{"cssClassNames":"page basicpage summit-page","templateName":"quickstart-page-template","allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"description":"Build document Q&A systems with Snowflake Cortex Search for hybrid semantic and keyword retrieval from enterprise knowledge bases.","language":"en","title":"Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search","analyticsPageType":"quickstart-page-template","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":hierarchyType":"page",":path":"/content/snowflake-site/global/en/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search","analyticsContentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/product/ai"],":type":"snowflake-site/components/structure/page",":mappedPath":"/en/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"markup_editor_1950346551":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","modal_container":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"experiencefragment-banner":{"id":"experiencefragment-130062822e","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/master/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"pushdown_banner_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-78abab1dc9",":type":"snowflake-site/components/container",":items":{"pushdown_banner_copy":{"id":"pushdown-banner-0309977610","contentHeadline":"Snowflake World Tour hits your city","contentDescription":"See how leading teams deploy agents at scale. Find a stop near you. Register free.","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"/en/world-tour/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Register now"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy"]},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"]},"experiencefragment-header":{"id":"experiencefragment-e6fcf6d686","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-80808f9538",":type":"snowflake-site/components/container",":items":{"markup_editor":{"id":"markup-editor-966b2f748b","title":" ","cssContent":".footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.mega-nav__sign-in.snowflake-button-container{display:none}@media screen and (min-width:768px){.mega-nav__sign-in.snowflake-button-container{display:inline-block;font-family:'Texta',sans-serif;font-weight:800 !important}}@media screen and (min-width:1024px) and (max-width:1199px){.snowflake-mega-nav-header-buttons-container .snowflake-button-blue .snowflake-button-container{font-size:13px !important}.snowflake-language-navigation .language-icon{width:18px !important;height:18px !important;margin-right:4px !important}}.mega-nav__sign-in svg{display:none}.nav-item__platform-parent-why-sf.snowflake-mega-nav-nav-item\u003Ea:hover,.nav-item__platform-parent.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent !important}.nav-platform-sidebar .snowflake-mega-nav-nav-item:hover.blue-icon .snowflake-mega-nav-nav-item-icon__inner{background-color:var(--ui-01) !important}@media screen and (min-width:1024px){.snowflake-mega-nav-navigation-dropdown{overflow:hidden}.meganav-platform-features{padding-left:64px}.meganav-platform-features::before{content:'';transform:translateX(-64px);display:block;z-index:0;width:100%;height:100%;position:absolute;top:0;background:#f7f9fa}.nav-item--si.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent}.nav-item--si{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:8px}.nav-item__platform-parent{border-bottom:1px solid #ccc;margin-bottom:8px;padding-bottom:16px}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description::after{content:'What Snowflake can do for you \u003E';display:block;color:var(--ui-01);margin-top:16px}.nav-item__platform-parent .snowflake-mega-nav-nav-item-description::after{content:'View the platform \u003E';display:block;color:var(--ui-01);margin-top:16px}}@media screen and (min-width:1367px){.snowflake-mega-nav-nav-item-description{font-size:13px !important;line-height:20px !important}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{font-size:17px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-title,.nav-item__platform-parent .snowflake-mega-nav-nav-item-title{font-size:24px !important;line-height:32px !important;margin-bottom:8px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description,.nav-item__platform-parent .snowflake-mega-nav-nav-item-description{font-size:14px !important;line-height:20px !important}}html.wf-texta-n9-loading .display-1-v2{font-size:48px!important;line-height:50px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-4-v2{font-size:18px!important;line-height:24px!important;font-family:sans-serif!important}@media screen and (min-width:768px){html.wf-texta-n9-loading .display-2-v2{font-size:48px!important;line-height:50px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:55.5px!important;line-height:54px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .heading-5-v2,html.wf-lato-n4-loading .snowflake-card-v2-advanced-text .snowflake-text p{font-size:15.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:34px!important;line-height:38px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-4,html.wf-texta-n8-loading .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-regular .snowflake-button-container{font-size:13px!important;line-height:20px!important;letter-spacing:.25px!important;font-family:sans-serif!important}}@media screen and (min-width:1024px){html.wf-lato-n4-loading .snowflake-mega-nav-nav-item-description{font-size:11.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .snowflake-button-compact .snowflake-button-container{font-size:12px!important;letter-spacing:0!important;line-height:18px!important}}@media screen and (min-width:1367px){html.wf-lato-n4-loading .hp-hero__eyebrow a\u003Eb:first-child{font-size:11px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .hp-hero__eyebrow a{font-size:13px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-2-v2{font-size:61px!important;line-height:60px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:74.5px!important;line-height:74px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:41px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-3-v2{font-family:sans-serif!important;letter-spacing:-.75px!important;font-size:33.75px!important}html.wf-texta-n9-loading .heading-4-v2{font-size:19.5px!important;line-height:26px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2{font-size:12px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:14px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-1,html.wf-lato-n4-loading .cq-Editable-dom[data-cq-data-path*=text] ol\u003Eli,html.wf-lato-n4-loading .snowflake-text li,html.wf-lato-n4-loading .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text li,html.wf-lato-n4-loading .text-size-large .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-large.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom span[data-testid=text-content],html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Ep,html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Eul\u003Eli{font-size:17.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content],html.wf-texta-n8-loading .snowflake-button-link .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-link-back .snowflake-button-container{font-size:15.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-3,html.wf-lato-n4-loading .text-size-small .snowflake-text li,html.wf-lato-n4-loading .text-size-small .snowflake-text p,html.wf-lato-n4-loading .text-size-small .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-small.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}}#industryPlatformSection,.sc-hero{background-position:top left;background-size:20% auto}.bwalignc,.bwalignr{list-style-position:inside}.snowflake-text p sup{font-size:10px}#industryPlatformSection .industry-platform__row .snowflake-flexible-column-container-items,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container,.snowflake-hero-system-content-container{gap:16px}.agenda-item p,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.partner-details p{margin:0!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::after,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::before,.hide-logo .snowflake-case-study-card-logo,.partner-page__powered-by-logo,.sc-hero div.code-toolbar\u003E.toolbar,.snowflake-card-v2-advanced.no-link .snowflake-card-v2-advanced-button,.snowflake-partner-hero-card-badge-container{display:none!important}.section--card-mobile-carousel .snowflake-flexible-column-container-items-with-carousel{max-width:100%!important}@media screen and (min-width:768px){.button-group-pair .snowflake-button-container.inline-button--desktop,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;display:inline-block!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:flex-start!important}.button-group-pair.center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center!important}.section--card-mobile-carousel{margin-left:var(--tablet-portrait-margin,48px)!important;margin-right:var(--tablet-portrait-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-portrait-margin) * 2)!important}}@media screen and (min-width:1024px){.section--card-mobile-carousel{margin-left:var(--tablet-horizontal-margin,48px)!important;margin-right:var(--tablet-horizontal-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-horizontal-margin) * 2)!important}.snowflake-mega-nav-header-mobile-icon{display:none!important}}@media screen and (min-width:1367px){.section--card-mobile-carousel{margin-left:var(--desktop-margin,6.5%)!important;margin-right:var(--desktop-margin,6.5%);width:87%!important;width:calc(100% - var(--desktop-margin) * 2)!important}.logo-container{min-width:143px}.sc-hero__headline .heading-1-v2{font-size:60px}.snowflake-mega-nav-navigation-title{font-size:17px}.snowflake-mega-nav-dropdown-footer-wrapper .snowflake-title-v2 .snowflake-title-v2-line:first-child{font-size:16px!important;line-height:24px!important}}.hero--home{overflow:hidden;background-color:var(--ui-01);z-index:2}.hp-hero__subheadline{width:90%}.hero--home .snowflake-button-container{transition:.3s}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-secondary a:hover,.hero--home .snowflake-button-white a:hover{transition:.3s;background-color:var(--ui-02)!important;color:var(--ui-05)!important}.hero--home .snowflake-button-secondary a:hover{border-color:var(--ui-05)!important}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-white a:hover{border-color:var(--ui-02)!important}.bwalignc,.hp-hero__eyebrow{text-align:center}.hp-hero__eyebrow a{display:inline-flex;flex-direction:column;justify-content:center;cursor:pointer;padding:8px;border-radius:var(--spacing-01);gap:8px;align-items:center;background-color:#45aee3;color:var(--ui-03);font-family:Texta,sans-serif;font-weight:800;font-size:16px;line-height:22px;transition:background-color .3s}.hp-hero__eyebrow a:hover{background-color:#7fc6ea;text-decoration:none;transition:background-color .3s}.hp-hero__eyebrow a\u003Eb:first-child{text-transform:uppercase;white-space:nowrap;display:inline-block;background-color:var(--ui-02);color:var(--ui-05);font-size:12px!important;line-height:16px!important;font-family:Lato,sans-serif;font-weight:500!important;padding:3px 6px;border-radius:2px;letter-spacing:1px}@media screen and (min-width:767px){.hp-hero__eyebrow{text-align:left}.hp-hero__eyebrow a{flex-direction:row;text-align:left}}.hero--home__inner .offset-video,.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{max-height:200px;overflow:hidden}.hero--home__inner .offset-video .wistia-responsive-padding{padding-top:100%}.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{position:absolute!important;top:0;left:0;width:100%}.offset-video__bg-image{z-index:-1}@media screen and (min-width:768px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{position:absolute!important;max-height:none;top:0;left:0;width:250%;padding-bottom:250%;transform:translate(0,-50%);height:0}.workloads_7.unistore{max-width:317px}}.promo-banner--homepage{z-index:2}.homepage-banner-offset-container::after{content:\"\";display:block;position:absolute;bottom:0;z-index:1;left:0;width:100%;height:80%;background:#fff}.section--quicklinks .snowflake-button-full-width a{padding-left:24px!important;padding-right:24px!important;transition:box-shadow .25s cubic-bezier(.4,0,.2,1);text-align:left;display:flex;justify-content:center;align-items:center}.section--quicklinks .snowflake-button-full-width a:hover{box-shadow:0 16px 16px 0 rgb(0 0 0 / .16);transition:box-shadow .25s cubic-bezier(.4,0,.2,1)}.section--quicklinks .snowflake-button-container:focus-visible a::before,.section--quicklinks .snowflake-button-full-width a::before{content:\"\";width:23px;height:23px;flex-shrink:0;margin-right:12px;display:inline-block;background-size:cover;background-repeat:no-repeat;background-position:center}#industryPartnerSlider .snowflake-navigation-icon.swiper-button-disabled,#partnerResources .section--resource-hub a svg,.button-tabs span.snowflake-tabs-navigation-item:after,.customer-card--hide-cta .snowflake-case-study-card-button,.dot-tabs span.snowflake-tabs-navigation-item::after,.partner-sidebar__mobile-expand,html:not(.aem-AuthorLayer-initial):not(.aem-AuthorLayer-Edit) .tab-content:not(.is-active){display:none}.section--quicklinks .snowflake-button-full-width a.pricing::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/decorative-icons/pricing-icon.svg)}.section--quicklinks .snowflake-button-full-width a.snowflake_on_snowflake::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon_snowflake-bug.svg)}.section--quicklinks .snowflake-button-full-width a.virtual_hands_on_labs::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__training.svg)}.section--quicklinks .snowflake-button-full-width a.weekly_demo::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__webinars.svg)}@media screen and (min-width:1024px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{left:-50%}.section--quicklinks .snowflake-flexible-column-container-items{gap:24px}.snowflake-quote-item-inner{padding:32px 24px 24px!important}}#communitiesOuter_overflowBottomGray::after{max-height:100px}#caseStudyOuter_overflowBottomMidBlue::after{max-height:180px}#caseStudyInner .snowflake-case-study-card .snowflake-wistia-video{border-radius:0!important}#caseStudyInner .snowflake-case-study-card{box-shadow:none!important;border-radius:0}#caseStudyInner{max-width:1200px;margin:0 auto;box-shadow:rgb(152 162 179 / .1) 0 10px 20px 0,rgb(152 162 179 / .25) 0 2px 6px 0;border-radius:8px;overflow:hidden;position:relative;z-index:1}.case-study__logo-bar\u003E.snowflake-flexible-column-container-items{background:#f7f9fa;padding:32px 16px 40px}.case-study__logo-bar .cmp-image__image{width:90%;margin:0 auto;max-width:240px}.hp-platform__text-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child),.sc-sidebar__group .snowflake-button-link{margin-top:8px}.workloads_7.unistore{margin-left:auto;margin-right:auto}#homepageFootnotesInner .snowflake-simple-stat-disclaimer .snowflake-text p{color:#fff!important}.snowflake-simple-stat-disclaimer .snowflake-text p\u003Ea{border-bottom:1px solid var(--ui-03);color:var(--text-03)}.snowflake-card-v2-advanced{color:inherit}#workloadCardGridOuter .snowflake-card-v2-base-front{gap:0}.video-modal.snowflake-modal-window-open-inner{background-color:#fff0;padding:8px;border:none}.snowflake-container-arrow-dotted-faded .snowflake-container-arrow-dotted-faded-image{width:40%!important;max-width:420px;top:4%!important}.list--blue-bullets ul{margin:0!important;padding:0!important;list-style-type:none}.list--blue-bullets li{margin:0;padding:0 0 0 32px;position:relative}.list--blue-bullets li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.list--blue-bullets li:not(:last-child){margin-bottom:1rem}.logo-tabs .snowflake-navigation-container,.snowflake-simple-stat-content:empty,.summit-speaker-card .snowflake-card-v2-advanced-text{margin-bottom:0}#techResourceInner,#techResourceOuter,div.overflow-bottom--blue,div.overflow-bottom--gray,div.overflow-bottom--mid-blue,div.overflow-bottom--white,div.overflow-top--blue,div.overflow-top--gray,div.overflow-top--mid-blue,div.overflow-top--white,div[id$=overflowBottomGray],div[id$=overflowBottomMidBlue],div[id$=overflowTopBlue],div[id$=overflowTopGray]{position:relative}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{content:\"\";display:block;position:absolute;left:0;width:100%;height:40%}div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{top:0}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after{bottom:0}div.overflow-bottom--white::after,div.overflow-top--white::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopWhite]::after{background:#fff!important}div.overflow-bottom--gray::after,div.overflow-top--gray::after,div[id$=overflowBottomGray]::after,div[id$=overflowTopGray]::after{background:#f6f9fa!important}div.overflow-bottom--mid-blue::after,div.overflow-top--mid-blue::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowTopMidBlue]::after{background:#11567f!important}div.overflow-bottom--blue::after,div.overflow-top--blue::after,div[id$=overflowBottomBlue]::after,div[id$=overflowTopBlue]::after{background:#259edc!important}.snowflake-premium-content-banner.promo-banner--no-shadow{box-shadow:none!important}#industryPartnerSlider .cmp-image__image,#industryPartnerSlider .section--partner-tabs .snowflake-image-container .cmp-image__image,#partnerSidebar,.has-shadow .cmp-image__image{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25)}.content-chip--has-desc{align-items:flex-start;padding:20px!important}.content-chip--has-desc .snowflake-content-chip-image{max-width:100px}.content-chip--has-desc .snowflake-content-chip-image__image{aspect-ratio:1}.content-chip--has-desc .snowflake-title-v2-line:first-child{font-size:18px!important}.content-chip--has-desc .snowflake-title-v2-line:nth-child(2){color:#000!important;font-weight:500!important;font-size:16px!important;line-height:22px!important;margin-top:2px!important}.content-chip--has-desc .snowflake-content-chip-button{margin-top:6px!important;font-size:18px!important;display:none}.square-image .snowflake-content-chip-image{aspect-ratio:1;max-width:120px}.section--logo-bar.smaller-logos .snowflake-image-container .cmp-image__image{max-width:200px;margin:0 auto}.snowflake-card-v2-advanced-tag,.snowflake-content-chip-tag{padding:3px 6px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-button,.snowflake-card-v2-advanced-title:first-child,.summit-pricing-block__aside ul{margin-top:0}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:40px;height:40px;display:flex;justify-content:center;align-items:center;margin:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{width:12px;height:12px;background:var(--ui-12);border-radius:100%}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p,.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{font-size:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{background:var(--ui-01)}.button-tabs .snowflake-navigation-container .swiper-wrapper{padding:8px 0}.button-tabs .snowflake-navigation-container .swiper-slide{margin:0 6px}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{padding:8px 24px;background-color:#f6f9fa;border-radius:48px;margin:0}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{text-transform:uppercase;font-family:Texta,sans-serif;font-weight:700}.button-tabs .border-top{border-top:1px solid #ccc}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{background-color:var(--ui-01);box-shadow:0 2px 6px 0 rgb(152 162 179 / .25),0 10px 20px 0 rgb(152 162 179 / .1)}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{color:#fff}.button-tabs.has-icons .snowflake-navigation-container .snowflake-tabs-navigation-item p::before{content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-position:center center;margin-right:12px;vertical-align:middle;margin-top:-3px}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:220px;padding-bottom:50%;height:0;margin:0 8px!important;background-size:cover;background-repeat:no-repeat;opacity:.5;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item:hover{opacity:.75;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{opacity:1;transition:opacity .3s}.dot-tabs .aem-container.cmp-tabs,.logo-tabs .aem-container.cmp-tabs{display:flex;flex-direction:column-reverse}.snowflake-icon.is-center{margin:0 auto;display:block}#industryPartnerSlider .snowflake-flexible-column-container-items,#partnerLogoSquare .snowflake-flexible-column-container-items{gap:24px}#techResourceOuter::after{content:\"\";display:block;position:absolute;top:0;left:0;width:100%;height:40%;background:#f6f9fa}#techResourceInner{z-index:1}.partner-tier-tag h6{display:inline-block!important;padding:2px 6px;border-radius:2px;color:#666}.partner-tier-tag.registered h6{background-color:#f6f9fa}.partner-tier-tag.elite h6{background-color:#11567f;color:#fff}.partner-tier-tag.premier h6{background-color:#b14c77;color:#fff}.partner-tier-tag.select h6{background-color:#5094a0;color:#fff}.partner-details\u003Espan{display:flex;gap:24px}.partner-details a{color:inherit!important;font-weight:400!important}.partner-details p::before{content:\"\";display:inline-block;vertical-align:middle;width:16px;height:16px;background-repeat:no-repeat;background-position:center;transform:translateY(-1px);background-size:auto 90%;margin-right:6px}.partner-details__location::before{background-image:url(\"data:image/svg+xml,%3Csvg width='13' height='18' viewBox='0 0 13 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M6.25 17.7531C6.4375 17.7531 6.6 17.6844 6.7375 17.5531C6.875 17.4219 6.95 17.2531 6.95 17.0531C6.95 16.8531 7.075 16.4281 7.3 15.7969C7.5875 15.0281 7.925 14.3156 8.30625 13.6406C8.8 12.7781 9.3125 12.1031 9.85 11.6094C10.75 10.7969 11.4125 9.96563 11.85 9.12188C12.2875 8.27813 12.5063 7.40313 12.5063 6.49063C12.5063 5.36563 12.2187 4.31563 11.6437 3.33438C11.0937 2.40313 10.3438 1.65938 9.4 1.10938C8.43125 .534376 7.375 .246876 6.24375 .246876C5.1125 .246876 4.06875 .534376 3.0875 1.10938C2.15625 1.65938 1.4125 2.40313 .862498 3.33438C.287498 4.31563 0 5.36563 0 6.49063C0 7.47188 .262499 8.42813 .787499 9.35938C1.14375 10.0031 1.65625 10.6656 2.3125 11.3344C2.75625 11.8031 3.24375 12.4781 3.78125 13.3656C4.225 14.0969 4.63125 14.8594 5 15.6656C5.35 16.3844 5.53125 16.8531 5.55625 17.0656C5.55625 17.2594 5.625 17.4156 5.7625 17.5531C5.9 17.6844 6.0625 17.7531 6.25 17.7531ZM6.16875 14.9156C5.775 14.0656 5.325 13.2469 4.825 12.4594C4.275 11.5594 3.7625 10.8719 3.28125 10.3969C2.625 9.71563 2.1375 9.05938 1.825 8.43438C1.5125 7.80313 1.35625 7.16563 1.35625 6.50313C1.35625 5.61563 1.575 4.80313 2.0125 4.05313C2.45 3.30313 3.04375 2.71563 3.7875 2.27813C4.5375 1.84063 5.35 1.62188 6.2375 1.62188C7.125 1.62188 7.9375 1.84063 8.6875 2.27813C9.4375 2.71563 10.0312 3.30313 10.475 4.04688C10.9187 4.80313 11.1375 5.62188 11.1375 6.50313C11.1375 7.90313 10.3937 9.26563 8.9125 10.5969C8.35 11.1094 7.8125 11.7906 7.3 12.6406C6.88125 13.3344 6.50625 14.0969 6.16875 14.9219V14.9156ZM6.26875 8.36563C6.65625 8.36563 7.01875 8.26563 7.35625 8.07188C7.69375 7.87813 7.95625 7.60938 8.14375 7.28438C8.3375 6.95313 8.43125 6.59063 8.43125 6.19688C8.43125 5.80313 8.33125 5.43438 8.1375 5.10313C7.9375 4.76563 7.675 4.50313 7.3375 4.31563C7 4.12813 6.6375 4.02813 6.24375 4.02813C5.85 4.02813 5.4875 4.12813 5.15625 4.32188C4.825 4.52188 4.56875 4.78438 4.375 5.12188C4.18125 5.45938 4.0875 5.82188 4.0875 6.20938C4.0875 6.59688 4.1875 6.95938 4.38125 7.29688C4.58125 7.63438 4.84375 7.89688 5.18125 8.08438C5.51875 8.27813 5.88125 8.37188 6.26875 8.37188V8.36563ZM6.24375 7.50313C5.8875 7.50313 5.575 7.37188 5.31875 7.11563C5.0625 6.85938 4.93125 6.55313 4.93125 6.19063C4.93125 5.82813 5.0625 5.52188 5.31875 5.26563C5.575 5.00938 5.88125 4.87813 6.24375 4.87813C6.60625 4.87813 6.9125 5.00938 7.16875 5.26563C7.425 5.52188 7.55625 5.82813 7.55625 6.19063C7.55625 6.55313 7.425 6.85938 7.16875 7.11563C6.9125 7.37188 6.60625 7.50313 6.24375 7.50313Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}.partner-details__website::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='16' viewBox='0 0 18 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M2.61587 2.96889C2.61587 2.75109 2.79633 2.57062 3.01413 2.57062C3.23192 2.57062 3.41238 2.75109 3.41238 2.96889C3.41238 3.18669 3.23192 3.36716 3.01413 3.36716C2.79633 3.36716 2.61587 3.18669 2.61587 2.96889ZM4.21512 2.96889C4.21512 2.75109 4.39558 2.57062 4.61338 2.57062C4.83117 2.57062 5.01163 2.75109 5.01163 2.96889C5.01163 3.18669 4.83117 3.36716 4.61338 3.36716C4.39558 3.36716 4.21512 3.18669 4.21512 2.96889ZM5.81438 2.96889C5.81438 2.75109 5.99484 2.57062 6.21264 2.57062C6.43043 2.57062 6.61089 2.75109 6.61089 2.96889C6.61089 3.18669 6.43043 3.36716 6.21264 3.36716C5.99484 3.36716 5.81438 3.18669 5.81438 2.96889ZM17.2518 .697559H1.19085C.811258 .697559 .506348 1.0025 .506348 1.38209V14.6179C.506348 14.9975 .811258 15.3024 1.19085 15.3024H17.2518C17.6314 15.3024 17.9363 14.9975 17.9363 14.6179V1.38209C17.9363 1.0025 17.6314 .697559 17.2518 .697559ZM16.5673 2.06035V3.90853H1.86914V2.06035H16.5673ZM1.86914 13.9334V4.78593H16.5673V13.9334H1.86914Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}#partnerSidebar{border-radius:4px;background-color:#fff;padding:24px 24px 32px;border-bottom:6px solid #29b5e8}#partnerSidebar h5,.newsletter-disclaimer p{font-size:14px!important}#partnerSidebar ul{margin-top:0;list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px}#partnerSidebar li{border:1px solid;border-radius:2px;padding:0 4px!important;font-size:11px!important;letter-spacing:.25px;text-transform:uppercase}div.snowflake-partner-hero-card{width:100%;margin:0}.partner-details__logo{max-width:380px;margin:0 auto}@media screen and (max-width:767px){.left-alignment .hp-hero__subheadline{margin-left:auto;margin-right:auto}.left-alignment .hp-hero__headline .snowflake-title-v2-line,.left-alignment .hp-hero__subheadline .snowflake-title-v2-line{text-align:center}.hero--home__inner .snowflake-flexible-column-container-items-top-padding-large{padding-top:var(--spacing-02)}.section--logo-bar\u003E.snowflake-flexible-column-container-items{display:flex;flex-wrap:wrap;flex-direction:row;justify-content:center;gap:8px}.section--logo-bar\u003E.snowflake-flexible-column-container-items\u003Ediv{width:calc(33.33% - 8px)}.partner-sidebar__mobile-expand{display:inline-block;color:#249edc;border-color:#249edc!important}#partnerSidebar li:nth-child(n+6),.summit-nav__links .snowflake-button-tertiary{display:none}.sc-body__sidebar{background-color:#f6f9fa;padding:24px}.sc-body__content{padding:0 24px 24px}.summit-speaker-card .snowflake-card-v2-advanced-content{padding:24px}}#partnerResources h6,.snowflake-tabs-navigation-item p.body-1{font-size:16px!important}#partnerResources .section--resource-hub{padding:0 16px}#partnerResources .section--resource-hub a,.bwalignl{text-align:left}@media screen and (max-width:1023px){.hero--workload .snowflake-hero-system-media-container{width:100%}}.section--timely-content .snowflake-content-chip,.snowflake-mega-nav-dropdown-footer-wrapper{align-items:center}.section--timely-content .snowflake-content-chip-image{max-width:94px}.section--timely-content .snowflake-content-chip-image__inner{line-height:0}.section--timely-content .snowflake-content-chip-image__image{aspect-ratio:1;height:auto}.section--workload-overview .workload-overview__headline{max-width:280px;margin:0 auto}#industryPartnerSlider .swiper-slide{margin-top:0!important;padding:0 12px}#industryPartnerSlider .snowflake-tabs-navigation-item{margin-left:0!important;margin-right:0!important}#industryPartnerSlider .snowflake-premium-content-banner-background-grad-white .snowflake-premium-content-banner{box-shadow:none}#industryPartnerSlider .logo-slider__slide .aem-container{display:flex;padding:0 8px!important;flex-wrap:wrap;gap:16px!important;justify-content:center}#industryPartnerSlider .logo-slider__slide .aem-container\u003Ediv{width:48%;max-width:200px}#useCaseTabs{padding-top:24px;padding-bottom:24px;padding-right:24px}#useCaseTabs .tab-content.is-active{display:block}#useCaseTabs .vert-tab{border-bottom:1px solid #a0bbcc;padding-bottom:16px}#useCaseTabs .vert-tab p{display:inline-block}#useCaseTabs .vert-tab p:hover{cursor:pointer}#useCaseTabs .vert-tab p,#useCaseTabs .vert-tab.is-active p.not-active{color:#249edc}#useCaseTabs .vert-tab p.is-active,#useCaseTabs .vert-tab.is-active p{color:#000}#industryPlatformSection{background-image:url(/adobe/dynamicmedia/deliver/dm-aid--db074ad5-7122-4c51-87a3-76c3aa466182/double-arrow-bg%403x.png);background-repeat:no-repeat}.snowflake-text p.featured-quote__source{font-weight:900!important;text-transform:uppercase;font-size:16px!important;margin-top:2rem!important}.snowflake-text p.featured-quote__title{margin-top:0!important;font-size:16px!important}.snowflake-case-study-card-logo img{width:auto!important;height:100px!important;transform:translateX(-15%)}.snowflake-quote-item-quote-text{font-weight:600!important}#customerStoryStatsInner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row}#customerStoryStat1,#customerStoryStat2{max-width:240px}#storyHighlights{border-radius:4px;padding:1rem}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line,.summit-pricing-block__tile .black-blue-text-color .snowflake-title-v2-line{color:#000!important}.snowflake-youtube-embedded-wrapper{border-radius:var(--small-border-radius)}#arcticNavItem::before,#offset::before,#open-source::before{color:var(--text-05);font-family:Texta,sans-serif!important}#offset,.sc-architecture-caption{margin-top:16px}.hero--press .snowflake-title-v2-line{text-transform:none!important}@media screen and (min-width:768px){.subpage-timely-content__inner\u003E.snowflake-flexible-column-container-items{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25);padding:var(--spacing-04);border-radius:4px;overflow:hidden}#partnerLogoSquare{padding:0 0 0 48px}.hero--workload .snowflake-container{max-width:1440px;margin:0 auto!important;align-items:center}#industryPartnerSlider.snowflake-flexible-column-container-2-column-40-60\u003E.snowflake-flexible-column-container-items{grid-template-columns:minmax(40%,4fr) minmax(0,6fr)}#industryPartnerSlider .swiper-slide{padding:0 24px}.sc-body{padding:48px}.sc-body\u003E.snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:124px}}.snowflake-button-container.has-icon{display:inline-flex;justify-content:center;align-items:center;text-align:left}.snowflake-button-container.has-icon::before{content:\"\";display:inline-block;width:20px;height:20px;margin-right:12px;background-size:contain;background-repeat:no-repeat;background-position:center}.snowflake-button-container.is-video::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M9 1.28663C13.2523 1.28663 16.7134 4.74768 16.7134 9C16.7134 13.2523 13.2523 16.7134 9 16.7134C4.74768 16.7198 1.28663 13.2588 1.28663 9C1.28663 4.74124 4.74768 1.28663 9 1.28663ZM9 0C4.0336 0 0 4.0336 0 9C0 13.9664 4.0336 18 9 18C13.9728 18 18 13.9664 18 9C18 4.0336 13.9728 0 9 0Z' fill='white'/%3E%3Cpath d='M7.75106 6.18211C7.42941 6.16925 7.16565 6.42658 7.16565 6.74823V11.2772C7.16565 11.7082 7.65457 11.9848 8.02126 11.7597L11.7975 9.4952C12.1578 9.27647 12.1578 8.74252 11.7975 8.52379L8.02126 6.25931C7.93763 6.21428 7.84756 6.18211 7.75106 6.18211Z' fill='white'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-github::before{background-image:url(\"data:image/svg+xml,%3Csvg width='20' height='21' viewBox='0 0 20 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 .651794C4.475 .651794 0 5.12679 0 10.6518C0 15.0768 2.8625 18.8143 6.8375 20.1393C7.3375 20.2268 7.525 19.9268 7.525 19.6643C7.525 19.4268 7.5125 18.6393 7.5125 17.8018C5 18.2643 4.35 17.1893 4.15 16.6268C4.0375 16.3393 3.55 15.4518 3.125 15.2143C2.775 15.0268 2.275 14.5643 3.1125 14.5518C3.9 14.5393 4.4625 15.2768 4.65 15.5768C5.55 17.0893 6.9875 16.6643 7.5625 16.4018C7.65 15.7518 7.9125 15.3143 8.2 15.0643C5.975 14.8143 3.65 13.9518 3.65 10.1268C3.65 9.03929 4.0375 8.13929 4.675 7.43929C4.575 7.18929 4.225 6.16429 4.775 4.78929C4.775 4.78929 5.6125 4.52679 7.525 5.81429C8.325 5.58929 9.175 5.47679 10.025 5.47679C10.875 5.47679 11.725 5.58929 12.525 5.81429C14.4375 4.51429 15.275 4.78929 15.275 4.78929C15.825 6.16429 15.475 7.18929 15.375 7.43929C16.0125 8.13929 16.4 9.02679 16.4 10.1268C16.4 13.9643 14.0625 14.8143 11.8375 15.0643C12.2 15.3768 12.5125 15.9768 12.5125 16.9143C12.5125 18.2518 12.5 19.3268 12.5 19.6643C12.5 19.9268 12.6875 20.2393 13.1875 20.1393C17.1375 18.8143 20 15.0643 20 10.6518C20 5.12679 15.525 .651794 10 .651794Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-quickstart::before{background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='21' viewBox='0 0 15 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M13.8489 2.79368H11.6439V2.38493C11.6439 1.71368 11.1451 .967427 10.4251 .967427H8.94762C8.80887 .359927 8.37387 .299927 7.89762 .299927H7.23012C6.85512 .299927 6.26637 .299927 6.08637 .967427H4.68387C3.94887 .967427 3.35637 1.74368 3.35637 2.38493V2.79368H1.15137C.738867 2.79368 .401367 3.13118 .401367 3.54368V20.2537C.401367 20.6662 .738867 21.0037 1.15137 21.0037H13.8489C14.2614 21.0037 14.5989 20.6662 14.5989 20.2537V3.54368C14.5989 3.13118 14.2614 2.79368 13.8489 2.79368ZM4.29387 2.38493C4.29387 2.18243 4.54137 1.90493 4.68387 1.90493H6.50262C6.76137 1.90493 6.97137 1.69493 6.97137 1.43618C6.97137 1.33868 6.97887 1.27868 6.98637 1.24118C7.05012 1.23368 7.15512 1.23368 7.23387 1.23368H7.90137C7.95012 1.23368 8.00637 1.23368 8.05137 1.23368C8.05512 1.27868 8.05887 1.34243 8.05887 1.43243C8.05887 1.69118 8.26887 1.90118 8.52762 1.90118H10.4289C10.5301 1.90118 10.7101 2.14493 10.7101 2.38118V2.78993H4.29762V2.38118L4.29387 2.38493ZM13.0989 19.4999H1.90137V4.29368H13.0989V19.5037V19.4999Z' fill='%23249EDC'/%3E%3Cpath d='M3.82512 16.0424H11.1751C11.4339 16.0424 11.6439 15.8324 11.6439 15.5736V6.88486C11.6439 6.62611 11.4339 6.41611 11.1751 6.41611H3.82512C3.56637 6.41611 3.35637 6.62611 3.35637 6.88486V15.5736C3.35637 15.8324 3.56637 16.0424 3.82512 16.0424ZM4.29387 15.1049V13.3686H10.7064V15.1049H4.29387ZM10.7101 7.35361V12.4311H4.29762V7.35361H10.7101Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 9.35989H8.83887C9.09762 9.35989 9.30762 9.14989 9.30762 8.89114C9.30762 8.63239 9.09762 8.42239 8.83887 8.42239H6.16512C5.90637 8.42239 5.69637 8.63239 5.69637 8.89114C5.69637 9.14989 5.90637 9.35989 6.16512 9.35989Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 11.3624H8.83887C9.09762 11.3624 9.30762 11.1524 9.30762 10.8937C9.30762 10.6349 9.09762 10.4249 8.83887 10.4249H6.16512C5.90637 10.4249 5.69637 10.6349 5.69637 10.8937C5.69637 11.1524 5.90637 11.3624 6.16512 11.3624Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-download::before{background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='18' viewBox='0 0 16 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M15.2017 17.1637H.798265C.364425 17.1637 0 16.7993 0 16.3655V12.3568C0 11.923 .364425 11.5585 .798265 11.5585C1.2321 11.5585 1.59653 11.923 1.59653 12.3568V15.5498H14.4035V12.3568C14.4035 11.923 14.7679 11.5585 15.2017 11.5585C15.6356 11.5585 16 11.923 16 12.3568V16.3655C16 16.7993 15.6529 17.1637 15.2017 17.1637Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.84381 