AI-Powered Chatbots

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margin-right: 0; } div#hs_cos_wrapper_module_161720035400929 { display: inline-block; } </style></div> </div> </div> </div> </header></div> <main id="main-content" class="body-container-wrapper"> <div class="container-fluid body-container body-container--about"> <div class="row-fluid-wrapper"> <div class="row-fluid"> <div class="span12 widget-span widget-type-cell " style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-1 dnd_area-row-0-background-layers dnd-section dnd_area-row-0-padding dnd_area-row-0-background-image"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-2 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_module_16896903067153" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> <div class="cr-clusters-items-wrapper " id="module_16896903067153"> <style> #module_16896903067153.cr-clusters-items-wrapper { /*max-width: 1760px;*/ margin: 0 auto; } #module_16896903067153.cr-clusters-items-wrapper.cr-clusters-limit, .cr-meta-inner { max-width: 1200px; } #module_16896903067153 .cr-clusters-container.cr-customers-header-sub { max-width: 1200px; margin: 0 auto; } #module_16896903067153 .cr-clusters-container { display: flex; align-items: center; } #module_16896903067153 .cr-clusters-header { margin-bottom: 20px; display: flex; align-items: baseline; } #module_16896903067153 .cr-clusters-header h1 { letter-spacing: -2.04px; max-width: 620px; margin-right: 20px; margin-bottom: 0px; font-family: Montserrat, Inter, Arial, sans-serif !important; font-weight: 600; line-height: 1.2; } #module_16896903067153 .cr-customers-header-sub 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#module_16896903067153 .cr-clusters-arrow { align-self: flex-end; } #module_16896903067153 .cr-clusters-arrow svg { max-width: 40px; height: auto; } .cr-landingpage-header { margin-bottom: 78px; } .cr-landingpage-header .dnd-column { padding: 0; } .cr-header-meta-bar { background: rgb(8, 29, 36); opacity: 1; padding: 20px 20px; position: absolute; bottom: -146px; left: 0; right: 0; margin-left: -20px; margin-right: -20px; } .cr-header-meta-bar > div > div { display: flex; font-family: Inter; font-weight: 600; font-size: 16px; line-height: 20px; letter-spacing: 0px; color: #FFFFFF; opacity: 1; } .cr-meta-inner { display: flex; flex-direction: row; align-items: center; justify-content: flex-start; margin: 0 auto; } .cr-meta-item { display: flex; align-items: center; margin-right: 30px; } .cr-meta-item img { margin-right: 10px; } .cr-header-speaker-image img { border-radius: 50%; max-width: 80px !important; } .cr-header-speaker-name { letter-spacing: -0.56px; color: #FFFFFF; font-size: 18px; line-height: 24px; font-weight: bold; font-family: Inter; text-align: center; margin-bottom: 2px; } .cr-header-speakers { display: flex; flex-direction: row; margin-top: 80px; } .cr-header-speakers-item:last-child { margin-left: 50px; } .cr-header-speaker-title { letter-spacing: -0.36px; color: #e3b15b; font-size: 16px; line-height: 20px; font-family: Inter; font-weight: 500; text-align: center; } .cr-header-speaker-image { text-align: center; } .cr-header-multipurpose { position: relative; } /*@media all and (min-width: 1200px) { .cr-header-speaker-image img { max-width: 100% !important; } }*/ @media all and (max-width: 1200px) { } @media all and (max-width: 980px) { #module_16896903067153 .cr-clusters-container { flex-direction: column; } #module_16896903067153 .cr-clusters-introtext { font-size: 18px; } #module_16896903067153 .cr-clusters-header h1 { width: auto; max-width: 100%; } #module_16896903067153 .cr-clusters-container { align-items: flex-start; } } @media all and (max-width: 767px) { #module_16896903067153 .cr-clusters-introtext { margin-right: 0; } .cr-header-meta-bar > div > div { flex-direction: column; text-align: center; font-size: 14px; } .cr-meta-item { margin-right: 10px; } .cr-header-speakers { margin-top: 40px; } } </style> <div class="cr-clusters-container cr-customers-header-sub"> <div class="cr-clusters-left"> <span class="cr-header-section">Use cases</span> <div class="cr-clusters-header "> <h1 style="color: rgba(255, 255, 255, 1.0);">Database for AI-Powered Chatbots</h1> </div> <div class="cr-clusters-introtext"> <p>Enhance your AI-powered chatbots with CrateDB's robust vector store support, and real-time data analysis, enabling intelligent, personalized responses. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-3 dnd_area-row-1-padding dnd_area-row-1-background-color dnd-section dnd_area-row-1-background-layers"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-4 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_module_17371221310025" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><div class="advanced-richtext body_text_regular"> <span id="hs_cos_wrapper_module_17371221310025_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="rich_text"><p>Imagine instantly accessing the precise information you need, hidden within mountains of documents. Imagine asking complex questions and receiving concise, accurate answers based on your company's unique knowledge. This is the power of AI-driven knowledge assistants built with Retrieval Augmented Generation (RAG) pipelines. Check out the multiple resources below to learn more.<strong> <br></strong></p></span> </div></div> </div> </div> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-5 dnd_area-row-2-padding dnd-section"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-6 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_widget_1737380036015" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> #widget_1737380036015 .cr-teaser-list { grid-template-columns: repeat(3,1fr); display: grid; grid-gap: 24px; height: 100%; font-style: normal; } #widget_1737380036015 .cr-teaser-box { height: 100%; } #widget_1737380036015 .cr-teaser-item { align-items: flex-start; align-self: stretch; background: transparent linear-gradient(154deg, #F3F3F3 0%, #FFF 100%) 0% 0% no-repeat padding-box; border-radius: 0; cursor: default; height: auto; justify-content: space-between; padding: 24px; position: relative; -webkit-transition: all .15s linear; -ms-transition: all .15s linear; transition: all .15s linear; box-shadow: 6px 7px 11px 7px #00000015; } #widget_1737380036015 .cr-teaser-item.cr-teaser-quote { background: rgb(25, 192, 234) !important; } #widget_1737380036015 .cr-teaser-quote .cr-teaser-cta-footer a, #widget_1737380036015 .cr-teaser-quote .cr-teaser-list, #widget_1737380036015 .cr-teaser-quote .cr-teaser-tag, #widget_1737380036015 .cr-teaser-quote .cr-teaser-title, #widget_1737380036015 .cr-teaser-quote .cr-teaser-content, #widget_1737380036015 .cr-teaser-quote .cr-teaser-content p, #widget_1737380036015 .cr-teaser-quotee p { font-family: Montserrat, Inter, Arial, sans-serif; color: #fff; } #widget_1737380036015 .cr-teaser-quote .cr-teaser-content p{ font-size: 18px !important; 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height: auto; margin-left: 5px; } #widget_1737380036015 .cr-teaser-tag { text-transform: uppercase; font-weight: bold; font-size: 18px; line-height: 21px; letter-spacing: -0.36px; color: #19C0EA; } #widget_1737380036015 .cr-teaser { display: flex; flex-direction: column; width: 100%; height: 100%; } #widget_1737380036015 .cr-teaser-top { display: flex; /*flex-grow: 1;*/ /*justify-content: space-between;*/ flex-direction: column; align-items: flex-start; margin-bottom: 10px; } #widget_1737380036015 .cr-teaser-content { font-size: 24px; flex-grow: 1; } #widget_1737380036015 .cr-teaser-item .cr-teaser-arrow svg path { stroke: #009DC7; -webkit-transition: all .15s linear; -ms-transition: all .15s linear; transition: all .15s linear; } #widget_1737380036015 .hs-cta-wrapper .hs-cta-node a { position: relative; } #widget_1737380036015 .cr-teaser-top img { max-width: 100%; height: auto; } #widget_1737380036015 .cr-teaser-arrow { display: flex; flex-direction: row; justify-content: center; align-items: center; width: 40px; height: 40px; background: #FFFFFF; border-radius: 100px; -webkit-transition: all .15s linear; -ms-transition: all .15s linear; transition: all .15s linear; } #widget_1737380036015 .cr-teaser-cta-footer a { text-transform: uppercase; font-size: 16px; color: #19C0EA; font-weight: 600; } #widget_1737380036015 .cr-teaser-list a.cr-teaser-a { position: absolute; top: 0; bottom: 0; left: 0; right: 0; } #widget_1737380036015 .cr-teaser-title { font-size: 24px; font-weight: bold; line-height: 1.3; letter-spacing: -0.72px; margin-bottom: 20px; } #widget_1737380036015 .cr-teaser-title a, #widget_1737380036015 .cr-teaser-title a:hover, #widget_1737380036015 .cr-teaser-title a:visited, #widget_1737380036015 .cr-teaser-title a:active { color: #000; text-decoration: none; } @media all and (max-width: 1024px) { #widget_1737380036015 .cr-teaser-list { grid-template-columns: repeat(2,1fr); } } @media all and (max-width: 767px) { #widget_1737380036015 .cr-teaser-list { grid-template-columns: repeat(1,1fr); } } @media all and (max-width: 600px) { #widget_1737380036015 .cr-teaser-item { width: 100% !important; } } </style> <div class="cr-teaser-box" id="widget_1737380036015"> <div class="cr-teaser-list"> <div class="cr-teaser-item "> <a href="https://cratedb.com/resources/webinars/lp-wb-from-documents-to-dialogue-unlocking-pdf-data-with-a-smart-chatbot" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Webinar </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=2000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png" alt="Screenshot of the webinar From Documents to Dialogue: Unlocking PDF Data with a Smart Chatbot. The speaker, Simon Prickett, is shown on the left while he shares his screen showing some source code of the the PDF chatbot." loading="lazy" width="2000" style="max-width: 100%; height: auto;" srcset="https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=1000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 1000w, https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=2000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 2000w, https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=3000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 3000w, https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=4000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 4000w, https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=5000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 5000w, https://cratedb.com/hs-fs/hubfs/2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png?width=6000&name=2025-Q1-WB-Unlocking-PDF-Data-with-a-Smart-Chatbot-Feb-12.png 6000w" sizes="(max-width: 2000px) 100vw, 2000px"> </div> <div class="cr-teaser-title"> From Documents to Dialogue: Unlocking PDF Data with a Smart Chatbot </div> <div class="cr-teaser-content"><p style="font-size: 16px;">In this webinar recording Simon Prickett reveals how to unlock text and image data trapped in PDF files and search it using the power of AI and CrateDB. </p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="https://cratedb.com/resources/webinars/lp-wb-from-documents-to-dialogue-unlocking-pdf-data-with-a-smart-chatbot"> Watch Now <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="https://cratedb.com/resources/white-papers/lp-wp-ai-driven-knowledge-assistants" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> White Paper </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=1200&height=628&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png" alt="Book-Cover-How-to-build-ai-driven-knowledge-assistants" loading="lazy" width="1200" height="628" srcset="https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=600&height=314&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 600w, https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=1200&height=628&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 1200w, https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=1800&height=942&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 1800w, https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=2400&height=1256&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 2400w, https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=3000&height=1570&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 3000w, https://cratedb.com/hs-fs/hubfs/Book-Cover-How-to-build-ai-driven-knowledge-assistants.png?width=3600&height=1884&name=Book-Cover-How-to-build-ai-driven-knowledge-assistants.png 3600w" sizes="(max-width: 1200px) 100vw, 1200px"> </div> <div class="cr-teaser-title"> How to Build AI-driven Knowledge Assistants with a Vector Store, LLMs and RAG Pipelines </div> <div class="cr-teaser-content"><p style="font-size: 16px;">This white paper explores how CrateDB provides a scalable platform to build Generative AI applications that cover the requirements of modern applications, such as AI-driven knowledge assistants. </p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="https://cratedb.com/resources/white-papers/lp-wp-ai-driven-knowledge-assistants"> Download the White Paper <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="/resources/videos/digital-twins-and-generative-ai" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Success Story </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=1920&height=1080&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png" alt="Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024" loading="lazy" width="1920" height="1080" srcset="https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=960&height=540&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 