UUF model catalog updates

Author: HeidiSteen

articles/search/cognitive-search-aml-skill.md

@@ -26,7 +26,7 @@ The **AML** skill can be called with the 2024-07-01 stable API version or the 20
 
 Starting in 2024-05-01-preview REST API and in the Azure portal (which also targets the 2024-05-01-preview), Azure AI Search introduced the [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for query time connections to the model catalog in Azure AI Foundry portal. If you want to use that vectorizer for queries, the **AML** skill is the *indexing counterpart* for generating embeddings using a model in the Azure AI Foundry model catalog. 
 
-During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [How to implement integrated vectorization using models from Azure AI Foundry](vector-search-integrated-vectorization-ai-studio.md) for details on this workflow.
+During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [Use embedding models from Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md) for details on this workflow.
 
 > [!NOTE]
 > The indexer will retry twice for certain standard HTTP status codes returned from the AML online endpoint. These HTTP status codes are:

articles/search/media/vector-search-integrated-vectorization-ai-studio/ai-studio-catalog-embeddings-filter.png

@@ -11,7 +11,7 @@ ms.topic: how-to
 ms.date: 12/03/2024
 ---
 
-# How to implement integrated vectorization using models from Azure AI Foundry
+# Use embedding models from Azure AI Foundry model catalog for integrated vectorization
 
 > [!IMPORTANT] 
 > This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2024-05-01-Preview REST API](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) supports this feature.
@@ -22,15 +22,29 @@ The workflow includes model deployment steps. The model catalog includes embeddi
 
 After the model is deployed, you can use it for [integrated vectorization](vector-search-integrated-vectorization.md) during indexing, or with the [AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for queries.
 
+## Prerequisites
+
++ Azure AI Search, any region and tier.
+
++ Azure AI Foundry and an [Azure AI Foundry project](/azure/ai-studio/how-to/create-projects).
+
+## Supported embedding models
+
+Integrated vectorization and the [Import and vectorize data wizard](search-import-data-portal.md) supports the following embedding models:
+
++ For text embeddings: Cohere-embed-v3-english, Cohere-embed-v3-multilingual
+
++ For image embeddings: Facebook-DinoV2-Image-Embeddings-ViT-Base, Facebook-DinoV2-Image-Embeddings-ViT-Giant
+
 ## Deploy an embedding model from the Azure AI Foundry model catalog
 
-1. Open the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models). 
+1. Open the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models). Create a project if you don't have one already.
 
 1. Apply a filter to show just the embedding models. Under **Inference tasks**, select **Embeddings**:
 
    :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" alt-text="Screenshot of the Azure AI Foundry model catalog page highlighting how to filter by embeddings models.":::
 
-1. Select the model you would like to vectorize your content with. Then select **Deploy** and pick a deployment option.
+1. Select a supported model, then select **Deploy** and pick a deployment option.
 
    :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" alt-text="Screenshot of deploying an endpoint via the Azure AI Foundry model catalog.":::
 
@@ -56,12 +70,12 @@ When you deploy embedding models from the [Azure AI Foundry model catalog](https
 
 This section describes the AML skill definition and index mappings. It includes sample payloads that are already configured to work with their corresponding deployed endpoints. For more technical details on how these payloads work, read about the [Skill context and input annotation language](cognitive-search-skill-annotation-language.md).
 
-### [**Text Input for "Inference" API**](#tab/inference-text)
+<!-- ### [**Text Input for "Inference" API**](#tab/inference-text)
 
 This AML skill payload works with the following models from AI Foundry:
 
-+ OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32
-+ OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336
++ Cohere-embed-v3-english
++ Cohere-embed-v3-multilingual
 
 It assumes that you're chunking your content using the [Text Split skill](cognitive-search-skill-textsplit.md) and that the text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path accordingly.
 
@@ -99,11 +113,11 @@ The URI and key are generated when you deploy the model from the catalog. For mo
     }
   ]
 }
-```
+``` -->
 
-### [**Image Input for "Inference" API**](#tab/inference-image)
+### [**Facebook embedding models**](#tab/inference-image)
 
-This AML skill payload works with the following models from AI Foundry:
+This AML skill payload works with the following image embedding models from AI Foundry:
 
