fixing bugs

Author: HeidiSteen

articles/search/knowledge-store-projection-example-long.md

@@ -7,22 +7,22 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
-ms.topic: conceptual
-ms.date: 06/17/2025
+ms.topic: concept-article
+ms.date: 07/28/2025
 ms.custom:
   - ignite-2023
   - sfi-ropc-nochange
 ---
 
-# Detailed example of shapes and projections in a knowledge store
+# Example of shapes and projections in a knowledge store
 
-This article provides a detailed example that supplements [high-level concepts](knowledge-store-projection-overview.md) and [syntax-based articles](knowledge-store-projections-examples.md) by walking you through the shaping and projection steps required for fully expressing the output of a rich skillset in a [knowledge store](knowledge-store-concept-intro.md).
+This article provides a detailed example that supplements [high-level concepts](knowledge-store-projection-overview.md) and [syntax-based articles](knowledge-store-projections-examples.md) by walking you through the shaping and projection steps required for fully expressing the output of a rich skillset in a [knowledge store](knowledge-store-concept-intro.md) in Azure Storage.
 
-If your application requirements call for multiple skills and projections, this example can give you a better idea of how shapes and projections intersect.
+If your application requirements call for multiple skills and projections, this example can give you a better idea of how shapes and projections interact.
 
 ## Set up sample data
 
-Sample documents aren't included with the Projections collection, but the [AI enrichment demo data files](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) contain text and images that work with the projections described in this example.
+Sample documents aren't included with the Projections collection, but the [AI enrichment demo data files](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) contain text and images that work with the projections described in this example. If you use this sample data, you can skip step that [attaches an Azure AI multi-service account](cognitive-search-attach-cognitive-services.md) because you stay under the daily indexer limit for free enrichments.
 
 Create a blob container in Azure Storage and upload all 14 items.
 
@@ -39,7 +39,7 @@ Pay close attention to skill outputs (targetNames). Outputs written to the enric
 ```json
 {
     "name": "projections-demo-ss",
-    "description": "Skillset that enriches blob data found in "merged_content". The enrichment granularity is a document.",
+    "description": "Skillset that enriches blob data found in the merged_content field. The enrichment granularity is a document.",
     "skills": [
         {
             "@odata.type": "#Microsoft.Skills.Text.V3.EntityRecognitionSkill",
@@ -182,12 +182,15 @@ Pay close attention to skill outputs (targetNames). Outputs written to the enric
     "cognitiveServices": {
         "@odata.type": "#Microsoft.Azure.Search.CognitiveServicesByKey",
         "description": "An Azure AI services resource in the same region as Search.",
-        "key": "<Azure AI services All-in-ONE KEY>"
+        "key": ""
     },
     "knowledgeStore": null
 }
 ```
 
+> [!NOTE]
+> Under `"cognitiveServices"`, the key field is unspecified because the indexer can use an Azure AI multi-service account in the same region as your search service and process up to 20 transactions daily at no charge. The sample data for this example stays under the 20 transaction limit.
+
 ## Example Shaper skill
 
 A [Shaper skill](cognitive-search-skill-shaper.md) is a utility for working with existing enriched content instead of creating new enriched content. Adding a Shaper to a skillset lets you create a custom shape that you can project into table or blob storage. Without a custom shape, projections are limited to referencing a single node (one projection per output), which isn't suitable for tables. Creating a custom shape aggregates various elements into a new logical whole that can be projected as a single table, or sliced and distributed across a collection of tables.