third con is that they cannot solve problems requiring multiple reasoning steps.

Experts have noted that GPT-4o, OpenAI\\'s latest model, has significantly improved its math (computational) capabilities. So, GPT-4o was tested with a high school-level math problem, as shown below:

If the speed of a plane is four times faster than that of a train, it takes the train 9 hours longer than the plane to travel 1,200 kilometers (about 745.65 mi).
Then, what is the speed of the train?

The following image shows the result of asking ChatGPT-4o about it. Initially, there was an error while solving the problem, but GPT-4o solved it after several retries. We can infer from this result that Python code is running in the background, and LLMs rely on LangChain to imitate calculation as a probabilistic model.

\"\"

Even \\'GPT-4o,\\' the latest model well known for significantly improved problem-solving ability in math, only achieved a score of 13% in the preliminary round of the International Mathematical Olympiad (IMO).

According to namuwiki (Wikipedia), the performance of GPT-4o is as follows: \"Although its computational abilities have greatly improved, it is barely able to solve four-point level math problems in the 2024 Korean college entrance exam (CSAT), and it cannot solve any Physics 1 questions except for the fundamental conceptual questions. GPT-4o can immediately solve problems like finding the derivative of a function at a specific point if one provides the equation in an ideal form or solving a standard fourth-degree polynomial equation. However, it fails to accurately determine undetermined coefficients based on given conditions or interpret and solve basic mechanics problems that neglect friction and air resistance. This phenomenon reflects that the AI model is not considered enough and programmed to handle mathematical structures, irrational numbers, imaginary numbers, and other related items.
It can barely manage problems up to three-point level CSAT questions; thus, based on the cutoff grades for the calculus in 2024 CSAT, GPT-4o\\'s performance is approximately equivalent to a grade 5 out of 9.\"

2. Solutions to overcome the limitations of LLMs

To solve the first issue, Retrieval-Augmented Generation (RAG), which forces the model to answer based solely on provided reference materials, is widely used because it is considered highly effective.

A method called \"Tooling\" is widely applied to solve the second issue, where various tools (i.e., calculators, web search, database search, program execution, and others) are predefined. Then, the appropriate tool pair for the given input problem is selected and applied.

To solve the third issue, a technique called \"Agent\" is used, which breaks down the problem-solving process into multiple steps and solves each step via using Tooling (including RAG) or LLMs, and if it achieves the desired result, the process moves to the next step; otherwise, the system retries. \"Agent\" is a selection algorithm that interprets natural language and combines LLMs with analysis platforms. The Agent autonomously selects and organizes actions from a Pool (a collection of AIs needed for analyses that LLMs cannot perform) and sequentially executes them to provide answers. In other words, the Agent method explores solutions \"autonomously\" by using LLMs, RAG, and Tooling until the correct answer is obtained.

\"\"

▲ (출처: Intro to LLM Agents with Langchain: When RAG is Not Enough)
The image above conceptualizes LangChain\\'s key modules: LLMs, RAG, and Agent. The RAG architecture enhances the LLMs with external memory, and the Agent extends this concept into memory, reasoning, tools, answers, and actions.

3. Cons of Agent Methods

When using the Agent method, predicting the outcome before executing each step is impossible. Many possibilities arise since the next step is determined based on the previous result. This approach makes it difficult to find the desired path, or even if there are few possibilities, loops between steps may occur, making the risk impossible to escape. 

Also, uncertainty increases as the number of steps increases, significantly increasing the possibility of getting incorrect answers.

Moreover, users cannot tell or control the problem-solving process because the Agent autonomously handles the process. In other words, debugging becomes impossible, so it is difficult to identify where things went wrong or correct the process midway, leaving users with no choice but to restart from the beginning.

Below is an image illustrating the operating principle of an Agent.

\"\"

Since the Agent autonomously selects algorithms, users cannot predict the execution time, nor do they know when it will finish—there is even the possibility that it may never finish (due to an infinite loop). Also, users cannot know which algorithms are selected or executed, so it functions like a black box, like AlphaGo.

4. The Emergence of TAG

A novel approach has emerged that combines databases with the inference capabilities of LLMs to handle natural language queries that require semantic reasoning or knowledge beyond what can be directly obtained from data sources. To overcome the limitations of LLMs, following the search-augmented generation (RAG) approach that retrieves web data, this recent technology, which directly connects to databases, is called \"Table-Augmented Generation (TAG).\"

On September 2nd, VentureBeat, Inc. reported that researchers from UC Berkeley and Stanford University published a paper on TAG, a new paradigm for answering natural language questions using databases in archives.

According to the paper, TAG consists of three processes: \"Query Synthesis,\" which transforms a user question into an executable database query; \"Query Execution,\" which runs the query to retrieve relevant data; and \"Answer Generation,\" which generates a natural language response using the data and query. These processes together enable TAG to handle a broader range of queries than systems like text-to-SQL or RAG, which are limited to specific queries. 

For example, it can answer a question like, \"Summarize reviews for the highest-grossing romance movie considered a classic.\"

This question is difficult for traditional text-to-SQL or RAG systems because it requires identifying the highest-grossing romance movie in the database and determining through world knowledge whether the movie is considered a \"classic.\"

However, using TAG\\'s three-step approach, the system can generate a query for the relevant movie data, use filters and a language model to create a table of classic romance movies ranked by revenue, and then summarize reviews for the highest-ranked movie in the table to provide the desired answer.

Below is an example of TAG\\'s implementation from the paper.

\"\"

According to benchmarks, the TAG baseline model consistently maintained an accuracy of over 50% across all query types and improved by 20% to 65% compared to standard baseline models. In addition, the researchers found that TAG\\'s implementation performed query execution three times faster than other baseline models.

5. Comparison with ThanoSQL

Similar to Agents, ThanoSQL utilizes LLMs, RAG, and Tooling. However, instead of solving problems step by step like Agents (such as solving a maze by addressing each branching path in sequence), it converts the problem into a SQL query (e.g., \"Give me the top 10 largest values\"). It executes the query to provide the results directly to the user.

The advantages of this approach are, first, there is no risk of getting stuck in an infinite loop. Since SQL is a declarative and deterministic language, there are no issues with exploring every path or getting caught in a loop. Instead of using commands to derive results, it specifies and retrieves the desired outcome in a non-procedural manner.

Second, because the LLMs express the process used to solve the problem as a query, users can modify it immediately if LLMs solve the problem incorrectly or inefficiently to obtain an improved answer. In the ThanoSQL workspace, you can click the \\'>>\\' button at the bottom right of the chat response window to check the executed query in the Query Editor, make modifications, and rerun it.

Solving problems by converting them into SQL offers these advantages, and combining it with RAG, LLMs, and Tooling doubles the accuracy compared to relying solely on the SQL functionality provided by traditional databases, which is the main point of the TAG paper. ThanoSQL extends the capabilities of conventional SQL to incorporate these features.

\"\"
Simply put, ThanoSQL can be considered a type of TAG. It incorporates not only LLMs but also RAG, external APIs, and even AI directly into SQL, maintaining the advantages of TAG even with more complex commands.

Compared to Agents, ThanoSQL always offers predictable execution times, provides accurate answers, is debuggable, and operates at significantly faster speeds.

Although TAG was introduced in a paper less than a month ago, SmartMind has been independently developing ThanoSQL since 2018. The company has applied for fourteen patents, registered twelve patents, and is pursuing six international PCT patents, with the product already commercialized.

'},{Slug:\"the-easiest-and-fastest-way-to-use-hugging-face-s-sota-model\",\":draft\":true,id:11,Title:\"The Easiest and Fastest Way to Use Hugging Face’s SOTA Model\",Date:\"2024-09-24T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/vrct0Vt2bFjmJUcNDYI7IWcYWm4.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

SOTA is an abbreviation for State-Of-The-Art,
referring to \"today\\'s leading AI models.\"

This post will analyze how to integrate and utilize Hugging Face\\'s SOTA models.
We will create an AI function based on user needs and, by submitting a natural language query via chat, demonstrate how the AI function responds using a practical example. This process involves importing and registering a SOTA model from Hugging Face.

For this example, we will use the CLIP model, one of many SOTA models available. CLIP stands for Contrastive Language-Image Pre-training model and was developed by OpenAI. This neural network architecture is designed to understand natural language and process computer vision, enabling machines to comprehend human language and images. Since 2022, CLIP has been a vital tool behind the rise of AI-generated images. OpenAI\\'s DALL-E was also built using CLIP.

Searching for CLIP in the \\'Models\\' tab on Hugging Face will find over 2,900 registered models (as of September 20, 2024), as shown in the image below.

\"\"
We will select the clip-vit-base-patch32 model, a zero-shot image classification model.
This model embeds text and images in the same space, and we will explore how it can be practically applied.

Creating an AI Function with a SOTA Model

We aim to create an AI function that can find images similar to a given input image or retrieve an image based on the user\\'s input.

We have named this function embed_clip, and its implementation is shown below.

\"\"
DROP FUNCTION IF EXISTS embed_clip(text);  \\n\\nCREATE or REPLACE FUNCTION embed_clip(text_or_image TEXT) \\nRETURNS VECTOR AS $$ \\nBEGIN\\n    RETURN thanosql.embed(\\n        engine := \\'huggingface\\',\\n        input := text_or_image,\\n        model := \\'openai/clip-vit-base-patch32\\',\\n        model_args := \\'{\"device_map\": \"cpu\"}\\'\\n    ); \\nEND; \\n$$ LANGUAGE plpgsql;

You can declare and implement the function in ThanoSQL\\'s Query Manager (QM). By setting the engine to Hugging Face and defining the model as openai/clip-vit-base-patch32, you can immediately start using the CLIP-based SOTA model from Hugging Face.

\"\"

By pressing the triangular button at the top of the Query Editor, you can execute the function, and the results will appear in the query results window below.

Creating a Sample Table for the AI Function

Next, we will create a sample table to use with this function.
We need a table where images are stored. If such a table does not exist, you can create a new one, import an existing one from a database, or upload a CSV file.

For this example, we downloaded ten images from the web and created a table named unsplash_meta_sample. The schema of that table is shown in the image below.

\"\"

You must embed the data first to classify and search through unstructured data like images. We will embed the sample table using the embed_clip function we created earlier. The picture below shows the query that applies the embed_clip function to the sample table and stores the results in a table named test_vector.

\"\"
DROP TABLE IF EXISTS test_vector;\\n\\nCREATE TABLE test_vector\\n  AS SELECT photo_id\\n          , photo_image_url\\n          , photo_submitted_at\\n          , photo_description\\n          , embed_clip(photo_image_url) AS embedding\\n     FROM my_data.unsplash_meta_sample;\\n\\nSELECT * FROM test_vector

Once the query is successfully executed, you can view the embedded results for each image in the Query Result window, as shown below.

\"\"

In the Data Viewer, the embedded images will appear as follows.

\"\"

Searching for Images Using an Image

Next, download an image from the web. We will download a desert photo from Unsplash. Copy the address (URL) of the image.

\"\"

Return to the ThanoSQL workspace, select AI Chat from the menu on the left, and choose the test_vector table created earlier

\"\"

Now everything is ready. Let\\'s use the chat to find three photos similar to the desert image from Unsplash.

\"\"

In the chat input box, enter the following:

\"Embed the image at https://images.unsplash.com/photo-1464822759023-fed622ff2c3b using the embed_clip function and find the three most similar photos.\"

This command passes the image URL to the custom function, which executes a query to find the three most similar photos from the sample table.

\"\"

After entering the chat input and pressing Enter, the query will be executed, and the results will appear as shown below.

\"\"

The executed query can also be viewed in the Query Editor, as shown in the image below.

\"\"

You can check the retrieved images through the Data Viewer.

\"\"

Searching for Images Using Text

Earlier, we searched for images using an image as input. Now, let\\'s search for images using text.

For example, to find a photo of a person surfing in the sea:
In the ThanoSQL workspace, select AI Chat from the menu on the left and choose the test_vector table created earlier.

\"\"

In the chat input box, enter the following:

\"Embed \\'a person surfing in the sea\\' using the embed_clip function and find the most similar photo.\"

This command embeds the user\\'s text input and passes it to the function, which then executes a query to find the most similar image from the sample table.

\"\"

After entering the chat input and pressing Enter, the query will be executed, and the results will appear as shown below.

\"\"

The executed query can also be viewed in the Query Editor, as shown in the image below.

\"\"

You can directly view the retrieved images by clicking the link in the response below.

\"\"

As with the previous example, you can view the images through the Data Viewer.

\"\"

Conclusion

In this post, we demonstrated how to import a SOTA model from Hugging Face, create a custom function, and use it to search for images in two diverse ways. As shown, ThanoSQL allows anyone to easily and quickly apply AI models from Hugging Face or other external sources and run them instantly.

Next time, we will explore a use case involving a machine learning model other than an image classification model.

'},{Slug:\"break-through-ai-adoption-barriers-how-thanosql-simplifies-business-data-analysis\",\":draft\":true,id:10,Title:\"Break Through AI Adoption Barriers: How ThanoSQL Simplifies Business Data Analysis\",Date:\"2024-09-07T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/cFgJP4WpPzPsDmqDdI6yrBUa3U.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

The Process of AI Adoption in Businesses and Its Challenges

\"How can we adopt AI in our company?\"

In the previous post, we examined the main challenges businesses face when adopting AI and how these can be addressed.
– What makes adopting AI so challenging for companies?

In this post, we will continue by discussing businesses\\' actual process when adopting AI applications or services and the challenges they face along the way.

A sales or marketing team member within the company may have needs such as the following:

\"\"

When a business attempts to adopt AI apps or AI services based on the person in charge\\'s needs or requirements, the general company\\'s workflow and each group\\'s roles are shown in the image below.

\"\"
Not only does it take a significant amount of time to begin actual development, but in many cases, the results, delivered months later, often fail to meet the expectations of the team in charge.

Why does AI adoption take so long, and why are the results often disappointing?

Controlling and analyzing unstructured data are essential to developing AI applications or adopting services. This is because more than 90% of today\\'s business data is estimated as unstructured data. However, most groups find data formats such as texts, audio, video, sensors, or other types challenging. According to foundry research, 65% of IT leaders responded that unstructured data management is troublesome to their group, and 24% answered that they do not even update their unstructured data on the analysis list.

Various tools for collecting and preprocessing these unstructured data are included, like the image below.

\"\"\"\"

In traditional methods, companies need to select the necessary tools from these options, install and run them, and then connect the data to build a pipeline. Each tool requires its own specialist, and managing the integration and maintenance of the pipeline is a very complex task. Additionally, many companies already have existing data pipelines built for structured data, and they must also consider how to integrate the newly developed unstructured data pipeline with the existing one.

Many companies have adopted LLM-based services recently. However, collecting and preprocessing unstructured data are causing severe bottleneck phenomena on LLM architectures, shifting from prototype to practice or operating level.

\"\"
For LLM-based AI services, it is crucial to convert unstructured data into a format suitable for LLMs and to preprocess the data so that the model can efficiently retrieve the required data at the inference stage.

If so, is there any way to overcome these challenges?The situation becomes quite different if a company has adopted or plans to adopt ThanoSQL. The image below represents the work process flow and the roles of each person in charge.

\"\"

Compared to the general process for other companies, the procedure is much simpler, and there is no need to collaborate with other organizations, such as procurement or external vendors. Even in one to two days, various analytics of the person in charge can be tried, and their results can be checked instantaneously.

\"\"

The above image indicates the conceptual targeting of data flow and processing method. Another post (The Easiest Way to Integrate and Utilize Hugging Face\\'s SOTA Models) discusses how to integrate and utilize Hugging Face\\'s SOTA models.

Let us see an example of the questions and queries in the following image.

\"\"
Like the example of the needs of the person in charge, helpful insight into the relationship between customer emotion and product success can be found by sorting out customer reviews, which are unstructured data with an AI model, and connecting them with the sales data. This type of analysis helps set marketing strategies or product development and customer services. It, ultimately, contributes to customer satisfaction improvements and business growth.

ThanoSQL by SmartMind is an all-in-one platform that supports companies to store structured and unstructured data in database tables and analyze using the latest AI models through an interactive interface. If you adopt ThanoSQL, you do not need to worry about various challenges when adopting AI apps or services in general.

'},{Slug:\"what-makes-adopting-ai-so-challenging-for-companies\",\":draft\":true,id:9,Title:\"What makes adopting AI so challenging for companies?\",Date:\"2024-09-05T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/bf096PEx8G6z23oA9R1GJavlmk.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

What makes adopting AI so challenging for companies?

Many organizations say, \"Implementing AI within our company has proven extremely difficult.\" But what\\'s truly behind these challenges?

This post explores the key obstacles companies face when adopting AI and offers practical, actionable solutions, drawing on insights from the article below.
– ZDNET news


7 out of 10 respondents stated, \"The barriers posed by security risks are high, and there is also a lack of proper data infrastructure, capabilities, and talent.\"


\"Small and medium-sized enterprises are often overwhelmed by how to implement AI. Where should they begin?\"

Both domestic and international companies have recently been striving to adopt AI to enhance operational efficiency. However, many face significant challenges due to insufficient data infrastructure and employee capabilities to leverage AI fully.

On August 10th, Cloudera, Inc. surveyed 600 IT leaders across the United States, Europe, the Middle East, Africa, and Asia-Pacific region to examine \\'The State of Enterprise AI and Modern Data Architecture.\\' The survey revealed that 8 out of 10 respondents had adopted AI for business processes. Still, many struggled to maintain their systems effectively.

Additionally, 74% of responding companies expressed significant concerns regarding security and compliance risks associated with AI. Challenges such as insufficient training or skilled personnel to manage AI tools (38%) and the high cost of AI tools (26%) also contributed to the difficulties in adopting AI.

\"\"

▲ Factors Making AI Adoption Difficult (Image source: Cloudera, Inc.)

A survey representative stated, \"AI has been gaining global attention in recent years due to its benefits, such as strengthening business operations, accelerating innovation, and enhancing experiences for both employees and customers.\" However, they also noted that \"not all companies are experiencing these benefits.\" Additionally, they emphasized that \"despite the rapid adoption of AI, many critical elements of a resilient AI strategy are being overlooked or ignored.\"

Domestic small and medium-sized enterprises (SMEs) face even more significant challenges in adopting AI. According to the \\'KOSTAT Statistics Plus\\' report published by Statistics Korea earlier this year, there is a substantial gap in adopting new technologies, such as AI and blockchain, between large enterprises and SMEs. In 2021, the adoption rate for big data was 12.7% among large enterprises compared to just 3.7% among SMEs. For AI, the adoption rate was 9.2% for large enterprises versus 2.9% for SMEs. Cloud adoption showed similar gaps, with 12.1% of large enterprises using it compared to just 3.7% of SMEs. The adoption rates of the Internet of Things (IoT) were 6.9% for large companies and 3.1% for smaller ones.

Even so, many companies see AI adoption as something they can\\'t afford to ignore. More and more businesses are recognizing that the benefits of AI outweigh the risks, and the number of industries adopting AI is increasing steadily. In Microsoft\\'s \\'Work Trend Index 2024,\\' released this past May, 79% of global business leaders said adopting AI is essential.

With that in mind, this section explores how ThanoSQL addresses one of the biggest challenges in AI adoption: security concerns.

ThanoSQL minimizes the risk of exposing Personally Identifiable Information (PII) or sensitive company data when exchanging information with public LLMs like ChatGPT through processes such as Masking (replacing data with symbols like * or O), Faker (generating fake data), and Re-transforming (restoring the original data).

Moreover, ThanoSQL only transfers the file path and embedding values (mathematical vector representations of the actual object) for unstructured data like images and videos, fundamentally preventing data leakage. ThanoSQL also shares the database schema, ensuring the original database remains internal. Furthermore, the column and table names are replaced with fake entities and re-transformed based on LLM responses, further strengthening data security.

Below are specific use cases that demonstrate how ThanoSQL detects anomalies and manages security risks for businesses:

1) Automatic Risk Analysis in Emails and Chats

2) Key Information Masking

\"\"

3) Unstructured Data Security: Managing Images and Attachments

4) Monitoring Anomalies Through Communication Pattern Analysis

ThanoSQL by SmartMind is an all-in-one platform that supports companies to store structured and unstructured data in database tables and analyze using the latest AI models through an interactive interface. As we have seen, implementing ThanoSQL can effectively prevent the leakage of sensitive internal data from a security standpoint.

'},{Slug:\"stt-ta-faqs-solutions-for-customer-service\",\":draft\":true,id:8,Title:\"STT, TA, FAQs – Solutions for Customer Service\",Date:\"2024-09-03T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/5rRG7bT9g1QL3comjhWJr79kg.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Generating and Executing STT/TA/FAQs using SQL

Introducing the core technologies of ThanoSQL\\'s VoC (Voice of Customer) analysis service: STT, TA, and FAQs.

You can view examples of VoC analysis using LLM-based natural language processing at the link below.

– VoC Analysis App
– Interactive Analysis Case – 01 Customer Service Interaction Analysis

Like the above case, even users without IT knowledge of queries or databases can easily and quickly analyze data using ThanoSQL\\'s interactive analysis service in natural language.

However, in this post, let us explore how to analyze VoC using the Query Manager option in ThansoSQL\\'s workspace. Users can intuitively analyze data using the Query Manager instead of natural language-based interactive analysis using relational database schemas and SQL queries.

The image below indicates that ThanoSQL includes STT, TA, and FAQ Gen modules for the VoC analysis. In customer service, call records of customers and customer service representatives get uploaded to ThanoSQL in voice file form, and the file will be transformed into a text file if one runs STT. The text file can be use for various analysis in the TA module. Also, repeated questions and answers can be collected, and FAQs can be autonomously created.

\"\"

Before researching each STT/TA/FAQ, let us introduce ThanoSQL\\'s Query Manager.

ThanoSQL\\'s Query Manager is the tool that adds the targeting data in the ThanoSQL database, runs SQL queries, and analyzes its results simultaneously.

\"\"

More detailed explanations on Query Manager can be found in the link below.
– Query Manager

Returning to the main topic, let us examine ThanoSQL\\'s STT/TA/FAQs.

1. STT (Speech To Text)

STT, or speech-to-text, literally converts spoken audio into text. It is challenging to analyze the human voice to analyze customer service information; the voice must be transformed into text first. The image below is the data flow and format of ThanoSQL\\'s STT.

\"\"

In customer service, every voice recording is stored in cloud storage, like Amazon S3 or the organization\\'s local storage. Upload mp3 or WAV format audio files from the storage to ThanoSQL STT\\'s database. Then, they will be transformed into text files through Open AI or Whisper-like AI models. The transferred files are in CSV or XLS format.

2. TA (Text Analysis)

TA is a function that can summarize context using AI models on transformed text, analyze customer emotions by extracting keywords and determining negativity or positivity from the text, or categorize it based on the topic.

\"\"

The below image is the initial starting screen of ThanoSQL Query Manager.

\"\"

Press the + button next to my_data to add text data for the analysis.

\"\"\"\"

Upload text file (excel or CSV format) created as STT\\'s output on ThanoSQL.

\"\"

This image shows the process of inserting the content of the uploaded text file into a table in the ThanoSQL database.
Since data is inserted in the database\\'s table, the data can be utilized freely via API in other locations.

\"\"

The above image is the screen that enters query statements, sorting out each conversational topic. The Mistral 7B model is being used in the example.

\"\"

The screen displays the 12 categories that query results on running. The execution result also gets stored as a table in the database, enabling the data to be called using API from outside and utilized for analysis.

3. FAQ Generation

It is the function of generating FAQs by collecting responses to repeated or frequently asked questions based on the analysis of the whole context of the text transformed in STT.

\"\"

The above image displays typing queries and making FAQs based on the call context. The GPT-4o model has been used in the example.

\"\"

The FAQ screen has been formed, resulting in query execution. Similarly, the generated FAQ is stored in a database table, allowing the data to be analyzed via API access.

Distinctness of ThanoSQL

Suppose an AI model or algorithm other than pre-built in ThanoSQL is needed. In that case, you can easily integrate external models and verify the results.
The above case is checking the result after applying ML needed for ThanoSQL by registering the recent model of Hugging Face on ThanoSQL\\'s FAQ result or STT result table, storing out and making queries based on the positivity and negativity in context, and then counting the result after placing in the table.

Expected Benefit - Utilization in AICC, CS Industry

Lastly, the table below organizes how AI-based STT and TA technology are used and their effects.

\"\"
As shown in the table above, utilizing AI-based STT/TA/FAQ can significantly enhance customer service efficiency and service quality. Customer service representatives can avoid simple and repetitive work and focus on more complex task-solving. Customers can receive fast and precise services.

Furthermore, organizations can provide a better customer experience by providing custom services and understanding customers\\' requirements earlier through data analysis. In addition, not only can the customer service representative\\'s education and management be enhanced, but the whole service task can also become efficient. SmartMind\\'s AI technology will bring innovative changes to the future of customer services, providing a better environment for both customers and customer service representatives.

'},{Slug:\"interactive-analysis-case-04-understanding-relational-databases-and-queries\",\":draft\":true,id:7,Title:\"Interactive Analysis Case – 04: Understanding Relational Databases and Queries\",Date:\"2024-08-29T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/r8iiY3wwgtal1sW7cy8VE9ISM.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Enhancing Understanding of SQL Syntax and Relational Databases through ThanoSQL

Introducing ThanoSQL\\'s interactive analysis service.

The interactive analysis service allows AI to take user queries in natural language and provide answers using a database.

▲ 한국어 내레이션

▲ English Narration

In this video, we will look at an example of asking questions about SQL syntax for relational databases using natural language.

The content explained in the video follows the sequence below:

1) As seen in the previous video (Interactive Analysis Case – 01 Customer Service Interaction Analysis, organizing and extracting the necessary information from a recorded customer service interaction database can be done using natural language.

\"\"

2) But what if you ask a general question about using a database (DB) or SQL queries rather than a question about data analysis? Choose a table and ask about \\'JOIN\\' function of the DB.

\"\"

3) Once the user enters a question, the LLM will respond based on its knowledge of relational databases learned through training. Interestingly, it explains using the table selected as an example.

\"\"

As shown in the illustration, it explains the basic SQL syntax, detailed descriptions, and usage based on the table the user chose.

\"\"


4) ThanoSQL\\'s interactive analysis service can also be used as a learning tool to improve understanding of SQL syntax and relational databases.

'},{Slug:\"interactive-analysis-case-03-searching-for-images-in-a-database\",\":draft\":true,id:6,Title:\"Interactive Analysis Case – 03: Searching for Images in a Database\",Date:\"2024-08-28T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/tpI7hAtixR8ofNYh2ol8ykJpA.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Searching for Images in a Database Using Natural Language with ThanoSQL

Introducing ThanoSQL\\'s interactive analysis service.

The interactive analysis service allows AI to take user queries in natural language and provide answers using a database.

▲ 한국어 내레이션

▲ English Narration

In this video, we will explore an example of searching for images stored in a database using natural language.

The content explained in the video follows the sequence below:

1) We have prepared a table with images collected from the internet, automatically labeled using AI functionality. After selecting this table, we will request to find images related to the ocean.

\"\"

2) ThanoSQL finds and displays images related to the ocean. Interestingly, the last image is of a desert, likely because it was interpreted as a sandy beach.

\"\"

3) Using the built-in query editor in ThanoSQL, you can see how the natural language query was translated into a database query (SQL). You can modify the query directly and execute a different one instantly if desired.

\"\"

4) When the data stored in the database consists of images, videos, or audio, you can immediately view the content through the data viewer, as shown in the image below.

\"\"'},{Slug:\"interactive-analysis-case-02-creating-a-report-by-analyzing-youtube-videos\",\":draft\":true,id:5,Title:\"Interactive Analysis Case – 02: Creating a Report by Analyzing YouTube Videos\",Date:\"2024-08-27T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/kavAioxrNHy6m98ODDYgneoy7s.png\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Summarizing video content with ThanoSQL to create a slide-style report.

Introducing ThanoSQL\\'s interactive analysis service.

The interactive analysis service allows AI to take user queries in natural language and provide answers using a database.

▲ 한국어 내레이션

▲ English Narration

In this video, we demonstrate the process of creating a PowerPoint-style summary report using collected YouTube video information.

The content explained in the video follows the sequence below:

1) We have prepared a database table containing recently collected AI-related YouTube videos.

\"\"

2) Before asking the chat bot, we choose which database table to use, select five YouTube videos with captions, and ask the chat bot to summarize the captions.

\"\"

3) The LLM processes the request and provides summaries of the five YouTube captions.

\"\"

4) 데이터베이스에서 다섯 건의 유튜브와 캡션을 추출하는 것은 데이터베이스의 기본 기능으로 가능한 것이지만 캡션 내용을 요약하는 것은 LLM 기술이 필요한 부분입니다. ThanoSQL은 LLM 등 인공지능을 데이터베이스에서 바로 활용할 수 있는 기능을 제공하고 있어서 이를 이용하여 결과가 나올 수 있었습니다.

5) 이번에는 앞의 처리 결과로 나온 다섯 개의 캡션 요약을 뭉쳐서 한 덩어리로 만들어 보겠습니다. ThanoSQL에서는 질문과 결과를 자동으로 저장합니다. 따라서 앞의 결과에서 ‘reply’ 버튼을 누르고 요청 사항을 입력하면 결과를 재사용하여 빠르게 답을 얻을 수 있습니다.

\"\"

6) 앞의 처리 결과를 이용하여 발표 슬라이드 스타일로 정리를 해달라고 요청하겠습니다. 앞의 처리 결과가 저장된 컬럼의 이름을 확인하고 앞의 결과에서 ‘reply’ 버튼을 누른 후 캡션 요약을 활용하여 슬라이드 스타일로 정리해 달라는 요청 사항을 입력하면 됩니다. 그럼 아래와 같이 파워포인트의 슬라이드 스타일로 결과가 나오게 됩니다.

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7) 슬라이드 스타일로 텍스트를 변환하는 기능도 데이터베이스의 내장 기능으로는 불가능하지만 ThanoSQL이 제공하는 데이터베이스에 내장된 LLM 기능으로 처리되어 슬라이드 형태로 정리된 결과를 만들 수 있습니다.

'},{Slug:\"interactive-analysis-case-01-customer-service-interaction-analysis\",\":draft\":true,id:4,Title:\"Interactive Analysis Case – 01 Customer Service Interaction Analysis\",Date:\"2024-08-26T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/rMxjSySasARrwl3GARpW8AW0R1s.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Analyzing phone call data using ThanoSQL

Introducing ThanoSQL\\'s interactive analysis service.

The interactive analysis service allows AI to take user queries in natural language and provide answers using a database.

▲ 한국어 내레이션

▲ English Narration

In this video, we demonstrate the process of analyzing customer service interactions using ThanoSQL.

The analysis process explained in the video follows this sequence:

1) The data used in the video comes from three tables: the agents table, which contains basic information about the customer service representatives; the calls table, which holds the call records; and the transcript table, which includes the actual conversation content.

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2) Before asking the chatbot a question, you choose which database table to use. Then, we ask the chatbot who has the most prolonged average call duration.
The LLM understands the user\\'s query and retrieves the answer from the database, which is \"Jennifer.\"

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3) You can open the query editor window to see how the natural language question was converted into a database query and view the results.

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4) Let us explore the transcript table, which contains the recorded conversational content.
After selecting the table, you can use the preview feature to review the data in the table if necessary.

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5) Next, we will extract all conversations lasting more than five minutes and classify the content of the conversations. We can identify various topics, ranging from technical questions to refunds and shipping issues.
Unlike the previous question, classifying the call content requires more advanced functionality, which is not part of a standard database. ThanoSQL provides built-in AI functionalities, such as LLM, enabling this classification.

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6) Now, we will count how many calls there are for each category. Since ThanoSQL automatically saves questions and results, we can quickly perform this aggregation using the previous classification result. Ensure to note the column\\'s name where the previous result is stored.

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7) To reference the previous result table, press the \"reply\" button from the previous query and enter the new question. This way, you can skip the time-consuming classification step.

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8) The chatbot neatly summarizes how many calls there were for each category.

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9) Likewise, power users can use the query editor to explore the data in more detail and, if needed, modify the query to gain additional insights.

\"\"'},{Slug:\"interactive-data-analysis-with-llm\",\":draft\":true,id:3,Title:\"Interactive Data Analysis with LLM\",Date:\"2024-08-12T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/Eq1l4Vk8Z06oKU3EZPWbp3dVPQ.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

Should our company adopt LLM?

Currently, there is significant investment in foundation models within the LLM field. However, investors and companies increasingly question the return on investment (ROI) and the specific revenue models.

Tesla revealed that it uses GitHub\\'s Copilot on 80% of their coding tasks, indicating that the IT sector has already been utilizing generative AI and LLM at a high level to maximize competitiveness and productivity. On the contrary, the adoption of generative AI in non-IT sectors remains relatively low because of concerns about data leakage and the high costs caused by the complexity of the technology. Even where it is used, applications are mainly limited to chatbots that handle inquiries about internal policies or company life guides, with little use for directly enhancing productivity or deriving business insights.

At SmartMind, we focus specifically on addressing this exact point—enhancing productivity in non-IT sectors. SmartMind\\'s solution, ThanoSQL, is an all-in-one platform that prevents the leakage of internal source data while unifying LLM and data analysis.

How can LLM improve productivity in non-IT sectors?

In representative non-IT areas such as sales and marketing, productivity can be enhanced by automating routine tasks for the person in charge or expanding their responsibilities. While RPA excels at automating everyday tasks, SmartMind focuses on helping broaden the scope of these responsibilities.

Previously, tasks likely involved exporting structured data from an RDB (Relational Database) into Excel or connecting it to BI tools for various pivot analyses. Additionally, analyzing unstructured data stored outside of RDBs, such as in NoSQL databases or data lakes, would have required assistance from the IT department, and organically combining each analysis result to derive integrated insights would have been a particularly challenging endeavor.

However, with ThanoSQL, all of this can be done effortlessly using natural language queries. Anyone can analyze any data without the need for IT support.

Consider the following use case:

Identify negative feedback from customer reviews and categorize it
-> Select the product with the most quality-related complaints
-> Provide the sales figures for that product in Q2 of this year
-> Compare it with the same quarter last year

This process is illustrated in the figures below.

The first image is the initial screen of the ThanoSQL workspace. After selecting the tables where the target data is stored, analysis begins by inputting a question into the query field.

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▲ Select the table target data is stored and input a question

In this example, customer reviews must be analyzed first, so the customer_review table is selected. Next, the product_list table is chosen to identify the product, followed by the sales table to check the sales volume.

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▲ Sentiment analysis of unstructured data (customer reviews) provides answers

The natural language query is automatically converted into SQL commands executed on the selected tables, incorporating various AI algorithms and machine learning techniques. ThanoSQL provides these algorithms and models by default, but users can also import external models from platforms like Hugging Face. The results are then presented in natural language format in the output field.

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▲ Combining structured sales data with analysis

With just a few natural language inputs, users can combine unstructured data, such as customer reviews, with structured data, like sales figures, viewing the results instantly.

In short, ThanoSQL is an analysis tool that allows users to perform interactive analysis of both structured and unstructured data stored in tables by combining machine learning and LLM. With ThanoSQL, users without technical expertise can quickly analyze all company data and derive meaningful insights.

What distinguishes ThanoSQL from other LangChain-based solutions?

Typically, the tech stack required to develop LLM services using LangChain is illustrated in the figure below.

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▲ Tech stack layers and key components of LLM technology (Source: codesmith.io/blog)

There are numerous product choices at each layer and even within individual components, and the chart does not cover everything.

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▲ Key products for each component (Source: Emerging Architectures for LLM Applications)

Companies aiming to build LangChain-based LLM services and integrate them into their tech stack face the challenge of selecting, purchasing, connecting, and managing a wide range of tools that data experts require.

Most companies lack these capabilities, and since they need to integrate disconnected tools and services, adopting LLM seamlessly requires significant time, cost, and specialized talent.

In contrast, with ThanoSQL, all those concerns and efforts become unnecessary. Users simply need to decide whether to use ChatGPT 4.0 as their LLM model or choose Hugging Face\\'s open-source model.

Another difference is the flexibility and speed of data analysis. The illustration below shows an example of using ThanoSQL to identify which customer support representative spent the most time on calls with customers.

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▲ Query that Identifies the customer support representative who spent the most time on phone calls

The following figure shows how the user\\'s natural language query was converted into SQL and the execution results.

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▲ View and modify the SQL conversion of the natural language query and review the results

If you need to run a different analysis from your original query, you can simply update the SQL command and get the results instantly.

With LangChain, though, since it is built on Python, functions are essentially locked behind preset code, so making quick adjustments is not as straightforward.

Conclusion

Setting up an LLM service with LangChain also requires integrating a complex array of technical tools, much like managing a full-scale SI project, and usually means revisiting and refining workflows repeatedly. For this reason, adopting ThanoSQL, which provides LLM and data analysis in a single-platform format, is a more efficient decision for everyone—not only in terms of time and cost but also for ease of use.

'},{Slug:\"problems-that-may-arise-when-companies-try-to-adopt-generative-ai-and-solutions\",id:2,Title:\"Problems that may arise when companies try to adopt generative AI, and solutions\",Date:\"2024-08-09T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/lICSilpsX2mTZQusQWgoslKwo.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:\"

The Era of Generative AI

Do you remember the Google DeepMind Challenge Match in 2016 when Google DeepMind's AlphaGo defeated Lee Sedol, a 9-dan professional? It was a big event of the era that simultaneously evoked amazement and awareness about artificial intelligence (AI) when it won 4 games to 1 against Lee Sedol, the world's best Go player, in the game of Go, which was considered an impossible domain for computers at the time.

After AlphaGo, the AI market was relatively quiet until the end of 2022 when an AI appeared like a comet - it was OpenAI's ChatGPT, which is now known to everyone. At the time of its release, ChatGPT made a huge impact, able to converse in human-like language and creating the illusion that it could think like a human. With the emergence of generative AI like ChatGPT, there coexists an anxiety that it will replace humans in the near future and an expectation that it can upgrade human productivity to the next level.

Is generative AI being widely adopted in various industries?

In the first quarter of this year, 32% of companies that reported earnings (out of more than 85,000 companies worldwide) mentioned generative AI at least once, which is a 150% increase compared to the same period last year. (IoT Analytics Research 2024) This shows that generative AI is still positioning itself as a hot issue in the market.

Then, is generative AI, which has received so much attention and expectation, actually being widely adopted by businesses?

The adoption of generative AI is becoming more active in the IT sector, which directly develops and operates AI. According to Capgemini research, up to 85% of respondents in the IT sector stated they would adopt generative AI within the next two years. However, non-IT sectors, such as marketing, sales, and finance, show different results. A survey conducted by Coatue of 600 CEOs revealed that only 9% have fully adopted AI, and even among these, 60-75% responded that they are still in the early verification stage.

Problems when adopting generative AI

1) External leakage of internal source data

What is the reason for this poor performance in non-IT sectors, which are directly linked to a company's revenue growth? The biggest cause is security issues. For most companies, especially large corporations, the biggest obstacle to adoption is the need to export internal company data to external clouds in order to use generative AI models. In fact, in July, there was a shocking incident in the United States where a hacker attack on Snowflake cloud resulted in the leak of call and message records of 110 million AT&T customers. For these reasons, many companies are blocking the use of external LLMs and are attempting to build their own LLMs internally.

2) High implementation costs and time

In cases where companies try to build their own LLM based applications internally, they can solve the problem of external leakage of internal data. However, another problem arises: the issue of cost and time. These problems occur because it needs to be built directly in an SI (System Integration). Especially if they try to build a platform for data analysis that can utilize LLM as well, the cost and time entry barriers will become even greater. Realistically, this becomes the reason why it's difficult for small and medium-sized enterprises to adopt generative AI, except for large corporations that have the capacity to invest. If they try to solve this by using public cloud services, the problem of data leakage occurs again.

Implementing generative AI intelligently

Generative AI is a powerful tool that can greatly help improve a company's productivity. How can companies easily and quickly adopt such generative AI? While it depends on the company's situation, the easiest way would be to eliminate the two major entry barriers we looked at earlier. In other words, a solution is needed that can reduce implementation costs and time while preventing the external leakage of internal source data.

SmartMind's Qurify solves the problem of external leakage of source data by utilizing only the company's metadata (table schema) for analysis. Additionally, it significantly reduces implementation costs and time by providing LLM and data analysis in a single platform.

Are you curious about SmartMind's Secure Enterprise Gen AI, Qurify, which is solving the market's pain points with its proprietary technology? Contact us right now.

\"},{Slug:\"optimizing-business-workflows-through-llm-integration\",id:1,Title:\"Optimizing Business Workflows Through LLM Integration\",Date:\"2024-08-02T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/isu13IajsW45MZ4uxMY4aWIhnMY.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"eng\",\"preview-contents\":\"\",Content:'

What is a Workflow?

According to Wikipedia, a workflow is defined as the operational aspect of a work procedure. It describes how tasks are structured, who performs them, their sequence, synchronization, the flow of supporting information, and how tasks are tracked.

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Automating workflows can significantly enhance convenience for both customers and employees and drive business growth, which is why many companies are actively adopting workflow automation. Essentially, automating workflows involves using computers or software programs to handle repetitive tasks or business processes, eliminating the need for manual intervention.

Challenges in Business Process Management and Pathways to Solutions

Today, most companies face challenges in managing business processes. Overseeing large teams, managing diverse tasks, and integrating multiple technologies into services often create bottlenecks. In their pursuit of efficiency in rapidly changing environments, these obstacles push companies to accelerate digital transformation and workflow automation initiatives.

In this fast-evolving digital landscape, AI technologies—especially Large Language Models (LLMs)—are revolutionizing how companies streamline operations. Tools like LLMs empower businesses to optimize and automate workflows, helping them stay competitive and agile.

LLMs are advanced AI models designed to understand and generate text like human communication. Although these language models continue to evolve, they still rely on training datasets to establish relationships and generate contextually relevant content. LLMs, however, offer more than basic language functions. Unlike traditional automation tools built for specific tasks, LLMs are versatile, handling various functions from data analysis to customer engagement.

To effectively integrate LLMs, companies must first identify operational inefficiencies. Once issues and bottlenecks are pinpointed, LLMs can analyze process data and deliver actionable solutions to address these challenges.

A typical process for integrating LLMs into a company\\'s internal systems includes:

Review Task Requirements \\n-> Integrate LLM \\n-> Train the Model with Relevant Data \\n-> Iterate and Improve Through Feedback

Benefits of LLM Integration

1) Automating Repetitive Tasks to Boost Productivity and Minimize Errors

Repetitive tasks such as data entry, report generation, and customer interactions are timeconsuming and can lead to human error. LLMs efficiently handle these tasks, improving productivity and accuracy.

In practice, LLMs have significantly enhanced corporate productivity by drafting email templates, generating content like blogs or reports, and automating FAQs in customer service centers. They can also integrate with popular project management tools like Trello, Asana, and Monday.com, allowing LLMs to interpret and act on user inputs, create tasks, update statuses, and set priorities without human intervention.

2) Enhancing Customer Interactions and Delivering Personalized Experiences

LLMs are essential for companies aiming to elevate customer support. By powering intelligent chatbots and enabling personalized interactions, LLMs can transform how businesses engage with their customers, delivering rich, customized experiences. Many companies operating customer service centers are already implementing or considering AI-powered contact centers (AICCs), and some have introduced LLM-based chatbots that provide 24/7 support, tailored to each customer’s needs.

Through sophisticated data analysis and learning algorithms, LLMs offer highly personalized customer interactions. For example, by using SmartMind\\'s Qurify, companies can securely analyze customer-specific data when a customer asks a question in natural language. The LLM generates a fully customized response without external data exposure, boosting customer satisfaction, increasing loyalty, and reducing service churn.

3) Improving Decision-Making with Data-Driven Insights

LLMs empower companies with strong capabilities to make data-driven decisions. By collecting, analyzing, and presenting data effectively, LLMs enhance decision-making with real-time insights and comprehensive data analysis.

For instance, an e-commerce company might analyze customer reviews (unstructured data) to identify products with negative feedback and then evaluate the sales trends of those products (structured data) to uncover revenue fluctuations and devise solutions.

SmartMind\\'s Qurify enables even non-technical staff, such as business leaders or sales and marketing teams, to access data easily, analyze it conversationally, and make well-informed business decisions.

4) Strengthening Collaboration and Communication

LLMs are increasingly integrated into enterprise collaboration tools, streamlining internal communication and collaboration. Companies using global platforms like Microsoft Teams, Slack, Flow, and Notion, or proprietary groupware, are adopting LLMs at a rapid pace.

LLMs enhance collaboration tools by enabling writing, editing, searching, translating, and even chatbot functions. Integrating LLMs into internal messaging platforms facilitates smooth communication across departments, promoting seamless collaboration and enhancing organizational efficiency.

The Next Step After LLM Integration

As companies experience the added value of LLMs firsthand, they increasingly recognize the importance of adapting to advancements in AI. To remain innovative and efficient, companies must actively prepare for the future of LLM technology and consider its operational impact.

Businesses should be ready for the next generation of LLMs, whether in the form of AI Agents, Large Vision Models (LVMs), or Large Action Models (LAMs). Companies that stay ahead of these advancements will continue to grow and secure their market position.

Integrating LLMs into business workflows is a transformative step toward heightened efficiency and productivity. As LLM technology continues to evolve, businesses that adapt and leverage these capabilities will drive innovation and success well into the future.

'},{Slug:\"더-빠르고-더-정확한-rag-구축-방법\",id:3,Title:\"더 빠르고 더 정확한 RAG 구축 방법\",Date:\"2024-11-17T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/PpbpUt3VafXrxaaf45H04VM8M.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"기업이 RAG 시스템을 도입할 때 마주치는 환각 문제와 검색 정확도 저하를 해결하기 위한 실용적 접근 방식을 다룹니다. 문서를 메타데이터 기반으로 분류하고, 시나리오에 따라 컬렉션을 구성함으로써 검색 속도와 정확도를 동시에 향상시키는 구조를 제안합니다. 또한 벡터 DB를 활용해 유사한 정보를 정밀하게 추출하는 절차까지 단계별로 설명합니다.\",Content:'

1. 생성형 AI와 LLM 활용의 한계

모두들 아시다시피 불과 1~2년 사이에 OpenAI의 ChatGPT, 구글의 Gemini, 메타의 LLaMa 등 여러 LLM을 기반으로 하는 생성형 AI가 대중화 되었습니다. 자연스럽게 기업들도 생성형 AI를 비즈니스에 도입하여 생산성을 높이려는 시도들을 많이 하고 있습니다.

생성형 AI는 굉장히 큰 규모의 데이터로 사전에 충분히 학습된 LLM을 기반으로 동작을 하는데, 이게 범용적이고 일반적인 질문들에는 비교적 정확한 답변을 합니다. 그러나 특정 도메인의 전문 지식이나 별도의 데이터를 필요로 하는 질문을 하면 생성 결과의 출처도 알 수 없는 완전히 엉뚱하거나 잘못된 답변을 하게 됩니다.

바로 이것이 거짓 정보를 사실인 것처럼 출력하는 환각(Hallucination) 현상인데 일반적인 생성형 AI 모델의 한계입니다.

비즈니스 의사 결정을 하는데 있어서 잘못된 정보를 기반으로 하면 절대 안 되므로 기업에서 생성형 AI를 도입하는 데 가장 큰 걸림돌이 되고 있습니다.

이 문제를 해결하기 위해 일반적으로 LLM에 특정한 지시를 전달하는 프롬프트 엔지니어링을 하거나, 모델의 매개변수를 조정하는 파인튜닝을 적용합니다. 그런데 프롬프트 엔지니어링은 프롬프트 입력 제한 등으로 인해 개선할 수 있는 성능에 한계가 있고, 파인튜닝은 기술적인 복잡함과 고성능 컴퓨팅 파워를 필요로 하기 때문에 비용이 많이 발생하는 문제가 있습니다.

2. 기업의 내부 데이터를 대상으로 하는 LLM+RAG 서비스 수요 증가

검색 증강 생성(RAG)은 바로 이러한 생성형 AI의 한계를 극복하기 위한 여러가지 방법중에 하나입니다. 간단하게 말하면 정보를 검색하는 부분(이후 Retriever라고 하겠습니다.)과 답변을 생성하는 부분을 결합하여 최종 결과물의 정확성을 높이는 방법입니다. 기업의 데이터를 벡터로 변환하여 DB에 저장하고, 사용자의 질문이 입력되면 쿼리로 변환하여 해당 쿼리와 유사한 데이터를 DB에서 검색하여 답변의 생성에 이용합니다.

이러한 RAG의 장점은 리트리버만 추가하면 모델의 추가 학습 없이도 빠르고 저렴하게 사용자 데이터를 반영할 수 있다는 점입니다. 그리고 임베딩 데이터는 인덱싱되어 관리되므로 답변의 생성에 어떤 데이터를 참조하였는지 알 수 있고 더 나아가 환각 여부도 판단할 수 있습니다.

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이렇게 RAG를 사용하게 되면 LLM만 사용했을때 보다 답변의 정확성이 크게 향상되므로, 최근 기업들이 내부 데이터를 대상으로 하는 질의 응답 시스템을 RAG로 구축하는 사례가 많아지고 있습니다.

3. RAG 구축 프로세스

데이터 소스 연결에서부터 답변을 받기까지 RAG 를 구축하는 전체 순서는 아래 그림과 같습니다.

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▲ RAG 구축 프로세스

각 단계별로 LangChain에서 제공하는 도구를 사용하거나 아니면 별도로 외부의 다양한 도구들을 선택하여 사용할 수도 있습니다.

문서를 임베딩 하는 모델로 무엇을 사용할 것인지, 리트리버를 구현할때 어떤 검색 기법을 사용할 것인지 등은 모두 여러가지 옵션이 있으며, 프롬프트 엔지니어링과 함께 RAG의 정확도를 개선하는데 영향을 미치게 됩니다.

4. 고품질 RAG 적용 사례

최근 스마트마인드가 고객사에 Qurify를 기반으로 RAG를 적용한 사례를 살펴보겠습니다.

고객사 내부에 축적되어 있는 비즈니스 문서를 대상으로 하는 RAG 챗봇을 구축하는 프로젝트 였는데, 어떻게 하면 기존 방식의 RAG 챗봇보다 더 정확하고 더 빠른 답을 얻을 수 있을까요?

위에서 언급한 RAG 구축 단계의 일반적인 내용들은 인터넷상에 참고할만한 좋은 자료들이 많기 때문에 여기서 자세히 언급하지는 않겠습니다.

본 게시글에서는 실제로 기업에 적용할때 답변의 품질을 높이기 위해, 특히 ‘검색’ 단계에서 속도와 정확성을 높이기 위해 무엇을 어떻게 했는지를 말씀드리겠습니다.

대부분의 경우 기업에서 RAG 기반의 챗봇을 도입한다고 할 때 RAG의 대상이 되는 데이터는 기업이 보유하고 있는 ‘문서’를 의미합니다.

하지만 모든 문서가 동등한 것이 아니라 여러 측면에서 차이가 있습니다. 대표적으로, (1) 문서가 담고 있는 내용(예, 특정 업무) (2) 문서에 대한 접근 권한(예, 대외비 또는 특정 부서만 접근 허용 등) (3) 공개 가능 여부 (예, 저작권 문제로 자료를 드러내면 안되는 경우) 등에서 차이가 있으므로 이러한 정보를 문서에 메타데이터로 덧붙여야 합니다.