12.9642 7.73969 12.9468 7.63557 12.8947C7.34056 12.7733 7.14967 12.4783 7.14967 12.1485L7.18437 .938127C7.18437 .504287 7.5488 .139862 7.98264 .139862C8.41648 .139862 8.7809 .504287 8.7809 .938127L8.7462 10.257L12.8416 6.33509C13.154 6.02273 13.6746 6.04008 13.9696 6.35244C14.282 6.66481 14.2646 7.18542 13.9523 7.48043L8.50325 12.7386C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.73969 12.9642 7.54881 12.8947 7.39262 12.7386L2.03037 7.53249C1.718 7.22012 1.70065 6.71687 2.01301 6.40451C2.32538 6.09214 2.82863 6.07479 3.141 6.38715L8.50325 11.5932C8.81562 11.9056 8.83297 12.4088 8.52061 12.7212C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-expand::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.64375 10.9125C6.9375 11.2062 6.93125 11.6812 6.64375 11.9687L2.57502 16H3.79375C4.20625 16 4.54376 16.3375 4.54376 16.75C4.54376 17.1625 4.20625 17.5 3.79375 17.5H.756264C.556264 17.5 .36876 17.4187 .22501 17.2812C.22501 17.2812 .206248 17.25 .193748 17.2375C.143748 17.1812 .100004 17.1125 .0625038 17.0437C.0375038 16.9687 .0187492 16.8937 .0187492 16.8187C.0187492 16.8 .0062561 16.7813 .0062561 16.7625V13.725C.0187561 13.3125 .356257 12.9875 .768757 12.9937C1.16876 13 1.48752 13.325 1.50002 13.725V14.9688L5.5875 10.9187C5.88125 10.6312 6.35 10.6312 6.64375 10.9187V10.9125ZM17.5063 .743732C17.5063 .543732 17.425 .356235 17.2875 .218735C17.2875 .218735 17.2562 .199998 17.2437 .193748C17.1875 .137498 17.1188 .0937347 17.0438 .0624847C16.9688 .0374847 16.8938 .0187492 16.8188 .0187492C16.8 .0187492 16.7813 .00623703 16.7625 .00623703H13.725C13.3125 .00623703 12.975 .343745 12.975 .756245C12.975 1.16874 13.3125 1.50623 13.725 1.50623H14.9688L11.1312 5.37498C10.8437 5.67498 10.8563 6.14999 11.1563 6.43124C11.45 6.71249 11.9063 6.70624 12.1938 6.43124L16.0125 2.575V3.79375C16.0125 4.20625 16.35 4.54372 16.7625 4.54372C17.175 4.54372 17.5125 4.20625 17.5125 3.79375V.756245L17.5063 .743732ZM16.7562 12.9688C16.3437 12.9688 16.0063 13.3063 16.0063 13.7188V14.8937L12.1938 10.925C11.9063 10.625 11.4375 10.6188 11.1375 10.9063C10.8375 11.1938 10.8313 11.6625 11.1188 11.9625L15 16.0062H13.7188C13.3063 16.0062 12.9688 16.3437 12.9688 16.7562C12.9688 17.1687 13.3063 17.5063 13.7188 17.5063H16.7562C16.85 17.5063 16.95 17.4875 17.0375 17.45C17.0875 17.425 17.1313 17.3937 17.175 17.3625C17.2063 17.3437 17.2438 17.325 17.275 17.3C17.3313 17.2375 17.375 17.1687 17.4125 17.1C17.4188 17.0875 17.4375 17.075 17.4438 17.0562C17.45 17.025 17.4563 16.9938 17.4625 16.9625C17.4813 16.9 17.5 16.8375 17.5 16.7687V13.725C17.5 13.3125 17.1687 12.975 16.7562 12.975V12.9688ZM.750008 4.53125C1.16251 4.53125 1.50002 4.19374 1.50002 3.78124V2.5L5.59376 6.43124C5.89376 6.71874 6.36251 6.70626 6.65001 6.41251C6.93751 6.11876 6.92501 5.64375 6.63126 5.35625L2.61251 1.49998H3.7875C4.2 1.49998 4.53751 1.16249 4.53751 .749989C4.53751 .337489 4.2 0 3.7875 0H.743752C.668752 0 .600004 .0187355 .531254 .0437355C.506254 .0499855 .481263 .0437477 .462513 .0562477C.443763 .0687477 .425015 .0812462 .406265 .0937462C.337515 .124996 .275004 .168741 .218754 .224991H.212498C.212498 .224991 .175 .28125 .15625 .3125C.11875 .3625 .0812477 .4125 .0562477 .46875C.0374977 .525 .0249992 .587499 .0187492 .643749C.0124992 .674999 0 .712482 0 .743732V3.78124C0 4.19374 .337508 4.53125 .750008 4.53125Z' fill='white'/%3E%3C/svg%3E%0A\")}@keyframes slow-scroll{100%{transform:translateY(-50%)}}.sc-hero{overflow:hidden;background-color:#212d35;background-repeat:repeat-y;background-image:url(\"data:image/svg+xml,%3Csvg width='389' height='17' viewBox='0 0 389 17' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M.638672 7.80824L.638672 9.2566C.638672 9.52364 .85538 9.74024 1.12262 9.74024H2.57204C2.83928 9.74024 3.05598 9.52364 3.05598 9.2566V7.80824C3.05598 7.54119 2.83928 7.32472 2.57204 7.32472L1.12262 7.32472C.85538 7.32472 .638672 7.54119 .638672 7.80824Z' fill='url(%23paint0_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.9639 7.80824V9.2566C10.9639 9.52364 11.1806 9.74024 11.4478 9.74024L12.8972 9.74024C13.1645 9.74024 13.3812 9.52364 13.3812 9.2566V7.80824C13.3812 7.54119 13.1645 7.32471 12.8972 7.32471L11.4478 7.32471C11.1806 7.32471 10.9639 7.54119 10.9639 7.80824Z' fill='url(%23paint1_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M21.2891 7.80823V9.2566C21.2891 9.52364 21.5058 9.74024 21.773 9.74024L23.2224 9.74024C23.4897 9.74024 23.7064 9.52364 23.7064 9.2566V7.80823C23.7064 7.54119 23.4897 7.32471 23.2224 7.32471L21.773 7.32471C21.5058 7.32471 21.2891 7.54119 21.2891 7.80823Z' fill='url(%23paint2_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M31.6143 7.80823V9.2566C31.6143 9.52364 31.831 9.74024 32.0982 9.74024H33.5476C33.8149 9.74024 34.0316 9.52364 34.0316 9.2566V7.80823C34.0316 7.54119 33.8149 7.32471 33.5476 7.32471L32.0982 7.32471C31.831 7.32471 31.6143 7.54119 31.6143 7.80823Z' fill='url(%23paint3_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M41.9395 7.80823V9.2566C41.9395 9.52364 42.1562 9.74024 42.4234 9.74024H43.8728C44.1401 9.74024 44.3568 9.52364 44.3568 9.2566V7.80823C44.3568 7.54119 44.1401 7.32471 43.8728 7.32471L42.4234 7.32471C42.1562 7.32471 41.9395 7.54119 41.9395 7.80823Z' fill='url(%23paint4_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M52.5076 7.80823V9.2566C52.5076 9.52364 52.7243 9.74024 52.9916 9.74024H54.441C54.7082 9.74024 54.9249 9.52364 54.9249 9.2566V7.80823C54.9249 7.54119 54.7082 7.32471 54.441 7.32471L52.9916 7.32471C52.7243 7.32471 52.5076 7.54119 52.5076 7.80823Z' fill='url(%23paint5_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M62.8331 7.80823V9.2566C62.8331 9.52364 63.0493 9.74024 63.3165 9.74024H64.7664C65.0332 9.74024 65.2504 9.52364 65.2504 9.2566V7.80823C65.2504 7.54119 65.0332 7.32471 64.7664 7.32471L63.3165 7.32471C63.0493 7.32471 62.8331 7.54119 62.8331 7.80823Z' fill='url(%23paint6_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M73.1583 7.80823V9.2566C73.1583 9.52364 73.3745 9.74024 73.6417 9.74024H75.0916C75.3584 9.74024 75.5756 9.52364 75.5756 9.2566V7.80823C75.5756 7.54119 75.3584 7.32471 75.0916 7.32471L73.6417 7.32471C73.3745 7.32471 73.1583 7.54119 73.1583 7.80823Z' fill='url(%23paint7_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M83.4835 7.80823V9.2566C83.4835 9.52364 83.6997 9.74024 83.9669 9.74024H85.4168C85.6836 9.74024 85.9008 9.52364 85.9008 9.2566V7.80823C85.9008 7.54119 85.6836 7.32471 85.4168 7.32471L83.9669 7.32471C83.6997 7.32471 83.4835 7.54119 83.4835 7.80823Z' fill='url(%23paint8_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M93.8087 7.80823V9.2566C93.8087 9.52364 94.0249 9.74024 94.2921 9.74024H95.742C96.0088 9.74024 96.226 9.52364 96.226 9.2566V7.80823C96.226 7.54119 96.0088 7.32471 95.742 7.32471L94.2921 7.32471C94.0249 7.32471 93.8087 7.54119 93.8087 7.80823Z' fill='url(%23paint9_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M104.134 7.80823V9.2566C104.134 9.52364 104.35 9.74024 104.617 9.74024H106.067C106.334 9.74024 106.551 9.52364 106.551 9.2566V7.80823C106.551 7.54119 106.334 7.32471 106.067 7.32471L104.617 7.32471C104.35 7.32471 104.134 7.54119 104.134 7.80823Z' fill='url(%23paint10_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M114.702 7.80823V9.2566C114.702 9.52364 114.918 9.74024 115.185 9.74024L116.635 9.74024C116.902 9.74024 117.119 9.52364 117.119 9.25659V7.80823C117.119 7.54119 116.902 7.32471 116.635 7.32471L115.185 7.32471C114.918 7.32471 114.702 7.54119 114.702 7.80823Z' fill='url(%23paint11_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M125.027 7.80823V9.25659C125.027 9.52364 125.243 9.74024 125.511 9.74024L126.961 9.74024C127.227 9.74024 127.445 9.52364 127.445 9.25659V7.80823C127.445 7.54119 127.227 7.32471 126.961 7.32471L125.511 7.32471C125.243 7.32471 125.027 7.54119 125.027 7.80823Z' fill='url(%23paint12_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M135.352 7.80823V9.25659C135.352 9.52364 135.569 9.74024 135.836 9.74024H137.286C137.553 9.74024 137.77 9.52364 137.77 9.25659V7.80823C137.77 7.54119 137.553 7.32471 137.286 7.32471L135.836 7.32471C135.569 7.32471 135.352 7.54119 135.352 7.80823Z' fill='url(%23paint13_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M145.678 7.80823V9.25659C145.678 9.52364 145.894 9.74024 146.161 9.74024H147.611C147.878 9.74024 148.095 9.52364 148.095 9.25659V7.80823C148.095 7.54119 147.878 7.32471 147.611 7.32471L146.161 7.32471C145.894 7.32471 145.678 7.54119 145.678 7.80823Z' fill='url(%23paint14_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M156.003 7.80823V9.25659C156.003 9.52364 156.219 9.74024 156.486 9.74024H157.936C158.203 9.74024 158.42 9.52364 158.42 9.25659V7.80823C158.42 7.54119 158.203 7.32471 157.936 7.32471L156.486 7.32471C156.219 7.32471 156.003 7.54119 156.003 7.80823Z' fill='url(%23paint15_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M166.328 7.80823V9.25659C166.328 9.52363 166.544 9.74024 166.811 9.74024H168.261C168.528 9.74024 168.745 9.52363 168.745 9.25659V7.80823C168.745 7.54119 168.528 7.32471 168.261 7.32471L166.811 7.32471C166.544 7.32471 166.328 7.54119 166.328 7.80823Z' fill='url(%23paint16_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M176.896 7.80823V9.25659C176.896 9.52363 177.112 9.74023 177.38 9.74023H178.83C179.096 9.74023 179.313 9.52363 179.313 9.25659V7.80823C179.313 7.54119 179.096 7.32471 178.83 7.32471L177.38 7.32471C177.112 7.32471 176.896 7.54119 176.896 7.80823Z' fill='url(%23paint17_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M187.221 7.80823V9.25659C187.221 9.52363 187.438 9.74023 187.705 9.74023H189.155C189.421 9.74023 189.639 9.52363 189.639 9.25659V7.80823C189.639 7.54119 189.421 7.32471 189.155 7.32471L187.705 7.32471C187.438 7.32471 187.221 7.54119 187.221 7.80823Z' fill='url(%23paint18_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M199.639 7.80824V9.2566C199.639 9.52364 199.855 9.74024 200.123 9.74024H201.572C201.839 9.74024 202.056 9.52364 202.056 9.2566V7.80824C202.056 7.54119 201.839 7.32472 201.572 7.32472L200.123 7.32472C199.855 7.32472 199.639 7.54119 199.639 7.80824Z' fill='url(%23paint19_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M209.964 7.80824V9.2566C209.964 9.52364 210.181 9.74024 210.448 9.74024L211.897 9.74024C212.164 9.74024 212.381 9.52364 212.381 9.2566V7.80824C212.381 7.54119 212.164 7.32471 211.897 7.32471L210.448 7.32471C210.181 7.32471 209.964 7.54119 209.964 7.80824Z' fill='url(%23paint20_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M220.289 7.80823V9.2566C220.289 9.52364 220.506 9.74024 220.773 9.74024L222.222 9.74024C222.49 9.74024 222.706 9.52364 222.706 9.2566V7.80823C222.706 7.54119 222.49 7.32471 222.222 7.32471L220.773 7.32471C220.506 7.32471 220.289 7.54119 220.289 7.80823Z' fill='url(%23paint21_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M230.614 7.80823V9.2566C230.614 9.52364 230.831 9.74024 231.098 9.74024H232.548C232.815 9.74024 233.032 9.52364 233.032 9.2566V7.80823C233.032 7.54119 232.815 7.32471 232.548 7.32471L231.098 7.32471C230.831 7.32471 230.614 7.54119 230.614 7.80823Z' fill='url(%23paint22_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M240.939 7.80823V9.2566C240.939 9.52364 241.156 9.74024 241.423 9.74024H242.873C243.14 9.74024 243.357 9.52364 243.357 9.2566V7.80823C243.357 7.54119 243.14 7.32471 242.873 7.32471L241.423 7.32471C241.156 7.32471 240.939 7.54119 240.939 7.80823Z' fill='url(%23paint23_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M251.508 7.80823V9.2566C251.508 9.52364 251.724 9.74024 251.992 9.74024H253.441C253.708 9.74024 253.925 9.52364 253.925 9.2566V7.80823C253.925 7.54119 253.708 7.32471 253.441 7.32471L251.992 7.32471C251.724 7.32471 251.508 7.54119 251.508 7.80823Z' fill='url(%23paint24_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M261.833 7.80823V9.2566C261.833 9.52364 262.049 9.74024 262.317 9.74024H263.766C264.033 9.74024 264.25 9.52364 264.25 9.2566V7.80823C264.25 7.54119 264.033 7.32471 263.766 7.32471L262.317 7.32471C262.049 7.32471 261.833 7.54119 261.833 7.80823Z' fill='url(%23paint25_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M272.158 7.80823V9.2566C272.158 9.52364 272.374 9.74024 272.642 9.74024H274.092C274.358 9.74024 274.576 9.52364 274.576 9.2566L274.576 7.80823C274.576 7.54119 274.358 7.32471 274.092 7.32471L272.642 7.32471C272.374 7.32471 272.158 7.54119 272.158 7.80823Z' fill='url(%23paint26_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M282.483 7.80823V9.2566C282.483 9.52364 282.7 9.74024 282.967 9.74024H284.417C284.684 9.74024 284.901 9.52364 284.901 9.2566V7.80823C284.901 7.54119 284.684 7.32471 284.417 7.32471L282.967 7.32471C282.7 7.32471 282.483 7.54119 282.483 7.80823Z' fill='url(%23paint27_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M292.809 7.80823L292.809 9.2566C292.809 9.52364 293.025 9.74024 293.292 9.74024H294.742C295.009 9.74024 295.226 9.52364 295.226 9.2566V7.80823C295.226 7.54119 295.009 7.32471 294.742 7.32471L293.292 7.32471C293.025 7.32471 292.809 7.54119 292.809 7.80823Z' fill='url(%23paint28_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M303.134 7.80823L303.134 9.2566C303.134 9.52364 303.35 9.74024 303.617 9.74024H305.067C305.334 9.74024 305.551 9.52364 305.551 9.2566V7.80823C305.551 7.54119 305.334 7.32471 305.067 7.32471L303.617 7.32471C303.35 7.32471 303.134 7.54119 303.134 7.80823Z' fill='url(%23paint29_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M313.702 7.80823L313.702 9.2566C313.702 9.52364 313.918 9.74024 314.185 9.74024L315.635 9.74024C315.902 9.74024 316.119 9.52364 316.119 9.25659V7.80823C316.119 7.54119 315.902 7.32471 315.635 7.32471L314.185 7.32471C313.918 7.32471 313.702 7.54119 313.702 7.80823Z' fill='url(%23paint30_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M324.027 7.80823V9.25659C324.027 9.52364 324.243 9.74024 324.511 9.74024L325.961 9.74024C326.227 9.74024 326.445 9.52364 326.445 9.25659V7.80823C326.445 7.54119 326.227 7.32471 325.961 7.32471L324.511 7.32471C324.243 7.32471 324.027 7.54119 324.027 7.80823Z' fill='url(%23paint31_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M334.352 7.80823V9.25659C334.352 9.52364 334.569 9.74024 334.836 9.74024H336.286C336.553 9.74024 336.77 9.52364 336.77 9.25659L336.77 7.80823C336.77 7.54119 336.553 7.32471 336.286 7.32471L334.836 7.32471C334.569 7.32471 334.352 7.54119 334.352 7.80823Z' fill='url(%23paint32_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M344.678 7.80823V9.25659C344.678 9.52364 344.894 9.74024 345.161 9.74024H346.611C346.878 9.74024 347.095 9.52364 347.095 9.25659L347.095 7.80823C347.095 7.54119 346.878 7.32471 346.611 7.32471L345.161 7.32471C344.894 7.32471 344.678 7.54119 344.678 7.80823Z' fill='url(%23paint33_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M355.003 7.80823V9.25659C355.003 9.52364 355.219 9.74024 355.486 9.74024H356.936C357.203 9.74024 357.42 9.52364 357.42 9.25659L357.42 7.80823C357.42 7.54119 357.203 7.32471 356.936 7.32471L355.486 7.32471C355.219 7.32471 355.003 7.54119 355.003 7.80823Z' fill='url(%23paint34_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M365.328 7.80823V9.25659C365.328 9.52363 365.544 9.74024 365.811 9.74024H367.261C367.528 9.74024 367.745 9.52363 367.745 9.25659V7.80823C367.745 7.54119 367.528 7.32471 367.261 7.32471L365.811 7.32471C365.544 7.32471 365.328 7.54119 365.328 7.80823Z' fill='url(%23paint35_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M375.896 7.80823V9.25659C375.896 9.52363 376.112 9.74023 376.38 9.74023H377.83C378.096 9.74023 378.313 9.52363 378.313 9.25659V7.80823C378.313 7.54119 378.096 7.32471 377.829 7.32471L376.38 7.32471C376.112 7.32471 375.896 7.54119 375.896 7.80823Z' fill='url(%23paint36_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M386.221 7.80823V9.25659C386.221 9.52363 386.438 9.74023 386.705 9.74023H388.155C388.421 9.74023 388.639 9.52363 388.639 9.25659V7.80823C388.639 7.54119 388.421 7.32471 388.155 7.32471L386.705 7.32471C386.438 7.32471 386.221 7.54119 386.221 7.80823Z' fill='url(%23paint37_linear_8295_70635)'/%3E%3Cdefs%3E%3ClinearGradient id='paint0_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint1_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint2_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint3_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint4_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint5_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint6_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint7_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint8_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint9_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint10_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint11_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint12_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint13_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint14_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint15_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint16_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint17_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint18_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint19_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint20_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint21_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint22_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint23_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint24_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint25_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint26_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint27_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint28_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint29_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint30_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint31_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint32_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint33_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint34_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint35_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint36_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint37_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3C/defs%3E%3C/svg%3E%0A\")}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:first-child{position:relative;z-index:3}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:absolute;height:100%;width:100%;top:0;left:-24px}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{content:\"\";display:block;z-index:1;position:absolute;top:-64px;left:0;width:150%;height:calc(100% + 160px);background-color:rgb(32 44 53 / .9)}.sc-body__content .heading-3-v2,.sc-hero__headline .heading-1-v2{text-transform:none}.sc-body__content span.snowflake-image-caption{display:block!important;font-style:italic}.sc-body__content .snowflake-text p+ul{margin-top:24px!important;padding-left:16px!important}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#e9eaeb!important;font-size:16px}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification.is-large .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#fff!important;font-size:18px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child{display:flex;justify-content:flex-start;align-items:center;gap:8px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child::before{content:\"\";display:inline-block;width:16px;height:16px;background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='16' viewBox='0 0 16 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M8 0C3.58146 0 0 3.58146 0 8C0 12.4185 3.58146 16 8 16C12.4185 16 16 12.4185 16 8C16 3.58146 12.4185 0 8 0ZM12.7184 5.91984L7.33471 11.3026C7.31293 11.3244 7.31293 11.3454 7.29198 11.3454L7.20653 11.4308C6.94933 11.688 6.54132 11.7525 6.21962 11.6235C6.11238 11.5808 6.00514 11.5163 5.9197 11.4308L5.83425 11.3454C5.83425 11.3454 5.83425 11.3236 5.81246 11.3236L3.28149 8.79347C2.93799 8.44997 2.93799 7.87107 3.28149 7.50664L3.36694 7.42119C3.71044 7.07769 4.28934 7.07769 4.65377 7.42119L6.58401 9.35143L11.3877 4.5477C11.7312 4.2042 12.3101 4.2042 12.6746 4.5477L12.76 4.63315C13.0826 4.99758 13.0828 5.55541 12.7184 5.91984Z' fill='%230E8A16'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-color:#fff;border-radius:100%}.sc-hero__byline{padding-top:8px}.sc-hero__byline p{color:#e2e2e2;margin-top:0!important}.sc-hero pre[class*=language-]{overflow:visible}.snowflake-code-snippet,.snowflake-code-snippet code,.snowflake-code-snippet pre{font-size:16px}.sc-hero__code-snippet:not(pre)\u003Ecode[class*=language-],.sc-hero__code-snippet pre[class*=language-]{background:0 0}.sc-hero__code-snippet{opacity:.8;background-color:transparent!important;position:absolute;top:0;right:0;width:100%;animation:240s linear 1s forwards slow-scroll}.sc-hero__button-container .snowflake-flexible-column-container-items{padding:0 0 24px;margin-top:-8px;margin-left:24px}.sc-sidebar__partner-logo{width:100%;max-width:140px;margin-top:8px}.sc-sidebar__partner-logo .cmp-image__image{border-radius:0}.sc-tag-cluster.snowflake-text ul{list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px;margin:0}.sc-tag-cluster.snowflake-text li{color:#373f41;border-radius:4px;display:inline-block;padding:6px;text-transform:uppercase;letter-spacing:1px;font-size:12px!important;line-height:12px!important;margin:0!important;background-color:#f3f3f3}.sc-body .share-icon svg{height:24px;cursor:pointer}.sc-body .share-icon svg:hover path{fill:var(--ui-02)}.sc-overview__webinar-promo-banner{align-items:center;border:1px solid #ccc;padding:var(--spacing-02)}.sc-overview__webinar-promo-banner .snowflake-content-chip-image{max-width:32px;margin-right:var(--spacing-02);line-height:0}.sc-overview__webinar-promo-banner .snowflake-content-chip-image__image,.summit-speaker-card .snowflake-card-v2-advanced-image__image{aspect-ratio:1}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{font-size:14px;font-family:Lato,sans-serif}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child){font-weight:400}.sc-overview__webinar-promo-banner .snowflake-content-chip-button .snowflake-button-container{font-size:14px!important}.diagram-group__button{position:absolute;bottom:24px;right:24px;background-color:#212c35!important}.section--mountains-bottom,.summit-hp-hero{position:relative}.sc-cert-banner{background-color:#212d35;border-radius:8px;padding:24px;overflow:hidden}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;align-items:center}:root{--text-secondary:#706f6f;--summit-bg-ltblue:#eaf8fd;--summit-bg-blue:#249edc;--summit-border:#d2d1d4;--summit-border-radius:8px;--summit-card-padding:32px;--summit-card-padding-sm:28px}.section--mountains-bottom::after,.section--mountains-bottom::before{content:\"\";display:block;position:absolute;bottom:-1px;max-width:400px;background-size:100% auto;height:100%;width:30%;line-height:0;background-repeat:no-repeat}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center;align-items:center}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;margin:0 8px!important}.button-group .snowflake-button-container{font-family:Texta,sans-serif}.section--summit-bg-ltblue{background-color:var(--summit-bg-ltblue)}.section--summit-bg-blue,.summit-hero-secondary{background-color:var(--summit-bg-blue)}.section--mountains-bottom::before{left:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M401.523 308.761H0V0L181.63 182.431L228.479 135.531L401.523 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom left}.section--mountains-bottom::after{right:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 308.761H401.523V0L219.893 182.431L173.044 135.531L0 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom right}.summit-hp-hero{overflow:hidden}.summit-hero__bg-video{position:absolute;top:50%;left:50%;width:120%;height:100%;opacity:.3;transform:translate(-50%,-50%)}.summit-hero__bg-svg,.summit-prefooter__bg-image,.summit-secondary-hero__bg-image{position:absolute;bottom:0;left:0;width:100%}.summit-hp-promo-banner__headline .heading-4-v2{font-weight:900}.summit-hero-secondary .hero-lottie__left{position:absolute;bottom:0;left:0;width:30%;line-height:0}.summit-timeline__card::after,.summit-timeline__card::before{bottom:0;left:50%;position:absolute;display:block;background-color:var(--ui-01);content:\"\"}.summit-hero-secondary .snowflake-text p{font-size:24px!important;line-height:32px!important;max-width:720px;margin:0 auto}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:center}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;max-width:25%}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid #fff}.summit-timeline__card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding);position:relative;background-color:#fff}.summit-timeline__card::before{width:20px;height:20px;border-radius:100%;transform:translate(-50%,50%)}.summit-timeline__card::after{width:3px;height:50px;transform:translate(-50%,100%)}.summit-timeline-card__icon{width:48px;height:48px}.summit-timeline-card__headline .heading-3-v2{font-size:32px}.faq-group{border:1px solid var(--ui-12);border-radius:4px;background-color:#fff}.faq-group__question{padding:24px}.faq-group__question:hover{color:var(--ui-01);cursor:pointer}.faq-group__question .heading-4-v2,.faq-group__question .heading-5-v2{position:relative;padding-right:64px}.faq-group__question .heading-4-v2::after,.faq-group__question .heading-5-v2::after{content:\"\";display:block;width:32px;height:32px;background-image:url(\"data:image/svg+xml,%3Csvg width='29' height='16' viewBox='0 0 29 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M14.16 14.6807C14.2537 14.7957 14.3719 14.8884 14.506 14.952C14.64 15.0157 14.7866 15.0487 14.935 15.0487C15.0834 15.0487 15.2299 15.0157 15.3639 14.952C15.498 14.8884 15.6162 14.7957 15.71 14.6807V14.6807L28.51 2.00068C29.07 1.43068 29.07 .92068 28.51 .44068C27.95 -.0393204 27.43 -.11932 26.96 .44068L14.94 12.0007L2.99996 .45068C2.90725 .322624 2.7855 .218374 2.6447 .146483C2.50389 .0745926 2.34805 .0371094 2.18996 .0371094C2.03187 .0371094 1.87603 .0745926 1.73522 .146483C1.59442 .218374 1.47267 .322624 1.37996 .45068C.819961 .93068 .819961 1.45068 1.37996 2.01068L14.16 14.6807Z' fill='black'/%3E%3C/svg%3E%0A\");background-size:80% auto;background-repeat:no-repeat;background-position:center;position:absolute;top:-2px;right:0;transition:.3s 150ms}.faq-group__question .heading-5-v2::after{top:-4px}.faq-group__answer{max-height:0;overflow:hidden;width:95%;padding:0 24px;transition:.5s}.faq-group__answer\u003Espan{display:block;padding-bottom:24px}.is-open .faq-group__answer{max-height:600px;transition:1s}.is-open .faq-group__question .heading-4-v2::after,.is-open .faq-group__question .heading-5-v2::after{transform:rotate(180deg);transition:.3s}.summit-agenda{box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);border-radius:8px;background-color:#fff;max-width:980px;margin-left:auto;margin-right:auto;padding:40px;width:90%}.agenda-item{border-radius:8px;background-color:#d4f0fa;padding:16px;border-left:4px solid var(--ui-01);position:relative}.summit-pricing-block__tile.is-past,.summit-pricing-block__tile.is-upcoming{pointer-events:none;border-color:#d2d1d4}p.agenda-item__time{width:25%;font-family:Texta!important;font-size:32px!important;font-weight:900!important;text-transform:uppercase!important;max-width:140px}@media screen and (max-width:991px){#partnerResources .section--resource-hub .snowflake-button-link .snowflake-button-container{font-size:14px!important;line-height:20px!important;margin-top:4px}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items{display:flex;flex-direction:column}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items\u003Ediv{width:100%}.sc-cert-banner__left{text-align:center}.sc-cert-banner__left .solution-center-hero__certification .snowflake-title-v2-line{justify-content:center}.summit-hero__bg-video{width:200%}.summit-leadership-grid .snowflake-flexible-column-container-items{grid-template-columns:repeat(2,1fr)}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:50%!important;max-width:50%!important}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:none!important}.summit-agenda{padding:24px}p.agenda-item__time{font-size:24px!important;width:auto;white-space:nowrap;padding-right:24px}}.agenda-item\u003Espan{display:flex;align-items:center}.summit-add-on-block,.summit-pricing-block{border:1px solid #d2d1d4;border-radius:8px;overflow:hidden;box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);background-color:#fff}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 20px 20px}.summit-pricing-block__tile{padding:24px 20px;border-radius:4px;background:#fff;border:1px solid var(--ui-01);position:relative;transition:background-color .3s}.summit-pricing-block__tile:hover{background-color:var(--ui-01);transition:background-color .3s}.summit-pricing-block__tile.is-past{background-color:#d4f0fa}.summit-pricing-block__tile:hover .black-blue-text-color .snowflake-title-v2-line{color:#fff!important;transition:color .3s}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::after,.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::after,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-pricing-block__tile.is-past .snowflake-content-chip-button,.summit-pricing-block__tile.is-upcoming .snowflake-content-chip-button,.summit-speaker-card .snowflake-card-v2-advanced-tag-indicator{display:none}.summit-pricing-block__tile.is-past .black-blue-text-color .snowflake-title-v2-line{color:#7cc7eb!important}.summit-pricing-block__tile.is-upcoming .black-blue-text-color .snowflake-title-v2-line{color:#8c8c8c!important}.summit-pricing-block__aside{background-color:#d4f0fa;border:1px solid #d2d1d4;border-radius:8px;padding:24px;width:100%}.summit-pricing-block__aside li::marker{color:var(--ui-01)}.summit-pricing-block__aside-headline .heading-5-v2{font-weight:900;margin-bottom:12px}.summit-pricing-block__header{background:#000;padding:24px 40px}.summit-pricing-block__header .heading-4-v2{font-weight:900;letter-spacing:.5px}.bwwidth100,.snowflake-mega-nav-dropdown-footer-content,.summit-pricing-block__tile .black-blue-text-color{width:100%}.summit-pricing-block__tile .heading-5-v2{position:static}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:first-child{text-transform:uppercase;font-weight:900!important;letter-spacing:.25px;font-size:24px!important}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:nth-child(2){margin-top:8px;font-family:Lato,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:16px}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{font-weight:900!important;font-size:40px!important}.snowflake-mega-nav-nav-item\u003Ea:hover .snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title,.summit-pricing-block__tile:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:var(--ui-01)!important}.summit-pricing-block__tile:hover:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:#fff!important}.summit-pricing-block__tile.is-past .heading-5-v2 span.snowflake-title-v2-line:last-child{text-decoration:line-through}.summit-pricing-block__tile .snowflake-content-chip-button{margin-top:0;white-space:nowrap;display:none}.snowflake-card-v2-advanced.no-link{pointer-events:none!important}.snowpro-card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding-sm);display:flex;height:100%}.snowpro-card__headline{margin:24px 0 12px}.snowpro-card__pricing{margin-top:48px}.snowpro-card .snowflake-text .snowpro-card__price{color:var(--ui-01);font-weight:900;font-size:40px!important;font-family:Texta,sans-serif}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid var(--summit-border)}.summit-stat-card{padding:0 40px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:first-child{font-size:64px;line-height:52px;margin-bottom:8px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:last-child{font-size:32px;line-height:30px;margin-bottom:16px}.summit-speaker-card .snowflake-card-v2-advanced-title{margin-bottom:var(--spacing-01)}.summit-add-on-card{padding:24px;border:1px solid #d2d1d4;border-radius:8px}.summit-add-on__subhead{padding-left:40px;padding-right:40px}.partner-card__logo-grid,.partner-card__logo-single{padding:40px}.partner-card__logo-grid .snowflake-image-container .cmp-image__image,.partner-card__logo-single .snowflake-image-container .cmp-image__image{border-radius:0;max-width:240px;margin:0 auto}.partner-card\u003E.container,.partner-card\u003E.container\u003E.aem-container,.partner-card\u003E.container\u003E.cmp-container{height:100%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;gap:24px;align-items:stretch}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap;gap:40px 24px;justify-content:center;align-items:center}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important}.partner-card{border-radius:8px;border:1px solid #d2d1d4;overflow:hidden;height:100%;background-color:#fff}.partner-card__header{padding:16px 24px;border-bottom:1px solid #d2d1d4}.partner-card__header.is-purple{background-color:#7d44cf}.partner-card__header h4{display:flex;flex-direction:row!important;align-items:center;gap:12px}.partner-card__header h4::before{vertical-align:middle;content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='black'/%3E%3C/svg%3E%0A\")}.partner-card__header.is-purple h4::before{background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='white'/%3E%3C/svg%3E%0A\")}.sf-blue-mountains{background-size:90% auto;background-repeat:no-repeat;background-position:center bottom;background-image:url(\"data:image/svg+xml,%3Csvg width='1361' height='410' viewBox='0 0 1361 410' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1360.25 410L1065.53 114.309L976.256 203.875L773.049 0L364.393 410H1360.25Z' fill='%233AA8DF'/%3E%3Cpath d='M274.778 410L137.467 272.238L.15625 410H274.778Z' fill='%233AA8DF'/%3E%3C/svg%3E%0A\")}.bwalignr,.main-pr-body .bwalignr{text-align:right}.bwblockalignl{margin-left:0;margin-right:auto}.bwcellpmargin{margin-top:0;margin-bottom:0}.bwlistdisc{list-style-type:disc}.bwpadb3{padding-bottom:4px}.bwpadb4{padding-bottom:5px}.bwpadl0{padding-left:0}.bwpadl3{padding-left:15px}.bwpadl6{padding-left:30px}.bwpadl9{padding-left:45px}.bwpadl12{padding-left:60px}.bwpadr0{padding-right:0}.bwtablemarginb{margin-bottom:10px}.bwvertalignb{vertical-align:bottom}.bwvertalignt{vertical-align:top}.bwsinglebottom{border-bottom:1pt solid #000}.bwdoublebottom{border-bottom:2.25pt double #000}.bwwidth1{width:1%}.bwwidth2{width:2%}.bwwidth6{width:6%}.bwwidth7{width:7%}.bwwidth8{width:8%}.bwwidth10{width:10%}.bwwidth12{width:12%}.bwwidth32{width:32%}.bwwidth44{width:44%}.bwwidth72{width:72%}.bwwidth97{width:97%}.main-pr-body{font-size:18px;line-height:26px}.main-pr-body img{display:block;width:100%;height:auto!important;border-radius:var(--small-border-radius)}.main-pr-body table{width:100%;display:block}.main-pr-body tbody{background-color:#f7f7f7}.main-pr-body .bwsinglebottom{border-bottom:1pt solid #000!important}.main-pr-body td.bwwidth44{padding-right:40px}.main-pr-body .bw-release-story{font-family:Lato,sans-serif}.main-pr-body .bw-release-story sup,.snowflake-mega-nav-dropdown-header-content-right a{white-space:nowrap}.main-pr-body .bw-release-story\u003E*,.main-pr-body\u003Espan\u003E*{margin-bottom:2rem!important}.snowflake-text.main-pr-body tbody,.snowflake-text.main-pr-body tbody p{font-size:14px!important;line-height:20px!important;width:100%;display:block}.press-body .snowflake-flexible-column-container-items{gap:var(--spacing-08)}.about-snowflake{border:1px solid #ccc;background-color:var(--ui-background-05);padding:24px;border-radius:8px;margin-top:0}.about-snowflake__logo{max-width:140px;margin-top:16px}.hero--press .snowflake-hero-system-inner{max-width:1408px;margin:0 auto!important}#arcticNavItem{flex-direction:column}#arcticNavItem::before{content:\"Featured Open Source Technologies\";display:block;margin-top:48px;margin-bottom:24px;font-size:16px!important;line-height:16px!important;font-weight:800!important;text-transform:uppercase}@media screen and (min-width:768px){.