Update links to multimodal

Author: pamelafox

docs/README.md

@@ -12,7 +12,7 @@ These are advanced topics that are not necessary for a basic deployment.
   - [Enabling optional features](deploy_features.md)
     - [All features](docs/deploy_features.md)
     - [Login and access control](login_and_acl.md)
-    - [GPT-4 Turbo with Vision](gpt4v.md) TODO
+    - [Multimodal](multimodal.md)
     - [Private endpoints](deploy_private.md)
     - [Agentic retrieval](agentic_retrieval.md)
   - [Sharing deployment environments](sharing_environments.md)

docs/customization.md

@@ -50,7 +50,7 @@ The prompts are currently tailored to the sample data since they start with "Ass
 
 TODO FIX THIS!
 
-If you followed the instructions in [the GPT vision guide](gpt4v.md) to enable the vision approach and the "Use GPT vision model" option is selected, then the chat tab will use the `chatreadretrievereadvision.py` approach instead. This approach is similar to the `chatreadretrieveread.py` approach, with a few differences:
+If you followed the instructions in [the multimodal guide](multimodal.md) to enable the vision approach and the "Use GPT vision model" option is selected, then the chat tab will use the `chatreadretrievereadvision.py` approach instead. This approach is similar to the `chatreadretrieveread.py` approach, with a few differences:
 
 1. Step 1 is the same as before, except it uses the GPT-4 Vision model instead of the default GPT-3.5 model.
 2. For this step, it also calculates a vector embedding for the user question using [the Computer Vision vectorize text API](https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/image-retrieval#call-the-vectorize-text-api), and passes that to the Azure AI Search to compare against the `imageEmbeddings` fields in the indexed documents. For each matching document, it downloads the image blob and converts it to a base 64 encoding.
@@ -70,7 +70,7 @@ The prompt for step 2 is currently tailored to the sample data since it starts w
 #### Ask with vision
 
 TODO FIX THIS!
-If you followed the instructions in [the GPT vision guide](gpt4v.md) to enable the vision approach and the "Use GPT vision model" option is selected, then the ask tab will use the `retrievethenreadvision.py` approach instead. This approach is similar to the `retrievethenread.py` approach, with a few differences:
+If you followed the instructions in [the multimodal guide](multimodal.md) to enable the vision approach and the "Use GPT vision model" option is selected, then the ask tab will use the `retrievethenreadvision.py` approach instead. This approach is similar to the `retrievethenread.py` approach, with a few differences:
 
 1. For this step, it also calculates a vector embedding for the user question using [the Computer Vision vectorize text API](https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/image-retrieval#call-the-vectorize-text-api), and passes that to the Azure AI Search to compare against the `imageEmbeddings` fields in the indexed documents. For each matching document, it downloads the image blob and converts it to a base 64 encoding.
 2. When it combines the search results and user question, it includes the base 64 encoded images, and sends along both the text and images to the GPT4 Vision model (similar to this [documentation example](https://platform.openai.com/docs/guides/vision/quick-start)). The model generates a response that includes citations to the images, and the UI renders the base64 encoded images when a citation is clicked.

docs/deploy_features.md

@@ -135,14 +135,14 @@ This process does *not* delete your previous model deployment. If you want to de
 
 ## Using reasoning models
 
-⚠️ This feature is not currently compatible with [vision integration](./gpt4v.md). TODO: OR IS IT?
+⚠️ This feature is not currently compatible with [multimodal feature](./multimodal.md). TODO: OR IS IT?
 
 This feature allows you to use reasoning models to generate responses based on retrieved content. These models spend more time processing and understanding the user's request.
 To enable reasoning models, follow the steps in [the reasoning models guide](./reasoning.md).
 
 ## Using agentic retrieval
 
-⚠️ This feature is not currently compatible with [vision integration](./gpt4v.md). TODO: OR IS IT?
+⚠️ This feature is not currently compatible with [multimodal feature](./multimodal.md). TODO: OR IS IT?
 
 This feature allows you to use agentic retrieval in place of the Search API. To enable agentic retrieval, follow the steps in [the agentic retrieval guide](./agentic_retrieval.md)
 
@@ -259,7 +259,7 @@ to experiment with different options before committing to them.
 
 ⚠️ This feature is not currently compatible with [integrated vectorization](#enabling-integrated-vectorization).
 
-It is compatible with [GPT vision integration](./gpt4v.md), but the features provide similar functionality. TODO: UPDATE
+It is compatible with the [multimodal feature](./multimodal.md), but the features provide similar functionality. TODO: UPDATE
 
 By default, if your documents contain image-like figures, the data ingestion process will ignore those figures,
 so users will not be able to ask questions about them.
@@ -347,7 +347,7 @@ azd env set USE_SPEECH_OUTPUT_BROWSER true
 
 ## Enabling Integrated Vectorization
 
-⚠️ This feature is not currently compatible with the [GPT vision integration](./gpt4v.md). TODO: UPDATE
+⚠️ This feature is not currently compatible with the [multimodal feature](./multimodal.md). TODO: UPDATE
 
 Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in-azure-ai-search/3960809). This feature is a cloud-based approach to data ingestion, which takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
 

docs/productionizing.md

@@ -106,7 +106,7 @@ First make sure you have the locust package installed in your Python environment
 python -m pip install locust
 ```
 
-Then run the locust command, specifying the name of the User class to use from `locustfile.py`. We've provided a `ChatUser` class that simulates a user asking questions and receiving answers, as well as a `ChatVisionUser` to simulate a user asking questions with [multimodal answering enabled](/docs/gpt4v.md). TODO
+Then run the locust command, specifying the name of the User class to use from `locustfile.py`. We've provided a `ChatUser` class that simulates a user asking questions and receiving answers, as well as a `ChatVisionUser` to simulate a user asking questions with [multimodal answering enabled](/docs/multimodal.md). TODO
 
 ```shell
 locust ChatUser