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Changing the llama stack client version number after peer review feedback
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modules/ingesting-content-into-a-llama-model.adoc

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@@ -10,7 +10,7 @@ You can quickly customize and prototype your retrievable content by ingesting ra
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* You have deployed a Llama 3.2 model with a vLLM model server and you have integrated LlamaStack.
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* You have created a project workbench within a data science project.
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* You have opened a Jupyter notebook and it is running in your workbench environment.
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* You have installed the `llama_stack_client` version 0.2.8 in your workbench environment.
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* You have installed the `llama_stack_client` version 0.2.14 or later in your workbench environment.
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* You have a created and configured a vector database instance and you know its identifier.
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ifdef::self-managed[]
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* Your environment has network access to the vector database service through {openshift-platform}.

modules/preparing-documents-with-docling-for-llama-stack-retrieval.adoc

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* You have installed local object storage buckets and created connections, as described in link:{odhdocshome}/working-on-data-science-projects/#adding-a-connection-to-your-data-science-project_projects[Adding a connection to your data science project].
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endif::[]
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ifndef::upstream[]
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* You have installed the `llama_stack_client` version 0.2.8 in your workbench environment.
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* You have installed the `llama_stack_client` version 0.2.14 or later in your workbench environment.
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* You have installed local object storage buckets and created connections, as described in link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects/using-connections_projects#adding-a-connection-to-your-data-science-project_projects[Adding a connection to your data science project].
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endif::[]
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* You have compiled to YAML a data science pipeline that includes a Docling transform, either one of the RAG demo samples or your own custom pipeline.

modules/querying-ingested-content-in-a-llama-model.adoc

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* You have configured a Llama Stack deployment by creating a `LlamaStackDistribution` instance to enable RAG functionality.
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* You have created a project workbench within a data science project.
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* You have opened a Jupyter notebook and it is running in your workbench environment.
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* You have installed the `llama_stack_client` version 0.2.8 in your workbench environment.
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* You have installed the `llama_stack_client` version 0.2.14 or later in your workbench environment.
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* You have ingested content into your model.
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[NOTE]

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