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:relative;height:100%;top:auto;left:auto;width:auto}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{background:linear-gradient(180deg,#202c35 -7.5%,#fff0 51.25%,#202c35 107.69%)}.sc-hero__byline\u003Espan{display:flex;flex-wrap:wrap}.sc-hero__byline p:not(:last-child)::after{content:\"|\";margin:0 12px;opacity:.5}.sc-hero__button-container .snowflake-flexible-column-container-items{position:absolute;bottom:0;padding:0;margin:0 24px 0 0}.sc-hero__button-container .hero-watch-the-demo{padding:12px 16px!important;float:right;margin-bottom:48px;background-color:rgb(35 45 54 / .8)}.summit-overview-stat{padding:0 40px}.summit-timeline{border-bottom:3px solid var(--ui-01);margin-bottom:64px}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 40px 40px}#arcticNavItem::before{font-size:12px!important;margin-bottom:8px;margin-top:16px}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{line-height:20px!important}.snowflake-card .heading-2.snowflake-title-line{font-size:24px!important;line-height:28px!important}}@media screen and (min-width:992px){.hp-hero__eyebrow a{gap:12px;margin-left:0;margin-right:0}.hp-hero__eyebrow a::after{content:\"\";background-image:url(\"data:image/svg+xml,%3Csvg width='6' height='11' viewBox='0 0 6 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M5.49134 5.79438C5.53447 5.75922 5.56923 5.71489 5.5931 5.66463C5.61697 5.61436 5.62935 5.55941 5.62935 5.50376C5.62935 5.44811 5.61697 5.39316 5.5931 5.34289C5.56923 5.29263 5.53447 5.2483 5.49134 5.21314L.736339 .413136C.522589 .203135 .331339 .203135 .151339 .413136C-.0286612 .623135 -.0586612 .818135 .151339 .994386L4.48634 5.50188L.155089 9.97938C.107068 10.0142 .0679743 10.0598 .0410153 10.1126C.0140562 10.1654 0 10.2238 0 10.2831C0 10.3424 .0140562 10.4009 .0410153 10.4537C.0679743 10.5065 .107068 10.5521 .155089 10.5869C.335089 10.7969 .530089 10.7969 .740089 10.5869L5.49134 5.79438Z' fill='black'/%3E%3C/svg%3E%0A\");display:inline-block;width:12px;height:12px;background-repeat:no-repeat;background-size:auto 100%;background-position:left center}.promo-banner--homepage{padding-top:32px}.homepage-banner-offset-container::after{height:50%}#storyHighlights{padding:2rem}.body-display-v2.snowflake-quote-item-quote-text{line-height:28px!important}.snowflake-hero-system-headline .heading-1-v2{line-height:48px;font-size:54px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-content{flex-direction:row;justify-content:space-between;align-items:center;width:100%}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{flex-direction:row}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child)::before{content:\"|\";margin:0 6px}.sc-cert-banner{padding:40px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{margin:0!important;width:50%}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;padding-right:24px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:240px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{width:70%;padding-left:40px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{width:30%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important;display:flex}.summit-pricing-block__tile .snowflake-content-chip-content{display:flex;flex-direction:row;align-items:center;width:calc(100% - 200px)}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{position:absolute;top:50%;transform:translate(0,-50%);right:40px}.press-body\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:sticky;top:120px}.snowflake-mega-nav-navigation-title:hover{color:var(--ui-01)}}@media screen and (min-width:1024px){.about-snowflake{padding:28px}.about-snowflake__logo{max-width:none;padding:0 0 0 48px;margin-bottom:0}.hero--press .snowflake-hero-system-layout-70-30 .snowflake-hero-system-content-container{width:85%}.snowflake-hero-system{padding-bottom:var(--spacing-04);padding-top:var(--spacing-07)}.hero--press .display-2-v2{font-size:64px;line-height:56px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container{flex-direction:row;flex-wrap:nowrap;align-items:center}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:280px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;margin-bottom:0!important}#polarisNavItem{margin-top:40px}.snowflake-mega-nav-nav-item-description{line-height:18px!important}.snowflake-mega-nav-column-items{gap:var(--spacing-01);grid-gap:var(--spacing-01)}.snowflake-mega-nav-navigation-title{text-transform:none}}div[id*=blueIcon] .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01);padding:8px}div[id*=blueIcon]:hover .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01)!important}.snowflake-mega-nav-nav-item-icon__inner{border-radius:4px;background:var(--ui-background-05);padding:6px}.snowflake-mega-nav-nav-item:hover .snowflake-mega-nav-nav-item-icon__inner{background:#fff!important}.snowflake-mega-nav-nav-item-icon.snowflake-image-container{height:40px;width:40px}.snowflake-mega-nav-dropdown-footer-links\u003E.snowflake-button-link\u003E.snowflake-button-container{font-size:16px!important;font-family:Texta!important;font-weight:800!important}.snowflake-mega-nav-dropdown-footer-icon.snowflake-image-container{margin-right:8px;width:40px!important;height:40px!important}#viewAllCapabilities a:hover{background:0 0!important}#platformFooter .snowflake-title-v2 .snowflake-title-v2-line:last-child{font-family:Lato;font-size:14px;font-weight:500}#platformFooter .snowflake-mega-nav-dropdown-footer-links{flex-grow:1;justify-content:flex-end;align-items:center}#platformFooter .snowflake-mega-nav-dropdown-footer-content{flex-direction:row}#offset,#open-source{flex-direction:column;border-top:1px solid #ccc}#offset::before,#open-source::before{content:\" \";display:block;width:100%;font-weight:800!important;font-size:12px!important;line-height:14px;text-transform:uppercase;white-space:nowrap;margin-top:16px;margin-bottom:8px}#open-source::before{content:\"Open Source Technologies\"}.snowflake-mega-nav-dropdown-menu-close-button{margin:var(--spacing-04) 0 var(--spacing-03)}.snowflake-mega-nav-column{gap:var(--spacing-02)!important}.snowflake-mega-nav-nav-item\u003Ea{width:100%;margin-left:-8px;padding:8px;border-radius:4px}.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:var(--ui-background-05)}.snowflake-mega-nav-nav-item-description{margin-top:2px;display:block}#promobanner_overflowBottomDarkBlue::before{content:'';display:block;position:absolute;bottom:0;left:0;width:100%;height:50%;background:#212d35}#promobanner_overflowTopDarkBlue::before{content:'';display:block;position:absolute;top:0;left:0;width:100%;height:50%;background:#212d35}.overview-card\u003Ediv{box-shadow:0 0 14px 0 rgba(0,0,0,.10);background-color:#fff;border-radius:16px;overflow:hidden}.overview-card-text{padding:40px}.overview-card-image img{border-radius:0 !important}.overview-card-text h3,.overview-card-text .heading-3-v2{font-size:18px;line-height:1.1;margin-top:0}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"mega_header":{"additionalClasses":"heap-nav-header","layout":"SIMPLE","id":"container-863e255504",":type":"snowflake-site/components/mega-header","appliedCssClassNames":"snowflake-header-container white",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-7f7dccefdc",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-eb05c277de","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-4209557962",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"additionalClasses":"nav-platform-sidebar","numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-2a5de10c8d",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-e4e714a878","additionalClasses":"nav-item__platform-parent is-platform","linkDescription":"Develop AI products, apps and more on a fully managed platform that securely connects businesses globally — across any type or scale of data.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/platform/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The Snowflake Platform"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-6d337e402e","additionalClasses":"nav-item nav-item--si is-si","linkDescription":"All your knowledge. One trusted enterprise agent.","flag":"NOW GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-cowork/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoWork"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_836345186":{"id":"nav-item-f91d02c964","additionalClasses":"blue-icon is-analytics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/analytics/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Analytics"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2":{"id":"nav-item-e1249fa8b3","additionalClasses":"blue-icon is-ai","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/ai/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1314771042":{"id":"nav-item-1e241852ba","additionalClasses":"blue-icon is-data-eng","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/data-engineering/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Data Engineering"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634":{"id":"nav-item-c97a1a3a5f","additionalClasses":"blue-icon is-apps-collab","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/applications-and-collaboration/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Applications & Collaboration"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_2013333117":{"id":"nav-item-8883b9365d","additionalClasses":"blue-icon is-transactions","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/transactions/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Transactions"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646","nav_item","nav_item_copy_copy_2_836345186","nav_item_copy_copy_2","nav_item_copy_copy_2_1314771042","nav_item_copy_144634","nav_item_copy_144634_2013333117"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","navColumnTitle":"Featured Capabilities","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-0be0e88fc2",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_212715":{"id":"nav-item-9705f34412","additionalClasses":"is-cortex-code","linkDescription":"Snowflake-native AI coding agent ","flag":"New","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-coco/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoCo"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-c5a6c90538","additionalClasses":"is-cortex-ai","linkDescription":"Instant access to industry-leading LLMs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/cortex/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Cortex AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590635":{"id":"nav-item-9be160b994","additionalClasses":"is-marketplace","linkDescription":"Third-party data sources connected within minutes","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/marketplace/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketplace"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-336c2558ac","additionalClasses":"is-snowpark","linkDescription":"Libraries and code execution environments that run Python and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/snowpark/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowpark"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_983061516":{"id":"nav-item-c3ac2bc35c","additionalClasses":"is-streamlit","linkDescription":"Framework for transforming Python scripts into web apps","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Streamlit"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_212715","nav_item","nav_item_copy_660590635","nav_item_copy_660590","nav_item_copy_660590_983061516"]},"nav_column_692142673":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-827d67c2bc",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_660590_1739526127":{"id":"nav-item-56234a6d0a","additionalClasses":"is-postgres","linkDescription":"Fully compatible open source Postgres running on Snowflake","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/postgres/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Postgres"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-20084966ed","additionalClasses":"is-dcr","linkDescription":"Streamlined model development and MLOps from a centralized UI","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/end-to-end-ml-workflows/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake ML"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_212715":{"id":"nav-item-084a333d24","additionalClasses":"is-openflow","linkDescription":"Effortless data movement for integrations","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/openflow/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Openflow"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-04a01d925f","additionalClasses":"is-notebooks","linkDescription":"Interactive dev environment for data and AI teams","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/notebooks/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Notebooks"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-878d07ac2d","propertiesId":"workload-nav-1","additionalClasses":"is-native-apps","linkDescription":"End-to-end, Snowflake-native app creation and distribution","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/native-apps/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Native Apps"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_660590_1739526127","nav_item_copy_185565","nav_item_copy_212715","nav_item_copy_660590","nav_item_258535199"]},"nav_column_782221091":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-1c7ded25f0",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-aa97adda7e","additionalClasses":"is-light-gray-icon is-horizon-catalog","linkDescription":"Universal AI catalog","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Catalog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_1293798742":{"id":"nav-item-91bf60e353","additionalClasses":"is-snowflake-ml","linkDescription":"Governed context layer that keeps AI, BI and data apps working from one truth","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon-context/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Context"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c":{"id":"nav-item-f448c94588","additionalClasses":"is-unistore","linkDescription":"Unify transactional and analytical workloads in Snowflake for enhanced simplicity","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/unistore/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Unistore"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1443811525":{"id":"nav-item-b24da41167","additionalClasses":"is-observe","linkDescription":"AI-powered observability for faster production troubleshooting","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/observe/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Observe"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1006104884":{"id":"nav-item-975a6f56df","additionalClasses":"is-observe","linkDescription":"Use any engine on a single governed data copy","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/use-cases/interoperable-lakehouse/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Interoperable Lakehouse"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item_copy_660590_1293798742","nav_item_511717659_c","nav_item_511717659_c_1443811525","nav_item_511717659_c_1006104884"]}},":itemsOrder":["nav_column","nav_column_copy_copy","nav_column_692142673","nav_column_782221091"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Product"},"nav_dropdown_menu_2":{"id":"nav-dropdown-menu-60f7845315","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-b63056471b",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"navColumnTitle":"INDUSTRIES","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-42e73dc74f",":type":"snowflake-site/components/nav/nav-column","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"nav_item_copy_361384_2056203141":{"id":"nav-item-d65e8d0569","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"All Industries"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-b96353c6c4","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/advertising-media-entertainment/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Advertising, Media & Entertainment"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-2e78530b3d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/financial-services/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Financial Services"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-b64abcc4b0","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/healthcare-and-life-sciences/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Healthcare & Life Sciences"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1533429516":{"id":"nav-item-e146be264b","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/manufacturing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Manufacturing"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1444458226":{"id":"nav-item-adf0b6a934","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/public-sector/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Public Sector"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1149488919":{"id":"nav-item-9b236f64c2","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Retail & Consumer Goods"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_57417040":{"id":"nav-item-a18d784ef9","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Technology"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384674":{"id":"nav-item-39e1950932","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/telecom/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Telecom"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384":{"id":"nav-item-b5f5d66f1a","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/travel-hospitality/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Travel & Hospitality"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_361384_2056203141","nav_item","nav_item_copy","nav_item_copy_1970515619","nav_item_copy_1533429516","nav_item_copy_1444458226","nav_item_copy_1149488919","nav_item_copy_57417040","nav_item_copy_361384674","nav_item_copy_361384"]},"nav_column_copy":{"navColumnTitle":"DEPARTMENTS","numberOfSubColumns":"one-column","minWidth":"160","layout":"SIMPLE","id":"container-621cdb5474",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-e7254bbf41","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/finance/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Finance"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-fbdf823738","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/information-technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"IT"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-caed4b0364","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/marketing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketing"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]},"nav_column_833417450":{"navColumnTitle":"Enablement Solutions","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-c16efbb715",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_107772":{"id":"nav-item-74cc2415fd","linkDescription":"Confident migration to a unified platform","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/migrate-to-the-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Migrate to the AI Data Cloud"},"icon":{"id":"icon","alt":"Cloud icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_107772/icon.coreimg.svg/1723828484100/nav-icon-cloud.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-a6c70f0371","linkDescription":"Snowflake experts to help you accelerate and achieve business goals","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/services-delivery/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Services Delivery"},"icon":{"id":"icon","alt":"Migrate icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_copy/icon.coreimg.svg/1768354429188/nav-icon--migrate.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_107772","nav_item_copy_copy"]},"nav_column_copy_copy":{"navColumnTitle":"PARTNER SOLUTIONS","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-eba872bfd7",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-9437cfd249","linkDescription":"Programs with product, solutions and cloud partners","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Partner Network"},"icon":{"id":"icon","alt":"Partner Network icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1723828498700/nav-icon--partner-network.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-029cb2a1fe","linkDescription":"Partners, apps and solutions for enhanced deployment","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/all-partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Partner Finder"},"icon":{"id":"icon","alt":"Partner Finder icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1726173927645/nav-icon--partner-finder.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-2e349ca3f8","linkDescription":"Live and virtual events","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/event-partnership-opportunities/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Event Partnership Opportunities"},"icon":{"id":"icon","alt":"Calendar icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy_1970515619/icon.coreimg.svg/1726173935655/nav-icon--events.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-4f9f2428bf","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-43536525c9",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-df7a5ed868",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-ab2c10b4d2","additionalClasses":"nav-item__platform-parent-why-sf","linkDescription":"Collaborate locally and globally to reveal new insights, create previously unforeseen business opportunities, and identify your customers with seamless experiences.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Why Snowflake"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","numberOfSubColumns":"two-columns","maxWidth":"1200","layout":"SIMPLE","id":"container-5c0e559351",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-8ca72849b4","propertiesId":"testID","linkDescription":"Case studies and videos showcasing how global organizations use Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Customers"},"icon":{"id":"icon","alt":"Customer icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1739839279367/nav-icon--partner-network.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-35b24ac5d7","propertiesId":"workload-nav-1","linkDescription":"Learn how to connect, share and integrate the data and apps on the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/what-is-data-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The AI Data Cloud Explained"},"icon":{"id":"icon","alt":"Cloud icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_258535199/icon.coreimg.svg/1739840490955/nav-icon-cloud.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-d1737f1513","linkDescription":"Comprehensive security through built-in features, robust cloud infrastructure protection, and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/snowflake-security-hub/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Security Hub"},"icon":{"id":"icon","alt":"User with security lock icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565/icon.coreimg.svg/1758909528089/user-security-admins-ciso-icon.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-a0b7b1d95f","additionalClasses":"is-light-gray-icon","linkDescription":"Maximize economic value through minimizing TCO and continuously optimizing price for performance","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/pricing-options/cost-and-performance-optimization/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Cost and Performance Optimization"},"icon":{"id":"icon","alt":"Cost Optimization icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1758909542267/nav-icon-cost-optimization-performance.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_903555964":{"id":"nav-item-604cfff736","linkDescription":"Startups building applications in the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/startup-program/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Startups"},"icon":{"id":"icon","alt":"Launch","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565_903555964/icon.coreimg.svg/1758732224323/launch.svg","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_258535199","nav_item_copy_185565","nav_item_copy","nav_item_copy_185565_903555964"]}},":itemsOrder":["nav_column","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Why Snowflake"},"item_1719961362824":{"id":"nav-dropdown-menu-2f9f182925","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-9af9624b2b",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","minWidth":"124","layout":"SIMPLE","id":"container-afa42f97a9",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-0cf2d7db60","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Blog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_180298689":{"id":"nav-item-2155da2f9d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/events/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Events"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_1639361946":{"id":"nav-item-0dacce3ee5","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/support/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Support"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_680912746":{"id":"nav-item-07a6255d9d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/contact/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Contact us"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_180298689","nav_item_1639361946","nav_item_680912746"]},"nav_column_44600420__826130542":{"navColumnTitle":"Learn","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-d78a39acef",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-afdc1b2315","linkDescription":"Ebooks, videos, white papers and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Resource Library"},"icon":{"id":"icon","alt":"Notebooks icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy/icon.coreimg.svg/1736877128196/nav-icon--notebooks.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-960ca3e5b0","linkDescription":"Overview of Snowflake's educational offerings","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/training/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Training"},"icon":{"id":"icon","alt":"Training icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item/icon.coreimg.svg/1722385094416/nav-icon--training.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-d4d70fb0dc","linkDescription":"Expert-led discussions and demos across industries and use cases","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Webinars"},"icon":{"id":"icon","alt":"Webinars icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_144634_1984107859/icon.coreimg.svg/1759424691990/nav-icon--webinars.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-cb082086c1","linkDescription":"Snowflake's technical industry professional certifications","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/certifications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Certifications"},"icon":{"id":"icon","alt":"Certification icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_1438098918/icon.coreimg.svg/1722382780833/nav-icon--cert.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-068d7003e5","linkDescription":"Weekly product demos showcasing key features and live Q&A ","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/demo/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Live Demos"},"icon":{"id":"icon","alt":"Live Demo icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_143809/icon.coreimg.svg/1759424359543/nav-icon--live-demo.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-245e864851","linkDescription":"Training courses for all levels, on-demand or instructor-led","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://learn.snowflake.com/en/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowflake University"},"icon":{"id":"icon","alt":"Education icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890638/icon.coreimg.svg/1722382769808/nav-icon--education.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-c744e187a1","linkDescription":"Instructor-led virtual workshops for exploring key Snowflake features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/virtual-hands-on-lab/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Hands-On Labs"},"icon":{"id":"icon","alt":"Hands-on Labs icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_189945/icon.coreimg.svg/1759388182903/nav-icon--labs.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890":{"id":"nav-item-d1567f75da","linkDescription":"Academic papers written by Snowflake researchers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/publications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Research Publications"},"icon":{"id":"icon","alt":"Copy","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890/icon.coreimg.svg/1756326371387/copy.svg","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890_930852828":{"id":"nav-item-18bebad3d7","linkDescription":"Informative articles about AI and data topics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/fundamentals/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Fundamentals"},"icon":{"id":"icon","alt":"Document with list","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890_930852828/icon.coreimg.svg/1756853637155/data-sheet.svg","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item","nav_item_copy_144634_1984107859","nav_item_copy_1438098918","nav_item_copy_143809","nav_item_copy_333890638","nav_item_copy_189945","nav_item_copy_333890","nav_item_copy_333890_930852828"]}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"]},"nav_promo_section":{"id":"nav-promo-section-ed9e5e6e35","experience_fragment_1":{"id":"experiencefragment-efd9c0e562","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master1/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f746f79872",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-22a1cfebfc","openInNewWindow":true,"layout":"horizontal","headline":"The Modern Marketing Data Stack 5th Edition","description":"AI agents are changing the marketing stack. See how to govern the shift. ","linkTitle":"Learn more","linkUrl":"/en/the-modern-marketing-data-stack-report/","image":{"id":"image","alt":"MMDS report 5th edition","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b3030d24-fd50-45e6-bfe6-9520d3eb46d8/web-inside-the-mmds-5th-960x540.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"]},"experience_fragment_2":{"id":"experiencefragment-a4105cf443","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-2/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-803f2782a1",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-aa0c64cbbd","openInNewWindow":true,"layout":"horizontal","headline":"The ROI of Gen AI and Agents 2026","description":"Discover how 92% of early adopters are achieving positive ROI with gen AI.","linkTitle":"Learn More","linkUrl":"/en/lp/radical-roi-generative-ai/","image":{"id":"image","alt":"roi of gen ai and agents","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0c15edae-1a97-4739-8b16-c7f3941a6d9e/web-roi-of-gen-ai-and-agents-2026-r02-960x540.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"]},"experience_fragment_3":{"id":"experiencefragment-b067afb0e0","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-3/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-375f182a83",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-440c389439","openInNewWindow":true,"layout":"horizontal","headline":"Startup 2026: AI Agents Mean Business","description":"Venture leaders weigh in on agentic AI. ","linkTitle":"Learn more","linkUrl":"/en/lp/building-startup-ai-age/","image":{"id":"image","alt":"alt","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a320b404-dca1-4477-b033-c79708538657/web-startup-2026-960x540.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"]},":type":"snowflake-site/components/nav/nav-promo-section"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Resources"},"item_1719963657751":{"id":"nav-dropdown-menu-ce2ce19532","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-96518dbae9",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy_copy":{"navColumnTitle":"Build","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-dbeedec7f3",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-8bc709d011","propertiesId":"testID","linkDescription":"Overview of the dev resources you need to build and scale","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Developers"},"icon":{"id":"icon","alt":"Developers icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1731362494574/nav-icon--devs.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-7d810342fa","linkDescription":"Reference architectures, use cases and best practices","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/guides/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Developer Guides"},"icon":{"id":"icon","alt":"Solution Center icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy_1855651246/icon.coreimg.svg/1761677891705/nav-icon--solution-center.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-910aa57f8d","additionalClasses":"is-light-gray-icon","linkDescription":"The latest software versions, drivers, libraries and relevant docs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/downloads/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Downloads"},"icon":{"id":"icon","alt":"Download icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1731362660050/nav-icon-download.svg","lazyEnabled":true,"height":"28","width":"28",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246","nav_item_copy"]},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Learn","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-2d86f9040c",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-3069ac279f","propertiesId":"testID","linkDescription":"Reference docs, guides, tutorials and announcements","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://docs.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Documentation"},"icon":{"id":"icon","alt":"Docs icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item/icon.coreimg.svg/1731361950527/nav-icon--docs.