[LLM] RAG Examples - Remove link to old registry (#2519)

Co-authored-by: Gerard <GerardJWoods@live.com>

sdk/python/generative-ai/rag/notebooks/faiss/url_to_faiss_incremental_embeddings_with_tabular_data.ipynb

@@ -358,7 +358,7 @@
     "### Setup Pipeline to process data into Index\n",
     "AzureML [Pipelines](https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2) connect together multiple [Components](https://learn.microsoft.com/en-us/azure/machine-learning/concept-component?view=azureml-api-2). Each Component defines inputs, code that consumes the inputs and outputs produced from the code. Pipelines themselves can have inputs, and outputs produced by connecting together individual sub Components. To process your data for embedding and indexing we will chain together multiple components each performing their own step of the workflow.\n",
     "\n",
-    "The Components are published to a [Registry](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2&tabs=cli), azureml-preview, which should have access to after signing up to the Generative AI Private Preview, it can be accessed from any Workspace as long as your Tenant has been granted access. In the below cell we get the Component Definitions from the azureml-preview registry."
+    "The Components are published to a [Registry](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2&tabs=cli), azureml, which should have access to after signing up to the Generative AI Private Preview, it can be accessed from any Workspace as long as your Tenant has been granted access. In the below cell we get the Component Definitions from the azureml registry."
    ]
   },
   {
@@ -380,7 +380,7 @@
    },
    "outputs": [],
    "source": [
-    "ml_registry = MLClient(credential=credential, registry_name=\"azureml-preview\")\n",
+    "ml_registry = MLClient(credential=credential, registry_name=\"azureml\")\n",
     "\n",
     "# Clones git repository to output folder of pipeline, by default this will be on the default Workspace Datastore `workspaceblobstore`\n",
     "git_clone_component = ml_registry.components.get(\"llm_rag_git_clone\", label=\"latest\")\n",