960w, https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=1920&height=1080&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 1920w, https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=2880&height=1620&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 2880w, https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=3840&height=2160&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 3840w, https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=4800&height=2700&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 4800w, https://cratedb.com/hs-fs/hubfs/custom-video-thumbnails/Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png?width=5760&height=3240&name=Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024.png 5760w" sizes="(max-width: 1920px) 100vw, 1920px"> </div> <div class="cr-teaser-title"> Digital Twins and Gen AI: How TGW Revolutionizes Warehouse Operations with CrateDB's Combination of Time Series, Documents, and Vectors </div> <div class="cr-teaser-content"><p style="font-size: 16px;">In this talk, TGW Logistics showcases their use of CrateDB to optimize distribution centers. With up to half a million items handled daily, they focus on automation and data-driven decisions.</p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="/resources/videos/digital-twins-and-generative-ai"> Watch now <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="/resources/videos/building-an-ai-chatbot-with-cratedb-and-langchain" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Demo </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=560&height=316&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png" alt="Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play" loading="lazy" width="560" height="316" srcset="https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=280&height=158&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 280w, https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=560&height=316&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 560w, https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=840&height=474&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 840w, https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=1120&height=632&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 1120w, https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=1400&height=790&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 1400w, https://cratedb.com/hs-fs/hubfs/Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png?width=1680&height=948&name=Building-an-AI-Chatbot-with-CrateDB-and-LangChain-play.png 1680w" sizes="(max-width: 560px) 100vw, 560px"> </div> <div class="cr-teaser-title"> Building an AI Chatbot with CrateDB and LangChain </div> <div class="cr-teaser-content"><p style="font-size: 16px;">This video shows step by step how to build an AI-powered chatbot using LangChain to connect to the different LLMs and CrateDB to store embeddings and run similarity searches against them.</p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="/resources/videos/building-an-ai-chatbot-with-cratedb-and-langchain"> Watch now <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="/resources/videos/how-to-use-private-data-in-generative-ai" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Video </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=1028&height=684&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png" alt="How to Use Private Data in Generative AI - new" loading="lazy" width="1028" height="684" srcset="https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=514&height=342&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 514w, https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=1028&height=684&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 1028w, https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=1542&height=1026&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 1542w, https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=2056&height=1368&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 2056w, https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=2570&height=1710&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 2570w, https://cratedb.com/hs-fs/hubfs/How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png?width=3084&height=2052&name=How%20to%20Use%20Private%20Data%20in%20Generative%20AI%20-%20new.png 