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-ac0be108a0","additionalClasses":"is-light-gray-icon","linkDescription":"Key projects Snowflake engineers maintain and support","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/open-source/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Open Source"},"icon":{"id":"icon","alt":"Open Source icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy/icon.coreimg.svg/1731365437016/nav-icon-open-source.svg","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-a0d4c4dfb9","additionalClasses":"is-light-gray-icon","linkDescription":"Online and in-person classes and workshops to upskill on Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/northstar/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Builder Education"},"icon":{"id":"icon","alt":"Northstar logo","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy_copy/icon.coreimg.svg/1731362475640/nav-icon--northstar.svg","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_copy"]},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-984ba9ea96",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-19378d8aea","propertiesId":"testID","linkDescription":"Snowflake’s technical leaders on what, why and how they build features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/engineering-blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Engineering Blog"},"icon":{"id":"icon","alt":"Developers icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item/icon.coreimg.svg/1757101368571/nav-icon--developer-center.svg","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-5bf33c6fbb","linkDescription":"Tips, tricks and discussion with fellow Snowflake developers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://community.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Community"},"icon":{"id":"icon","alt":"Partner Network icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item_copy_1855651246/icon.coreimg.svg/1731362644348/nav-icon--partner-network.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246"]}},":itemsOrder":["nav_column_copy_copy","nav_column_copy_copy_1367930678","nav_column_copy_copy_1101894776"]},"nav_promo_section":{"id":"nav-promo-section-f7709a6774","experience_fragment_1":{"id":"experiencefragment-08d6d5eade","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-5/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-dd2c5dd382",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-fce53a53de","openInNewWindow":false,"layout":"horizontal","headline":"Get started with your first Snowflake Notebook","description":"Write and execute code, visualize results, and tell the story of your analysis all in one place.","linkTitle":"Learn More","linkUrl":"/en/developers/solutions-center/getting-started-with-your-first-snowflake-notebook-project/","image":{"id":"image","alt":"alt","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dc7e334a-c38b-4283-b1de-fcf829952eef/nav-promo-first-notebook.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"210","width":"415",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]},"experience_fragment_2":{"id":"experiencefragment-8b41120c7c","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-053de67b29",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-ec1600b946","openInNewWindow":true,"layout":"horizontal","headline":"Northstar Builder Workshops","description":"Join other developers as you roll up your sleeves and explore the possibilities of Snowflake.","linkTitle":"Learn More","linkUrl":"/en/nav-promos/northstar-builders-workshop/","image":{"id":"image","alt":"Snowflake Northstar logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14341ced-bc5e-4a29-9762-b7857f6cadfc/nav-promo-northstar.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"700","width":"1440",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]},":type":"snowflake-site/components/nav/nav-promo-section"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Developers"},"item_1718247180324":{"id":"nav-dropdown-menu-827dff05df","enableDropdown":false,"link_url":"/en/pricing-options/",":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Pricing"}},":itemsOrder":["item_1719963657751_c_663444255","nav_dropdown_menu_2","item_1719963657751_c","item_1719961362824","item_1719963657751","item_1718247180324"]},"languagenavigation":{"id":"language-navigation-0a1435b91c","languageNavItems":[{"title":"English","path":"/en/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/","locale":"en","active":true},{"title":"日本語","path":"/ja/","locale":"ja","active":false},{"title":"한국어","path":"/ko/","locale":"ko","active":false},{"title":"中文（简体）","path":"/zh_cn/","locale":"zh-cn","active":false},{"title":"Português","path":"/pt_br/","locale":"pt-br","active":false},{"title":"Deutsch","path":"/de/","locale":"de","active":false},{"title":"Français","path":"/fr/","locale":"fr","active":false},{"title":"Español","path":"/es/","locale":"es","active":false},{"title":"Italiano","path":"/it/","locale":"it","active":false}],":type":"snowflake-site/components/nav/language-navigation"},"button_1177328691":{"id":"button-8655ee9248","heapButtonClasses":["mega-nav__sign-in"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://app.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-link snowflake-button-black snowflake-button-compact","text":"Sign in"},"button":{"id":"button-c7951576c2","heapButtonClasses":["contact_nav","heap-nav-contact"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"/en/contact-sales/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-secondary snowflake-button-blue snowflake-button-compact","text":"CONTACT SALES"},"button_288358396":{"id":"button-59f61fbde0","heapButtonClasses":["start_for_free_nav","heap-nav-start-for-free"],"showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://signup.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-primary snowflake-button-blue snowflake-button-compact","text":"start for free"}},":itemsOrder":["nav_mega","languagenavigation","button_1177328691","button","button_288358396"]}},":itemsOrder":["markup_editor","mega_header"]},"image":{":type":"nt:unstructured"},"cq:targetMetadata":{"cq:targetStatus":"OUT_OF_SYNC","cq:exportTime":1781280015540,"cq:targetOfferId":860250,":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:targetMetadata"]},"markup_editor_1950346551":{"id":"markup-editor-69852c29c5","title":" ","cssContent":".snowflake-markdown-table code[class*=language-],.snowflake-markdown-table code[class*=language-],.snowflake-markdown .snowflake-text code[class*=language-],.snowflake-markdown .snowflake-text pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-9c7723bd09","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroForkRepoLink":{"id":"button-5db66ec645","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/ask-questions-to-your-own-documents-with-snowflake-cortex-search"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/ask-questions-to-your-own-documents-with-snowflake-cortex-search","quickstartHeroBreadcrumbs":[{"title":"Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"quickstartHeroTitle":{"lines":["Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Carlos Carrero","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""}},"flexible_column_cont":{"id":"flexible-column-container-0ce992fe5b","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-fa8190e5e3",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-1b65e0662d","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/large-language-model/\"\u003ELarge Language Models\u003C/a\u003E (LLMs) are incredibly powerful, but they can sometimes guess or provide incorrect responses, which is what we call a “hallucination.” To reduce hallucinations, LLMs can be combined with private datasets. Today, the most common approach for reducing hallucinations without having to change the model (e.g. fine-tuning) is the \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERetrieval Augmented Generation\u003C/a\u003E (RAG) framework. RAG allows you to “ground” the model’s responses by making a set of relevant documents available to the LLM as context in the response.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart we will show you how to quickly and securely build a full-stack RAG application in Snowflake without having to build integrations, manage any infrastructure or deal with security concerns with data moving outside of the Snowflake governance framework.\u003C/p\u003E\n","\u003Cp\u003EThis guide will leverage \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E, a fully managed service that automatically creates embeddings for your data and perform retrievals using a hybrid search engine, using embeddings for semantic similarity plus keyword search for lexical similarity, achieving state-of-the-art retrieval quality.\u003C/p\u003E\n","\u003Cp\u003EWe will show you how easy it is to implement RAG via a chat assistant that knows everything about products. To make the assistant an expert in your bikes and snow gears, we are going to give it access to a few user manuals. This template can be easily adapted to any documents you want your assistant to learn from — whether its financial reports, research documents or anything else!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/qs_cortex_search_intro.gif\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERAG Overview\u003C/h3\u003E\n","\u003Cp\u003EIn a typical RAG application, there is a process to prepare and use your documents. The content is first broken down into chunks and the content is embedded into a vector store. When a user asks a question, the vector store is searched to retrieve the most similar chunks of content. These relevant chunks are passed to the LLM as context to provide an accurate answer:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig1_rag.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESnowflake Cortex Search is a fully managed indexing and retrieval service that simplifies and empowers RAG applications. The service automatically creates embeddings and indexes your data, providing a robust enterprise search service that can be accessed via APIs:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig2_rag_cortex_search_arch.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create functions that use Python libraries using \u003Ca href=\"https://snowflake.com/snowpark\"\u003ESnowpark\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to use power search experiences using \u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/cortex-search/cortex-search-overview\"\u003ESnowflake Cortex Search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to use LLMs using serverless functions in \u003Ca href=\"https://snowflake.com/cortex\"\u003ESnowflake Cortex AI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to build a front-end with Python using \u003Ca href=\"/en/data-cloud/overview/streamlit-in-snowflake/\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EYou will create a working product chat assistant in Streamlit. The final product includes an application that lets users test how the LLM responds with and without the context document(s) to show how RAG can address hallucinations.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account in a cloud region that supports Snowflake Cortex \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003ELLM functions\u003C/a\u003E are supported\u003C/li\u003E\u003Cli\u003ECheck \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions?_ga=2.5151286.405859672.1709568467-277021311.1701887324&_gac=1.124754680.1707955750.Cj0KCQiA5rGuBhCnARIsAN11vgRLWfK6RIoIEqcZ7cFas8qwN4yCoL0q9nttp5UEmSocnPmhdBG57fgaAjqNEALw_wcB&_fsi=j2b82Wl3#availability\"\u003ELLM availability\u003C/a\u003E to help you decide where you want to create your Snowflake account\u003C/li\u003E\u003Cli\u003EA Snowflake account with \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003EAnaconda Packages\u003C/a\u003E enabled by ORGADMIN\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: For an end-to-end setup experience using Snowflake Notebook, download this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/RAG_Using_Snowflake_Cortex_Search_Setup_Notebook.ipynb\"\u003E.ipynb\u003C/a\u003E file from the GitHub repository and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-create#label-notebooks-import\"\u003Eimport\u003C/a\u003E it into a Snowflake Notebook. The Notebook covers setup steps 2, 3, and 4.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOrganize Documents\u003C/h2\u003E\n","\u003Cp\u003EBefore we can ask our LLM assistant questions about our products, we need to get our product manual PDFs into Snowflake. In Snowflake, databases and schemas are used to organize and govern access to data and logic. Let's start by getting a few documents locally and then create a database that will hold the PDFs.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1\u003C/strong\u003E. Download example documents\u003C/p\u003E\n","\u003Cp\u003EWe have created a small set of example user manuals for fictitious bikes and skis that contain specific product details. This information is what we will use to test how the LLM responds with and without access to the information in the documents.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: You can substitute these documents with your own product sheets, financial reports, or any other documents you want to try asking questions against.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EDownload all documents from this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/docs.zip\"\u003Ezip file\u003C/a\u003E on the GitHub repository.\u003C/p\u003E\n","\u003Cp\u003EThe zip file contains these individual documents:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Mondracer_Infant_Bike.pdf\"\u003EMondracer Infant Bike\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Premium_Bicycle_User_Guide.pdf\"\u003EPremium Bycycle User Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Ski_Boots_TDBootz_Special.pdf\"\u003ESki Boots TDBootz Special\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/The_Ultimate_Downhill_Bike.pdf\"\u003EThe Ultimate Downhill Bike\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/The_Xtreme_Road_Bike_105_SL.pdf\"\u003EThe Xtreme Road Bike 105 SL\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Carver%20Skis%20Specification%20Guide.pdf\"\u003ECarver Skis Specification Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/OutPiste%20Skis%20Specification%20Guide.pdf\"\u003EOutPiste Skis Specification Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/RacingFast%20Skis%20Specification%20Guide.pdf\"\u003ERacingFast Skis Specification Guide\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2\u003C/strong\u003E. Open a new Worksheet\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-in-sf-web-interface\"\u003ECreating Snowflake Worksheets documentation\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3\u003C/strong\u003E. Create a database and a schema\u003C/p\u003E\n","\u003Cp\u003ERun the following code inside your newly created worksheet\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE DATABASE CC_QUICKSTART_CORTEX_SEARCH_DOCS;\nCREATE SCHEMA DATA;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/ddl-database\"\u003EDatabase and Schema management documentation\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4\u003C/strong\u003E. Create a Stage with Directory Table where you will be uploading your documents\u003C/p\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-stage\"\u003EStage\u003C/a\u003E is used for loading data from files into Snowflake tables and unloading data from tables into files. A \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-dirtables\"\u003EDirectory Table\u003C/a\u003E stores file-level metadata about the data files in the stage.\u003C/p\u003E\n","\u003Cp\u003ERun the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace stage docs ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE') DIRECTORY = ( ENABLE = true );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5\u003C/strong\u003E. Upload documents to your staging area\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECatalog » Database Explorer\u003C/strong\u003E on the left of Snowsight\u003C/li\u003E\u003Cli\u003EClick on your database \u003Cstrong\u003ECC_QUICKSTART_CORTEX_SEARCH_DOCS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on your schema \u003Cstrong\u003EDATA\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003EStages\u003C/strong\u003E and select \u003Cstrong\u003EDOCS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EOn the top right click on the +Files button\u003C/li\u003E\u003Cli\u003EDrag and drop the two PDF files from the docs folder you downloaded\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig3_stage.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 6\u003C/strong\u003E. Check files has been successfully uploaded\u003C/p\u003E\n","\u003Cp\u003ERun this query in the Worksheet to check that all your documents are in the staging area\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Els @docs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see a list of all uploaded PDF file names.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig4_ls_docs.png\" alt=\"App\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPre-process Documents\u003C/h2\u003E\n","\u003Cp\u003EIn this step we are going to leverage Snowflake native document processing functions to prepare documents before enabling \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E. We are also going to use Cortex \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex\"\u003ECLASSIFY_TEXT\u003C/a\u003E function in order to label the type of document being processed so that we can use that metadata to filter searches.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig5_build.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1\u003C/strong\u003E. Use the function \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex\"\u003ESNOWFLAKE.CORTEX.PARSE_DOCUMENT\u003C/a\u003E to read the PDF documents directly from the staging area we created. Here, we use the LAYOUT mode to extract the text and maintain the document’s structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE TEMPORARY TABLE RAW_TEXT AS\nWITH FILE_TABLE as (\n  (SELECT \n        RELATIVE_PATH,\n        SIZE,\n        FILE_URL,\n        build_scoped_file_url(@docs, relative_path) as scoped_file_url,\n        TO_FILE('@DOCS', RELATIVE_PATH) AS docs \n    FROM \n        DIRECTORY(@DOCS))\n)\nSELECT \n    RELATIVE_PATH,\n    SIZE,\n    FILE_URL,\n    scoped_file_url,\n    TO_VARCHAR (\n        SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT (\n            docs,\n            {'mode': 'LAYOUT'} ):content\n        ) AS EXTRACTED_LAYOUT \nFROM \n    FILE_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2\u003C/strong\u003E. Create the table where we are going to store the chunks for each PDF from the processed text.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace TABLE DOCS_CHUNKS_TABLE ( \n    RELATIVE_PATH VARCHAR(16777216), -- Relative path to the PDF file\n    SIZE NUMBER(38,0), -- Size of the PDF\n    FILE_URL VARCHAR(16777216), -- URL for the PDF\n    SCOPED_FILE_URL VARCHAR(16777216), -- Scoped url (you can choose which one to keep depending on your use case)\n    CHUNK VARCHAR(16777216), -- Piece of text\n    CHUNK_INDEX INTEGER, -- Index for the text\n    CATEGORY VARCHAR(16777216) -- Will hold the document category to enable filtering\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3\u003C/strong\u003E. The extracted text can sometimes be too large to be an effective piece of context for an LLM. We will use the function \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/split_text_recursive_character-snowflake-cortex\"\u003ESNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER\u003C/a\u003E to split the text into shorter strings. We will then insert these smaller chunks into our table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Einsert into docs_chunks_table (relative_path, size, file_url,\n                            scoped_file_url, chunk, chunk_index)\n\n    select relative_path, \n            size,\n            file_url, \n            scoped_file_url,\n            c.value::TEXT as chunk,\n            c.INDEX::INTEGER as chunk_index\n            \n    from \n        raw_text,\n        LATERAL FLATTEN( input => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELabel the product category\u003C/h3\u003E\n","\u003Cp\u003EWe are going to use the power of Large Language Models and the function \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex\"\u003ECLASSIFY_TEXT\u003C/a\u003E  to easily classify the documents we are ingesting in our RAG application. We are going to pass the document name and the first chunk of text into the CLASSIFY_TEXT function to determine categories. In this example, we want to use the LLM to automatically label each document as either ‘Bike’ or ‘Snow.’\u003C/p\u003E\n","\u003Cp\u003EFirst, we will create a temporary table with each unique file name. We will then pass the file name and the first chunk of text to CLASSIFY_TEXT. Classification is not mandatory for Cortex Search but we want to use it here to also use hybrid search.\u003C/p\u003E\n","\u003Cp\u003ERun this SQL to create the table containing the classification results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE TEMPORARY TABLE docs_categories AS WITH unique_documents AS (\n  SELECT\n    DISTINCT relative_path, chunk\n  FROM\n    docs_chunks_table\n  WHERE \n    chunk_index = 0\n  ),\n docs_category_cte AS (\n  SELECT\n    relative_path,\n    TRIM(snowflake.cortex.AI_CLASSIFY (\n      'Title:' || relative_path || 'Content:' || chunk, ['Bike', 'Snow']\n     )['labels'][0], '"') AS CATEGORY\n  FROM\n    unique_documents\n)\nSELECT\n  *\nFROM\n  docs_category_cte;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can check the table to identify how many categories have been created and if the classifications are correct:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Eselect * from docs_categories;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll documents should be correctly classified now. This data will let users filter their searches later with Cortex Search, making retrieval faster and more accurate:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig7_docs_categories.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we can update the table with the chunks of text that will be used by Cortex Search service to include the category for each document:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Eupdate docs_chunks_table \n  SET category = docs_categories.category\n  from docs_categories\n  where  docs_chunks_table.relative_path = docs_categories.relative_path;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe table is now fully prepared and ready to enable the Cortex Search service.\u003C/p\u003E\n","\u003Cp\u003EAfter completing all the steps in this section you should see the following objects in your database:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Cstrong\u003EDOCS_CHUNKS_TABLE\u003C/strong\u003E that contains the text and categories for each document\u003C/li\u003E\u003Cli\u003EThe \u003Cstrong\u003EDOCS\u003C/strong\u003E Stage with your PDF files\u003C/li\u003E\u003Cli\u003EThe \u003Cstrong\u003EPDF_TEXT_CHUNKER\u003C/strong\u003E function that extracts and chunk the text from the PDF files\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig8_db_objects.png\" alt=\"App\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Search Service\u003C/h2\u003E\n","\u003Cp\u003EThe next step is to create the Cortex Search Service in the table we have made before. We will execute this SQL command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace CORTEX SEARCH SERVICE CC_SEARCH_SERVICE_CS\nON chunk\nATTRIBUTES category\nwarehouse = COMPUTE_WH\nTARGET_LAG = '1 minute'\nas (\n    select chunk,\n        chunk_index,\n        relative_path,\n        file_url,\n        category\n    from docs_chunks_table\n);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EThe name of the service is \u003Cstrong\u003ECC_SEARCH_SERVICE_CS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EThe service will use the column \u003Cstrong\u003Echunk\u003C/strong\u003E to create embeddings and perform retrieval based on similarity search\u003C/li\u003E\u003Cli\u003EThe column \u003Cstrong\u003Ecategory\u003C/strong\u003E could be used as a filter\u003C/li\u003E\u003Cli\u003ETo keep this service updated the warehouse \u003Cstrong\u003ECOMPUTE_WH\u003C/strong\u003E will be used. If you want to use a different warehouse, replace this with the name of your chosen warehouse\u003C/li\u003E\u003Cli\u003EThe service will be refreshed every minute\u003C/li\u003E\u003Cli\u003EThe data retrieved will contain the \u003Cstrong\u003Echunk, relative_path, file_url and category\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThat’s it! There is no need to create embeddings as that is done automatically. We can now use the API to query the service.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild Chat UI with Retrieval and Generation Logic\u003C/h2\u003E\n","\u003Cp\u003ENow that your data is indexed, the next step is to create the user interface (UI) to interact with it. We will use \u003Ca href=\"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/\"\u003EStreamlit in Snowflake\u003C/a\u003E (SiS) to build a secure, full-stack application entirely within your account.\u003C/p\u003E\n","\u003Cp\u003ETo make it easy for anyone to ask questions against the Cortex Search service, we will create a fairly simple front-end using Streamlit. As part of the app, we will provide the end-user with a toggle that allows testing of LLM responses with and without access to the context to observe the differences. The app will also show the pieces of text that the service is returning and which are being used to fill the calls to LLM to create the answers.\u003C/p\u003E\n","\u003Cp\u003EStreamlit in Snowflake allows you to run the app and share it with other Snowflake users within the same account. This ensures data remains secure and protected and is only available to users that meet your role-based access policies.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EIntroduction to Streamlit in Snowflake documentation\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003EClick on the \u003Cstrong\u003ECatalog » Streamlit\u003C/strong\u003E tab on the left-hand navigation pane in Snowsight\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E button on the right\u003C/li\u003E\u003Cli\u003EName the App (Use CC_CORTEX_SEARCH_APP or another name)\u003C/li\u003E\u003Cli\u003ESelect the warehouse to run the App (a Small WH is sufficient)\u003C/li\u003E\u003Cli\u003EChoose the \u003Cstrong\u003ECC_QUICKSTART_CORTEX_SEARCH_DOCS\u003C/strong\u003E database and \u003Cstrong\u003EDATA\u003C/strong\u003E schema\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig9_streamlit_app.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Streamlit app comes with a default template. Delete the default code and replace it with the code provided below (front-end elements, retrieval, and generation logic are included):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETable with list of available documents\u003C/li\u003E\u003Cli\u003EToggle to choose to use documents as context\u003C/li\u003E\u003Cli\u003EQuestion input box\u003C/li\u003E\u003Cli\u003EFilter for a type of product\u003C/li\u003E\u003Cli\u003EUsing the Cortex Search service to retrieve the relevant chunks of text\u003C/li\u003E\u003Cli\u003EPrompt template that instructs LLM how to answer using relevant chunks\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st # Import python packages\nfrom snowflake.snowpark.context import get_active_session\n\nfrom snowflake.core import Root\n\nimport pandas as pd\nimport json\n\npd.set_option("max_colwidth",None)\n\n### Default Values\nNUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\n\n# service parameters\nCORTEX_SEARCH_DATABASE = "CC_QUICKSTART_CORTEX_SEARCH_DOCS"\nCORTEX_SEARCH_SCHEMA = "DATA"\nCORTEX_SEARCH_SERVICE = "CC_SEARCH_SERVICE_CS"\n######\n######\n\n# columns to query in the service\nCOLUMNS = [\n    "chunk",\n    "chunk_index",\n    "relative_path",\n    "category"\n]\n\nsession = get_active_session()\nroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n   \n### Functions\n     \ndef config_options():\n\n    st.sidebar.selectbox('Select your model:',('mistral-large2', 'llama3.1-70b',\n                        'llama3.1-8b', 'snowflake-arctic'), key="model_name")\n\n    categories = session.sql("select category from docs_chunks_table group by category").collect()\n\n    cat_list = ['ALL']\n    for cat in categories:\n        cat_list.append(cat.CATEGORY)\n            \n    st.sidebar.selectbox('Select what products you are looking for', cat_list, key = "category_value")\n\n    st.sidebar.expander("Session State").write(st.session_state)\n\ndef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == "ALL":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {"@eq": {"category": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n\ndef create_prompt (myquestion):\n\n    if st.session_state.rag == 1:\n        prompt_context = get_similar_chunks_search_service(myquestion)\n  \n        prompt = f"""\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between <context> and </context> tags.\n           When ansering the question contained between <question> and </question> tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           Do not mention the CONTEXT used in your answer.\n    \n           <context>          \n           {prompt_context}\n           </context>\n           <question>  \n           {myquestion}\n           </question>\n           Answer: \n           """\n\n        json_data = json.loads(prompt_context)\n\n        relative_paths = set(item['relative_path'] for item in json_data['results'])\n        \n    else:     \n        prompt = f"""[0]\n         'Question:  \n           {myquestion} \n           Answer: '\n           """\n        relative_paths = "None"\n            \n    return prompt, relative_paths\n\ndef complete(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n    cmd = """\n            select snowflake.cortex.complete(?, ?) as response\n          """\n    \n    df_response = session.sql(cmd, params=[st.session_state.model_name, prompt]).collect()\n    return df_response, relative_paths\n\ndef main():\n    \n    st.title(f":speech_balloon: Chat Document Assistant with Snowflake Cortex")\n    st.write("This is the list of documents you already have and that will be used to answer your questions:")\n    docs_available = session.sql("ls @docs").collect()\n    list_docs = []\n    for doc in docs_available:\n        list_docs.append(doc["name"])\n    st.dataframe(list_docs)\n\n    config_options()\n\n    st.session_state.rag = st.sidebar.checkbox('Use your own documents as context?')\n\n    question = st.text_input("Enter question", placeholder="Is there any special lubricant to be used with the premium bike?", label_visibility="collapsed")\n\n    if question:\n        response, relative_paths = complete(question)\n        res_text = response[0].RESPONSE\n        st.markdown(res_text)\n\n        if relative_paths != "None":\n            with st.sidebar.expander("Related Documents"):\n                for path in relative_paths:\n                    cmd2 = f"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)"\n                    df_url_link = session.sql(cmd2).to_pandas()\n                    url_link = df_url_link._get_value(0,'URL_LINK')\n        \n                    display_url = f"Doc: [{path}]({url_link})"\n                    st.sidebar.markdown(display_url)\n                \nif __name__ == "__main__":\n    main()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: When you first paste and run the code, you will get an error because we need to add the package \u003Cstrong\u003Esnowflake.core\u003C/strong\u003E in our Streamlit App.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig10_error.png\" alt=\"App\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Cstrong\u003EPackages\u003C/strong\u003E (found near the top of the code editor)\u003C/li\u003E\u003Cli\u003ESearch for \u003Cstrong\u003Esnowflake.core\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESet the Python Environment. You may want to use the latest versions available. This is the combination that has been tested for this lab:\n\u003Cul\u003E\u003Cli\u003Esnowflake.core = 1.2.0\u003C/li\u003E\u003Cli\u003Epython = 3.11\u003C/li\u003E\u003Cli\u003Esnowflake-snowpark-python = 1.30.0\u003C/li\u003E\u003Cli\u003Estreamlit = 1.42.0\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick Run after adding the package. The app should now load successfully\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig11_packages.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECode Walkthrough\u003C/h3\u003E\n","\u003Cp\u003ELet's go step by step to see what the code is doing:\u003C/p\u003E\n","\u003Cp\u003EFirst, we use st.sidebar to place all interactive options on the left-hand sidebar. There we include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA selectbox for the LLM to be used\u003C/li\u003E\u003Cli\u003EA selectbox to allow filtering for one category (this will be used by Cortex Search\u003C/li\u003E\u003Cli\u003ESession state (for debugging purposes when building the app)\u003C/li\u003E\u003Cli\u003EChunks used and the links to the relevant documents\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe main() function collects the user's question and calls a complete() function with the question. This one calls the create_prompt() function that uses get_similar_chunks_search_service() with the question. This is the function that calls Cortex Search service for retrieval with this code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == "ALL":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {"@eq": {"category": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDepending if we want to filter for any category, we can use the filter object or not. Previously, we have created the \u003Cstrong\u003Esvc\u003C/strong\u003E handler that is pointing to the Cortex Search service:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe complete() function combines the LLM, the prompt template and whether to use the context or not to generate a response that includes a link to the asset from which the answer was obtained.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef complete(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n    cmd = """\n            select snowflake.cortex.complete(?, ?) as response\n          """\n    \n    df_response = session.sql(cmd, params=[st.session_state.model_name, prompt]).collect()\n    return df_response, relative_paths\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDeploy and share your AI-powered app\u003C/h3\u003E\n","\u003Cp\u003EStreamlit in Snowflake provides a side-by-side editor and preview screen that makes it easy and fast to iterate and visualize changes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig12_streamlit.