Merge branch 'diberry/0605-ai-search-rag' of https://github.com/diberry/azure-ai-docs-pr into diberry/0605-ai-search-rag

Author: diberry

.gitignore

@@ -11,6 +11,8 @@ _repo.*/
 
 .openpublishing.buildcore.ps1
 
+.vscode/
+
 *sec.endpointdlp
 
 # CoPilot instructions and prompts

.vscode/settings.json

@@ -8,7 +8,7 @@ manager: scottpolly
 ms.reviewer: deeikele
 ms.date: 04/28/2025
 ms.service: azure-ai-foundry
-ms.topic: conceptual
+ms.topic: concept-article
 ms.custom:
   - ignite-2023
   - build-2024

articles/ai-foundry/concepts/ai-resources.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - build-2024
   - ignite-2024
-ms.topic: conceptual
+ms.topic: concept-article
 ms.date: 04/28/2025
 ms.reviewer: deeikele
 ms.author: larryfr
@@ -16,8 +16,7 @@ author: Blackmist
 
 # Azure AI Foundry architecture 
 
-> [!NOTE]
-> The architecture discussed in this article is specific to a **[!INCLUDE [hub](../includes/hub-project-name.md)]**. For more information, see [Types of projects](../what-is-azure-ai-foundry.md#project-types).
+[!INCLUDE [hub-only-alt](../includes/uses-hub-only-alt.md)]
 
 Azure AI Foundry provides a unified experience for AI developers and data scientists to build, evaluate, and deploy AI models through a web portal, SDK, or CLI. Azure AI Foundry is built on capabilities and services provided by other Azure services.
 

articles/ai-foundry/concepts/architecture.md

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
   - build-2024
   - ignite-2024
-ms.topic: conceptual
+ms.topic: concept-article
 ms.date: 05/31/2025
 ms.reviewer: eur
 ms.author: pafarley

articles/ai-foundry/concepts/concept-playgrounds.md

@@ -117,6 +117,9 @@ Managed identity must be enabled as a prerequisite for using customer-managed ke
 
 Customer-managed key encryption is configured via Azure portal in a similar way for each Azure resource:
 
+> [!IMPORTANT]
+> The Azure Key Vault used for encryption **must be in the same resource group** as the AI Foundry project. Key Vaults in other resource groups are not currently supported by the deployment wizards or project configuration workflows.
+
 1. Create a new Azure AI Foundry resource in the [Azure portal](https://portal.azure.com/).
 1. Under the **Encryption** tab, select **Customer-managed key**, **Select vault and key**, and then select the key vault and key to use.
 

articles/ai-foundry/concepts/content-filtering.md

@@ -70,7 +70,7 @@ intent_resolution(
 
 ### Intent resolution output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason and additional fields can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason and additional fields can help you understand why the score is high or low.
 
 ```python
 {
@@ -137,7 +137,7 @@ tool_call_accuracy(
 
 ### Tool call accuracy output
 
-The numerical score (passing rate of correct tool calls) is 0-1 and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason and tool call detail fields can help you understand why the score is high or low.
+The numerical score (passing rate of correct tool calls) is 0-1 and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason and tool call detail fields can help you understand why the score is high or low.
 
 ```python
 {
@@ -174,7 +174,7 @@ task_adherence(
 
 ### Task adherence output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {

articles/ai-foundry/concepts/encryption-keys-portal.md

@@ -59,7 +59,7 @@ coherence(
 
 ### Coherence output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {
@@ -88,7 +88,7 @@ fluency(
 
 ### Fluency output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {
@@ -127,7 +127,7 @@ qa_eval(
 
 ### QA output
 
-While F1 score outputs a numerical score on 0-1 float scale, the other evaluators output numerical scores on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+While F1 score outputs a numerical score on 0-1 float scale, the other evaluators output numerical scores on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {

articles/ai-foundry/concepts/evaluation-evaluators/agent-evaluators.md

@@ -63,7 +63,7 @@ retrieval(
 
 ### Retrieval output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (a default is set), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (a default is set), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {
@@ -163,7 +163,7 @@ document_retrieval_evaluator(retrieval_ground_truth=retrieval_ground_truth, retr
 
 ### Document retrieval output
 
-All numerical scores have `high_is_better=True` except for `holes` and `holes_ratio` which have `high_is_better=False`. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. 
+All numerical scores have `high_is_better=True` except for `holes` and `holes_ratio` which have `high_is_better=False`. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. 
 
 ```python
 {
@@ -206,7 +206,7 @@ groundedness(
 
 ### Groundedness output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {
@@ -276,7 +276,7 @@ relevance(
 
 ### Relevance output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {
@@ -306,7 +306,7 @@ response_completeness(
 
 ### Response completeness output
 
-The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
+The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
 
 ```python
 {