 + Facebook-DinoV2-Image-Embeddings-ViT-Base
 + Facebook-DinoV2-Image-Embeddings-ViT-Giant
@@ -116,8 +130,8 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 {
   "@odata.type": "#Microsoft.Skills.Custom.AmlSkill",
   "context": "/document/normalized_images/*",
-  "uri": "<YOUR_MODEL_URL_HERE>",
-  "key": "<YOUR_MODEL_HERE>",
+  "uri": "https://myproject-1a1a-abcd.eastus.inference.ml.azure.com/score",
+  "key": "bbbbbbbb-1c1c-2d2d-3e3e-444444444444",
   "inputs": [
     {
       "name": "input_data",
@@ -146,27 +160,27 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 }
 ```
 
-### [**Cohere**](#tab/cohere)
+### [**Cohere embedding models**](#tab/cohere)
 
-This AML skill payload works with the following models from AI Foundry:
+This AML skill payload works with the following text embedding models from AI Foundry:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
 
-It assumes that you're chunking your content using the SplitSkill and therefore your text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path according.
+It assumes that you're chunking your content using the Text Split skill and therefore your text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path according.
 
 You must add the `/v1/embed` path onto the end of the URL that you copied from your AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-studio/how-to/deploy-models-cohere-embed).
 
 The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy Cohere Embed models with Azure AI Foundry](/azure/ai-studio/how-to/deploy-models-cohere-embed).
 
-Note that image URIs are not supported by this integration at this time.
+Note that image URIs aren't supported by this integration at this time.
 
 ```json
 {
   "@odata.type": "#Microsoft.Skills.Custom.AmlSkill",
   "context": "/document/pages/*",
-  "uri": "<YOUR_MODEL_URL_HERE>/v1/embed",
-  "key": "<YOUR_MODEL_KEY_HERE>",
+  "uri": "https://Cohere-embed-v3-multilingual-hin.eastus.models.ai.azure.com/v1/embed",
+  "key": "aaaaaaaa-0b0b-1c1c-2d2d-333333333333",
   "inputs": [
     {
       "name": "texts",

articles/search/media/vector-search-integrated-vectorization-ai-studio/ai-studio-deploy-endpoint.png

@@ -27,7 +27,7 @@ Parameters are case-sensitive. Which parameters you choose to use depends on wha
 | Parameter name | Description |
 |--------------------|-------------|
 | `uri` | (Required) The [URI of the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md) to which the _JSON_ payload is sent. Only the **https** URI scheme is allowed. |
-| `modelName` | (Required) The model ID from the AI Foundry model catalog that is deployed at the provided endpoint. Currently supported models are <ul><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
+| `modelName` | (Required) The model ID from the AI Foundry model catalog that is deployed at the provided endpoint. Supported models are: <ul><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
 | `key` | (Required for [key authentication](#WhatParametersToUse)) The [key for the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md). |
 | `resourceId` | (Required for [token authentication](#WhatParametersToUse)). The Azure Resource Manager resource ID of the AML online endpoint. It should be in the format subscriptions/{guid}/resourceGroups/{resource-group-name}/Microsoft.MachineLearningServices/workspaces/{workspace-name}/onlineendpoints/{endpoint_name}. |
 | `region` | (Optional for [token authentication](#WhatParametersToUse)). The [region](https://azure.microsoft.com/global-infrastructure/regions/) the AML online endpoint is deployed in. Needed if the region is different from the region of the search service. |
@@ -49,7 +49,7 @@ Which authentication parameters are required depends on what authentication your
 
 Which vector query types are supported by the AI Foundry model catalog vectorizer depends on the `modelName` that is configured.
 
-| `modelName` | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
+| Embedding model | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
 |--------------------|-------------|-------------|-------------|
 | Facebook-DinoV2-Image-Embeddings-ViT-Base |  | X | X |
 | Facebook-DinoV2-Image-Embeddings-ViT-Giant |  | X | X |
@@ -69,16 +69,18 @@ The expected field dimensions for a field configured with an AI Foundry model ca
 
 ## Sample definition
 
+Suggested model names in the Azure AI Foundry model catalog consist of the base model plus a random three-letter suffix. The name of your model will be different from the one shown in this example.
+
 ```json
 "vectorizers": [
     {
-        "name": "my-ai-studio-catalog-vectorizer",
+        "name": "my-model-catalog-vectorizer",
         "kind": "aml",
         "amlParameters": {
-            "uri": "https://my-aml-endpoint.eastus.inference.ml.azure.com/score",
-            "key": "0000000000000000000000000000000000000",
+            "uri": "https://Cohere-embed-v3-multilingual-hin.eastus.models.ai.azure.com",
+            "key": "aaaaaaaa-0b0b-1c1c-2d2d-333333333333",
             "timeout": "PT60S",
-            "modelName": "OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch3",
+            "modelName": "Cohere-embed-v3-multilingual-hin",
             "resourceId": null,
             "region": null,
         },