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the app, we can see the documents we had uploaded previously. We can also ask questions while trying multiple options using interactive widgets available on the side.\u003C/p\u003E\n","\u003Cp\u003ETo test out the RAG framework, here are a few questions you can ask. Use the interactive widgets to compare the results when using a different LLM or when choosing to get a response without the context. This is related to very specific information that we have added into the documents and it is very unique to our products.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIs there any special lubricant to be used with the premium bike?\u003C/li\u003E\u003Cli\u003EDoes the mondracer infant bike need any special tool?\u003C/li\u003E\u003Cli\u003EIs there any temperature to be considered with the premium bicycle?\u003C/li\u003E\u003Cli\u003EWhat is the temperature to store the ski boots?\u003C/li\u003E\u003Cli\u003EWhere have the ski boots been tested and who tested them?\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EOther things to test\u003C/h3\u003E\n","\u003Cp\u003EIn this example, we have just decided a fixed format for chunks and used only the top result in the retrieval process. This \u003Ca href=\"https://medium.com/@thechosentom/rag-made-simple-with-snowflake-cortex-74d1df5143fd\"\u003Eblog\u003C/a\u003E provides some considerations about settings in the chunking strategy.\u003C/p\u003E\n","\u003Cp\u003EThis is the recommendation from the \u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/cortex-search/cortex-search-overview\"\u003ECortex Search documentation\u003C/a\u003E:\u003C/p\u003E\n","\u003Cp\u003EFor optimal search results with Cortex Search, Snowflake recommends splitting the text in your search column into chunks of no more than 512 tokens (about 385 English words). A token is the smallest unit of text processed by a large language model.\u003C/p\u003E\n","\u003Cp\u003EWhen an entry in the search column contains more than 512 tokens, Cortex Search performs keyword-based retrieval on the entire body of text, but only uses the first 512 tokens for semantic (i.e., vector-based) retrieval.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E        LATERAL FLATTEN( input => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also try different instructions in your prompt and see how the responses may vary. Simply replace any of the text with another instruction and run the app again. The prompt is a crucial part of LLM generation. Make sure you are clear in your instructions. Results may change depending on your own use case.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E        prompt = f"""\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between <context> and </context> tags.\n           When ansering the question contained between <question> and </question> tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           Do not mention the CONTEXT used in your answer.\n    \n           <context>          \n           {prompt_context}\n           </context>\n           <question>  \n           {myquestion}\n           </question>\n           Answer: \n           """\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also try to change the number of chunks that are provided as context by simply modifying this value:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ENUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild a ChatBot UI with Conversation History\u003C/h2\u003E\n","\u003Cp\u003EIn the previous section, we have created a simple interface where we can ask questions about our documents and select the LLM running within Snowflake Cortex to answer the question. We have seen that when no context from our documents is provided, we just get a general answer. However, when we use context from the PDFs, we get a specific answer related to our data. But what happens if we want to have it be a conversation?\u003C/p\u003E\n","\u003Cp\u003ELarge Language Models (LLMs) are stateless. That means when we make a call to any of them, it does not remember what  the previous call was about. With Snowflake Cortex, each complete() call we make is independent, even when using the same model. There are several techniques that can be used to keep the conversation flow and chat history. To maintain context, the app needs to keep a memory of the chat history and send it along with the new question in every single call to the LLM. Since LLM calls are restricted by a context window length (the total number of tokens the model can process), we need a smart way to manage this history.\u003C/p\u003E\n","\u003Cp\u003EStreamlit makes it very easy to create chat interfaces with its \u003Ca href=\"https://docs.streamlit.io/library/api-reference/chat\"\u003EChat Elements\u003C/a\u003E. In this section we are going to see how we can write a simple app that can be used to have a conversation, and how it uses the data from our documents to provide answers. We are going to use a slide window concept. This helps to remember just a number of past interactions when calling the LLM. We are also going to summarize the previous conversation in order to find the right chunk in our documents that will help the LLM and provide the right answer. In the previous section, we were embedding the new question to find the right chunk for context. Here, we are also going to include the previous summary to find the chunks that will be used as context.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/chatbot.gif\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst let's create a new Streamlit App. Give it a name and create it within the database and schema we already made (CC_QUICKSTART_CORTEX_SEARCH_DOCS.DATA or whatever name you may have chosen).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig13_streamlit_chatbot.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this second app, we are going to demo how to access the Complete function from Snowflake Cortex via Python API instead of SQL as we did in the previous app. To get access to the API, you need to specify the \u003Cstrong\u003Esnowflake-ml-python\u003C/strong\u003E package in your Streamlit app. This combination of packages version is known to work well:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Esnowflake-ml-python = 1.8.1\u003C/li\u003E\u003Cli\u003Esnowflake.core = 1.2.0\u003C/li\u003E\u003Cli\u003Epython = 3.11\u003C/li\u003E\u003Cli\u003Esnowflake-snowpark-python = 1.30.0\u003C/li\u003E\u003Cli\u003Estreamlit = 1.42.0\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig13_2_ml_python_package.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Streamlit app comes with a default template that you can delete and replace with this code, which includes the front-end elements:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st # Import python packages\nfrom snowflake.snowpark.context import get_active_session\n\nfrom snowflake.cortex import Complete\nfrom snowflake.core import Root\n\nimport pandas as pd\nimport json\n\npd.set_option("max_colwidth",None)\n\n### Default Values\nNUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\nslide_window = 7 # how many last conversations to remember. This is the slide window.\n\n# service parameters\nCORTEX_SEARCH_DATABASE = "CC_QUICKSTART_CORTEX_SEARCH_DOCS"\nCORTEX_SEARCH_SCHEMA = "DATA"\nCORTEX_SEARCH_SERVICE = "CC_SEARCH_SERVICE_CS"\n######\n######\n\n# columns to query in the service\nCOLUMNS = [\n    "chunk",\n    "chunk_index",\n    "relative_path",\n    "category"\n]\n\nsession = get_active_session()\nroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n   \n### Functions\n     \ndef config_options():\n\n    st.sidebar.selectbox('Select your model:',('mistral-large2', 'llama3.1-70b',\n                                    'llama3.1-8b', 'snowflake-arctic'), key="model_name")\n\n    categories = session.table('docs_chunks_table').select('category').distinct().collect()\n\n    cat_list = ['ALL']\n    for cat in categories:\n        cat_list.append(cat.CATEGORY)\n            \n    st.sidebar.selectbox('Select what products you are looking for', cat_list, key = "category_value")\n\n    st.sidebar.checkbox('Do you want that I remember the chat history?', key="use_chat_history", value = True)\n\n    st.sidebar.checkbox('Debug: Click to see summary generated of previous conversation', key="debug", value = True)\n    st.sidebar.button("Start Over", key="clear_conversation", on_click=init_messages)\n    st.sidebar.expander("Session State").write(st.session_state)\n\ndef init_messages():\n\n    # Initialize chat history\n    if st.session_state.clear_conversation or "messages" not in st.session_state:\n        st.session_state.messages = []\n\ndef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == "ALL":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {"@eq": {"category": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n\ndef get_chat_history():\n#Get the history from the st.session_stage.messages according to the slide window parameter\n    \n    chat_history = []\n    \n    start_index = max(0, len(st.session_state.messages) - slide_window)\n    for i in range (start_index , len(st.session_state.messages) -1):\n         chat_history.append(st.session_state.messages[i])\n\n    return chat_history\n\ndef summarize_question_with_history(chat_history, question):\n# To get the right context, use the LLM to first summarize the previous conversation\n# This will be used to get embeddings and find similar chunks in the docs for context\n\n    prompt = f"""\n        Based on the chat history below and the question, generate a query that extend the question\n        with the chat history provided. The query should be in natual language. \n        Answer with only the query. Do not add any explanation.\n        \n        <chat_history>\n        {chat_history}\n        </chat_history>\n        <question>\n        {question}\n        </question>\n        """\n    \n    sumary = Complete(st.session_state.model_name, prompt)   \n\n    if st.session_state.debug:\n        st.sidebar.text("Summary to be used to find similar chunks in the docs:")\n        st.sidebar.caption(sumary)\n\n    sumary = sumary.replace("'", "")\n\n    return sumary\n\ndef create_prompt (myquestion):\n\n    if st.session_state.use_chat_history:\n        chat_history = get_chat_history()\n\n        if chat_history != []: #There is chat_history, so not first question\n            question_summary = summarize_question_with_history(chat_history, myquestion)\n            prompt_context =  get_similar_chunks_search_service(question_summary)\n        else:\n            prompt_context = get_similar_chunks_search_service(myquestion) #First question when using history\n    else:\n        prompt_context = get_similar_chunks_search_service(myquestion)\n        chat_history = ""\n  \n    prompt = f"""\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between <context> and </context> tags.\n           You offer a chat experience considering the information included in the CHAT HISTORY\n           provided between <chat_history> and </chat_history> tags..\n           When ansering the question contained between <question> and </question> tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           \n           Do not mention the CONTEXT used in your answer.\n           Do not mention the CHAT HISTORY used in your asnwer.\n\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           <chat_history>\n           {chat_history}\n           </chat_history>\n           <context>          \n           {prompt_context}\n           </context>\n           <question>  \n           {myquestion}\n           </question>\n           Answer: \n           """\n    \n    json_data = json.loads(prompt_context)\n\n    relative_paths = set(item['relative_path'] for item in json_data['results'])\n\n    return prompt, relative_paths\n\n\ndef answer_question(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n\n    response = Complete(st.session_state.model_name, prompt)   \n\n    return response, relative_paths\n\ndef main():\n    \n    st.title(f":speech_balloon: Chat Document Assistant with Snowflake Cortex")\n    st.write("This is the list of documents you already have and that will be used to answer your questions:")\n    docs_available = session.sql("ls @docs").collect()\n    list_docs = []\n    for doc in docs_available:\n        list_docs.append(doc["name"])\n    st.dataframe(list_docs)\n\n    config_options()\n    init_messages()\n     \n    # Display chat messages from history on app rerun\n    for message in st.session_state.messages:\n        with st.chat_message(message["role"]):\n            st.markdown(message["content"])\n    \n    # Accept user input\n    if question := st.chat_input("What do you want to know about your products?"):\n        # Add user message to chat history\n        st.session_state.messages.append({"role": "user", "content": question})\n        # Display user message in chat message container\n        with st.chat_message("user"):\n            st.markdown(question)\n        # Display assistant response in chat message container\n        with st.chat_message("assistant"):\n            message_placeholder = st.empty()\n    \n            question = question.replace("'","")\n    \n            with st.spinner(f"{st.session_state.model_name} thinking..."):\n                response, relative_paths = answer_question(question)            \n                response = response.replace("'", "")\n                message_placeholder.markdown(response)\n\n                if relative_paths != "None":\n                    with st.sidebar.expander("Related Documents"):\n                        for path in relative_paths:\n                            cmd2 = f"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)"\n                            df_url_link = session.sql(cmd2).to_pandas()\n                            url_link = df_url_link._get_value(0,'URL_LINK')\n                \n                            display_url = f"Doc: [{path}]({url_link})"\n                            st.sidebar.markdown(display_url)\n\n        \n        st.session_state.messages.append({"role": "assistant", "content": response})\n\n\nif __name__ == "__main__":\n    main()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor educational purposes we have enabled the check for "Do you want that I remember the chat history?" When disabled, the app will not use memory. Each question will be independent from the previous ones. This is what happens when we are not using memory:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig14_no_memory.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBasically, each question is answered individually. The second question loses the context.\u003C/p\u003E\n","\u003Cp\u003EWhen asking the same questions but using memory the first thing to notice is that we have re-created the new question adding the previous context:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig15_memory.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe new question that is used to search for similar chunks is "What is the Rincon del Cielo bike made of?"\u003C/p\u003E\n","\u003Ch3\u003ECode Walkthrough\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef get_chat_history():\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis function is going to return the previous conversation in the chat up to a limit defined by the global variable slide_window.\u003C/p\u003E\n","\u003Cp\u003EStreamlit stores the conversation history in <code>st.session_state.messages</code>.\u003C/p\u003E\n","\u003Cp\u003EEverytime a question is asked, it is stored in the state with this code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Est.session_state.messages.append({"role": "user", "content": question})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd the responses provided are also stored with this code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Est.session_state.messages.append({"role": "assistant", "content": res_text})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETherefore <code>st.session_state.messages</code> is used as the chat memory\u003C/p\u003E\n","\u003Cp\u003EThis next function <code>summarize_question_with_history()</code> takes the chat_history that we have had previously and the new question being asked. Remember that we are using the information from the PDF documents to answer questions. Therefore, we need to identify using Cortex Search what chunk of text will be relevant to answer the question.\u003C/p\u003E\n","\u003Cp\u003EIf we just use "what is it made of" sentence to look for similar chunks, we likely will not get the right one. We need to include more information in the call we make to <code>get_similar_chunks()</code>.\u003C/p\u003E\n","\u003Cp\u003ETake a look this <code>summarize_question_with_history()</code> function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef summarize_question_with_history(chat_history, question):\n# To get the right context, use the LLM to first summarize the previous conversation\n# This will be used to get embeddings and find similar chunks in the docs for context\n\n    prompt = f"""\n        Based on the chat history below and the question, generate a query that extend the question\n        with the chat history provided. The query should be in natual language. \n        Answer with only the query. Do not add any explanation.\n        \n        <chat_history>\n        {chat_history}\n        </chat_history>\n        <question>\n        {question}\n        </question>\n        """\n    \n    sumary = Complete(st.session_state.model_name, prompt)   \n\n    if st.session_state.debug:\n        st.sidebar.text("Summary to be used to find similar chunks in the docs:")\n        st.sidebar.caption(sumary)\n\n    sumary = sumary.replace("'", "")\n\n    return sumary\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn that function we are using the <code>Complete()</code> API. This is where we have created a specific prompt to provide the chat history and the new question in order to get the query that will be used to find the right context.\u003C/p\u003E\n","\u003Cp\u003EIn order to experiment with this, we have also created a selection check "Debug: Click to see summary generated of previous conversation" that will allow you to see what is the question being used to look for similar chunks.\u003C/p\u003E\n","\u003Cp\u003ENow the function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef create_prompt (myquestion):\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUse all those previous functions to get the <b>chat_history</b> and <b>prompt_context</b> to build the prompt. This prompt is adding the previous conversation plus the context extracted from the PDFs and the new question.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E   prompt = f"""\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between <context> and </context> tags.\n           You offer a chat experience considering the information included in the CHAT HISTORY\n           provided between <chat_history> and </chat_history> tags..\n           When ansering the question contained between <question> and </question> tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           \n           Do not mention the CONTEXT used in your answer.\n           Do not mention the CHAT HISTORY used in your asnwer.\n\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           <chat_history>\n           {chat_history}\n           </chat_history>\n           <context>          \n           {prompt_context}\n           </context>\n           <question>  \n           {myquestion}\n           </question>\n           Answer: \n           """\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhat makes this very easy with Streamlit are the \u003Ca href=\"https://docs.streamlit.io/library/api-reference/chat/st.chat_input\"\u003Est.chat_input\u003C/a\u003E and \u003Ca href=\"https://docs.streamlit.io/library/api-reference/chat/st.chat_message\"\u003Est.chat_message\u003C/a\u003E functions. This is the code that get the question with st.chat_input, add it to st.chat_message and to the memory with st.session_state.messages.append and call the <b>answer_question()</b> function to get the answer that is also printed and stored:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E    if question := st.chat_input("What do you want to know about your products?"):\n        # Add user message to chat history\n        st.session_state.messages.append({"role": "user", "content": question})\n        # Display user message in chat message container\n        with st.chat_message("user"):\n            st.markdown(question)\n        # Display assistant response in chat message container\n        with st.chat_message("assistant"):\n            message_placeholder = st.empty()\n    \n            question = question.replace("'","")\n    \n            with st.spinner(f"{st.session_state.model_name} thinking..."):\n                response, relative_paths = answer_question(question)            \n                response = response.replace("'", "")\n                message_placeholder.markdown(response)\n\n                if relative_paths != "None":\n                    with st.sidebar.expander("Related Documents"):\n                        for path in relative_paths:\n                            cmd2 = f"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)"\n                            df_url_link = session.sql(cmd2).to_pandas()\n                            url_link = df_url_link._get_value(0,'URL_LINK')\n                \n                            display_url = f"Doc: [{path}]({url_link})"\n                            st.sidebar.markdown(display_url)\n\n        \n        st.session_state.messages.append({"role": "assistant", "content": response})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are also printing the links to the documents being used to answer the questions.\u003C/p\u003E\n","\u003Cp\u003EHere are some other suggestions based on specific info from the documents (unique for us):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat is the name of the ski boots?\u003C/li\u003E\u003Cli\u003EWhere have been tested?\u003C/li\u003E\u003Cli\u003EWho tested them?\u003C/li\u003E\u003Cli\u003EDo they include any special component?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou can try this out with your own documents. You will notice different performance depending on the LLM you use.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAutomatic Processing of New Documents\u003C/h2\u003E\n","\u003Cp\u003EThe next step in building an enterprise-ready RAG system is ensuring it stays up-to-date. If a new product manual is released or an old one is retired, your search index must update automatically.\u003C/p\u003E\n","\u003Cp\u003EMaintaining your RAG system when new documents are added, deleted or updated can be tedious. Snowflake makes it very easy. Cortex Search is a self-managed service. We only need to add, delete or update rows in the table where Cortex Search Service has been enabled. The service will then automatically update the indexes and create new embeddings based on the frequency defined during service creation.\u003C/p\u003E\n","\u003Cp\u003EIn addition, we can use Snowflake features like \u003Ca href=\"https://docs.snowflake.com/en/user-guide/streams-intro\"\u003EStreams\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ETasks\u003C/a\u003E, and \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/stored-procedure/stored-procedures-overview\"\u003EStored Procedures\u003C/a\u003E to automatically process new PDF files as they are added into Snowflake.\u003C/p\u003E\n","\u003Cp\u003EFirst we create two streams for the DOCS staging area. One is going to be used to process deletes and the other to process inserts. The Streams capture the changes on the Directory Table used for the DOCS staging area. This way, we can track new updates and deletes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace stream insert_docs_stream on stage docs;\ncreate or replace stream delete_docs_stream on stage docs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESecond, we are going to define a Stored Procedure. These extend the system with procedural code. With a procedure, you can use branching, looping, and other programmatic constructs. You can reuse a procedure multiple times by calling it from other code. This will process those streams to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDelete from the docs_chunk_table the content for files that has been deleted from the stagin area, so they are no longer relevant\u003C/li\u003E\u003Cli\u003EParse new PDF documents that has been added into the staging area using PARSE_DOCUMENT\u003C/li\u003E\u003Cli\u003EChunk the new document into pieces using SPLIT_TEXT_RECURSIVE_CHARACTER\u003C/li\u003E\u003Cli\u003EClassify the new documents and update the label (this step is optional just to show the part of the possible)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECreate the Stored Procedure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace procedure insert_delete_docs_sp()\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\n$$\nBEGIN\n\nDELETE FROM docs_chunks_table\n    USING delete_docs_stream\n    WHERE docs_chunks_table.RELATIVE_PATH = delete_docs_stream.RELATIVE_PATH\n    and delete_docs_stream.METADATA$ACTION = 'DELETE';\n\nCREATE OR REPLACE TEMPORARY TABLE RAW_TEXT AS\n    WITH FILE_TABLE as \n      (SELECT \n            RELATIVE_PATH,\n            SIZE,\n            FILE_URL,\n            build_scoped_file_url(@docs, relative_path) as scoped_file_url,\n            TO_FILE('@DOCS', RELATIVE_PATH) AS docs \n        FROM \n            insert_docs_stream\n        WHERE \n            METADATA$ACTION = 'INSERT'        \n        )\n    SELECT \n        RELATIVE_PATH,\n        SIZE,\n        FILE_URL,\n        scoped_file_url,\n        TO_VARCHAR (\n            SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT (\n                docs,\n                {'mode': 'LAYOUT'} ):content\n            ) AS EXTRACTED_LAYOUT \n    FROM \n        FILE_TABLE;\n    \n\n    -- Insert new docs chunks\ninsert into docs_chunks_table (relative_path, size, file_url,\n                            scoped_file_url, chunk, chunk_index)\n\nselect relative_path, \n            size,\n            file_url, \n            scoped_file_url,\n            c.value::TEXT as chunk,\n            c.INDEX::INTEGER as chunk_index\n            \n    from \n        RAW_TEXT,\n        LATERAL FLATTEN( input => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n\n    -- Classify the new documents\n\n    CREATE OR REPLACE TEMPORARY TABLE docs_categories AS \n    WITH unique_documents AS (\n      SELECT DISTINCT\n        d.relative_path, d.chunk\n      FROM\n        docs_chunks_table d\n      INNER JOIN\n        RAW_TEXT r\n        ON d.relative_path = r.relative_path\n      WHERE \n        d.chunk_index = 0\n    ),\n    docs_category_cte AS (\n      SELECT\n        relative_path,\n        TRIM(snowflake.cortex.AI_CLASSIFY (\n            'Title:' || relative_path || 'Content:' || chunk, ['Bike', 'Snow']\n            )['labels'][0], '"') AS CATEGORY\n      FROM\n        unique_documents\n    )\n    SELECT\n      *\n    FROM\n      docs_category_cte;\n\n    -- Update cathegories\n\n    update docs_chunks_table \n        SET category = docs_categories.category\n        from docs_categories\n        where  docs_chunks_table.relative_path = docs_categories.relative_path;\nEND;\n$$;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can create a Task that every X minutes can check if there is new data in the stream and take an action. We are setting the schedule to 5 minutes so you can follow the execution, but feel free to reduce the time to 1 minute if needed. Consider what would be best for your app and how often new docs are updated.\u003C/p\u003E\n","\u003Cp\u003EWe define:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhere: This is going to be executed using warehouse \u003Cstrong\u003ECOMPUTE_WH\u003C/strong\u003E. Please name to your own Warehouse.\u003C/li\u003E\u003Cli\u003EWhen: Check every 5 minutes. Execute in the case of new records in the delete_docs_stream stream (we could also use the other stream)\u003C/li\u003E\u003Cli\u003EWhat to do: Call the stored procedure insert_delete_docs_sp()\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EExecute this code in your Snowflake Worksheet to create the task:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace task insert_delete_docs_task\n    warehouse = COMPUTE_WH\n    schedule = '5 minute'\n    when system$stream_has_data('delete_docs_stream')\nas\n    call insert_delete_docs_sp();\n\n\nalter task  insert_delete_docs_task resume;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can prove the RAG system updates automatically when documents change.\u003C/p\u003E\n","\u003Cp\u003EAfter uploading the document (and if you are fast enough before the doc is automatically processed) you can see the doc in the stream:\u003C/p\u003E\n","\u003Cp\u003ENow let's test how it works.\u003C/p\u003E\n","\u003Cp\u003ERun these queries. Your streams will be empty:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Eselect * from delete_docs_stream;\nselect * from insert_docs_stream;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are going to test the modification of a document and the deletion of another.\u003C/p\u003E\n","\u003Cp\u003EFirst you can check what is the response to the question:\u003C/p\u003E\n","\u003Cp\u003E"Is there any special lubricant to be used with the premium bike?"\u003C/p\u003E\n","\u003Cp\u003EYou should get something pointing to Premium Oil 2287:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig16_search_1.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, let's update the document for the Premium Bike as we have a new lubricant to be used. Also you can test the deletion of another document.\u003C/p\u003E\n","\u003Cp\u003EGo to your database, select the schema DATA and the Stage DOCS. Click on "Premium_Bicycle_User_Guide.pdf" document and select Remove:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig17_remove_file.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe no longer have Ski boots, so you can also remove the Ski_Boots_TDBootz_Special.pdf document.\u003C/p\u003E\n","\u003Cp\u003ENow you can add the new version of the "Premium_Bicycle_User_Guide.pdf" document. Download the new version from:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/NEW_DOCS/Premium_Bicycle_User_Guide.pdf\"\u003EPremium_Bicycle_User_Guide.pdf\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you read the document, you will notice that the new Oil recommended is Premium Oil 5555\u003C/p\u003E\n","\u003Cp\u003EOn the top right, click on +Files and add this new file:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig18_upload_file.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you have done this fast enough (in less than 5 minutes), you can check that both streams have detected the changes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Eselect * from delete_docs_stream;\nselect * from insert_docs_stream;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe column METADATA$ACTION tells us if this is a DELETE or an INSERT.\u003C/p\u003E\n","\u003Cp\u003ESelect specific columns so you can see the columns from the Stream:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig18_2_streams.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can also force the task execution with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Eexecute task insert_delete_docs_task;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that once the Stream has been processed, it will be empty.\u003C/p\u003E\n","\u003Cp\u003EUnder Transformation » Tasks, you can find your task and monitor the execution:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig19_task_history.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd now you can go back to your Streamlit app to ask the question again:\u003C/p\u003E\n","\u003Cp\u003E"Is there any special lubricant to be used with the premium bike?"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig20_search_2.png\" alt=\"App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can check that the new document is being used.\u003C/p\u003E\n","\u003Cp\u003EIf you ask the question: "Who tested the ski boots?", you should get something like "The context provided does not mention anyone testing ski boots" as the document has been deleted.\u003C/p\u003E\n","\u003Cp\u003EOnce you have finished testing uploading new documents and asking questions, you may want to suspend the task to ensure it doesn’t try to run and consume resources:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ealter task insert_delete_docs_task suspend;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EIn order to avoid consuming credits, you can also drop the databse used for this guide.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Edrop database CC_QUICKSTART_CORTEX_SEARCH_DOCS;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully performed RAG using Snowflake Cortex Search and securely built a full-stack RAG application in Snowflake without having to build integrations, manage any infrastructure, or deal with security concerns with data moving outside of the Snowflake governance framework.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating functions to automatically extract text and chunk PDF files\u003C/li\u003E\u003Cli\u003EAutomatically identify document categories using LLMs\u003C/li\u003E\u003Cli\u003EUsing Snowflake Cortex Search to use LLMs to answer questions\u003C/li\u003E\u003Cli\u003EBuilding an application front-end with Streamlit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003EDocs: Snowflake Cortex LLM functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/blog/easy-secure-llm-inference-retrieval-augmented-generation-rag-cortex\"\u003EBlog: RAG explainer and how it works in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/@jason.summer/developing-a-product-chatbot-with-airmiles-in-snowflake-6b197d3fc424\"\u003EBlog: Customizing RAG framework components using Snowpark Container Services\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-dirtables\"\u003EDocs: Snowflake Directory Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udtfs\"\u003EDocs: Creating User-Defined Table Functions\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E"],"description":"Build document Q&A systems with Snowflake Cortex Search for hybrid semantic and keyword retrieval from enterprise knowledge bases.","title":"Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n[Large Language Models](https://www.snowflake.com/en/fundamentals/large-language-model/) (LLMs) are incredibly powerful, but they can sometimes guess or provide incorrect responses, which is what we call a “hallucination.” To reduce hallucinations, LLMs can be combined with private datasets. Today, the most common approach for reducing hallucinations without having to change the model (e.g. fine-tuning) is the [Retrieval Augmented Generation](https://www.snowflake.com/en/fundamentals/rag/) (RAG) framework. RAG allows you to “ground” the model’s responses by making a set of relevant documents available to the LLM as context in the response. \n\nIn this quickstart we will show you how to quickly and securely build a full-stack RAG application in Snowflake without having to build integrations, manage any infrastructure or deal with security concerns with data moving outside of the Snowflake governance framework. \n\nThis guide will leverage [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview), a fully managed service that automatically creates embeddings for your data and perform retrievals using a hybrid search engine, using embeddings for semantic similarity plus keyword search for lexical similarity, achieving state-of-the-art retrieval quality.