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▲ 문서와 메타데이터

예를 들어 ‘S/W 개발 방법론’이라는 문서가 있다면 이 문서는 개발(문서 주제)에 관련된 것이고 개발 조직에 속한 사람들만 열람(접근 권한) 가능하며 대외비(공개여부) 라는 메타데이터를 추가하게 됩니다. 또 다른 예로 ‘회사생활 가이드’라는 문서는 주제가 회사 일반이고 전체 직원 누구나 볼 수 있으며 공개도 가능하다는 메타데이터를 추가할 수 있습니다.

모든 문서에 다 별도의 메타데이터를 붙이는 것도 가능하지만 그렇게 되면 관리의 부담이 커지므로 같은 메타데이터를 가진 문서들끼리 모아서 그룹화하여 ‘컬렉션’이라고 이름을 붙였습니다. 그리고 메타데이터는 이 컬렉션에 부여하는 방식을 사용했는데 그렇게 되면 특정 컬렉션 내의 문서들은 모두 동일한 메타데이터를 가지게 됩니다.

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▲ 메타데이터 기반의 문서 컬렉션

한편 기업에서 도입하는 챗봇의 용도도 여러가지가 있을 수 있습니다. 특정 부서 또는 특정 업무 담당자들을 위한 챗봇(예, IT 보안 부서의 보안 모니터링 담당자를 위한 챗봇)이 있을 수 있고, 전체 임직원을 대상으로 한 특정 분야 챗봇(예, 회사 복지 업무 지원 챗봇)도 있을 수 있습니다. 이렇게 다양한 챗봇 활용 상황을 Qurify에서는 ‘시나리오’라고 부릅니다. 각 시나리오마다 필요로 하는 RAG 자료가 다르므로 앞에서 정의한 문서의 묶음인 컬렉션을 시나리오와 잘 매핑한다면 리트리버가 불필요한 데이터까지 모두 검색하는 시간과 비용을 아낄수 있고, 꼭 필요한 자료만을 활용하므로 답변의 정확도 역시 높일 수 있습니다.

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▲ 컬렉션과 시나리오의 매핑

예를 들어 법무 담당자가 특정 프로젝트의 하도급 계약 내용과 관련된 질의를 한다면 챗봇은 수행 프로젝트 컬렉션과 법령/규정 관련 컬렉션을 선택하여 해당 컬렉션들 내에서만 검색을 수행하고 그 결과를 가지고 답변을 생성하게 됩니다. 즉 관련이 없는 컬렉션은 검색하지 않으므로 거짓 정보를 생성하지 않게될 뿐만 아니라 응답시간도 짧아지게 됩니다.

5. 마치며

기업이 보유한 문서의 종류와 성격 그리고 적용할 시나리오의 다양성 등에 따라 위에서 언급한 세가지 메타데이터 이외에 다른 메타데이터가 추가로 필요할 수도 있습니다. 컬렉션 구성을 위한 이러한 메타데이터 태깅은 번거로울 수 있지만 일정 부분은 AI로 자동화가 가능하며 RAG 검색의 속도와 정확성을 높이기 위해서는 필요한 과정이라고 할 수 있습니다.

이처럼 Qurify를 이용하여 컬렉션과 시나리오를 매핑하는 방식으로 RAG를 구현한다면 다양한 분야에서 고품질의 서비스를 신속하게 제공할 수 있습니다.

'},{Slug:\"랭체인-기반으로-ai-어플리케이션을-만들려면-비용이-얼마나-들까\",\":draft\":true,id:0,Title:\"랭체인 기반으로 AI 어플리케이션을 만들려면 비용이 얼마나 들까?\",Date:\"2024-10-26T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/tjJOm6zQkogUcrVk2c0N1LIY0E8.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

최근 LLM 서비스가 우후죽순으로 쏟아져 나오고 있지만 우리 회사에 딱 맞는 제품은 없는 것 같고 그나마 괜찮아 보이는 제품은 가격이 너무 비싸서 차라리 자체적으로 만드는 것을 검토하는 경우도 있습니다.

오늘은 기업이 랭체인 기반의 AI 어플리케이션을 만들려면 실제로 비용이 어느 정도나 필요한지 대략적으로 알아보겠습니다.

1. 랭체인 기반의 AI 어플리케이션

랭체인(LangChain)은 대규모 언어 모델(LLM)로 구동되는 어플리케이션을 빌드할 수 있는 프레임워크입니다. 랭체인은 다양한 구성요소들을 연결하는 표준 인터페이스를 제공하고 체이닝, 데이터 인식, 에이전트 등과 같은 기능을 통해 어플리케이션을 더 쉽고 빠르게 빌드할 수 있도록 해줍니다.

아래 그림은 랭체인과 에이전트가 어떻게 동작하는지를 나타냅니다.

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▲ 랭체인과 에이전트의 동작 메커니즘

메커니즘을 간단히 설명하면, 에이전트는 백그라운드에서 Python 에이전트를 호출하여 기능하고, 이는 LLM에서 생성된 Python 코드를 실행합니다. 사용자의 질문이나 프롬프트는 LLM에서 해석하여 Python 코드로 변환하고, 생성된 코드는 실행을 위해 Python 엔진으로 전달됩니다. Python 엔진은 프로세스에 필요한 데이터에 액세스하고 읽을 수 있는 기능을 가지고 있습니다.

2. 랭체인(LLM & AI Agent) 에코시스템

아래 그림은 랭체인 에코시스템을 나타냅니다.

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▲ 랭체인 마켓 맵 (출처 : CBInsights)

그림에서 보다시피 랭체인의 각 영역 별로 수많은 솔루션들이 있는데(심지어 이게 전부가 아닙니다), 목적과 상황에 맞게 솔루션들을 선택하고 그것들을 오케스트레이션 하는 어려운 작업은 전적으로 기업의 몫이 됩니다.

모든 영역에서 솔루션을 하나씩 골라서 각각의 비용을 모두 추정해보기는 어려우니 아래처럼 몇가지 요소들로 제한하여 살펴보도록 하겠습니다.

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▲ 랭체인 에코시스템 내의 주요 비용 요소들

아래에서 각 요소 별로 하나씩 살펴보겠습니다.

3. 각 영역별 솔루션과 추정 비용

위의 그림에서 알 수 있듯이 랭체인 에코시스템을 구성하는 항목들은 매우 다양하지만 여기서는 편의상 몇가지로 단순화 하였습니다.

크게 LLM과 벡터DB, 인메모리 데이터 스토어, 그리고 데이터 플랫폼입니다.

대략적인 비용이 얼마나 될지 감을 잡아보기 위해서 대표적인 솔루션을 선정하고 임의의 기준에서의 비용을 산출해보겠습니다.

정확한 비용은 처리해야 할 데이터셋의 규모와 목표하는 성능 또는 품질 기준 등 개별 기업의 요구 사항에 따라 매우 크게 달라질 수 있으므로 본 게시글에 언급된 금액은 단순히 참고만 하시기를 바랍니다.

1) LLM

LLM은 컨텍스트(Context)에 따라 다르긴 하지만 대표적인 모델들의 경우 100만 토큰 기준으로 가격은 아래와 같습니다.

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▲ 주요 모델별 100만 토큰 기준 가격

이를 기반으로 하루 1,000 토큰과 100만 토큰 사용량을 가정하고 연간 비용을 계산해보면 대략 아래 표와 같습니다.

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▲ 사용량 및 OpenAI /클라우드 호스팅 모델에 따른 연간 비용 (출처 : Skanda Vivek)

OpenAI의 경우 연간 비용은 모델에 따라 사용량이 적을 때(하루 1,000개 토큰)는 연간 1,000 ~ 50,000달러이고, 사용량이 많을 때(하루 100만개 토큰) 는 연간 100만~5,600만 달러입니다.

반면에 Falcon-7B, LLaMa2-7B, Vicuna-33B 같은 오픈소스 모델의 호스팅 비용은 사용량이 많은 경우에 OpenAI 대비 상대적으로 매우 낮은 비용이 드는 것을 알 수 있습니다.

2) 벡터 DB

벡터DB는 대표적인 솔루션으로 파인콘(Pinecone)을 선정하였고, 비용 계산을 위해 아래처럼 임의의 기준을 설정하였습니다.

+a는 0으로 가정하고 위 금액에 12를 곱하여 연간 금액을 계산해보면 약 1.64억원이 됩니다.
(다시 말씀드리지만 이 금액은 절대적인 기준이 아니며 단순 참고용으로만 이해해 주시기 바랍니다.)

위의 기준은 비교적 작은 데이터 세트이며 고성능을 위한 런타임 설정이 아니라는 점을 유의해야 합니다. 이러한 설정을 사용하면 데이터베이스 용량은 초당 30개의 쿼리에 불과합니다. 즉 이 속도로는 1,000만 개의 벡터를 초기화하는 데 4일이 걸립니다. 만약 이러한 성능과 속도에 만족하지 못한다면 더 많은 replica가 필요합니다. 그렇게 되면 비용은 이 계산보다 훨씬 더 커지게 됩니다. 대규모 말뭉치(corpora, 데이터 집합)를 보유한 기업은 RAG 애플리케이션을 만들기 위해서 10~100배 더 많은 비용을 지불해야 할 수도 있습니다.

3) 인메모리 데이터 스토어

인메모리 데이터 스토어는 milli-second 이하의 데이터 액세스, 확장성, 고가용성을 제공하는 인메모리 DB입니다.
인메모리 데이터 스토어로 대표적인 솔루션으로 레디스(Redis)를 선정하였고 비용 계산을 위해 아래처럼 임의의 기준을 설정하였습니다.

위와 같은 사양으로 Managed Service 형태로 사용하면 시간당 요금은 약 $6이고 월 금액은 $4,300입니다. 여기에 12를 곱하여 연간 금액을 계산해보면 약 0.71억원이 됩니다.

4) 데이터 플랫폼

현재 시장을 리딩하는 데이터 플랫폼 솔루션으로는 데이터브릭스(Databricks), 스노우플레이크(Snowflake), 데이터이쿠(Dataiku) 등이 있습니다.

모든 데이터 플랫폼의 비용은 사용량에 따라 달라지므로 한마디로 비용이 얼마다 라고 잘라 말할 수는 없습니다.

오픈된 정보가 많이 없긴하지만 데이터브릭스는 미국 기준으로 중견 기업의 평균 데이터 사용량을 기준으로 볼 때 중견 기업의 지출은 연간 10만 달러에서 100만 달러 사이인 것으로 알려져 있습니다.(출처)

즉, 연간 금액이 약 1.38억원~13.8원억 정도 됩니다.

5) 파이프라인 구축과 통합

마지막으로 랭체인의 에코시스템을 구성하는 모든 항목들을 연계하여 파이프라인을 구축하고 통합하는 비용이 있습니다. 이 부분은 기준을 설정하기도 어렵고 케이스마다 모두 다를 수 있어서 비용 산정을 하지는 않겠습니다.

다만, 파이프라인 구축과 통합은 다수의 전문 인력이 상당 시간 투입되어야 하는 난이도가 높은 작업이며 결코 작지 않은(어쩌면 가장 클 수도 있는) 비용이 발생할 것이라는 점을 염두에 두셔야겠습니다.

그리고 MLOps나 LLMOps 등과 관련된 보이지 않는 추가적인 비용도 발생하게 됩니다.

1번부터 5번까지 모두 다 합하면 얼마가 될까요? 네 그렇습니다, 랭체인 기반으로 AI 어플리케이션을 만드는 것은 아~주 비쌉니다. 가격이 비쌀뿐만 아니라 수많은 요소들을 조화롭게 오케스트레이션하고 운영 및 유지보수도 해야 합니다. 바로 이런 이유 때문에 대부분의 기업들이 AI 도입을 어려워하고 도입을 시작하더라도 실패하는 경우를 볼 수 있습니다.

4. 데이터 플랫폼을 꼭 사용해야 할까?

위에서 살펴본 비용 요소 중 LLM을 제외하면 데이터 플랫폼의 비중이 가장 큰데요, 현재 AI 어플리케이션을 랭체인 기반으로 개발하려면 거의 대부분의 경우 데이터 플랫폼을 필수적으로 사용하게 됩니다. 물론 데이터 플랫폼 솔루션을 사용하지 않고도 AI 서비스를 만들 수는 있지만 자체적으로(또는 외주) 개발해야 할 것들이 굉장히 많고 개발의 난이도 또한 매우 높아지게 됩니다.

아래 표에서는 데이터 플랫폼 사용 여부에 따른 각 방식별 특징과 비용, 난이도 등을 간단하게 비교하였습니다.

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위 표에 언급한 솔루션 비용과 인건비 등은 특정 조건에서의 대략적인 금액이며 비교를 위한 참고용입니다.

5. 마치며

랭체인은 LLM과 Prompt engineering, RAG, Text to SQL, 다른 머신러닝 서비스 및 외부 API들을 모두 사용할 수 있도록 준비된 오픈소스 툴키트 입니다.

RAG와 Prompt Engineering을 사용하기 위해서는 벡터 DB와 인메모리 데이터 스토어가 반드시 필요하고, 다른 머신러닝 모델을 사용하기 위해서는 데이터 플랫폼이 필요합니다.

랭체인은 일종의 규약으로 어떤 솔루션이 그 규약에 맞추기만 한다면 랭체인 기능 내에 통합될 수 있습니다. 고객 관점에서는 랭체인을 쓰더라도 벡터 DB, 인메모리 데이터 스토어, 데이터 플랫폼 등 필요한 솔루션들을 일일이 선택해야 하며 비용 또한 각각 개별로 지불해야 합니다.

한편 데이터 분석 및 머신러닝 개발에 사용하는 데이터 플랫폼은 LLM 및 GPT 모델 전에 개발된 서비스들이기 때문에 데이터를 벡터 DB에 저장하고 처리하지 않습니다. 다시 말하면 이미지, 비디오, 텍스트 등으로 구성된 비정형 데이터를 데이터 플랫폼 내의 값비싼 데이터 스토리지에 저장해야 함을 의미합니다.

만약 기업이 랭체인 방식이 아닌 ThanoSQL을 사용하여 AI 어플리케이션을 개발한다면 위와 같은 복잡한 문제들이 모두 한번에 해결됩니다.

또한 ThanoSQL은 랭체인 방식 대비 정확도 및 실행 속도가 월등하면서도 비용까지 저렴한데요, 그 이유는 LLM 뿐 아니라 다른 머신러닝을 개발할 때에도 트랜스포머(Transformer) 모델을 사용하므로 내장된 벡터 DB를 적극 활용하기 때문입니다. 즉 원천 데이터는 매우 싼 S3 같은 분산 스토리지에 저장해 두고, 실제 분석에 사용되는 데이터는 모두 크기가 월등히 작은 임베딩 벡터 형태로 변환하여 벡터 DB에 저장 및 처리하는 방식입니다.

ThanoSQL은 LLM 및 기타 머신러닝(분석)에도 트랜스포머 모델이 사용 가능하도록 되어있기 때문에 비로소 TAG 방식(SQL내에서 LLM, prompt engineering, RAG, text to SQL, 또다른 MLs, 외부API 를 사용할 수 있는 구조)이 동작할 수 있습니다.


ThanoSQL 도입 문의

'},{Slug:\"ai-에이전트로-복잡한-문제를-풀거나-분석을-할-수-있을까\",\":draft\":true,id:13,Title:\"AI 에이전트로 복잡한 문제를 풀거나 분석을 할 수 있을까?\",Date:\"2024-10-12T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/OzQGqZjCvfigPL1FkcP0G3rpY8.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

최근 LLM(대규모 언어 모델)만큼이나 자주 등장하는 단어가 AI 에이전트 입니다. 지난 8월 달만 해도 국내외에서 10개가 넘는 업체들이 각자 AI 에이전트라고 주장하는 제품을 공개하기도 하였습니다.

오늘은 AI 에이전트가 무엇이고 현실 세계의 비즈니스에서 발생하는 복잡한 문제들을 풀거나 실제로 분석에 활용할 수 있는지를 살펴보겠습니다.

1. AI 에이전트(Agent)에 대하여

LLM의 관점에서 에이전트는 LLM을 기본적인 구성 요소로 사용하여 적절한 추론을 통해 계획을 구성하고, 사용 가능한 도구와 리소스를 사용하여 문제를 해결하는 시스템입니다. 문제가 주어지면 전략을 고민하고 필요한 도구를 활용하여 문제를 해결하는 인간과 유사합니다. LLM은 에이전트에서 인간의 뇌와 유사하게 작동합니다. 문제를 해결하는 이상적인 방법은 문제를 순차적으로 또는 독립적으로 수행할 수 있는 하나 이상의 작은 문제로 분할하여 해결하는 것입니다. 에이전트도 이와 동일한 작업을 수행합니다.

일반적으로 LLM은 문제를 해결할 방법을 계획하고 에이전트는 하나 이상의 도구를 사용합니다. 에이전트는 의도된 기능을 제대로 수행하기 위해 도구 이외에도 프롬프트나 메모리 같은 다른 구성 요소를 갖습니다. 아래 그림은 단일 에이전트 시스템의 구성도입니다.

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단일 에이전트 시스템은 주어진 문제를 해결하기 위해 여러 도구를 사용할 수 있는 특정 AI 에이전트로 구성됩니다. 이러한 시스템은 LLM의 추론 기능과 함께 도구의 결합된 기능을 활용하여 작업을 자율적으로 처리하도록 설계되었습니다. 

단일 에이전트 시스템의 메모리 제약과 처리 능력의 한계로 인해 더 광범위한 작업이나 복잡한 문제를 처리해야 할 때는 멀티 에이전트가 필요합니다. 멀티 에이전트 시스템은 복수개의 단일 에이전트로 구성되어 있으며, 각각은 자율적으로 작업을 수행할 수 있지만 공통의 목표를 향해 협력하도록 설계되었습니다.

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위 그림은 멀티 에이전트 시스템의 구성도입니다. 만능처럼 보이는 멀티 에이전트 시스템에도 여전히 한계가 존재합니다. (지난 게시글 LLM과 랭체인의 한계와 Agent의 문제점 그리고 TAG을 참조하시면 됩니다.)

2. 사용자의 복잡한 자연어 질의를 푸는 방식

사용자가 자연어로 길고 복잡한 질문(요청)을 했을 때 문제를 풀기 위한 방식은 아래 그림처럼 3가지가 있습니다.

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▲ LLM을 활용하여 복잡한 자연어 질의 처리하기


사용자의 입력을 LLM이 1) 에이전트 언어로 바꿔서 에이전트 방식으로 처리, 2) Text-to-SQL을 이용해서 SQL로 변환하여 TAG로 처리, 3) ThanoSQL로 처리하는 방식입니다.

여기서 Text-to-SQL이 무엇인지 잠시 짚어보고 가겠습니다.

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다시 본론으로 돌아와서, 현시점에서 에이전트 시스템이 대세이긴 하지만 느린 속도와 낮은 정확성 때문에 기업에서 활용하기에는 명백한 한계가 있습니다. 그리고 지난달에 논문으로 발표된 TAG(Table-Augmented Generation)가 에이전트의 문제를 어느 정도 해소시켜 줄 수 있을것으로 기대되지만 아직은 논문 수준이라 당장 기업에서 도입할 수는 없습니다.

스마트마인드의 ThanoSQL은 바로 이 TAG의 개념을 LLM뿐 아니라 RAG, 외부API 등 모든 Action으로 확대 적용하여 구현한 솔루션입니다.

3. TAG의 개념을 확장 구현하여 상용화한 ThanoSQL

사용자가 자연어로 길고 복잡한 질문(요청)을 했을 때 문제를 풀기 위한 방식은 아래 그림처럼 3가지가 있습니다.

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▲ LLM 기반 분석 도구로서의 ThanoSQL

ThanoSQL은 비IT 인력을 위한 LLM 기반의 분석 도구로서 복잡한 데이터를 쉽게 분석할 수 있도록 지원합니다. 전통적으로 데이터를 분석하기 위해서는 SQL, RAG, AI/ML, 외부 API와 같은 다양한 도구들이 필요합니다. 이러한 도구들을 효과적으로 연결하기 위해서는 통합적인 솔루션이 필수적이며, 랭체인과 같은 기술이 이를 가능하게 합니다. 랭체인은 여러 도구를 프로그래밍 언어인 파이썬이나 자바스크립트를 이용해 체인 형태로 연결하여 사용할 수 있도록 돕습니다.

그러나 이러한 도구들을 연결하고 문제에 맞게 적절히 활용하는 과정은 단순하지 않습니다. 각 도구의 선택과 사용 순서를 AI에 의존해야 하며, AI의 답변이 항상 정확하다는 보장이 없기 때문에 잘못된 결과가 나오기도 합니다. 사용자는 최종 결과물이 제대로 생성되었는지 확인하기 어려우며 수정이 필요할 때 이를 자연어로 다시 설명해야 하는 비효율적인 상황이 발생합니다.

SQL은 데이터베이스와의 정확한 소통을 위해 개발된 언어로 하나의 작업을 수행하고 필요 시 수정할 수 있는 장점이 있습니다. Nested SQL을 활용하면 도구의 적용 순서가 반복되거나 잘못될 위험을 최소화할 수 있습니다.

ThanoSQL은 이러한 문제를 해결하고자 설계되었습니다. 랭체인과 같은 목표를 공유하지만 비IT 인력을 주요 대상으로 하고 있으며 복잡한 문제를 해결하는 방식 역시 다릅니다. ThanoSQL은 에이전트 기반의 순차적인 접근 대신 Nested SQL 형태로 복잡한 문제를 해결합니다. 현재 시장에는 개별 기능을 제공하는 다양한 제품들이 존재하지만, ThanoSQL처럼 복합적인 질문을 한번에 해결 할 수 있는 All-in-One 솔루션은 없습니다.

4. 복잡한 질의 분석 사례

다음과 같은 두 가지 질문에 대해 ThanoSQL을 이용해서 처리하는 과정을 살펴보겠습니다.

질문 1) “최근 3일 내 평점이 2점 이하인 리뷰를 추출하고, 부작용 문제와 관련된 내용을 요약한 후, 요약본을 만들어주고, 웹에서 유사한 게시물을 찾아줘.”

먼저 필요한 함수를 만듭니다. 그 함수들은 ① 리뷰 내용을 요약, ② 내용이 ‘부작용’인 리뷰를 검색, ③ 웹 검색(외부 API 활용) 이렇게 3개가 되겠지요?

그 다음으로는 순서대로 하나씩 Text-to-SQL을 통해 SQL 쿼리를 생성해서 실행하게 됩니다. 최근 3일 내 평점이 2점 이하인 리뷰를 선택 –> 해당 리뷰들을 요약  –> 내용이 부작용인 리뷰들을 선택 –> 선택 결과 출력 –> 웹 검색 결과 출력

아래 그림은 ThanoSQL에서 처리되는 SQL 구문입니다.

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다음으로 2번째 질문입니다.

질문 2) “최근 일주일 동안의 평균 매출 변화를 분석하고, 고객 리뷰의 키워드 변화를 파악한 뒤, 해당 키워드로 작성된 리뷰를 추출하여 최근 급상승 검색어와 비교하고, 이를 바탕으로 새로운 마케팅 전략을 제안해줘.”

앞의 사례와 동일하게 필요한 함수들을 먼저 생성하고, 이어서 단계 별로 SQL 쿼리를 만들어 실행합니다.

아래 그림은 ThanoSQL에서 처리되는 실제 SQL 구문입니다.

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ThanoSQL은 위와 같이 복잡한 질문에 대해 SQL, RAG, AI/ML, 외부 API를 활용한 nested SQL 문을 자동으로 생성합니다. 이러한 방식은 각 도구를 순차적으로 실행하여 일관된 답변을 제공하는 것을 보장합니다.

ThanoSQL의 자동 쿼리 생성 결과를 검토하여 변환된 내용이 의도한 대로 이루어졌는지 확인할 수 있으며 필요한 경우 질의를 통해 즉시 수정하거나 보완할 수도 있습니다. 그리고 자동 생성된 쿼리의 답변을 바탕으로 추가적인 질문을 하고 그 답변이 정확한지 다시 확인하는 것도 가능합니다.

이와 같은 ‘과정 검증’과 ‘결과 검증’은 랭체인을 기반으로 하는 에이전트 방식에서는 불가능하며 오직 ThanoSQL에서만 가능합니다.

5. 마치며

위의 사례에서 살펴본 것처럼, 사용자의 복잡한 자연어 질의를 SQL 네스티드 쿼리로 짜서 나온 결과 테이블을 대상으로 자연어 질의를 하게 되면 에이전트 방식의 문제점과 한계를 극복할 수 있을 뿐 아니라 다음과 같은 이점이 있습니다.

■ 만들어진 테이블을 바탕으로 추가 질문을 하고 분석할 수 있습니다.
■ 이러한 방식으로 생성된 SQL은 반복적으로 사용할 수 있으며, 저장하여 공유하고 지속적으로 활용할 수 있습니다. 즉, 자주 사용하는 또는 최근 사용한 인사이트 추출용 SQL을 재활용하는 등 반복 업무를 효율화 할 수 있습니다.
■ SQL 기반의 구조이기 때문에 분석 결과를 개선하는 과정이 매우 간편합니다. 이는 사용자가 문제를 신속하게 파악하고 수정할 수 있도록 합니다.
■ 더 나아가 잘 정제된 자연어 질의로 생성된 SQL 자체가 독립적인 어플리케이션으로도 사용될 수 있습니다.
■ 이러한 SQL문 여러 개를 조합하면 더욱 고차원적인 어플리케이션을 쉽고 빠르게 만들 수 있습니다.

앞으로도 ThanoSQL에 많은 관심 부탁드립니다.

'},{Slug:\"llm과-랭체인의-한계와-agent의-문제점-그리고-tag\",\":draft\":true,id:12,Title:\"LLM과 랭체인의 한계와 Agent의 문제점 그리고 TAG\",Date:\"2024-09-27T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/h36hERQclhOpBRywzSAn5FoMbs.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

최근에 나오고 있는 LLM 기반의 AI 서비스들은 대부분이 랭체인(Langchain)으로 개발되고 있습니다. 랭체인은 LLM을 사용하여 어플리케이션 생성을 단순화하도록 설계된 프레임워크 입니다.

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▲ 랭체인 구성도 (출처: LangChain 101: Build Your Own GPT-Powered Applications)

랭체인은 시작하기에 매우 좋고 훌륭한 교육 도구이지만 실제 운영 상황에 맞게 설계되지는 않았습니다. 마치 서버를 직접 조립하기 위해 용산 전자상가에 가는 것처럼 기업에서 프로덕션 레벨로 사용하기 위해 자체적인 파이프라인과 체인을 구축 및 실행하는 것은 컴퓨터의 각 부품을 직접 구매하고 조립하는 것과 같습니다.

LLM과 랭체인은 어떤 한계가 있고 극복 하기 위한 방안은 무엇인지, 그리고 최근에 발표된 TAG(Table Augmented Generation)란 무엇인지 등을 살펴보겠습니다.

1. LLM의 한계

LLM은 질문에 ‘짝이 되는’ 가장 적절한 답변을 ‘생성’하도록 만들어진 AI 모델입니다. 매우 많은 샘플 문서로 훈련이 되어 있어서 다양한 질문에 대하여 그럴듯한 답변을 잘 만들어 내어 큰 관심을 끌었지만 금방 몇 가지 단점이 드러났습니다.

첫번째는 모르는 것을 그럴 듯 하게 답변을 하는 환각 현상이고

두번째는 ‘생성’ 방식으로는 풀 수 없는 문제(예를 들어, 수학이나 물리 같은 연산 문제)가 많이 있다는 것이고 

세번째는 여러 단계의 추론이 필요한 문제를 풀 수 없다는 것입니다.

Open AI의 최신 모델인 GPT-4o의 경우 Math(수학 연산) 관련 기능들이 크게 개선되었다고 알려져 있습니다. 그래서 실제로 GPT-4o에게 아래처럼 고등학교 수준의 수학 문제를 한번 풀어보게 해봤습니다.

비행기의 속력이 기차의 속력보다 4배 빠르다면, 1200km를 가는데 기차는
비행기보다 9시간이 더 걸린다. 기차의 속력은 얼마인가?

아래 그림은 ChatGPT-4o에게 물어본 결과입니다. 우선은 문제를 풀다가 에러가 났고 몇번의 재시도 후에 풀기는 했습니다. 여기서 알 수 있는 것은 내부에서는 파이썬 코드가 동작을 하고있으며 LLM은 결국 랭체인에 의존해서 계산을 모방하는 확률적 모델이라는 것입니다.

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수학 분야의 문제 해결 능력이 크게 개선되었다고 알려진 최신 모델인 ‘GPT-4o’조차 국제 수학 올림피아드(IMO) 예선 시험에서 13%의 성적을 받는 수준입니다.

나무위키에 따르면 GPT-4o의 성능은 다음과 같습니다. “연산 실력은 월등히 향상되었다곤 하나 2024년 현재 수능 수학 4점짜리 수준의 문제는 거의 풀지 못하며, 물리학 1 문제는 아주 쉬운 개념형 문제를 제외하고 전혀 풀지 못하고 있다. 식이 완벽하게 주어진 함수의 특정 점에서의 미분계수를 구하거나, 평범한 4차방정식 수준의 문제는 곧장 풀 수 있지만, 주어진 조건을 이용하여 미정계수를 정확히 구하는 것이나, 마찰과 공기저항을 무시한 매우 기초적인 수준의 역학 문제를 해석하고 푸는 것은 전혀 하지 못하는 상태이다. 이는 수학적 구조와 무리수, 허수 등에 관한 정확한 프로그래밍이 제대로 되어있지 않은 모델로 인한 문제로 보인다.
수능 3점 수준의 문제까지는 어찌저찌 풀 수 있는 수준이므로, 2024학년도 대학수학능력시험 미적분 등급컷 기준, 현재 GPT-4o의 성적은 약 5등급 정도이다.”

2. LLM의 한계를 극복하기 위한 방법들

첫번째 문제를 해결하기 위해서는 근거 자료를 제공해주고 그 자료에만 근거하여 답변을 하도록 강제하는 RAG 이며 효과가 좋은 것으로 판단되어 널리 활용되고 있습니다.

두번째 문제를 해결하기 위해서는 각종 도구(예를 들어, 계산기, 웹 검색, 데이터베이스 검색, 프로그램 실행 등)를 미리 정해 놓고 입력으로 들어온 문제에 맞는 도구 짝을 찾아서 적용하는 방식을 사용하는 툴링(Tooling) 기법이 널리 활용되고 있습니다.

세번째 문제를 해결하기 위해서는 문제의 풀이 과정을 여러 단계로 나눈 뒤 각 단계를 툴링이나 (RAG를 포함하여) LLM을 이용해서 풀고 원하는 결과가 나왔으면 다음 단계로 가고 아니면 다시 푸는 에이전트(Agent) 기법이 사용되고 있습니다. 여기서 에이전트란 자연어를 해석하여 LLM과 분석 플랫폼을 조합하기 위한 선택 알고리즘으로 Pool(LLM이 할 수 없는 종류의 분석에 필요한 AI 묶음)에서 Action(LLM과 분석을 위한 AI의 조합)을 스스로 선택하여 순서를 조합한 후 순차적으로 실행하여 답변하는 랭체인 모듈을 의미합니다. 즉 에이전트 기법은 LLM, RAG, 툴링을 모두 사용하면서 ‘자율적으로’ 답이 나올 때까지 탐색하는 방식입니다.

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▲ (출처: Intro to LLM Agents with Langchain: When RAG is Not Enough)
위의 그림은 랭체인의 주요 모듈인 LLM과 RAG, Agent의 개념도 입니다. LLM은 RAG 아키텍처를 통해 외부 메모리로 보강되는 개념이며, 에이전트는 이 개념을 메모리, 추론, 도구, 답변 및 action으로 확장합니다.

3. 에이전트(Agent) 방식의 단점들

에이전트 방식을 사용하면 어떤 단계를 실행하기 전에는 결과가 어떻게 나올지 모르는데, 그 결과에 따라서 그 다음 단계를 결정하므로 기하급수적으로 많은 경우의 수가 발생하여 원하는 방향을 찾기 어렵거나, 경우의 수가 많지 않더라도 단계들 사이에 루프가 발생하면 빠져나오지 못하는 위험이 있습니다. 

또한 단계가 많아질수록 불확실성이 점점 커지므로 답변이 틀릴 가능성도 그만큼 급격하게 높아지게 됩니다.

게다가 그 과정이 에이전트의 자율로 진행되므로 사용자는 문제를 푸는 과정을 알 수도 없고 통제할 수도 없습니다. 즉 디버깅이 불가능하여 어디서부터 잘못됐는지 알수가 없고 중간에 바로잡을 수 도 없으므로 아예 처음부터 질문을 다시할 수밖에 없습니다.

아래는 에이전트의 동작 원리를 나타내는 그림입니다.

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에이전트가 알고리즘을 스스로 선택함에 따라 사용자는 실행 시간을 전혀 예측 할 수 없으며 언제 끝날지도 모르고 심지어 끝나지 않을(무한 루프) 수도 있습니다. 또한 어떤 알고리즘이 선택되고 어떻게 실행되는지 조차 알 수가 없어서 마치 알파고처럼 블랙박스로 동작합니다.

4. TAG의 등장

데이터베이스와 LLM의 추론 기능을 결합하여 의미적 추론이나 데이터 소스에서 직접 얻을 수 있는 것 이상의 지식을 요구하는 자연어 쿼리를 처리하도록 하는 새로운 접근 방식이 나왔습니다. LLM 한계를 넘기 위해 웹 데이터를 찾는 검색 증강 생성(RAG)에 이어, 데이터베이스를 직접 연결하는 이번 기술에는 ‘테이블 증강 생성(TAG: Table-Augmented Generation)’이라는 이름이 붙었습니다.

벤처비트는 9월 2일 UC 버클리와 스탠포드대학교 연구진이 데이터베이스에서 자연어 질문에 답하기 위한 새로운 패러다임인 TAG에 관한 논문을 아카이브에 게재했다고 보도했습니다.

논문에 따르면 TAG는 사용자 질문을 실행 가능한 데이터베이스 쿼리로 변환하는 ‘쿼리 합성(Query Synthesis)’ 과정, 이 쿼리를 실행해 관련 데이터를 검색하는 ‘쿼리 실행(Query Execution)’ 과정, 그리고 이 데이터와 쿼리를 사용해 자연어 답변을 생성하는 ‘답변 생성(Answer Generation)’ 과정으로 구성됩니다. 이를 통해 특정 쿼리에 제한되는 텍스트-SQL이나 RAG와 달리 더 넓은 범위의 쿼리를 처리할 수 있게 됩니다. 

예를 들어, “고전으로 여겨지는 최고 수익을 올린 로맨스 영화의 리뷰 요약”이라는 질문에 답할 수 있습니다.

이 질문은 전통적인 텍스트-SQL나 RAG 시스템에게는 어려운 문제인데요, 왜냐하면 데이터베이스에서 최고의 수익을 올린 로맨스 영화를 찾아야 할 뿐만 아니라, 세계 지식을 활용해 해당 영화가 ‘고전’인지도 판단해야 하기 때문입니다.

하지만 TAG의 3단계 접근 방식을 통하면, 시스템은 관련 영화 데이터를 위한 쿼리를 생성하고, 필터와 언어 모델을 사용해 수익 순으로 정렬된 고전 로맨스 영화 테이블을 생성한 뒤, 테이블에서 가장 높은 순위의 영화에 대한 리뷰를 요약해 원하는 답변을 제공할 수 있습니다.

아래 그림은 논문에서 제공한 TAG의 구현 예제입니다.

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벤치마크에 따르면 TAG 기준 모델은 모든 쿼리 유형에서 50% 이상의 정확도를 꾸준히 유지했으며, 전반적으로 표준 기준 모델들에 비해 20%에서 65%까지 정확도 향상을 제공했다고 밝혔습니다. 이 외에도 연구진은 TAG 구현이 다른 기준 모델들에 비해 쿼리 실행 속도가 3배 빠르다는 것을 발견했다고 합니다.

5. ThanoSQL과의 비교

ThanoSQL도 에이전트와 마찬가지로 LLM, RAG, 툴링을 활용하지만 에이전트처럼 단계별로 풀어나가는 것(미로찾기 문제를 풀 때, 갈림길을 하나씩 차례대로 해결하는 방식)이 아니라 원하는 결과(예, 가장 큰 값 10개를 뽑아줘)만을 제시하는 SQL 언어로 문제를 변환하고, 그 변환의 결과로 나온 질의(query)를 실행하여 그 결과를 사용자에게 제공합니다.

이러한 방식의 장점은 첫째, 무한 루프에 빠지는 위험이 없습니다. SQL은 선언적(declarative) 언어이고 결정론적(deterministic) 특성을 가지므로 모든 경로를 탐색하거나 루프에 빠지는 등의 문제가 없습니다. 명령어를 이용해서 결과를 도출하는 것이 아니라 원하는 결과를 명시하고 그 결과를 얻는 비절차적 방식이기 때문입니다.

둘째, LLM이 문제를 어떻게 풀었는지가 질의로 표현이 되어 있으므로 혹시 틀리게 풀었거나 비효율적으로 풀었다면 질의 자체를 수정하여 즉시 개선된 답변을 얻을 수 있습니다. ThanoSQL 워크스페이스의 채팅 대답창 오른쪽 아래에 있는 >> 버튼을 누르면 Query Editor 화면에서 실행된 쿼리문을 확인하고 바로 수정하여 다시 실행해볼 수 있습니다.

문제를 SQL로 변환하여 푸는 것은 위와 같은 장점이 있는데 종래의 데이터베이스에서 제공하는 SQL의 기능만으로 하는 것 보다는 RAG, LLM, 툴링 등을 결합하면 정확도가 2배 이상 높아진 다는 것이 TAG 논문의 요지입니다. ThanoSQL는 기존의 SQL을 확장하여 이러한 기능을 결합할 수 있는 기능을 내장하고 있습니다.

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쉽게 말하면 ThanoSQL은 TAG의 일종이라고 할 수 있으며 LLM뿐 아니라 RAG, 외부API, AI 까지도 모두 SQL에 삽입하여 처리하므로 보다 복잡한 명령에서도 TAG의 장점을 유지합니다.

에이전트와 비교하면, 항상 예측 가능한 실행 시간을 제공하고 정확한 답변을 얻을 수 있으며 디버깅이 가능하고 실행 속도가 월등히 빠릅니다.

TAG는 ‘논문’으로 발표 된 지 아직 한 달이 채 되지 않았지만, 스마트마인드의 ThanoSQL은 지난 2018년부터 자체 개발을 해왔고 다수의 특허출원(14건), 특허등록(12건), 국제특허 PCT(6건)가 진행 중이며 상용화를 하였습니다.

'},{Slug:\"허깅페이스의-sota-모델을-사용하는-가장-쉽고-빠른-방법\",\":draft\":true,id:11,Title:\"허깅페이스의 SOTA 모델을 사용하는 가장 쉽고 빠른 방법\",Date:\"2024-09-24T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/OqBgfPfprvObLEF1tNm0UE65I.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

SOTA는 State-of-the-Art의 약자이며,
“현재 시점에서 최고 수준의 AI 모델”을 의미합니다.

본 게시글 에서는 허깅페이스(Hugging Face)에 있는 SOTA 모델을 가져와서 활용하는 방법을 살펴보려고 합니다.
사용자에게 필요한 AI 함수를 만들고, 그 다음에 채팅으로 자연어 질의를 하여 만들어진 AI 함수가 답을 하는 경우를 예로 들어 설명하겠습니다. 바로 그 AI 함수를 만들 때 허깅페이스의 SOTA 모델을 불러와서 등록하게 됩니다.

여기서는 많은 SOTA 모델들 중에서 CLIP 모델을 사용해보겠습니다. CLIP은 Contrastive Language-Image Pre-training model의 약자이며 OpenAI에서 개발한 신경망 아키텍처로 자연어를 이해하고 컴퓨터 비전을 구현하는 등 인간의 언어 또는 이미지를 컴퓨터로 처리할 수 있게 해주는 대표적인 모델입니다. CLIP은 2022년부터 쏟아져나온 AI 그림의 기반이 모두 여기에 있다고 할만큼 매우 강력한 도구 중에 하나입니다. 실제로 OpenAI의 DALL-E도 이 CLIP을 이용하여 만들었습니다.

허깅페이스의 ‘모델(Models)’ 탭에서 CLIP을 검색하면 아래 그림과 같이 2,900개가 넘는(‘24.09.20 기준) 모델들이 등록되어 있음을 알 수 있습니다.

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우리는 이중에서 제로샷 이미지 분류 모델인 clip-vit-base-patch32 모델을 선택하겠습니다.
이 모델은 텍스트와 이미지를 동일하게 임베딩(embedding) 시키는 모델이며 실제로 어떻게 활용되는지를 이제부터 자세히 알아보겠습니다.

SOTA 모델로 AI 함수 만들기

우리는 특정 이미지를 input으로 넣으면 그와 유사한 이미지를 찾아주거나, 또는 사용자의 텍스트 입력을 받아서 그에 해당하는 이미지를 찾아주는 AI 함수를 만들려고 합니다.

그 함수의 이름은 embed_clip이라고 명명하였고 함수 구현은 아래와 같습니다.

\"\"
DROP FUNCTION IF EXISTS embed_clip(text);  \\n\\nCREATE or REPLACE FUNCTION embed_clip(text_or_image TEXT) \\nRETURNS VECTOR AS $$ \\nBEGIN\\n    RETURN thanosql.embed(\\n        engine := \\'huggingface\\',\\n        input := text_or_image,\\n        model := \\'openai/clip-vit-base-patch32\\',\\n        model_args := \\'{\"device_map\": \"cpu\"}\\'\\n    ); \\nEND; \\n$$ LANGUAGE plpgsql;

ThanoSQL의 쿼리매니저(QM)에서 위와 같이 함수를 선언하고 구현합니다. 이때 engine을 ‘huggingface’로 하고 model을 ‘openai/clip-vit-base-patch32’으로 정의하면 허깅페이스의 CLIP 관련 SOTA 모델인 clip-vit-base-patch32를 즉시 사용할 수 있게 됩니다.

\"\"

쿼리 에디터 상단의 세모 버튼을 눌러서 실행하면 아래 쿼리 결과창에서 실행 결과가 나타납니다.

AI 함수가 동작할 샘플 테이블 만들기

이제 이 함수를 사용하기 위한 샘플 테이블을 만들도록 하겠습니다.
이미지들이 저장되어 있는 테이블이 필요한데 그런 테이블이 없다면 새로 만들어도 되고 기존의 DB에 있는 테이블을 가져오거나 csv 파일을 업로드해도 됩니다.

여기서는 임의로 웹에서 10개의 이미지를 다운받아서 unsplash_meta_sample 라는 이름의 테이블을 만들었고 그 테이블의 스키마(schema)는 아래 그림과 같습니다.

\"\"

이미지 같은 비정형 데이터를 분류하고 검색하기 위해서는 먼저 해당 데이터를 임베딩해야 하므로 위에서 만든 embed_clip 함수를 사용해서 샘플 테이블을 임베딩합니다. 아래 그림은 embed_clip 함수를 샘플 테이블에 적용하여 그 결과를 test_vector라는 이름의 테이블에 저장하는 쿼리문입니다.

\"\"
DROP TABLE IF EXISTS test_vector;\\n\\nCREATE TABLE test_vector\\n  AS SELECT photo_id\\n          , photo_image_url\\n          , photo_submitted_at\\n          , photo_description\\n          , embed_clip(photo_image_url) AS embedding\\n     FROM my_data.unsplash_meta_sample;\\n\\nSELECT * FROM test_vector

쿼리가 정상적으로 실행되면 아래 그림처럼 Query Result 창에서 각각의 이미지들이 임베딩된 결과를 확인할 수 있습니다.

\"\"

Data Viewer에서 확인해보면 임베딩 처리된 각각의 이미지들은 아래와 같이 나타납니다.

\"\"

이미지로 이미지 검색하기

다음으로 웹에서 이미지를 하나 다운받습니다. 우리는 Unsplash라는 사이트에서 사막 사진을 한 장 받아보겠습니다. 해당 이미지의 주소(URL)를 복사해둡니다.

\"\"

다시 ThanoSQL 워크스페이스로 돌아와서 왼편의 메뉴 중에 AI Chat을 선택하고 이전 단계에서 만들어둔 test_vector 테이블을 선택합니다.

\"\"

이제 모든 준비가 끝났습니다. 채팅을 통해서 Unsplash에서 다운 받은 사막 사진과 유사한 사진 3장을 찾아보도록 하겠습니다.

\"\"

채팅 입력 창에 다음과 같이 입력합니다.

“embed_clip 함수로 https://images.unsplash.com/photo-1464822759023-fed622ff2c3b 를 임베딩하고 가장 비슷한 사진 3개 찾아줘”

이 채팅의 의미는 사용자가 만든 함수에 다운받은 이미지 주소를 넘겨주고 샘플 테이블에서 그와 비슷한 사진 3장을 찾는 쿼리문을 실행하는 것입니다.

\"\"

채팅 입력 후 엔터키를 누르면 쿼리가 실행되고 그 결과는 아래와 같이 나타납니다.

\"\"

이때 실행된 쿼리는 아래 그림처럼 Query Editor에서 확인할 수 있습니다.

\"\"

또한 Data Viewer를 통해 어떤 이미지가 검색되었는지도 확인할 수 있습니다.

\"\"

텍스트로 이미지 검색하기

앞에서는 이미지로 이미지를 검색했는데 이번에는 텍스트로 이미지를 검색해보겠습니다.

아래는 바다에서 서핑하는 사람이 있는 사진을 찾는 예제입니다.
마찬가지로 ThanoSQL 워크스페이스에서 왼편의 메뉴 중에 AI Chat을 선택하고 이전 단계에서 만들어둔 test_vector 테이블을 선택합니다.

\"\"

채팅 입력 창에 다음과 같이 입력합니다.

“embed_clip 함수로 ‘바다에서 서핑하는 사람’을 임베딩하고 가장 비슷한 사진 찾아줘”

이 채팅의 의미는 사용자의 텍스트 입력값을 임베딩하여 함수로 넘겨주고 샘플 테이블에서 그와 가장 비슷한 사진 1장을 찾는 쿼리문을 실행하는 것입니다.

\"\"

채팅 입력 후 엔터키를 누르면 쿼리가 실행되고 그 결과는 아래와 같이 나타납니다.

\"\"

이때 실행된 쿼리는 아래 그림처럼 Query Editor에서 확인할 수 있습니다.

\"\"

답변에 있는 링크를 누르면 아래와 같이 검색된 이미지를 바로 확인할 수 있습니다.

\"\"

마찬가지로 Data Viewer를 통해서도 이미지를 확인할 수 있습니다.

\"\"

마치며

위과 같이 허깅페이스의 SOTA 모델을 불러와서 사용자 정의 함수를 만들고, 그 함수를 활용하여 2가지 방법으로 이미지를 검색하는 유스케이스를 살펴보았습니다. 이처럼 ThanoSQL을 사용하면 누구든 허깅페이스 또는 외부의 AI 모델들을 쉽고 빠르게 적용하여 즉시 실행해 볼 수 있습니다.

다음에는 이미지 분류 모델이 아닌 다른 머신러닝 모델을 활용한 유스케이스로 돌아오겠습니다.

'},{Slug:\"기업이-ai를-도입하는-절차와-그-어려움에-대하여\",\":draft\":true,id:10,Title:\"기업이 AI를 도입하는 절차와 그 어려움에 대하여\",Date:\"2024-09-07T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/cFgJP4WpPzPsDmqDdI6yrBUa3U.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

기업이 AI를 도입하는 절차와 그 어려움에 대하여

\"우리 회사에 AI를 도입하려면 어떻게 해야 할까?\"

지난 게시글 에서는 기업들이 AI 도입을 어려워하는 가장 큰 원인은 무엇이고 어떻게 하면 해결할 수 있는지 살펴보았습니다.
– 기업 AI 도입의 걸림돌은 무엇일까?

여기서는 지난 글에 이어서 실제로 기업들이 AI 앱이나 서비스를 도입하는 절차가 어떻게 되고 그 과정에서 어떤 어려움들이 있는지를 알아보겠습니다.

기업 내의 세일즈나 마케팅 부서의 실무 담당자는 아래와 같은 니즈가 있을 수 있습니다.

\"\"

이러한 실무 담당자의 필요나 요청에 의해 AI 앱 또는 AI 서비스를 도입하려고 할 때, 일반적인 기업들에서 일어나는 업무 처리 흐름과 각 조직별 역할은 아래 그림과 많이 다르지 않을 것입니다.

\"\"

실제 개발을 시작하기까지도 적지 않은 시간이 소요될 뿐 아니라 실무자 입장에서 수 개월 후에 확인한 결과물이 만족스럽지 못한 경우도 상당히 많습니다.

도대체 AI 도입은 왜 이렇게 오래 걸리고 결과도 실망스러울까요?

AI 어플리케이션의 개발이나 서비스 도입을 위해서는 비정형 데이터에 대한 처리와 분석이 필수적입니다. 오늘날 기업 데이터의 90% 이상이 비정형 데이터인 것으로 추정되기 때문입니다. 그러나 대부분의 조직에게 텍스트, 오디오, 비디오, 센서 및 기타 형식의 데이터는 활용하기 어려운 존재입니다. 파운드리의 조사에 따르면 IT 리더의 61%가 비정형 데이터 관리가 조직에게 골칫거리라고 답했으며, 24%는 비정형 데이터를 분석 목록에 올리지도 않는다고 답했습니다.

이러한 비정형 데이터 분석을 위하여 필요한 수집 및 사전 처리를 위한 도구들은 아래 그림과 같이 매우 다양합니다.

\"\"\"\"

기존 방식의 경우 이러한 도구들 중에서 필요한 것들을 선정하여 설치/실행하고 데이터를 연결하여 파이프라인을 구축해야 합니다. 각 도구별로 전문가가 필요하고 그것들의 유기적인 연계와 구축된 파이프라인의 관리도 굉장히 어려운 작업입니다. 또한 많은 기업들은 정형 데이터 처리를 위해 구축된 기존 데이터 파이프라인을 보유하고 있는데, 새롭게 구축되는 비정형 데이터 파이프라인과는 어떻게 통합할지도 고민해야 합니다.

최근 많은 기업들이 LLM을 기반으로 하는 서비스를 도입하는 추세인데, 이러한 비정형 데이터의 수집 및 사전 처리가 LLM 아키텍처를 프로토타입에서 실행이나 운영 레벨로 옮기는데 심각한 병목 현상을 일으키고 있습니다.

\"\"\"\"

LLM 기반의 AI 서비스를 위해서는 비정형 데이터를 LLM에 적합한 형태로 변환하고 추론 시점에 모델이 필요한 데이터를 잘 찾을 수 있도록 사전 처리를 잘하는 것이 중요하기 때문입니다.

그렇다면 이런 어려움들을 극복할 수 있는 방법은 없을까요? 만약 기업이 ThanoSQL을 도입했거나 도입할 계획이라면 상황은 크게 달라집니다. 아래 그림은 ThanoSQL을 도입하였을 때의 업무 처리 흐름과 담당자별 역할을 나타냅니다.

위의 일반적인 기업들의 경우와 비교해보면 절차도 매우 단순하고 구매나 외주 업체 같은 다른 조직과의 협업도 필요가 없다는 것을 알 수 있습니다. 심지어 하루이틀 만에 실무자가 원하는 다양한 분석을 시도해보고 결과도 즉시 확인할 수가 있습니다.

\"\"

위의 그림에서는 분석하고자 하는 데이터의 흐름과 처리 방식을 간단하게 개념적으로 나타내었습니다. 허깅페이스의 SOTA 모델을 가져와서 활용하는 방법은 별도의 게시글(허깅페이스의 SOTA 모델을 가져와서 활용하는 가장 쉬운 방법)에서 소개하겠습니다.

