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articles/AI.asm.xml

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</structure>
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<structure renderas="article" xml:id="ai-deployment-intro" xml:lang="en">
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<merge>
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<title>Installing &productname;</title>
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<title>Deploying and Installing &productname;</title>
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<revhistory xml:id="rh-deployment-intro">
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<revision><date>2024-09-04</date>
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<revdescription>
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</varlistentry>
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</variablelist>
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<para>
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&productname; is a complex product consisting of multiple software
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&productname; is a versatile product consisting of multiple software
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layers and components. This document outlines the complete workflow
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for deploying and installing all &productname; dependencies, as well
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as &productname; itself. You can also find references to recommended
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<title>Installing &ailibrary;</title>
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<abstract>
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<para>
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&productname; is being delivered as a set of components that you can
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&productname; is delivered as a set of components that you can
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combine to meet specific use cases. This provides extraordinary
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flexibility but means that there is not a single &helm; chart that
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installs the whole stack. To enable the full integrated stack, you
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<!-- TODO AI: Support for Various Environments: the documentation should reflect
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support for various Kubernetes clusters and operating systems, not just SUSE's,
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while emphasizing preferred configurations. -->
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while emphasizing preferred configurations. Link to the supported OSes
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for RKE2 matrix as well:
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https://www.suse.com/suse-rancher/support-matrix/all-supported-versions/rancher-v2-10-4/
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-->
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<!-- TODO AI: Ensure consistency in terminology and aligning it with product
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marketing messaging -->

concepts/AI-intro-how-works.xml

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<meta name="maintainer" content="tbazant@suse.com" its:translate="no"/>
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<abstract>
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<para>
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&productname; is a cluster-oriented solution and consists of multiple
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&productname; is a cloud native solution that comprises multiple
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software building blocks. These blocks include the Linux operating
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system running on bare metal or virtualized, &kube; cluster with a Web
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UI management layer, supportive tools to utilize GPU capabilities, and

snippets/ai-library-requirement.xml

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<para>
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Before the installation, you need to get user access to the &sappco;, create
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a &kube; namespace, and log in to the &helm; registry as described in
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<xref linkend="ai-deployment-ailibrary-installing"/>.
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<xref linkend="suse-ai-deploy-suse-ai"/>.
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</para>
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</tip>

tasks/AI-deployment-ailibrary-installing.xml

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<!ENTITY % entities SYSTEM "../common/generic-entities.ent">
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%entities;
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]>
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<topic xml:id="ai-deployment-ailibrary-installing"
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<topic xml:id="suse-ai-deploy-suse-ai"
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role="concept" xml:lang="en"
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xmlns="http://docbook.org/ns/docbook" version="5.2"
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xmlns:its="http://www.w3.org/2005/11/its"

tasks/AI-deployment-cluster-preparation.xml

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<!ENTITY % entities SYSTEM "../common/generic-entities.ent">
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%entities;
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]>
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<topic xml:id="ai-cluster-preparation"
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<topic xml:id="suse-ai-deploy-prepare"
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role="concept" xml:lang="en"
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xmlns="http://docbook.org/ns/docbook" version="5.2"
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xmlns:its="http://www.w3.org/2005/11/its"

tasks/AI-deployment-kube-installing.xml

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&kube; distribution for users who start deploying on cluster nodes from
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scratch. If you already have a &kube; cluster installed and running, you
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can skip this procedure and continue with
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<xref linkend="ai-cluster-preparation"/>.
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<xref linkend="suse-ai-deploy-suse-ai"/>.
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</para>
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</abstract>
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</info>

tasks/AI-deployment-steps-after.xml

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<!ENTITY % entities SYSTEM "../common/generic-entities.ent">
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%entities;
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]>
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<topic xml:id="ai-deployment-steps-afterward"
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<topic xml:id="suse-ai-deploy-post"
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role="concept" xml:lang="en"
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xmlns="http://docbook.org/ns/docbook" version="5.2"
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xmlns:its="http://www.w3.org/2005/11/its"
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<meta name="maintainer" content="tbazant@suse.com" its:translate="no"/>
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<abstract>
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<para>
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This procedure lists tasks to accomplish after the &productname;
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installation is complete.
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Once the &productname; installation is finished, follow these tasks to
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complete the initial setup and configuration.
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</para>
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</abstract>
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</info>
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</step>
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<step>
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<para>
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Configure <emphasis>retrieval-augmented generation</emphasis>(RAG) to
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Configure <emphasis>retrieval-augmented generation</emphasis> (RAG) to
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let the model process content relevant to the customer.
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</para>
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</step>

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