\n\nWe will show you how easy it is to implement RAG via a chat assistant that knows everything about products. To make the assistant an expert in your bikes and snow gears, we are going to give it access to a few user manuals. This template can be easily adapted to any documents you want your assistant to learn from — whether its financial reports, research documents or anything else! \n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/qs_cortex_search_intro.gif)\n\n### RAG Overview\n\nIn a typical RAG application, there is a process to prepare and use your documents. The content is first broken down into chunks and the content is embedded into a vector store. When a user asks a question, the vector store is searched to retrieve the most similar chunks of content. These relevant chunks are passed to the LLM as context to provide an accurate answer:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig1_rag.png)\n\nSnowflake Cortex Search is a fully managed indexing and retrieval service that simplifies and empowers RAG applications. The service automatically creates embeddings and indexes your data, providing a robust enterprise search service that can be accessed via APIs:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig2_rag_cortex_search_arch.png)\n\n### What You Will Learn \n- How to create functions that use Python libraries using [Snowpark](https://snowflake.com/snowpark)\n- How to use power search experiences using [Snowflake Cortex Search](https://docs.snowflake.com/LIMITEDACCESS/cortex-search/cortex-search-overview)\n- How to use LLMs using serverless functions in [Snowflake Cortex AI](https://snowflake.com/cortex)\n- How to build a front-end with Python using [Streamlit in Snowflake](/en/data-cloud/overview/streamlit-in-snowflake/)\n\n### What You Will Build\nYou will create a working product chat assistant in Streamlit. The final product includes an application that lets users test how the LLM responds with and without the context document(s) to show how RAG can address hallucinations.  \n\n### Prerequisites\n- A Snowflake account in a cloud region that supports Snowflake Cortex [LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql) are supported\n- Check [LLM availability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions?_ga=2.5151286.405859672.1709568467-277021311.1701887324&_gac=1.124754680.1707955750.Cj0KCQiA5rGuBhCnARIsAN11vgRLWfK6RIoIEqcZ7cFas8qwN4yCoL0q9nttp5UEmSocnPmhdBG57fgaAjqNEALw_wcB&_fsi=j2b82Wl3#availability) to help you decide where you want to create your Snowflake account\n- A Snowflake account with [Anaconda Packages](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda) enabled by ORGADMIN\n\n\u003E NOTE: For an end-to-end setup experience using Snowflake Notebook, download this [.ipynb](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/RAG_Using_Snowflake_Cortex_Search_Setup_Notebook.ipynb) file from the GitHub repository and [import](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-create#label-notebooks-import) it into a Snowflake Notebook. The Notebook covers setup steps 2, 3, and 4.\n\n\u003C!-- ------------------------ --\u003E\n## Organize Documents\n\nBefore we can ask our LLM assistant questions about our products, we need to get our product manual PDFs into Snowflake. In Snowflake, databases and schemas are used to organize and govern access to data and logic. Let's start by getting a few documents locally and then create a database that will hold the PDFs.\n\n**Step 1**. Download example documents\n\nWe have created a small set of example user manuals for fictitious bikes and skis that contain specific product details. This information is what we will use to test how the LLM responds with and without access to the information in the documents. \n\n\u003E NOTE: You can substitute these documents with your own product sheets, financial reports, or any other documents you want to try asking questions against.\n\nDownload all documents from this [zip file](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/docs.zip) on the GitHub repository.\n\nThe zip file contains these individual documents:\n\n- [Mondracer Infant Bike](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Mondracer_Infant_Bike.pdf)\n- [Premium Bycycle User Guide](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Premium_Bicycle_User_Guide.pdf)\n- [Ski Boots TDBootz Special](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Ski_Boots_TDBootz_Special.pdf)\n- [The Ultimate Downhill Bike](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/The_Ultimate_Downhill_Bike.pdf)\n- [The Xtreme Road Bike 105 SL](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/The_Xtreme_Road_Bike_105_SL.pdf)\n- [Carver Skis Specification Guide](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/Carver%20Skis%20Specification%20Guide.pdf)\n- [OutPiste Skis Specification Guide](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/OutPiste%20Skis%20Specification%20Guide.pdf)\n- [RacingFast Skis Specification Guide](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/RacingFast%20Skis%20Specification%20Guide.pdf)\n\n**Step 2**. Open a new Worksheet\n\n\u003E NOTE: [Creating Snowflake Worksheets documentation](https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-in-sf-web-interface). \n\n**Step 3**. Create a database and a schema\n\nRun the following code inside your newly created worksheet\n\n```SQL\nCREATE DATABASE CC_QUICKSTART_CORTEX_SEARCH_DOCS;\nCREATE SCHEMA DATA;\n```\n\u003E NOTE: [Database and Schema management documentation](https://docs.snowflake.com/en/sql-reference/ddl-database)\n\n\n**Step 4**. Create a Stage with Directory Table where you will be uploading your documents\n\nA [Stage](https://docs.snowflake.com/en/sql-reference/sql/create-stage) is used for loading data from files into Snowflake tables and unloading data from tables into files. A [Directory Table](https://docs.snowflake.com/en/user-guide/data-load-dirtables) stores file-level metadata about the data files in the stage. \n\nRun the following code:\n```SQL\ncreate or replace stage docs ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE') DIRECTORY = ( ENABLE = true );\n```\n\n**Step 5**. Upload documents to your staging area\n- Select **Catalog » Database Explorer** on the left of Snowsight\n- Click on your database **CC_QUICKSTART_CORTEX_SEARCH_DOCS**\n- Click on your schema **DATA**\n- Click on **Stages** and select **DOCS**\n- On the top right click on the +Files button\n- Drag and drop the two PDF files from the docs folder you downloaded\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig3_stage.png)\n\n**Step 6**. Check files has been successfully uploaded\n\nRun this query in the Worksheet to check that all your documents are in the staging area\n\n```SQL\nls @docs;\n```\n\nYou should see a list of all uploaded PDF file names. \n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig4_ls_docs.png)\n\n\u003C!-- ------------------------ --\u003E\n## Pre-process Documents\n\nIn this step we are going to leverage Snowflake native document processing functions to prepare documents before enabling [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview). We are also going to use Cortex [CLASSIFY_TEXT](https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex) function in order to label the type of document being processed so that we can use that metadata to filter searches.\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig5_build.png)\n\n**Step 1**. Use the function [SNOWFLAKE.CORTEX.PARSE_DOCUMENT](https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex) to read the PDF documents directly from the staging area we created. Here, we use the LAYOUT mode to extract the text and maintain the document’s structure:\n\n```SQL\nCREATE OR REPLACE TEMPORARY TABLE RAW_TEXT AS\nWITH FILE_TABLE as (\n  (SELECT \n        RELATIVE_PATH,\n        SIZE,\n        FILE_URL,\n        build_scoped_file_url(@docs, relative_path) as scoped_file_url,\n        TO_FILE('@DOCS', RELATIVE_PATH) AS docs \n    FROM \n        DIRECTORY(@DOCS))\n)\nSELECT \n    RELATIVE_PATH,\n    SIZE,\n    FILE_URL,\n    scoped_file_url,\n    TO_VARCHAR (\n        SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT (\n            docs,\n            {'mode': 'LAYOUT'} ):content\n        ) AS EXTRACTED_LAYOUT \nFROM \n    FILE_TABLE;\n```\n\n\n**Step 2**. Create the table where we are going to store the chunks for each PDF from the processed text. \n\n```SQL\ncreate or replace TABLE DOCS_CHUNKS_TABLE ( \n    RELATIVE_PATH VARCHAR(16777216), -- Relative path to the PDF file\n    SIZE NUMBER(38,0), -- Size of the PDF\n    FILE_URL VARCHAR(16777216), -- URL for the PDF\n    SCOPED_FILE_URL VARCHAR(16777216), -- Scoped url (you can choose which one to keep depending on your use case)\n    CHUNK VARCHAR(16777216), -- Piece of text\n    CHUNK_INDEX INTEGER, -- Index for the text\n    CATEGORY VARCHAR(16777216) -- Will hold the document category to enable filtering\n);\n```\n\n**Step 3**. The extracted text can sometimes be too large to be an effective piece of context for an LLM. We will use the function [SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER](https://docs.snowflake.com/en/sql-reference/functions/split_text_recursive_character-snowflake-cortex) to split the text into shorter strings. We will then insert these smaller chunks into our table: \n\n```SQL\ninsert into docs_chunks_table (relative_path, size, file_url,\n                            scoped_file_url, chunk, chunk_index)\n\n    select relative_path, \n            size,\n            file_url, \n            scoped_file_url,\n            c.value::TEXT as chunk,\n            c.INDEX::INTEGER as chunk_index\n            \n    from \n        raw_text,\n        LATERAL FLATTEN( input =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n\n```\n\n### Label the product category\n\nWe are going to use the power of Large Language Models and the function [CLASSIFY_TEXT](https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex)  to easily classify the documents we are ingesting in our RAG application. We are going to pass the document name and the first chunk of text into the CLASSIFY_TEXT function to determine categories. In this example, we want to use the LLM to automatically label each document as either ‘Bike’ or ‘Snow.’\n\nFirst, we will create a temporary table with each unique file name. We will then pass the file name and the first chunk of text to CLASSIFY_TEXT. Classification is not mandatory for Cortex Search but we want to use it here to also use hybrid search.\n\nRun this SQL to create the table containing the classification results:\n\n```SQL\nCREATE OR REPLACE TEMPORARY TABLE docs_categories AS WITH unique_documents AS (\n  SELECT\n    DISTINCT relative_path, chunk\n  FROM\n    docs_chunks_table\n  WHERE \n    chunk_index = 0\n  ),\n docs_category_cte AS (\n  SELECT\n    relative_path,\n    TRIM(snowflake.cortex.AI_CLASSIFY (\n      'Title:' || relative_path || 'Content:' || chunk, ['Bike', 'Snow']\n     )['labels'][0], '\"') AS CATEGORY\n  FROM\n    unique_documents\n)\nSELECT\n  *\nFROM\n  docs_category_cte;\n```\n\nYou can check the table to identify how many categories have been created and if the classifications are correct:\n\n```SQL\nselect * from docs_categories;\n```\n\nAll documents should be correctly classified now. This data will let users filter their searches later with Cortex Search, making retrieval faster and more accurate:\n\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig7_docs_categories.png)\n\nNow we can update the table with the chunks of text that will be used by Cortex Search service to include the category for each document:\n\n```SQL\nupdate docs_chunks_table \n  SET category = docs_categories.category\n  from docs_categories\n  where  docs_chunks_table.relative_path = docs_categories.relative_path;\n```\nThe table is now fully prepared and ready to enable the Cortex Search service.\n\nAfter completing all the steps in this section you should see the following objects in your database:\n\n- The **DOCS_CHUNKS_TABLE** that contains the text and categories for each document\n- The **DOCS** Stage with your PDF files\n- The **PDF_TEXT_CHUNKER** function that extracts and chunk the text from the PDF files\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig8_db_objects.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create Cortex Search Service\n\nThe next step is to create the Cortex Search Service in the table we have made before. We will execute this SQL command:\n\n```SQL\ncreate or replace CORTEX SEARCH SERVICE CC_SEARCH_SERVICE_CS\nON chunk\nATTRIBUTES category\nwarehouse = COMPUTE_WH\nTARGET_LAG = '1 minute'\nas (\n    select chunk,\n        chunk_index,\n        relative_path,\n        file_url,\n        category\n    from docs_chunks_table\n);\n```\n\n- The name of the service is **CC_SEARCH_SERVICE_CS**\n- The service will use the column **chunk** to create embeddings and perform retrieval based on similarity search\n- The column **category** could be used as a filter\n- To keep this service updated the warehouse **COMPUTE_WH** will be used. If you want to use a different warehouse, replace this with the name of your chosen warehouse\n- The service will be refreshed every minute\n- The data retrieved will contain the **chunk, relative_path, file_url and category**\n\nThat’s it! There is no need to create embeddings as that is done automatically. We can now use the API to query the service.\n\n\u003C!-- ------------------------ --\u003E\n## Build Chat UI with Retrieval and Generation Logic\n\nNow that your data is indexed, the next step is to create the user interface (UI) to interact with it. We will use [Streamlit in Snowflake](https://www.snowflake.com/en/product/features/streamlit-in-snowflake/) (SiS) to build a secure, full-stack application entirely within your account.\n\nTo make it easy for anyone to ask questions against the Cortex Search service, we will create a fairly simple front-end using Streamlit. As part of the app, we will provide the end-user with a toggle that allows testing of LLM responses with and without access to the context to observe the differences. The app will also show the pieces of text that the service is returning and which are being used to fill the calls to LLM to create the answers.\n\nStreamlit in Snowflake allows you to run the app and share it with other Snowflake users within the same account. This ensures data remains secure and protected and is only available to users that meet your role-based access policies. \n\n\u003E NOTE: [Introduction to Streamlit in Snowflake documentation](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n\n1. Click on the **Catalog » Streamlit** tab on the left-hand navigation pane in Snowsight\n2. Click on **+ Streamlit App** button on the right\n3. Name the App (Use CC_CORTEX_SEARCH_APP or another name)\n4. Select the warehouse to run the App (a Small WH is sufficient)\n5. Choose the **CC_QUICKSTART_CORTEX_SEARCH_DOCS** database and **DATA** schema\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig9_streamlit_app.png)\n\nThe Streamlit app comes with a default template. Delete the default code and replace it with the code provided below (front-end elements, retrieval, and generation logic are included):\n\n- Table with list of available documents\n- Toggle to choose to use documents as context\n- Question input box\n- Filter for a type of product\n- Using the Cortex Search service to retrieve the relevant chunks of text\n- Prompt template that instructs LLM how to answer using relevant chunks\n\n```python\nimport streamlit as st # Import python packages\nfrom snowflake.snowpark.context import get_active_session\n\nfrom snowflake.core import Root\n\nimport pandas as pd\nimport json\n\npd.set_option(\"max_colwidth\",None)\n\n### Default Values\nNUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\n\n# service parameters\nCORTEX_SEARCH_DATABASE = \"CC_QUICKSTART_CORTEX_SEARCH_DOCS\"\nCORTEX_SEARCH_SCHEMA = \"DATA\"\nCORTEX_SEARCH_SERVICE = \"CC_SEARCH_SERVICE_CS\"\n######\n######\n\n# columns to query in the service\nCOLUMNS = [\n    \"chunk\",\n    \"chunk_index\",\n    \"relative_path\",\n    \"category\"\n]\n\nsession = get_active_session()\nroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n   \n### Functions\n     \ndef config_options():\n\n    st.sidebar.selectbox('Select your model:',('mistral-large2', 'llama3.1-70b',\n                        'llama3.1-8b', 'snowflake-arctic'), key=\"model_name\")\n\n    categories = session.sql(\"select category from docs_chunks_table group by category\").collect()\n\n    cat_list = ['ALL']\n    for cat in categories:\n        cat_list.append(cat.CATEGORY)\n            \n    st.sidebar.selectbox('Select what products you are looking for', cat_list, key = \"category_value\")\n\n    st.sidebar.expander(\"Session State\").write(st.session_state)\n\ndef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == \"ALL\":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {\"@eq\": {\"category\": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n\ndef create_prompt (myquestion):\n\n    if st.session_state.rag == 1:\n        prompt_context = get_similar_chunks_search_service(myquestion)\n  \n        prompt = f\"\"\"\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between \u003Ccontext\u003E and \u003C/context\u003E tags.\n           When ansering the question contained between \u003Cquestion\u003E and \u003C/question\u003E tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           Do not mention the CONTEXT used in your answer.\n    \n           \u003Ccontext\u003E          \n           {prompt_context}\n           \u003C/context\u003E\n           \u003Cquestion\u003E  \n           {myquestion}\n           \u003C/question\u003E\n           Answer: \n           \"\"\"\n\n        json_data = json.loads(prompt_context)\n\n        relative_paths = set(item['relative_path'] for item in json_data['results'])\n        \n    else:     \n        prompt = f\"\"\"[0]\n         'Question:  \n           {myquestion} \n           Answer: '\n           \"\"\"\n        relative_paths = \"None\"\n            \n    return prompt, relative_paths\n\ndef complete(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n    cmd = \"\"\"\n            select snowflake.cortex.complete(?, ?) as response\n          \"\"\"\n    \n    df_response = session.sql(cmd, params=[st.session_state.model_name, prompt]).collect()\n    return df_response, relative_paths\n\ndef main():\n    \n    st.title(f\":speech_balloon: Chat Document Assistant with Snowflake Cortex\")\n    st.write(\"This is the list of documents you already have and that will be used to answer your questions:\")\n    docs_available = session.sql(\"ls @docs\").collect()\n    list_docs = []\n    for doc in docs_available:\n        list_docs.append(doc[\"name\"])\n    st.dataframe(list_docs)\n\n    config_options()\n\n    st.session_state.rag = st.sidebar.checkbox('Use your own documents as context?')\n\n    question = st.text_input(\"Enter question\", placeholder=\"Is there any special lubricant to be used with the premium bike?\", label_visibility=\"collapsed\")\n\n    if question:\n        response, relative_paths = complete(question)\n        res_text = response[0].RESPONSE\n        st.markdown(res_text)\n\n        if relative_paths != \"None\":\n            with st.sidebar.expander(\"Related Documents\"):\n                for path in relative_paths:\n                    cmd2 = f\"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)\"\n                    df_url_link = session.sql(cmd2).to_pandas()\n                    url_link = df_url_link._get_value(0,'URL_LINK')\n        \n                    display_url = f\"Doc: [{path}]({url_link})\"\n                    st.sidebar.markdown(display_url)\n                \nif __name__ == \"__main__\":\n    main()\n```\n\n\u003E NOTE: When you first paste and run the code, you will get an error because we need to add the package **snowflake.core** in our Streamlit App.\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig10_error.png)\n\n1. Click on **Packages** (found near the top of the code editor) \n2. Search for **snowflake.core**\n3. Set the Python Environment. You may want to use the latest versions available. This is the combination that has been tested for this lab:\n   * snowflake.core = 1.2.0\n   * python = 3.11\n   * snowflake-snowpark-python = 1.30.0\n   * streamlit = 1.42.0\n4. Click Run after adding the package. The app should now load successfully\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig11_packages.png)\n\n### Code Walkthrough\n\nLet's go step by step to see what the code is doing:\n\nFirst, we use st.sidebar to place all interactive options on the left-hand sidebar. There we include:\n- A selectbox for the LLM to be used\n- A selectbox to allow filtering for one category (this will be used by Cortex Search\n- Session state (for debugging purposes when building the app)\n- Chunks used and the links to the relevant documents\n\nThe main() function collects the user's question and calls a complete() function with the question. This one calls the create_prompt() function that uses get_similar_chunks_search_service() with the question. This is the function that calls Cortex Search service for retrieval with this code:\n\n```python\ndef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == \"ALL\":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {\"@eq\": {\"category\": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n```\n\nDepending if we want to filter for any category, we can use the filter object or not. Previously, we have created the **svc** handler that is pointing to the Cortex Search service:\n\n```python\nroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n```\n\nThe complete() function combines the LLM, the prompt template and whether to use the context or not to generate a response that includes a link to the asset from which the answer was obtained. \n\n```python\ndef complete(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n    cmd = \"\"\"\n            select snowflake.cortex.complete(?, ?) as response\n          \"\"\"\n    \n    df_response = session.sql(cmd, params=[st.session_state.model_name, prompt]).collect()\n    return df_response, relative_paths\n```\n\n### Deploy and share your AI-powered app\n\nStreamlit in Snowflake provides a side-by-side editor and preview screen that makes it easy and fast to iterate and visualize changes.\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig12_streamlit.png)\n\nIn the app, we can see the documents we had uploaded previously. We can also ask questions while trying multiple options using interactive widgets available on the side.\n\nTo test out the RAG framework, here are a few questions you can ask. Use the interactive widgets to compare the results when using a different LLM or when choosing to get a response without the context. This is related to very specific information that we have added into the documents and it is very unique to our products.\n\n- Is there any special lubricant to be used with the premium bike?\n- Does the mondracer infant bike need any special tool?\n- Is there any temperature to be considered with the premium bicycle?\n- What is the temperature to store the ski boots?\n- Where have the ski boots been tested and who tested them?\n\n\n### Other things to test\n\nIn this example, we have just decided a fixed format for chunks and used only the top result in the retrieval process. This [blog](https://medium.com/@thechosentom/rag-made-simple-with-snowflake-cortex-74d1df5143fd) provides some considerations about settings in the chunking strategy. \n\nThis is the recommendation from the [Cortex Search documentation](https://docs.snowflake.com/LIMITEDACCESS/cortex-search/cortex-search-overview):\n\nFor optimal search results with Cortex Search, Snowflake recommends splitting the text in your search column into chunks of no more than 512 tokens (about 385 English words). A token is the smallest unit of text processed by a large language model.\n\nWhen an entry in the search column contains more than 512 tokens, Cortex Search performs keyword-based retrieval on the entire body of text, but only uses the first 512 tokens for semantic (i.e., vector-based) retrieval.\n\n\n```SQL\n        LATERAL FLATTEN( input =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n```\n\nYou can also try different instructions in your prompt and see how the responses may vary. Simply replace any of the text with another instruction and run the app again. The prompt is a crucial part of LLM generation. Make sure you are clear in your instructions. Results may change depending on your own use case.\n\n```python\n        prompt = f\"\"\"\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between \u003Ccontext\u003E and \u003C/context\u003E tags.\n           When ansering the question contained between \u003Cquestion\u003E and \u003C/question\u003E tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           Do not mention the CONTEXT used in your answer.\n    \n           \u003Ccontext\u003E          \n           {prompt_context}\n           \u003C/context\u003E\n           \u003Cquestion\u003E  \n           {myquestion}\n           \u003C/question\u003E\n           Answer: \n           \"\"\"\n```\n\nYou can also try to change the number of chunks that are provided as context by simply modifying this value:\n\n```python\nNUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\n```\n\n\u003C!-- ------------------------ --\u003E\n## Build a ChatBot UI with Conversation History\n\nIn the previous section, we have created a simple interface where we can ask questions about our documents and select the LLM running within Snowflake Cortex to answer the question. We have seen that when no context from our documents is provided, we just get a general answer. However, when we use context from the PDFs, we get a specific answer related to our data. But what happens if we want to have it be a conversation?\n\nLarge Language Models (LLMs) are stateless. That means when we make a call to any of them, it does not remember what  the previous call was about. With Snowflake Cortex, each complete() call we make is independent, even when using the same model. There are several techniques that can be used to keep the conversation flow and chat history. To maintain context, the app needs to keep a memory of the chat history and send it along with the new question in every single call to the LLM. Since LLM calls are restricted by a context window length (the total number of tokens the model can process), we need a smart way to manage this history.\n\nStreamlit makes it very easy to create chat interfaces with its [Chat Elements](https://docs.streamlit.io/library/api-reference/chat). In this section we are going to see how we can write a simple app that can be used to have a conversation, and how it uses the data from our documents to provide answers. We are going to use a slide window concept. This helps to remember just a number of past interactions when calling the LLM. We are also going to summarize the previous conversation in order to find the right chunk in our documents that will help the LLM and provide the right answer. In the previous section, we were embedding the new question to find the right chunk for context. Here, we are also going to include the previous summary to find the chunks that will be used as context.\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/chatbot.gif)\n\nFirst let's create a new Streamlit App. Give it a name and create it within the database and schema we already made (CC_QUICKSTART_CORTEX_SEARCH_DOCS.DATA or whatever name you may have chosen).\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig13_streamlit_chatbot.png)\n\nIn this second app, we are going to demo how to access the Complete function from Snowflake Cortex via Python API instead of SQL as we did in the previous app. To get access to the API, you need to specify the **snowflake-ml-python** package in your Streamlit app. This combination of packages version is known to work well:\n\n- snowflake-ml-python = 1.8.1\n- snowflake.core = 1.2.0\n- python = 3.11\n- snowflake-snowpark-python = 1.30.0\n- streamlit = 1.42.0\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig13_2_ml_python_package.png)\n\n\nThe Streamlit app comes with a default template that you can delete and replace with this code, which includes the front-end elements:\n\n```python\nimport streamlit as st # Import python packages\nfrom snowflake.snowpark.context import get_active_session\n\nfrom snowflake.cortex import Complete\nfrom snowflake.core import Root\n\nimport pandas as pd\nimport json\n\npd.set_option(\"max_colwidth\",None)\n\n### Default Values\nNUM_CHUNKS = 3 # Num-chunks provided as context. Play with this to check how it affects your accuracy\nslide_window = 7 # how many last conversations to remember. This is the slide window.\n\n# service parameters\nCORTEX_SEARCH_DATABASE = \"CC_QUICKSTART_CORTEX_SEARCH_DOCS\"\nCORTEX_SEARCH_SCHEMA = \"DATA\"\nCORTEX_SEARCH_SERVICE = \"CC_SEARCH_SERVICE_CS\"\n######\n######\n\n# columns to query in the service\nCOLUMNS = [\n    \"chunk\",\n    \"chunk_index\",\n    \"relative_path\",\n    \"category\"\n]\n\nsession = get_active_session()\nroot = Root(session)                         \n\nsvc = root.databases[CORTEX_SEARCH_DATABASE].schemas[CORTEX_SEARCH_SCHEMA].cortex_search_services[CORTEX_SEARCH_SERVICE]\n   \n### Functions\n     \ndef config_options():\n\n    st.sidebar.selectbox('Select your model:',('mistral-large2', 'llama3.1-70b',\n                                    'llama3.1-8b', 'snowflake-arctic'), key=\"model_name\")\n\n    categories = session.table('docs_chunks_table').select('category').distinct().collect()\n\n    cat_list = ['ALL']\n    for cat in categories:\n        cat_list.append(cat.CATEGORY)\n            \n    st.sidebar.selectbox('Select what products you are looking for', cat_list, key = \"category_value\")\n\n    st.sidebar.checkbox('Do you want that I remember the chat history?', key=\"use_chat_history\", value = True)\n\n    st.sidebar.checkbox('Debug: Click to see summary generated of previous conversation', key=\"debug\", value = True)\n    st.sidebar.button(\"Start Over\", key=\"clear_conversation\", on_click=init_messages)\n    st.sidebar.expander(\"Session State\").write(st.session_state)\n\ndef init_messages():\n\n    # Initialize chat history\n    if st.session_state.clear_conversation or \"messages\" not in st.session_state:\n        st.session_state.messages = []\n\ndef get_similar_chunks_search_service(query):\n\n    if st.session_state.category_value == \"ALL\":\n        response = svc.search(query, COLUMNS, limit=NUM_CHUNKS)\n    else: \n        filter_obj = {\"@eq\": {\"category\": st.session_state.category_value} }\n        response = svc.search(query, COLUMNS, filter=filter_obj, limit=NUM_CHUNKS)\n\n    st.sidebar.json(response.json())\n    \n    return response.json()  \n\ndef get_chat_history():\n#Get the history from the st.session_stage.messages according to the slide window parameter\n    \n    chat_history = []\n    \n    start_index = max(0, len(st.session_state.messages) - slide_window)\n    for i in range (start_index , len(st.session_state.messages) -1):\n         chat_history.append(st.session_state.messages[i])\n\n    return chat_history\n\ndef summarize_question_with_history(chat_history, question):\n# To get the right context, use the LLM to first summarize the previous conversation\n# This will be used to get embeddings and find similar chunks in the docs for context\n\n    prompt = f\"\"\"\n        Based on the chat history below and the question, generate a query that extend the question\n        with the chat history provided. The query should be in natual language. \n        Answer with only the query. Do not add any explanation.\n        \n        \u003Cchat_history\u003E\n        {chat_history}\n        \u003C/chat_history\u003E\n        \u003Cquestion\u003E\n        {question}\n        \u003C/question\u003E\n        \"\"\"\n    \n    sumary = Complete(st.session_state.model_name, prompt)   \n\n    if st.session_state.debug:\n        st.sidebar.text(\"Summary to be used to find similar chunks in the docs:\")\n        st.sidebar.caption(sumary)\n\n    sumary = sumary.replace(\"'\", \"\")\n\n    return sumary\n\ndef create_prompt (myquestion):\n\n    if st.session_state.use_chat_history:\n        chat_history = get_chat_history()\n\n        if chat_history != []: #There is chat_history, so not first question\n            question_summary = summarize_question_with_history(chat_history, myquestion)\n            prompt_context =  get_similar_chunks_search_service(question_summary)\n        else:\n            prompt_context = get_similar_chunks_search_service(myquestion) #First question when using history\n    else:\n        prompt_context = get_similar_chunks_search_service(myquestion)\n        chat_history = \"\"\n  \n    prompt = f\"\"\"\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between \u003Ccontext\u003E and \u003C/context\u003E tags.\n           You offer a chat experience considering the information included in the CHAT HISTORY\n           provided between \u003Cchat_history\u003E and \u003C/chat_history\u003E tags..\n           When ansering the question contained between \u003Cquestion\u003E and \u003C/question\u003E tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           \n           Do not mention the CONTEXT used in your answer.\n           Do not mention the CHAT HISTORY used in your asnwer.