다음 그림에서는 실제로 어떤 질문과 쿼리들이 실행되는지 간단한 예를 들어 살펴보겠습니다.

\"\"

이 글에서 예로 든 실무 담당자의 니즈처럼, 비정형 데이터인 고객 리뷰를 AI 모델로 분류하고 판매 데이터와 연결하면 고객 감정과 제품 성공과의 상관 관계를 이해하는 데 도움이 되는 인사이트를 발견할 수 있습니다. 이러한 방식의 분석은 마케팅 전략 수립이나 제품 개발 및 고객 서비스에 도움을 줄 수 있을 뿐만 아니라 궁극적으로 고객 만족도 향상과 사업 성장에 기여할 수 있습니다.

스마트마인드의 ThanoSQL은 기업내의 모든 정형/비정형 데이터를 데이터베이스의 테이블에 저장하고 최신의 AI 모델을 불러와서 대화형 인터페이스로 분석할 수 있는 올인원 플랫폼입니다. ThanoSQL을 도입하면 일반적인 방식으로 AI 앱이나 서비스를 도입할 때의 많은 어려움들을 고민하지 않아도 됩니다.

'},{Slug:\"기업-ai-도입의-걸림돌은-무엇일까\",\":draft\":true,id:9,Title:\"기업 AI 도입의 걸림돌은 무엇일까?\",Date:\"2024-09-05T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/bf096PEx8G6z23oA9R1GJavlmk.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

“우리 회사에 AI를 도입하기가 너무 어렵다” 진짜 이유는 무엇일까?

본 게시글 에서는 아래 기사의 내용을 바탕으로 기업들이 AI 도입을 어려워하는 원인은 무엇이고 어떻게 하면 해결할 수 있을지 방안을 제시하려고 합니다.

– ZDNET 기사


응답자 10명 중 7명 “보안 위험 장벽 높아…제대로 된 데이터 인프라\\xb7역량\\xb7인재도 부족”


“중소기업은 AI를 어떻게 도입해야 할 지 막막합니다. 무엇부터 해야 할까요?”

최근 국내외 기업들이 업무 효율성을 높이고자 AI를 도입하기 위해 앞다퉈 나서고 있지만 상당한 어려움을 겪고 있는 것으로 나타났습니다. AI를 제대로 이용하기 위해 필요한 데이터 인프라와 직원 역량이 뒷받침되지 않고 있기 때문입니다.

지난 8월 10일 클라우데라(Cloudera)가 미국, 유럽, 중동, 아프리카, 아시아-태평양 지역 600명의 IT 리더를 대상으로 ‘기업용 AI 및 최신 데이터 아키텍처 현황’을 조사한 결과, 10명 중 8명이 업무 처리를 위해 AI를 도입했지만 시스템을 유지하기 힘겨워하는 것으로 조사됐습니다.

또 응답 기업의 74%는 AI로 인한 보안과 규정 준수 위험에 대한 우려가 상당히 높다고 밝혔습니다. AI 도구를 관리할 적절한 교육이나 인재 부족(38%), AI 도구의 높은 가격(26%) 등의 문제로 AI 도입을 어렵게 느끼는 기업들도 있었습니다.

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▲ AI 도입을 어렵게 하는 요인들 (이미지=클라우데라)

조사를 진행한 관계자는 “AI는 지난 몇 년간 비즈니스 운영을 강화하고 정보에 기반한 의사결정을 내리는 등 혁신을 가속하고 직원과 고객 모두의 경험을 향상시킨다는 이점으로 전 세계적으로 주목받고 있다”면서도 “하지만 모든 기업이 이러한 이점을 경험하진 않는다”고 지적했습니다. 그러면서 “AI가 빠르게 도입되고 있음에도 불구하고 탄력적인 AI 전략의 많은 요소가 무시되거나 고려되지 않고 있다”고 덧붙였습니다.

특히 국내 중소기업들이 AI 도입 시 느끼는 어려움은 더 한 것으로 나타났습니다. 통계청이 올 초 발간한 ‘KOSTAT 통계플러스’ 보고서에 따르면 국내 대기업과 중소기업의 AI, 블록체인 등 신기술 도입 비율은 상당한 격차를 보였습니다. 빅데이터 도입율에선 2021년 기준 대기업이 12.7%, 중소기업이 3.7%로 나타났고, AI는 9.2% 대 2.9%로 조사됐습니다. 클라우드는 12.1%대 3.7%, 사물인터넷(IoT)는 6.9%대 3.1%였습니다.

하지만 많은 기업들이 AI 도입은 거스를 수 없는 흐름이라고 보고 있습니다. AI 기술의 잠재적 이점이 위험보다 많다고 인식하는 이들이 점차 많아지고 있는데다 활용하는 곳도 많아지고 있기 때문입니다. 마이크로소프트가 지난 5월 발표한 ‘업무동향 지표 2024’에선 글로벌 고객사 리더들의 79%가 AI 도입은 필수라고 응답한 바 있습니다.

그렇다면 AI 도입의 가장 큰 걸림돌인 보안 문제를 해결하기 위해 ThanoSQL은 어떻게 하고 있는지 살펴보겠습니다.

ThanoSQL은 ChatGPT 같은 퍼블릭 LLM과 데이터를 주고받을 때 마스킹(데이터를 * 또는 O 로 표시하는 방법)과 Faker(가짜 데이터 생성), 재변환(원래 데이터로 복구) 등의 과정을 통해 개인 식별 정보(PII: Personally Identifiable Information) 또는 기업의 민감한 내부 정보의 유출을 최소화합니다.

그리고 이미지나 동영상 같은 비정형 데이터는 파일이 위치한 경로와 임베딩(실제 객체를 수학적으로 표현한 벡터 뭉치) 값 만을 주고받아 데이터 유출을 원천적으로 차단합니다. 또한 데이터베이스의 스키마(Schema)만을 주고받기 때문에 DB의 내용 자체가 외부로 나가지 않으며, 데이터베이스의 컬럼명이나 테이블명도 Fake Entity를 사용하고 LLM 답변으로부터 재변환 시켜서 보안을 강화합니다.

기업의 보안과 관련하여 이상을 감지하고 리스크를 관리하는 조금 더 구체적인 유스케이스를 살펴보면 다음과 같습니다.

1) 이메일/채팅 등에서 위험도 자동 분석

2) 주요 정보 마스킹 기능

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3) 이미지나 첨부 파일 등 비정형 데이터 보안 관리


4) 송수신 패턴 비교를 통한 이상 신호 모니터링


스마트마인드의 ThanoSQL은 기업내의 모든 정형/비정형 데이터를 데이터베이스의 테이블에 저장하고 최신의 AI 모델을 불러와서 대화형 인터페이스로 분석할 수 있는 올인원 플랫폼입니다. 위에서 살펴본 것처럼 ThanoSQL을 도입하면 데이터 보안 관점에서도 기업 내부의 민감한 데이터 유출을 원천적으로 차단할 수 있습니다.

'},{Slug:\"stt-ta-faq-고객센터-솔루션\",\":draft\":true,id:8,Title:\"STT, TA, FAQ – 고객센터 솔루션\",Date:\"2024-09-03T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/5rRG7bT9g1QL3comjhWJr79kg.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

SQL로 STT/TA/FAQ 생성 실행하기

ThanoSQL(타노시퀄)의 VoC 분석 서비스의 핵심 기술인 STT와 TA, FAQ 를 소개합니다.

LLM을 기반으로 자연어로 대화하듯이 VoC를 분석하는 사례는 아래 링크에서 확인하실 수 있습니다.

– VoC 분석 앱
– 대화형 분석 사례 – 01 콜 센터의 상담 통화 분석

위의 경우처럼 ThanoSQL의 대화형 분석 서비스를 이용하면 쿼리나 DB 같은 IT 지식이 없는 사람들도 자연어로 대화하듯이 쉽고 빠르게 분석을 할 수 있습니다.

하지만 여기서는 ThanoSQL 워크스페이스 내의 쿼리 매니저(Query Manager)라는 기능으로 VoC를 분석하는 방법을 살펴보겠습니다. 쿼리 매니저를 사용하게 되면 자연어 기반의 대화형 분석이 아니라 관계형 데이터베이스의 스키마(schema)와 SQL 쿼리문을 사용하는 직관적인 방법으로 분석합니다.

아래 그림을 보시면 ThanoSQL에는 VoC 분석을 위하여 STT와 TA 그리고 FAQ Gen 모듈이 포함되어 있습니다. 콜센터에서 고객과 상담사가 통화한 내용을 녹음한 음성 파일을 ThanoSQL에 업로드하고 STT를 실행하면 녹음된 음성이 텍스트로 변환됩니다. 변환된 텍스트는 TA 모듈에서 여러 가지 다양한 분석을 할 수 있습니다. 그리고 반복되는 질문과 답변들은 모아서 FAQ를 자동으로 생성할 수도 있습니다.

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STT/TA/FAQ 각각에 대하여 자세히 살펴보기 전에 먼저 ThanoSQL의 쿼리 매니저를 간단히 소개해 드리겠습니다.

ThanoSQL의 쿼리 매니저는 분석하고자 하는 데이터를 ThanoSQL DB에 추가하여 SQL 쿼리를 실행하고 즉시 그 결과를 분석할 수 있는 도구입니다.

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쿼리 매니저에 대한 더 자세한 설명은 다음 링크를 참조해주십시오.
– Query Manager

다시 본론으로 돌아와서 ThanoSQL의 STT/TA/FAQ에 대하여 하나씩 살펴보겠습니다.

1. STT (Speech To Text)

STT는 말 그대로 음성을 글자(텍스트)로 바꾸는 기능입니다. 사람의 목소리 그 자체로는 분석이 어렵기 때문에 상담 내역을 분석하기 위해서는 가장 먼저 음성을 텍스트로 변환해야 합니다. 아래 그림은 ThanoSQL STT의 구성과 데이터 흐름입니다.

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콜센터나 고객센터에서 생성되는 녹음 파일들은 보통 아마존 S3 같은 클라우드 스토리지 또는 기업 내부의 로컬 스토리지에 저장이 되어있습니다. 해당 스토리지에서 mp3나 wav 같은 형식으로 저장된 오디오 파일들을 업로드하여 ThanoSQL STT의 DB에 넣으면 Open AI의 Whisper 같은 AI 모델을 통해 텍스트로 변환됩니다. 변환된 내용은 csv 또는 xls 형식의 파일로 최종 결과물이 만들어집니다.

2. TA (Text Analysis)

TA는 STT에서 변환된 텍스트에 여러가지 AI 모델들을 적용하여 내용을 요약하거나 핵심 키워드를 추출하거나 글의 내용이 부정인지 긍정인지 등 고객의 감성을 분석하거나, 글의 주제를 파악하여 카테고리를 분류할 수 있는 기능입니다.

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아래는 ThanoSQL 쿼리 매니저의 처음 시작 화면입니다.

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my_data 옆에있는 + 버튼을 눌러서 분석에 사용할 텍스트 데이터를 추가합니다.

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STT의 결과물로 생성된 텍스트 파일(엑셀 또는 csv 포맷)을 ThanoSQL로 업로드합니다.

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업로드한 텍스트 파일의 내용들을 ThanoSQL DB의 테이블로 인서트(insert)한 모습입니다.
이렇게 DB의 테이블에 들어가 있으므로 해당 데이터를 다른 곳에서도 API를 통해 얼마든지 자유롭게 활용 할 수 있습니다.

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위 그림은 각각의 대화들을 주제 별로 분류하도록 하는 쿼리문을 입력하는 화면입니다. 예제에서는 Mistral 7B 모델을 사용하였습니다.

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쿼리 실행 결과 12개의 카테고리로 분류된 화면입니다. 쿼리 실행 결과 역시 DB의 테이블로 저장되므로 외부에서 API를 통해 해당 데이터를 호출하여 분석에 활용 가능합니다.

3. FAQ 생성

STT로 변환된 텍스트 내용 전체를 분석하여 반복되거나 자주 묻는 질문과 그에 대한 답변을 모아서 FAQ를 생성하는 기능입니다.

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위 그림은 통화 내용을 기반으로 FAQ를 생성하는 쿼리문을 입력하는 화면입니다. 예제에서는 GPT-4o 모델을 사용하였습니다.

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쿼리 실행 결과 FAQ가 생성된 화면입니다. 이렇게 생성된 FAQ도 마찬가지로 DB의 테이블로 저장되므로 API를 통해 해당 데이터를 분석에 활용할 수 있습니다.

ThanoSQL 만의 차별점

ThanoSQL에 Pre-Built 되어 있는 모델 외에 다른 AI 모델이나 알고리즘이 필요한 경우 간단하게 외부 모델을 추가하여 등록하고 결과를 확인할 수 있습니다.
위의 사례에서는 ThanoSQL의 FAQ 결과 또는 STT 결과 테이블에 허깅페이스(Hugging Face)의 최신 모델을 적용(register)하여 내용상 긍정/부정을 따로 쿼리하여 테이블에 넣고 결과를 카운트 하는 등 ThanoSQL에 필요한 ML을 적용한 뒤 결과를 확인할 수 있습니다.

기대 효과 – AICC, CS 업계에서의 활용

마지막으로 아래 표에 AI 기반의 STT와 TA 기술이 어떻게 활용되는지, 그로 인한 효과는 무엇인지 정리하였습니다.

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위에서 살펴본 것처럼 AI 기반의 STT/TA/FAQ 를 사용하면 콜센터의 효율성과 서비스 품질을 극대화할 수 있습니다. 상담원은 단순하고 반복적인 업무에서 벗어나 보다 더 복잡한 문제 해결에 집중할 수 있고 고객은 신속하고 정확한 서비스를 받을 수 있게 됩니다.

그리고 기업은 데이터 분석을 통해 고객의 요구를 미리 파악하고 맞춤형 서비스를 제공함으로써 더 나은 고객 경험을 제공할 수 있습니다. 또한 상담원 교육과 관리는 물론 전체적인 상담 업무를 효율화하고 상담 품질을 향상 시킬 수도 있습니다. 스마트마인드의 AI 기술은 고객과 상담원 모두에게 더 나은 환경을 제공하며 콜센터와 고객센터의 미래를 혁신적으로 변화 시킬 것입니다.

'},{Slug:\"대화형-분석-사례-04-관계형-데이터베이스와-쿼리문에-대한-이해\",\":draft\":true,id:7,Title:\"대화형 분석 사례 – 04 관계형 데이터베이스와 쿼리문에 대한 이해\",Date:\"2024-08-29T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/r8iiY3wwgtal1sW7cy8VE9ISM.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

ThanoSQL을 사용하여 SQL 구문과 관계형 데이터베이스에 대한 이해도 높이기

ThanoSQL(타노시퀄)의 대화형 분석 서비스를 소개합니다.

대화형 분석 서비스는 AI가 사용자의 질문을 자연어로 받아서 데이터베이스를 사용하여 답변을 해주는 서비스입니다.

▲ 한국어 내레이션

▲ English Narration

이번 영상에서는 자연어를 사용하여 관계형 데이터베이스의 SQL 구문에 관련된 질문을 하는 사례를 살펴보겠습니다.

영상에서 설명드리는 내용은 아래 순서와 같습니다.

1) 이전 동영상(대화형 분석 사례 – 01 콜 센터의 상담 통화 분석)에서 본 것처럼 고객 센터의 상담 기록 데이터베이스에서 필요한 정보를 정리하고 추출하는 과정을 자연어로 할 수 있습니다.

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2) 그런데 만약 데이터 분석에 대한 질문이 아니라 데이터베이스(DB)나 SQL 쿼리문의 사용법에 대한 일반적인 질문을 하면 어떤 답을 얻을 수 있을까요? 테이블을 적당히 선택하고 데이터베이스의 ‘JOIN’ 기능에 대해서 물어보겠습니다.

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3) 질문이 입력되면 LLM은 학습을 통해 이미 알고 있는 관계형 데이터베이스에 대한 내용을 기준으로 답변을 해줍니다. 재미있는 점은 예시를 선택한 테이블을 이용해서 설명해준다는 점입니다.

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그림과 같이 사용자가 선택한 테이블을 기준으로 SQL의 기본적인 문법과 자세한 설명, 활용 방법까지 알려줍니다.

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4) 따라서 ThanoSQL의 대화형 분석 서비스는 SQL 구문과 관계형 데이터베이스에 대한 이해도를 높일 수 있는 학습 도구로도 활용이 가능합니다.

'},{Slug:\"대화형-분석-사례-03-데이터베이스에서-이미지-검색하기\",\":draft\":true,id:6,Title:\"대화형 분석 사례 – 03 데이터베이스에서 이미지 검색하기\",Date:\"2024-08-28T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/YAMPkGG1OgSqzv1bRAMAQ9x6u8.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

ThanoSQL을 사용하여 자연어로 데이터베이스에서 이미지를 검색하기

ThanoSQL(타노시퀄)의 대화형 분석 서비스를 소개합니다.

대화형 분석 서비스는 AI가 사용자의 질문을 자연어로 받아서 데이터베이스를 사용하여 답변을 해주는 서비스입니다.

▲ 한국어 내레이션

▲ English Narration

이번 영상에서는 자연어를 사용하여 데이터베이스에 저장된 사진을 검색하는 사례를 살펴보겠습니다.

영상에서 설명드리는 내용은 아래 순서와 같습니다.

1) 인터넷에서 수집한 이미지에 AI 기능을 이용하여 자동으로 설명을 붙여 놓은(라벨링) 테이블을 미리 준비하였습니다. 이 테이블을 선택하고 바다 관련 이미지를 찾아 달라고 요청을 합니다.

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2) ThanoSQL은 바다와 관련된 이미지를 찾아서 보여줍니다. 마지막 사진은 바다가 아닌 사막인데 아마도 해변의 모래사장으로 판단한 것 같습니다.

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3) ThanoSQL에 내장된 쿼리 편집기 기능을 이용하면 자연어가 어떻게 데이터베이스 쿼리(SQL)로 바뀌었는지 확인할 수 있고, 원한다면 여기서 바로 수정하여 다른 쿼리를 즉시 실행해볼 수도 있습니다.

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4) 데이터베이스에 저장된 데이터가 이미지, 비디오, 오디오인 경우에는 아래 그림처럼 데이터 뷰어에서 그 내용을 바로 확인할 수 있는 기능도 제공합니다.

\"\"'},{Slug:\"대화형-분석-사례-02-유튜브-동영상을-분석하여-보고서-작성\",\":draft\":true,id:5,Title:\"대화형 분석 사례 – 02 유튜브 동영상을 분석하여 보고서 작성\",Date:\"2024-08-27T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/kavAioxrNHy6m98ODDYgneoy7s.png\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

ThanoSQL로 동영상 내용을 정리하여 슬라이드 형태의 보고서 생성하기

ThanoSQL(타노시퀄)의 대화형 분석 서비스를 소개합니다.

대화형 분석 서비스는 AI가 사용자의 질문을 자연어로 받아서 데이터베이스를 사용하여 답변을 해주는 서비스입니다.

▲ 한국어 내레이션

▲ English Narration

위 영상에서는 수집한 유튜브 동영상 정보를 활용하여 파워포인트 슬라이드 형태의 요약 보고서를 만드는 과정을 보여줍니다.

영상에서 설명드리는 내용은 아래 순서와 같습니다.

1) 최근 AI 관련 유튜브 동영상을 수집한 데이터베이스 테이블을 만들어 두었습니다.

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2) 챗봇에게 질문을 하기에 앞서 어느 데이터베이스 테이블을 활용할 것인지 선택하고 캡션까지 수집된 유튜브 동영상 5개를 선정하고 캡션을 요약해 달라고 챗봇에게 요청합니다.

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3) LLM은 요청을 처리하여 다섯 건의 유튜브 캡션을 요약해서 보여줍니다.

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4) 데이터베이스에서 다섯 건의 유튜브와 캡션을 추출하는 것은 데이터베이스의 기본 기능으로 가능한 것이지만 캡션 내용을 요약하는 것은 LLM 기술이 필요한 부분입니다. ThanoSQL은 LLM 등 인공지능을 데이터베이스에서 바로 활용할 수 있는 기능을 제공하고 있어서 이를 이용하여 결과가 나올 수 있었습니다.

5) 이번에는 앞의 처리 결과로 나온 다섯 개의 캡션 요약을 뭉쳐서 한 덩어리로 만들어 보겠습니다. ThanoSQL에서는 질문과 결과를 자동으로 저장합니다. 따라서 앞의 결과에서 ‘reply’ 버튼을 누르고 요청 사항을 입력하면 결과를 재사용하여 빠르게 답을 얻을 수 있습니다.

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6) 앞의 처리 결과를 이용하여 발표 슬라이드 스타일로 정리를 해달라고 요청하겠습니다. 앞의 처리 결과가 저장된 컬럼의 이름을 확인하고 앞의 결과에서 ‘reply’ 버튼을 누른 후 캡션 요약을 활용하여 슬라이드 스타일로 정리해 달라는 요청 사항을 입력하면 됩니다. 그럼 아래와 같이 파워포인트의 슬라이드 스타일로 결과가 나오게 됩니다.

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7) 슬라이드 스타일로 텍스트를 변환하는 기능도 데이터베이스의 내장 기능으로는 불가능하지만 ThanoSQL이 제공하는 데이터베이스에 내장된 LLM 기능으로 처리되어 슬라이드 형태로 정리된 결과를 만들 수 있습니다.

'},{Slug:\"대화형-분석-사례-01-콜-센터의-상담-통화-분석\",\":draft\":true,id:4,Title:\"대화형 분석 사례 – 01 콜 센터의 상담 통화 분석\",Date:\"2024-08-26T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/rMxjSySasARrwl3GARpW8AW0R1s.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

ThanoSQL로 전화 상담 내용 분석하기

ThanoSQL(타노시퀄)의 대화형 분석 서비스를 소개합니다.

대화형 분석 서비스는 AI가 사용자의 질문을 자연어로 받아서 데이터베이스를 사용하여 답변을 해주는 서비스입니다.


▲ 한국어 내레이션

▲ English Narration

위 영상에서는 ThanoSQL을 사용하여 콜 센터의 상담 통화를 분석하는 과정을 보여드리고 있습니다.

영상에서 설명드리는 분석 과정은 아래 흐름과 같습니다.

1) 영상에서 사용할 데이터는 상담원 기본 정보가 들어 있는 agents 테이블, 통화 기록이 들어 있는 calls 테이블, 그리고 실제 통화 내용이 기록되어 있는 transcript 테이블 이렇게 3개의 테이블을 사용합니다.

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2) 챗봇에게 질문을 하기에 앞서 어느 데이터베이스 테이블을 활용할 것인지 선택합니다. 그리고 가장 평균 통화 시간이 긴 상담사가 누구인지 물어보도록 하겠습니다.
LLM이 사용자의 질문을 이해하고 데이터베이스에서 답을 찾아서 제니퍼라고 답을 하였습니다.

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3) 쿼리 에디터 창을 열어서 내가 한 질문이 데이터베이스 쿼리로 어떻게 바뀌었는지 볼 수 있으며 그 결과도 볼 수 있습니다.

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4) 통화 내용이 기록되어 있는 transcript 테이블을 활용해보겠습니다.
해당 테이블을 선택한 후 필요하다면 테이블 미리 보기 기능을 이용하여 테이블에 어떤 데이터가 들어 있는지도 살펴볼 수 있습니다.

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5) 다음으로 5분 이상 통화한 내용을 모두 뽑아서 통화 내용을 분류하도록 시켜보겠습니다. 기술적인 질문부터 환불이나 배송 등 여러 질문이 있다는 것을 알 수 있습니다.
앞의 질문과는 달리 이 질문을 처리 하기 위해서는 통화 내용을 분류할 수 있어야 하는데 데이터베이스의 기본 기능에는 이러한 기능이 없습니다. ThanoSQL은 LLM 등의 인공지능을 데이터베이스에서 바로 활용할 수 있는 기능을 제공하고 있으며 그러한 기능 덕분에 분류가 가능하게 된 것입니다.

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6) 다음으로는 각 분류 별로 몇 건의 통화가 있었는지 집계를 하려고 합니다. ThanoSQL에서는 질문과 결과를 자동으로 저장해주기 때문에 앞의 분류 결과를 이용해서 바로 집계가 가능합니다. 앞의 질문에서 분류한 결과가 저장된 컬럼의 이름을 확인해둡니다.

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7) 그리고 앞 질문의 결과 테이블을 활용하겠다는 것을 표현하기 위해서 앞 질문의 결과에서 ‘reply’ 버튼을 눌러 준 뒤 질문을 입력하면 됩니다. 이렇게 함으로써 시간이 오래 걸리는 분류 작업을 건너 뛸 수 있게 됩니다.

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8) 챗봇이 분류별로 몇 건의 통화가 있었는지 잘 정리해서 보여주고 있습니다.

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9) 마찬가지로 파워 유저들은 쿼리 편집기 기능을 이용하여 좀 더 자세히 살펴볼 수 있고 필요하다면 쿼리를 직접 변형하여 추가적인 인사이트를 도출할 수 있습니다.

\"\"'},{Slug:\"llm으로-대화하듯이-데이터-분석하기\",\":draft\":true,id:3,Title:\"LLM으로 대화하듯이 데이터 분석하기\",Date:\"2024-08-12T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/Eq1l4Vk8Z06oKU3EZPWbp3dVPQ.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

우리 회사도 LLM을 도입해야 할까?

현재 LLM 분야에서 파운데이션 모델 쪽은 과하다 싶을 만큼 많은 투자가 이루어지고 있습니다. 그러나 투자자들과 기업들은 실제로 ROI는 어떻게 되는지, 구체적인 수익 모델은 어떻게 되는지 의문이 커져가고 있는 상황입니다.

테슬라는 깃허브(GitHub)의 코파일럿(Copilot)을 사용해서 코딩 작업의 80%를 한다고 밝혔을 정도로 기업의 IT 부문에서는 이미 생성형 AI와 LLM을 상당히 높은 수준으로 활용하여 경쟁력과 생산성을 극대화하고 있습니다. 그러나 비 IT 부문의 경우는 기업 내부 데이터의 외부 유출에 대한 우려와 기술의 복잡성으로 인한 높은 비용으로 인해 생성형 AI 도입이 상대적으로 저조한 실정입니다. 그나마도 주로 챗봇을 통해 회사 내부의 규정이나 회사생활 가이드에 대한 질의응답 등을 하는 수준이 대부분이며, 직접적인 생산성 향상이나 비즈니스 인사이트 도출에 활용하지는 못하고 있는 것으로 보입니다.

그래서 스마트마인드는 바로 그 포인트, 즉 비IT 부문의 생산성 향상에 집중하고자 합니다. 스마트마인드의 솔루션 ThanoSQL은 기업 내부의 원천 데이터를 외부로 유출하지 않으며 LLM과 데이터 분석을 하나로 통합한 올인원 플랫폼입니다.

LLM으로 비IT 부문의 생산성을 어떻게 향상시킬 수 있나요?

대표적인 비IT 부문인 세일즈/마케팅 분야를 예로 들면, 담당자의 일상 업무를 자동화하거나 업무 영역을 넓게 확장함으로써 생산성이 향상될 수 있습니다. 일상 업무 자동화는 RPA가 잘하는 영역이므로 스마트마인드는 담당자의 업무 영역을 확장하는데 도움을 드리고자 합니다.

아마도 기존의 업무는 RDB(관계형 데이터베이스)에 있는 정형 데이터를 엑셀로 export하거나 BI툴로 연결해서 여러 형태로 pivot해보는 차원이었을 겁니다. 또한 RDB가 아닌 NoSQL이나 데이터 레이크 등에 저장된 비정형 데이터에 대한 분석은 IT 부서의 도움을 받아야만 가능할 것이며, 각각의 분석 결과들에 대한 유기적인 결합이나 통합적인 인사이트를 찾는 것은 굉장히 어려운 일이 될 것입니다.

하지만 ThanoSQL을 사용한다면 이 모든 것들을 자연어로 대화하듯이 쉽게 할 수 있으며 IT 부서의 도움 없이도 누구나 모든 종류의 데이터에 대해서 분석이 가능합니다.

예를 들어 다음과 같은 유스 케이스(Use Case)를 생각해보겠습니다.

고객 리뷰 중에 부정적인 피드백들을 찾아서 카테고리를 구분해줘
-> 그중 품질에 대한 불만이 가장 많은 제품을 골라줘
-> 해당 제품의 올해 2분기 판매량을 알려줘
-> 작년 동분기와 비교해줘

각 단계별 흐름은 아래 그림들과 같습니다.

첫번째 그림은 ThanoSQL 워크스페이스의 초기 화면입니다. 먼저 분석하고자 하는 대상 데이터가 저장된 테이블을 선택하고 입력창에 질문을 입력하는 것으로 분석을 시작하게 됩니다.

\"분석할

▲ 분석할 데이터가 저장된 테이블들을 선택 후 질문 입력

위 예시에서는 먼저 고객의 리뷰를 분석해야 하므로 customer_review 테이블을, 그 다음으로 제품을 찾아야 하므로 product_list 테이블을, 마지막으로 판매량을 확인해야 하므로 sales 테이블을 선택하였습니다.

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▲ 비정형 데이터인 리뷰들의 감성(Sentiment) 분석 후 답변 생성

자연어로 입력된 질문은 ThanoSQL 내부에서 자동으로 SQL구문으로 변환되어 선택한 테이블들을 대상으로 실행되는데, 이때 여러가지 AI 알고리즘과 머신 러닝 기법들이 적용됩니다. 여기서 사용되는 알고리즘과 모델들은 ThanoSQL이 기본적으로 제공하는 것이며 사용자가 원한다면 허깅페이스 등에서 외부의 모델을 가져와서 사용할 수도 있습니다. 그리고 그렇게 분석된 실행 결과는 다시 LLM을 통해 자연어 형태로 만들어져서 대답창에 나타나게 됩니다.

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▲ 정형 데이터인 세일즈 데이터와 결합 분석

사용자는 단지 몇번의 자연어 입력만으로 고객 리뷰 같은 비정형 데이터와 세일즈/마케팅 같은 정형 데이터를 결합해서 분석하고 결과를 즉시 확인할 수 있습니다.

즉 ThanoSQL은 정형 데이터와 비정형 데이터가 모두 테이블로 저장된 DB에 머신 러닝과 LLM을 결합하여 대화형으로 분석할 수 있는 분석 도구입니다. 따라서 ThanoSQL을 사용하면 위의 예시처럼 IT에 대한 전문적인 지식이 없어도 사내의 모든 데이터에 대해서 빠르게 분석할 수 있고, 그 결과로부터 의미 있는 인사이트도 찾을 수 있게 됩니다.

랭체인 기반의 다른 솔루션과의 차이점은 무엇인가요?

일반적으로 랭체인 기반으로 LLM 서비스를 개발하기 위해 필요한 기술 스택은 다음 그림과 같습니다.

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▲ LLM 기술 스택의 계층과 주요 구성 요소 (출처: codesmith.io/blog)

각 계층별로 또 계층 내에서도 각 요소별로 선택 가능한 제품들은 아래와 같이 매우 다양합니다. 심지어 이 표에 있는 것들이 전부 다가 아닙니다.

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▲ 구성 요소별 주요 제품 리스트 (출처: Emerging Architectures for LLM Applications)

랭체인 기반의 LLM 서비스를 구축하고 자체 기술 스택에 통합하려는 기업들은 위와 같은 데이터 전문가들이 필요로 하는 굉장히 많은 도구들을 일일이 선정하고 구매해서 연결하고 운영까지 해야 합니다.

대부분의 기업들은 이런 역량이 부족한 것이 현실이고 단절된 도구들과 서비스들을 유기적으로 통합해야 하므로 LLM 도입은 엄청난 시간과 비용, 전문 인력을 필요로 합니다.

반면 ThanoSQL을 도입하면 그런 고민과 노력들은 모두 필요 없게 됩니다. 사용자는 단지 LLM 모델로 챗GPT 4.0을 사용할지 아니면 허깅페이스에 있는 오픈소스 모델을 사용할 것인지만 결정하면 됩니다.

또 다른 차이점은 데이터 분석에 대한 유연성과 신속성입니다. 아래 그림은 ThanoSQL을 사용해서 고객 상담 센터의 어느 직원이 고객과 가장 오랜 시간을 통화했는지 찾는 예시입니다.

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▲ 고객과 가장 오랜 시간 동안 통화를 한 상담 직원을 찾는 질문

다음 그림에서는 사용자가 입력한 자연어 질문이 어떻게 SQL로 변환되었고 그 실행 결과는 무엇인지 확인할 수 있습니다.

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▲ 자연어 질문의 SQL 변환과 실행 결과 확인 및 쿼리 수정

만약 처음 질문과 다른 분석이 필요하다면 SQL 구문을 즉시 수정하고 바로 실행하여 결과를 확인할 수도 있습니다.

반면 랭체인은 파이썬 기반이므로 해당 부분이 함수 형태로 제공되어 블랙박스일 뿐만 아니라 사용자가 원하는 대로 빠르게 수정도 불가능합니다.

마치며

랭체인 기반으로 LLM 서비스를 구축하려면 SI 프로젝트를 진행하듯이 복잡다양한 기술 스택들을 통합해야 하고 워크플로우의 개발도 수없이 반복해야 합니다. 따라서 시간과 비용 뿐만 아니라 사용 편의성 측면에서도 LLM과 데이터 분석을 단일 플랫폼 형태로 제공하는 ThanoSQL을 도입하는 것이 모두에게 효율적인 의사결정이 될 것입니다.

'},{Slug:\"기업이-생성형-ai를-도입하고자-할-때-발생하는-문제점과-해결방안\",id:2,Title:\"기업이 생성형 AI를 도입하고자 할 때 발생하는 문제점과 해결방안\",Date:\"2024-08-09T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/lICSilpsX2mTZQusQWgoslKwo.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:\"

생성형 AI의 시대

2016년 Google Deepmind의 알파고(AlphaGo)가 이세돌 9단과의 바둑 대결에서 승리한 구글 딥마인드 챌린지 매치를 기억하시나요? 당시 컴퓨터에게는 불가능한 영역이라고 여겨지던 바둑에서 세계 최고의 바둑 기사 이세돌을 4승 1패로 이기면서 인공지능(AI)에 대한 놀라움과 경각심을 동시에 불러 일으킨 시대의 빅 이벤트였습니다.

알파고 이후 뜸하던 AI 시장에 2022년 말 혜성같이 등장한 AI가 있었으니, 바로 지금은 누구나 알고 있는 OpenAI의 ChatGPT입니다. 발표 당시 ChatGPT는 사람과 같은 언어로 대화를 할 수 있고, 마치 사람과 같이 생각할 수 있다는 착각을 불러일으킬 정도로 커다란 임팩트를 남겼습니다. ChatGPT와 같은 생성형 AI의 등장으로 빠른 시일 안에 인간을 대체할 것이라는 불안감과 인류의 생산성을 한 단계 업그레이드할 수 있을 것이라는 기대감이 공존하고 있습니다.

생성형 AI가 산업 현장에 실제로 많이 도입되고 있나?

올해 1분기에 실적발표를 한 기업(전세계 85,000개 이상의 기업 대상) 중 32%가 생성형 AI를 한번 이상 언급했는데 이는 전년 동기 대비 150% 증가한 수준입니다 (출처: IoT Analytics Research 2024). 생성형 AI가 여전히 시장의 뜨거운 이슈로 자리매김하고 있음을 알 수 있습니다.

그렇다면 이렇게 많은 관심과 기대를 받은 생성형 AI는 실제로 기업에 많이 도입되고 있을까요?

AI를 직접 개발하고 운영하는 IT 부문에서는 생성형 AI의 도입이 활성화되고 있습니다. Capgemini research에 따르면 향후 2년 내 IT 부문에서 생성형 AI를 도입하겠다고 밝히 비중이 85%에 이릅니다. 하지만 비IT 부문, 즉 마케팅, 세일즈, 재무 등의 영역에서는 다른 결과를 보여줍니다. Coatue가 CEO 600명을 대상으로 설문 조사한 결과, 실제로 AI를 완전히 채택한 비율은 9%에 불과하며, 이마저도 60~75%는 여전히 초기 검증단계에 머물러 있다고 응답했습니다.

생성형 AI의 도입 시 문제점들

1) 내부 원천 데이터의 외부 유출

기업의 매출 성장과 직결되는 이러한 비IT 부문에서의 부진은 어떤 이유에서 비롯된 것일까요? 가장 큰 원인은 보안 이슈입니다. 대다수의 기업, 특히 대기업을 중심으로 생성형 AI 모델을 사용하기 위해 외부 클라우드로 기업 내부의 데이터를 내보내야만 하는 상황이 도입을 가로막고 있는 가장 큰 장애물이라고 할 수 있습니다. 실제로 지난 7월에는 Snowflake 클라우드에 대한 해커 공격으로 AT&T의 고객 1.1억 명에 대한 통화와 메시지 기록이 유출되는 충격적인 사건이 미국에서 벌어지기도 했었죠. 이러한 이유로 많은 기업에서는 외부 LLM을 사용하지 못하게 막고, 내부적으로 자체 LLM을 구축하고자 하는 시도를 하고 있습니다.

2) 높은 도입 비용과 시간

이렇게 내부적으로 자체 LLM을 구축하고자 하는 경우에는 내부 데이터의 외부 유출 문제를 해결할 수 있습니다. 하지만 또다른 문제가 발생하게 되는데 바로 비용과 시간의 문제입니다. SI 형식으로 직접 구축해야 하다 보니 이러한 문제가 발생하는 것이지요. 특히 LLM과 더불어 이를 활용할 수 있는 데이터 분석을 위한 플랫폼까지 구축하려고 하는 경우에는 비용과 시간의 진입 장벽이 더욱 커질 것입니다. 현실적으로는 투자의 여력이 있는 대기업을 제외하고 중소기업에서 생성형 AI를 도입하기 어려운 이유가 되는 것이죠. 이를 해결하고자 퍼블릭 클라우드를 이용하게 되면 또다시 데이터 유출의 문제가 발생하게 되는 것입니다.

스마트하게 생성형 AI를 도입하기

생성형 AI는 기업의 생산성 향상에 많은 도움을 줄 수 있는 파워풀한 도구입니다. 어떻게 하면 이러한 생성형 AI를 기업에서 쉽고 빠르게 도입할 수 있을까요? 기업의 상황에 따라 다르지만 가장 쉬운 방법은 앞서 살펴본 두가지 커다란 진입장벽을 없애 주면 될 것입니다. 즉, 내부의 원천 데이터를 외부로 유출하지 않으면서 도입 비용과 시간은 줄일 수 있는 솔루션이 필요합니다.

스마트마인드의 Qurify는 기업의 메타 데이터(테이블 스키마)만을 활용하여 분석하기 때문에 원천 데이터의 외부 유출 문제를 해결합니다. 또한 LLM과 데이터 분석을 하나의 플랫폼에서 제공하므로 도입 비용과 시간을 획기적으로 줄여 줍니다.

자체 개발한 기술력으로 시장의 페인 포인트를 해결해 나가고 있는 스마트마인드의 Secure Enterprise Gen AI, Qurify가 궁금하신가요? 지금 바로 연락주세요.

\"},{Slug:\"llm을-통합하여-비즈니스-워크플로우-최적화하기\",id:1,Title:\"LLM을 통합하여 비즈니스 워크플로우 최적화하기\",Date:\"2024-08-02T00:00:00.000Z\",Image:{url:\"https://framerusercontent.com/images/isu13IajsW45MZ4uxMY4aWIhnMY.jpg\"},Author:\"smartmind\",category:\"blog\",language:\"kor\",\"preview-contents\":\"\",Content:'

워크플로우란 무엇인가?

위키피디아에서는 워크플로우를 작업 절차의 운영적 측면이라고 정의합니다. 업무들이 어떻게 구성되고 누가 수행하며 순서는 어떻게 되며 어떻게 동기화를 시킬지, 업무를 지원하기 위한 정보는 어떻게 흐르는지 그리고 업무가 어떻게 추적되는지 등을 포함합니다.

\"\"

이러한 워크플로우를 자동화하게 되면 고객과 직원의 편의성을 높여주고 비즈니스의 성장에도 큰 영향을 준다는 점에서 많은 기업들이 앞다퉈 도입하고 있습니다. 워크플로우를 자동화한다는 의미는 쉽게 말해서 반복적인 작업이나 업무 프로세스를 컴퓨터나 프로그램으로 대신 처리하는 것입니다.

기업의 공통적인 문제점들과 해결 방향

현대의 기업들은 대부분 수많은 프로세스 관리 문제에 직면해 있습니다. 방대한 팀을 관리하고 다양한 작업을 수행하며 동시에 여러 기술을 서비스에 통합해야 하는 복잡성으로 인해 병목 현상도 발생합니다. 기업들은 급변하는 환경에서도 효율성을 유지하려고 하는데 이런 문제들까지 더해져서 디지털 전환과 업무 프로세스의 자동화 추진을 가속화하고 있습니다.

오늘날 빠르게 진화하는 디지털 환경에서 AI 기술들, 특히 LLM(Large Language Models)은 기업들이 운영을 효율화 하는 방식을 크게 혁신하고 있습니다. LLM을 비롯한 이러한 강력한 도구들은 각 기업들의 워크플로우를 효율화하고 자동화하여 경쟁 우위를 유지할 수 있도록 해줍니다.

LLM은 본질적으로 사람과 유사한 방식으로 텍스트를 이해하고 생성하는 데 능숙한 AI 모델입니다. AI 기반 언어 모델인 LLM은 지금도 계속 진화 중이지만 여전히 관계를 추론하고 컨텐츠를 생성할 수 있는 데이터 세트에 대하여 학습을 해야 하는 한계도 존재합니다. 그럼에도 불구하고 LLM으로 할 수 있는 것들은 단순한 언어 작업 뿐만이 아닙니다. 일반적으로 특정 작업을 위해 만들어진 기존의 자동화 도구들과 달리 LLM은 데이터 분석부터 고객과의 상호 작용에 이르기까지 매우 다양한 역할을 할 수 있습니다.

이러한 LLM을 기업 내부에 효과적으로 통합하려면 기업은 먼저 운영상의 비효율성을 파악해야 합니다. 문제점과 병목 현상이 정확히 파악되면 LLM을 사용하여 프로세스 데이터를 분석하고, 이어서 이런 문제들을 해결하고 바로잡기 위한 실행 방안을 제공할 수 있습니다.

LLM을 기업 내부 시스템에 통합하는 일반적인 과정은 다음과 같습니다.

작업의 요구 사항 검토 -> LLM 통합 -> 관련 데이터로 모델 학습 -> 피드백을 통한 반복과 개선

LLM 통합으로 얻을 수 있는 효과들

1) 반복적인 작업을 자동화하여 생산성 향상 및 휴먼 에러 감소

기업 내부에서 매일같이 일어나는 데이터 입력, 보고서 작성, 고객 상호 작용 같은 반복적인 작업은 귀찮으면서 ​시간도 많이 소요되는 작업들입니다. LLM은 이러한 작업을 처리하는데 능숙하여 작업을 효율적으로 수행할 수 있습니다.

실제로 이메일 초안을 작성하거나 블로그나 보고서 같은 컨텐츠를 생성하고 고객 센터의 FAQ를 자동화하는 등 LLM이 기업의 생산성 향상에 크게 기여하고 있습니다. 또한 기업에서 많이 사용하는 트렐로(Trello)나 아사나(Asana), 먼데이 닷컴(Monday.com) 같은 다양한 프로젝트 관리 시스템과 통합되어 작업을 자동화할 수도 있습니다. 즉 자연어 처리를 사용하여 사람의 개입 없이 사용자 입력을 이해하고 해석하여 작업을 생성하고 상태를 업데이트하고 우선순위를 할당할 수 있습니다.

2) 고객 상호작용 강화 및 개인화된 고객 경험 제공

LLM은 기업들이 고객 지원을 강화하는 데 있어서 주춧돌의 역할을 합니다. 지능형 챗봇을 구동하는 것부터 고객과의 상호 작용을 개인화 하는 것까지, LLM은 개인화된 풍부한 경험을 제공함으로써 기업이 고객과 소통하는 방식을 크게 바꿀 수 있습니다. 현재 고객 센터를 운영하는 상당수의 기업들이 AICC(인공지능 컨택센터)를 도입하거나 도입을 검토 중에 있습니다. 최근에는 더 나아가 LLM 기반의 챗봇을 도입하여 24시간 365일 고객 대응은 물론이고 각 고객별 개인화 서비스를 제공하는 등 대고객 접점 업무를 혁신하려는 사례도 있습니다.

LLM은 통찰력 있는 데이터 분석과 학습 알고리즘을 통해 고객과 매우 개인화된 상호작용을 만들 수 있습니다. 스마트마인드의 Qurify를 사용하게 되면 고객이 자연어로 질문했을 때 기업 내부에 있는 고객 고유의 데이터를 외부 유출 없이 분석하여, LLM을 통해 답변을 생성하는 형태로 완전히 개인화된 서비스를 제공할 수 있습니다. 이러한 접근 방식은 고객 만족도를 향상시킬 뿐만 아니라 기업 또는 브랜드 충성도를 높이고 서비스 이탈 가능성을 줄여줍니다.

3) 의사 결정 프로세스 개선

LLM은 기업이 데이터에 기반한 의사 결정을 내릴 수 있도록 지원하는 강력한 기능을 갖추고 있습니다. LLM은 매우 능숙하게 데이터를 수집, 분석, 제시할 수 있기 때문에 실시간 인사이트와 종합적인 데이터 분석을 기반으로 의사 결정 프로세스를 개선할 수 있습니다.

온라인 상거래 업체를 예로 들면, 고객 리뷰(비정형 데이터)를 분석하여 부정적인 내용이 담긴 제품을 찾고 해당 제품의 판매 추세(정형 데이터)를 분석하면 매출 변동의 원인과 그에 대한 대응책을 수립할 수 있게 되는 것입니다.

스마트마인드의 Qurify를 사용함으로써 비즈니스 리더나 세일즈/마케팅 인력처럼 IT 기술에 대한 전문 지식이 없는 사람들도 쉽게 데이터에 접근해서 대화하듯이 분석하고 효율적으로 비즈니스 의사결정을 내릴 수 있습니다.

4) 협업 및 커뮤니케이션 강화

최근에 LLM은 기업용 업무 협업 도구에 통합되고 있습니다. MS 팀즈(Teams), 슬랙(Slack), 플로우(Flow), 노션(Notion) 등의 글로벌 협업 도구를 사용중인 기업들은 물론이고 자체적인 그룹웨어를 사용하는 기업들도 앞다투어 LLM을 도입하는 추세입니다.

직원들은 협업 도구 안에서 글쓰기, 편집, 검색, 번역, 챗봇 등 다양한 기능들을 LLM을 통해 사용할 수 있습니다. 또한 LLM은 조직 내 여러 ​​부서에 걸쳐 원활한 커뮤니케이션을 가능하게 합니다. 기업은 메신저 등의 사내 커뮤니케이션 플랫폼에 LLM을 통합함으로써 의사소통의 효율성을 높이고 직원들 간의 협업을 원활하게 하고 있습니다.

LLM 통합 그 다음에는 무엇을 해야 하나

최근 기업들은 경험을 통해 LLM으로 인해 추가되는 가치들을 매우 잘 인식하고 있습니다. 기업이 혁신성과 효율성을 유지하기 위해서는 기술의 발전에 적극적으로 적응해야 하며 특히 LLM의 발전이 기업 운영에 어떤 영향을 미칠지 고민할 필요가 있습니다.

기업들은 경쟁적인 비즈니스 환경에서 앞서 나가기 위해 LLM의 다음 물결에 대비해야 합니다. 누군가는 LLM의 다음 물결로 AI 에이전트를 말하고 누군가는 LVM(Large Vision Model)을 말하며 또다른 누군가는 LAM(Large Action Model)을 얘기합니다. 하지만 그게 무엇이 되든 어떤 형태로 진화하든지 변화에 맞춰서 잘 준비하는 기업만이 지속적인 성장을 유지하고 시장 주도권을 확보할 수 있을 것입니다.

비즈니스 워크플로우에 LLM을 도입하는 것은 효율성과 생산성을 위한 커다란 변화를 의미합니다. 앞으로도 LLM은 놀랍도록 발전할 것이고 지속적으로 기업 운영을 혁신하도록 할 것입니다. 따라서 기업들은 LLM의 진화에 발맞추어 나아가야 하겠습니다.