3084w" sizes="(max-width: 1028px) 100vw, 1028px"> </div> <div class="cr-teaser-title"> How to Use Private Data in Generative AI </div> <div class="cr-teaser-content"><p style="font-size: 16px;">This talk focuses on the synergistic combination of CrateDB and LangChain: it helps to get started with using private data as context for large language models through LangChain, incorporating the concept of Retrieval Augmented Generation (RAG). </p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="/resources/videos/how-to-use-private-data-in-generative-ai"> Watch now <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="/use-cases/chatbots/unlocking-the-power-of-knowledge-assistants" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Ebook </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/Context-data.png?width=1471&height=1100&name=Context-data.png" alt="Context Data" loading="lazy" width="1471" height="1100" srcset="https://cratedb.com/hs-fs/hubfs/Context-data.png?width=736&height=550&name=Context-data.png 736w, https://cratedb.com/hs-fs/hubfs/Context-data.png?width=1471&height=1100&name=Context-data.png 1471w, https://cratedb.com/hs-fs/hubfs/Context-data.png?width=2207&height=1650&name=Context-data.png 2207w, https://cratedb.com/hs-fs/hubfs/Context-data.png?width=2942&height=2200&name=Context-data.png 2942w, https://cratedb.com/hs-fs/hubfs/Context-data.png?width=3678&height=2750&name=Context-data.png 3678w, https://cratedb.com/hs-fs/hubfs/Context-data.png?width=4413&height=3300&name=Context-data.png 4413w" sizes="(max-width: 1471px) 100vw, 1471px"> </div> <div class="cr-teaser-title"> Unlocking the Power of Knowledge Assistants with CrateDB </div> <div class="cr-teaser-content"><p style="font-size: 16px;">As a cutting-edge real-time analytics database, CrateDB provides the foundation for building chatbots and knowledge assistants that are not only fast and reliable but also intelligent and scalable. </p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="/use-cases/chatbots/unlocking-the-power-of-knowledge-assistants"> Read more <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> <div class="cr-teaser-item "> <a href="/docs/crate/clients-tools/en/latest/integrate/ml.html" class="cr-teaser-a"></a> <div class="cr-teaser"> <div class="cr-teaser-top"> <div class="cr-teaser-tag"> Documentation </div> <style> .cr-teaser-top img { margin-top: 10px; } </style> <img src="https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=750&height=275&name=MLflow-logo.png" alt="MLflow logo" loading="lazy" width="750" height="275" srcset="https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=375&height=138&name=MLflow-logo.png 375w, https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=750&height=275&name=MLflow-logo.png 750w, https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=1125&height=413&name=MLflow-logo.png 1125w, https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=1500&height=550&name=MLflow-logo.png 1500w, https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=1875&height=688&name=MLflow-logo.png 1875w, https://cratedb.com/hs-fs/hubfs/MLflow-logo.png?width=2250&height=825&name=MLflow-logo.png 2250w" sizes="(max-width: 750px) 100vw, 750px"> </div> <div class="cr-teaser-title"> Machine learning applications and frameworks which can be used together with CrateDB </div> <div class="cr-teaser-content"><p style="font-size: 16px;">Learn how to integrate CrateDB with machine learning frameworks and tools, for MLOps and Vector database operations.</p></div> <div class="cr-teaser-cta-footer"> <div class="cr-teaser-date"> </div> <a href="/docs/crate/clients-tools/en/latest/integrate/ml.html"> Read more <svg xmlns="http://www.w3.org/2000/svg" width="69.969" height="56.814" viewbox="0 0 69.969 56.814"> <g id="subheader" transform="translate(96.312 88.344) rotate(180)"> <g id="back" transform="translate(32 34.358)"> <path id="Path_42" data-name="Path 42" d="M0,36.174H36.174V0" transform="translate(51.158 25.579) rotate(135)" fill="none" stroke="#18b8e7" stroke-width="8" /> <line id="Line_1" data-name="Line 1" x2="63.051" transform="translate(1.261 25.579)" fill="none" stroke="#18b8e7" stroke-linejoin="round" stroke-width="8" /> </g> </g> </svg> </a> </div> </div> </div> </div> </div></div> </div> </div> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-7 dnd-section"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-8 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_module_17385964335505" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> <div class="cr-related-post-items-wrapper" id="module_17385964335505"> <style> #module_17385964335505.cr-related-post-items-wrapper { margin: 0 auto; 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Once you have your pipeline from extraction to chatbot functionality in place, the next critical steps involve ...