\n\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           \u003Cchat_history\u003E\n           {chat_history}\n           \u003C/chat_history\u003E\n           \u003Ccontext\u003E          \n           {prompt_context}\n           \u003C/context\u003E\n           \u003Cquestion\u003E  \n           {myquestion}\n           \u003C/question\u003E\n           Answer: \n           \"\"\"\n    \n    json_data = json.loads(prompt_context)\n\n    relative_paths = set(item['relative_path'] for item in json_data['results'])\n\n    return prompt, relative_paths\n\n\ndef answer_question(myquestion):\n\n    prompt, relative_paths =create_prompt (myquestion)\n\n    response = Complete(st.session_state.model_name, prompt)   \n\n    return response, relative_paths\n\ndef main():\n    \n    st.title(f\":speech_balloon: Chat Document Assistant with Snowflake Cortex\")\n    st.write(\"This is the list of documents you already have and that will be used to answer your questions:\")\n    docs_available = session.sql(\"ls @docs\").collect()\n    list_docs = []\n    for doc in docs_available:\n        list_docs.append(doc[\"name\"])\n    st.dataframe(list_docs)\n\n    config_options()\n    init_messages()\n     \n    # Display chat messages from history on app rerun\n    for message in st.session_state.messages:\n        with st.chat_message(message[\"role\"]):\n            st.markdown(message[\"content\"])\n    \n    # Accept user input\n    if question := st.chat_input(\"What do you want to know about your products?\"):\n        # Add user message to chat history\n        st.session_state.messages.append({\"role\": \"user\", \"content\": question})\n        # Display user message in chat message container\n        with st.chat_message(\"user\"):\n            st.markdown(question)\n        # Display assistant response in chat message container\n        with st.chat_message(\"assistant\"):\n            message_placeholder = st.empty()\n    \n            question = question.replace(\"'\",\"\")\n    \n            with st.spinner(f\"{st.session_state.model_name} thinking...\"):\n                response, relative_paths = answer_question(question)            \n                response = response.replace(\"'\", \"\")\n                message_placeholder.markdown(response)\n\n                if relative_paths != \"None\":\n                    with st.sidebar.expander(\"Related Documents\"):\n                        for path in relative_paths:\n                            cmd2 = f\"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)\"\n                            df_url_link = session.sql(cmd2).to_pandas()\n                            url_link = df_url_link._get_value(0,'URL_LINK')\n                \n                            display_url = f\"Doc: [{path}]({url_link})\"\n                            st.sidebar.markdown(display_url)\n\n        \n        st.session_state.messages.append({\"role\": \"assistant\", \"content\": response})\n\n\nif __name__ == \"__main__\":\n    main()\n```\n\nFor educational purposes we have enabled the check for \"Do you want that I remember the chat history?\" When disabled, the app will not use memory. Each question will be independent from the previous ones. This is what happens when we are not using memory:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig14_no_memory.png)\n\nBasically, each question is answered individually. The second question loses the context.\n\nWhen asking the same questions but using memory the first thing to notice is that we have re-created the new question adding the previous context:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig15_memory.png)\n\nThe new question that is used to search for similar chunks is \"What is the Rincon del Cielo bike made of?\"\n\n\n### Code Walkthrough\n\n```python\ndef get_chat_history():\n```\nThis function is going to return the previous conversation in the chat up to a limit defined by the global variable slide_window.\n\nStreamlit stores the conversation history in \u003Ccode\u003Est.session_state.messages\u003C/code\u003E.\n\nEverytime a question is asked, it is stored in the state with this code:\n\n    st.session_state.messages.append({\"role\": \"user\", \"content\": question})\n\nAnd the responses provided are also stored with this code:\n\n    st.session_state.messages.append({\"role\": \"assistant\", \"content\": res_text})\n\nTherefore \u003Ccode\u003Est.session_state.messages\u003C/code\u003E is used as the chat memory\n\nThis next function \u003Ccode\u003Esummarize_question_with_history()\u003C/code\u003E takes the chat_history that we have had previously and the new question being asked. Remember that we are using the information from the PDF documents to answer questions. Therefore, we need to identify using Cortex Search what chunk of text will be relevant to answer the question. \n\nIf we just use \"what is it made of\" sentence to look for similar chunks, we likely will not get the right one. We need to include more information in the call we make to \u003Ccode\u003Eget_similar_chunks()\u003C/code\u003E. \n\nTake a look this \u003Ccode\u003Esummarize_question_with_history()\u003C/code\u003E function:\n\n```python\ndef summarize_question_with_history(chat_history, question):\n# To get the right context, use the LLM to first summarize the previous conversation\n# This will be used to get embeddings and find similar chunks in the docs for context\n\n    prompt = f\"\"\"\n        Based on the chat history below and the question, generate a query that extend the question\n        with the chat history provided. The query should be in natual language. \n        Answer with only the query. Do not add any explanation.\n        \n        \u003Cchat_history\u003E\n        {chat_history}\n        \u003C/chat_history\u003E\n        \u003Cquestion\u003E\n        {question}\n        \u003C/question\u003E\n        \"\"\"\n    \n    sumary = Complete(st.session_state.model_name, prompt)   \n\n    if st.session_state.debug:\n        st.sidebar.text(\"Summary to be used to find similar chunks in the docs:\")\n        st.sidebar.caption(sumary)\n\n    sumary = sumary.replace(\"'\", \"\")\n\n    return sumary\n```\n\nIn that function we are using the \u003Ccode\u003EComplete()\u003C/code\u003E API. This is where we have created a specific prompt to provide the chat history and the new question in order to get the query that will be used to find the right context.\n\nIn order to experiment with this, we have also created a selection check \"Debug: Click to see summary generated of previous conversation\" that will allow you to see what is the question being used to look for similar chunks.\n\n\nNow the function:\n\n```python\ndef create_prompt (myquestion):\n```\n\nUse all those previous functions to get the \u003Cb\u003Echat_history\u003C/b\u003E and \u003Cb\u003Eprompt_context\u003C/b\u003E to build the prompt. This prompt is adding the previous conversation plus the context extracted from the PDFs and the new question.\n\n```python\n   prompt = f\"\"\"\n           You are an expert chat assistance that extracs information from the CONTEXT provided\n           between \u003Ccontext\u003E and \u003C/context\u003E tags.\n           You offer a chat experience considering the information included in the CHAT HISTORY\n           provided between \u003Cchat_history\u003E and \u003C/chat_history\u003E tags..\n           When ansering the question contained between \u003Cquestion\u003E and \u003C/question\u003E tags\n           be concise and do not hallucinate. \n           If you don´t have the information just say so.\n           \n           Do not mention the CONTEXT used in your answer.\n           Do not mention the CHAT HISTORY used in your asnwer.\n\n           Only anwer the question if you can extract it from the CONTEXT provideed.\n           \n           \u003Cchat_history\u003E\n           {chat_history}\n           \u003C/chat_history\u003E\n           \u003Ccontext\u003E          \n           {prompt_context}\n           \u003C/context\u003E\n           \u003Cquestion\u003E  \n           {myquestion}\n           \u003C/question\u003E\n           Answer: \n           \"\"\"\n ```\n\nWhat makes this very easy with Streamlit are the [st.chat_input](https://docs.streamlit.io/library/api-reference/chat/st.chat_input) and [st.chat_message](https://docs.streamlit.io/library/api-reference/chat/st.chat_message) functions. This is the code that get the question with st.chat_input, add it to st.chat_message and to the memory with st.session_state.messages.append and call the \u003Cb\u003Eanswer_question()\u003C/b\u003E function to get the answer that is also printed and stored:\n\n```python\n    if question := st.chat_input(\"What do you want to know about your products?\"):\n        # Add user message to chat history\n        st.session_state.messages.append({\"role\": \"user\", \"content\": question})\n        # Display user message in chat message container\n        with st.chat_message(\"user\"):\n            st.markdown(question)\n        # Display assistant response in chat message container\n        with st.chat_message(\"assistant\"):\n            message_placeholder = st.empty()\n    \n            question = question.replace(\"'\",\"\")\n    \n            with st.spinner(f\"{st.session_state.model_name} thinking...\"):\n                response, relative_paths = answer_question(question)            \n                response = response.replace(\"'\", \"\")\n                message_placeholder.markdown(response)\n\n                if relative_paths != \"None\":\n                    with st.sidebar.expander(\"Related Documents\"):\n                        for path in relative_paths:\n                            cmd2 = f\"select GET_PRESIGNED_URL(@docs, '{path}', 360) as URL_LINK from directory(@docs)\"\n                            df_url_link = session.sql(cmd2).to_pandas()\n                            url_link = df_url_link._get_value(0,'URL_LINK')\n                \n                            display_url = f\"Doc: [{path}]({url_link})\"\n                            st.sidebar.markdown(display_url)\n\n        \n        st.session_state.messages.append({\"role\": \"assistant\", \"content\": response})\n```\nWe are also printing the links to the documents being used to answer the questions.\n\nHere are some other suggestions based on specific info from the documents (unique for us):\n\n- What is the name of the ski boots?\n- Where have been tested?\n- Who tested them?\n- Do they include any special component?\n\n\nYou can try this out with your own documents. You will notice different performance depending on the LLM you use. \n\n\u003C!-- ------------------------ --\u003E\n## Automatic Processing of New Documents\n\nThe next step in building an enterprise-ready RAG system is ensuring it stays up-to-date. If a new product manual is released or an old one is retired, your search index must update automatically. \n\nMaintaining your RAG system when new documents are added, deleted or updated can be tedious. Snowflake makes it very easy. Cortex Search is a self-managed service. We only need to add, delete or update rows in the table where Cortex Search Service has been enabled. The service will then automatically update the indexes and create new embeddings based on the frequency defined during service creation.\n\nIn addition, we can use Snowflake features like [Streams](https://docs.snowflake.com/en/user-guide/streams-intro), [Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro), and [Stored Procedures](https://docs.snowflake.com/en/developer-guide/stored-procedure/stored-procedures-overview) to automatically process new PDF files as they are added into Snowflake. \n\nFirst we create two streams for the DOCS staging area. One is going to be used to process deletes and the other to process inserts. The Streams capture the changes on the Directory Table used for the DOCS staging area. This way, we can track new updates and deletes:\n\n```SQL\ncreate or replace stream insert_docs_stream on stage docs;\ncreate or replace stream delete_docs_stream on stage docs;\n```\n\nSecond, we are going to define a Stored Procedure. These extend the system with procedural code. With a procedure, you can use branching, looping, and other programmatic constructs. You can reuse a procedure multiple times by calling it from other code. This will process those streams to:\n\n- Delete from the docs_chunk_table the content for files that has been deleted from the stagin area, so they are no longer relevant\n- Parse new PDF documents that has been added into the staging area using PARSE_DOCUMENT\n- Chunk the new document into pieces using SPLIT_TEXT_RECURSIVE_CHARACTER\n- Classify the new documents and update the label (this step is optional just to show the part of the possible)\n\nCreate the Stored Procedure:\n\n```SQL\ncreate or replace procedure insert_delete_docs_sp()\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\n$$\nBEGIN\n\nDELETE FROM docs_chunks_table\n    USING delete_docs_stream\n    WHERE docs_chunks_table.RELATIVE_PATH = delete_docs_stream.RELATIVE_PATH\n    and delete_docs_stream.METADATA$ACTION = 'DELETE';\n\nCREATE OR REPLACE TEMPORARY TABLE RAW_TEXT AS\n    WITH FILE_TABLE as \n      (SELECT \n            RELATIVE_PATH,\n            SIZE,\n            FILE_URL,\n            build_scoped_file_url(@docs, relative_path) as scoped_file_url,\n            TO_FILE('@DOCS', RELATIVE_PATH) AS docs \n        FROM \n            insert_docs_stream\n        WHERE \n            METADATA$ACTION = 'INSERT'        \n        )\n    SELECT \n        RELATIVE_PATH,\n        SIZE,\n        FILE_URL,\n        scoped_file_url,\n        TO_VARCHAR (\n            SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT (\n                docs,\n                {'mode': 'LAYOUT'} ):content\n            ) AS EXTRACTED_LAYOUT \n    FROM \n        FILE_TABLE;\n    \n\n    -- Insert new docs chunks\ninsert into docs_chunks_table (relative_path, size, file_url,\n                            scoped_file_url, chunk, chunk_index)\n\nselect relative_path, \n            size,\n            file_url, \n            scoped_file_url,\n            c.value::TEXT as chunk,\n            c.INDEX::INTEGER as chunk_index\n            \n    from \n        RAW_TEXT,\n        LATERAL FLATTEN( input =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n              EXTRACTED_LAYOUT,\n              'markdown',\n              1512,\n              256,\n              ['\\n\\n', '\\n', ' ', '']\n           )) c;\n\n    -- Classify the new documents\n\n    CREATE OR REPLACE TEMPORARY TABLE docs_categories AS \n    WITH unique_documents AS (\n      SELECT DISTINCT\n        d.relative_path, d.chunk\n      FROM\n        docs_chunks_table d\n      INNER JOIN\n        RAW_TEXT r\n        ON d.relative_path = r.relative_path\n      WHERE \n        d.chunk_index = 0\n    ),\n    docs_category_cte AS (\n      SELECT\n        relative_path,\n        TRIM(snowflake.cortex.AI_CLASSIFY (\n            'Title:' || relative_path || 'Content:' || chunk, ['Bike', 'Snow']\n            )['labels'][0], '\"') AS CATEGORY\n      FROM\n        unique_documents\n    )\n    SELECT\n      *\n    FROM\n      docs_category_cte;\n\n    -- Update cathegories\n\n    update docs_chunks_table \n        SET category = docs_categories.category\n        from docs_categories\n        where  docs_chunks_table.relative_path = docs_categories.relative_path;\nEND;\n$$;\n\n```\n\nNow we can create a Task that every X minutes can check if there is new data in the stream and take an action. We are setting the schedule to 5 minutes so you can follow the execution, but feel free to reduce the time to 1 minute if needed. Consider what would be best for your app and how often new docs are updated.\n\nWe define:\n  - Where: This is going to be executed using warehouse **COMPUTE_WH**. Please name to your own Warehouse.\n  - When: Check every 5 minutes. Execute in the case of new records in the delete_docs_stream stream (we could also use the other stream)\n  - What to do: Call the stored procedure insert_delete_docs_sp()\n\nExecute this code in your Snowflake Worksheet to create the task:\n\n```SQL\ncreate or replace task insert_delete_docs_task\n    warehouse = COMPUTE_WH\n    schedule = '5 minute'\n    when system$stream_has_data('delete_docs_stream')\nas\n    call insert_delete_docs_sp();\n\n\nalter task  insert_delete_docs_task resume;\n```\nNow we can prove the RAG system updates automatically when documents change. \n\nAfter uploading the document (and if you are fast enough before the doc is automatically processed) you can see the doc in the stream:\n\nNow let's test how it works. \n\nRun these queries. Your streams will be empty:\n\n```SQL\nselect * from delete_docs_stream;\nselect * from insert_docs_stream;\n```\nWe are going to test the modification of a document and the deletion of another.\n\nFirst you can check what is the response to the question:\n\n\"Is there any special lubricant to be used with the premium bike?\"\n\nYou should get something pointing to Premium Oil 2287:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig16_search_1.png)\n\nNow, let's update the document for the Premium Bike as we have a new lubricant to be used. Also you can test the deletion of another document.\n\nGo to your database, select the schema DATA and the Stage DOCS. Click on \"Premium_Bicycle_User_Guide.pdf\" document and select Remove:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig17_remove_file.png)\n\nWe no longer have Ski boots, so you can also remove the Ski_Boots_TDBootz_Special.pdf document.\n\nNow you can add the new version of the \"Premium_Bicycle_User_Guide.pdf\" document. Download the new version from:\n\n- [Premium_Bicycle_User_Guide.pdf](https://github.com/Snowflake-Labs/sfguide-ask-questions-to-your-documents-using-rag-with-snowflake-cortex-search/blob/main/NEW_DOCS/Premium_Bicycle_User_Guide.pdf)\n\nIf you read the document, you will notice that the new Oil recommended is Premium Oil 5555\n\nOn the top right, click on +Files and add this new file:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig18_upload_file.png)\n\nIf you have done this fast enough (in less than 5 minutes), you can check that both streams have detected the changes:\n\n```SQL\nselect * from delete_docs_stream;\nselect * from insert_docs_stream;\n```\nThe column METADATA$ACTION tells us if this is a DELETE or an INSERT.\n\nSelect specific columns so you can see the columns from the Stream:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig18_2_streams.png)\n\nYou can also force the task execution with:\n\n```SQL\nexecute task insert_delete_docs_task;\n```\n\nNote that once the Stream has been processed, it will be empty.\n\nUnder Transformation » Tasks, you can find your task and monitor the execution:\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig19_task_history.png)\n\nAnd now you can go back to your Streamlit app to ask the question again:\n\n\"Is there any special lubricant to be used with the premium bike?\"\n\n![App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search/fig20_search_2.png)\n\nYou can check that the new document is being used.\n\nIf you ask the question: \"Who tested the ski boots?\", you should get something like \"The context provided does not mention anyone testing ski boots\" as the document has been deleted.\n\nOnce you have finished testing uploading new documents and asking questions, you may want to suspend the task to ensure it doesn’t try to run and consume resources:\n\n```SQL\nalter task insert_delete_docs_task suspend;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nIn order to avoid consuming credits, you can also drop the databse used for this guide.\n\n```SQL\ndrop database CC_QUICKSTART_CORTEX_SEARCH_DOCS;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully performed RAG using Snowflake Cortex Search and securely built a full-stack RAG application in Snowflake without having to build integrations, manage any infrastructure, or deal with security concerns with data moving outside of the Snowflake governance framework. \n\n### What You Learned\n- Creating functions to automatically extract text and chunk PDF files\n- Automatically identify document categories using LLMs\n- Using Snowflake Cortex Search to use LLMs to answer questions\n- Building an application front-end with Streamlit\n\n### Related Resources\n\n- [Docs: Snowflake Cortex LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)\n- [Blog: RAG explainer and how it works in Snowflake](/blog/easy-secure-llm-inference-retrieval-augmented-generation-rag-cortex)\n- [Blog: Customizing RAG framework components using Snowpark Container Services](https://medium.com/@jason.summer/developing-a-product-chatbot-with-airmiles-in-snowflake-6b197d3fc424)\n- [Docs: Snowflake Directory Tables](https://docs.snowflake.com/en/user-guide/data-load-dirtables)\n- [Docs: Creating User-Defined Table Functions](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udtfs)\n\n---\n\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c9bbcc0837","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-29b8a7fd16",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-e17210915c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c7b72572fd","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-8c95d4477a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-7a0c235f82",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-98c2a27351","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-41c81afa33",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/ask-questions-to-your-own-documents-with-snowflake-cortex-search","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EOrganize Documents\u003C/h2\u003E","\u003Ch2\u003EPre-process Documents\u003C/h2\u003E","\u003Ch2\u003ECreate Cortex Search Service\u003C/h2\u003E","\u003Ch2\u003EBuild Chat UI with Retrieval and Generation Logic\u003C/h2\u003E","\u003Ch2\u003EBuild a ChatBot UI with Conversation History\u003C/h2\u003E","\u003Ch2\u003EAutomatic Processing of New Documents\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-9c646146c2",":type":"snowflake-site/components/quickstart/quickstart-button","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/ask-questions-to-your-own-documents-with-snowflake-cortex-search","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-4124dbf17d","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:1fr 0}.qs-disclaimer-text p \u003E span{font-size:15px !important}@media (min-width:768px){#quickstart-template-main-flexible-container{padding:24px 32px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:48px}}@media (max-width:767px){#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:0}}@media (min-width:1024px){#quickstart-template-main-flexible-container{padding:0 92px 48px 92px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:117px}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["quickstart_hero","flexible_column_cont","markup_editor"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"layout":"SIMPLE","id":"container-75543f3497",":type":"snowflake-site/components/modal/modal-container",":items":{},":itemsOrder":[]},"experiencefragment-footer":{"id":"experiencefragment-89ab2d8538","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"additionalClasses":"sf-footer","layout":"SIMPLE","id":"container-432256a5ba",":type":"snowflake-site/components/container",":items":{"container_copy":{"additionalClasses":"sf-footer__inner","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-8b1343bd99",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-7217cb2d5f","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"sf-footer-grid","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c9a5acbece",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_1622723482":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-8ae0882a78",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f183bc310e",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-7eee43f55f",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-b3681a2619","additionalClasses":"sf-footer__newsletter-title","text":"\u003Cp\u003E\u003Cb\u003ESubscribe to our monthly newsletter\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EStay up to date on Snowflake’s latest products, expert insights and resources—right in your inbox!\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-04"},"marketo_v2":{"id":"marketo-v2-b4cc782893","marketoForm":{"formId":"45871","edit":false,"successUrl":null,"hidden":null,"script":null,"values":null},"marketoConfigured":true,"formConfigured":true,"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com",":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"]}},":itemsOrder":["container"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f0fdfbea09",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text":{"id":"text-d26d387ada","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-2e2f94ff62","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text","text_copy"]},"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-9bef6aec3d",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-9f68b94c10","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003E\u003Ca href=\"/en/solutions/industries/\"\u003EIndustries\u003C/a\u003E\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/advertising-media-entertainment/\"\u003EAdvertising, Media & Entertainment\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/financial-services/\"\u003EFinancial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/healthcare-and-life-sciences/\"\u003EHealthcare & Life Sciences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/manufacturing/\"\u003EManufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/public-sector/\"\u003EPublic Sector\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/retail-consumer-goods/\"\u003ERetail & Consumer Goods\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/telecom/\"\u003ETelecom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/solutions/industries/technology/\"\u003ETechnology\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-2a70efab2a",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-3000da6e1b","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ECompany\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/about-snowflake/\"\u003EAbout Snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/leadership-and-board/\"\u003ELeadership & Board\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://careers.snowflake.com/us/en\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECareers\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://investors.snowflake.com/overview/default.aspx\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EInvestor Relations\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://trust.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETrust Center\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/brand-guidelines/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBrand Guidelines\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/contact/\"\u003EContact\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/news/\"\u003ENewsroom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/esg/\"\u003EEnvironmental, Social & Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/snowflake-ventures/\"\u003ESnowflake Ventures\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/end-data-disparity/\"\u003EEnd Data Disparity\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/summit/\"\u003ESnowflake Summit 26\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-5a3ea713a9",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-a8d2734883","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ELearn\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/en/resources/\"\u003EResource Library\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/webinars/demo/\"\u003ELive Demos\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/\"\u003EFundamentals\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/training/\"\u003ETraining\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/certifications/\"\u003ECertifications\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://learn.snowflake.com/en/\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides\"\u003EDeveloper Guides\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/data-governance/\"\u003EData Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"]}},":itemsOrder":["container_1622723482","container","container_copy_copy","container_copy","container_copy_copy_"]}},":itemsOrder":["container"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["flexible_column_cont"]},"container_573483281_":{"additionalClasses":"sf-footer__bottom","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f9c2108db6",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":items":{"container_112062425":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-8a40c97ed6",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-5494b2b850","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-2b2c178b2c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer__legal-container","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_16360":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-4bdec27555",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-7c9588cf45",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"image":{"id":"image-c954cbf4db","additionalClasses":"sf-footer__logo","alt":"Snowflake logo","imageLink":{"valid":true,"url":"/en/"},"src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/_jcr_content/root/container_573483281_/container_112062425/flexible_column_cont/flexible_column_content_container_1/container/container/image.coreimg.svg/1747882370694/nav-icon-snowflake-bug.svg","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"]},"text_copy_copy_16360":{"id":"text-feaa087e71","additionalClasses":"sf-footer__legal-links","text":"\u003Cul\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. All Rights Reserved\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/privacy/privacy-policy/\"\u003EPrivacy Policy\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/en/legal/snowflake-site-terms/\"\u003ESite Terms\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://info.snowflake.com/Preference-center.html\"\u003ECommunication Preferences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cbutton id=\"ot-sdk-btn\" class=\"ot-sdk-show-settings\"\u003ECookie Settings\u003C/button\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/privacy/privacy-policy/#12\"\u003EDo Not Share My Personal Information\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/\"\u003ELegal\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"markup_editor":{"id":"markup-editor-e1d32f603a","title":" ","htmlContent":"\u003Cdiv class=\"sf-footer__social\"\u003E\r\n\u003Cdiv data-testid=\"snowflake-footer-twitter\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://x.com/Snowflake\" data-testid=\"button-external\" aria-label=\"X (Twitter)\" role=\"button\" class=\"snowflake-button-container\" title=\"X (Twitter)\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"none\" viewBox=\"0 0 59 53\" class=\"button-icon\"\u003E\u003Cpath fill=\"currentColor\" d=\"M46.614 0h9.044L35.8 22.49 59 53H40.795L26.54 34.46 10.223 53H1.18l21.036-24.055L0 0h18.657l12.878 16.937zM43.45 47.72h5.013L16.023 5.085h-5.387z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-linkedin\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.linkedin.com/company/3653845\" data-testid=\"button-external\" aria-label=\"LinkedIn\" role=\"button\" class=\"snowflake-button-container\" title=\"LinkedIn\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M22.223 0H1.772C.792 0 0 .773 0 1.73v20.536C0 23.222.792 24 1.772 24h20.451c.98 0 1.777-.778 1.777-1.73V1.73C24 .773 23.203 0 22.223 0ZM7.12 20.452H3.558V8.995H7.12v11.457ZM5.34 7.434a2.064 2.064 0 1 1 0-4.125 2.063 2.063 0 0 1 0 4.125Zm15.112 13.018h-3.558v-5.57c0-1.326-.024-3.037-1.852-3.037-1.851 0-2.133 1.449-2.133 2.944v5.663H9.356V8.995h3.413v1.566h.047c.473-.9 1.636-1.852 3.365-1.852 3.605 0 4.27 2.372 4.27 5.457v6.286Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-facebook\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.facebook.com/snowflakedb/\" data-testid=\"button-external\" aria-label=\"Facebook\" role=\"button\" class=\"snowflake-button-container\" title=\"Facebook\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M24 12c0-6.627-5.373-12-12-12S0 5.373 0 12c0 5.99 4.388 10.954 10.125 11.854V15.47H7.078V12h3.047V9.356c0-3.007 1.792-4.668 4.533-4.668 1.312 0 2.686.234 2.686.234v2.953H15.83c-1.491 0-1.956.925-1.956 1.875V12h3.328l-.532 3.469h-2.796v8.385C19.612 22.954 24 17.99 24 12Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-youtube\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.youtube.com/user/snowflakecomputing\" data-testid=\"button-external\" aria-label=\"YouTube\" role=\"button\" class=\"snowflake-button-container\" title=\"YouTube\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M23.76 7.2s-.233-1.655-.955-2.381c-.914-.956-1.936-.961-2.405-1.017-3.356-.244-8.395-.244-8.395-.244h-.01s-5.039 0-8.395.244c-.469.056-1.49.06-2.405 1.017C.473 5.545.244 7.2.244 7.2S0 9.145 0 11.086v1.819c0 1.94.24 3.886.24 3.886s.233 1.654.95 2.38c.915.957 2.115.924 2.65 1.027 1.92.183 8.16.24 8.16.24s5.044-.01 8.4-.249c.469-.056 1.49-.06 2.405-1.017.722-.727.956-2.381.956-2.381S24 14.85 24 12.905v-1.819c0-1.94-.24-3.886-.24-3.886ZM9.52 15.113V8.367l6.483 3.385-6.483 3.36Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\r\n\u003C/div\u003E",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container","text_copy_copy_16360","markup_editor"]}},":itemsOrder":["container"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["flexible_column_cont"]}},":itemsOrder":["container_112062425"]},"markup_editor_copy":{"id":"markup-editor-e00b604a0c","title":"New css","cssContent":".snowflake-image-container img{background-color:transparent}div.snowflake-person-chip-avatar{width:80px !important}#snowflake-blog-template-main-container .snowflake-quote-item-card{margin-top:40px}#snowflake-blog-template-main-container .aem-GridColumn:has(.vertical-video){background-color:#000;border-radius:16px;overflow:hidden}#snowflake-blog-template-main-container .is-vertical img{max-width:400px;margin-left:auto;margin-right:auto}#snowflake-blog-template-main-container .vertical-video{max-width:240px;margin-left:auto;margin-right:auto}@media screen and (min-width:1367px){.dynamic .heading-1-v2 .snowflake-title-v2-line{font-size:72px !important;line-height:60px !important}}.snowflake-flexible-column-container-items-alignment-match-height .download-card,.snowflake-flexible-column-container-items-alignment-match-height .download-card\u003E.container{height:100%}.download-card div.code-toolbar\u003E.toolbar .copy-to-clipboard-button{background-color:white;border:1px solid #a9e1f6;margin-right:4px;top:6px;border-radius:16px;height:26px;width:40px}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:before{content:'';background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23249EDC;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23249EDC;'%3E%3C/path%3E%3C/svg%3E\");background-size:auto 65%;background-position:center;background-repeat:no-repeat;top:0;left:0;width:100%;height:100%}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:hover:before{background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23fff;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23fff;'%3E%3C/path%3E%3C/svg%3E\")}.download-card\u003Ediv{background-color:#fff;border:1px