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O=createLucideIcon(\"AlignVerticalJustifyEnd\",[[\"rect\",{x:\"5\",y:\"12\",width:\"14\",height:\"6\",rx:\"2\",key:\"12nflp\"}],[\"rect\",{x:\"7\",y:\"2\",width:\"10\",height:\"6\",rx:\"2\",key:\"1dm79a\"}],[\"path\",{d:\"M2 22h20\",key:\"272qi7\"}]]);const R=createLucideIcon(\"AlignVerticalJustifyStart\",[[\"rect\",{x:\"5\",y:\"16\",width:\"14\",height:\"6\",rx:\"2\",key:\"1xmr5l\"}],[\"rect\",{x:\"7\",y:\"6\",width:\"10\",height:\"6\",rx:\"2\",key:\"q2ofyd\"}],[\"path\",{d:\"M2 2h20\",key:\"1ennik\"}]]);const T=createLucideIcon(\"AlignVerticalSpaceAround\",[[\"rect\",{x:\"7\",y:\"9\",width:\"10\",height:\"6\",rx:\"2\",key:\"1iy9tl\"}],[\"path\",{d:\"M22 20H2\",key:\"1p1f7z\"}],[\"path\",{d:\"M22 4H2\",key:\"1b7qnq\"}]]);const Z=createLucideIcon(\"AlignVerticalSpaceBetween\",[[\"rect\",{x:\"5\",y:\"15\",width:\"14\",height:\"6\",rx:\"2\",key:\"hytrht\"}],[\"rect\",{x:\"7\",y:\"3\",width:\"10\",height:\"6\",rx:\"2\",key:\"y09b40\"}],[\"path\",{d:\"M2 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ce=createLucideIcon(\"ArrowLeftCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 8 8 12 12 16\",key:\"bz1698\"}],[\"line\",{x1:\"16\",y1:\"12\",x2:\"8\",y2:\"12\",key:\"i2qu8k\"}]]);const ie=createLucideIcon(\"ArrowLeftRight\",[[\"polyline\",{points:\"17 11 21 7 17 3\",key:\"l3l6r3\"}],[\"line\",{x1:\"21\",y1:\"7\",x2:\"9\",y2:\"7\",key:\"17x2jj\"}],[\"polyline\",{points:\"7 21 3 17 7 13\",key:\"lfumnw\"}],[\"line\",{x1:\"15\",y1:\"17\",x2:\"3\",y2:\"17\",key:\"gusd5o\"}]]);const ne=createLucideIcon(\"ArrowLeft\",[[\"line\",{x1:\"19\",y1:\"12\",x2:\"5\",y2:\"12\",key:\"17g05t\"}],[\"polyline\",{points:\"12 19 5 12 12 5\",key:\"1ksm0z\"}]]);const he=createLucideIcon(\"ArrowRightCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 16 16 12 12 8\",key:\"1byh5s\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}]]);const oe=createLucideIcon(\"ArrowRight\",[[\"line\",{x1:\"5\",y1:\"12\",x2:\"19\",y2:\"12\",key:\"1smlys\"}],[\"polyline\",{points:\"12 5 19 12 12 19\",key:\"sfr3i6\"}]]);const de=createLucideIcon(\"ArrowUpCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"16 12 12 8 8 12\",key:\"1gpmhk\"}],[\"line\",{x1:\"12\",y1:\"16\",x2:\"12\",y2:\"8\",key:\"r2mfdg\"}]]);const se=createLucideIcon(\"ArrowUpDown\",[[\"polyline\",{points:\"11 17 7 21 3 17\",key:\"dv0ycv\"}],[\"line\",{x1:\"7\",y1:\"21\",x2:\"7\",y2:\"9\",key:\"1cxv4h\"}],[\"polyline\",{points:\"21 7 17 3 13 7\",key:\"1su31j\"}],[\"line\",{x1:\"17\",y1:\"15\",x2:\"17\",y2:\"3\",key:\"r3527w\"}]]);const ke=createLucideIcon(\"ArrowUpLeft\",[[\"line\",{x1:\"17\",y1:\"17\",x2:\"7\",y2:\"7\",key:\"814yaz\"}],[\"polyline\",{points:\"7 17 7 7 17 7\",key:\"1jae2c\"}]]);const le=createLucideIcon(\"ArrowUpRight\",[[\"line\",{x1:\"7\",y1:\"17\",x2:\"17\",y2:\"7\",key:\"16hgw2\"}],[\"polyline\",{points:\"7 7 17 7 17 17\",key:\"blehsp\"}]]);const pe=createLucideIcon(\"ArrowUp\",[[\"line\",{x1:\"12\",y1:\"19\",x2:\"12\",y2:\"5\",key:\"yrd7g6\"}],[\"polyline\",{points:\"5 12 12 5 19 12\",key:\"1y7d7k\"}]]);const re=createLucideIcon(\"Asterisk\",[[\"path\",{d:\"M12 6v12\",key:\"1vza4d\"}],[\"path\",{d:\"M17.196 9 6.804 15\",key:\"1ah31z\"}],[\"path\",{d:\"m6.804 9 10.392 6\",key:\"1b6pxd\"}]]);const ue=createLucideIcon(\"AtSign\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"4\",key:\"4exip2\"}],[\"path\",{d:\"M16 8v5a3 3 0 0 0 6 0v-1a10 10 0 1 0-3.92 7.94\",key:\"1m6qx5\"}]]);const Me=createLucideIcon(\"Award\",[[\"circle\",{cx:\"12\",cy:\"8\",r:\"6\",key:\"1vp47v\"}],[\"path\",{d:\"M15.477 12.89 17 22l-5-3-5 3 1.523-9.11\",key:\"em7aur\"}]]);const xe=createLucideIcon(\"Axe\",[[\"path\",{d:\"m14 12-8.501 8.501a2.12 2.12 0 0 1-2.998 0h-.002a2.12 2.12 0 0 1 0-2.998L11 9.002\",key:\"ha6v2k\"}],[\"path\",{d:\"m9 7 4-4 6 6h3l-.13.648a7.648 7.648 0 0 1-5.081 5.756L15 16v-3z\",key:\"1mosh2\"}]]);const ve=createLucideIcon(\"Axis3d\",[[\"path\",{d:\"M4 4v16h16\",key:\"1s015l\"}],[\"path\",{d:\"m4 20 7-7\",key:\"17qe9y\"}]]);const ge=createLucideIcon(\"Baby\",[[\"path\",{d:\"M9 12h0.01\",key:\"u5n1lm\"}],[\"path\",{d:\"M15 12h0.01\",key:\"10zk70\"}],[\"path\",{d:\"M10 16c.5.3 1.2.5 2 .5s1.5-.2 2-.5\",key:\"1u7htd\"}],[\"path\",{d:\"M19 6.3a9 9 0 0 1 1.8 3.9 2 2 0 0 1 0 3.6 9 9 0 0 1-17.6 0 2 2 0 0 1 0-3.6A9 9 0 0 1 12 3c2 0 3.5 1.1 3.5 2.5s-.9 2.5-2 2.5c-.8 0-1.5-.4-1.5-1\",key:\"5yv0yz\"}]]);const me=createLucideIcon(\"Backpack\",[[\"path\",{d:\"M4 20V10a4 4 0 0 1 4-4h8a4 4 0 0 1 4 4v10a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2Z\",key:\"tunmdx\"}],[\"path\",{d:\"M9 6V4a2 2 0 0 1 2-2h2a2 2 0 0 1 2 2v2\",key:\"donm21\"}],[\"path\",{d:\"M8 21v-5a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v5\",key:\"xk3gvk\"}],[\"path\",{d:\"M8 10h8\",key:\"c7uz4u\"}],[\"path\",{d:\"M8 18h8\",key:\"1no2b1\"}]]);const Le=createLucideIcon(\"BaggageClaim\",[[\"path\",{d:\"M22 18H6a2 2 0 0 1-2-2V7a2 2 0 0 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Ie=createLucideIcon(\"BarChart2\",[[\"line\",{x1:\"18\",y1:\"20\",x2:\"18\",y2:\"10\",key:\"1e8c49\"}],[\"line\",{x1:\"12\",y1:\"20\",x2:\"12\",y2:\"4\",key:\"65j799\"}],[\"line\",{x1:\"6\",y1:\"20\",x2:\"6\",y2:\"14\",key:\"4svqks\"}]]);const Ce=createLucideIcon(\"BarChart3\",[[\"path\",{d:\"M3 3v18h18\",key:\"1s2lah\"}],[\"path\",{d:\"M18 17V9\",key:\"2bz60n\"}],[\"path\",{d:\"M13 17V5\",key:\"1frdt8\"}],[\"path\",{d:\"M8 17v-3\",key:\"17ska0\"}]]);const be=createLucideIcon(\"BarChart4\",[[\"path\",{d:\"M3 3v18h18\",key:\"1s2lah\"}],[\"path\",{d:\"M13 17V9\",key:\"1fwyjl\"}],[\"path\",{d:\"M18 17V5\",key:\"sfb6ij\"}],[\"path\",{d:\"M8 17v-3\",key:\"17ska0\"}]]);const ze=createLucideIcon(\"BarChartHorizontal\",[[\"path\",{d:\"M3 3v18h18\",key:\"1s2lah\"}],[\"path\",{d:\"M7 16h8\",key:\"srdodz\"}],[\"path\",{d:\"M7 11h12\",key:\"127s9w\"}],[\"path\",{d:\"M7 6h3\",key:\"w9rmul\"}]]);const He=createLucideIcon(\"BarChart\",[[\"line\",{x1:\"12\",y1:\"20\",x2:\"12\",y2:\"10\",key:\"1wi7jb\"}],[\"line\",{x1:\"18\",y1:\"20\",x2:\"18\",y2:\"4\",key:\"1mwru6\"}],[\"line\",{x1:\"6\",y1:\"20\",x2:\"6\",y2:\"16\",key:\"zj13da\"}]]);const Se=createLucideIcon(\"Baseline\",[[\"path\",{d:\"M4 20h16\",key:\"14thso\"}],[\"path\",{d:\"m6 16 6-12 6 12\",key:\"1b4byz\"}],[\"path\",{d:\"M8 12h8\",key:\"1wcyev\"}]]);const Ve=createLucideIcon(\"Bath\",[[\"path\",{d:\"M9 6 6.5 3.5a1.5 1.5 0 0 0-1-.5C4.683 3 4 3.683 4 4.5V17a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2v-5\",key:\"1r8yf5\"}],[\"line\",{x1:\"10\",y1:\"5\",x2:\"8\",y2:\"7\",key:\"d858pc\"}],[\"line\",{x1:\"2\",y1:\"12\",x2:\"22\",y2:\"12\",key:\"zvmn4p\"}],[\"line\",{x1:\"7\",y1:\"19\",x2:\"7\",y2:\"21\",key:\"cpl2n4\"}],[\"line\",{x1:\"17\",y1:\"19\",x2:\"17\",y2:\"21\",key:\"ywtigw\"}]]);const qe=createLucideIcon(\"BatteryCharging\",[[\"path\",{d:\"M15 7h1a2 2 0 0 1 2 2v6a2 2 0 0 1-2 2h-2\",key:\"1sdynx\"}],[\"path\",{d:\"M6 7H4a2 2 0 0 0-2 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je=createLucideIcon(\"BatteryMedium\",[[\"rect\",{x:\"2\",y:\"7\",width:\"16\",height:\"10\",rx:\"2\",ry:\"2\",key:\"5j9scf\"}],[\"line\",{x1:\"22\",x2:\"22\",y1:\"11\",y2:\"13\",key:\"4dh1rd\"}],[\"line\",{x1:\"6\",x2:\"6\",y1:\"11\",y2:\"13\",key:\"1wd6dw\"}],[\"line\",{x1:\"10\",x2:\"10\",y1:\"11\",y2:\"13\",key:\"haxvl5\"}]]);const Pe=createLucideIcon(\"BatteryWarning\",[[\"path\",{d:\"M14 7h2a2 2 0 0 1 2 2v6c0 1 -1 2 -2 2h-2\",key:\"7x2rx5\"}],[\"path\",{d:\"M6 7h-2a2 2 0 0 0-2 2v6c0 1 1 2 2 2h2\",key:\"1r83cn\"}],[\"line\",{x1:\"22\",x2:\"22\",y1:\"11\",y2:\"13\",key:\"4dh1rd\"}],[\"line\",{x1:\"10\",x2:\"10\",y1:\"7\",y2:\"13\",key:\"1uzyus\"}],[\"line\",{x1:\"10\",x2:\"10\",y1:\"17\",y2:\"17.01\",key:\"1y8k4g\"}]]);const Be=createLucideIcon(\"Battery\",[[\"rect\",{x:\"2\",y:\"7\",width:\"16\",height:\"10\",rx:\"2\",ry:\"2\",key:\"5j9scf\"}],[\"line\",{x1:\"22\",x2:\"22\",y1:\"11\",y2:\"13\",key:\"4dh1rd\"}]]);const De=createLucideIcon(\"Beaker\",[[\"path\",{d:\"M4.5 3h15\",key:\"c7n0jr\"}],[\"path\",{d:\"M6 3v16a2 2 0 0 0 2 2h8a2 2 0 0 0 2-2V3\",key:\"m1uhx7\"}],[\"path\",{d:\"M6 14h12\",key:\"4cwo0f\"}]]);const Oe=createLucideIcon(\"BeanOff\",[[\"path\",{d:\"M9 9c-.64.64-1.521.954-2.402 1.165A6 6 0 0 0 8 22a13.96 13.96 0 0 0 9.9-4.1\",key:\"bq3udt\"}],[\"path\",{d:\"M10.75 5.093A6 6 0 0 1 22 8c0 2.411-.61 4.68-1.683 6.66\",key:\"17ccse\"}],[\"path\",{d:\"M5.341 10.62a4 4 0 0 0 6.487 1.208M10.62 5.341a4.015 4.015 0 0 1 2.039 2.04\",key:\"18zqgq\"}],[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}]]);const Re=createLucideIcon(\"Bean\",[[\"path\",{d:\"M10.165 6.598C9.954 7.478 9.64 8.36 9 9c-.64.64-1.521.954-2.402 1.165A6 6 0 0 0 8 22c7.732 0 14-6.268 14-14a6 6 0 0 0-11.835-1.402Z\",key:\"1tvzk7\"}],[\"path\",{d:\"M5.341 10.62a4 4 0 1 0 5.279-5.28\",key:\"2cyri2\"}]]);const Te=createLucideIcon(\"BedDouble\",[[\"path\",{d:\"M2 20v-8a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2v8\",key:\"1k78r4\"}],[\"path\",{d:\"M4 10V6a2 2 0 0 1 2-2h12a2 2 0 0 1 2 2v4\",key:\"fb3tl2\"}],[\"path\",{d:\"M12 4v6\",key:\"1dcgq2\"}],[\"path\",{d:\"M2 18h20\",key:\"ajqnye\"}]]);const Ze=createLucideIcon(\"BedSingle\",[[\"path\",{d:\"M3 20v-8a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2v8\",key:\"1wm6mi\"}],[\"path\",{d:\"M5 10V6a2 2 0 0 1 2-2h10a2 2 0 0 1 2 2v4\",key:\"4k93s5\"}],[\"path\",{d:\"M3 18h18\",key:\"1h113x\"}]]);const Ue=createLucideIcon(\"Bed\",[[\"path\",{d:\"M2 4v16\",key:\"vw9hq8\"}],[\"path\",{d:\"M2 8h18a2 2 0 0 1 2 2v10\",key:\"1dgv2r\"}],[\"path\",{d:\"M2 17h20\",key:\"18nfp3\"}],[\"path\",{d:\"M6 8v9\",key:\"1yriud\"}]]);const Ge=createLucideIcon(\"Beef\",[[\"circle\",{cx:\"12.5\",cy:\"8.5\",r:\"2.5\",key:\"9738u8\"}],[\"path\",{d:\"M12.5 2a6.5 6.5 0 0 0-6.22 4.6c-1.1 3.13-.78 3.9-3.18 6.08A3 3 0 0 0 5 18c4 0 8.4-1.8 11.4-4.3A6.5 6.5 0 0 0 12.5 2Z\",key:\"o0f6za\"}],[\"path\",{d:\"m18.5 6 2.19 4.5a6.48 6.48 0 0 1 .31 2 6.49 6.49 0 0 1-2.6 5.2C15.4 20.2 11 22 7 22a3 3 0 0 1-2.68-1.66L2.4 16.5\",key:\"k7p6i0\"}]]);const Ee=createLucideIcon(\"Beer\",[[\"path\",{d:\"M17 11h1a3 3 0 0 1 0 6h-1\",key:\"1yp76v\"}],[\"path\",{d:\"M9 12v6\",key:\"1u1cab\"}],[\"path\",{d:\"M13 12v6\",key:\"1sugkk\"}],[\"path\",{d:\"M14 7.5c-1 0-1.44.5-3 .5s-2-.5-3-.5-1.72.5-2.5.5a2.5 2.5 0 0 1 0-5c.78 0 1.57.5 2.5.5S9.44 2 11 2s2 1.5 3 1.5 1.72-.5 2.5-.5a2.5 2.5 0 0 1 0 5c-.78 0-1.5-.5-2.5-.5Z\",key:\"1510fo\"}],[\"path\",{d:\"M5 8v12a2 2 0 0 0 2 2h8a2 2 0 0 0 2-2V8\",key:\"19jb7n\"}]]);const We=createLucideIcon(\"BellMinus\",[[\"path\",{d:\"M13.73 21a2 2 0 0 1-3.46 0\",key:\"6o5tke\"}],[\"path\",{d:\"M21 5h-6\",key:\"14nobq\"}],[\"path\",{d:\"M18.021 9C18.29 15.193 21 17 21 17H3s3-2 3-9a6 6 0 0 1 7-5.916\",key:\"1wldvb\"}]]);const Xe=createLucideIcon(\"BellOff\",[[\"path\",{d:\"M13.73 21a2 2 0 0 1-3.46 0\",key:\"6o5tke\"}],[\"path\",{d:\"M18.63 13A17.888 17.888 0 0 1 18 8\",key:\"d5seqe\"}],[\"path\",{d:\"M6.26 6.26A5.86 5.86 0 0 0 6 8c0 7-3 9-3 9h14\",key:\"cae0gx\"}],[\"path\",{d:\"M18 8a6 6 0 0 0-9.33-5\",key:\"4mngwl\"}],[\"path\",{d:\"m2 2 20 20\",key:\"1ooewy\"}]]);const Je=createLucideIcon(\"BellPlus\",[[\"path\",{d:\"M18.387 12C19.198 15.799 21 17 21 17H3s3-2 3-9a6 6 0 0 1 7-5.916\",key:\"1dhkt2\"}],[\"path\",{d:\"M13.73 21a2 2 0 0 1-3.46 0\",key:\"6o5tke\"}],[\"path\",{d:\"M18 2v6\",key:\"163kbd\"}],[\"path\",{d:\"M21 5h-6\",key:\"14nobq\"}]]);const Ne=createLucideIcon(\"BellRing\",[[\"path\",{d:\"M18 8A6 6 0 0 0 6 8c0 7-3 9-3 9h18s-3-2-3-9\",key:\"oql5b8\"}],[\"path\",{d:\"M13.73 21a2 2 0 0 1-3.46 0\",key:\"6o5tke\"}],[\"path\",{d:\"M2 8c0-2.2.7-4.3 2-6\",key:\"1c7u9x\"}],[\"path\",{d:\"M22 8a10 10 0 0 0-2-6\",key:\"1vnyda\"}]]);const Ke=createLucideIcon(\"Bell\",[[\"path\",{d:\"M18 8A6 6 0 0 0 6 8c0 7-3 9-3 9h18s-3-2-3-9\",key:\"oql5b8\"}],[\"path\",{d:\"M13.73 21a2 2 0 0 1-3.46 0\",key:\"6o5tke\"}]]);const Qe=createLucideIcon(\"Bike\",[[\"circle\",{cx:\"5.5\",cy:\"17.5\",r:\"3.5\",key:\"1noe27\"}],[\"circle\",{cx:\"18.5\",cy:\"17.5\",r:\"3.5\",key:\"15x4ox\"}],[\"path\",{d:\"M15 6a1 1 0 1 0 0-2 1 1 0 0 0 0 2zm-3 11.5V14l-3-3 4-3 2 3h2\",key:\"1932na\"}]]);const Ye=createLucideIcon(\"Binary\",[[\"path\",{d:\"M6 20h4\",key:\"1i6q5t\"}],[\"path\",{d:\"M14 10h4\",key:\"ru81e7\"}],[\"path\",{d:\"M6 14h2v6\",key:\"16z9wg\"}],[\"path\",{d:\"M14 4h2v6\",key:\"1idq9u\"}],[\"rect\",{x:\"6\",y:\"4\",width:\"4\",height:\"6\",key:\"btwfzx\"}],[\"rect\",{x:\"14\",y:\"14\",width:\"4\",height:\"6\",key:\"1cym0j\"}]]);const $e=createLucideIcon(\"Bitcoin\",[[\"path\",{d:\"M11.767 19.089c4.924.868 6.14-6.025 1.216-6.894m-1.216 6.894L5.86 18.047m5.908 1.042-.347 1.97m1.563-8.864c4.924.869 6.14-6.025 1.215-6.893m-1.215 6.893-3.94-.694m5.155-6.2L8.29 4.26m5.908 1.042.348-1.97M7.48 20.364l3.126-17.727\",key:\"yr8idg\"}]]);const _e=createLucideIcon(\"BluetoothConnected\",[[\"path\",{d:\"m7 7 10 10-5 5V2l5 5L7 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0 0 0 0-3.4l-1.6-1.6a2.41 2.41 0 0 0-3.4 0l-1.8 1.8\",key:\"9owvxi\"}],[\"path\",{d:\"m22 2-1.5 1.5\",key:\"ay92ug\"}]]);const ia=createLucideIcon(\"Bone\",[[\"path\",{d:\"M18.6 9.82c-.52-.21-1.15-.25-1.54.15l-7.07 7.06c-.39.39-.36 1.03-.15 1.54.12.3.16.6.16.93a2.5 2.5 0 0 1-5 0c0-.26-.24-.5-.5-.5a2.5 2.5 0 1 1 .96-4.82c.5.21 1.14.25 1.53-.15l7.07-7.06c.39-.39.36-1.03.15-1.54-.12-.3-.21-.6-.21-.93a2.5 2.5 0 0 1 5 0c.01.26.24.49.5.5a2.5 2.5 0 1 1-.9 4.82Z\",key:\"134x1i\"}]]);const na=createLucideIcon(\"BookOpenCheck\",[[\"path\",{d:\"M8 3H2v15h7c1.7 0 3 1.3 3 3V7c0-2.2-1.8-4-4-4Z\",key:\"1i8u0n\"}],[\"path\",{d:\"m16 12 2 2 4-4\",key:\"mdajum\"}],[\"path\",{d:\"M22 6V3h-6c-2.2 0-4 1.8-4 4v14c0-1.7 1.3-3 3-3h7v-2.3\",key:\"jb5l51\"}]]);const ha=createLucideIcon(\"BookOpen\",[[\"path\",{d:\"M2 3h6a4 4 0 0 1 4 4v14a3 3 0 0 0-3-3H2z\",key:\"vv98re\"}],[\"path\",{d:\"M22 3h-6a4 4 0 0 0-4 4v14a3 3 0 0 1 3-3h7z\",key:\"1cyq3y\"}]]);const oa=createLucideIcon(\"Book\",[[\"path\",{d:\"M4 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7v4\",key:\"xawao1\"}],[\"line\",{x1:\"8\",y1:\"16\",x2:\"8\",y2:\"16\",key:\"717jkb\"}],[\"line\",{x1:\"16\",y1:\"16\",x2:\"16\",y2:\"16\",key:\"19gr12\"}]]);const pa=createLucideIcon(\"BoxSelect\",[[\"path\",{d:\"M5 3a2 2 0 0 0-2 2\",key:\"y57alp\"}],[\"path\",{d:\"M19 3a2 2 0 0 1 2 2\",key:\"18rm91\"}],[\"path\",{d:\"M21 19a2 2 0 0 1-2 2\",key:\"1j7049\"}],[\"path\",{d:\"M5 21a2 2 0 0 1-2-2\",key:\"sbafld\"}],[\"path\",{d:\"M9 3h1\",key:\"1yesri\"}],[\"path\",{d:\"M9 21h1\",key:\"15o7lz\"}],[\"path\",{d:\"M14 3h1\",key:\"1ec4yj\"}],[\"path\",{d:\"M14 21h1\",key:\"v9vybs\"}],[\"path\",{d:\"M3 9v1\",key:\"1r0deq\"}],[\"path\",{d:\"M21 9v1\",key:\"mxsmne\"}],[\"path\",{d:\"M3 14v1\",key:\"vnatye\"}],[\"path\",{d:\"M21 14v1\",key:\"169vum\"}]]);const ra=createLucideIcon(\"Box\",[[\"path\",{d:\"M21 16V8a2 2 0 0 0-1-1.73l-7-4a2 2 0 0 0-2 0l-7 4A2 2 0 0 0 3 8v8a2 2 0 0 0 1 1.73l7 4a2 2 0 0 0 2 0l7-4A2 2 0 0 0 21 16z\",key:\"yt0hxn\"}],[\"polyline\",{points:\"3.29 7 12 12 20.71 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Ma=createLucideIcon(\"Briefcase\",[[\"rect\",{x:\"2\",y:\"7\",width:\"20\",height:\"14\",rx:\"2\",ry:\"2\",key:\"1fj28a\"}],[\"path\",{d:\"M16 21V5a2 2 0 0 0-2-2h-4a2 2 0 0 0-2 2v16\",key:\"zwj3tp\"}]]);const xa=createLucideIcon(\"Brush\",[[\"path\",{d:\"m9.06 11.9 8.07-8.06a2.85 2.85 0 1 1 4.03 4.03l-8.06 8.08\",key:\"1styjt\"}],[\"path\",{d:\"M7.07 14.94c-1.66 0-3 1.35-3 3.02 0 1.33-2.5 1.52-2 2.02 1.08 1.1 2.49 2.02 4 2.02 2.2 0 4-1.8 4-4.04a3.01 3.01 0 0 0-3-3.02z\",key:\"z0l1mu\"}]]);const va=createLucideIcon(\"Bug\",[[\"rect\",{width:\"8\",height:\"14\",x:\"8\",y:\"6\",rx:\"4\",key:\"hq8nra\"}],[\"path\",{d:\"m19 7-3 2\",key:\"fmg8ec\"}],[\"path\",{d:\"m5 7 3 2\",key:\"dkxqes\"}],[\"path\",{d:\"m19 19-3-2\",key:\"1hbhi4\"}],[\"path\",{d:\"m5 19 3-2\",key:\"dvt2ee\"}],[\"path\",{d:\"M20 13h-4\",key:\"1agfp2\"}],[\"path\",{d:\"M4 13h4\",key:\"1bwh8b\"}],[\"path\",{d:\"m10 4 1 2\",key:\"1pot59\"}],[\"path\",{d:\"m14 4-1 2\",key:\"m8sn0o\"}]]);const ga=createLucideIcon(\"Building2\",[[\"path\",{d:\"M6 22V4c0-.27 0-.55.07-.82a1.477 1.477 0 0 1 1.1-1.11C7.46 2 8.73 2 9 2h7c.27 0 .55 0 .82.07a1.477 1.477 0 0 1 1.11 1.1c.07.28.07.56.07.83v18H6Z\",key:\"1b1x9d\"}],[\"path\",{d:\"M2 14v6c0 1.1.9 2 2 2h2V12H4c-.27 0-.55 0-.82.07-.27.07-.52.2-.72.4-.19.19-.32.44-.39.71A3.4 3.4 0 0 0 2 14Z\",key:\"12g3jd\"}],[\"path\",{d:\"M20.82 9.07A3.4 3.4 0 0 0 20 9h-2v13h2a2 2 0 0 0 2-2v-9c0-.28 0-.55-.07-.82-.07-.27-.2-.52-.4-.72-.19-.19-.44-.32-.71-.39Z\",key:\"o1w3ll\"}],[\"path\",{d:\"M10 6h4\",key:\"1itunk\"}],[\"path\",{d:\"M10 10h4\",key:\"tcdvrf\"}],[\"path\",{d:\"M10 14h4\",key:\"kelpxr\"}],[\"path\",{d:\"M10 18h4\",key:\"1ulq68\"}]]);const ma=createLucideIcon(\"Building\",[[\"rect\",{x:\"4\",y:\"2\",width:\"16\",height:\"20\",rx:\"2\",ry:\"2\",key:\"152kg8\"}],[\"path\",{d:\"M9 22v-4h6v4\",key:\"r93iot\"}],[\"path\",{d:\"M8 6h.01\",key:\"1dz90k\"}],[\"path\",{d:\"M16 6h.01\",key:\"1x0f13\"}],[\"path\",{d:\"M12 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qa=createLucideIcon(\"CalendarPlus\",[[\"path\",{d:\"M21 13V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h8\",key:\"3spt84\"}],[\"line\",{x1:\"16\",y1:\"2\",x2:\"16\",y2:\"6\",key:\"18saeg\"}],[\"line\",{x1:\"8\",y1:\"2\",x2:\"8\",y2:\"6\",key:\"1u51jw\"}],[\"line\",{x1:\"3\",y1:\"10\",x2:\"21\",y2:\"10\",key:\"6sq0yj\"}],[\"line\",{x1:\"19\",y1:\"16\",x2:\"19\",y2:\"22\",key:\"537lsc\"}],[\"line\",{x1:\"16\",y1:\"19\",x2:\"22\",y2:\"19\",key:\"qkgpxo\"}]]);const Fa=createLucideIcon(\"CalendarRange\",[[\"rect\",{x:\"3\",y:\"4\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"1dtxiw\"}],[\"line\",{x1:\"16\",y1:\"2\",x2:\"16\",y2:\"6\",key:\"18saeg\"}],[\"line\",{x1:\"8\",y1:\"2\",x2:\"8\",y2:\"6\",key:\"1u51jw\"}],[\"line\",{x1:\"3\",y1:\"10\",x2:\"21\",y2:\"10\",key:\"6sq0yj\"}],[\"path\",{d:\"M17 14h-6\",key:\"bkmgh3\"}],[\"path\",{d:\"M13 18H7\",key:\"bb0bb7\"}],[\"path\",{d:\"M7 14h.01\",key:\"1qa3f1\"}],[\"path\",{d:\"M17 18h.01\",key:\"1bdyru\"}]]);const Aa=createLucideIcon(\"CalendarSearch\",[[\"path\",{d:\"M21 12V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14c0 1.1.9 2 2 2h7.5\",key:\"18ncp8\"}],[\"path\",{d:\"M16 2v4\",key:\"4m81vk\"}],[\"path\",{d:\"M8 2v4\",key:\"1cmpym\"}],[\"path\",{d:\"M3 10h18\",key:\"8toen8\"}],[\"path\",{d:\"M18 21a3 3 0 1 0 0-6 3 3 0 0 0 0 6v0Z\",key:\"mgbru4\"}],[\"path\",{d:\"m22 22-1.5-1.5\",key:\"1x83k4\"}]]);const ja=createLucideIcon(\"CalendarX2\",[[\"path\",{d:\"M21 13V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h8\",key:\"3spt84\"}],[\"line\",{x1:\"16\",y1:\"2\",x2:\"16\",y2:\"6\",key:\"18saeg\"}],[\"line\",{x1:\"8\",y1:\"2\",x2:\"8\",y2:\"6\",key:\"1u51jw\"}],[\"line\",{x1:\"3\",y1:\"10\",x2:\"21\",y2:\"10\",key:\"6sq0yj\"}],[\"line\",{x1:\"17\",y1:\"17\",x2:\"22\",y2:\"22\",key:\"tvgkbv\"}],[\"line\",{x1:\"17\",y1:\"22\",x2:\"22\",y2:\"17\",key:\"1l23f3\"}]]);const Pa=createLucideIcon(\"CalendarX\",[[\"rect\",{x:\"3\",y:\"4\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"1dtxiw\"}],[\"line\",{x1:\"16\",y1:\"2\",x2:\"16\",y2:\"6\",key:\"18saeg\"}],[\"line\",{x1:\"8\",y1:\"2\",x2:\"8\",y2:\"6\",key:\"1u51jw\"}],[\"line\",{x1:\"3\",y1:\"10\",x2:\"21\",y2:\"10\",key:\"6sq0yj\"}],[\"line\",{x1:\"10\",y1:\"14\",x2:\"14\",y2:\"18\",key:\"fs6roj\"}],[\"line\",{x1:\"14\",y1:\"14\",x2:\"10\",y2:\"18\",key:\"1kdrv6\"}]]);const Ba=createLucideIcon(\"Calendar\",[[\"rect\",{x:\"3\",y:\"4\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"1dtxiw\"}],[\"line\",{x1:\"16\",y1:\"2\",x2:\"16\",y2:\"6\",key:\"18saeg\"}],[\"line\",{x1:\"8\",y1:\"2\",x2:\"8\",y2:\"6\",key:\"1u51jw\"}],[\"line\",{x1:\"3\",y1:\"10\",x2:\"21\",y2:\"10\",key:\"6sq0yj\"}]]);const Da=createLucideIcon(\"CameraOff\",[[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}],[\"path\",{d:\"M7 7H4a2 2 0 0 0-2 2v9a2 2 0 0 0 2 2h16\",key:\"qmtpty\"}],[\"path\",{d:\"M9.5 4h5L17 7h3a2 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Ta=createLucideIcon(\"Candy\",[[\"path\",{d:\"m9.5 7.5-2 2a4.95 4.95 0 1 0 7 7l2-2a4.95 4.95 0 1 0-7-7Z\",key:\"ue6khb\"}],[\"path\",{d:\"M14 6.5v10\",key:\"5xnk7c\"}],[\"path\",{d:\"M10 7.5v10\",key:\"1uew51\"}],[\"path\",{d:\"m16 7 1-5 1.37.68A3 3 0 0 0 19.7 3H21v1.3c0 .46.1.92.32 1.33L22 7l-5 1\",key:\"b9cp6k\"}],[\"path\",{d:\"m8 17-1 5-1.37-.68A3 3 0 0 0 4.3 21H3v-1.3a3 3 0 0 0-.32-1.33L2 17l5-1\",key:\"5lney8\"}]]);const Za=createLucideIcon(\"Car\",[[\"path\",{d:\"M14 16H9m10 0h3v-3.15a1 1 0 0 0-.84-.99L16 11l-2.7-3.6a1 1 0 0 0-.8-.4H5.24a2 2 0 0 0-1.8 1.1l-.8 1.63A6 6 0 0 0 2 12.42V16h2\",key:\"l5np60\"}],[\"circle\",{cx:\"6.5\",cy:\"16.5\",r:\"2.5\",key:\"ae40ju\"}],[\"circle\",{cx:\"16.5\",cy:\"16.5\",r:\"2.5\",key:\"1smtlt\"}]]);const Ua=createLucideIcon(\"Carrot\",[[\"path\",{d:\"M2.27 21.7s9.87-3.5 12.73-6.36a4.5 4.5 0 0 0-6.36-6.37C5.77 11.84 2.27 21.7 2.27 21.7zM8.64 14l-2.05-2.04M15.34 15l-2.46-2.46\",key:\"rfqxbe\"}],[\"path\",{d:\"M22 9s-1.33-2-3.5-2C16.86 7 15 9 15 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tt=createLucideIcon(\"ChevronRight\",[[\"polyline\",{points:\"9 18 15 12 9 6\",key:\"1rtp27\"}]]);const yt=createLucideIcon(\"ChevronUp\",[[\"polyline\",{points:\"18 15 12 9 6 15\",key:\"1uugdp\"}]]);const ct=createLucideIcon(\"ChevronsDownUp\",[[\"path\",{d:\"m7 20 5-5 5 5\",key:\"13a0gw\"}],[\"path\",{d:\"m7 4 5 5 5-5\",key:\"1kwcof\"}]]);const it=createLucideIcon(\"ChevronsDown\",[[\"polyline\",{points:\"7 13 12 18 17 13\",key:\"am1j83\"}],[\"polyline\",{points:\"7 6 12 11 17 6\",key:\"pjsmwq\"}]]);const nt=createLucideIcon(\"ChevronsLeftRight\",[[\"path\",{d:\"m9 7-5 5 5 5\",key:\"j5w590\"}],[\"path\",{d:\"m15 7 5 5-5 5\",key:\"1bl6da\"}]]);const ht=createLucideIcon(\"ChevronsLeft\",[[\"polyline\",{points:\"11 17 6 12 11 7\",key:\"1ueymj\"}],[\"polyline\",{points:\"18 17 13 12 18 7\",key:\"18fy0m\"}]]);const ot=createLucideIcon(\"ChevronsRightLeft\",[[\"path\",{d:\"m20 17-5-5 5-5\",key:\"30x0n2\"}],[\"path\",{d:\"m4 17 5-5-5-5\",key:\"16spf4\"}]]);const dt=createLucideIcon(\"ChevronsRight\",[[\"polyline\",{points:\"13 17 18 12 13 7\",key:\"oq0h83\"}],[\"polyline\",{points:\"6 17 11 12 6 7\",key:\"3k300q\"}]]);const st=createLucideIcon(\"ChevronsUpDown\",[[\"path\",{d:\"m7 15 5 5 5-5\",key:\"1hf1tw\"}],[\"path\",{d:\"m7 9 5-5 5 5\",key:\"sgt6xg\"}]]);const kt=createLucideIcon(\"ChevronsUp\",[[\"polyline\",{points:\"17 11 12 6 7 11\",key:\"1u9xa9\"}],[\"polyline\",{points:\"17 18 12 13 7 18\",key:\"1fl4au\"}]]);const lt=createLucideIcon(\"Chrome\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"4\",key:\"4exip2\"}],[\"line\",{x1:\"21.17\",y1:\"8\",x2:\"12\",y2:\"8\",key:\"1a1nqa\"}],[\"line\",{x1:\"3.95\",y1:\"6.06\",x2:\"8.54\",y2:\"14\",key:\"irv2k6\"}],[\"line\",{x1:\"10.88\",y1:\"21.94\",x2:\"15.46\",y2:\"14\",key:\"fokf7t\"}]]);const pt=createLucideIcon(\"CigaretteOff\",[[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}],[\"path\",{d:\"M12 12H2v4h14\",key:\"91gsaq\"}],[\"path\",{d:\"M22 12v4\",key:\"142cbu\"}],[\"path\",{d:\"M18 12h-.5\",key:\"12ymji\"}],[\"path\",{d:\"M7 12v4\",key:\"jqww69\"}],[\"path\",{d:\"M18 8c0-2.5-2-2.5-2-5\",key:\"1il607\"}],[\"path\",{d:\"M22 8c0-2.5-2-2.5-2-5\",key:\"1gah44\"}]]);const rt=createLucideIcon(\"Cigarette\",[[\"path\",{d:\"M18 12H2v4h16\",key:\"2rt1hm\"}],[\"path\",{d:\"M22 12v4\",key:\"142cbu\"}],[\"path\",{d:\"M7 12v4\",key:\"jqww69\"}],[\"path\",{d:\"M18 8c0-2.5-2-2.5-2-5\",key:\"1il607\"}],[\"path\",{d:\"M22 8c0-2.5-2-2.5-2-5\",key:\"1gah44\"}]]);const ut=createLucideIcon(\"CircleDot\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"1\",key:\"41hilf\"}]]);const Mt=createLucideIcon(\"CircleEllipsis\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"path\",{d:\"M17 12h.01\",key:\"1m0b6t\"}],[\"path\",{d:\"M12 12h.01\",key:\"1mp3jc\"}],[\"path\",{d:\"M7 12h.01\",key:\"eqddd0\"}]]);const xt=createLucideIcon(\"CircleSlashed\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"path\",{d:\"M22 2 2 22\",key:\"y4kqgn\"}]]);const vt=createLucideIcon(\"Circle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}]]);const gt=createLucideIcon(\"Citrus\",[[\"path\",{d:\"M5.51 18.49a12 12 0 0 0 16.12.78c.49-.41.49-1.15.03-1.6L6.34 2.33a1.08 1.08 0 0 0-1.6.03A12 12 0 0 0 5.5 18.5Z\",key:\"cpj97m\"}],[\"path\",{d:\"M8.34 15.66a8 8 0 0 0 10.4.78c.54-.4.54-1.16.06-1.64L9.2 5.2c-.48-.48-1.25-.48-1.64.06a8 8 0 0 0 .78 10.4Z\",key:\"vhgi9a\"}],[\"path\",{d:\"m14 10-5.5 5.5\",key:\"92pfem\"}],[\"path\",{d:\"M14 10v8\",key:\"3sxk0t\"}],[\"path\",{d:\"M14 10H6\",key:\"1aai03\"}]]);const mt=createLucideIcon(\"Clapperboard\",[[\"path\",{d:\"M4 11v8a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2v-8H4Z\",key:\"1hxvyx\"}],[\"path\",{d:\"m4 11-.88-2.87a2 2 0 0 1 1.33-2.5l11.48-3.5a2 2 0 0 1 2.5 1.32l.87 2.87L4 11.01Z\",key:\"1vz1k2\"}],[\"path\",{d:\"m6.6 4.99 3.38 4.2\",key:\"192ida\"}],[\"path\",{d:\"m11.86 3.38 3.38 4.2\",key:\"hhucvz\"}]]);const Lt=createLucideIcon(\"ClipboardCheck\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\",key:\"116196\"}],[\"path\",{d:\"m9 14 2 2 4-4\",key:\"df797q\"}]]);const ft=createLucideIcon(\"ClipboardCopy\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M8 4H6a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2v-2\",key:\"4jdomd\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v4\",key:\"3hqy98\"}],[\"path\",{d:\"M21 14H11\",key:\"1bme5i\"}],[\"path\",{d:\"m15 10-4 4 4 4\",key:\"5dvupr\"}]]);const wt=createLucideIcon(\"ClipboardEdit\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M10.42 12.61a2.1 2.1 0 1 1 2.97 2.97L7.95 21 4 22l.99-3.95 5.43-5.44Z\",key:\"1rgxu8\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2h-5.5\",key:\"cereej\"}],[\"path\",{d:\"M4 13.5V6a2 2 0 0 1 2-2h2\",key:\"5ua5vh\"}]]);const It=createLucideIcon(\"ClipboardList\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\",key:\"116196\"}],[\"path\",{d:\"M12 11h4\",key:\"1jrz19\"}],[\"path\",{d:\"M12 16h4\",key:\"n85exb\"}],[\"path\",{d:\"M8 11h.01\",key:\"1dfujw\"}],[\"path\",{d:\"M8 16h.01\",key:\"18s6g9\"}]]);const Ct=createLucideIcon(\"ClipboardSignature\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M8 4H6a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2v-.5\",key:\"1but9f\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 1.73 1\",key:\"1p8n7l\"}],[\"path\",{d:\"M18.42 9.61a2.1 2.1 0 1 1 2.97 2.97L16.95 17 13 18l.99-3.95 4.43-4.44Z\",key:\"johvi5\"}],[\"path\",{d:\"M8 18h1\",key:\"13wk12\"}]]);const bt=createLucideIcon(\"ClipboardType\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\",key:\"116196\"}],[\"path\",{d:\"M9 12v-1h6v1\",key:\"iehl6m\"}],[\"path\",{d:\"M11 17h2\",key:\"12w5me\"}],[\"path\",{d:\"M12 11v6\",key:\"1bwqyc\"}]]);const zt=createLucideIcon(\"ClipboardX\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\",key:\"116196\"}],[\"path\",{d:\"m15 11-6 6\",key:\"1toa9n\"}],[\"path\",{d:\"m9 11 6 6\",key:\"wlibny\"}]]);const Ht=createLucideIcon(\"Clipboard\",[[\"rect\",{x:\"8\",y:\"2\",width:\"8\",height:\"4\",rx:\"1\",ry:\"1\",key:\"wz2j3u\"}],[\"path\",{d:\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\",key:\"116196\"}]]);const St=createLucideIcon(\"Clock1\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 14.5 8\",key:\"12zbmj\"}]]);const Vt=createLucideIcon(\"Clock10\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 8 10\",key:\"atfzqc\"}]]);const qt=createLucideIcon(\"Clock11\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 9.5 8\",key:\"l5bg6f\"}]]);const Ft=createLucideIcon(\"Clock12\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12\",key:\"1fub01\"}]]);const At=createLucideIcon(\"Clock2\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 16 10\",key:\"1g230d\"}]]);const jt=createLucideIcon(\"Clock3\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 16.5 12\",key:\"1aq6pp\"}]]);const Pt=createLucideIcon(\"Clock4\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 16 14\",key:\"68esgv\"}]]);const Bt=createLucideIcon(\"Clock5\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 14.5 16\",key:\"1pcbox\"}]]);const Dt=createLucideIcon(\"Clock6\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 12 16.5\",key:\"hb2qv6\"}]]);const Ot=createLucideIcon(\"Clock7\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 9.5 16\",key:\"ka3394\"}]]);const Rt=createLucideIcon(\"Clock8\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 8 14\",key:\"tmc9b4\"}]]);const Tt=createLucideIcon(\"Clock9\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 7.5 12\",key:\"1k60p0\"}]]);const Zt=createLucideIcon(\"Clock\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polyline\",{points:\"12 6 12 12 16 14\",key:\"68esgv\"}]]);const Ut=createLucideIcon(\"CloudCog\",[[\"path\",{d:\"M20 16.2A4.5 4.5 0 0 0 17.5 8h-1.8A7 7 0 1 0 4 14.9\",key:\"19hoja\"}],[\"circle\",{cx:\"12\",cy:\"17\",r:\"3\",key:\"1spfwm\"}],[\"path\",{d:\"M12 13v1\",key:\"176q98\"}],[\"path\",{d:\"M12 20v1\",key:\"1wcdkc\"}],[\"path\",{d:\"M16 17h-1\",key:\"y560le\"}],[\"path\",{d:\"M9 17H8\",key:\"1lfe9z\"}],[\"path\",{d:\"m15 14-.88.88\",key:\"12ytk1\"}],[\"path\",{d:\"M9.88 19.12 9 20\",key:\"1kmb4r\"}],[\"path\",{d:\"m15 20-.88-.88\",key:\"1ipjcf\"}],[\"path\",{d:\"M9.88 14.88 9 14\",key:\"c4uok7\"}]]);const Gt=createLucideIcon(\"CloudDrizzle\",[[\"path\",{d:\"M4 14.899A7 7 0 1 1 15.71 8h1.79a4.5 4.5 0 0 1 2.5 8.242\",key:\"1pljnt\"}],[\"path\",{d:\"M8 19v1\",key:\"1dk2by\"}],[\"path\",{d:\"M8 14v1\",key:\"84yxot\"}],[\"path\",{d:\"M16 19v1\",key:\"v220m7\"}],[\"path\",{d:\"M16 14v1\",key:\"g12gj6\"}],[\"path\",{d:\"M12 21v1\",key:\"q8vafk\"}],[\"path\",{d:\"M12 16v1\",key:\"1mx6rx\"}]]);const Et=createLucideIcon(\"CloudFog\",[[\"path\",{d:\"M4 14.899A7 7 0 1 1 15.71 8h1.79a4.5 4.5 0 0 1 2.5 8.242\",key:\"1pljnt\"}],[\"path\",{d:\"M16 17H7\",key:\"pygtm1\"}],[\"path\",{d:\"M17 21H9\",key:\"1u2q02\"}]]);const Wt=createLucideIcon(\"CloudHail\",[[\"path\",{d:\"M4 14.899A7 7 0 1 1 15.71 8h1.79a4.5 4.5 0 0 1 2.5 8.242\",key:\"1pljnt\"}],[\"path\",{d:\"M16 14v2\",key:\"a1is7l\"}],[\"path\",{d:\"M8 14v2\",key:\"1e9m6t\"}],[\"path\",{d:\"M16 20h.01\",key:\"xwek51\"}],[\"path\",{d:\"M8 20h.01\",key:\"1vjney\"}],[\"path\",{d:\"M12 16v2\",key:\"z66u1j\"}],[\"path\",{d:\"M12 22h.01\",key:\"1urd7a\"}]]);const Xt=createLucideIcon(\"CloudLightning\",[[\"path\",{d:\"M6 16.326A7 7 0 1 1 15.71 8h1.79a4.5 4.5 0 0 1 .5 8.973\",key:\"1cez44\"}],[\"path\",{d:\"m13 12-3 5h4l-3 5\",key:\"1t22er\"}]]);const Jt=createLucideIcon(\"CloudMoonRain\",[[\"path\",{d:\"M10.083 9A6.002 6.002 0 0 1 16 4a4.243 4.243 0 0 0 6 6c0 2.22-1.206 4.16-3 5.197\",key:\"u82z8m\"}],[\"path\",{d:\"M3 20a5 5 0 1 1 8.9-4H13a3 3 0 0 1 2 5.24\",key:\"1qmrp3\"}],[\"path\",{d:\"M11 20v2\",key:\"174qtz\"}],[\"path\",{d:\"M7 19v2\",key:\"12npes\"}]]);const Nt=createLucideIcon(\"CloudMoon\",[[\"path\",{d:\"M13 22H7a5 5 0 1 1 4.9-6H13a3 3 0 0 1 0 6Z\",key:\"s09mg5\"}],[\"path\",{d:\"M10.083 9A6.002 6.002 0 0 1 16 4a4.243 4.243 0 0 0 6 6c0 2.22-1.206 4.16-3 5.197\",key:\"u82z8m\"}]]);const Kt=createLucideIcon(\"CloudOff\",[[\"path\",{d:\"m2 2 20 20\",key:\"1ooewy\"}],[\"path\",{d:\"M5.782 5.782A7 7 0 0 0 9 19h8.5a4.5 4.5 0 0 0 1.307-.193\",key:\"yfwify\"}],[\"path\",{d:\"M21.532 16.5A4.5 4.5 0 0 0 17.5 10h-1.79A7.008 7.008 0 0 0 10 5.07\",key:\"jlfiyv\"}]]);const Qt=createLucideIcon(\"CloudRainWind\",[[\"path\",{d:\"M4 14.899A7 7 0 1 1 15.71 8h1.79a4.5 4.5 0 0 1 2.5 8.242\",key:\"1pljnt\"}],[\"path\",{d:\"m9.2 22 3-7\",key:\"sb5f6j\"}],[\"path\",{d:\"m9 13-3 7\",key:\"500co5\"}],[\"path\",{d:\"m17 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Vy=createLucideIcon(\"Cpu\",[[\"rect\",{x:\"4\",y:\"4\",width:\"16\",height:\"16\",rx:\"2\",ry:\"2\",key:\"19czt8\"}],[\"rect\",{x:\"9\",y:\"9\",width:\"6\",height:\"6\",key:\"o3kz5p\"}],[\"line\",{x1:\"9\",y1:\"2\",x2:\"9\",y2:\"4\",key:\"1tcqwn\"}],[\"line\",{x1:\"15\",y1:\"2\",x2:\"15\",y2:\"4\",key:\"1yvlak\"}],[\"line\",{x1:\"9\",y1:\"21\",x2:\"9\",y2:\"22\",key:\"tuhu08\"}],[\"line\",{x1:\"15\",y1:\"20\",x2:\"15\",y2:\"22\",key:\"1o5cyu\"}],[\"line\",{x1:\"20\",y1:\"9\",x2:\"22\",y2:\"9\",key:\"b8rlm1\"}],[\"line\",{x1:\"20\",y1:\"14\",x2:\"22\",y2:\"14\",key:\"1mk8c1\"}],[\"line\",{x1:\"2\",y1:\"9\",x2:\"4\",y2:\"9\",key:\"bt7uvh\"}],[\"line\",{x1:\"2\",y1:\"14\",x2:\"4\",y2:\"14\",key:\"hp71sd\"}]]);const qy=createLucideIcon(\"CreativeCommons\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"path\",{d:\"M10 9.3a2.8 2.8 0 0 0-3.5 1 3.1 3.1 0 0 0 0 3.4 2.7 2.7 0 0 0 3.5 1\",key:\"1ss3eq\"}],[\"path\",{d:\"M17 9.3a2.8 2.8 0 0 0-3.5 1 3.1 3.1 0 0 0 0 3.4 2.7 2.7 0 0 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Ry=createLucideIcon(\"CurlyBraces\",[[\"path\",{d:\"M8 3H7a2 2 0 0 0-2 2v5a2 2 0 0 1-2 2 2 2 0 0 1 2 2v5c0 1.1.9 2 2 2h1\",key:\"ezmyqa\"}],[\"path\",{d:\"M16 21h1a2 2 0 0 0 2-2v-5c0-1.1.9-2 2-2a2 2 0 0 1-2-2V5a2 2 0 0 0-2-2h-1\",key:\"e1hn23\"}]]);const Ty=createLucideIcon(\"Currency\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"8\",key:\"46899m\"}],[\"line\",{x1:\"3\",y1:\"3\",x2:\"6\",y2:\"6\",key:\"zkqcdn\"}],[\"line\",{x1:\"21\",y1:\"3\",x2:\"18\",y2:\"6\",key:\"n7spic\"}],[\"line\",{x1:\"3\",y1:\"21\",x2:\"6\",y2:\"18\",key:\"7xq9ok\"}],[\"line\",{x1:\"21\",y1:\"21\",x2:\"18\",y2:\"18\",key:\"mzvtez\"}]]);const Zy=createLucideIcon(\"DatabaseBackup\",[[\"ellipse\",{cx:\"12\",cy:\"5\",rx:\"9\",ry:\"3\",key:\"msslwz\"}],[\"path\",{d:\"M3 12c0 1.18 2.03 2.2 5 2.7\",key:\"ecdgp9\"}],[\"path\",{d:\"M21 5v4.5\",key:\"18xpbp\"}],[\"path\",{d:\"m12 16 1.27-1.35a4.75 4.75 0 1 1 .41 5.74\",key:\"1wi0wz\"}],[\"path\",{d:\"M12 12v4h4\",key:\"1bxaet\"}],[\"path\",{d:\"M3 5v14c0 1.43 2.97 2.63 7 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Xy=createLucideIcon(\"Dice2\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M15 9h.01\",key:\"x1ddxp\"}],[\"path\",{d:\"M9 