</p> <p class="cr-related-readmore read_more_holder"><a href="https://cratedb.com/blog/making-a-production-ready-ai-knowledge-assistant">Read more</a></p> </div> </div> <div class="related-blog-item hs-related-blog-item"> <a class="related-blog-image-url" href="https://cratedb.com/blog/step-by-step-guide-to-building-a-pdf-knowledge-assistant"><img loading="lazy" src="https://cratedb.com/hubfs/AI-Generated%20Media/Images/The%20image%20depicts%20a%20clean%2c%20modern%20workspace%20featuring%20a%20sleek%20laptop%20open%20to%20a%20code%20editor%20displaying%20Python%20script.jpeg" alt=""></a> <div class="related-meta"> <div class="cr-related-tag"><a href="https://cratedb.com/blog/tag/chatbot">Chatbot</a></div> <div class="cr-related-tag"><a href="https://cratedb.com/blog/tag/case-studies">Case Studies</a></div> <a class="related-blog-title" href="https://cratedb.com/blog/step-by-step-guide-to-building-a-pdf-knowledge-assistant"><h3>Step by Step Guide to Building a PDF Knowledge Assistant</h3></a> <span class="related-blog-date blog-index_post_timestamp">2025-01-15</span> <p class="cr-meta-content">This guide outlines how to build a PDF Knowledge Assistant, covering: Setting up a project folder. Installing dependencies. 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This ...</p> <p class="cr-related-readmore read_more_holder"><a href="https://cratedb.com/blog/designing-the-consumption-layer-for-enterprise-knowledge-assistants">Read more</a></p> </div> </div> <div class="related-blog-item hs-related-blog-item"> <a class="related-blog-image-url" href="https://cratedb.com/blog/core-techniques-in-an-enterprise-knowledge-assistants"><img loading="lazy" src="https://cratedb.com/hubfs/AI-Generated%20Media/Images/The%20image%20depicts%20a%20modern%20office%20environment%20with%20a%20sleek%2c%20minimalist%20design.jpeg" alt=""></a> <div class="related-meta"> <div class="cr-related-tag"><a href="https://cratedb.com/blog/tag/chatbot">Chatbot</a></div> <div class="cr-related-tag"><a href="https://cratedb.com/blog/tag/case-studies">Case Studies</a></div> <a class="related-blog-title" href="https://cratedb.com/blog/core-techniques-in-an-enterprise-knowledge-assistants"><h3>Core Techniques Powering Enterprise Knowledge Assistants</h3></a> <span class="related-blog-date blog-index_post_timestamp">2025-01-15</span> <p class="cr-meta-content">To harness the potential of RAG, organizations need to master a few crucial building blocks.</p> <p class="cr-related-readmore read_more_holder"><a href="https://cratedb.com/blog/core-techniques-in-an-enterprise-knowledge-assistants">Read more</a></p> </div> </div> <div class="related-blog-item hs-related-blog-item"> <a class="related-blog-image-url" href="https://cratedb.com/blog/building-ai-knowledge-assistants-for-enterprise-pdfs"><img loading="lazy" src="https://cratedb.com/hubfs/image-1%5B1%5D.png" alt=""></a> <div class="related-meta"> <div class="cr-related-tag"><a href="https://cratedb.com/blog/tag/chatbot">Chatbot</a></div> <a class="related-blog-title" href="https://cratedb.com/blog/building-ai-knowledge-assistants-for-enterprise-pdfs"><h3>Building AI Knowledge Assistants for Enterprise PDFs: A Strategic Approach</h3></a> <span class="related-blog-date blog-index_post_timestamp">2025-01-15</span> <p class="cr-meta-content">In today’s increasingly data-driven world, many organizations are sitting on mountains of information locked away in PDFs. 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These powerful AI systems have reshaped how we interact with ...</p> <p class="cr-related-readmore read_more_holder"><a href="https://cratedb.com/blog/leverage-vector-search-to-use-embeddings-and-generative-ai-retrieval-augmented-generation-rag-with-cratedb">Read more</a></p> </div> </div> </div> </div> </div> </div></div> </div> </div> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-9 dnd-section dnd_area-row-4-background-layers dnd_area-row-4-background-color dnd_area-row-4-padding"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-10 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_widget_1737121801927" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><div class="advanced-richtext body_text_regular"> <span id="hs_cos_wrapper_widget_1737121801927_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="rich_text"><h3>Ready to unleash the power of AI-driven assistants?