solid #ccc;border-radius:8px;padding:24px}.download-chip__headline{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:16px}.download-chip{padding:8px 12px !important;border-radius:4px;transition:300ms ease background-color}.download-chip .black-blue-text-color .snowflake-title-v2-line{color:#000 !important;padding-right:24px;font-family:'Lato',sans-serif;font-size:14px !important;font-weight:500 !important}.download-chip .black-blue-text-color .snowflake-title-v2-line:not(:first-child){opacity:.6;font-style:italic !important}.download-chip .snowflake-content-chip-button{display:none}.download-chip.is-external-link{background-size:16px 16px;background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='15' viewBox='0 0 15 15' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1.06055 13.0607L11.8605 2.26067M13.0605 10.6607V1.06067H3.46055' stroke='%23249EDC' stroke-width='2.12132' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-chip{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_883_7979)'%3E%3Cpath d='M3.375 16.875H14.625' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M9 1.125V11.25' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M4.5 7.875L9 12.375L13.5 7.875' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/g%3E%3Cdefs%3E%3CclipPath id='clip0_883_7979'%3E%3Crect width='18' height='18' fill='white'/%3E%3C/clipPath%3E%3C/defs%3E%3C/svg%3E%0A\");background-size:24px auto;background-repeat:no-repeat;background-position:calc(100% - 12px) center}.download-chip__headline{display:flex;gap:16px;flex-direction:row !important;flex-wrap:nowrap}.download-chip__headline::before{content:'';display:inline-block;width:24px;height:24px;background-position:center;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='21' viewBox='0 0 21 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 9.89999C8.13657 9.89999 8.74702 9.64713 9.19711 9.19704C9.64719 8.74696 9.90005 8.13651 9.90005 7.49999V2.69999C9.90005 2.06347 9.64719 1.45302 9.19711 1.00293C8.74702 .552844 8.13657 .299988 7.50005 .299988H2.70005C2.06353 .299988 1.45308 .552844 1.00299 1.00293C.552905 1.45302 .300049 2.06347 .300049 2.69999V7.49999C.300049 8.13651 .552905 8.74696 1.00299 9.19704C1.45308 9.64713 2.06353 9.89999 2.70005 9.89999H7.50005ZM7.50005 7.49999H2.70005V2.69999H7.50005V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 20.3C8.13657 20.3 8.74702 20.0472 9.19711 19.5971C9.64719 19.147 9.90005 18.5365 9.90005 17.9V13.1C9.90005 12.4635 9.64719 11.853 9.19711 11.403C8.74702 10.9529 8.13657 10.7 7.50005 10.7H2.70005C2.06353 10.7 1.45308 10.9529 1.00299 11.403C.552905 11.853 .300049 12.4635 .300049 13.1V17.9C.300049 18.5365 .552905 19.147 1.00299 19.5971C1.45308 20.0472 2.06353 20.3 2.70005 20.3H7.50005ZM7.50005 17.9H2.70005V13.1H7.50005V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 9.89999C18.5366 9.89999 19.147 9.64713 19.5971 9.19704C20.0472 8.74696 20.3001 8.13651 20.3001 7.49999V2.69999C20.3001 2.06347 20.0472 1.45302 19.5971 1.00293C19.147 .552844 18.5366 .299988 17.9001 .299988H13.1001C12.4636 .299988 11.8531 .552844 11.403 1.00293C10.9529 1.45302 10.7001 2.06347 10.7001 2.69999V7.49999C10.7001 8.13651 10.9529 8.74696 11.403 9.19704C11.8531 9.64713 12.4636 9.89999 13.1001 9.89999H17.9001ZM17.9001 7.49999H13.1001V2.69999H17.9001V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 20.3C18.5366 20.3 19.147 20.0472 19.5971 19.5971C20.0472 19.147 20.3001 18.5365 20.3001 17.9V13.1C20.3001 12.4635 20.0472 11.853 19.5971 11.403C19.147 10.9529 18.5366 10.7 17.9001 10.7H13.1001C12.4636 10.7 11.8531 10.9529 11.403 11.403C10.9529 11.853 10.7001 12.4635 10.7001 13.1V17.9C10.7001 18.5365 10.9529 19.147 11.403 19.5971C11.8531 20.0472 12.4636 20.3 13.1001 20.3H17.9001ZM17.9001 17.9H13.1001V13.1H17.9001V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat}.download-chip__headline.is-cli::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M4 17L10 11L4 5' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M12 19H20' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-card pre[class*=language-]{padding:8px 12px;background-color:var(--ui-background-05);overflow:hidden}.download-chip__headline.is-windows,.download-chip__headline.is-mac{gap:12px}.download-chip__headline.is-windows::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='4875' height='4875' viewBox='0 0 4875 4875' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_122_201)'%3E%3Cpath d='M0 0H2311V2310H0V0ZM2564 0H4875V2310H2564V0ZM0 2564H2311V4875H0V2564ZM2564 2564H4875V4875H2564' fill='%23000'/%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-mac::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg version='1.1' id='Layer_1' xmlns:x='ns_extend;' xmlns:i='ns_ai;' xmlns:graph='ns_graphs;' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' x='0' y='0' viewBox='0 0 41.5 51' style='enable-background:new 0 0 41.5 51;' xml:space='preserve'%3E%3Cmetadata%3E%3Csfw xmlns='ns_sfw;'%3E%3Cslices%3E%3C/slices%3E%3CsliceSourceBounds bottomLeftOrigin='true' height='51' width='41.5' x='166.1' y='-208.1'%3E%3C/sliceSourceBounds%3E%3C/sfw%3E%3C/metadata%3E%3Cg%3E%3Cpath d='M40.2,17.4c-3.4,2.1-5.5,5.7-5.5,9.7c0,4.5,2.7,8.6,6.8,10.3c-.8,2.6-2,5-3.5,7.2c-2.2,3.1-4.5,6.3-7.9,6.3s-4.4-2-8.4-2 c-3.9,0-5.3,2.1-8.5,2.1s-5.4-2.9-7.9-6.5C2,39.5,.1,33.7,0,27.6c0-9.9,6.4-15.2,12.8-15.2c3.4,0,6.2,2.2,8.3,2.2 c2,0,5.2-2.3,9-2.3C34.1,12.2,37.9,14.1,40.2,17.4z M28.3,8.1C30,6.1,30.9,3.6,31,1c0-.3,0-.7-.1-1c-2.9,.3-5.6,1.7-7.5,3.9 c-1.7,1.9-2.7,4.3-2.8,6.9c0,.3,0,.6,.1,.9c.2,0,.5,.1,.7,.1C24.1,11.6,26.6,10.2,28.3,8.1z'%3E%3C/path%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-desktop::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg opacity='.8'%3E%3Cpath d='M1.5 21H22.5V18H1.5V21Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M19.5 15C20.2956 15 21.0587 14.6839 21.6213 14.1213C22.1839 13.5587 22.5 12.7956 22.5 12V6C22.5 5.20435 22.1839 4.44129 21.6213 3.87868C21.0587 3.31607 20.2956 3 19.5 3H4.5C3.70435 3 2.94129 3.31607 2.37868 3.87868C1.81607 4.44129 1.5 5.20435 1.5 6V12C1.5 12.7956 1.81607 13.5587 2.37868 14.1213C2.94129 14.6839 3.70435 15 4.5 15H19.5ZM19.5 12H4.5V6H19.5V12Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3C/g%3E%3C/svg%3E%0A\")}.download-card .snowflake-code-snippet,.download-card .snowflake-code-snippet code,.download-card .snowflake-code-snippet pre{font-size:14px;color:#000;text-shadow:none !important}.download-chip:hover{background-color:var(--ui-background-05) !important;transition:300ms ease background-color}body:has(.snowflake-skip-to-content[style]) #subNav,.pushdown-banner-dismissed #subNav{top:var(--scroll-padding-top) !important;transition:300ms ease top}body:has(.snowflake-skip-to-content[style*=\"58\"]) #subNav{top:34px !important}body:has(.snowflake-skip-to-content[style*=\"82\"]) #subNav{top:58px !important}body:has(.snowflake-skip-to-content[style*=\"130\"]) #subNav{top:106px !important}body:has(.snowflake-skip-to-content[style*=\"138\"]) #subNav{top:114px !important}body:has(.snowflake-skip-to-content[style*=\"146\"]) #subNav{top:122px !important}.is-hidden .snowflake-person-chip-avatar{display:none}.is-small .snowflake-person-chip-avatar{width:56px;height:56px}.ai-summary ul{margin:16px 0 0 0 !important;padding:0 !important;list-style-type:none}.ai-summary li{margin:0;padding:0 0 0 32px;position:relative}.ai-summary li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.ai-summary li:not(:last-child){margin-bottom:1rem}.snowflake-content-chip-image__image{aspect-ratio:5 / 3 !important}.content-chip-new .snowflake-content-chip-image__image{height:100% !important;aspect-ratio:unset !important}.snapshot-card .snowflake-text p:not(:first-child){margin-top:var(--spacing-01)}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p:has(b){font-family:'Texta',sans-serif;margin-top:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p b{font-weight:700 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){border-bottom:1px solid #ccc;padding-bottom:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) p:first-child:has(b){font-family:'Texta',sans-serif;font-size:20px !important;margin-bottom:1rem !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li{display:inline-block}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li a{display:inline-block;text-decoration:none;padding:4px 16px !important;border:1px solid #ccc;border-radius:24px;color:#666 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) ul{list-style-type:none;display:flex;padding:0 !important;margin:0 !important;gap:12px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container img{width:90%;max-width:240px;margin:0 auto}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container{padding:40px;max-width:450px;margin:0 0 0 auto;background-color:#fff;box-shadow:0 2px 6px 0 rgba(152,162,179,.25),0 10px 20px 0 rgba(152,162,179,.10);border-radius:8px;border-top:4px solid var(--ui-01)}.ai-summary{background-color:#f3fbfe;border-left:2px solid var(--ui-01);padding:40px}.ai-summary\u003Espan p:last-child:has(i){color:#666;font-size:14px !important}.ai-summary\u003Espan p:last-child:has(i) a{color:#666 !important;text-decoration:underline !important}.ai-summary\u003Espan p:last-child:has(i) a:hover{color:var(--ui-01) !Important}.ai-summary\u003Espan p:first-child:has(b)::after{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9.3158 3.15226C8.6475 6.2258 6.22698 8.64545 3.15232 9.31587C2.94923 9.36072 2.94923 9.63928 3.15232 9.68413C6.22698 10.3522 8.6475 12.7742 9.3158 15.8477C9.36067 16.0508 9.63933 16.0508 9.6842 15.8477C10.3525 12.7742 12.773 10.3545 15.8477 9.68413C16.0508 9.63928 16.0508 9.36072 15.8477 9.31587C12.773 8.64781 10.3525 6.2258 9.6842 3.15226C9.63933 2.94925 9.36067 2.94925 9.3158 3.15226Z' fill='%23249EDC'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.3725 11.5461C16.9098 13.6739 15.2341 15.3491 13.1054 15.8132C12.9649 15.8443 12.9649 16.0371 13.1054 16.0681C15.2341 16.5307 16.9098 18.2074 17.3725 20.3353C17.4035 20.4758 17.5965 20.4758 17.6275 20.3353C18.0902 18.2074 19.7659 16.5323 21.8946 16.0681C22.0352 16.0371 22.0352 15.8443 21.8946 15.8132C19.7659 15.3507 18.0902 13.6739 17.6275 11.5461C17.5965 11.4055 17.4035 11.4055 17.3725 11.5461Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-repeat:no-repeat;background-size:contain;background-position:center;vertical-align:middle;margin-left:8px}.ai-summary\u003Espan p:first-child:has(b){color:var(--ui-01) !important;text-transform:uppercase}.border-top{border-top:1px solid rgba(0,0,0,.2)}.border-top\u003Espan{display:block;padding-top:32px}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9 !important}.content-chip-new .snowflake-content-chip-image__image{border-radius:0;object-fit:cover;height:100%}.sf-footer #ot-sdk-btn.ot-sdk-show-settings,.sf-footer #ot-sdk-btn.optanon-show-settings{color:rgba(255,255,255,.7) !important;text-underline-offset:4px;border-top:none;border-left:none;border-right:none;border-bottom:1px dotted transparent;background-color:transparent !important;background-image:none !important;transition:300ms ease text-decoration-color;padding:0 !important;font-size:12px;font-family:'Lato',sans-serif}.sf-footer #ot-sdk-btn.ot-sdk-show-settings:hover,.sf-footer #ot-sdk-btn.optanon-show-settings:hover{color:rgba(255,255,255,1) !important;border-bottom:1px dotted var(--ui-01);transition:300ms ease text-decoration-color}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{flex-shrink:0}.sf-footer__disclaimers{background-color:#042130}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p a{color:inherit;text-decoration:none !important}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p sup{margin-right:2px}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p{text-indent:-5px;padding-left:5px}.sf-footer__disclaimers-inner{border-top:1px solid rgba(255,255,255,.25);padding:40px 0}.sf-footer__disclaimers .snowflake-simple-stat{align-items:flex-start;text-align:left;color:rgba(255,255,255,.7);margin-bottom:10px}.sf-footer__social{display:flex;justify-content:center;gap:12px}.sf-footer .snowflake-footer-social-item{margin:0 !important}.sf-footer .snowflake-footer-social-item a{line-height:0;background-color:rgba(3,24,35,.8);display:inline-block;width:48px !important;height:48px;border-radius:8px;display:inline-flex;justify-content:center;align-items:center;transition:300ms ease background-color}.sf-footer .snowflake-footer-social-item a:hover{background-color:var(--ui-01) !important;transition:300ms ease background-color}.sf-footer__bottom{padding-bottom:40px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoError .mktoErrorMsg{max-width:100%;color:#fff}.sf-footer .mktoForm .mktoError .mktoErrorMsg .mktoErrorDetail{display:inline-block}.sf-footer .mktoFormRow:has(.mktoHtmlText:empty){display:none}.sf-footer .mktoFormRow .mktoHtmlText span{color:#fff !important}.sf-footer{background-color:#042130}.sf-footer .optanon-toggle-display:hover{text-decoration-color:var(--ui-01) !important;cursor:pointer !important;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:var(--ui-01);color:#fff !important;transition:300ms ease text-decoration-color;text-decoration:underline;opacity:1}.sf-footer__logo{width:40px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{row-gap:32px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;align-items:center;text-align:center;row-gap:16px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){text-align:center;flex-grow:1}.sf-footer__legal-links li button,.sf-footer__legal-links li a,.sf-footer__legal-links li{margin:0;color:rgba(255,255,255,.7) !important;font-weight:500}.sf-footer__legal-links li a:hover{color:rgba(255,255,255,1) !important}.sf-footer div.sf-footer__copyright p,.sf-footer div.sf-footer__legal-links li,.sf-footer div.sf-footer__legal-links a,.sf-footer div.sf-footer__legal-links p{font-size:12px !important}.sf-footer__legal-links ul{list-style-type:none;margin:0;padding:0;display:flex;gap:20px;row-gap:4px;justify-content:center;flex-wrap:wrap;text-align:center}.sf-footer__legal-links li:last-child{width:100%}.sf-footer .mktoFormRow:has(.mktoPlaceholder),.sf-footer .mktoFormRow:has(input[type=\"hidden\"]){display:none !important}.sf-footer .mktoFormCol{margin-bottom:0 !important}.sf-footer label[for=\"adhoc1\"]{width:auto !important;flex-grow:1;margin-left:16px}.sf-footer .mktoFieldWrap:has(label[for=\"adhoc1\"]){display:flex;flex-direction:row-reverse;margin-top:22px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoCheckboxList input[type=checkbox]{background-color:transparent !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-color:transparent !important;color:#fff !important;height:auto !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important;padding:12px 18px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select:focus{border-color:var(--ui-01) !important}.sf-footer .mktoForm *{padding:0 !important}.sf-footer .mktoForm,.sf-footer .snowflake-marketo-form-container{padding:0 !important;background:transparent;margin-bottom:0;box-shadow:none}.sf-footer .mktoHtmlText.mktoHasWidth{width:100% !important;margin:24px 0}.sf-footer .mktoFormRow{flex-direction:column}.sf-footer .mktoForm .mktoButtonWrap{margin:0 !important}.sf-footer select{background-image:url(\"data:image/svg+xml,%3Csvg width='14' height='8' viewBox='0 0 14 8' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M.981445 1.43496L6.90897 7.32496L12.9314 1.33496' stroke='white' stroke-width='1.33333' stroke-miterlimit='10' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\") !important}.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative{justify-content:flex-start}.sf-footer *::placeholder{color:#fff !important;opacity:.8}.sf-footer .mktoForm .mktoButtonWrap.mktoSimple .mktoButton{background-color:var(--ui-01) !important;color:#fff !important;width:100% !important;padding:12px 16px !important;border:1px solid var(--ui-01) !important;background-image:none !important;border-radius:48px;text-transform:uppercase;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:16px !important;line-height:1.2}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoHtmlText\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoLabel\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap label.mktoLabel{color:#fff !important}.sf-footer__newsletter-title p:not(:first-child){margin-top:8px !important}.sf-footer__newsletter-title p b{font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:22px !important;line-height:1.2}.sf-footer__newsletter-title p:last-child{font-size:14px !important;opacity:.8}.sf-footer__link-group li a[target=\"_blank\"]::after{content:'';display:inline-block;width:10px;height:10px;margin-left:5px;background-image:url(\"data:image/svg+xml,%3Csvg width='11' height='11' viewBox='0 0 11 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.72222 1.22222C6.38471 1.22222 6.11111 .948616 6.11111 .611111C6.11111 .273607 6.38471 0 6.72222 0H10.3889C10.551 0 10.7064 .0643867 10.821 .178988C10.9356 .293596 11 .449032 11 .611111V4.27778C11 4.61529 10.7264 4.88889 10.3889 4.88889C10.0514 4.88889 9.77778 4.61529 9.77778 4.27778V2.08647L4.09879 7.76545C3.86013 8.00409 3.4732 8.00409 3.23454 7.76545C2.99589 7.52681 2.99589 7.13986 3.23454 6.90122L8.91355 1.22222H6.72222ZM0 2.44444C0 1.76943 .547207 1.22222 1.22222 1.22222H4.27778C4.61529 1.22222 4.88889 1.49583 4.88889 1.83333C4.88889 2.17084 4.61529 2.44444 4.27778 2.44444H1.22222V9.77778H8.55556V6.72222C8.55556 6.38471 8.82915 6.11111 9.16667 6.11111C9.50418 6.11111 9.77778 6.38471 9.77778 6.72222V9.77778C9.77778 10.4528 9.23059 11 8.55556 11H1.22222C.547207 11 0 10.4528 0 9.77778V2.44444Z' fill='white'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-position:center}.sf-footer__link-group ul,.sf-footer__link-group li{margin:0;padding:0;list-style-type:none}.sf-footer__link-group ul{margin-top:20px !important}.sf-footer__link-group li{margin-top:15px}.sf-footer div.sf-footer__link-group\u003Espan\u003Ep\u003Ea,.sf-footer div.sf-footer__link-group\u003Espan\u003Ep{color:var(--ui-01) !important;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:20px !important;line-height:1.2}.sf-footer__link-group li a{opacity:.9;color:#fff !important;font-weight:500 !important;font-size:15px !important;line-height:1.3}.sf-footer__link-group li a:hover{opacity:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::before,.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::after{display:none}.sf-footer__column{flex-grow:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child){width:50%}@media (min-width:800px){.sf-footer__legal-links ul{justify-content:flex-start;text-align:left}.sf-footer__social{justify-content:flex-end}.sf-footer__legal-links ul{padding-left:24px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{text-align:right;flex-wrap:nowrap}.sf-footer__legal-links.align-left ul{justify-content:flex-start}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;flex-direction:row}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto !important;max-width:200px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;order:2;width:100% !important;max-width:none}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto}}@media screen and (min-width:1380px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{flex-wrap:nowrap}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{padding-right:48px;max-width:380px;background-color:rgba(3,24,35,.4);padding:32px;margin-left:48px;border-radius:16px}.sf-footer__link-group li,.sf-footer__link-group li a{font-size:14px !important;line-height:1.3}}@media screen and (max-width:991px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{order:2;margin-top:24px !important}}@media screen and (max-width:420px){.is-reduced-mobile .heading-1-v2,.is-reduced-mobile .heading-1-v2-sm{font-size:32px;line-height:28px}}.quote-content-chip{background-color:var(--ui-background-05);padding:24px;border-radius:12px;position:relative}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan{color:rgba(0,0,0,.8) !important;font-size:15px !important;line-height:1.5 !important;font-family:'Lato',sans-serif;font-weight:400 !important}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:not(:first-child){max-width:calc(100% - 200px)}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:nth-child(2){font-family:'Texta',sans-serif;color:#000 !important;font-size:20px !important;font-weight:800 !important;margin-top:24px}.quote-content-chip .snowflake-content-chip-image{width:140px !important}@media screen and (min-width:992px){.quote-content-chip .snowflake-content-chip-image{position:absolute !important;bottom:24px;right:16px}}@media screen and (max-width:991px){.quote-content-chip .snowflake-content-chip-image{margin-bottom:40px}.quote-content-chip{flex-direction:column}}#spa-root{background-color:#fff}.lowercase .snowflake-title-v2-line{text-transform:none !important}.centered .snowflake-logo-content-container-inner{justify-content:center}div.snowflake-linklist-dropdown-menu{max-height:380px}.first-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:first-child{color:var(--ui-01) !important}.is-front{position:relative;z-index:2}.use-case-body .snowflake-text h1,.use-case-body .snowflake-text h2,.use-case-body .snowflake-text h3,.use-case-body .snowflake-text h4,.use-case-body .snowflake-text h5,.use-case-body .snowflake-text h6{font-family:'Texta',sans-serif;color:#000;margin:.25rem 0 0 0}.pc-hero .button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:flex-start}.sf-footer .mktoFormRow .mktoHtmlText span{font-family:'Lato',sans-serif !important}.snowflake-button-primary.snowflake-button-blue .snowflake-button-container{justify-content:center}.related-chip-25{background-color:#fff;border:1px solid rgba(204,204,204,.5);border-radius:8px;padding:20px;position:relative}.related-chip-25:hover{box-shadow:rgba(152,162,179,.1) 0 10px 20px 0}.related-chip-25:hover::after{right:24px;transition:300ms ease right}.related-chip-25::after{content:'';display:block;transition:300ms ease right;background-image:url(\"data:image/svg+xml,%3Csvg width='8' height='14' viewBox='0 0 8 14' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.66699 7C7.66699 6.6571 7.53559 6.32825 7.30169 6.08578L2.34446 .947072C1.84529 .429617 1.0164 .429617 .517219 .947072C.0427878 1.43887 .042788 2.21798 .517219 2.70978L4.65591 7L.51722 11.2902C.0427889 11.782 .0427887 12.5611 .51722 13.0529C1.0164 13.5704 1.84529 13.5704 2.34447 13.0529L7.30169 7.91421C7.53559 7.67175 7.66699 7.34289 7.66699 7Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\");width:8px;height:14px;display:block;position:absolute;right:30px;top:50%;transform:translateY(-50%);background-size:contain;background-position:center;background-repeat:no-repeat}.related-chip-25 .heading-5-v2{font-size:22px;line-height:1.1}.related-chip-25 .snowflake-content-chip-image{width:48px;flex-shrink:0}.related-chip-25 .snowflake-content-chip-image__image{aspect-ratio:1;height:auto;object-fit:contain}.related-chip-25 .snowflake-content-chip-button{display:none}.related-chip-25 .snowflake-content-chip-content-without-tag{flex-grow:1;padding-right:24px}.case-study-25.small-logo .snowflake-case-study-card-logo img{width:60px !important}.swiper-slide .case-study-25{width:95%;margin-left:auto;margin-right:auto}.case-study-25 .snowflake-case-study-card-logo img{width:140px !important;height:auto !important;transform:none !important;margin:24px 0 8px 0}.case-study-25 .snowflake-case-study-card-image__image{object-position:left center}.case-study-25 .snowflake-case-study-card-information-container{padding-right:24px}.case-study-25 ul{list-style-type:none;padding:0;margin:8px 0 0 0}.case-study-25 li{font-size:15px !important;line-height:1.3 !important;display:flex;flex-direction:column;border-left:4px solid var(--ui-01);padding-left:24px;margin-top:24px;color:#535862;gap:4px}.case-study-25 li b{display:block;font-family:'Texta',sans-serif;font-weight:900 !important;font-size:48px !important;line-height:.9 !important;color:var(--ui-01)}.case-study-25 .snowflake-case-study-card-description p{color:#535862}.case-study-25 .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){color:#000;font-family:Texta;font-size:30px !important;line-height:1 !important;font-style:normal;font-weight:700;text-indent:-8px}.case-study-25.is-story .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){text-indent:0}.case-study-25 .snowflake-case-study-card-key-card{background-color:transparent}.case-study-25 .snowflake-case-study-card-button{display:none}.case-study-25{border-radius:24px;overflow:hidden}@media screen and (min-width:1024px){.case-study-25 .snowflake-case-study-card-left-container{position:static;width:60%;min-height:0}.case-study-25 .snowflake-case-study-card-right-container::after{content:'';display:block;width:60%;max-width:340px;padding-bottom:50%;background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 22 16' class='snowflake-pushdown-banner-placeholder-arrow'%3E%3Cpath fill='%2329B5E8' fill-rule='evenodd' d='M17.865 8.756c.088-.274.124-.555.118-.834a2.551 2.551 0 0 0-1.3-2.142L7.887.76C6.645.055 5.063.475 4.35 1.7a2.535 2.535 0 0 0 .947 3.494l4.916 2.809-4.916 2.801a2.543 2.543 0 0 0-.947 3.502c.713 1.222 2.295 1.64 3.537.934l8.796-5.024a2.541 2.541 0 0 0 1.182-1.46Z' clip-rule='evenodd'%3E%3C/path%3E%3C/svg%3E\");background-size:contain;background-repeat:no-repeat;position:absolute;top:-10%;left:-20%}.case-study-25 .snowflake-case-study-card-right-container{max-width:none;width:40%;position:absolute;top:-5%;right:-5%;z-index:0;height:110%}}@media screen and (min-width:768px){.case-study-25 li{max-width:50%}.case-study-25 ul{display:flex;gap:48px}}.snowflake-text.section-eyebrow p{margin-left:auto;margin-right:auto;margin-bottom:16px !important}.snowflake-text.section-eyebrow p,.snowflake-text.eyebrow-text p{text-transform:uppercase;font-family:'Texta',sans-serif !important;font-weight:800 !important;letter-spacing:.025em;margin-bottom:12px;line-height:1.1 !important}.snowflake-title-v2.dynamic .heading-2-v2 span.snowflake-title-v2-line{font-size:clamp(2.5rem,4.5vw,4rem) !important;line-height:.82 !important}.checklist ul{padding:0;margin:0}.checklist ul li{list-style-type:none;padding-left:32px;position:relative}.checklist ul li:not(:last-child){margin-bottom:1em}.checklist ul li::before{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='25' viewBox='0 0 24 25' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Crect y='.985352' width='24' height='24' rx='12' fill='%23D4F0FA'/%3E%3Cpath d='M7.28613 13.2967L10.7147 16.7253L17.5718 9.86816' stroke='%2329B5E8' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;position:absolute;top:3px;left:0}.last-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:last-child{color:var(--ui-01)}.snowflake-text p sup{line-height:0}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px;line-height:1;text-transform:none;font-weight:700}.snowflake-title-v2.lowercase .heading-2-v2{font-size:32px;line-height:1;text-transform:none;font-weight:700}.content-chip-new{border:1px solid rgba(204,204,204,.5);border-radius:16px;overflow:hidden}.content-chip-new .snowflake-image-container{border-radius:0;display:none}.content-chip-new .snowflake-content-chip-image{margin-right:0;max-width:180px;flex-shrink:0}.content-chip-new .snowflake-content-chip-content{padding:24px}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:first-child{font-size:24px;line-height:1.1}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){font-family:'Lato',sans-serif;font-size:17px;color:#535862 !important;font-weight:500;line-height:1.45;margin-top:8px;display:none}div.snowflake-text a{font-weight:normal;color:var(--ui-01);text-decoration:underline;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:transparent;transition:300ms ease text-decoration-color}div.snowflake-text a:hover{text-decoration-color:var(--ui-01);transition:300ms ease text-decoration-color}.footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container .snowflake-button-primary+.snowflake-button-link{margin-left:12px !important}.snowflake-button-regular.snowflake-button-link .snowflake-button-container{font-size:18px !important;text-align:left;justify-content:flex-start;line-height:1.4 !important}body .snowflake-card-v2-advanced{border:1px solid rgba(204,204,204,.5);border-radius:var(--spacing-02);transition:300ms ease all}body .snowflake-card-v2-advanced:hover{box-shadow:rgba(152,162,179,.1) 0 10px 20px 0;transition:300ms ease all}body .snowflake-card-v2-advanced-inner{border-bottom:none}body .snowflake-card-v2-advanced-image{line-height:0}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9}body .snowflake-card-v2-advanced-content{position:relative}body .snowflake-card-v2-advanced-content::after{content:'';display:block;position:absolute;bottom:0;left:0;transition:300ms ease all;width:20%;height:4px;background-color:var(--ui-01);opacity:0}body .snowflake-card-v2-advanced:hover .snowflake-card-v2-advanced-content::after{width:100%;opacity:1;transition:300ms ease all}body .snowflake-card-v2-advanced .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transition:300ms ease transform}body .snowflake-card-v2-advanced:hover .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transform:translateX(4px);transition:300ms ease transform}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-wrap:wrap;gap:24px}.six-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:100%;margin:0 !important}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px);margin:0 !important}@media screen and (min-width:768px){.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}}@media screen and (min-width:1024px){.snowflake-title-v2.lowercase .heading-3-v2{font-size:34px}.snowflake-title-v2.lowercase.larger .heading-2-v2{font-size:44px;line-height:.95}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(25% - 18px)}.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(20% - 19.2px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(16.6666% - 20px)}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px !important}}@media screen and (min-width:1200px){.snowflake-title-v2.lowercase .heading-2-v2{font-size:40px}.content-chip-new .snowflake-content-chip-content{padding:32px}.content-chip-new .snowflake-image-container,.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){display:block}}.promo-banner-25{border-radius:16px;overflow:hidden}.promo-banner-25 .snowflake-premium-content-banner-image-container{position:relative;max-width:380px}.promo-banner-25 .snowflake-text{color:#535862}.promo-banner-25 .snowflake-premium-content-banner-image__image{transform:translateY(8px);transition:300ms ease transform;border-radius:0;width:85%;margin:0 auto;display:block;position:relative;z-index:1}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover .snowflake-premium-content-banner-image__image{transform:translateY(0);transition:300ms ease transform}.promo-banner-25 .snowflake-premium-content-banner-image__inner{height:auto;padding-top:24px}.promo-banner-25 .snowflake-premium-content-banner-image__link{position:relative;z-index:1;height:auto}.promo-banner-25 .snowflake-premium-content-banner-image__link::after{content:'';display:block;position:absolute;clip-path:polygon(0 0,66% 0,100% 100%,0 100%);bottom:0;left:0;width:100%;height:100%;background:var(--ui-01);transition:300ms ease width}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover::after{width:110%;transition:300ms ease width}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-position:95% 50%}.sf-footer__disclaimers .text-size-small .snowflake-text p{color:#fff !important;font-size:10px !important;opacity:.8}@media screen and (min-width:768px){.sf-footer__disclaimers .text-size-small .snowflake-text p{font-size:12px !important}}@media screen and (max-width:1023px){.mobile-top-padding{padding-top:64px}}@media (max-width:799px){.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative .mktoButton{width:100% !important}.sf-footer__logo{text-align:center;display:block;margin:0 auto}}.customer-card .snowflake-card-v2-advanced-image{aspect-ratio:4.35 / 1}.customer-card .snowflake-card-v2-advanced-image__image{width:100%;height:100%;padding-left:8px;object-fit:contain;object-position:left center;margin:0 !important;aspect-ratio:initial}.customer-card .snowflake-card-v2-advanced-image__inner{height:110px}.customer-card .snowflake-card-v2-advanced-tag-indicator{display:none}.pc-hero .snowflake-container-arrow-small-gray-image{top:-34% !important;width:18% !important}.pc-hero .snowflake-container-arrow-small-gray-image path{fill:var(--ui-01);opacity:1}@media screen and (max-width:767px){.mobile-padding-top{padding-top:64px}.hide-mobile{display:none !important}.pc-hero{padding-top:52px}.pc-hero .snowflake-text p,.pc-hero .left-alignment .snowflake-title-v2-line,.pc-hero h1 span{text-align:center !important}}div.snowflake-pushdown-banner-button{margin-top:0}.button-group.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:center !important}.text-center .snowflake-breadcrumb-swiper .swiper-wrapper{justify-content:center}div.snowflake-breadcrumb a.snowflake-breadcrumb-item,.snowflake-breadcrumb div.snowflake-breadcrumb-item{text-transform:none;font-weight:500}.snowflake-breadcrumb svg{display:none !important}.snowflake-breadcrumb a:has(svg)::after{content:'/';margin:0 12px;color:#666}.hide-filters .snowflake-filterable-and-searchable-grid-top-part{display:none !important}.page-section{padding-left:24px;padding-right:24px}@media screen and (min-width:768px){.page-section{padding-left:48px;padding-right:48px}}.download-card pre[class*=language-]{overflow-x:scroll !important}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container_copy","container_573483281_","markup_editor_copy"]}},":itemsOrder":["root"]},"markup_editor":{"id":"markup-editor-3e83a6c217","title":"Quickstarts Overrides","cssContent":".snowflake-markdown blockquote{padding:24px 32px;background:#f6f9fa;border:1px solid #29b5e8;border-radius:16px}.snowflake-markdown .snowflake-image-container img{width:auto !important;max-width:100%}.snowflake-markdown .snowflake-text ol{padding-left:20px !important}.snowflake-markdown .snowflake-text li{margin:0 0 12px 0 !important}.snowflake-markdown h3.snowflake-markdown-h3{font-size:20px !important;font-family:Texta,sans-serif !important}@media (min-width:768px){.snowflake-markdown h3.snowflake-markdown-h3{font-size:28px !important}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","markup_editor_1950346551","responsivegrid","modal_container","experiencefragment-footer","markup_editor"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"","pageType":"quickstart-page-template","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/content/snowflake-site/global/en/developers/guides/ask-questions-to-your-own-documents-with-snowflake-cortex-search","language":"en","category":"general","pageName":"Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search","contentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/product/ai"]},"locale":"en","analyticsEnabled":true,"isPasswordProtected":false,"coveoConfig":{"pipeline":"snowflake.com","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","organizationId":"snowflakecomputingproduction8neljofn","searchHub":"snowflake.com"}}