15h.01\",key:\"fzyn71\"}]]);const Jy=createLucideIcon(\"Dice3\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M16 8h.01\",key:\"cr5u4v\"}],[\"path\",{d:\"M12 12h.01\",key:\"1mp3jc\"}],[\"path\",{d:\"M8 16h.01\",key:\"18s6g9\"}]]);const Ny=createLucideIcon(\"Dice4\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M16 8h.01\",key:\"cr5u4v\"}],[\"path\",{d:\"M8 8h.01\",key:\"1e4136\"}],[\"path\",{d:\"M8 16h.01\",key:\"18s6g9\"}],[\"path\",{d:\"M16 16h.01\",key:\"1f9h7w\"}]]);const Ky=createLucideIcon(\"Dice5\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M16 8h.01\",key:\"cr5u4v\"}],[\"path\",{d:\"M8 8h.01\",key:\"1e4136\"}],[\"path\",{d:\"M8 16h.01\",key:\"18s6g9\"}],[\"path\",{d:\"M16 16h.01\",key:\"1f9h7w\"}],[\"path\",{d:\"M12 12h.01\",key:\"1mp3jc\"}]]);const Qy=createLucideIcon(\"Dice6\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M16 8h.01\",key:\"cr5u4v\"}],[\"path\",{d:\"M16 12h.01\",key:\"1l6xoz\"}],[\"path\",{d:\"M16 16h.01\",key:\"1f9h7w\"}],[\"path\",{d:\"M8 8h.01\",key:\"1e4136\"}],[\"path\",{d:\"M8 12h.01\",key:\"czm47f\"}],[\"path\",{d:\"M8 16h.01\",key:\"18s6g9\"}]]);const Yy=createLucideIcon(\"Dices\",[[\"rect\",{x:\"2\",y:\"10\",width:\"12\",height:\"12\",rx:\"2\",ry:\"2\",key:\"73wp2n\"}],[\"path\",{d:\"m17.92 14 3.5-3.5a2.24 2.24 0 0 0 0-3l-5-4.92a2.24 2.24 0 0 0-3 0L10 6\",key:\"1o487t\"}],[\"path\",{d:\"M6 18h.01\",key:\"uhywen\"}],[\"path\",{d:\"M10 14h.01\",key:\"ssrbsk\"}],[\"path\",{d:\"M15 6h.01\",key:\"cblpky\"}],[\"path\",{d:\"M18 9h.01\",key:\"2061c0\"}]]);const $y=createLucideIcon(\"Diff\",[[\"path\",{d:\"M12 3v14\",key:\"7cf3v8\"}],[\"path\",{d:\"M5 10h14\",key:\"elsbfy\"}],[\"path\",{d:\"M5 21h14\",key:\"11awu3\"}]]);const _y=createLucideIcon(\"Disc\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"3\",key:\"1v7zrd\"}]]);const ec=createLucideIcon(\"DivideCircle\",[[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}],[\"line\",{x1:\"12\",y1:\"16\",x2:\"12\",y2:\"16\",key:\"4v5xkb\"}],[\"line\",{x1:\"12\",y1:\"8\",x2:\"12\",y2:\"8\",key:\"1vrb7x\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}]]);const ac=createLucideIcon(\"DivideSquare\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}],[\"line\",{x1:\"12\",y1:\"16\",x2:\"12\",y2:\"16\",key:\"4v5xkb\"}],[\"line\",{x1:\"12\",y1:\"8\",x2:\"12\",y2:\"8\",key:\"1vrb7x\"}]]);const tc=createLucideIcon(\"Divide\",[[\"circle\",{cx:\"12\",cy:\"6\",r:\"1\",key:\"1bh7o1\"}],[\"line\",{x1:\"5\",y1:\"12\",x2:\"19\",y2:\"12\",key:\"1smlys\"}],[\"circle\",{cx:\"12\",cy:\"18\",r:\"1\",key:\"lqb9t5\"}]]);const yc=createLucideIcon(\"DnaOff\",[[\"path\",{d:\"M15 2c-1.35 1.5-2.092 3-2.5 4.5M9 22c1.35-1.5 2.092-3 2.5-4.5\",key:\"sxiaad\"}],[\"path\",{d:\"M2 15c3.333-3 6.667-3 10-3m10-3c-1.5 1.35-3 2.092-4.5 2.5\",key:\"yn4bs1\"}],[\"path\",{d:\"m17 6-2.5-2.5\",key:\"5cdfhj\"}],[\"path\",{d:\"m14 8-1.5-1.5\",key:\"1ohn8i\"}],[\"path\",{d:\"m7 18 2.5 2.5\",key:\"16tu1a\"}],[\"path\",{d:\"m3.5 14.5.5.5\",key:\"hapbhd\"}],[\"path\",{d:\"m20 9 .5.5\",key:\"1n7z02\"}],[\"path\",{d:\"m6.5 12.5 1 1\",key:\"cs35ky\"}],[\"path\",{d:\"m16.5 10.5 1 1\",key:\"696xn5\"}],[\"path\",{d:\"m10 16 1.5 1.5\",key:\"11lckj\"}],[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}]]);const cc=createLucideIcon(\"Dna\",[[\"path\",{d:\"M2 15c6.667-6 13.333 0 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dc=createLucideIcon(\"Dribbble\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"path\",{d:\"M19.13 5.09C15.22 9.14 10 10.44 2.25 10.94\",key:\"hpej1\"}],[\"path\",{d:\"M21.75 12.84c-6.62-1.41-12.14 1-16.38 6.32\",key:\"1tr44o\"}],[\"path\",{d:\"M8.56 2.75c4.37 6 6 9.42 8 17.72\",key:\"kbh691\"}]]);const sc=createLucideIcon(\"Droplet\",[[\"path\",{d:\"M12 22a7 7 0 0 0 7-7c0-2-1-3.9-3-5.5s-3.5-4-4-6.5c-.5 2.5-2 4.9-4 6.5C6 11.1 5 13 5 15a7 7 0 0 0 7 7z\",key:\"c7niix\"}]]);const kc=createLucideIcon(\"Droplets\",[[\"path\",{d:\"M7 16.3c2.2 0 4-1.83 4-4.05 0-1.16-.57-2.26-1.71-3.19S7.29 6.75 7 5.3c-.29 1.45-1.14 2.84-2.29 3.76S3 11.1 3 12.25c0 2.22 1.8 4.05 4 4.05z\",key:\"1ptgy4\"}],[\"path\",{d:\"M12.56 6.6A10.97 10.97 0 0 0 14 3.02c.5 2.5 2 4.9 4 6.5s3 3.5 3 5.5a6.98 6.98 0 0 1-11.91 4.97\",key:\"1sl1rz\"}]]);const lc=createLucideIcon(\"Drumstick\",[[\"path\",{d:\"M15.45 15.4c-2.13.65-4.3.32-5.7-1.1-2.29-2.27-1.76-6.5 1.17-9.42 2.93-2.93 7.15-3.46 9.43-1.18 1.41 1.41 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wc=createLucideIcon(\"Equal\",[[\"line\",{x1:\"5\",y1:\"9\",x2:\"19\",y2:\"9\",key:\"p612hi\"}],[\"line\",{x1:\"5\",y1:\"15\",x2:\"19\",y2:\"15\",key:\"e39u1i\"}]]);const Ic=createLucideIcon(\"Eraser\",[[\"path\",{d:\"m7 21-4.3-4.3c-1-1-1-2.5 0-3.4l9.6-9.6c1-1 2.5-1 3.4 0l5.6 5.6c1 1 1 2.5 0 3.4L13 21\",key:\"182aya\"}],[\"path\",{d:\"M22 21H7\",key:\"t4ddhn\"}],[\"path\",{d:\"m5 11 9 9\",key:\"1mo9qw\"}]]);const Cc=createLucideIcon(\"Euro\",[[\"path\",{d:\"M4 10h12\",key:\"1y6xl8\"}],[\"path\",{d:\"M4 14h9\",key:\"1loblj\"}],[\"path\",{d:\"M19 6a7.7 7.7 0 0 0-5.2-2A7.9 7.9 0 0 0 6 12c0 4.4 3.5 8 7.8 8 2 0 3.8-.8 5.2-2\",key:\"1j6lzo\"}]]);const bc=createLucideIcon(\"Expand\",[[\"path\",{d:\"m21 21-6-6m6 6v-4.8m0 4.8h-4.8\",key:\"1c15vz\"}],[\"path\",{d:\"M3 16.2V21m0 0h4.8M3 21l6-6\",key:\"1fsnz2\"}],[\"path\",{d:\"M21 7.8V3m0 0h-4.8M21 3l-6 6\",key:\"hawz9i\"}],[\"path\",{d:\"M3 7.8V3m0 0h4.8M3 3l6 6\",key:\"u9ee12\"}]]);const zc=createLucideIcon(\"ExternalLink\",[[\"path\",{d:\"M18 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li=createLucideIcon(\"FileMinus\",[[\"path\",{d:\"M14.5 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7.5L14.5 2z\",key:\"1nnpy2\"}],[\"polyline\",{points:\"14 2 14 8 20 8\",key:\"1ew0cm\"}],[\"line\",{x1:\"9\",y1:\"15\",x2:\"15\",y2:\"15\",key:\"1hgpwl\"}]]);const pi=createLucideIcon(\"FileOutput\",[[\"path\",{d:\"M4 22h14a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 2v4\",key:\"702lig\"}],[\"polyline\",{points:\"14 2 14 8 20 8\",key:\"1ew0cm\"}],[\"path\",{d:\"M2 15h10\",key:\"jfw4w8\"}],[\"path\",{d:\"m5 12-3 3 3 3\",key:\"oke12k\"}]]);const ri=createLucideIcon(\"FilePieChart\",[[\"path\",{d:\"M16 22h2a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 2v3\",key:\"zhyrez\"}],[\"polyline\",{points:\"14 2 14 8 20 8\",key:\"1ew0cm\"}],[\"path\",{d:\"M4.04 11.71a5.84 5.84 0 1 0 8.2 8.29\",key:\"f1t5jc\"}],[\"path\",{d:\"M13.83 16A5.83 5.83 0 0 0 8 10.17V16h5.83Z\",key:\"7q54ec\"}]]);const ui=createLucideIcon(\"FilePlus2\",[[\"path\",{d:\"M4 22h14a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 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3v-1\",key:\"3pnvol\"}],[\"circle\",{cx:\"12\",cy:\"8\",r:\"2\",key:\"1822b1\"}],[\"path\",{d:\"M12 10v12\",key:\"6ubwww\"}],[\"path\",{d:\"M12 22c4.2 0 7-1.667 7-5-4.2 0-7 1.667-7 5Z\",key:\"9hd38g\"}],[\"path\",{d:\"M12 22c-4.2 0-7-1.667-7-5 4.2 0 7 1.667 7 5Z\",key:\"ufn41s\"}]]);const cn=createLucideIcon(\"Flower\",[[\"path\",{d:\"M12 7.5a4.5 4.5 0 1 1 4.5 4.5M12 7.5A4.5 4.5 0 1 0 7.5 12M12 7.5V9m-4.5 3a4.5 4.5 0 1 0 4.5 4.5M7.5 12H9m7.5 0a4.5 4.5 0 1 1-4.5 4.5m4.5-4.5H15m-3 4.5V15\",key:\"51z86h\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"3\",key:\"1v7zrd\"}],[\"path\",{d:\"m8 16 1.5-1.5\",key:\"ce6zph\"}],[\"path\",{d:\"M14.5 9.5 16 8\",key:\"1kzrzb\"}],[\"path\",{d:\"m8 8 1.5 1.5\",key:\"1yv88w\"}],[\"path\",{d:\"M14.5 14.5 16 16\",key:\"12xhjh\"}]]);const nn=createLucideIcon(\"Focus\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"3\",key:\"1v7zrd\"}],[\"path\",{d:\"M3 7V5a2 2 0 0 1 2-2h2\",key:\"aa7l1z\"}],[\"path\",{d:\"M17 3h2a2 2 0 0 1 2 2v2\",key:\"4qcy5o\"}],[\"path\",{d:\"M21 17v2a2 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sn=createLucideIcon(\"FolderClosed\",[[\"path\",{d:\"M4 20h16a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.93a2 2 0 0 1-1.66-.9l-.82-1.2A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13c0 1.1.9 2 2 2Z\",key:\"1fr9dc\"}],[\"path\",{d:\"M2 10h20\",key:\"1ir3d8\"}]]);const kn=createLucideIcon(\"FolderCog2\",[[\"path\",{d:\"M4 20h16a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.93a2 2 0 0 1-1.66-.9l-.82-1.2A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13c0 1.1.9 2 2 2Z\",key:\"1fr9dc\"}],[\"circle\",{cx:\"12\",cy:\"13\",r:\"2\",key:\"1c1ljs\"}],[\"path\",{d:\"M12 10v1\",key:\"ngorzm\"}],[\"path\",{d:\"M12 15v1\",key:\"1ovrzm\"}],[\"path\",{d:\"m14.6 11.5-.87.5\",key:\"zm6w6e\"}],[\"path\",{d:\"m10.27 14-.87.5\",key:\"idea33\"}],[\"path\",{d:\"m14.6 14.5-.87-.5\",key:\"1ii18h\"}],[\"path\",{d:\"m10.27 12-.87-.5\",key:\"tf2vd0\"}]]);const ln=createLucideIcon(\"FolderCog\",[[\"path\",{d:\"M10.5 20H4a2 2 0 0 1-2-2V5c0-1.1.9-2 2-2h3.93a2 2 0 0 1 1.66.9l.82 1.2a2 2 0 0 0 1.66.9H20a2 2 0 0 1 2 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qn=createLucideIcon(\"FormInput\",[[\"rect\",{x:\"2\",y:\"6\",width:\"20\",height:\"12\",rx:\"2\",key:\"1wpnh2\"}],[\"path\",{d:\"M12 12h.01\",key:\"1mp3jc\"}],[\"path\",{d:\"M17 12h.01\",key:\"1m0b6t\"}],[\"path\",{d:\"M7 12h.01\",key:\"eqddd0\"}]]);const Fn=createLucideIcon(\"Forward\",[[\"polyline\",{points:\"15 17 20 12 15 7\",key:\"1w3sku\"}],[\"path\",{d:\"M4 18v-2a4 4 0 0 1 4-4h12\",key:\"jmiej9\"}]]);const An=createLucideIcon(\"Frame\",[[\"line\",{x1:\"22\",y1:\"6\",x2:\"2\",y2:\"6\",key:\"181agm\"}],[\"line\",{x1:\"22\",y1:\"18\",x2:\"2\",y2:\"18\",key:\"12x4ne\"}],[\"line\",{x1:\"6\",y1:\"2\",x2:\"6\",y2:\"22\",key:\"gjs6u1\"}],[\"line\",{x1:\"18\",y1:\"2\",x2:\"18\",y2:\"22\",key:\"1hbgm0\"}]]);const jn=createLucideIcon(\"Framer\",[[\"path\",{d:\"M5 16V9h14V2H5l14 14h-7m-7 0 7 7v-7m-7 0h7\",key:\"1a2nng\"}]]);const Pn=createLucideIcon(\"Frown\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"path\",{d:\"M16 16s-1.5-2-4-2-4 2-4 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$h=createLucideIcon(\"Info\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"12\",y1:\"16\",x2:\"12\",y2:\"12\",key:\"dkqlv3\"}],[\"line\",{x1:\"12\",y1:\"8\",x2:\"12.01\",y2:\"8\",key:\"1kl4hv\"}]]);const _h=createLucideIcon(\"Inspect\",[[\"path\",{d:\"M21 11V5a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h6\",key:\"14rsvq\"}],[\"path\",{d:\"m12 12 4 10 1.7-4.3L22 16Z\",key:\"64ilsv\"}]]);const eo=createLucideIcon(\"Instagram\",[[\"rect\",{x:\"2\",y:\"2\",width:\"20\",height:\"20\",rx:\"5\",ry:\"5\",key:\"cdfzoc\"}],[\"path\",{d:\"M16 11.37A4 4 0 1 1 12.63 8 4 4 0 0 1 16 11.37z\",key:\"9exkf1\"}],[\"line\",{x1:\"17.5\",y1:\"6.5\",x2:\"17.51\",y2:\"6.5\",key:\"643fve\"}]]);const ao=createLucideIcon(\"Italic\",[[\"line\",{x1:\"19\",y1:\"4\",x2:\"10\",y2:\"4\",key:\"ohvhe\"}],[\"line\",{x1:\"14\",y1:\"20\",x2:\"5\",y2:\"20\",key:\"pl6qj\"}],[\"line\",{x1:\"15\",y1:\"4\",x2:\"9\",y2:\"20\",key:\"baf5vk\"}]]);const 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wo=createLucideIcon(\"LayoutList\",[[\"rect\",{x:\"3\",y:\"14\",width:\"7\",height:\"7\",key:\"1omegr\"}],[\"rect\",{x:\"3\",y:\"3\",width:\"7\",height:\"7\",key:\"1q9d4d\"}],[\"line\",{x1:\"14\",y1:\"4\",x2:\"21\",y2:\"4\",key:\"1klf7b\"}],[\"line\",{x1:\"14\",y1:\"9\",x2:\"21\",y2:\"9\",key:\"1kf9x0\"}],[\"line\",{x1:\"14\",y1:\"15\",x2:\"21\",y2:\"15\",key:\"s6i7v1\"}],[\"line\",{x1:\"14\",y1:\"20\",x2:\"21\",y2:\"20\",key:\"yxpbil\"}]]);const Io=createLucideIcon(\"LayoutTemplate\",[[\"path\",{d:\"M21 3H3v7h18V3z\",key:\"cq2tmr\"}],[\"path\",{d:\"M21 14h-5v7h5v-7z\",key:\"1dv32i\"}],[\"path\",{d:\"M12 14H3v7h9v-7z\",key:\"1k92lm\"}]]);const Co=createLucideIcon(\"Layout\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"3\",y1:\"9\",x2:\"21\",y2:\"9\",key:\"1uch6j\"}],[\"line\",{x1:\"9\",y1:\"21\",x2:\"9\",y2:\"9\",key:\"97zt75\"}]]);const bo=createLucideIcon(\"Leaf\",[[\"path\",{d:\"M11 20A7 7 0 0 1 9.8 6.1C15.5 5 17 4.48 19 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Jo=createLucideIcon(\"Loader\",[[\"line\",{x1:\"12\",y1:\"2\",x2:\"12\",y2:\"6\",key:\"1pduqs\"}],[\"line\",{x1:\"12\",y1:\"18\",x2:\"12\",y2:\"22\",key:\"1b94uv\"}],[\"line\",{x1:\"4.93\",y1:\"4.93\",x2:\"7.76\",y2:\"7.76\",key:\"1a736z\"}],[\"line\",{x1:\"16.24\",y1:\"16.24\",x2:\"19.07\",y2:\"19.07\",key:\"gt096z\"}],[\"line\",{x1:\"2\",y1:\"12\",x2:\"6\",y2:\"12\",key:\"rkbu33\"}],[\"line\",{x1:\"18\",y1:\"12\",x2:\"22\",y2:\"12\",key:\"1vrbnu\"}],[\"line\",{x1:\"4.93\",y1:\"19.07\",x2:\"7.76\",y2:\"16.24\",key:\"59c6el\"}],[\"line\",{x1:\"16.24\",y1:\"7.76\",x2:\"19.07\",y2:\"4.93\",key:\"8m03gt\"}]]);const 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2-2h4\",key:\"1uf3rs\"}],[\"polyline\",{points:\"16 17 21 12 16 7\",key:\"1gabdz\"}],[\"line\",{x1:\"21\",y1:\"12\",x2:\"9\",y2:\"12\",key:\"1stwgr\"}]]);const ed=createLucideIcon(\"Luggage\",[[\"path\",{d:\"M6 20h0a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h12a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h0\",key:\"1h5fkc\"}],[\"path\",{d:\"M8 18V4a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v14\",key:\"1l99gc\"}],[\"path\",{d:\"M10 20h4\",key:\"ni2waw\"}],[\"circle\",{cx:\"16\",cy:\"20\",r:\"2\",key:\"1vifvg\"}],[\"circle\",{cx:\"8\",cy:\"20\",r:\"2\",key:\"ckkr5m\"}]]);const ad=createLucideIcon(\"Magnet\",[[\"path\",{d:\"m6 15-4-4 6.75-6.77a7.79 7.79 0 0 1 11 11L13 22l-4-4 6.39-6.36a2.14 2.14 0 0 0-3-3L6 15\",key:\"1i3lhw\"}],[\"path\",{d:\"m5 8 4 4\",key:\"j6kj7e\"}],[\"path\",{d:\"m12 15 4 4\",key:\"lnac28\"}]]);const td=createLucideIcon(\"MailCheck\",[[\"path\",{d:\"M22 13V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v12c0 1.1.9 2 2 2h8\",key:\"12jkf8\"}],[\"path\",{d:\"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7\",key:\"1ocrg3\"}],[\"path\",{d:\"m16 19 2 2 4-4\",key:\"1b14m6\"}]]);const yd=createLucideIcon(\"MailMinus\",[[\"path\",{d:\"M22 15V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v12c0 1.1.9 2 2 2h8\",key:\"fuxbkv\"}],[\"path\",{d:\"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7\",key:\"1ocrg3\"}],[\"path\",{d:\"M16 19h6\",key:\"xwg31i\"}]]);const cd=createLucideIcon(\"MailOpen\",[[\"path\",{d:\"M21.2 8.4c.5.38.8.97.8 1.6v10a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V10a2 2 0 0 1 .8-1.6l8-6a2 2 0 0 1 2.4 0l8 6Z\",key:\"1jhwl8\"}],[\"path\",{d:\"m22 10-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 10\",key:\"1qfld7\"}]]);const id=createLucideIcon(\"MailPlus\",[[\"path\",{d:\"M22 13V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v12c0 1.1.9 2 2 2h8\",key:\"12jkf8\"}],[\"path\",{d:\"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7\",key:\"1ocrg3\"}],[\"path\",{d:\"M19 16v6\",key:\"tddt3s\"}],[\"path\",{d:\"M16 19h6\",key:\"xwg31i\"}]]);const nd=createLucideIcon(\"MailQuestion\",[[\"path\",{d:\"M22 10.5V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v12c0 1.1.9 2 2 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dd=createLucideIcon(\"MailX\",[[\"path\",{d:\"M22 13V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v12c0 1.1.9 2 2 2h9\",key:\"1j9vog\"}],[\"path\",{d:\"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7\",key:\"1ocrg3\"}],[\"path\",{d:\"m17 17 4 4\",key:\"1b3523\"}],[\"path\",{d:\"m21 17-4 4\",key:\"uinynz\"}]]);const sd=createLucideIcon(\"Mail\",[[\"rect\",{x:\"2\",y:\"4\",width:\"20\",height:\"16\",rx:\"2\",key:\"izxlao\"}],[\"path\",{d:\"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7\",key:\"1ocrg3\"}]]);const kd=createLucideIcon(\"Mails\",[[\"rect\",{x:\"6\",y:\"4\",width:\"16\",height:\"13\",rx:\"2\",key:\"q6n4z8\"}],[\"path\",{d:\"m22 7-7.1 3.78c-.57.3-1.23.3-1.8 0L6 7\",key:\"xn252p\"}],[\"path\",{d:\"M2 8v11c0 1.1.9 2 2 2h14\",key:\"n13cji\"}]]);const ld=createLucideIcon(\"MapPinOff\",[[\"path\",{d:\"M5.43 5.43A8.06 8.06 0 0 0 4 10c0 6 8 12 8 12a29.94 29.94 0 0 0 5-5\",key:\"12a8pk\"}],[\"path\",{d:\"M19.18 13.52A8.66 8.66 0 0 0 20 10a8 8 0 0 0-8-8 7.88 7.88 0 0 0-3.52.82\",key:\"1r9f6y\"}],[\"path\",{d:\"M9.13 9.13A2.78 2.78 0 0 0 9 10a3 3 0 0 0 3 3 2.78 2.78 0 0 0 .87-.13\",key:\"erynq7\"}],[\"path\",{d:\"M14.9 9.25a3 3 0 0 0-2.15-2.16\",key:\"1hwwmx\"}],[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}]]);const pd=createLucideIcon(\"MapPin\",[[\"path\",{d:\"M20 10c0 6-8 12-8 12s-8-6-8-12a8 8 0 0 1 16 0Z\",key:\"2oe9fu\"}],[\"circle\",{cx:\"12\",cy:\"10\",r:\"3\",key:\"ilqhr7\"}]]);const rd=createLucideIcon(\"Map\",[[\"polygon\",{points:\"3 6 9 3 15 6 21 3 21 18 15 21 9 18 3 21\",key:\"ok2ie8\"}],[\"line\",{x1:\"9\",y1:\"3\",x2:\"9\",y2:\"18\",key:\"3zqglt\"}],[\"line\",{x1:\"15\",y1:\"6\",x2:\"15\",y2:\"21\",key:\"1c9xoo\"}]]);const ud=createLucideIcon(\"Martini\",[[\"path\",{d:\"M8 22h8\",key:\"rmew8v\"}],[\"path\",{d:\"M12 11v11\",key:\"ur9y6a\"}],[\"path\",{d:\"m19 3-7 8-7-8Z\",key:\"1sgpiw\"}]]);const Md=createLucideIcon(\"Maximize2\",[[\"polyline\",{points:\"15 3 21 3 21 9\",key:\"mznyad\"}],[\"polyline\",{points:\"9 21 3 21 3 15\",key:\"1avn1i\"}],[\"line\",{x1:\"21\",y1:\"3\",x2:\"14\",y2:\"10\",key:\"8isubj\"}],[\"line\",{x1:\"3\",y1:\"21\",x2:\"10\",y2:\"14\",key:\"c1a6xr\"}]]);const xd=createLucideIcon(\"Maximize\",[[\"path\",{d:\"M8 3H5a2 2 0 0 0-2 2v3\",key:\"1dcmit\"}],[\"path\",{d:\"M21 8V5a2 2 0 0 0-2-2h-3\",key:\"1e4gt3\"}],[\"path\",{d:\"M3 16v3a2 2 0 0 0 2 2h3\",key:\"wsl5sc\"}],[\"path\",{d:\"M16 21h3a2 2 0 0 0 2-2v-3\",key:\"18trek\"}]]);const vd=createLucideIcon(\"Medal\",[[\"path\",{d:\"M7.21 15 2.66 7.14a2 2 0 0 1 .13-2.2L4.4 2.8A2 2 0 0 1 6 2h12a2 2 0 0 1 1.6.8l1.6 2.14a2 2 0 0 1 .14 2.2L16.79 15\",key:\"143lza\"}],[\"path\",{d:\"M11 12 5.12 2.2\",key:\"qhuxz6\"}],[\"path\",{d:\"m13 12 5.88-9.8\",key:\"hbye0f\"}],[\"path\",{d:\"M8 7h8\",key:\"i86dvs\"}],[\"circle\",{cx:\"12\",cy:\"17\",r:\"5\",key:\"qbz8iq\"}],[\"path\",{d:\"M12 18v-2h-.5\",key:\"fawc4q\"}]]);const gd=createLucideIcon(\"MegaphoneOff\",[[\"path\",{d:\"M9.26 9.26 3 11v3l14.14 3.14\",key:\"3429n\"}],[\"path\",{d:\"M21 15.34V6l-7.31 2.03\",key:\"4o1dh8\"}],[\"path\",{d:\"M11.6 16.8a3 3 0 1 1-5.8-1.6\",key:\"1yl0tm\"}],[\"line\",{x1:\"2\",x2:\"22\",y1:\"2\",y2:\"22\",key:\"a6p6uj\"}]]);const md=createLucideIcon(\"Megaphone\",[[\"path\",{d:\"m3 11 18-5v12L3 14v-3z\",key:\"n962bs\"}],[\"path\",{d:\"M11.6 16.8a3 3 0 1 1-5.8-1.6\",key:\"1yl0tm\"}]]);const Ld=createLucideIcon(\"Meh\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"8\",y1:\"15\",x2:\"16\",y2:\"15\",key:\"29ieok\"}],[\"line\",{x1:\"9\",y1:\"9\",x2:\"9.01\",y2:\"9\",key:\"141aaf\"}],[\"line\",{x1:\"15\",y1:\"9\",x2:\"15.01\",y2:\"9\",key:\"1cyg3o\"}]]);const fd=createLucideIcon(\"Menu\",[[\"line\",{x1:\"4\",y1:\"12\",x2:\"20\",y2:\"12\",key:\"1q6rtp\"}],[\"line\",{x1:\"4\",y1:\"6\",x2:\"20\",y2:\"6\",key:\"1jr6gt\"}],[\"line\",{x1:\"4\",y1:\"18\",x2:\"20\",y2:\"18\",key:\"98tuvx\"}]]);const wd=createLucideIcon(\"MessageCircle\",[[\"path\",{d:\"M21 11.5a8.38 8.38 0 0 1-.9 3.8 8.5 8.5 0 0 1-7.6 4.7 8.38 8.38 0 0 1-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 0 1-.9-3.8 8.5 8.5 0 0 1 4.7-7.6 8.38 8.38 0 0 1 3.8-.9h.5a8.48 8.48 0 0 1 8 8v.5z\",key:\"5iho0c\"}]]);const Id=createLucideIcon(\"MessageSquare\",[[\"path\",{d:\"M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z\",key:\"1lielz\"}]]);const Cd=createLucideIcon(\"Mic2\",[[\"path\",{d:\"m12 8-9.04 9.06a2.82 2.82 0 1 0 3.98 3.98L16 12\",key:\"zoua8r\"}],[\"circle\",{cx:\"17\",cy:\"7\",r:\"5\",key:\"1fomce\"}]]);const bd=createLucideIcon(\"MicOff\",[[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}],[\"path\",{d:\"M18.89 13.23A7.12 7.12 0 0 0 19 12v-2\",key:\"80xlxr\"}],[\"path\",{d:\"M5 10v2a7 7 0 0 0 12 5\",key:\"p2k8kg\"}],[\"path\",{d:\"M15 9.34V5a3 3 0 0 0-5.68-1.33\",key:\"1gzdoj\"}],[\"path\",{d:\"M9 9v3a3 3 0 0 0 5.12 2.12\",key:\"r2i35w\"}],[\"line\",{x1:\"12\",y1:\"19\",x2:\"12\",y2:\"22\",key:\"1l505v\"}]]);const zd=createLucideIcon(\"Mic\",[[\"path\",{d:\"M12 2a3 3 0 0 0-3 3v7a3 3 0 0 0 6 0V5a3 3 0 0 0-3-3Z\",key:\"131961\"}],[\"path\",{d:\"M19 10v2a7 7 0 0 1-14 0v-2\",key:\"1vc78b\"}],[\"line\",{x1:\"12\",y1:\"19\",x2:\"12\",y2:\"22\",key:\"1l505v\"}]]);const Hd=createLucideIcon(\"Microscope\",[[\"path\",{d:\"M6 18h8\",key:\"1borvv\"}],[\"path\",{d:\"M3 22h18\",key:\"8prr45\"}],[\"path\",{d:\"M14 22a7 7 0 1 0 0-14h-1\",key:\"1jwaiy\"}],[\"path\",{d:\"M9 14h2\",key:\"197e7h\"}],[\"path\",{d:\"M8 6h4\",key:\"i9thid\"}],[\"path\",{d:\"M13 10V6.5a.5.5 0 0 0-.5-.5.5.5 0 0 1-.5-.5V3a1 1 0 0 0-1-1H9a1 1 0 0 0-1 1v2.5a.5.5 0 0 1-.5.5.5.5 0 0 0-.5.5V10c0 1.1.9 2 2 2h2a2 2 0 0 0 2-2Z\",key:\"z62yqi\"}]]);const Sd=createLucideIcon(\"Microwave\",[[\"rect\",{x:\"2\",y:\"4\",width:\"20\",height:\"15\",rx:\"2\",key:\"1rfv8z\"}],[\"rect\",{x:\"6\",y:\"8\",width:\"8\",height:\"7\",rx:\"1\",key:\"i43qc1\"}],[\"path\",{d:\"M18 8v7\",key:\"o5zi4n\"}],[\"path\",{d:\"M6 19v2\",key:\"1loha6\"}],[\"path\",{d:\"M18 19v2\",key:\"1dawf0\"}]]);const Vd=createLucideIcon(\"Milestone\",[[\"path\",{d:\"M18 6H5a2 2 0 0 0-2 2v3a2 2 0 0 0 2 2h13l4-3.5L18 6Z\",key:\"1mp5s7\"}],[\"path\",{d:\"M12 13v9\",key:\"keea1l\"}],[\"path\",{d:\"M12 2v4\",key:\"3427ic\"}]]);const qd=createLucideIcon(\"MilkOff\",[[\"path\",{d:\"M8 2h8\",key:\"1ssgc1\"}],[\"path\",{d:\"M9 2v1.343M15 2v2.789a4 4 0 0 0 .672 2.219l.656.984a4 4 0 0 1 .672 2.22v1.131M7.8 7.8l-.128.192A4 4 0 0 0 7 10.212V20a2 2 0 0 0 2 2h6a2 2 0 0 0 2-2v-3\",key:\"y0ejgx\"}],[\"path\",{d:\"M7 15a6.47 6.47 0 0 1 5 0 6.472 6.472 0 0 0 3.435.435\",key:\"iaxqsy\"}],[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}]]);const Fd=createLucideIcon(\"Milk\",[[\"path\",{d:\"M8 2h8\",key:\"1ssgc1\"}],[\"path\",{d:\"M9 2v2.789a4 4 0 0 1-.672 2.219l-.656.984A4 4 0 0 0 7 10.212V20a2 2 0 0 0 2 2h6a2 2 0 0 0 2-2v-9.789a4 4 0 0 0-.672-2.219l-.656-.984A4 4 0 0 1 15 4.788V2\",key:\"qtp12x\"}],[\"path\",{d:\"M7 15a6.472 6.472 0 0 1 5 0 6.47 6.47 0 0 0 5 0\",key:\"ygeh44\"}]]);const Ad=createLucideIcon(\"Minimize2\",[[\"polyline\",{points:\"4 14 10 14 10 20\",key:\"11kfnr\"}],[\"polyline\",{points:\"20 10 14 10 14 4\",key:\"rlmsce\"}],[\"line\",{x1:\"14\",y1:\"10\",x2:\"21\",y2:\"3\",key:\"6dvi8v\"}],[\"line\",{x1:\"3\",y1:\"21\",x2:\"10\",y2:\"14\",key:\"c1a6xr\"}]]);const jd=createLucideIcon(\"Minimize\",[[\"path\",{d:\"M8 3v3a2 2 0 0 1-2 2H3\",key:\"hohbtr\"}],[\"path\",{d:\"M21 8h-3a2 2 0 0 1-2-2V3\",key:\"5jw1f3\"}],[\"path\",{d:\"M3 16h3a2 2 0 0 1 2 2v3\",key:\"198tvr\"}],[\"path\",{d:\"M16 21v-3a2 2 0 0 1 2-2h3\",key:\"ph8mxp\"}]]);const Pd=createLucideIcon(\"MinusCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}]]);const Bd=createLucideIcon(\"MinusSquare\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}]]);const Dd=createLucideIcon(\"Minus\",[[\"line\",{x1:\"5\",y1:\"12\",x2:\"19\",y2:\"12\",key:\"1smlys\"}]]);const Od=createLucideIcon(\"MonitorOff\",[[\"path\",{d:\"M17 17H4a2 2 0 0 1-2-2V5c0-1.5 1-2 1-2\",key:\"k0q8oc\"}],[\"path\",{d:\"M22 15V5a2 2 0 0 0-2-2H9\",key:\"cp1ac0\"}],[\"path\",{d:\"M8 21h8\",key:\"1ev6f3\"}],[\"path\",{d:\"M12 17v4\",key:\"1riwvh\"}],[\"path\",{d:\"m2 2 20 20\",key:\"1ooewy\"}]]);const Rd=createLucideIcon(\"MonitorSmartphone\",[[\"path\",{d:\"M18 8V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h8\",key:\"10dyio\"}],[\"path\",{d:\"M10 19v-3.96 3.15\",key:\"1irgej\"}],[\"path\",{d:\"M7 19h5\",key:\"qswx4l\"}],[\"rect\",{x:\"16\",y:\"12\",width:\"6\",height:\"10\",rx:\"2\",key:\"k4sdfk\"}]]);const Td=createLucideIcon(\"MonitorSpeaker\",[[\"path\",{d:\"M5.5 20H8\",key:\"1k40s5\"}],[\"path\",{d:\"M17 9h.01\",key:\"1j24nn\"}],[\"rect\",{x:\"12\",y:\"4\",width:\"10\",height:\"16\",rx:\"2\",key:\"1gi0i2\"}],[\"path\",{d:\"M8 6H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h4\",key:\"1mp6e1\"}],[\"circle\",{cx:\"17\",cy:\"15\",r:\"1\",key:\"tqvash\"}]]);const Zd=createLucideIcon(\"Monitor\",[[\"rect\",{x:\"2\",y:\"3\",width:\"20\",height:\"14\",rx:\"2\",ry:\"2\",key:\"1q87ek\"}],[\"line\",{x1:\"8\",y1:\"21\",x2:\"16\",y2:\"21\",key:\"bcbiac\"}],[\"line\",{x1:\"12\",y1:\"17\",x2:\"12\",y2:\"21\",key:\"1v4d7v\"}]]);const Ud=createLucideIcon(\"Moon\",[[\"path\",{d:\"M12 3a6.364 6.364 0 0 0 9 9 9 9 0 1 1-9-9Z\",key:\"1rit1i\"}]]);const Gd=createLucideIcon(\"MoreHorizontal\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"1\",key:\"41hilf\"}],[\"circle\",{cx:\"19\",cy:\"12\",r:\"1\",key:\"1wjl8i\"}],[\"circle\",{cx:\"5\",cy:\"12\",r:\"1\",key:\"1pcz8c\"}]]);const Ed=createLucideIcon(\"MoreVertical\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"1\",key:\"41hilf\"}],[\"circle\",{cx:\"12\",cy:\"5\",r:\"1\",key:\"gxeob9\"}],[\"circle\",{cx:\"12\",cy:\"19\",r:\"1\",key:\"lyex9k\"}]]);const Wd=createLucideIcon(\"MountainSnow\",[[\"path\",{d:\"m8 3 4 8 5-5 5 15H2L8 3z\",key:\"otkl63\"}],[\"path\",{d:\"M4.14 15.08c2.62-1.57 5.24-1.43 7.86.42 2.74 1.94 5.49 2 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6\",key:\"d60sxy\"}],[\"line\",{x1:\"12\",y1:\"2\",x2:\"12\",y2:\"22\",key:\"1k6o5o\"}]]);const ts=createLucideIcon(\"Move\",[[\"polyline\",{points:\"5 9 2 12 5 15\",key:\"1r5uj5\"}],[\"polyline\",{points:\"9 5 12 2 15 5\",key:\"5v383o\"}],[\"polyline\",{points:\"15 19 12 22 9 19\",key:\"g7qi8m\"}],[\"polyline\",{points:\"19 9 22 12 19 15\",key:\"tpp73q\"}],[\"line\",{x1:\"2\",y1:\"12\",x2:\"22\",y2:\"12\",key:\"zvmn4p\"}],[\"line\",{x1:\"12\",y1:\"2\",x2:\"12\",y2:\"22\",key:\"1k6o5o\"}]]);const ys=createLucideIcon(\"Music2\",[[\"circle\",{cx:\"8\",cy:\"18\",r:\"4\",key:\"1fc0mg\"}],[\"path\",{d:\"M12 18V2l7 4\",key:\"g04rme\"}]]);const cs=createLucideIcon(\"Music3\",[[\"circle\",{cx:\"12\",cy:\"18\",r:\"4\",key:\"m3r9ws\"}],[\"path\",{d:\"M16 18V2\",key:\"40x2m5\"}]]);const is=createLucideIcon(\"Music4\",[[\"path\",{d:\"M9 18V5l12-2v13\",key:\"1jmyc2\"}],[\"path\",{d:\"m9 9 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ss=createLucideIcon(\"Navigation\",[[\"polygon\",{points:\"3 11 22 2 13 21 11 13 3 11\",key:\"1ltx0t\"}]]);const ks=createLucideIcon(\"Network\",[[\"rect\",{x:\"9\",y:\"2\",width:\"6\",height:\"6\",key:\"1iwon9\"}],[\"rect\",{x:\"16\",y:\"16\",width:\"6\",height:\"6\",key:\"gonbwd\"}],[\"rect\",{x:\"2\",y:\"16\",width:\"6\",height:\"6\",key:\"1q0lzr\"}],[\"path\",{d:\"M5 16v-4h14v4\",key:\"8njgxx\"}],[\"path\",{d:\"M12 12V8\",key:\"2874zd\"}]]);const ls=createLucideIcon(\"Newspaper\",[[\"path\",{d:\"M4 22h16a2 2 0 0 0 2-2V4a2 2 0 0 0-2-2H8a2 2 0 0 0-2 2v16a2 2 0 0 1-2 2Zm0 0a2 2 0 0 1-2-2v-9c0-1.1.9-2 2-2h2\",key:\"7pis2x\"}],[\"path\",{d:\"M18 14h-8\",key:\"sponae\"}],[\"path\",{d:\"M15 18h-5\",key:\"95g1m2\"}],[\"path\",{d:\"M10 6h8v4h-8V6Z\",key:\"smlsk5\"}]]);const ps=createLucideIcon(\"NutOff\",[[\"path\",{d:\"M12 4V2\",key:\"1k5q1u\"}],[\"path\",{d:\"M5 10v4a7.004 7.004 0 0 0 5.277 6.787c.412.104.802.292 1.102.592L12 22l.621-.621c.3-.3.69-.488 1.102-.592a7.01 7.01 0 0 0 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0-3-3Z\",key:\"m6k5sh\"}],[\"path\",{d:\"M9 8c-2 3-4 3.5-7 4l8 10c2-1 6-5 6-7\",key:\"arzq70\"}],[\"path\",{d:\"M14.5 17.5 4.5 15\",key:\"s7fvrz\"}]]);const Ss=createLucideIcon(\"Palette\",[[\"circle\",{cx:\"13.5\",cy:\"6.5\",r:\".5\",key:\"1xcu5\"}],[\"circle\",{cx:\"17.5\",cy:\"10.5\",r:\".5\",key:\"736e4u\"}],[\"circle\",{cx:\"8.5\",cy:\"7.5\",r:\".5\",key:\"clrty\"}],[\"circle\",{cx:\"6.5\",cy:\"12.5\",r:\".5\",key:\"1s4xz9\"}],[\"path\",{d:\"M12 2C6.5 2 2 6.5 2 12s4.5 10 10 10c.926 0 1.648-.746 1.648-1.688 0-.437-.18-.835-.437-1.125-.29-.289-.438-.652-.438-1.125a1.64 1.64 0 0 1 1.668-1.668h1.996c3.051 0 5.555-2.503 5.555-5.554C21.965 6.012 17.461 2 12 2z\",key:\"12rzf8\"}]]);const Vs=createLucideIcon(\"Palmtree\",[[\"path\",{d:\"M13 8c0-2.76-2.46-5-5.5-5S2 5.24 2 8h2l1-1 1 1h4\",key:\"foxbe7\"}],[\"path\",{d:\"M13 7.14A5.82 5.82 0 0 1 16.5 6c3.04 0 5.5 2.24 5.5 5h-3l-1-1-1 1h-3\",key:\"18arnh\"}],[\"path\",{d:\"M5.89 9.71c-2.15 2.15-2.3 5.47-.35 7.43l4.24-4.25.7-.7.71-.71 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5-.83.83-3.07-.07-5-2-1.93-1.93-2.83-4.17-2-5 .83-.83 3.07.07 5 2Z\",key:\"4kbmks\"}]]);const As=createLucideIcon(\"PauseCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"10\",y1:\"15\",x2:\"10\",y2:\"9\",key:\"2cxng6\"}],[\"line\",{x1:\"14\",y1:\"15\",x2:\"14\",y2:\"9\",key:\"wsglx2\"}]]);const js=createLucideIcon(\"PauseOctagon\",[[\"path\",{d:\"M10 15V9\",key:\"1lckn7\"}],[\"path\",{d:\"M14 15V9\",key:\"1muqhk\"}],[\"path\",{d:\"M7.714 2h8.572L22 7.714v8.572L16.286 22H7.714L2 16.286V7.714L7.714 2z\",key:\"1m7qra\"}]]);const Ps=createLucideIcon(\"Pause\",[[\"rect\",{x:\"6\",y:\"4\",width:\"4\",height:\"16\",key:\"5yltu4\"}],[\"rect\",{x:\"14\",y:\"4\",width:\"4\",height:\"16\",key:\"duxydb\"}]]);const Bs=createLucideIcon(\"PenTool\",[[\"path\",{d:\"m12 19 7-7 3 3-7 7-3-3z\",key:\"rklqx2\"}],[\"path\",{d:\"m18 13-1.5-7.5L2 2l3.5 14.5L13 18l5-5z\",key:\"1et58u\"}],[\"path\",{d:\"m2 2 7.586 7.586\",key:\"etlp93\"}],[\"circle\",{cx:\"11\",cy:\"11\",r:\"2\",key:\"xmgehs\"}]]);const Ds=createLucideIcon(\"Pencil\",[[\"line\",{x1:\"18\",y1:\"2\",x2:\"22\",y2:\"6\",key:\"1k5sg2\"}],[\"path\",{d:\"M7.5 20.5 19 9l-4-4L3.5 16.5 2 22z\",key:\"12iwkt\"}]]);const Os=createLucideIcon(\"Percent\",[[\"line\",{x1:\"19\",y1:\"5\",x2:\"5\",y2:\"19\",key:\"t1677v\"}],[\"circle\",{cx:\"6.5\",cy:\"6.5\",r:\"2.5\",key:\"4mh3h7\"}],[\"circle\",{cx:\"17.5\",cy:\"17.5\",r:\"2.5\",key:\"1mdrzq\"}]]);const Rs=createLucideIcon(\"PersonStanding\",[[\"circle\",{cx:\"12\",cy:\"5\",r:\"1\",key:\"gxeob9\"}],[\"path\",{d:\"m9 20 3-6 3 6\",key:\"se2kox\"}],[\"path\",{d:\"m6 8 6 2 6-2\",key:\"4o3us4\"}],[\"path\",{d:\"M12 10v4\",key:\"1kjpxc\"}]]);const Ts=createLucideIcon(\"PhoneCall\",[[\"path\",{d:\"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 1-3.07-8.67A2 2 0 0 1 4.11 2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91a16 16 0 0 0 6 6l1.27-1.27a2 2 0 0 1 2.11-.45 12.84 12.84 0 0 0 2.81.7A2 2 0 0 1 22 16.92z\",key:\"foiqr5\"}],[\"path\",{d:\"M14.05 2a9 9 0 0 1 8 7.94\",key:\"vmijpz\"}],[\"path\",{d:\"M14.05 6A5 5 0 0 1 18 10\",key:\"13nbpp\"}]]);const Zs=createLucideIcon(\"PhoneForwarded\",[[\"polyline\",{points:\"18 2 22 6 18 10\",key:\"6vjanh\"}],[\"line\",{x1:\"14\",y1:\"6\",x2:\"22\",y2:\"6\",key:\"1yuov7\"}],[\"path\",{d:\"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 1-3.07-8.67A2 2 0 0 1 4.11 2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91a16 16 0 0 0 6 6l1.27-1.27a2 2 0 0 1 2.11-.45 12.84 12.84 0 0 0 2.81.7A2 2 0 0 1 22 16.92z\",key:\"foiqr5\"}]]);const Us=createLucideIcon(\"PhoneIncoming\",[[\"polyline\",{points:\"16 2 16 8 22 8\",key:\"1ygljm\"}],[\"line\",{x1:\"22\",y1:\"2\",x2:\"16\",y2:\"8\",key:\"kb9lty\"}],[\"path\",{d:\"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 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2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91\",key:\"z86iuo\"}],[\"line\",{x1:\"22\",y1:\"2\",x2:\"2\",y2:\"22\",key:\"1sphze\"}]]);const Ws=createLucideIcon(\"PhoneOutgoing\",[[\"polyline\",{points:\"22 8 22 2 16 2\",key:\"1g204g\"}],[\"line\",{x1:\"16\",y1:\"8\",x2:\"22\",y2:\"2\",key:\"1hkegm\"}],[\"path\",{d:\"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 1-3.07-8.67A2 2 0 0 1 4.11 2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91a16 16 0 0 0 6 6l1.27-1.27a2 2 0 0 1 2.11-.45 12.84 12.84 0 0 0 2.81.7A2 2 0 0 1 22 16.92z\",key:\"foiqr5\"}]]);const Xs=createLucideIcon(\"Phone\",[[\"path\",{d:\"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 1-3.07-8.67A2 2 0 0 1 4.11 2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91a16 16 0 0 0 6 6l1.27-1.27a2 2 0 0 1 2.11-.45 12.84 12.84 0 0 0 2.81.7A2 2 0 0 1 22 16.92z\",key:\"foiqr5\"}]]);const Js=createLucideIcon(\"PictureInPicture2\",[[\"path\",{d:\"M21 9V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v10c0 1.1.9 2 2 2h4\",key:\"daa4of\"}],[\"rect\",{x:\"12\",y:\"13\",width:\"10\",height:\"7\",rx:\"2\",key:\"1wczht\"}]]);const Ns=createLucideIcon(\"PictureInPicture\",[[\"path\",{d:\"M8 4.5v5H3m-1-6 6 6m13 0v-3c0-1.16-.84-2-2-2h-7m-9 9v2c0 1.05.95 2 2 2h3\",key:\"bcd8fb\"}],[\"rect\",{width:\"10\",height:\"7\",x:\"12\",y:\"13.5\",ry:\"2\",key:\"136fx3\"}]]);const Ks=createLucideIcon(\"PieChart\",[[\"path\",{d:\"M21.21 15.89A10 10 0 1 1 8 2.83\",key:\"k2fpak\"}],[\"path\",{d:\"M22 12A10 10 0 0 0 12 2v10z\",key:\"1rfc4y\"}]]);const Qs=createLucideIcon(\"PiggyBank\",[[\"path\",{d:\"M19 5c-1.5 0-2.8 1.4-3 2-3.5-1.5-11-.3-11 5 0 1.8 0 3 2 4.5V20h4v-2h3v2h4v-4c1-.5 1.7-1 2-2h2v-4h-2c0-1-.5-1.5-1-2h0V5z\",key:\"uf6l00\"}],[\"path\",{d:\"M2 9v1c0 1.1.9 2 2 2h1\",key:\"nm575m\"}],[\"path\",{d:\"M16 11h0\",key:\"k2aug8\"}]]);const Ys=createLucideIcon(\"Pilcrow\",[[\"path\",{d:\"M13 4v16\",key:\"8vvj80\"}],[\"path\",{d:\"M17 4v16\",key:\"7dpous\"}],[\"path\",{d:\"M19 4H9.5a4.5 4.5 0 0 0 0 9H13\",key:\"sh4n9v\"}]]);const $s=createLucideIcon(\"PinOff\",[[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}],[\"line\",{x1:\"12\",y1:\"17\",x2:\"12\",y2:\"22\",key:\"fb3qrx\"}],[\"path\",{d:\"M9 9v1.76a2 2 0 0 1-1.11 1.79l-1.78.9A2 2 0 0 0 5 15.24V17h12\",key:\"13x2n8\"}],[\"path\",{d:\"M15 9.34V6h1a2 2 0 0 0 0-4H7.89\",key:\"reo3ki\"}]]);const _s=createLucideIcon(\"Pin\",[[\"line\",{x1:\"12\",y1:\"17\",x2:\"12\",y2:\"22\",key:\"fb3qrx\"}],[\"path\",{d:\"M5 17h14v-1.76a2 2 0 0 0-1.11-1.79l-1.78-.9A2 2 0 0 1 15 10.76V6h1a2 2 0 0 0 0-4H8a2 2 0 0 0 0 4h1v4.76a2 2 0 0 1-1.11 1.79l-1.78.9A2 2 0 0 0 5 15.24Z\",key:\"13yl11\"}]]);const ek=createLucideIcon(\"Pipette\",[[\"path\",{d:\"m2 22 1-1h3l9-9\",key:\"1sre89\"}],[\"path\",{d:\"M3 21v-3l9-9\",key:\"hpe2y6\"}],[\"path\",{d:\"m15 6 3.4-3.4a2.1 2.1 0 1 1 3 3L18 9l.4.4a2.1 2.1 0 1 1-3 3l-3.8-3.8a2.1 2.1 0 1 1 3-3l.4.4Z\",key:\"196du1\"}]]);const ak=createLucideIcon(\"Pizza\",[[\"path\",{d:\"M15 11h.01\",key:\"rns66s\"}],[\"path\",{d:\"M11 15h.01\",key:\"k85uqc\"}],[\"path\",{d:\"M16 16h.01\",key:\"1f9h7w\"}],[\"path\",{d:\"m2 16 20 6-6-20c-3.36.9-6.42 2.67-8.88 5.12A19.876 19.876 0 0 0 2 16Z\",key:\"1akyvp\"}],[\"path\",{d:\"M17 6c-6.29 1.47-9.43 5.13-11 11\",key:\"1dsok0\"}]]);const tk=createLucideIcon(\"Plane\",[[\"path\",{d:\"M17.8 19.2 16 11l3.5-3.5C21 6 21.5 