</h3> <p></p><div class="hs-web-interactive-inline" style="" data-hubspot-wrapper-cta-id="136876233821"> <a href="https://cta-service-cms2.hubspot.com/web-interactives/public/v1/track/click?encryptedPayload=AVxigLJ%2FEE9ZQqyKKlDRqaBhzDNdCsSfxZnnW1bBrKXxrUVA78gaHFfadHuTm%2Byaq1lhMZ9XUGBwwFyYFACuPNoOKj4HCQ0k9r7oYWpfrIByPZAUvjgFMGHv6H4bpgxzq%2BtIjkVDkXMe4fKGbYJjdo6aY7jkVOZM4JOrrSTqs0IcOg%3D%3D&portalId=19927462" class="hs-inline-web-interactive-136876233821 hs-button " data-hubspot-cta-id="136876233821"> Contact us </a> </div><p></p></span> </div></div> </div> </div> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-11 dnd-section dnd_area-row-5-padding"> <div class="row-fluid "> <div class="span12 widget-span widget-type-cell dnd-column" style="" data-widget-type="cell" data-x="0" data-w="12"> <div class="row-fluid-wrapper row-depth-1 row-number-12 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_module_1720017121888" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> #module_1720017121888 h2.cr-headline { font-size: 30px; letter-spacing: -1.7px; margin-right: 20px; margin-bottom: 40px; font-family: Montserrat, Inter, Arial, sans-serif !important; font-weight: 600; line-height: 1.3; } h3.cr-subheadline { font-style: normal; font-weight: 400; font-size: 20px; line-height: 1.3; color: #000; margin-top: 4px; margin-bottom: 0; } @media all and (max-width: 767px) { #module_1720017121888 h2.cr-headline { font-size: 20px; margin-bottom: 20px; } h3.cr-subheadline { font-size: 18px; } } </style> <h2 id="module_1720017121888" class="cr-headline" style="">FAQ</h2> </div> </div> </div> </div> <div class="row-fluid-wrapper row-depth-1 row-number-13 dnd-row"> <div class="row-fluid "> <div class="span12 widget-span widget-type-custom_widget dnd-module" style="" data-widget-type="custom_widget" data-x="0" data-w="12"> <div id="hs_cos_wrapper_widget_1720017115566" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><div class="hs-accordion hs-accordion-regular cr-accordion " itemscope itemtype="https://schema.org/FAQPage"> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> What are AI-powered chatbots? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>AI chatbots are applications or interfaces that engage in human-like conversations using natural language understanding (NLU), natural language processing (NLP), and machine learning (ML). These chatbots use conversational AI to communicate with users, though not all chatbots employ this technology. Databases such as CrateDB can enhance AI-powered chatbots by providing robust <a href="/data-model/vector" rel="noopener">vector store support</a> and <a href="/use-cases/real-time-analytics-database" rel="noopener">real-time data analysis</a>, enabling them to deliver intelligent and personalized responses.</p> </div> </main> </div> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> Does an AI chatbot need a database? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>AI chatbot needs a database to store chat information and relevant user metadata. This database is the chatbot's memory, organizing data to enable quick and accurate responses. AI applications benefit from flexible data modeling, so a database that supports various data structures is advantageous. Examples of databases suitable for AI-powered chatbots include CrateDB, MongoDB, MySQL, and Postgres. CrateDB offers support for <a href="/database/multi-model" rel="noopener">multiple data formats</a> within a single database and even within a single table, thanks to its <a href="/product/features/dynamic-schemas" rel="noopener">dynamic data schema</a>.</p> </div> </main> </div> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> Where do AI chatbots get their data? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>AI chatbots gather data from a variety of sources, including publicly available data, databases, websites, APIs, and structured knowledge bases. They also rely on real-time information and updates to provide accurate responses. CrateDB’s <a href="/product/features/dynamic-schemas" rel="noopener">dynamic schema</a> supports multiple data formats from various data sources seamlessly, including structured, unstructured, semi-structured, and binary data.