still fixing links

Author: fosteramanda

articles/ai-services/agents/how-to/tools/azure-ai-search.md

@@ -33,7 +33,7 @@ Use an existing Azure AI Search index with the agent's Azure AI Search tool.
 **Index with semantic configuration**
 - By default, the Azure AI Search tool runs hybrid + semantic search on all text fields.
 
-<a id="overview"></a>
+<a id="overview-pivot"></a>
 
 [!INCLUDE [setup](../../includes/azure-search/setup.md)]
 

articles/ai-services/agents/includes/azure-search/code-examples.md

@@ -11,7 +11,7 @@ ms.date: 12/10/2024
 This quickstart shows how to use an existing Azure AI Search index with the Azure AI Search tool.
 
 ### Prerequisites
-Complete the [Azure AI Search tool setup](../../how-to/tools/azure-ai-search.md#overview).
+Complete the [Azure AI Search tool setup](../../how-to/tools/azure-ai-search.md#overview-pivot).
 
 ### Step 1: Create an Azure AI Client
 First, create an Azure AI Client using the connection string of your project.

articles/ai-services/agents/includes/file-search/deep-dive.md

@@ -1,4 +1,11 @@
-
+---
+manager: nitinme
+author: fosteramanda
+ms.author: fosteramanda
+ms.service: azure
+ms.topic: include
+ms.date: 12/10/2024
+---
 ## Creating vector stores and adding files 
 
 You can create a vector store and add files to it in a single API call:
@@ -67,12 +74,6 @@ project_client.agents.delete_vector_store(vector_store.id)
 print("Deleted vector store")
 ```
 
-## Ensuring vector store readiness before creating runs
-
-We highly recommend that you ensure all files in a vector_store are fully processed before you create a run. This approach ensures that all the data in your vector store is searchable. You can check for vector store readiness by using the polling helpers in the SDKs, or by manually polling the `vector_store` object to ensure the status is completed.
-
-As a fallback, there's a 60-second maximum wait in the run object when the thread's vector store contains files that are still being processed. This step ensures that any files your users upload in a thread are fully searchable before the run proceeds. This fallback wait does not apply to the agent's vector store.
-
 ## Managing costs with expiration policies
 
 For basic agent setup, the `file_search` tool uses the `vector_stores`  object as its resource and you are billed based on the size of the vector_store objects created. The size of the vector store object is the sum of all the parsed chunks from your files and their corresponding embeddings.