4 21 3c-1-.5-3 0-4.5 1.5L13 8 4.8 6.2c-.5-.1-.9.1-1.1.5l-.3.5c-.2.5-.1 1 .3 1.3L9 12l-2 3H4l-1 1 3 2 2 3 1-1v-3l3-2 3.5 5.3c.3.4.8.5 1.3.3l.5-.2c.4-.3.6-.7.5-1.2z\",key:\"1v9wt8\"}]]);const yk=createLucideIcon(\"PlayCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"polygon\",{points:\"10 8 16 12 10 16 10 8\",key:\"1cimsy\"}]]);const ck=createLucideIcon(\"Play\",[[\"polygon\",{points:\"5 3 19 12 5 21 5 3\",key:\"191637\"}]]);const ik=createLucideIcon(\"Plug2\",[[\"path\",{d:\"M9 2v6\",key:\"17ngun\"}],[\"path\",{d:\"M15 2v6\",key:\"s7yy2p\"}],[\"path\",{d:\"M12 17v5\",key:\"bb1du9\"}],[\"path\",{d:\"M5 8h14\",key:\"pcz4l3\"}],[\"path\",{d:\"M6 11V8h12v3a6 6 0 1 1-12 0v0Z\",key:\"nd4hoy\"}]]);const nk=createLucideIcon(\"PlugZap\",[[\"path\",{d:\"m13 2-2 2.5h3L12 7\",key:\"1me98u\"}],[\"path\",{d:\"M12 22v-3\",key:\"kmzjlo\"}],[\"path\",{d:\"M10 13v-2.5\",key:\"1g2mrq\"}],[\"path\",{d:\"M10 12.5v-2\",key:\"pcvzbb\"}],[\"path\",{d:\"M14 12.5v-2\",key:\"qv1toj\"}],[\"path\",{d:\"M16 15a2 2 0 0 0-2-2h-4a2 2 0 0 0-2 2v2a2 2 0 0 0 2 2h4a2 2 0 0 0 2-2v-2z\",key:\"17xmdd\"}]]);const hk=createLucideIcon(\"Plug\",[[\"path\",{d:\"M12 22v-5\",key:\"1ega77\"}],[\"path\",{d:\"M9 7V2\",key:\"1r97uf\"}],[\"path\",{d:\"M15 7V2\",key:\"1uo4jc\"}],[\"path\",{d:\"M6 13V8h12v5a4 4 0 0 1-4 4h-4a4 4 0 0 1-4-4Z\",key:\"1xki7n\"}]]);const ok=createLucideIcon(\"PlusCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"12\",y1:\"8\",x2:\"12\",y2:\"16\",key:\"55jlg\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}]]);const dk=createLucideIcon(\"PlusSquare\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"12\",y1:\"8\",x2:\"12\",y2:\"16\",key:\"55jlg\"}],[\"line\",{x1:\"8\",y1:\"12\",x2:\"16\",y2:\"12\",key:\"1myapg\"}]]);const sk=createLucideIcon(\"Plus\",[[\"line\",{x1:\"12\",y1:\"5\",x2:\"12\",y2:\"19\",key:\"myz83a\"}],[\"line\",{x1:\"5\",y1:\"12\",x2:\"19\",y2:\"12\",key:\"1smlys\"}]]);const kk=createLucideIcon(\"Pocket\",[[\"path\",{d:\"M4 3h16a2 2 0 0 1 2 2v6a10 10 0 0 1-10 10A10 10 0 0 1 2 11V5a2 2 0 0 1 2-2z\",key:\"1mz881\"}],[\"polyline\",{points:\"8 10 12 14 16 10\",key:\"w4mbv5\"}]]);const lk=createLucideIcon(\"Podcast\",[[\"circle\",{cx:\"12\",cy:\"11\",r:\"1\",key:\"1gvufo\"}],[\"path\",{d:\"M11 17a1 1 0 0 1 2 0c0 .5-.34 3-.5 4.5a.5.5 0 0 1-1 0c-.16-1.5-.5-4-.5-4.5Z\",key:\"1n5fvv\"}],[\"path\",{d:\"M8 14a5 5 0 1 1 8 0\",key:\"fc81rn\"}],[\"path\",{d:\"M17 18.5a9 9 0 1 0-10 0\",key:\"jqtxkf\"}]]);const pk=createLucideIcon(\"Pointer\",[[\"path\",{d:\"M22 14a8 8 0 0 1-8 8\",key:\"56vcr3\"}],[\"path\",{d:\"M18 11v-1a2 2 0 0 0-2-2v0a2 2 0 0 0-2 2v0\",key:\"1pp0yd\"}],[\"path\",{d:\"M14 10V9a2 2 0 0 0-2-2v0a2 2 0 0 0-2 2v1\",key:\"u654g\"}],[\"path\",{d:\"M10 9.5V4a2 2 0 0 0-2-2v0a2 2 0 0 0-2 2v10\",key:\"1e2dtv\"}],[\"path\",{d:\"M18 11a2 2 0 1 1 4 0v3a8 8 0 0 1-8 8h-2c-2.8 0-4.5-.86-5.99-2.34l-3.6-3.6a2 2 0 0 1 2.83-2.82L7 15\",key:\"g6ys72\"}]]);const rk=createLucideIcon(\"PoundSterling\",[[\"path\",{d:\"M18 7c0-5.333-8-5.333-8 0\",key:\"1prm2n\"}],[\"path\",{d:\"M10 7v14\",key:\"18tmcs\"}],[\"path\",{d:\"M6 21h12\",key:\"4dkmi1\"}],[\"path\",{d:\"M6 13h10\",key:\"ybwr4a\"}]]);const uk=createLucideIcon(\"PowerOff\",[[\"path\",{d:\"M18.36 6.64A9 9 0 0 1 20.77 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2H7\",key:\"8crl2c\"}],[\"path\",{d:\"M3 12h.01\",key:\"nlz23k\"}],[\"path\",{d:\"M12 3h.01\",key:\"n36tog\"}],[\"path\",{d:\"M12 16v.01\",key:\"133mhm\"}],[\"path\",{d:\"M16 12h1\",key:\"1slzba\"}],[\"path\",{d:\"M21 12v.01\",key:\"1lwtk9\"}],[\"path\",{d:\"M12 21v-1\",key:\"1880an\"}]]);const mk=createLucideIcon(\"Quote\",[[\"path\",{d:\"M3 21c3 0 7-1 7-8V5c0-1.25-.756-2.017-2-2H4c-1.25 0-2 .75-2 1.972V11c0 1.25.75 2 2 2 1 0 1 0 1 1v1c0 1-1 2-2 2s-1 .008-1 1.031V20c0 1 0 1 1 1z\",key:\"4rm80e\"}],[\"path\",{d:\"M15 21c3 0 7-1 7-8V5c0-1.25-.757-2.017-2-2h-4c-1.25 0-2 .75-2 1.972V11c0 1.25.75 2 2 2h.75c0 2.25.25 4-2.75 4v3c0 1 0 1 1 1z\",key:\"10za9r\"}]]);const Lk=createLucideIcon(\"RadioReceiver\",[[\"path\",{d:\"M5 16v2\",key:\"g5qcv5\"}],[\"path\",{d:\"M19 16v2\",key:\"1gbaio\"}],[\"rect\",{x:\"2\",y:\"8\",width:\"20\",height:\"8\",rx:\"2\",key:\"1xflse\"}],[\"path\",{d:\"M18 12h0\",key:\"1ucjzd\"}]]);const fk=createLucideIcon(\"Radio\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"2\",key:\"1c9p78\"}],[\"path\",{d:\"M4.93 19.07a10 10 0 0 1 0-14.14\",key:\"r41b39\"}],[\"path\",{d:\"M7.76 16.24a6 6 0 0 1-1.3-1.95 6 6 0 0 1 0-4.59 6 6 0 0 1 1.3-1.95\",key:\"1pc8et\"}],[\"path\",{d:\"M16.24 7.76a6 6 0 0 1 1.3 2 6 6 0 0 1 0 4.59 6 6 0 0 1-1.3 1.95\",key:\"8dzjga\"}],[\"path\",{d:\"M19.07 4.93a10 10 0 0 1 0 14.14\",key:\"1kegas\"}]]);const wk=createLucideIcon(\"RectangleHorizontal\",[[\"rect\",{x:\"2\",y:\"6\",width:\"20\",height:\"12\",rx:\"2\",key:\"1wpnh2\"}]]);const Ik=createLucideIcon(\"RectangleVertical\",[[\"rect\",{x:\"6\",y:\"2\",width:\"12\",height:\"20\",rx:\"2\",key:\"749fme\"}]]);const Ck=createLucideIcon(\"Recycle\",[[\"path\",{d:\"M7 19H4.815a1.83 1.83 0 0 1-1.57-.881 1.785 1.785 0 0 1-.004-1.784L7.196 9.5\",key:\"x6z5xu\"}],[\"path\",{d:\"M11 19h8.203a1.83 1.83 0 0 0 1.556-.89 1.784 1.784 0 0 0 0-1.775l-1.226-2.12\",key:\"1x4zh5\"}],[\"path\",{d:\"m14 16-3 3 3 3\",key:\"f6jyew\"}],[\"path\",{d:\"M8.293 13.596 7.196 9.5 3.1 10.598\",key:\"wf1obh\"}],[\"path\",{d:\"m9.344 5.811 1.093-1.892A1.83 1.83 0 0 1 11.985 3a1.784 1.784 0 0 1 1.546.888l3.943 6.843\",key:\"9tzpgr\"}],[\"path\",{d:\"m13.378 9.633 4.096 1.098 1.097-4.096\",key:\"1oe83g\"}]]);const bk=createLucideIcon(\"Redo2\",[[\"path\",{d:\"m15 14 5-5-5-5\",key:\"12vg1m\"}],[\"path\",{d:\"M20 9H9.5A5.5 5.5 0 0 0 4 14.5v0A5.5 5.5 0 0 0 9.5 20H13\",key:\"19mnr4\"}]]);const zk=createLucideIcon(\"Redo\",[[\"path\",{d:\"M21 7v6h-6\",key:\"3ptur4\"}],[\"path\",{d:\"M3 17a9 9 0 0 1 9-9 9 9 0 0 1 6 2.3l3 2.7\",key:\"1kgawr\"}]]);const Hk=createLucideIcon(\"RefreshCcw\",[[\"path\",{d:\"M3 2v6h6\",key:\"18ldww\"}],[\"path\",{d:\"M21 12A9 9 0 0 0 6 5.3L3 8\",key:\"1pbrqz\"}],[\"path\",{d:\"M21 22v-6h-6\",key:\"usdfbe\"}],[\"path\",{d:\"M3 12a9 9 0 0 0 15 6.7l3-2.7\",key:\"1hosoe\"}]]);const Sk=createLucideIcon(\"RefreshCw\",[[\"path\",{d:\"M21 2v6h-6\",key:\"1lwg0q\"}],[\"path\",{d:\"M3 12a9 9 0 0 1 15-6.7L21 8\",key:\"vaktt2\"}],[\"path\",{d:\"M3 22v-6h6\",key:\"6llvyv\"}],[\"path\",{d:\"M21 12a9 9 0 0 1-15 6.7L3 16\",key:\"i52hsp\"}]]);const Vk=createLucideIcon(\"Refrigerator\",[[\"path\",{d:\"M5 6a4 4 0 0 1 4-4h6a4 4 0 0 1 4 4v14a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2V6Z\",key:\"fpq118\"}],[\"path\",{d:\"M5 10h14\",key:\"elsbfy\"}],[\"path\",{d:\"M15 7v6\",key:\"1nx30x\"}]]);const qk=createLucideIcon(\"Regex\",[[\"path\",{d:\"M17 3v10\",key:\"15fgeh\"}],[\"path\",{d:\"m12.67 5.5 8.66 5\",key:\"1gpheq\"}],[\"path\",{d:\"m12.67 10.5 8.66-5\",key:\"1dkfa6\"}],[\"path\",{d:\"M9 17a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v2a2 2 0 0 0 2 2h2a2 2 0 0 0 2-2v-2z\",key:\"swwfx4\"}]]);const Fk=createLucideIcon(\"RemoveFormatting\",[[\"path\",{d:\"M4 7V4h16v3\",key:\"9msm58\"}],[\"path\",{d:\"M5 20h6\",key:\"1h6pxn\"}],[\"path\",{d:\"M13 4 8 20\",key:\"kqq6aj\"}],[\"path\",{d:\"m15 15 5 5\",key:\"me55sn\"}],[\"path\",{d:\"m20 15-5 5\",key:\"11p7ol\"}]]);const Ak=createLucideIcon(\"Repeat1\",[[\"path\",{d:\"m17 2 4 4-4 4\",key:\"nntrym\"}],[\"path\",{d:\"M3 11v-1a4 4 0 0 1 4-4h14\",key:\"84bu3i\"}],[\"path\",{d:\"m7 22-4-4 4-4\",key:\"1wqhfi\"}],[\"path\",{d:\"M21 13v1a4 4 0 0 1-4 4H3\",key:\"1rx37r\"}],[\"path\",{d:\"M11 10h1v4\",key:\"70cz1p\"}]]);const jk=createLucideIcon(\"Repeat\",[[\"path\",{d:\"m17 2 4 4-4 4\",key:\"nntrym\"}],[\"path\",{d:\"M3 11v-1a4 4 0 0 1 4-4h14\",key:\"84bu3i\"}],[\"path\",{d:\"m7 22-4-4 4-4\",key:\"1wqhfi\"}],[\"path\",{d:\"M21 13v1a4 4 0 0 1-4 4H3\",key:\"1rx37r\"}]]);const Pk=createLucideIcon(\"ReplyAll\",[[\"polyline\",{points:\"7 17 2 12 7 7\",key:\"t83bqg\"}],[\"polyline\",{points:\"12 17 7 12 12 7\",key:\"1g4ajm\"}],[\"path\",{d:\"M22 18v-2a4 4 0 0 0-4-4H7\",key:\"1fcyog\"}]]);const Bk=createLucideIcon(\"Reply\",[[\"polyline\",{points:\"9 17 4 12 9 7\",key:\"hvgpf2\"}],[\"path\",{d:\"M20 18v-2a4 4 0 0 0-4-4H4\",key:\"5vmcpk\"}]]);const Dk=createLucideIcon(\"Rewind\",[[\"polygon\",{points:\"11 19 2 12 11 5 11 19\",key:\"14yba5\"}],[\"polygon\",{points:\"22 19 13 12 22 5 22 19\",key:\"1pi1cj\"}]]);const Ok=createLucideIcon(\"Rocket\",[[\"path\",{d:\"M4.5 16.5c-1.5 1.26-2 5-2 5s3.74-.5 5-2c.71-.84.7-2.13-.09-2.91a2.18 2.18 0 0 0-2.91-.09z\",key:\"m3kijz\"}],[\"path\",{d:\"m12 15-3-3a22 22 0 0 1 2-3.95A12.88 12.88 0 0 1 22 2c0 2.72-.78 7.5-6 11a22.35 22.35 0 0 1-4 2z\",key:\"1fmvmk\"}],[\"path\",{d:\"M9 12H4s.55-3.03 2-4c1.62-1.08 5 0 5 0\",key:\"1f8sc4\"}],[\"path\",{d:\"M12 15v5s3.03-.55 4-2c1.08-1.62 0-5 0-5\",key:\"qeys4\"}]]);const Rk=createLucideIcon(\"RockingChair\",[[\"polyline\",{points:\"3.5 2 6.5 12.5 18 12.5\",key:\"y3iy52\"}],[\"line\",{x1:\"9.5\",y1:\"12.5\",x2:\"5.5\",y2:\"20\",key:\"ivgihp\"}],[\"line\",{x1:\"15\",y1:\"12.5\",x2:\"18.5\",y2:\"20\",key:\"1palb4\"}],[\"path\",{d:\"M2.75 18a13 13 0 0 0 18.5 0\",key:\"1nquas\"}]]);const Tk=createLucideIcon(\"Rotate3d\",[[\"path\",{d:\"M16.466 7.5C15.643 4.237 13.952 2 12 2 9.239 2 7 6.477 7 12s2.239 10 5 10c.342 0 .677-.069 1-.2\",key:\"10n0gc\"}],[\"path\",{d:\"m15.194 13.707 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Yk=createLucideIcon(\"Scale\",[[\"path\",{d:\"m16 16 3-8 3 8c-.87.65-1.92 1-3 1s-2.13-.35-3-1Z\",key:\"7g6ntu\"}],[\"path\",{d:\"m2 16 3-8 3 8c-.87.65-1.92 1-3 1s-2.13-.35-3-1Z\",key:\"ijws7r\"}],[\"path\",{d:\"M7 21h10\",key:\"1b0cd5\"}],[\"path\",{d:\"M12 3v18\",key:\"108xh3\"}],[\"path\",{d:\"M3 7h2c2 0 5-1 7-2 2 1 5 2 7 2h2\",key:\"3gwbw2\"}]]);const $k=createLucideIcon(\"Scaling\",[[\"path\",{d:\"M21 3 9 15\",key:\"15kdhq\"}],[\"path\",{d:\"M12 3H3v18h18v-9\",key:\"8suug0\"}],[\"path\",{d:\"M16 3h5v5\",key:\"1806ms\"}],[\"path\",{d:\"M14 15H9v-5\",key:\"pi4jk9\"}]]);const _k=createLucideIcon(\"ScanFace\",[[\"path\",{d:\"M3 7V5a2 2 0 0 1 2-2h2\",key:\"aa7l1z\"}],[\"path\",{d:\"M17 3h2a2 2 0 0 1 2 2v2\",key:\"4qcy5o\"}],[\"path\",{d:\"M21 17v2a2 2 0 0 1-2 2h-2\",key:\"6vwrx8\"}],[\"path\",{d:\"M7 21H5a2 2 0 0 1-2-2v-2\",key:\"ioqczr\"}],[\"path\",{d:\"M8 14s1.5 2 4 2 4-2 4-2\",key:\"1y1vjs\"}],[\"path\",{d:\"M9 9h.01\",key:\"1q5me6\"}],[\"path\",{d:\"M15 9h.01\",key:\"x1ddxp\"}]]);const el=createLucideIcon(\"ScanLine\",[[\"path\",{d:\"M3 7V5a2 2 0 0 1 2-2h2\",key:\"aa7l1z\"}],[\"path\",{d:\"M17 3h2a2 2 0 0 1 2 2v2\",key:\"4qcy5o\"}],[\"path\",{d:\"M21 17v2a2 2 0 0 1-2 2h-2\",key:\"6vwrx8\"}],[\"path\",{d:\"M7 21H5a2 2 0 0 1-2-2v-2\",key:\"ioqczr\"}],[\"line\",{x1:\"7\",y1:\"12\",x2:\"17\",y2:\"12\",key:\"bc9tui\"}]]);const al=createLucideIcon(\"Scan\",[[\"path\",{d:\"M3 7V5a2 2 0 0 1 2-2h2\",key:\"aa7l1z\"}],[\"path\",{d:\"M17 3h2a2 2 0 0 1 2 2v2\",key:\"4qcy5o\"}],[\"path\",{d:\"M21 17v2a2 2 0 0 1-2 2h-2\",key:\"6vwrx8\"}],[\"path\",{d:\"M7 21H5a2 2 0 0 1-2-2v-2\",key:\"ioqczr\"}]]);const tl=createLucideIcon(\"Scissors\",[[\"circle\",{cx:\"6\",cy:\"6\",r:\"3\",key:\"1lh9wr\"}],[\"circle\",{cx:\"6\",cy:\"18\",r:\"3\",key:\"fqmcym\"}],[\"line\",{x1:\"20\",y1:\"4\",x2:\"8.12\",y2:\"15.88\",key:\"3cwkde\"}],[\"line\",{x1:\"14.47\",y1:\"14.48\",x2:\"20\",y2:\"20\",key:\"1keerz\"}],[\"line\",{x1:\"8.12\",y1:\"8.12\",x2:\"12\",y2:\"12\",key:\"spxzcb\"}]]);const yl=createLucideIcon(\"ScreenShareOff\",[[\"path\",{d:\"M13 3H4a2 2 0 0 0-2 2v10a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2v-3\",key:\"i8wdob\"}],[\"path\",{d:\"M8 21h8\",key:\"1ev6f3\"}],[\"path\",{d:\"M12 17v4\",key:\"1riwvh\"}],[\"path\",{d:\"m22 3-5 5\",key:\"12jva0\"}],[\"path\",{d:\"m17 3 5 5\",key:\"k36vhe\"}]]);const cl=createLucideIcon(\"ScreenShare\",[[\"path\",{d:\"M13 3H4a2 2 0 0 0-2 2v10a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2v-3\",key:\"i8wdob\"}],[\"path\",{d:\"M8 21h8\",key:\"1ev6f3\"}],[\"path\",{d:\"M12 17v4\",key:\"1riwvh\"}],[\"path\",{d:\"m17 8 5-5\",key:\"fqif7o\"}],[\"path\",{d:\"M17 3h5v5\",key:\"1o3tu8\"}]]);const il=createLucideIcon(\"Scroll\",[[\"path\",{d:\"M10 17v2a2 2 0 0 1-2 2v0a2 2 0 0 1-2-2V5a2 2 0 0 0-2-2v0a2 2 0 0 0-2 2v3h3\",key:\"1knuaj\"}],[\"path\",{d:\"M22 17v2a2 2 0 0 1-2 2H8\",key:\"62edg8\"}],[\"path\",{d:\"M19 17V5a2 2 0 0 0-2-2H4\",key:\"zz82l3\"}],[\"path\",{d:\"M22 17H10\",key:\"1dr9mz\"}]]);const nl=createLucideIcon(\"Search\",[[\"circle\",{cx:\"11\",cy:\"11\",r:\"8\",key:\"4ej97u\"}],[\"line\",{x1:\"21\",y1:\"21\",x2:\"16.65\",y2:\"16.65\",key:\"1p50m8\"}]]);const hl=createLucideIcon(\"Send\",[[\"line\",{x1:\"22\",y1:\"2\",x2:\"11\",y2:\"13\",key:\"10auo0\"}],[\"polygon\",{points:\"22 2 15 22 11 13 2 9 22 2\",key:\"12uapv\"}]]);const ol=createLucideIcon(\"SeparatorHorizontal\",[[\"line\",{x1:\"3\",y1:\"12\",x2:\"21\",y2:\"12\",key:\"1aui40\"}],[\"polyline\",{points:\"8 8 12 4 16 8\",key:\"zo8t4w\"}],[\"polyline\",{points:\"16 16 12 20 8 16\",key:\"1oyrid\"}]]);const dl=createLucideIcon(\"SeparatorVertical\",[[\"line\",{x1:\"12\",y1:\"3\",x2:\"12\",y2:\"21\",key:\"essbwb\"}],[\"polyline\",{points:\"8 8 4 12 8 16\",key:\"bnfmv4\"}],[\"polyline\",{points:\"16 16 20 12 16 8\",key:\"u90052\"}]]);const sl=createLucideIcon(\"ServerCog\",[[\"path\",{d:\"M5 10H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2v4a2 2 0 0 1-2 2h-1\",key:\"1qm4no\"}],[\"path\",{d:\"M5 14H4a2 2 0 0 0-2 2v4a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2v-4a2 2 0 0 0-2-2h-1\",key:\"1lpaho\"}],[\"path\",{d:\"M6 6h.01\",key:\"1utrut\"}],[\"path\",{d:\"M6 18h.01\",key:\"uhywen\"}],[\"circle\",{cx:\"12\",cy:\"12\",r:\"3\",key:\"1v7zrd\"}],[\"path\",{d:\"M12 8v1\",key:\"1rj8u4\"}],[\"path\",{d:\"M12 15v1\",key:\"1ovrzm\"}],[\"path\",{d:\"M16 12h-1\",key:\"1qpdyp\"}],[\"path\",{d:\"M9 12H8\",key:\"1l15iv\"}],[\"path\",{d:\"m15 9-.88.88\",key:\"3hwatj\"}],[\"path\",{d:\"M9.88 14.12 9 15\",key:\"13ldc9\"}],[\"path\",{d:\"m15 15-.88-.88\",key:\"45priv\"}],[\"path\",{d:\"M9.88 9.88 9 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pl=createLucideIcon(\"Server\",[[\"rect\",{x:\"2\",y:\"2\",width:\"20\",height:\"8\",rx:\"2\",ry:\"2\",key:\"e1v5fq\"}],[\"rect\",{x:\"2\",y:\"14\",width:\"20\",height:\"8\",rx:\"2\",ry:\"2\",key:\"10c4lq\"}],[\"line\",{x1:\"6\",y1:\"6\",x2:\"6.01\",y2:\"6\",key:\"1g0o6g\"}],[\"line\",{x1:\"6\",y1:\"18\",x2:\"6.01\",y2:\"18\",key:\"y2j7fo\"}]]);const rl=createLucideIcon(\"Settings2\",[[\"path\",{d:\"M20 7h-9\",key:\"3s1dr2\"}],[\"path\",{d:\"M14 17H5\",key:\"gfn3mx\"}],[\"circle\",{cx:\"17\",cy:\"17\",r:\"3\",key:\"18b49y\"}],[\"circle\",{cx:\"7\",cy:\"7\",r:\"3\",key:\"dfmy0x\"}]]);const ul=createLucideIcon(\"Settings\",[[\"path\",{d:\"M12.22 2h-.44a2 2 0 0 0-2 2v.18a2 2 0 0 1-1 1.73l-.43.25a2 2 0 0 1-2 0l-.15-.08a2 2 0 0 0-2.73.73l-.22.38a2 2 0 0 0 .73 2.73l.15.1a2 2 0 0 1 1 1.72v.51a2 2 0 0 1-1 1.74l-.15.09a2 2 0 0 0-.73 2.73l.22.38a2 2 0 0 0 2.73.73l.15-.08a2 2 0 0 1 2 0l.43.25a2 2 0 0 1 1 1.73V20a2 2 0 0 0 2 2h.44a2 2 0 0 0 2-2v-.18a2 2 0 0 1 1-1.73l.43-.25a2 2 0 0 1 2 0l.15.08a2 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vl=createLucideIcon(\"Sheet\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"3\",y1:\"9\",x2:\"21\",y2:\"9\",key:\"1uch6j\"}],[\"line\",{x1:\"3\",y1:\"15\",x2:\"21\",y2:\"15\",key:\"1xojw2\"}],[\"line\",{x1:\"9\",y1:\"9\",x2:\"9\",y2:\"21\",key:\"x5ianl\"}],[\"line\",{x1:\"15\",y1:\"9\",x2:\"15\",y2:\"21\",key:\"13a17d\"}]]);const gl=createLucideIcon(\"ShieldAlert\",[[\"path\",{d:\"M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z\",key:\"3xmgem\"}],[\"path\",{d:\"M12 8v4\",key:\"1got3b\"}],[\"path\",{d:\"M12 16h.01\",key:\"1drbdi\"}]]);const ml=createLucideIcon(\"ShieldCheck\",[[\"path\",{d:\"M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z\",key:\"3xmgem\"}],[\"path\",{d:\"m9 12 2 2 4-4\",key:\"dzmm74\"}]]);const Ll=createLucideIcon(\"ShieldClose\",[[\"path\",{d:\"M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 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13v.01\",key:\"1v1k97\"}],[\"path\",{d:\"M16 10v.01\",key:\"5169yg\"}],[\"path\",{d:\"M11 20v.01\",key:\"cj92p8\"}],[\"path\",{d:\"M17 14v.01\",key:\"11cswd\"}],[\"path\",{d:\"M20 11v.01\",key:\"19e0od\"}]]);const Sl=createLucideIcon(\"Shrink\",[[\"path\",{d:\"m15 15 6 6m-6-6v4.8m0-4.8h4.8\",key:\"17vawe\"}],[\"path\",{d:\"M9 19.8V15m0 0H4.2M9 15l-6 6\",key:\"chjx8e\"}],[\"path\",{d:\"M15 4.2V9m0 0h4.8M15 9l6-6\",key:\"lav6yq\"}],[\"path\",{d:\"M9 4.2V9m0 0H4.2M9 9 3 3\",key:\"1pxi2q\"}]]);const Vl=createLucideIcon(\"Shrub\",[[\"path\",{d:\"M12 22v-7l-2-2\",key:\"eqv9mc\"}],[\"path\",{d:\"M17 8v.8A6 6 0 0 1 13.8 20v0H10v0A6.5 6.5 0 0 1 7 8h0a5 5 0 0 1 10 0Z\",key:\"12jcau\"}],[\"path\",{d:\"m14 14-2 2\",key:\"847xa2\"}]]);const ql=createLucideIcon(\"Shuffle\",[[\"polyline\",{points:\"16 3 21 3 21 8\",key:\"11391h\"}],[\"line\",{x1:\"4\",y1:\"20\",x2:\"21\",y2:\"3\",key:\"pnd031\"}],[\"polyline\",{points:\"21 16 21 21 16 21\",key:\"1j0gwc\"}],[\"line\",{x1:\"15\",y1:\"15\",x2:\"21\",y2:\"21\",key:\"ygtzor\"}],[\"line\",{x1:\"4\",y1:\"4\",x2:\"9\",y2:\"9\",key:\"q17lez\"}]]);const Fl=createLucideIcon(\"SidebarClose\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M9 3v18\",key:\"fh3hqa\"}],[\"path\",{d:\"m16 15-3-3 3-3\",key:\"14y99z\"}]]);const Al=createLucideIcon(\"SidebarOpen\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"path\",{d:\"M9 3v18\",key:\"fh3hqa\"}],[\"path\",{d:\"m14 9 3 3-3 3\",key:\"8010ee\"}]]);const jl=createLucideIcon(\"Sidebar\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"9\",y1:\"3\",x2:\"9\",y2:\"21\",key:\"nvcl17\"}]]);const Pl=createLucideIcon(\"Sigma\",[[\"path\",{d:\"M18 7V4H6l6 8-6 8h12v-3\",key:\"zis8ev\"}]]);const Bl=createLucideIcon(\"SignalHigh\",[[\"path\",{d:\"M2 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kp=createLucideIcon(\"Star\",[[\"polygon\",{points:\"12 2 15.09 8.26 22 9.27 17 14.14 18.18 21.02 12 17.77 5.82 21.02 7 14.14 2 9.27 8.91 8.26 12 2\",key:\"8f66p6\"}]]);const lp=createLucideIcon(\"Stethoscope\",[[\"path\",{d:\"M4.8 2.3A.3.3 0 1 0 5 2H4a2 2 0 0 0-2 2v5a6 6 0 0 0 6 6v0a6 6 0 0 0 6-6V4a2 2 0 0 0-2-2h-1a.2.2 0 1 0 .3.3\",key:\"1jd90r\"}],[\"path\",{d:\"M8 15v1a6 6 0 0 0 6 6v0a6 6 0 0 0 6-6v-4\",key:\"126ukv\"}],[\"circle\",{cx:\"20\",cy:\"10\",r:\"2\",key:\"ts1r5v\"}]]);const pp=createLucideIcon(\"Sticker\",[[\"path\",{d:\"M15.5 3H5a2 2 0 0 0-2 2v14c0 1.1.9 2 2 2h14a2 2 0 0 0 2-2V8.5L15.5 3Z\",key:\"1wis1t\"}],[\"path\",{d:\"M15 3v6h6\",key:\"edgan2\"}],[\"path\",{d:\"M10 16s.8 1 2 1c1.3 0 2-1 2-1\",key:\"1vvgv3\"}],[\"path\",{d:\"M8 13h0\",key:\"jdup5h\"}],[\"path\",{d:\"M16 13h0\",key:\"l4i2ga\"}]]);const rp=createLucideIcon(\"StickyNote\",[[\"path\",{d:\"M15.5 3H5a2 2 0 0 0-2 2v14c0 1.1.9 2 2 2h14a2 2 0 0 0 2-2V8.5L15.5 3Z\",key:\"1wis1t\"}],[\"path\",{d:\"M15 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$p=createLucideIcon(\"TimerReset\",[[\"path\",{d:\"M10 2h4\",key:\"n1abiw\"}],[\"path\",{d:\"M12 14v-4\",key:\"1evpnu\"}],[\"path\",{d:\"M4 13a8 8 0 0 1 8-7 8 8 0 1 1-5.3 14L4 17.6\",key:\"1ts96g\"}],[\"path\",{d:\"M9 17H4v5\",key:\"8t5av\"}]]);const _p=createLucideIcon(\"Timer\",[[\"line\",{x1:\"10\",x2:\"14\",y1:\"2\",y2:\"2\",key:\"14vaq8\"}],[\"line\",{x1:\"12\",x2:\"15\",y1:\"14\",y2:\"11\",key:\"17fdiu\"}],[\"circle\",{cx:\"12\",cy:\"14\",r:\"8\",key:\"1e1u0o\"}]]);const er=createLucideIcon(\"ToggleLeft\",[[\"rect\",{x:\"2\",y:\"6\",width:\"20\",height:\"12\",rx:\"6\",ry:\"6\",key:\"cdopvd\"}],[\"circle\",{cx:\"8\",cy:\"12\",r:\"2\",key:\"1nvbw3\"}]]);const ar=createLucideIcon(\"ToggleRight\",[[\"rect\",{x:\"2\",y:\"6\",width:\"20\",height:\"12\",rx:\"6\",ry:\"6\",key:\"cdopvd\"}],[\"circle\",{cx:\"16\",cy:\"12\",r:\"2\",key:\"4ma0v8\"}]]);const tr=createLucideIcon(\"Tornado\",[[\"path\",{d:\"M21 4H3\",key:\"1hwok0\"}],[\"path\",{d:\"M18 8H6\",key:\"41n648\"}],[\"path\",{d:\"M19 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dr=createLucideIcon(\"Trees\",[[\"path\",{d:\"M10 10v.2A3 3 0 0 1 8.9 16v0H5v0h0a3 3 0 0 1-1-5.8V10a3 3 0 0 1 6 0Z\",key:\"yh07w9\"}],[\"path\",{d:\"M7 16v6\",key:\"1a82de\"}],[\"path\",{d:\"M13 19v3\",key:\"13sx9i\"}],[\"path\",{d:\"M12 19h8.3a1 1 0 0 0 .7-1.7L18 14h.3a1 1 0 0 0 .7-1.7L16 9h.2a1 1 0 0 0 .8-1.7L13 3l-1.4 1.5\",key:\"1sj9kv\"}]]);const sr=createLucideIcon(\"Trello\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"rect\",{x:\"7\",y:\"7\",width:\"3\",height:\"9\",key:\"1xk0xp\"}],[\"rect\",{x:\"14\",y:\"7\",width:\"3\",height:\"5\",key:\"1otkhn\"}]]);const kr=createLucideIcon(\"TrendingDown\",[[\"polyline\",{points:\"22 17 13.5 8.5 8.5 13.5 2 7\",key:\"1r2t7k\"}],[\"polyline\",{points:\"16 17 22 17 22 11\",key:\"11uiuu\"}]]);const lr=createLucideIcon(\"TrendingUp\",[[\"polyline\",{points:\"22 7 13.5 15.5 8.5 10.5 2 17\",key:\"126l90\"}],[\"polyline\",{points:\"16 7 22 7 22 13\",key:\"kwv8wd\"}]]);const pr=createLucideIcon(\"Triangle\",[[\"path\",{d:\"m21.73 18-8-14a2 2 0 0 0-3.48 0l-8 14A2 2 0 0 0 4 21h16a2 2 0 0 0 1.73-3Z\",key:\"c3ski4\"}]]);const rr=createLucideIcon(\"Trophy\",[[\"path\",{d:\"M6 9H4.5a2.5 2.5 0 0 1 0-5H6\",key:\"17hqa7\"}],[\"path\",{d:\"M18 9h1.5a2.5 2.5 0 0 0 0-5H18\",key:\"lmptdp\"}],[\"path\",{d:\"M4 22h16\",key:\"57wxv0\"}],[\"path\",{d:\"M10 14.66V17c0 .55-.47.98-.97 1.21C7.85 18.75 7 20.24 7 22\",key:\"1nw9bq\"}],[\"path\",{d:\"M14 14.66V17c0 .55.47.98.97 1.21C16.15 18.75 17 20.24 17 22\",key:\"1np0yb\"}],[\"path\",{d:\"M18 2H6v7a6 6 0 0 0 12 0V2Z\",key:\"u46fv3\"}]]);const ur=createLucideIcon(\"Truck\",[[\"path\",{d:\"M10 17h4V5H2v12h3\",key:\"1jq12e\"}],[\"path\",{d:\"M20 17h2v-3.34a4 4 0 0 0-1.17-2.83L19 9h-5\",key:\"1xb3ft\"}],[\"path\",{d:\"M14 17h1\",key:\"nufu4t\"}],[\"circle\",{cx:\"7.5\",cy:\"17.5\",r:\"2.5\",key:\"a7aife\"}],[\"circle\",{cx:\"17.5\",cy:\"17.5\",r:\"2.5\",key:\"1mdrzq\"}]]);const Mr=createLucideIcon(\"Tv2\",[[\"path\",{d:\"M7 21h10\",key:\"1b0cd5\"}],[\"rect\",{x:\"2\",y:\"3\",width:\"20\",height:\"14\",rx:\"2\",key:\"x3v2xh\"}]]);const xr=createLucideIcon(\"Tv\",[[\"rect\",{x:\"2\",y:\"7\",width:\"20\",height:\"15\",rx:\"2\",ry:\"2\",key:\"f237mn\"}],[\"polyline\",{points:\"17 2 12 7 7 2\",key:\"11pgbg\"}]]);const vr=createLucideIcon(\"Twitch\",[[\"path\",{d:\"M21 2H3v16h5v4l4-4h5l4-4V2zm-10 9V7m5 4V7\",key:\"c0yzno\"}]]);const gr=createLucideIcon(\"Twitter\",[[\"path\",{d:\"M22 4s-.7 2.1-2 3.4c1.6 10-9.4 17.3-18 11.6 2.2.1 4.4-.6 6-2C3 15.5.5 9.6 3 5c2.2 2.6 5.6 4.1 9 4-.9-4.2 4-6.6 7-3.8 1.1 0 3-1.2 3-1.2z\",key:\"pff0z6\"}]]);const mr=createLucideIcon(\"Type\",[[\"polyline\",{points:\"4 7 4 4 20 4 20 7\",key:\"1nosan\"}],[\"line\",{x1:\"9\",y1:\"20\",x2:\"15\",y2:\"20\",key:\"10hqwk\"}],[\"line\",{x1:\"12\",y1:\"4\",x2:\"12\",y2:\"20\",key:\"8v58sd\"}]]);const Lr=createLucideIcon(\"Umbrella\",[[\"path\",{d:\"M22 12a9.92 9.92 0 0 0-3.24-6.41 10.12 10.12 0 0 0-13.52 0A9.92 9.92 0 0 0 2 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Vr=createLucideIcon(\"Usb\",[[\"circle\",{cx:\"4\",cy:\"20\",r:\"1\",key:\"22iqad\"}],[\"circle\",{cx:\"10\",cy:\"7\",r:\"1\",key:\"dypaad\"}],[\"path\",{d:\"M4 20 19 5\",key:\"15hogs\"}],[\"path\",{d:\"m21 3-3 1 2 2 1-3Z\",key:\"ew8vct\"}],[\"path\",{d:\"m10 7-5 5 2 5\",key:\"148pqf\"}],[\"path\",{d:\"m10 14 5 2 4-4\",key:\"1ivjwr\"}],[\"path\",{d:\"m18 12 1-1 1 1-1 1-1-1Z\",key:\"tus6kn\"}]]);const qr=createLucideIcon(\"UserCheck\",[[\"path\",{d:\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\",key:\"1yyitq\"}],[\"circle\",{cx:\"9\",cy:\"7\",r:\"4\",key:\"nufk8\"}],[\"polyline\",{points:\"16 11 18 13 22 9\",key:\"1pwet4\"}]]);const Fr=createLucideIcon(\"UserCog\",[[\"path\",{d:\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\",key:\"1yyitq\"}],[\"circle\",{cx:\"9\",cy:\"7\",r:\"4\",key:\"nufk8\"}],[\"circle\",{cx:\"19\",cy:\"11\",r:\"2\",key:\"1rxg02\"}],[\"path\",{d:\"M19 8v1\",key:\"1iffrw\"}],[\"path\",{d:\"M19 13v1\",key:\"z4xc62\"}],[\"path\",{d:\"m21.6 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2\",key:\"101i4y\"}],[\"rect\",{x:\"8\",y:\"5\",width:\"8\",height:\"14\",rx:\"1\",key:\"bi6xeo\"}]]);const Wr=createLucideIcon(\"VideoOff\",[[\"path\",{d:\"M10.66 6H14a2 2 0 0 1 2 2v2.34l1 1L22 8v8\",key:\"ubwiq0\"}],[\"path\",{d:\"M16 16a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h2l10 10Z\",key:\"1l10zd\"}],[\"line\",{x1:\"2\",y1:\"2\",x2:\"22\",y2:\"22\",key:\"1w4vcy\"}]]);const Xr=createLucideIcon(\"Video\",[[\"path\",{d:\"m22 8-6 4 6 4V8Z\",key:\"50v9me\"}],[\"rect\",{x:\"2\",y:\"6\",width:\"14\",height:\"12\",rx:\"2\",ry:\"2\",key:\"14il7g\"}]]);const Jr=createLucideIcon(\"View\",[[\"path\",{d:\"M5 12s2.545-5 7-5c4.454 0 7 5 7 5s-2.546 5-7 5c-4.455 0-7-5-7-5z\",key:\"vptub8\"}],[\"path\",{d:\"M12 13a1 1 0 1 0 0-2 1 1 0 0 0 0 2z\",key:\"10lhjs\"}],[\"path\",{d:\"M21 17v2a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-2\",key:\"mrq65r\"}],[\"path\",{d:\"M21 7V5a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v2\",key:\"be3xqs\"}]]);const Nr=createLucideIcon(\"Voicemail\",[[\"circle\",{cx:\"6\",cy:\"12\",r:\"4\",key:\"1ehtga\"}],[\"circle\",{cx:\"18\",cy:\"12\",r:\"4\",key:\"4vafl8\"}],[\"line\",{x1:\"6\",y1:\"16\",x2:\"18\",y2:\"16\",key:\"1xgyj1\"}]]);const Kr=createLucideIcon(\"Volume1\",[[\"polygon\",{points:\"11 5 6 9 2 9 2 15 6 15 11 19 11 5\",key:\"16drj5\"}],[\"path\",{d:\"M15.54 8.46a5 5 0 0 1 0 7.07\",key:\"ltjumu\"}]]);const Qr=createLucideIcon(\"Volume2\",[[\"polygon\",{points:\"11 5 6 9 2 9 2 15 6 15 11 19 11 5\",key:\"16drj5\"}],[\"path\",{d:\"M15.54 8.46a5 5 0 0 1 0 7.07\",key:\"ltjumu\"}],[\"path\",{d:\"M19.07 4.93a10 10 0 0 1 0 14.14\",key:\"1kegas\"}]]);const Yr=createLucideIcon(\"VolumeX\",[[\"polygon\",{points:\"11 5 6 9 2 9 2 15 6 15 11 19 11 5\",key:\"16drj5\"}],[\"line\",{x1:\"22\",y1:\"9\",x2:\"16\",y2:\"15\",key:\"3gspht\"}],[\"line\",{x1:\"16\",y1:\"9\",x2:\"22\",y2:\"15\",key:\"2tltpt\"}]]);const $r=createLucideIcon(\"Volume\",[[\"polygon\",{points:\"11 5 6 9 2 9 2 15 6 15 11 19 11 5\",key:\"16drj5\"}]]);const _r=createLucideIcon(\"Vote\",[[\"path\",{d:\"m9 12 2 2 4-4\",key:\"dzmm74\"}],[\"path\",{d:\"M5 7c0-1.1.9-2 2-2h10a2 2 0 0 1 2 2v12H5V7Z\",key:\"1ezoue\"}],[\"path\",{d:\"M22 19H2\",key:\"nuriw5\"}]]);const eu=createLucideIcon(\"Wallet\",[[\"path\",{d:\"M20 12V8H6a2 2 0 0 1-2-2c0-1.1.9-2 2-2h12v4\",key:\"st805m\"}],[\"path\",{d:\"M4 6v12c0 1.1.9 2 2 2h14v-4\",key:\"16cu1e\"}],[\"path\",{d:\"M18 12a2 2 0 0 0-2 2c0 1.1.9 2 2 2h4v-4h-4z\",key:\"lwd56p\"}]]);const au=createLucideIcon(\"Wand2\",[[\"path\",{d:\"m21.64 3.64-1.28-1.28a1.21 1.21 0 0 0-1.72 0L2.36 18.64a1.21 1.21 0 0 0 0 1.72l1.28 1.28a1.2 1.2 0 0 0 1.72 0L21.64 5.36a1.2 1.2 0 0 0 0-1.72Z\",key:\"1bcowg\"}],[\"path\",{d:\"m14 7 3 3\",key:\"1r5n42\"}],[\"path\",{d:\"M5 6v4\",key:\"ilb8ba\"}],[\"path\",{d:\"M19 14v4\",key:\"blhpug\"}],[\"path\",{d:\"M10 2v2\",key:\"7u0qdc\"}],[\"path\",{d:\"M7 8H3\",key:\"zfb6yr\"}],[\"path\",{d:\"M21 16h-4\",key:\"1cnmox\"}],[\"path\",{d:\"M11 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Mu=createLucideIcon(\"XCircle\",[[\"circle\",{cx:\"12\",cy:\"12\",r:\"10\",key:\"1mglay\"}],[\"line\",{x1:\"15\",y1:\"9\",x2:\"9\",y2:\"15\",key:\"19zs77\"}],[\"line\",{x1:\"9\",y1:\"9\",x2:\"15\",y2:\"15\",key:\"10u9bu\"}]]);const xu=createLucideIcon(\"XOctagon\",[[\"polygon\",{points:\"7.86 2 16.14 2 22 7.86 22 16.14 16.14 22 7.86 22 2 16.14 2 7.86 7.86 2\",key:\"h1p8hx\"}],[\"line\",{x1:\"15\",y1:\"9\",x2:\"9\",y2:\"15\",key:\"19zs77\"}],[\"line\",{x1:\"9\",y1:\"9\",x2:\"15\",y2:\"15\",key:\"10u9bu\"}]]);const vu=createLucideIcon(\"XSquare\",[[\"rect\",{x:\"3\",y:\"3\",width:\"18\",height:\"18\",rx:\"2\",ry:\"2\",key:\"maln0c\"}],[\"line\",{x1:\"9\",y1:\"9\",x2:\"15\",y2:\"15\",key:\"10u9bu\"}],[\"line\",{x1:\"15\",y1:\"9\",x2:\"9\",y2:\"15\",key:\"19zs77\"}]]);const gu=createLucideIcon(\"X\",[[\"line\",{x1:\"18\",y1:\"6\",x2:\"6\",y2:\"18\",key:\"1o5bob\"}],[\"line\",{x1:\"6\",y1:\"6\",x2:\"18\",y2:\"18\",key:\"z4dcbv\"}]]);const mu=createLucideIcon(\"Youtube\",[[\"path\",{d:\"M12 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wu=createLucideIcon(\"ZoomIn\",[[\"circle\",{cx:\"11\",cy:\"11\",r:\"8\",key:\"4ej97u\"}],[\"line\",{x1:\"21\",y1:\"21\",x2:\"16.65\",y2:\"16.65\",key:\"1p50m8\"}],[\"line\",{x1:\"11\",y1:\"8\",x2:\"11\",y2:\"14\",key:\"jw7mvq\"}],[\"line\",{x1:\"8\",y1:\"11\",x2:\"14\",y2:\"11\",key:\"1nivud\"}]]);const Iu=createLucideIcon(\"ZoomOut\",[[\"circle\",{cx:\"11\",cy:\"11\",r:\"8\",key:\"4ej97u\"}],[\"line\",{x1:\"21\",y1:\"21\",x2:\"16.65\",y2:\"16.65\",key:\"1p50m8\"}],[\"line\",{x1:\"8\",y1:\"11\",x2:\"14\",y2:\"11\",key:\"1nivud\"}]]);var Cu=Object.freeze(Object.defineProperty({__proto__:null,Accessibility:y,Activity:c,AirVent:i,Airplay:n,AlarmCheck:h,AlarmClockOff:o,AlarmClock:d,AlarmMinus:s,AlarmPlus:k,Album:l,AlertCircle:p,AlertOctagon:r,AlertTriangle:u,AlignCenterHorizontal:M,AlignCenterVertical:x,AlignCenter:v,AlignEndHorizontal:g,AlignEndVertical:m,AlignHorizontalDistributeCenter:L,AlignHorizontalDistributeEnd:f,AlignHorizontalDistributeStart:w,AlignHorizontalJustifyCenter:I,AlignHorizontalJustifyEnd:C,AlignHorizontalJustifyStart:b,AlignHorizontalSpaceAround:z,AlignHorizontalSpaceBetween:H,AlignJustify:S,AlignLeft:V,AlignRight:q,AlignStartHorizontal:F,AlignStartVertical:A,AlignVerticalDistributeCenter:j,AlignVerticalDistributeEnd:P,AlignVerticalDistributeStart:B,AlignVerticalJustifyCenter:D,AlignVerticalJustifyEnd:O,AlignVerticalJustifyStart:R,AlignVerticalSpaceAround:T,AlignVerticalSpaceBetween:Z,Anchor:U,Angry:G,Annoyed:E,Aperture:W,Apple:X,ArchiveRestore:J,Archive:N,Armchair:K,ArrowBigDown:Q,ArrowBigLeft:Y,ArrowBigRight:$,ArrowBigUp:_,ArrowDownCircle:ee,ArrowDownLeft:ae,ArrowDownRight:te,ArrowDown:ye,ArrowLeftCircle:ce,ArrowLeftRight:ie,ArrowLeft:ne,ArrowRightCircle:he,ArrowRight:oe,ArrowUpCircle:de,ArrowUpDown:se,ArrowUpLeft:ke,ArrowUpRight:le,ArrowUp:pe,Asterisk:re,AtSign:ue,Award:Me,Axe:xe,Axis3d:ve,Baby:ge,Backpack:me,BaggageClaim:Le,Banana:fe,Banknote:we,BarChart2:Ie,BarChart3:Ce,BarChart4:be,BarChartHorizontal:ze,BarChart:He,Baseline:Se,Bath:Ve,BatteryCharging:qe,BatteryFull:Fe,BatteryLow:Ae,BatteryMedium:je,BatteryWarning:Pe,Battery:Be,Beaker:De,BeanOff:Oe,Bean:Re,BedDouble:Te,BedSingle:Ze,Bed:Ue,Beef:Ge,Beer:Ee,BellMinus:We,BellOff:Xe,BellPlus:Je,BellRing:Ne,Bell:Ke,Bike:Qe,Binary:Ye,Bitcoin:$e,BluetoothConnected:_e,BluetoothOff:ea,BluetoothSearching:aa,Bluetooth:ta,Bold:ya,Bomb:ca,Bone:ia,BookOpenCheck:na,BookOpen:ha,Book:oa,BookmarkMinus:da,BookmarkPlus:sa,Bookmark:ka,Bot:la,BoxSelect:pa,Box:ra,Boxes:ua,Briefcase:Ma,Brush:xa,Bug:va,Building2:ga,Building:ma,Bus:La,Cake:fa,Calculator:wa,CalendarCheck2:Ia,CalendarCheck:Ca,CalendarClock:ba,CalendarDays:za,CalendarHeart:Ha,CalendarMinus:Sa,CalendarOff:Va,CalendarPlus:qa,CalendarRange:Fa,CalendarSearch:Aa,CalendarX2:ja,CalendarX:Pa,Calendar:Ba,CameraOff:Da,Camera:Oa,CandyOff:Ra,Candy:Ta,Car:Za,Carrot:Ua,Cast:Ga,Cat:Ea,CheckCheck:Wa,CheckCircle2:Xa,CheckCircle:Ja,CheckSquare:Na,Check:Ka,ChefHat:Qa,Cherry:Ya,ChevronDown:$a,ChevronFirst:_a,ChevronLast:et,ChevronLeft:at,ChevronRight:tt,ChevronUp:yt,ChevronsDownUp:ct,ChevronsDown:it,ChevronsLeftRight:nt,ChevronsLeft:ht,ChevronsRightLeft:ot,ChevronsRight:dt,ChevronsUpDown:st,ChevronsUp:kt,Chrome:lt,CigaretteOff:pt,Cigarette:rt,CircleDot:ut,CircleEllipsis:Mt,CircleSlashed:xt,Circle:vt,Citrus:gt,Clapperboard:mt,ClipboardCheck:Lt,ClipboardCopy:ft,ClipboardEdit:wt,ClipboardList:It,ClipboardSignature:Ct,ClipboardType:bt,ClipboardX:zt,Clipboard:Ht,Clock1:St,Clock10:Vt,Clock11:qt,Clock12:Ft,Clock2:At,Clock3:jt,Clock4:Pt,Clock5:Bt,Clock6:Dt,Clock7:Ot,Clock8:Rt,Clock9:Tt,Clock:Zt,CloudCog:Ut,CloudDrizzle:Gt,CloudFog:Et,CloudHail:Wt,CloudLightning:Xt,CloudMoonRain:Jt,CloudMoon:Nt,CloudOff:Kt,CloudRainWind:Qt,CloudRain:Yt,CloudSnow:$t,CloudSunRain:_t,CloudSun:ey,Cloud:ay,Cloudy:ty,Clover:yy,Code2:cy,Code:iy,Codepen:ny,Codesandbox:hy,Coffee:oy,Cog:dy,Coins:sy,Columns:ky,Command:ly,Compass:py,Component:ry,ConciergeBell:uy,Contact:My,Contrast:xy,Cookie:vy,Copy:gy,Copyleft:my,Copyright:Ly,CornerDownLeft:fy,CornerDownRight:wy,CornerLeftDown:Iy,CornerLeftUp:Cy,CornerRightDown:by,CornerRightUp:zy,CornerUpLeft:Hy,CornerUpRight:Sy,Cpu:Vy,CreativeCommons:qy,CreditCard:Fy,Croissant:Ay,Crop:jy,Cross:Py,Crosshair:By,Crown:Dy,CupSoda:Oy,CurlyBraces:Ry,Currency:Ty,DatabaseBackup:Zy,Database:Uy,Delete:Gy,Diamond:Ey,Dice1:Wy,Dice2:Xy,Dice3:Jy,Dice4:Ny,Dice5:Ky,Dice6:Qy,Dices:Yy,Diff:$y,Disc:_y,DivideCircle:ec,DivideSquare:ac,Divide:tc,DnaOff:yc,Dna:cc,Dog:ic,DollarSign:nc,DownloadCloud:hc,Download:oc,Dribbble:dc,Droplet:sc,Droplets:kc,Drumstick:lc,Dumbbell:pc,EarOff:rc,Ear:uc,Edit2:Mc,Edit3:xc,Edit:vc,EggFried:gc,EggOff:mc,Egg:Lc,EqualNot:fc,Equal:wc,Eraser:Ic,Euro:Cc,Expand:bc,ExternalLink:zc,EyeOff:Hc,Eye:Sc,Facebook:Vc,Factory:qc,Fan:Fc,FastForward:Ac,Feather:jc,Figma:Pc,FileArchive:Bc,FileAudio2:Dc,FileAudio:Oc,FileAxis3d:Rc,FileBadge2:Tc,FileBadge:Zc,FileBarChart2:Uc,FileBarChart:Gc,FileBox:Ec,FileCheck2:Wc,FileCheck:Xc,FileClock:Jc,FileCode:Nc,FileCog2:Kc,FileCog:Qc,FileDiff:Yc,FileDigit:$c,FileDown:_c,FileEdit:ei,FileHeart:ai,FileImage:ti,FileInput:yi,FileJson2:ci,FileJson:ii,FileKey2:ni,FileKey:hi,FileLineChart:oi,FileLock2:di,FileLock:si,FileMinus2:ki,FileMinus:li,FileOutput:pi,FilePieChart:ri,FilePlus2:ui,FilePlus:Mi,FileQuestion:xi,FileScan:vi,FileSearch2:gi,FileSearch:mi,FileSignature:Li,FileSpreadsheet:fi,FileSymlink:wi,FileTerminal:Ii,FileText:Ci,FileType2:bi,FileType:zi,FileUp:Hi,FileVideo2:Si,FileVideo:Vi,FileVolume2:qi,FileVolume:Fi,FileWarning:Ai,FileX2:ji,FileX:Pi,File:Bi,Files:Di,Film:Oi,Filter:Ri,Fingerprint:Ti,FishOff:Zi,Fish:Ui,FlagOff:Gi,FlagTriangleLeft:Ei,FlagTriangleRight:Wi,Flag:Xi,Flame:Ji,FlashlightOff:Ni,Flashlight:Ki,FlaskConicalOff:Qi,FlaskConical:Yi,FlaskRound:$i,FlipHorizontal2:_i,FlipHorizontal:en,FlipVertical2:an,FlipVertical:tn,Flower2:yn,Flower:cn,Focus:nn,FolderArchive:hn,FolderCheck:on,FolderClock:dn,FolderClosed:sn,FolderCog2:kn,FolderCog:ln,FolderDown:pn,FolderEdit:rn,FolderHeart:un,FolderInput:Mn,FolderKey:xn,FolderLock:vn,FolderMinus:gn,FolderOpen:mn,FolderOutput:Ln,FolderPlus:fn,FolderSearch2:wn,FolderSearch:In,FolderSymlink:Cn,FolderTree:bn,FolderUp:zn,FolderX:Hn,Folder:Sn,Folders:Vn,FormInput:qn,Forward:Fn,Frame:An,Framer:jn,Frown:Pn,Fuel:Bn,FunctionSquare:Dn,Gamepad2:On,Gamepad:Rn,Gauge:Tn,Gavel:Zn,Gem:Un,Ghost:Gn,Gift:En,GitBranchPlus:Wn,GitBranch:Xn,GitCommit:Jn,GitCompare:Nn,GitFork:Kn,GitMerge:Qn,GitPullRequestClosed:Yn,GitPullRequestDraft:$n,GitPullRequest:_n,Github:eh,Gitlab:ah,GlassWater:th,Glasses:yh,Globe2:ch,Globe:ih,Grab:nh,GraduationCap:hh,Grape:oh,Grid:dh,GripHorizontal:sh,GripVertical:kh,Grip:lh,Hammer:ph,HandMetal:rh,Hand:uh,HardDrive:Mh,HardHat:xh,Hash:vh,Haze:gh,Heading1:mh,Heading2:Lh,Heading3:fh,Heading4:wh,Heading5:Ih,Heading6:Ch,Heading:bh,Headphones:zh,HeartCrack:Hh,HeartHandshake:Sh,HeartOff:Vh,HeartPulse:qh,Heart:Fh,HelpCircle:Ah,Hexagon:jh,Highlighter:Ph,History:Bh,Home:Dh,HopOff:Oh,Hop:Rh,Hourglass:Th,IceCream2:Zh,IceCream:Uh,ImageMinus:Gh,ImageOff:Eh,ImagePlus:Wh,Image:Xh,Import:Jh,Inbox:Nh,Indent:Kh,IndianRupee:Qh,Infinity:Yh,Info:$h,Inspect:_h,Instagram:eo,Italic:ao,JapaneseYen:to,Joystick:yo,Key:co,Keyboard:io,LampCeiling:no,LampDesk:ho,LampFloor:oo,LampWallDown:so,LampWallUp:ko,Lamp:lo,Landmark:po,Languages:ro,Laptop2:uo,Laptop:Mo,LassoSelect:xo,Lasso:vo,Laugh:go,Layers:mo,LayoutDashboard:Lo,LayoutGrid:fo,LayoutList:wo,LayoutTemplate:Io,Layout:Co,Leaf:bo,Library:zo,LifeBuoy:Ho,LightbulbOff:So,Lightbulb:Vo,LineChart:qo,Link2Off:Fo,Link2:Ao,Link:jo,Linkedin:Po,ListChecks:Bo,ListEnd:Do,ListMinus:Oo,ListMusic:Ro,ListOrdered:To,ListPlus:Zo,ListStart:Uo,ListVideo:Go,ListX:Eo,List:Wo,Loader2:Xo,Loader:Jo,LocateFixed:No,LocateOff:Ko,Locate:Qo,Lock:Yo,LogIn:$o,LogOut:_o,Luggage:ed,Magnet:ad,MailCheck:td,MailMinus:yd,MailOpen:cd,MailPlus:id,MailQuestion:nd,MailSearch:hd,MailWarning:od,MailX:dd,Mail:sd,Mails:kd,MapPinOff:ld,MapPin:pd,Map:rd,Martini:ud,Maximize2:Md,Maximize:xd,Medal:vd,MegaphoneOff:gd,Megaphone:md,Meh:Ld,Menu:fd,MessageCircle:wd,MessageSquare:Id,Mic2:Cd,MicOff:bd,Mic:zd,Microscope:Hd,Microwave:Sd,Milestone:Vd,MilkOff:qd,Milk:Fd,Minimize2:Ad,Minimize:jd,MinusCircle:Pd,MinusSquare:Bd,Minus:Dd,MonitorOff:Od,MonitorSmartphone:Rd,MonitorSpeaker:Td,Monitor:Zd,Moon:Ud,MoreHorizontal:Gd,MoreVertical:Ed,MountainSnow:Wd,Mountain:Xd,MousePointer2:Jd,MousePointerClick:Nd,MousePointer:Kd,Mouse:Qd,Move3d:Yd,MoveDiagonal2:$d,MoveDiagonal:_d,MoveHorizontal:es,MoveVertical:as,Move:ts,Music2:ys,Music3:cs,Music4:is,Music:ns,Navigation2Off:hs,Navigation2:os,NavigationOff:ds,Navigation:ss,Network:ks,Newspaper:ls,NutOff:ps,Nut:rs,Octagon:us,Option:Ms,Outdent:xs,Package2:vs,PackageCheck:gs,PackageMinus:ms,PackageOpen:Ls,PackagePlus:fs,PackageSearch:ws,PackageX:Is,Package:Cs,PaintBucket:bs,Paintbrush2:zs,Paintbrush:Hs,Palette:Ss,Palmtree:Vs,Paperclip:qs,PartyPopper:Fs,PauseCircle:As,PauseOctagon:js,Pause:Ps,PenTool:Bs,Pencil:Ds,Percent:Os,PersonStanding:Rs,PhoneCall:Ts,PhoneForwarded:Zs,PhoneIncoming:Us,PhoneMissed:Gs,PhoneOff:Es,PhoneOutgoing:Ws,Phone:Xs,PictureInPicture2:Js,PictureInPicture:Ns,PieChart:Ks,PiggyBank:Qs,Pilcrow:Ys,PinOff:$s,Pin:_s,Pipette:ek,Pizza:ak,Plane:tk,PlayCircle:yk,Play:ck,Plug2:ik,PlugZap:nk,Plug:hk,PlusCircle:ok,PlusSquare:dk,Plus:sk,Pocket:kk,Podcast:lk,Pointer:pk,PoundSterling:rk,PowerOff:uk,Power:Mk,Printer:xk,Puzzle:vk,QrCode:gk,Quote:mk,RadioReceiver:Lk,Radio:fk,RectangleHorizontal:wk,RectangleVertical:Ik,Recycle:Ck,Redo2:bk,Redo:zk,RefreshCcw:Hk,RefreshCw:Sk,Refrigerator:Vk,Regex:qk,RemoveFormatting:Fk,Repeat1:Ak,Repeat:jk,ReplyAll:Pk,Reply:Bk,Rewind:Dk,Rocket:Ok,RockingChair:Rk,Rotate3d:Tk,RotateCcw:Zk,RotateCw:Uk,Rss:Gk,Ruler:Ek,RussianRuble:Wk,Sailboat:Xk,Salad:Jk,Sandwich:Nk,Save:Kk,Scale3d:Qk,Scale:Yk,Scaling:$k,ScanFace:_k,ScanLine:el,Scan:al,Scissors:tl,ScreenShareOff:yl,ScreenShare:cl,Scroll:il,Search:nl,Send:hl,SeparatorHorizontal:ol,SeparatorVertical:dl,ServerCog:sl,ServerCrash:kl,ServerOff:ll,Server:pl,Settings2:rl,Settings:ul,Share2:Ml,Share:xl,Sheet:vl,ShieldAlert:gl,ShieldCheck:ml,ShieldClose:Ll,ShieldOff:fl,Shield:wl,Shirt:Il,ShoppingBag:Cl,ShoppingCart:bl,Shovel:zl,ShowerHead:Hl,Shrink:Sl,Shrub:Vl,Shuffle:ql,SidebarClose:Fl,SidebarOpen:Al,Sidebar:jl,Sigma:Pl,SignalHigh:Bl,SignalLow:Dl,SignalMedium:Ol,SignalZero:Rl,Signal:Tl,Siren:Zl,SkipBack:Ul,SkipForward:Gl,Skull:El,Slack:Wl,Slash:Xl,Slice:Jl,SlidersHorizontal:Nl,Sliders:Kl,SmartphoneCharging:Ql,Smartphone:Yl,SmilePlus:$l,Smile:_l,Snowflake:ep,Sofa:ap,SortAsc:tp,SortDesc:yp,Soup:cp,Speaker:ip,Spline:np,Sprout:hp,Square:op,StarHalf:dp,StarOff:sp,Star:kp,Stethoscope:lp,Sticker:pp,StickyNote:rp,StopCircle:up,StretchHorizontal:Mp,StretchVertical:xp,Strikethrough:vp,Subscript:gp,Subtitles:mp,SunDim:Lp,SunMedium:fp,SunMoon:wp,SunSnow:Ip,Sun:Cp,Sunrise:bp,Sunset:zp,Superscript:Hp,SwissFranc:Sp,SwitchCamera:Vp,Sword:qp,Swords:Fp,Syringe:Ap,Table2:jp,Table:Pp,Tablet:Bp,Tag:Dp,Tags:Op,Target:Rp,Tent:Tp,TerminalSquare:Zp,Terminal:Up,TextCursorInput:Gp,TextCursor:Ep,ThermometerSnowflake:Wp,ThermometerSun:Xp,Thermometer:Jp,ThumbsDown:Np,ThumbsUp:Kp,Ticket:Qp,TimerOff:Yp,TimerReset:$p,Timer:_p,ToggleLeft:er,ToggleRight:ar,Tornado:tr,ToyBrick:yr,Train:cr,Trash2:ir,Trash:nr,TreeDeciduous:hr,TreePine:or,Trees:dr,Trello:sr,TrendingDown:kr,TrendingUp:lr,Triangle:pr,Trophy:rr,Truck:ur,Tv2:Mr,Tv:xr,Twitch:vr,Twitter:gr,Type:mr,Umbrella:Lr,Underline:fr,Undo2:wr,Undo:Ir,Unlink2:Cr,Unlink:br,Unlock:zr,UploadCloud:Hr,Upload:Sr,Usb:Vr,UserCheck:qr,UserCog:Fr,UserMinus:Ar,UserPlus:jr,UserX:Pr,User:Br,Users:Dr,UtensilsCrossed:Or,Utensils:Rr,Vegan:Tr,VenetianMask:Zr,Verified:Ur,VibrateOff:Gr,Vibrate:Er,VideoOff:Wr,Video:Xr,View:Jr,Voicemail:Nr,Volume1:Kr,Volume2:Qr,VolumeX:Yr,Volume:$r,Vote:_r,Wallet:eu,Wand2:au,Wand:tu,Watch:yu,Waves:cu,Webcam:iu,Webhook:nu,WheatOff:hu,Wheat:ou,WifiOff:du,Wifi:su,Wind:ku,WineOff:lu,Wine:pu,WrapText:ru,Wrench:uu,XCircle:Mu,XOctagon:xu,XSquare:vu,X:gu,Youtube:mu,ZapOff:Lu,Zap:fu,ZoomIn:wu,ZoomOut:Iu},Symbol.toStringTag,{value:\"Module\"}));export{y as Accessibility,y as AccessibilityIcon,c as Activity,c as ActivityIcon,i as AirVent,i as AirVentIcon,n as Airplay,n as AirplayIcon,h as AlarmCheck,h as AlarmCheckIcon,d as AlarmClock,d as AlarmClockIcon,o as AlarmClockOff,o as AlarmClockOffIcon,s as AlarmMinus,s as AlarmMinusIcon,k as AlarmPlus,k as AlarmPlusIcon,l as Album,l as AlbumIcon,p as AlertCircle,p as AlertCircleIcon,r as AlertOctagon,r as AlertOctagonIcon,u as AlertTriangle,u as AlertTriangleIcon,v as AlignCenter,M as AlignCenterHorizontal,M as AlignCenterHorizontalIcon,v as AlignCenterIcon,x as AlignCenterVertical,x as AlignCenterVerticalIcon,g as AlignEndHorizontal,g as AlignEndHorizontalIcon,m as AlignEndVertical,m as AlignEndVerticalIcon,L as AlignHorizontalDistributeCenter,L as AlignHorizontalDistributeCenterIcon,f as AlignHorizontalDistributeEnd,f as AlignHorizontalDistributeEndIcon,w as AlignHorizontalDistributeStart,w as AlignHorizontalDistributeStartIcon,I as AlignHorizontalJustifyCenter,I as AlignHorizontalJustifyCenterIcon,C as AlignHorizontalJustifyEnd,C as AlignHorizontalJustifyEndIcon,b as AlignHorizontalJustifyStart,b as AlignHorizontalJustifyStartIcon,z as AlignHorizontalSpaceAround,z as AlignHorizontalSpaceAroundIcon,H as AlignHorizontalSpaceBetween,H as AlignHorizontalSpaceBetweenIcon,S as AlignJustify,S as AlignJustifyIcon,V as AlignLeft,V as AlignLeftIcon,q as AlignRight,q as AlignRightIcon,F as AlignStartHorizontal,F as AlignStartHorizontalIcon,A as AlignStartVertical,A as AlignStartVerticalIcon,j as AlignVerticalDistributeCenter,j as AlignVerticalDistributeCenterIcon,P as AlignVerticalDistributeEnd,P as AlignVerticalDistributeEndIcon,B as AlignVerticalDistributeStart,B as AlignVerticalDistributeStartIcon,D as AlignVerticalJustifyCenter,D as AlignVerticalJustifyCenterIcon,O as AlignVerticalJustifyEnd,O as AlignVerticalJustifyEndIcon,R as AlignVerticalJustifyStart,R as AlignVerticalJustifyStartIcon,T as AlignVerticalSpaceAround,T as AlignVerticalSpaceAroundIcon,Z as AlignVerticalSpaceBetween,Z as AlignVerticalSpaceBetweenIcon,U as Anchor,U as AnchorIcon,G as Angry,G as AngryIcon,E as Annoyed,E as AnnoyedIcon,W as Aperture,W as ApertureIcon,X as Apple,X as AppleIcon,N as Archive,N as ArchiveIcon,J as ArchiveRestore,J as ArchiveRestoreIcon,K as Armchair,K as ArmchairIcon,Q as ArrowBigDown,Q as ArrowBigDownIcon,Y as ArrowBigLeft,Y as ArrowBigLeftIcon,$ as ArrowBigRight,$ as ArrowBigRightIcon,_ as ArrowBigUp,_ as ArrowBigUpIcon,ye as ArrowDown,ee as ArrowDownCircle,ee as ArrowDownCircleIcon,ye as ArrowDownIcon,ae as ArrowDownLeft,ae as ArrowDownLeftIcon,te as ArrowDownRight,te as ArrowDownRightIcon,ne as ArrowLeft,ce as ArrowLeftCircle,ce as ArrowLeftCircleIcon,ne as ArrowLeftIcon,ie as ArrowLeftRight,ie as ArrowLeftRightIcon,oe as ArrowRight,he as ArrowRightCircle,he as ArrowRightCircleIcon,oe as ArrowRightIcon,pe as ArrowUp,de as ArrowUpCircle,de as ArrowUpCircleIcon,se as ArrowUpDown,se as ArrowUpDownIcon,pe as ArrowUpIcon,ke as ArrowUpLeft,ke as ArrowUpLeftIcon,le as ArrowUpRight,le as ArrowUpRightIcon,re as Asterisk,re as AsteriskIcon,ue as AtSign,ue as AtSignIcon,Me as Award,Me as AwardIcon,xe as Axe,xe as AxeIcon,ve as Axis3d,ve as Axis3dIcon,ge as Baby,ge as BabyIcon,me as Backpack,me as BackpackIcon,Le as BaggageClaim,Le as BaggageClaimIcon,fe as Banana,fe as BananaIcon,we as Banknote,we as BanknoteIcon,He as BarChart,Ie as BarChart2,Ie as BarChart2Icon,Ce as BarChart3,Ce as BarChart3Icon,be as BarChart4,be as BarChart4Icon,ze as BarChartHorizontal,ze as BarChartHorizontalIcon,He as BarChartIcon,Se as Baseline,Se as BaselineIcon,Ve as Bath,Ve as BathIcon,Be as Battery,qe as BatteryCharging,qe as BatteryChargingIcon,Fe as BatteryFull,Fe as BatteryFullIcon,Be as BatteryIcon,Ae as BatteryLow,Ae as BatteryLowIcon,je as BatteryMedium,je as BatteryMediumIcon,Pe as BatteryWarning,Pe as BatteryWarningIcon,De as Beaker,De as BeakerIcon,Re as Bean,Re as BeanIcon,Oe as BeanOff,Oe as BeanOffIcon,Ue as Bed,Te as BedDouble,Te as BedDoubleIcon,Ue as BedIcon,Ze as BedSingle,Ze as BedSingleIcon,Ge as Beef,Ge as BeefIcon,Ee as Beer,Ee as BeerIcon,Ke as Bell,Ke as BellIcon,We as BellMinus,We as BellMinusIcon,Xe as BellOff,Xe as BellOffIcon,Je as BellPlus,Je as BellPlusIcon,Ne as BellRing,Ne as BellRingIcon,Qe as Bike,Qe as BikeIcon,Ye as Binary,Ye as BinaryIcon,$e as Bitcoin,$e as BitcoinIcon,ta as Bluetooth,_e as BluetoothConnected,_e as BluetoothConnectedIcon,ta as BluetoothIcon,ea as BluetoothOff,ea as BluetoothOffIcon,aa as BluetoothSearching,aa as BluetoothSearchingIcon,ya as Bold,ya as BoldIcon,ca as Bomb,ca as BombIcon,ia as Bone,ia as BoneIcon,oa as Book,oa as BookIcon,ha as BookOpen,na as BookOpenCheck,na as BookOpenCheckIcon,ha as BookOpenIcon,ka as Bookmark,ka as BookmarkIcon,da as BookmarkMinus,da as BookmarkMinusIcon,sa as BookmarkPlus,sa as BookmarkPlusIcon,la as Bot,la as BotIcon,ra as Box,ra as BoxIcon,pa as BoxSelect,pa as BoxSelectIcon,ua as Boxes,ua as BoxesIcon,Ma as Briefcase,Ma as BriefcaseIcon,xa as Brush,xa as BrushIcon,va as Bug,va as BugIcon,ma as Building,ga as Building2,ga as Building2Icon,ma as BuildingIcon,La as Bus,La as BusIcon,fa as Cake,fa as CakeIcon,wa as Calculator,wa as CalculatorIcon,Ba as Calendar,Ca as CalendarCheck,Ia as CalendarCheck2,Ia as CalendarCheck2Icon,Ca as CalendarCheckIcon,ba as CalendarClock,ba as CalendarClockIcon,za as CalendarDays,za as CalendarDaysIcon,Ha as CalendarHeart,Ha as CalendarHeartIcon,Ba as CalendarIcon,Sa as CalendarMinus,Sa as CalendarMinusIcon,Va as CalendarOff,Va as CalendarOffIcon,qa as CalendarPlus,qa as CalendarPlusIcon,Fa as CalendarRange,Fa as CalendarRangeIcon,Aa as CalendarSearch,Aa as CalendarSearchIcon,Pa as CalendarX,ja as CalendarX2,ja as CalendarX2Icon,Pa as CalendarXIcon,Oa as Camera,Oa as CameraIcon,Da as CameraOff,Da as CameraOffIcon,Ta as Candy,Ta as CandyIcon,Ra as CandyOff,Ra as CandyOffIcon,Za as Car,Za as CarIcon,Ua as Carrot,Ua as CarrotIcon,Ga as Cast,Ga as CastIcon,Ea as Cat,Ea as CatIcon,Ka as Check,Wa as CheckCheck,Wa as CheckCheckIcon,Ja as CheckCircle,Xa as CheckCircle2,Xa as CheckCircle2Icon,Ja as CheckCircleIcon,Ka as CheckIcon,Na as CheckSquare,Na as CheckSquareIcon,Qa as ChefHat,Qa as ChefHatIcon,Ya as Cherry,Ya as CherryIcon,$a as ChevronDown,$a as ChevronDownIcon,_a as ChevronFirst,_a as ChevronFirstIcon,et as ChevronLast,et as ChevronLastIcon,at as ChevronLeft,at as ChevronLeftIcon,tt as ChevronRight,tt as ChevronRightIcon,yt as ChevronUp,yt as ChevronUpIcon,it as ChevronsDown,it as ChevronsDownIcon,ct as ChevronsDownUp,ct as ChevronsDownUpIcon,ht as ChevronsLeft,ht as ChevronsLeftIcon,nt as ChevronsLeftRight,nt as ChevronsLeftRightIcon,dt as ChevronsRight,dt as ChevronsRightIcon,ot as ChevronsRightLeft,ot as ChevronsRightLeftIcon,kt as ChevronsUp,st as ChevronsUpDown,st as ChevronsUpDownIcon,kt as ChevronsUpIcon,lt as Chrome,lt as ChromeIcon,rt as Cigarette,rt as CigaretteIcon,pt as CigaretteOff,pt as CigaretteOffIcon,vt as Circle,ut as CircleDot,ut as CircleDotIcon,Mt as CircleEllipsis,Mt as CircleEllipsisIcon,vt as CircleIcon,xt as CircleSlashed,xt as CircleSlashedIcon,gt as Citrus,gt as CitrusIcon,mt as Clapperboard,mt as ClapperboardIcon,Ht as Clipboard,Lt as ClipboardCheck,Lt as ClipboardCheckIcon,ft as ClipboardCopy,ft as ClipboardCopyIcon,wt as ClipboardEdit,wt as ClipboardEditIcon,Ht as ClipboardIcon,It as ClipboardList,It as ClipboardListIcon,Ct as ClipboardSignature,Ct as ClipboardSignatureIcon,bt as ClipboardType,bt as ClipboardTypeIcon,zt as ClipboardX,zt as ClipboardXIcon,Zt as Clock,St as Clock1,Vt as Clock10,Vt as Clock10Icon,qt as Clock11,qt as Clock11Icon,Ft as Clock12,Ft as Clock12Icon,St as Clock1Icon,At as Clock2,At as Clock2Icon,jt as Clock3,jt as Clock3Icon,Pt as Clock4,Pt as Clock4Icon,Bt as Clock5,Bt as Clock5Icon,Dt as Clock6,Dt as Clock6Icon,Ot as Clock7,Ot as Clock7Icon,Rt as Clock8,Rt as Clock8Icon,Tt as Clock9,Tt as Clock9Icon,Zt as ClockIcon,ay as Cloud,Ut as CloudCog,Ut as CloudCogIcon,Gt as CloudDrizzle,Gt as CloudDrizzleIcon,Et as CloudFog,Et as CloudFogIcon,Wt as CloudHail,Wt as CloudHailIcon,ay as CloudIcon,Xt as CloudLightning,Xt as CloudLightningIcon,Nt as CloudMoon,Nt as CloudMoonIcon,Jt as CloudMoonRain,Jt as CloudMoonRainIcon,Kt as CloudOff,Kt as CloudOffIcon,Yt as CloudRain,Yt as CloudRainIcon,Qt as CloudRainWind,Qt as CloudRainWindIcon,$t as CloudSnow,$t as CloudSnowIcon,ey as CloudSun,ey as CloudSunIcon,_t as CloudSunRain,_t as CloudSunRainIcon,ty as Cloudy,ty as CloudyIcon,yy as Clover,yy as CloverIcon,iy as Code,cy as Code2,cy as Code2Icon,iy as CodeIcon,ny as Codepen,ny as CodepenIcon,hy as Codesandbox,hy as CodesandboxIcon,oy as Coffee,oy as CoffeeIcon,dy as Cog,dy as CogIcon,sy as Coins,sy as CoinsIcon,ky as Columns,ky as ColumnsIcon,ly as Command,ly as CommandIcon,py as Compass,py as CompassIcon,ry as Component,ry as ComponentIcon,uy as ConciergeBell,uy as ConciergeBellIcon,My as Contact,My as ContactIcon,xy as Contrast,xy as ContrastIcon,vy as Cookie,vy as CookieIcon,gy as Copy,gy as CopyIcon,my as Copyleft,my as CopyleftIcon,Ly as Copyright,Ly as CopyrightIcon,fy as CornerDownLeft,fy as CornerDownLeftIcon,wy as CornerDownRight,wy as CornerDownRightIcon,Iy as CornerLeftDown,Iy as CornerLeftDownIcon,Cy as CornerLeftUp,Cy as CornerLeftUpIcon,by as CornerRightDown,by as CornerRightDownIcon,zy as CornerRightUp,zy as CornerRightUpIcon,Hy as CornerUpLeft,Hy as CornerUpLeftIcon,Sy as CornerUpRight,Sy as CornerUpRightIcon,Vy as Cpu,Vy as CpuIcon,qy as CreativeCommons,qy as CreativeCommonsIcon,Fy as CreditCard,Fy as CreditCardIcon,Ay as Croissant,Ay as CroissantIcon,jy as Crop,jy as CropIcon,Py as Cross,Py as CrossIcon,By as Crosshair,By as CrosshairIcon,Dy as Crown,Dy as CrownIcon,Oy as CupSoda,Oy as CupSodaIcon,Ry as CurlyBraces,Ry as CurlyBracesIcon,Ty as Currency,Ty as CurrencyIcon,Uy as Database,Zy as DatabaseBackup,Zy as DatabaseBackupIcon,Uy as DatabaseIcon,Gy as Delete,Gy as DeleteIcon,Ey as Diamond,Ey as DiamondIcon,Wy as Dice1,Wy as Dice1Icon,Xy as Dice2,Xy as Dice2Icon,Jy as Dice3,Jy as Dice3Icon,Ny as Dice4,Ny as Dice4Icon,Ky as Dice5,Ky as Dice5Icon,Qy as Dice6,Qy as Dice6Icon,Yy as Dices,Yy as DicesIcon,$y as Diff,$y as DiffIcon,_y as Disc,_y as DiscIcon,tc as Divide,ec as DivideCircle,ec as DivideCircleIcon,tc as DivideIcon,ac as DivideSquare,ac as DivideSquareIcon,cc as Dna,cc as DnaIcon,yc as DnaOff,yc as DnaOffIcon,ic as Dog,ic as DogIcon,nc as DollarSign,nc as DollarSignIcon,oc as Download,hc as DownloadCloud,hc as DownloadCloudIcon,oc as DownloadIcon,dc as Dribbble,dc as DribbbleIcon,sc as Droplet,sc as DropletIcon,kc as Droplets,kc as DropletsIcon,lc as Drumstick,lc as DrumstickIcon,pc as Dumbbell,pc as DumbbellIcon,uc as Ear,uc as EarIcon,rc as EarOff,rc as EarOffIcon,vc as Edit,Mc as Edit2,Mc as Edit2Icon,xc as Edit3,xc as Edit3Icon,vc as EditIcon,Lc as Egg,gc as EggFried,gc as EggFriedIcon,Lc as EggIcon,mc as EggOff,mc as EggOffIcon,wc as Equal,wc as EqualIcon,fc as EqualNot,fc as EqualNotIcon,Ic as Eraser,Ic as EraserIcon,Cc as Euro,Cc as EuroIcon,bc as Expand,bc as ExpandIcon,zc as ExternalLink,zc as ExternalLinkIcon,Sc as Eye,Sc as EyeIcon,Hc as EyeOff,Hc as EyeOffIcon,Vc as Facebook,Vc as FacebookIcon,qc as Factory,qc as FactoryIcon,Fc as Fan,Fc as FanIcon,Ac as FastForward,Ac as FastForwardIcon,jc as Feather,jc as FeatherIcon,Pc as Figma,Pc as FigmaIcon,Bi as File,Bc as FileArchive,Bc as FileArchiveIcon,Oc as FileAudio,Dc as FileAudio2,Dc as FileAudio2Icon,Oc as FileAudioIcon,Rc as FileAxis3d,Rc as FileAxis3dIcon,Zc as FileBadge,Tc as FileBadge2,Tc as FileBadge2Icon,Zc as FileBadgeIcon,Gc as FileBarChart,Uc as FileBarChart2,Uc as FileBarChart2Icon,Gc as FileBarChartIcon,Ec as FileBox,Ec as FileBoxIcon,Xc as FileCheck,Wc as FileCheck2,Wc as FileCheck2Icon,Xc as FileCheckIcon,Jc as FileClock,Jc as FileClockIcon,Nc as FileCode,Nc as FileCodeIcon,Qc as FileCog,Kc as FileCog2,Kc as FileCog2Icon,Qc as FileCogIcon,Yc as FileDiff,Yc as FileDiffIcon,$c as FileDigit,$c as FileDigitIcon,_c as FileDown,_c as FileDownIcon,ei as FileEdit,ei as FileEditIcon,ai as FileHeart,ai as FileHeartIcon,Bi as FileIcon,ti as FileImage,ti as FileImageIcon,yi as FileInput,yi as FileInputIcon,ii as FileJson,ci as FileJson2,ci as FileJson2Icon,ii as FileJsonIcon,hi as FileKey,ni as FileKey2,ni as FileKey2Icon,hi as FileKeyIcon,oi as FileLineChart,oi as FileLineChartIcon,si as FileLock,di as FileLock2,di as FileLock2Icon,si as FileLockIcon,li as FileMinus,ki as FileMinus2,ki as FileMinus2Icon,li as FileMinusIcon,pi as FileOutput,pi as FileOutputIcon,ri as FilePieChart,ri as FilePieChartIcon,Mi as FilePlus,ui as FilePlus2,ui as FilePlus2Icon,Mi as FilePlusIcon,xi as FileQuestion,xi as FileQuestionIcon,vi as FileScan,vi as FileScanIcon,mi as FileSearch,gi as FileSearch2,gi as FileSearch2Icon,mi as FileSearchIcon,Li as FileSignature,Li as FileSignatureIcon,fi as FileSpreadsheet,fi as FileSpreadsheetIcon,wi as FileSymlink,wi as FileSymlinkIcon,Ii as FileTerminal,Ii as FileTerminalIcon,Ci as FileText,Ci as FileTextIcon,zi as FileType,bi as FileType2,bi as FileType2Icon,zi as FileTypeIcon,Hi as FileUp,Hi as FileUpIcon,Vi as FileVideo,Si as FileVideo2,Si as FileVideo2Icon,Vi as FileVideoIcon,Fi as FileVolume,qi as FileVolume2,qi as FileVolume2Icon,Fi as FileVolumeIcon,Ai as FileWarning,Ai as FileWarningIcon,Pi as FileX,ji as FileX2,ji as FileX2Icon,Pi as FileXIcon,Di as Files,Di as FilesIcon,Oi as Film,Oi as FilmIcon,Ri as Filter,Ri as FilterIcon,Ti as Fingerprint,Ti as FingerprintIcon,Ui as Fish,Ui as FishIcon,Zi as FishOff,Zi as FishOffIcon,Xi as Flag,Xi as FlagIcon,Gi as FlagOff,Gi as FlagOffIcon,Ei as FlagTriangleLeft,Ei as FlagTriangleLeftIcon,Wi as FlagTriangleRight,Wi as FlagTriangleRightIcon,Ji as Flame,Ji as FlameIcon,Ki as Flashlight,Ki as FlashlightIcon,Ni as FlashlightOff,Ni as FlashlightOffIcon,Yi as FlaskConical,Yi as FlaskConicalIcon,Qi as FlaskConicalOff,Qi as FlaskConicalOffIcon,$i as FlaskRound,$i as FlaskRoundIcon,en as FlipHorizontal,_i as FlipHorizontal2,_i as FlipHorizontal2Icon,en as FlipHorizontalIcon,tn as FlipVertical,an as FlipVertical2,an as FlipVertical2Icon,tn as FlipVerticalIcon,cn as Flower,yn as Flower2,yn as Flower2Icon,cn as FlowerIcon,nn as Focus,nn as FocusIcon,Sn as Folder,hn as FolderArchive,hn as FolderArchiveIcon,on as FolderCheck,on as FolderCheckIcon,dn as FolderClock,dn as FolderClockIcon,sn as FolderClosed,sn as FolderClosedIcon,ln as FolderCog,kn as FolderCog2,kn as FolderCog2Icon,ln as FolderCogIcon,pn as FolderDown,pn as FolderDownIcon,rn as FolderEdit,rn as FolderEditIcon,un as FolderHeart,un as FolderHeartIcon,Sn as FolderIcon,Mn as FolderInput,Mn as FolderInputIcon,xn as FolderKey,xn as FolderKeyIcon,vn as FolderLock,vn as FolderLockIcon,gn as FolderMinus,gn as FolderMinusIcon,mn as FolderOpen,mn as FolderOpenIcon,Ln as FolderOutput,Ln as FolderOutputIcon,fn as FolderPlus,fn as FolderPlusIcon,In as FolderSearch,wn as FolderSearch2,wn as FolderSearch2Icon,In as FolderSearchIcon,Cn as FolderSymlink,Cn as FolderSymlinkIcon,bn as FolderTree,bn as FolderTreeIcon,zn as FolderUp,zn as FolderUpIcon,Hn as FolderX,Hn as FolderXIcon,Vn as Folders,Vn as FoldersIcon,qn as FormInput,qn as FormInputIcon,Fn as Forward,Fn as ForwardIcon,An as Frame,An as FrameIcon,jn as Framer,jn as FramerIcon,Pn as Frown,Pn as FrownIcon,Bn as Fuel,Bn as FuelIcon,Dn as FunctionSquare,Dn as FunctionSquareIcon,Rn as Gamepad,On as Gamepad2,On as Gamepad2Icon,Rn as GamepadIcon,Tn as Gauge,Tn as GaugeIcon,Zn as Gavel,Zn as GavelIcon,Un as Gem,Un as GemIcon,Gn as Ghost,Gn as GhostIcon,En as Gift,En as GiftIcon,Xn as GitBranch,Xn as GitBranchIcon,Wn as GitBranchPlus,Wn as GitBranchPlusIcon,Jn as GitCommit,Jn as GitCommitIcon,Nn as GitCompare,Nn as GitCompareIcon,Kn as GitFork,Kn as GitForkIcon,Qn as GitMerge,Qn as GitMergeIcon,_n as GitPullRequest,Yn as GitPullRequestClosed,Yn as GitPullRequestClosedIcon,$n as GitPullRequestDraft,$n as GitPullRequestDraftIcon,_n as GitPullRequestIcon,eh as Github,eh as GithubIcon,ah as Gitlab,ah as GitlabIcon,th as GlassWater,th as GlassWaterIcon,yh as Glasses,yh as GlassesIcon,ih as Globe,ch as Globe2,ch as Globe2Icon,ih as GlobeIcon,nh as Grab,nh as GrabIcon,hh as GraduationCap,hh as GraduationCapIcon,oh as Grape,oh as GrapeIcon,dh as Grid,dh as GridIcon,lh as Grip,sh as GripHorizontal,sh as GripHorizontalIcon,lh as GripIcon,kh as GripVertical,kh as GripVerticalIcon,ph as Hammer,ph as HammerIcon,uh as Hand,uh as HandIcon,rh as HandMetal,rh as HandMetalIcon,Mh as HardDrive,Mh as HardDriveIcon,xh as HardHat,xh as HardHatIcon,vh as Hash,vh as HashIcon,gh as Haze,gh as HazeIcon,bh as Heading,mh as Heading1,mh as Heading1Icon,Lh as Heading2,Lh as Heading2Icon,fh as Heading3,fh as Heading3Icon,wh as Heading4,wh as Heading4Icon,Ih as Heading5,Ih as Heading5Icon,Ch as Heading6,Ch as Heading6Icon,bh as HeadingIcon,zh as Headphones,zh as HeadphonesIcon,Fh as Heart,Hh as HeartCrack,Hh as HeartCrackIcon,Sh as HeartHandshake,Sh as HeartHandshakeIcon,Fh as HeartIcon,Vh as HeartOff,Vh as HeartOffIcon,qh as HeartPulse,qh as HeartPulseIcon,Ah as HelpCircle,Ah as HelpCircleIcon,jh as Hexagon,jh as HexagonIcon,Ph as Highlighter,Ph as HighlighterIcon,Bh as History,Bh as HistoryIcon,Dh as Home,Dh as HomeIcon,Rh as Hop,Rh as HopIcon,Oh as HopOff,Oh as HopOffIcon,Th as Hourglass,Th as HourglassIcon,Uh as IceCream,Zh as IceCream2,Zh as IceCream2Icon,Uh as IceCreamIcon,Xh as Image,Xh as ImageIcon,Gh as ImageMinus,Gh as ImageMinusIcon,Eh as ImageOff,Eh as ImageOffIcon,Wh as ImagePlus,Wh as ImagePlusIcon,Jh as Import,Jh as ImportIcon,Nh as Inbox,Nh as InboxIcon,Kh as Indent,Kh as IndentIcon,Qh as IndianRupee,Qh as IndianRupeeIcon,Yh as Infinity,Yh as InfinityIcon,$h as Info,$h as InfoIcon,_h as Inspect,_h as InspectIcon,eo as Instagram,eo as InstagramIcon,ao as Italic,ao as ItalicIcon,to as JapaneseYen,to as JapaneseYenIcon,yo as Joystick,yo as JoystickIcon,co as Key,co as KeyIcon,io as Keyboard,io as KeyboardIcon,lo as Lamp,no as LampCeiling,no as LampCeilingIcon,ho as LampDesk,ho as LampDeskIcon,oo as LampFloor,oo as LampFloorIcon,lo as LampIcon,so as LampWallDown,so as LampWallDownIcon,ko as LampWallUp,ko as LampWallUpIcon,po as Landmark,po as LandmarkIcon,ro as Languages,ro as LanguagesIcon,Mo as Laptop,uo as Laptop2,uo as Laptop2Icon,Mo as LaptopIcon,vo as Lasso,vo as LassoIcon,xo as LassoSelect,xo as LassoSelectIcon,go as Laugh,go as LaughIcon,mo as Layers,mo as LayersIcon,Co as Layout,Lo as LayoutDashboard,Lo as LayoutDashboardIcon,fo as LayoutGrid,fo as LayoutGridIcon,Co as LayoutIcon,wo as LayoutList,wo as LayoutListIcon,Io as LayoutTemplate,Io as LayoutTemplateIcon,bo as Leaf,bo as LeafIcon,zo as Library,zo as LibraryIcon,Ho as LifeBuoy,Ho as LifeBuoyIcon,Vo as Lightbulb,Vo as LightbulbIcon,So as LightbulbOff,So as LightbulbOffIcon,qo as LineChart,qo as LineChartIcon,jo as Link,Ao as Link2,Ao as Link2Icon,Fo as Link2Off,Fo as Link2OffIcon,jo as LinkIcon,Po as Linkedin,Po as LinkedinIcon,Wo as List,Bo as ListChecks,Bo as ListChecksIcon,Do as ListEnd,Do as ListEndIcon,Wo as ListIcon,Oo as ListMinus,Oo as ListMinusIcon,Ro as ListMusic,Ro as ListMusicIcon,To as ListOrdered,To as ListOrderedIcon,Zo as ListPlus,Zo as ListPlusIcon,Uo as ListStart,Uo as ListStartIcon,Go as ListVideo,Go as ListVideoIcon,Eo as ListX,Eo as ListXIcon,Jo as Loader,Xo as Loader2,Xo as Loader2Icon,Jo as LoaderIcon,Qo as Locate,No as LocateFixed,No as LocateFixedIcon,Qo as LocateIcon,Ko as LocateOff,Ko as LocateOffIcon,Yo as Lock,Yo as LockIcon,$o as LogIn,$o as LogInIcon,_o as LogOut,_o as LogOutIcon,y as LucideAccessibility,c as LucideActivity,i as LucideAirVent,n as LucideAirplay,h as LucideAlarmCheck,d as LucideAlarmClock,o as LucideAlarmClockOff,s as LucideAlarmMinus,k as LucideAlarmPlus,l as LucideAlbum,p as LucideAlertCircle,r as LucideAlertOctagon,u as LucideAlertTriangle,v as LucideAlignCenter,M as LucideAlignCenterHorizontal,x as LucideAlignCenterVertical,g as LucideAlignEndHorizontal,m as LucideAlignEndVertical,L as LucideAlignHorizontalDistributeCenter,f as LucideAlignHorizontalDistributeEnd,w as LucideAlignHorizontalDistributeStart,I as LucideAlignHorizontalJustifyCenter,C as LucideAlignHorizontalJustifyEnd,b as LucideAlignHorizontalJustifyStart,z as LucideAlignHorizontalSpaceAround,H as LucideAlignHorizontalSpaceBetween,S as LucideAlignJustify,V as LucideAlignLeft,q as LucideAlignRight,F as LucideAlignStartHorizontal,A as LucideAlignStartVertical,j as LucideAlignVerticalDistributeCenter,P as LucideAlignVerticalDistributeEnd,B as LucideAlignVerticalDistributeStart,D as LucideAlignVerticalJustifyCenter,O as LucideAlignVerticalJustifyEnd,R as LucideAlignVerticalJustifyStart,T as LucideAlignVerticalSpaceAround,Z as LucideAlignVerticalSpaceBetween,U as LucideAnchor,G as LucideAngry,E as LucideAnnoyed,W as LucideAperture,X as LucideApple,N as LucideArchive,J as LucideArchiveRestore,K as LucideArmchair,Q as LucideArrowBigDown,Y as LucideArrowBigLeft,$ as LucideArrowBigRight,_ as LucideArrowBigUp,ye as LucideArrowDown,ee as LucideArrowDownCircle,ae as LucideArrowDownLeft,te as LucideArrowDownRight,ne as LucideArrowLeft,ce as LucideArrowLeftCircle,ie as LucideArrowLeftRight,oe as LucideArrowRight,he as LucideArrowRightCircle,pe as LucideArrowUp,de as LucideArrowUpCircle,se as LucideArrowUpDown,ke as LucideArrowUpLeft,le as LucideArrowUpRight,re as LucideAsterisk,ue as LucideAtSign,Me as LucideAward,xe as LucideAxe,ve as LucideAxis3d,ge as LucideBaby,me as LucideBackpack,Le as LucideBaggageClaim,fe as LucideBanana,we as LucideBanknote,He as LucideBarChart,Ie as LucideBarChart2,Ce as LucideBarChart3,be as LucideBarChart4,ze as LucideBarChartHorizontal,Se as LucideBaseline,Ve as LucideBath,Be as LucideBattery,qe as LucideBatteryCharging,Fe as LucideBatteryFull,Ae as LucideBatteryLow,je as LucideBatteryMedium,Pe as LucideBatteryWarning,De as LucideBeaker,Re as LucideBean,Oe as LucideBeanOff,Ue as LucideBed,Te as LucideBedDouble,Ze as LucideBedSingle,Ge as LucideBeef,Ee as LucideBeer,Ke as LucideBell,We as LucideBellMinus,Xe as LucideBellOff,Je as LucideBellPlus,Ne as LucideBellRing,Qe as LucideBike,Ye as LucideBinary,$e as LucideBitcoin,ta as LucideBluetooth,_e as LucideBluetoothConnected,ea as LucideBluetoothOff,aa as LucideBluetoothSearching,ya as LucideBold,ca as LucideBomb,ia as LucideBone,oa as LucideBook,ha as LucideBookOpen,na as LucideBookOpenCheck,ka as LucideBookmark,da as LucideBookmarkMinus,sa as LucideBookmarkPlus,la as LucideBot,ra as LucideBox,pa as LucideBoxSelect,ua as LucideBoxes,Ma as LucideBriefcase,xa as LucideBrush,va as LucideBug,ma as LucideBuilding,ga as LucideBuilding2,La as LucideBus,fa as LucideCake,wa as LucideCalculator,Ba as LucideCalendar,Ca as LucideCalendarCheck,Ia as LucideCalendarCheck2,ba as LucideCalendarClock,za as LucideCalendarDays,Ha as LucideCalendarHeart,Sa as LucideCalendarMinus,Va as LucideCalendarOff,qa as LucideCalendarPlus,Fa as LucideCalendarRange,Aa as LucideCalendarSearch,Pa as LucideCalendarX,ja as LucideCalendarX2,Oa as LucideCamera,Da as LucideCameraOff,Ta as LucideCandy,Ra as LucideCandyOff,Za as LucideCar,Ua as LucideCarrot,Ga as LucideCast,Ea as LucideCat,Ka as LucideCheck,Wa as LucideCheckCheck,Ja as LucideCheckCircle,Xa as LucideCheckCircle2,Na as LucideCheckSquare,Qa as LucideChefHat,Ya as LucideCherry,$a as LucideChevronDown,_a as LucideChevronFirst,et as LucideChevronLast,at as LucideChevronLeft,tt as LucideChevronRight,yt as LucideChevronUp,it as LucideChevronsDown,ct as LucideChevronsDownUp,ht as LucideChevronsLeft,nt as LucideChevronsLeftRight,dt as LucideChevronsRight,ot as LucideChevronsRightLeft,kt as LucideChevronsUp,st as LucideChevronsUpDown,lt as LucideChrome,rt as LucideCigarette,pt as LucideCigaretteOff,vt as LucideCircle,ut as LucideCircleDot,Mt as LucideCircleEllipsis,xt as LucideCircleSlashed,gt as LucideCitrus,mt as LucideClapperboard,Ht as LucideClipboard,Lt as LucideClipboardCheck,ft as LucideClipboardCopy,wt as LucideClipboardEdit,It as LucideClipboardList,Ct as LucideClipboardSignature,bt as LucideClipboardType,zt as LucideClipboardX,Zt as LucideClock,St as LucideClock1,Vt as LucideClock10,qt as LucideClock11,Ft as LucideClock12,At as LucideClock2,jt as LucideClock3,Pt as LucideClock4,Bt as LucideClock5,Dt as LucideClock6,Ot as LucideClock7,Rt as LucideClock8,Tt as LucideClock9,ay as LucideCloud,Ut as LucideCloudCog,Gt as LucideCloudDrizzle,Et as LucideCloudFog,Wt as LucideCloudHail,Xt as LucideCloudLightning,Nt as LucideCloudMoon,Jt as LucideCloudMoonRain,Kt as LucideCloudOff,Yt as LucideCloudRain,Qt as LucideCloudRainWind,$t as LucideCloudSnow,ey as LucideCloudSun,_t as LucideCloudSunRain,ty as LucideCloudy,yy as LucideClover,iy as LucideCode,cy as LucideCode2,ny as LucideCodepen,hy as LucideCodesandbox,oy as LucideCoffee,dy as LucideCog,sy as LucideCoins,ky as LucideColumns,ly as LucideCommand,py as LucideCompass,ry as LucideComponent,uy as LucideConciergeBell,My as LucideContact,xy as LucideContrast,vy as LucideCookie,gy as LucideCopy,my as LucideCopyleft,Ly as LucideCopyright,fy as LucideCornerDownLeft,wy as LucideCornerDownRight,Iy as LucideCornerLeftDown,Cy as LucideCornerLeftUp,by as LucideCornerRightDown,zy as LucideCornerRightUp,Hy as LucideCornerUpLeft,Sy as LucideCornerUpRight,Vy as LucideCpu,qy as LucideCreativeCommons,Fy as LucideCreditCard,Ay as LucideCroissant,jy as LucideCrop,Py as LucideCross,By as LucideCrosshair,Dy as LucideCrown,Oy as LucideCupSoda,Ry as LucideCurlyBraces,Ty as LucideCurrency,Uy as LucideDatabase,Zy as LucideDatabaseBackup,Gy as LucideDelete,Ey as LucideDiamond,Wy as LucideDice1,Xy as LucideDice2,Jy as LucideDice3,Ny as LucideDice4,Ky as LucideDice5,Qy as LucideDice6,Yy as LucideDices,$y as LucideDiff,_y as LucideDisc,tc as LucideDivide,ec as LucideDivideCircle,ac as LucideDivideSquare,cc as LucideDna,yc as LucideDnaOff,ic as LucideDog,nc as LucideDollarSign,oc as LucideDownload,hc as LucideDownloadCloud,dc as LucideDribbble,sc as LucideDroplet,kc as LucideDroplets,lc as LucideDrumstick,pc as LucideDumbbell,uc as LucideEar,rc as LucideEarOff,vc as LucideEdit,Mc as LucideEdit2,xc as LucideEdit3,Lc as LucideEgg,gc as LucideEggFried,mc as LucideEggOff,wc as LucideEqual,fc as LucideEqualNot,Ic as LucideEraser,Cc as LucideEuro,bc as LucideExpand,zc as LucideExternalLink,Sc as LucideEye,Hc as LucideEyeOff,Vc as LucideFacebook,qc as LucideFactory,Fc as LucideFan,Ac as LucideFastForward,jc as LucideFeather,Pc as LucideFigma,Bi as LucideFile,Bc as LucideFileArchive,Oc as LucideFileAudio,Dc as LucideFileAudio2,Rc as LucideFileAxis3d,Zc as LucideFileBadge,Tc as LucideFileBadge2,Gc as LucideFileBarChart,Uc as LucideFileBarChart2,Ec as LucideFileBox,Xc as LucideFileCheck,Wc as LucideFileCheck2,Jc as LucideFileClock,Nc as LucideFileCode,Qc as LucideFileCog,Kc as LucideFileCog2,Yc as LucideFileDiff,$c as LucideFileDigit,_c as LucideFileDown,ei as LucideFileEdit,ai as LucideFileHeart,ti as LucideFileImage,yi as LucideFileInput,ii as LucideFileJson,ci as LucideFileJson2,hi as LucideFileKey,ni as LucideFileKey2,oi as LucideFileLineChart,si as LucideFileLock,di as LucideFileLock2,li as LucideFileMinus,ki as LucideFileMinus2,pi as LucideFileOutput,ri as LucideFilePieChart,Mi as LucideFilePlus,ui as LucideFilePlus2,xi as LucideFileQuestion,vi as LucideFileScan,mi as LucideFileSearch,gi as LucideFileSearch2,Li as LucideFileSignature,fi as LucideFileSpreadsheet,wi as LucideFileSymlink,Ii as LucideFileTerminal,Ci as LucideFileText,zi as LucideFileType,bi as LucideFileType2,Hi as LucideFileUp,Vi as