</p> </div> </main> </div> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> What is a RAG Pipeline? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>RAG Pipelines, short for Retrieval Augmented Generation Pipelines, are a crucial component of generative AI, that combines the vast knowledge of large language models (LLMs) with the specific context of your private data.</p> </div> </main> </div> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> How does a RAG Pipeline work? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>A RAG Pipeline works by breaking down your data (text, PDFs, images, etc.) into smaller chunks, creating a unique "fingerprint" for each chunk called an embedding, and storing these embeddings in a database. When you ask a question, the system identifies the most relevant chunks based on your query and feeds this information to the LLM, ensuring accurate and context-aware answers. They operate through a streamlined process involving data preparation, data retrieval, and response generation.</p> <ol> <li><strong>Phase 1: Data Preparation</strong><br>During the data preparation phase, raw data such as text, audio, etc., is extracted and divided into smaller chunks. These chunks are then translated into embeddings and stored in a vector database. It is important to store the chunks and their metadata together with the embeddings in order to reference back to the actual source of information in the retrieval phase.<br><br></li> <li><strong>Phase 2: Data Retrieval</strong><br>The retrieval phase is initiated by a user prompt or question. An embedding of this prompt is created and used to search for the most similar pieces of content in the vector database. The relevant data extracted from the source data is used as context, along with the original question, for the Large Language Model (LLM) to generate a response.</li> </ol> <p><br><img src="https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=900&height=282&name=RAG-Pipelines.png" class="hs-image-widget" width="900" height="282" alt="Retrieval augmented generation (RAG) Pipeline" title="Retrieval augmented generation (RAG) Pipeline" loading="lazy" srcset="https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=450&height=141&name=RAG-Pipelines.png 450w, https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=900&height=282&name=RAG-Pipelines.png 900w, https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=1350&height=423&name=RAG-Pipelines.png 1350w, https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=1800&height=564&name=RAG-Pipelines.png 1800w, https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=2250&height=705&name=RAG-Pipelines.png 2250w, https://cratedb.com/hs-fs/hubfs/RAG-Pipelines.png?width=2700&height=846&name=RAG-Pipelines.png 2700w" sizes="(max-width: 900px) 100vw, 900px"></p> <p><span>While this is a simplified representation of the process, the real-world implementation involves more intricate steps. Questions such as how to properly chunk and extract information from sources like PDF files or documentation and how to define and measure relevance for re-ranking results are part of broader considerations.</span></p> </div> </main> </div> <div class="hs-accordion__item " aria-expanded="" itemscope itemprop="mainEntity" itemtype="https://schema.org/Question"> <button type="button" itemprop="name"> Why is a Database Needed for RAG Pipelines? <span class="hs-accordion__item-icon"></span> </button> <main role="region" itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer"> <div class="hs-accordion__item-content" itemprop="text"> <p>A database is crucial for storing and efficiently retrieving the embeddings and associated data chunks.  It acts as the “memory” of your knowledge assistant, enabling lightning-fast access to relevant information when a user asks a question.</p> <p>Without a database, searching for relevant information would be incredibly slow and inefficient, hindering the responsiveness and usefulness of your knowledge assistant.</p> <p>As AI adoption continues to grow, the need for databases that can adapt to complex data landscapes becomes paramount. Leveraging a multi-model database capable of managing both structured, semi-structured, and unstructured data, is an ideal fit to serve as the foundation for data modelling and application development in AI/ML scenarios. 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