Initial integrated vectorization improvements

Author: pamelafox

app/backend/requirements.in

@@ -7,7 +7,7 @@ tiktoken
 tenacity
 azure-ai-documentintelligence
 azure-cognitiveservices-speech
-azure-search-documents==11.6.0b1
+azure-search-documents==11.6.0b5
 azure-storage-blob
 azure-storage-file-datalake
 uvicorn

docs/data_ingestion.md

@@ -65,11 +65,13 @@ You can also remove individual documents by using the `--remove` flag. Open eith
 
 ## Overview of Integrated Vectorization
 
-Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers). 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.
+Azure AI Search includes an [integrated vectorization feature](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers), a cloud-based approach to data ingestion. Integrated vectorization takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
 
 See [this notebook](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) to understand the process of setting up integrated vectorization.
 We have integrated that code into our `prepdocs` script, so you can use it without needing to understand the details.
 
+You must first explicitly [enable integrated vectorization](./deploy_features.md#enabling-integrated-vectorization) in the `azd` environment to use this feature.
+
 This feature cannot be used on existing index. You need to create a new index or drop and recreate an existing index.
 In the newly created index schema, a new field 'parent_id' is added. This is used internally by the indexer to manage life cycle of chunks.
 

docs/deploy_features.md

@@ -192,12 +192,26 @@ Azure AI search recently introduced an [integrated vectorization feature in prev
 
 To enable integrated vectorization with this sample:
 
-1. If you've previously deployed, delete the existing search index.
-2. Run `azd env set USE_FEATURE_INT_VECTORIZATION true`
-3. Run `azd up` to update system and user roles
+1. If you've previously deployed, delete the existing search index. 🗑️
+2. To enable the use of integrated vectorization, run:
+
+    ```shell
+    azd env set USE_FEATURE_INT_VECTORIZATION true
+    ```
+
+3. If you've already deployed your app, then you can run just the `provision` step:
+
+    ```shell
+    azd provision
+    ```
+
+    That will set up necessary RBAC roles and configure the integrated vectorization feature on your search service.
+
+    If you haven't deployed your app yet, then you should run the full `azd up` after configuring all optional features.
+
 4. You can view the resources such as the indexer and skillset in Azure Portal and monitor the status of the vectorization process.
 
-This feature is not currently compatible with GPT4-vision or the newer text-embedding-3 models.
+⚠️ This feature is not currently compatible with the [GPT vision integration](./gpt4v.md).
 
 ## Enabling authentication