LucideFileVideo,Si as LucideFileVideo2,Fi as LucideFileVolume,qi as LucideFileVolume2,Ai as LucideFileWarning,Pi as LucideFileX,ji as LucideFileX2,Di as LucideFiles,Oi as LucideFilm,Ri as LucideFilter,Ti as LucideFingerprint,Ui as LucideFish,Zi as LucideFishOff,Xi as LucideFlag,Gi as LucideFlagOff,Ei as LucideFlagTriangleLeft,Wi as LucideFlagTriangleRight,Ji as LucideFlame,Ki as LucideFlashlight,Ni as LucideFlashlightOff,Yi as LucideFlaskConical,Qi as LucideFlaskConicalOff,$i as LucideFlaskRound,en as LucideFlipHorizontal,_i as LucideFlipHorizontal2,tn as LucideFlipVertical,an as LucideFlipVertical2,cn as LucideFlower,yn as LucideFlower2,nn as LucideFocus,Sn as LucideFolder,hn as LucideFolderArchive,on as LucideFolderCheck,dn as LucideFolderClock,sn as LucideFolderClosed,ln as LucideFolderCog,kn as LucideFolderCog2,pn as LucideFolderDown,rn as LucideFolderEdit,un as LucideFolderHeart,Mn as LucideFolderInput,xn as LucideFolderKey,vn as LucideFolderLock,gn as LucideFolderMinus,mn as LucideFolderOpen,Ln as LucideFolderOutput,fn as LucideFolderPlus,In as LucideFolderSearch,wn as LucideFolderSearch2,Cn as LucideFolderSymlink,bn as LucideFolderTree,zn as LucideFolderUp,Hn as LucideFolderX,Vn as LucideFolders,qn as LucideFormInput,Fn as LucideForward,An as LucideFrame,jn as LucideFramer,Pn as LucideFrown,Bn as LucideFuel,Dn as LucideFunctionSquare,Rn as LucideGamepad,On as LucideGamepad2,Tn as LucideGauge,Zn as LucideGavel,Un as LucideGem,Gn as LucideGhost,En as LucideGift,Xn as LucideGitBranch,Wn as LucideGitBranchPlus,Jn as LucideGitCommit,Nn as LucideGitCompare,Kn as LucideGitFork,Qn as LucideGitMerge,_n as LucideGitPullRequest,Yn as LucideGitPullRequestClosed,$n as LucideGitPullRequestDraft,eh as LucideGithub,ah as LucideGitlab,th as LucideGlassWater,yh as LucideGlasses,ih as LucideGlobe,ch as LucideGlobe2,nh as LucideGrab,hh as LucideGraduationCap,oh as LucideGrape,dh as LucideGrid,lh as LucideGrip,sh as LucideGripHorizontal,kh as LucideGripVertical,ph as LucideHammer,uh as LucideHand,rh as LucideHandMetal,Mh as LucideHardDrive,xh as LucideHardHat,vh as LucideHash,gh as LucideHaze,bh as LucideHeading,mh as LucideHeading1,Lh as LucideHeading2,fh as LucideHeading3,wh as LucideHeading4,Ih as LucideHeading5,Ch as LucideHeading6,zh as LucideHeadphones,Fh as LucideHeart,Hh as LucideHeartCrack,Sh as LucideHeartHandshake,Vh as LucideHeartOff,qh as LucideHeartPulse,Ah as LucideHelpCircle,jh as LucideHexagon,Ph as LucideHighlighter,Bh as LucideHistory,Dh as LucideHome,Rh as LucideHop,Oh as LucideHopOff,Th as LucideHourglass,Uh as LucideIceCream,Zh as LucideIceCream2,Xh as LucideImage,Gh as LucideImageMinus,Eh as LucideImageOff,Wh as LucideImagePlus,Jh as LucideImport,Nh as LucideInbox,Kh as LucideIndent,Qh as LucideIndianRupee,Yh as LucideInfinity,$h as LucideInfo,_h as LucideInspect,eo as LucideInstagram,ao as LucideItalic,to as LucideJapaneseYen,yo as LucideJoystick,co as LucideKey,io as LucideKeyboard,lo as LucideLamp,no as LucideLampCeiling,ho as LucideLampDesk,oo as LucideLampFloor,so as LucideLampWallDown,ko as LucideLampWallUp,po as LucideLandmark,ro as LucideLanguages,Mo as LucideLaptop,uo as LucideLaptop2,vo as LucideLasso,xo as LucideLassoSelect,go as LucideLaugh,mo as LucideLayers,Co as LucideLayout,Lo as LucideLayoutDashboard,fo as LucideLayoutGrid,wo as LucideLayoutList,Io as LucideLayoutTemplate,bo as LucideLeaf,zo as LucideLibrary,Ho as LucideLifeBuoy,Vo as LucideLightbulb,So as LucideLightbulbOff,qo as LucideLineChart,jo as LucideLink,Ao as LucideLink2,Fo as LucideLink2Off,Po as LucideLinkedin,Wo as LucideList,Bo as LucideListChecks,Do as LucideListEnd,Oo as LucideListMinus,Ro as LucideListMusic,To as LucideListOrdered,Zo as LucideListPlus,Uo as LucideListStart,Go as LucideListVideo,Eo as LucideListX,Jo as LucideLoader,Xo as LucideLoader2,Qo as LucideLocate,No as LucideLocateFixed,Ko as LucideLocateOff,Yo as LucideLock,$o as LucideLogIn,_o as LucideLogOut,ed as LucideLuggage,ad as LucideMagnet,sd as LucideMail,td as LucideMailCheck,yd as LucideMailMinus,cd as LucideMailOpen,id as LucideMailPlus,nd as LucideMailQuestion,hd as LucideMailSearch,od as LucideMailWarning,dd as LucideMailX,kd as LucideMails,rd as LucideMap,pd as LucideMapPin,ld as LucideMapPinOff,ud as LucideMartini,xd as LucideMaximize,Md as LucideMaximize2,vd as LucideMedal,md as LucideMegaphone,gd as LucideMegaphoneOff,Ld as LucideMeh,fd as LucideMenu,wd as LucideMessageCircle,Id as LucideMessageSquare,zd as LucideMic,Cd as LucideMic2,bd as LucideMicOff,Hd as LucideMicroscope,Sd as LucideMicrowave,Vd as LucideMilestone,Fd as LucideMilk,qd as LucideMilkOff,jd as LucideMinimize,Ad as LucideMinimize2,Dd as LucideMinus,Pd as LucideMinusCircle,Bd as LucideMinusSquare,Zd as LucideMonitor,Od as LucideMonitorOff,Rd as LucideMonitorSmartphone,Td as LucideMonitorSpeaker,Ud as LucideMoon,Gd as LucideMoreHorizontal,Ed as LucideMoreVertical,Xd as LucideMountain,Wd as LucideMountainSnow,Qd as LucideMouse,Kd as LucideMousePointer,Jd as LucideMousePointer2,Nd as LucideMousePointerClick,ts as LucideMove,Yd as LucideMove3d,_d as LucideMoveDiagonal,$d as LucideMoveDiagonal2,es as LucideMoveHorizontal,as as LucideMoveVertical,ns as LucideMusic,ys as LucideMusic2,cs as LucideMusic3,is as LucideMusic4,ss as LucideNavigation,os as LucideNavigation2,hs as LucideNavigation2Off,ds as LucideNavigationOff,ks as LucideNetwork,ls as LucideNewspaper,rs as LucideNut,ps as LucideNutOff,us as LucideOctagon,Ms as LucideOption,xs as LucideOutdent,Cs as LucidePackage,vs as LucidePackage2,gs as LucidePackageCheck,ms as LucidePackageMinus,Ls as LucidePackageOpen,fs as LucidePackagePlus,ws as LucidePackageSearch,Is as LucidePackageX,bs as LucidePaintBucket,Hs as LucidePaintbrush,zs as LucidePaintbrush2,Ss as LucidePalette,Vs as LucidePalmtree,qs as LucidePaperclip,Fs as LucidePartyPopper,Ps as LucidePause,As as LucidePauseCircle,js as LucidePauseOctagon,Bs as LucidePenTool,Ds as LucidePencil,Os as LucidePercent,Rs as LucidePersonStanding,Xs as LucidePhone,Ts as LucidePhoneCall,Zs as LucidePhoneForwarded,Us as LucidePhoneIncoming,Gs as LucidePhoneMissed,Es as LucidePhoneOff,Ws as LucidePhoneOutgoing,Ns as LucidePictureInPicture,Js as LucidePictureInPicture2,Ks as LucidePieChart,Qs as LucidePiggyBank,Ys as LucidePilcrow,_s as LucidePin,$s as LucidePinOff,ek as LucidePipette,ak as LucidePizza,tk as LucidePlane,ck as LucidePlay,yk as LucidePlayCircle,hk as LucidePlug,ik as LucidePlug2,nk as LucidePlugZap,sk as LucidePlus,ok as LucidePlusCircle,dk as LucidePlusSquare,kk as LucidePocket,lk as LucidePodcast,pk as LucidePointer,rk as LucidePoundSterling,Mk as LucidePower,uk as LucidePowerOff,xk as LucidePrinter,vk as LucidePuzzle,gk as LucideQrCode,mk as LucideQuote,fk as LucideRadio,Lk as LucideRadioReceiver,wk as LucideRectangleHorizontal,Ik as LucideRectangleVertical,Ck as LucideRecycle,zk as LucideRedo,bk as LucideRedo2,Hk as LucideRefreshCcw,Sk as LucideRefreshCw,Vk as LucideRefrigerator,qk as LucideRegex,Fk as LucideRemoveFormatting,jk as LucideRepeat,Ak as LucideRepeat1,Bk as LucideReply,Pk as LucideReplyAll,Dk as LucideRewind,Ok as LucideRocket,Rk as LucideRockingChair,Tk as LucideRotate3d,Zk as LucideRotateCcw,Uk as LucideRotateCw,Gk as LucideRss,Ek as LucideRuler,Wk as LucideRussianRuble,Xk as LucideSailboat,Jk as LucideSalad,Nk as LucideSandwich,Kk as LucideSave,Yk as LucideScale,Qk as LucideScale3d,$k as LucideScaling,al as LucideScan,_k as LucideScanFace,el as LucideScanLine,tl as LucideScissors,cl as LucideScreenShare,yl as LucideScreenShareOff,il as LucideScroll,nl as LucideSearch,hl as LucideSend,ol as LucideSeparatorHorizontal,dl as LucideSeparatorVertical,pl as LucideServer,sl as LucideServerCog,kl as LucideServerCrash,ll as LucideServerOff,ul as LucideSettings,rl as LucideSettings2,xl as LucideShare,Ml as LucideShare2,vl as LucideSheet,wl as LucideShield,gl as LucideShieldAlert,ml as LucideShieldCheck,Ll as LucideShieldClose,fl as LucideShieldOff,Il as LucideShirt,Cl as LucideShoppingBag,bl as LucideShoppingCart,zl as LucideShovel,Hl as LucideShowerHead,Sl as LucideShrink,Vl as LucideShrub,ql as LucideShuffle,jl as LucideSidebar,Fl as LucideSidebarClose,Al as LucideSidebarOpen,Pl as LucideSigma,Tl as LucideSignal,Bl as LucideSignalHigh,Dl as LucideSignalLow,Ol as LucideSignalMedium,Rl as LucideSignalZero,Zl as LucideSiren,Ul as LucideSkipBack,Gl as LucideSkipForward,El as LucideSkull,Wl as LucideSlack,Xl as LucideSlash,Jl as LucideSlice,Kl as LucideSliders,Nl as LucideSlidersHorizontal,Yl as LucideSmartphone,Ql as LucideSmartphoneCharging,_l as LucideSmile,$l as LucideSmilePlus,ep as LucideSnowflake,ap as LucideSofa,tp as LucideSortAsc,yp as LucideSortDesc,cp as LucideSoup,ip as LucideSpeaker,np as LucideSpline,hp as LucideSprout,op as LucideSquare,kp as LucideStar,dp as LucideStarHalf,sp as LucideStarOff,lp as LucideStethoscope,pp as LucideSticker,rp as LucideStickyNote,up as LucideStopCircle,Mp as LucideStretchHorizontal,xp as LucideStretchVertical,vp as LucideStrikethrough,gp as LucideSubscript,mp as LucideSubtitles,Cp as LucideSun,Lp as LucideSunDim,fp as LucideSunMedium,wp as LucideSunMoon,Ip as LucideSunSnow,bp as LucideSunrise,zp as LucideSunset,Hp as LucideSuperscript,Sp as LucideSwissFranc,Vp as LucideSwitchCamera,qp as LucideSword,Fp as LucideSwords,Ap as LucideSyringe,Pp as LucideTable,jp as LucideTable2,Bp as LucideTablet,Dp as LucideTag,Op as LucideTags,Rp as LucideTarget,Tp as LucideTent,Up as LucideTerminal,Zp as LucideTerminalSquare,Ep as LucideTextCursor,Gp as LucideTextCursorInput,Jp as LucideThermometer,Wp as LucideThermometerSnowflake,Xp as LucideThermometerSun,Np as LucideThumbsDown,Kp as LucideThumbsUp,Qp as LucideTicket,_p as LucideTimer,Yp as LucideTimerOff,$p as LucideTimerReset,er as LucideToggleLeft,ar as LucideToggleRight,tr as LucideTornado,yr as LucideToyBrick,cr as LucideTrain,nr as LucideTrash,ir as LucideTrash2,hr as LucideTreeDeciduous,or as LucideTreePine,dr as LucideTrees,sr as LucideTrello,kr as LucideTrendingDown,lr as LucideTrendingUp,pr as LucideTriangle,rr as LucideTrophy,ur as LucideTruck,xr as LucideTv,Mr as LucideTv2,vr as LucideTwitch,gr as LucideTwitter,mr as LucideType,Lr as LucideUmbrella,fr as LucideUnderline,Ir as LucideUndo,wr as LucideUndo2,br as LucideUnlink,Cr as LucideUnlink2,zr as LucideUnlock,Sr as LucideUpload,Hr as LucideUploadCloud,Vr as LucideUsb,Br as LucideUser,qr as LucideUserCheck,Fr as LucideUserCog,Ar as LucideUserMinus,jr as LucideUserPlus,Pr as LucideUserX,Dr as LucideUsers,Rr as LucideUtensils,Or as LucideUtensilsCrossed,Tr as LucideVegan,Zr as LucideVenetianMask,Ur as LucideVerified,Er as LucideVibrate,Gr as LucideVibrateOff,Xr as LucideVideo,Wr as LucideVideoOff,Jr as LucideView,Nr as LucideVoicemail,$r as LucideVolume,Kr as LucideVolume1,Qr as LucideVolume2,Yr as LucideVolumeX,_r as LucideVote,eu as LucideWallet,tu as LucideWand,au as LucideWand2,yu as LucideWatch,cu as LucideWaves,iu as LucideWebcam,nu as LucideWebhook,ou as LucideWheat,hu as LucideWheatOff,su as LucideWifi,du as LucideWifiOff,ku as LucideWind,pu as LucideWine,lu as LucideWineOff,ru as LucideWrapText,uu as LucideWrench,gu as LucideX,Mu as LucideXCircle,xu as LucideXOctagon,vu as LucideXSquare,mu as LucideYoutube,fu as LucideZap,Lu as LucideZapOff,wu as LucideZoomIn,Iu as LucideZoomOut,ed as Luggage,ed as LuggageIcon,ad as Magnet,ad as MagnetIcon,sd as Mail,td as MailCheck,td as MailCheckIcon,sd as MailIcon,yd as MailMinus,yd as MailMinusIcon,cd as MailOpen,cd as MailOpenIcon,id as MailPlus,id as MailPlusIcon,nd as MailQuestion,nd as MailQuestionIcon,hd as MailSearch,hd as MailSearchIcon,od as MailWarning,od as MailWarningIcon,dd as MailX,dd as MailXIcon,kd as Mails,kd as MailsIcon,rd as Map,rd as MapIcon,pd as MapPin,pd as MapPinIcon,ld as MapPinOff,ld as MapPinOffIcon,ud as Martini,ud as MartiniIcon,xd as Maximize,Md as Maximize2,Md as Maximize2Icon,xd as MaximizeIcon,vd as Medal,vd as MedalIcon,md as Megaphone,md as MegaphoneIcon,gd as MegaphoneOff,gd as MegaphoneOffIcon,Ld as Meh,Ld as MehIcon,fd as Menu,fd as MenuIcon,wd as MessageCircle,wd as MessageCircleIcon,Id as MessageSquare,Id as MessageSquareIcon,zd as Mic,Cd as Mic2,Cd as Mic2Icon,zd as MicIcon,bd as MicOff,bd as MicOffIcon,Hd as Microscope,Hd as MicroscopeIcon,Sd as Microwave,Sd as MicrowaveIcon,Vd as Milestone,Vd as MilestoneIcon,Fd as Milk,Fd as MilkIcon,qd as MilkOff,qd as MilkOffIcon,jd as Minimize,Ad as Minimize2,Ad as Minimize2Icon,jd as MinimizeIcon,Dd as Minus,Pd as MinusCircle,Pd as MinusCircleIcon,Dd as MinusIcon,Bd as MinusSquare,Bd as MinusSquareIcon,Zd as Monitor,Zd as MonitorIcon,Od as MonitorOff,Od as MonitorOffIcon,Rd as MonitorSmartphone,Rd as MonitorSmartphoneIcon,Td as MonitorSpeaker,Td as MonitorSpeakerIcon,Ud as Moon,Ud as MoonIcon,Gd as MoreHorizontal,Gd as MoreHorizontalIcon,Ed as MoreVertical,Ed as MoreVerticalIcon,Xd as Mountain,Xd as MountainIcon,Wd as MountainSnow,Wd as MountainSnowIcon,Qd as Mouse,Qd as MouseIcon,Kd as MousePointer,Jd as MousePointer2,Jd as MousePointer2Icon,Nd as MousePointerClick,Nd as MousePointerClickIcon,Kd as MousePointerIcon,ts as Move,Yd as Move3d,Yd as Move3dIcon,_d as MoveDiagonal,$d as MoveDiagonal2,$d as MoveDiagonal2Icon,_d as MoveDiagonalIcon,es as MoveHorizontal,es as MoveHorizontalIcon,ts as MoveIcon,as as MoveVertical,as as MoveVerticalIcon,ns as Music,ys as Music2,ys as Music2Icon,cs as Music3,cs as Music3Icon,is as Music4,is as Music4Icon,ns as MusicIcon,ss as Navigation,os as Navigation2,os as Navigation2Icon,hs as Navigation2Off,hs as Navigation2OffIcon,ss as NavigationIcon,ds as NavigationOff,ds as NavigationOffIcon,ks as Network,ks as NetworkIcon,ls as Newspaper,ls as NewspaperIcon,rs as Nut,rs as NutIcon,ps as NutOff,ps as NutOffIcon,us as Octagon,us as OctagonIcon,Ms as Option,Ms as OptionIcon,xs as Outdent,xs as OutdentIcon,Cs as Package,vs as Package2,vs as Package2Icon,gs as PackageCheck,gs as PackageCheckIcon,Cs as PackageIcon,ms as PackageMinus,ms as PackageMinusIcon,Ls as PackageOpen,Ls as PackageOpenIcon,fs as PackagePlus,fs as PackagePlusIcon,ws as PackageSearch,ws as PackageSearchIcon,Is as PackageX,Is as PackageXIcon,bs as PaintBucket,bs as PaintBucketIcon,Hs as Paintbrush,zs as Paintbrush2,zs as Paintbrush2Icon,Hs as PaintbrushIcon,Ss as Palette,Ss as PaletteIcon,Vs as Palmtree,Vs as PalmtreeIcon,qs as Paperclip,qs as PaperclipIcon,Fs as PartyPopper,Fs as PartyPopperIcon,Ps as Pause,As as PauseCircle,As as PauseCircleIcon,Ps as PauseIcon,js as PauseOctagon,js as PauseOctagonIcon,Mc as Pen,vc as PenBox,xc as PenLine,Bs as PenTool,Bs as PenToolIcon,Ds as Pencil,Ds as PencilIcon,Os as Percent,Os as PercentIcon,Rs as PersonStanding,Rs as PersonStandingIcon,Xs as Phone,Ts as PhoneCall,Ts as PhoneCallIcon,Zs as PhoneForwarded,Zs as PhoneForwardedIcon,Xs as PhoneIcon,Us as PhoneIncoming,Us as PhoneIncomingIcon,Gs as PhoneMissed,Gs as PhoneMissedIcon,Es as PhoneOff,Es as PhoneOffIcon,Ws as PhoneOutgoing,Ws as PhoneOutgoingIcon,Ns as PictureInPicture,Js as PictureInPicture2,Js as PictureInPicture2Icon,Ns as PictureInPictureIcon,Ks as PieChart,Ks as PieChartIcon,Qs as PiggyBank,Qs as PiggyBankIcon,Ys as Pilcrow,Ys as PilcrowIcon,_s as Pin,_s as PinIcon,$s as PinOff,$s as PinOffIcon,ek as Pipette,ek as PipetteIcon,ak as Pizza,ak as PizzaIcon,tk as Plane,tk as PlaneIcon,ck as Play,yk as PlayCircle,yk as PlayCircleIcon,ck as PlayIcon,hk as Plug,ik as Plug2,ik as Plug2Icon,hk as PlugIcon,nk as PlugZap,nk as PlugZapIcon,sk as Plus,ok as PlusCircle,ok as PlusCircleIcon,sk as PlusIcon,dk as PlusSquare,dk as PlusSquareIcon,kk as Pocket,kk as PocketIcon,lk as Podcast,lk as PodcastIcon,pk as Pointer,pk as PointerIcon,rk as PoundSterling,rk as PoundSterlingIcon,Mk as Power,Mk as PowerIcon,uk as PowerOff,uk as PowerOffIcon,xk as Printer,xk as PrinterIcon,vk as Puzzle,vk as PuzzleIcon,gk as QrCode,gk as QrCodeIcon,mk as Quote,mk as QuoteIcon,fk as Radio,fk as RadioIcon,Lk as RadioReceiver,Lk as RadioReceiverIcon,wk as RectangleHorizontal,wk as RectangleHorizontalIcon,Ik as RectangleVertical,Ik as RectangleVerticalIcon,Ck as Recycle,Ck as RecycleIcon,zk as Redo,bk as Redo2,bk as Redo2Icon,zk as RedoIcon,Hk as RefreshCcw,Hk as RefreshCcwIcon,Sk as RefreshCw,Sk as RefreshCwIcon,Vk as Refrigerator,Vk as RefrigeratorIcon,qk as Regex,qk as RegexIcon,Fk as RemoveFormatting,Fk as RemoveFormattingIcon,jk as Repeat,Ak as Repeat1,Ak as Repeat1Icon,jk as RepeatIcon,Bk as Reply,Pk as ReplyAll,Pk as ReplyAllIcon,Bk as ReplyIcon,Dk as Rewind,Dk as RewindIcon,Ok as Rocket,Ok as RocketIcon,Rk as RockingChair,Rk as RockingChairIcon,Tk as Rotate3d,Tk as Rotate3dIcon,Zk as RotateCcw,Zk as RotateCcwIcon,Uk as RotateCw,Uk as RotateCwIcon,Gk as Rss,Gk as RssIcon,Ek as Ruler,Ek as RulerIcon,Wk as RussianRuble,Wk as RussianRubleIcon,Xk as Sailboat,Xk as SailboatIcon,Jk as Salad,Jk as SaladIcon,Nk as Sandwich,Nk as SandwichIcon,Kk as Save,Kk as SaveIcon,Yk as Scale,Qk as Scale3d,Qk as Scale3dIcon,Yk as ScaleIcon,$k as Scaling,$k as ScalingIcon,al as Scan,_k as ScanFace,_k as ScanFaceIcon,al as ScanIcon,el as ScanLine,el as ScanLineIcon,tl as Scissors,tl as ScissorsIcon,cl as ScreenShare,cl as ScreenShareIcon,yl as ScreenShareOff,yl as ScreenShareOffIcon,il as Scroll,il as ScrollIcon,nl as Search,nl as SearchIcon,hl as Send,hl as SendIcon,ol as SeparatorHorizontal,ol as SeparatorHorizontalIcon,dl as SeparatorVertical,dl as SeparatorVerticalIcon,pl as Server,sl as ServerCog,sl as ServerCogIcon,kl as ServerCrash,kl as ServerCrashIcon,pl as ServerIcon,ll as ServerOff,ll as ServerOffIcon,ul as Settings,rl as Settings2,rl as Settings2Icon,ul as SettingsIcon,xl as Share,Ml as Share2,Ml as Share2Icon,xl as ShareIcon,vl as Sheet,vl as SheetIcon,wl as Shield,gl as ShieldAlert,gl as ShieldAlertIcon,ml as ShieldCheck,ml as ShieldCheckIcon,Ll as ShieldClose,Ll as ShieldCloseIcon,wl as ShieldIcon,fl as ShieldOff,fl as ShieldOffIcon,Il as Shirt,Il as ShirtIcon,Cl as ShoppingBag,Cl as ShoppingBagIcon,bl as ShoppingCart,bl as ShoppingCartIcon,zl as Shovel,zl as ShovelIcon,Hl as ShowerHead,Hl as ShowerHeadIcon,Sl as Shrink,Sl as ShrinkIcon,Vl as Shrub,Vl as ShrubIcon,ql as Shuffle,ql as ShuffleIcon,jl as Sidebar,Fl as SidebarClose,Fl as SidebarCloseIcon,jl as SidebarIcon,Al as SidebarOpen,Al as SidebarOpenIcon,Pl as Sigma,Pl as SigmaIcon,Tl as Signal,Bl as SignalHigh,Bl as SignalHighIcon,Tl as SignalIcon,Dl as SignalLow,Dl as SignalLowIcon,Ol as SignalMedium,Ol as SignalMediumIcon,Rl as SignalZero,Rl as SignalZeroIcon,Zl as Siren,Zl as SirenIcon,Ul as SkipBack,Ul as SkipBackIcon,Gl as SkipForward,Gl as SkipForwardIcon,El as Skull,El as SkullIcon,Wl as Slack,Wl as SlackIcon,Xl as Slash,Xl as SlashIcon,Jl as Slice,Jl as SliceIcon,Kl as Sliders,Nl as SlidersHorizontal,Nl as SlidersHorizontalIcon,Kl as SlidersIcon,Yl as Smartphone,Ql as SmartphoneCharging,Ql as SmartphoneChargingIcon,Yl as SmartphoneIcon,_l as Smile,_l as SmileIcon,$l as SmilePlus,$l as SmilePlusIcon,ep as Snowflake,ep as SnowflakeIcon,ap as Sofa,ap as SofaIcon,tp as SortAsc,tp as SortAscIcon,yp as SortDesc,yp as SortDescIcon,cp as Soup,cp as SoupIcon,ip as Speaker,ip as SpeakerIcon,np as Spline,np as SplineIcon,hp as Sprout,hp as SproutIcon,op as Square,op as SquareIcon,kp as Star,dp as StarHalf,dp as StarHalfIcon,kp as StarIcon,sp as StarOff,sp as StarOffIcon,lp as Stethoscope,lp as StethoscopeIcon,pp as Sticker,pp as StickerIcon,rp as StickyNote,rp as StickyNoteIcon,up as StopCircle,up as StopCircleIcon,Mp as StretchHorizontal,Mp as StretchHorizontalIcon,xp as StretchVertical,xp as StretchVerticalIcon,vp as Strikethrough,vp as StrikethroughIcon,gp as Subscript,gp as SubscriptIcon,mp as Subtitles,mp as SubtitlesIcon,Cp as Sun,Lp as SunDim,Lp as SunDimIcon,Cp as SunIcon,fp as SunMedium,fp as SunMediumIcon,wp as SunMoon,wp as SunMoonIcon,Ip as SunSnow,Ip as SunSnowIcon,bp as Sunrise,bp as SunriseIcon,zp as Sunset,zp as SunsetIcon,Hp as Superscript,Hp as SuperscriptIcon,Sp as SwissFranc,Sp as SwissFrancIcon,Vp as SwitchCamera,Vp as SwitchCameraIcon,qp as Sword,qp as SwordIcon,Fp as Swords,Fp as SwordsIcon,Ap as Syringe,Ap as SyringeIcon,Pp as Table,jp as Table2,jp as Table2Icon,Pp as TableIcon,Bp as Tablet,Bp as TabletIcon,Dp as Tag,Dp as TagIcon,Op as Tags,Op as TagsIcon,Rp as Target,Rp as TargetIcon,Tp as Tent,Tp as TentIcon,Up as Terminal,Up as TerminalIcon,Zp as TerminalSquare,Zp as TerminalSquareIcon,Ep as TextCursor,Ep as TextCursorIcon,Gp as TextCursorInput,Gp as TextCursorInputIcon,Jp as Thermometer,Jp as ThermometerIcon,Wp as ThermometerSnowflake,Wp as ThermometerSnowflakeIcon,Xp as ThermometerSun,Xp as ThermometerSunIcon,Np as ThumbsDown,Np as ThumbsDownIcon,Kp as ThumbsUp,Kp as ThumbsUpIcon,Qp as Ticket,Qp as TicketIcon,_p as Timer,_p as TimerIcon,Yp as TimerOff,Yp as TimerOffIcon,$p as TimerReset,$p as TimerResetIcon,er as ToggleLeft,er as ToggleLeftIcon,ar as ToggleRight,ar as ToggleRightIcon,tr as Tornado,tr as TornadoIcon,yr as ToyBrick,yr as ToyBrickIcon,cr as Train,cr as TrainIcon,nr as Trash,ir as Trash2,ir as Trash2Icon,nr as TrashIcon,hr as TreeDeciduous,hr as TreeDeciduousIcon,or as TreePine,or as TreePineIcon,dr as Trees,dr as TreesIcon,sr as Trello,sr as TrelloIcon,kr as TrendingDown,kr as TrendingDownIcon,lr as TrendingUp,lr as TrendingUpIcon,pr as Triangle,pr as TriangleIcon,rr as Trophy,rr as TrophyIcon,ur as Truck,ur as TruckIcon,xr as Tv,Mr as Tv2,Mr as Tv2Icon,xr as TvIcon,vr as Twitch,vr as TwitchIcon,gr as Twitter,gr as TwitterIcon,mr as Type,mr as TypeIcon,Lr as Umbrella,Lr as UmbrellaIcon,fr as Underline,fr as UnderlineIcon,Ir as Undo,wr as Undo2,wr as Undo2Icon,Ir as UndoIcon,br as Unlink,Cr as Unlink2,Cr as Unlink2Icon,br as UnlinkIcon,zr as Unlock,zr as UnlockIcon,Sr as Upload,Hr as UploadCloud,Hr as UploadCloudIcon,Sr as UploadIcon,Vr as Usb,Vr as UsbIcon,Br as User,qr as UserCheck,qr as UserCheckIcon,Fr as UserCog,Fr as UserCogIcon,Br as UserIcon,Ar as UserMinus,Ar as UserMinusIcon,jr as UserPlus,jr as UserPlusIcon,Pr as UserX,Pr as UserXIcon,Dr as Users,Dr as UsersIcon,Rr as Utensils,Or as UtensilsCrossed,Or as UtensilsCrossedIcon,Rr as UtensilsIcon,Tr as Vegan,Tr as VeganIcon,Zr as VenetianMask,Zr as VenetianMaskIcon,Ur as Verified,Ur as VerifiedIcon,Er as Vibrate,Er as VibrateIcon,Gr as VibrateOff,Gr as VibrateOffIcon,Xr as Video,Xr as VideoIcon,Wr as VideoOff,Wr as VideoOffIcon,Jr as View,Jr as ViewIcon,Nr as Voicemail,Nr as VoicemailIcon,$r as Volume,Kr as Volume1,Kr as Volume1Icon,Qr as Volume2,Qr as Volume2Icon,$r as VolumeIcon,Yr as VolumeX,Yr as VolumeXIcon,_r as Vote,_r as VoteIcon,eu as Wallet,eu as WalletIcon,tu as Wand,au as Wand2,au as Wand2Icon,tu as WandIcon,yu as Watch,yu as WatchIcon,cu as Waves,cu as WavesIcon,iu as Webcam,iu as WebcamIcon,nu as Webhook,nu as WebhookIcon,ou as Wheat,ou as WheatIcon,hu as WheatOff,hu as WheatOffIcon,su as Wifi,su as WifiIcon,du as WifiOff,du as WifiOffIcon,ku as Wind,ku as WindIcon,pu as Wine,pu as WineIcon,lu as WineOff,lu as WineOffIcon,ru as WrapText,ru as WrapTextIcon,uu as Wrench,uu as WrenchIcon,gu as X,Mu as XCircle,Mu as XCircleIcon,gu as XIcon,xu as XOctagon,xu as XOctagonIcon,vu as XSquare,vu as XSquareIcon,mu as Youtube,mu as YoutubeIcon,fu as Zap,fu as ZapIcon,Lu as ZapOff,Lu as ZapOffIcon,wu as ZoomIn,wu as ZoomInIcon,Iu as ZoomOut,Iu as ZoomOutIcon,createLucideIcon,Cu as icons};\n\n//# sourceMappingURL=lucide-react.js.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import{addPropertyControls,ControlType}from\"framer\";import{useState,useEffect,useMemo}from\"react\";import{BlogPosts}from\"https://framerusercontent.com/modules/o2TSQ8a9GN8vGOdyQvqN/2OIHzydm6EHa0tFTqJk9/BlogPostsToJson.js\";import{Search}from\"lucide-react\";export default function CMSSearch(props){const[searchTerm,setSearchTerm]=useState(\"\");const[isLoading,setIsLoading]=useState(false);const[allPosts,setAllPosts]=useState([]);const[showResults,setShowResults]=useState(false);// CMS 데이터 로드 및 필터링\nuseEffect(()=>{const loadCMSData=async()=>{setIsLoading(true);try{// 필터링\nconst filteredPosts=BlogPosts.filter(post=>post.category===props.contentType&&post.language===props.contentLanguage);setAllPosts(filteredPosts);}catch(error){console.error(\"Failed to load CMS data:\",error);}finally{setIsLoading(false);}};loadCMSData();},[]);// 검색 로직 - useMemo로 실시간 계산\nconst searchResults=useMemo(()=>{if(!searchTerm.trim()){return[];}const term=searchTerm.toLowerCase();return allPosts.filter(post=>{const isNotDraft=!post[\":draft\"];const titleMatch=post.Title.toLowerCase().includes(term);const contentMatch=props.contentType===\"release-note\"?post.Content.toLowerCase().includes(term):false;return isNotDraft&&(titleMatch||contentMatch);}).slice(0,props.maxResults||10)// 결과 개수 제한\n;},[searchTerm,allPosts,props.maxResults,props.contentType]);// 검색어 변경 핸들러\nconst handleSearchChange=e=>{const value=e.target.value;setSearchTerm(value);// 검색어가 있으면 결과창 표시, 없으면 숨김\nsetShowResults(value.length>0);};// 검색 결과 클릭 핸들러\nconst handleResultClick=post=>{if(props.onResultClick){props.onResultClick(post);}// 페이지 이동 (Framer 라우팅)\nif(typeof window!==\"undefined\"&&props.contentType===\"blog\"){window.location.href=`/blogs/tech-blog/${post.Slug}`;}setShowResults(false);setSearchTerm(\"\");};// 검색어 하이라이팅\nconst highlightSearchTerm=(text,term)=>{if(!term)return text;const regex=new RegExp(`(${term})`,\"gi\");const parts=(text||\"\").split(regex);return parts.map((part,index)=>regex.test(part)?/*#__PURE__*/_jsx(\"mark\",{style:{backgroundColor:props.highlightColor||\"#ffeb3b\",padding:\"0 2px\",borderRadius:\"2px\"},children:part},index):part);};// 검색창 클리어 함수\nconst clearSearch=()=>{setSearchTerm(\"\");setShowResults(false);};// 검색창 포커스 처리\nconst handleFocus=()=>{if(searchTerm.trim()){setShowResults(true);}};// 검색창 블러 처리\nconst handleBlur=e=>{// 검색 결과 클릭을 위해 지연 시간 증가\nsetTimeout(()=>{setShowResults(false);},300);};const calculateRightPadding=()=>{const iconSize=props.iconSize||16;const iconPadding=props.iconPadding||12;const extraPadding=8// 아이콘과 텍스트 사이 여백\n;return iconPadding+iconSize+extraPadding;};return /*#__PURE__*/_jsxs(\"div\",{style:{position:\"relative\",width:\"100%\",maxWidth:props.maxWidth||\"600px\",margin:\"0 auto\"},children:[/*#__PURE__*/_jsx(\"form\",{style:{position:\"relative\"},children:/*#__PURE__*/_jsxs(\"div\",{style:{position:\"relative\",display:\"flex\",alignItems:\"center\"},children:[/*#__PURE__*/_jsx(\"input\",{type:\"text\",value:searchTerm,onChange:handleSearchChange,placeholder:props.placeholder||\"Search...\",style:{width:\"100%\",padding:`${props.paddingY||12}px ${calculateRightPadding()}px ${props.paddingY||12}px ${props.paddingX||16}px`,border:`${props.borderWidth||1}px solid ${props.borderColor||\"#e0e0e0\"}`,borderRadius:props.borderRadius||\"8px\",fontSize:props.fontFamily.fontSize||\"16px\",fontFamily:props.fontFamily.fontFamily||\"inherit\",color:props.textColor||\"#333333\",backgroundColor:props.backgroundColor||\"#ffffff\",outline:\"none\",transition:\"all 0.2s ease\",boxShadow:showResults?`0 2px 8px 1px ${props.focusShadowColor||\"rgba(0,0,0,0.1)\"}`:\"none\"},onFocus:handleFocus,onBlur:handleBlur}),/*#__PURE__*/_jsx(\"div\",{style:{position:\"absolute\",top:props.iconPadding||\"14px\",right:props.iconPadding+4||\"18px\",zIndex:1,color:props.iconColor||\"#666666\"},children:/*#__PURE__*/_jsx(Search,{size:props.iconSize})}),isLoading&&/*#__PURE__*/_jsx(\"div\",{style:{position:\"absolute\",right:props.iconPadding||\"12px\",color:props.loadingColor||\"#999999\"},children:\"⏳\"})]})}),showResults&&searchTerm.trim()&&/*#__PURE__*/_jsx(\"div\",{style:{position:\"absolute\",top:\"100%\",left:\"0\",right:\"0\",backgroundColor:props.resultBackgroundColor||\"#ffffff\",border:`1px solid ${props.resultBorderColor||\"#e0e0e0\"}`,borderRadius:props.resultBorderRadius||\"8px\",boxShadow:`0 2px 8px 1px ${props.focusShadowColor||\"rgba(0,0,0,0.1)\"}`||\"0 4px 16px rgba(0,0,0,0.1)\",maxHeight:props.resultMaxHeight||\"400px\",overflowY:\"auto\",zIndex:3,marginTop:\"2px\"},onMouseDown:e=>{// 검색 결과 영역 클릭 시 입력 필드의 블러 이벤트 방지\ne.preventDefault();},children:searchResults.length>0?/*#__PURE__*/_jsxs(_Fragment,{children:[/*#__PURE__*/_jsx(\"div\",{style:{padding:\"12px 16px\",borderBottom:`1px solid ${props.resultBorderColor||\"#e0e0e0\"}`,fontSize:\"12px\",color:props.resultHeaderColor||\"#666666\",fontWeight:\"500\"},children:props.contentLanguage===\"kor\"?/*#__PURE__*/_jsxs(\"p\",{style:{margin:0},children:[searchResults.length,\"개의 검색 결과\"]}):/*#__PURE__*/_jsxs(\"p\",{style:{margin:0},children:[searchResults.length,\" results\"]})}),searchResults.map((post,index)=>/*#__PURE__*/_jsxs(\"div\",{onMouseDown:e=>{// 마우스 다운에서 처리하여 블러보다 먼저 실행\n e.preventDefault();if(props.contentType===\"blog\"){handleResultClick(post);}},style:{padding:\"12px 16px\",borderBottom:index{e.target.style.backgroundColor=props.resultHoverColor||\"#f8f9fa\";},onMouseLeave:e=>{e.target.style.backgroundColor=\"transparent\";},children:[/*#__PURE__*/_jsx(\"div\",{style:{fontSize:props.resultTitleSize||\"14px\",fontWeight:\"500\",color:props.resultTitleColor||\"#333333\",marginBottom:\"4px\",lineHeight:\"1.4\"},children:highlightSearchTerm(post.Title,searchTerm)}),post.Date&&/*#__PURE__*/_jsx(\"div\",{style:{fontSize:props.resultExcerptSize||\"12px\",color:props.resultExcerptColor||\"#666666\",lineHeight:\"1.4\",marginBottom:\"4px\",overflow:\"hidden\",display:\"-webkit-box\",WebkitLineClamp:2,WebkitBoxOrient:\"vertical\"},children:highlightSearchTerm(post.Date.substring(0,10),searchTerm)})]},post.id||index))]}):/*#__PURE__*/_jsx(\"div\",{style:{padding:\"20px 16px\",textAlign:\"center\",color:props.noResultsColor||\"#666666\",fontSize:\"14px\",wordBreak:\"break-word\",whiteSpace:\"normal\",overflowWrap:\"break-word\",lineHeight:\"1.4\"},children:props.contentLanguage===\"kor\"?/*#__PURE__*/_jsxs(\"p\",{children:['\"',searchTerm,'\"에 대한 검색 결과가 없습니다.']}):/*#__PURE__*/_jsx(\"p\",{children:\"No results\"})})}),props.showDebugInfo&&/*#__PURE__*/_jsxs(\"div\",{style:{marginTop:\"8px\",padding:\"8px\",backgroundColor:\"#f8f9fa\",borderRadius:\"4px\",fontSize:\"12px\",color:\"#666666\",fontFamily:\"monospace\"},children:[/*#__PURE__*/_jsxs(\"div\",{children:['검색어: \"',searchTerm,'\"']}),/*#__PURE__*/_jsxs(\"div\",{children:[\"결과 개수: \",searchResults.length]}),/*#__PURE__*/_jsxs(\"div\",{children:[\"결과창 표시: \",showResults?\"Yes\":\"No\"]}),/*#__PURE__*/_jsxs(\"div\",{children:[\"전체 포스트: \",allPosts.length]})]})]});}CMSSearch.displayName=\"CMS Filtered Search\";addPropertyControls(CMSSearch,{// 기본 설정\nplaceholder:{type:ControlType.String,title:\"Placeholder\",defaultValue:\"블로그 검색...\"},maxResults:{type:ControlType.Number,title:\"Max Results\",defaultValue:10,min:1,max:20},maxWidth:{type:ControlType.String,title:\"Max Width\",defaultValue:\"600px\"},contentType:{type:ControlType.Enum,defaultValue:\"blog\",options:[\"blog\",\"release-note\"],optionTitles:[\"Tech Blog\",\"Release Note\"]},contentLanguage:{type:ControlType.Enum,defaultValue:\"kor\",options:[\"kor\",\"eng\"],optionTitles:[\"Korean\",\"English\"]},// 입력 필드 스타일\nfontSize:{type:ControlType.Number,title:\"Font Size\",defaultValue:16,min:12,max:24,unit:\"px\"},paddingY:{type:ControlType.Number,title:\"Padding Y\",defaultValue:7,min:4,max:20,unit:\"px\"},paddingX:{type:ControlType.Number,title:\"Padding X\",defaultValue:16,min:8,max:30,unit:\"px\"},borderRadius:{type:ControlType.Number,title:\"Border Radius\",defaultValue:8,min:0,max:20,unit:\"px\"},borderWidth:{type:ControlType.Number,title:\"Border Width\",defaultValue:1,min:0,max:3,unit:\"px\"},// 색상\ntextColor:{type:ControlType.Color,title:\"Text Color\",defaultValue:\"#333333\"},backgroundColor:{type:ControlType.Color,title:\"Background Color\",defaultValue:\"#ffffff\"},borderColor:{type:ControlType.Color,title:\"Border Color\",defaultValue:\"#e0e0e0\"},iconColor:{type:ControlType.Color,title:\"Icon Color\",defaultValue:\"#666666\"},highlightColor:{type:ControlType.Color,title:\"Highlight Color\",defaultValue:\"#ffeb3b\"},focusShadowColor:{type:ControlType.Color,title:\"Focus Shadow Color\",defaultValue:\"#666666\"},// 아이콘 설정\niconSize:{type:ControlType.Number,title:\"Icon Size\",defaultValue:16,min:12,max:24,unit:\"px\"},iconPadding:{type:ControlType.Number,title:\"Icon Padding\",defaultValue:12,min:8,max:20,unit:\"px\"},// 검색 결과 스타일\nresultBackgroundColor:{type:ControlType.Color,title:\"Result Background\",defaultValue:\"#ffffff\"},resultBorderColor:{type:ControlType.Color,title:\"Result Border\",defaultValue:\"#e0e0e0\"},resultHoverColor:{type:ControlType.Color,title:\"Result Hover\",defaultValue:\"#f8f9fa\"},resultTitleColor:{type:ControlType.Color,title:\"Result Title\",defaultValue:\"#333333\"},resultExcerptColor:{type:ControlType.Color,title:\"Result Excerpt\",defaultValue:\"#666666\"},resultMetaColor:{type:ControlType.Color,title:\"Result Meta\",defaultValue:\"#999999\"},// 결과 크기\nresultTitleSize:{type:ControlType.Number,title:\"Result Title Size\",defaultValue:14,min:12,max:18,unit:\"px\"},resultExcerptSize:{type:ControlType.Number,title:\"Result Excerpt Size\",defaultValue:12,min:10,max:16,unit:\"px\"},resultMaxHeight:{type:ControlType.String,title:\"Result Max Height\",defaultValue:\"400px\"},resultBorderRadius:{type:ControlType.Number,title:\"Result Border Radius\",defaultValue:8,min:0,max:16,unit:\"px\"},// 폰트\nfontFamily:{type:ControlType.Font,title:\"Font\",controls:\"extended\",defaultFontType:\"sans-serif\",defaultValue:{fontSize:16,letterSpacing:0,lineHeight:\"1em\",textAlign:\"left\"}},// 디버그 (개발용)\nshowDebugInfo:{type:ControlType.Boolean,title:\"Show Debug Info\",defaultValue:false}});\nexport const __FramerMetadata__ = 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