fixed links

Author: HeidiSteen

articles/search/search-api-preview.md

@@ -23,9 +23,9 @@ Preview features are removed from this list if they're retired or transition to
 
 |Feature                          | Category | Description | Availability  |
 |---------|------------------|-------------|---------------|
-|  [**Scalar quantization**](vector-search-how-to-configure-compression-storage.md#option-3-configure-scalar-quantization) | Index | Compress vector index size in memory and on disk using built-in scalar quantization. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to add a `compressions` section to a vector profile. |
-|  [**Narrow data types**](vector-search-how-to-configure-compression-storage.md#option-1-assign-narrow-data-types-to-vector-fields) | Index | Assign a smaller data type on vector fields, assuming incoming data is of that data type. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to specify a vector field definition. [Binary vector support](vector-search-how-to-index-binary-data.md) is added in 2024-05-01-preview.|
-|  [**stored property**](vector-search-how-to-configure-compression-storage.md#option-2-set-the-stored-property-to-remove-retrievable-storage) | Index | Boolean that reduces storage of vector indexes by *not* storing retrievable vectors. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to set `stored` on a vector field. |
+|  [**Scalar quantization**](vector-search-how-to-configure-compression-storage.md#option-1-configure-scalar-quantization) | Index | Compress vector index size in memory and on disk using built-in scalar quantization. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to add a `compressions` section to a vector profile. |
+|  [**Narrow data types**](vector-search-how-to-configure-compression-storage.md#option-2-assign-narrow-data-types-to-vector-fields) | Index | Assign a smaller data type on vector fields, assuming incoming data is of that data type. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to specify a vector field definition. [Binary vector support](vector-search-how-to-index-binary-data.md) is added in 2024-05-01-preview.|
+|  [**stored property**](vector-search-how-to-configure-compression-storage.md#option-3-set-the-stored-property-to-remove-retrievable-storage) | Index | Boolean that reduces storage of vector indexes by *not* storing retrievable vectors. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to set `stored` on a vector field. |
 |  [**Vectorizers**](vector-search-integrated-vectorization.md) | Queries | Text-to-vector conversion during query execution. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) to define a `vectorizer`. [Search POST (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2023-10-01-preview&preserve-view=true) for `vectorQueries`, 2023-10-01-Preview or later. |
 |  [**Integrated vectorization**](vector-search-integrated-vectorization.md) | Index, skillset | Skills-driven data chunking and embedding during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) for AzureOpenAIEmbedding skill and the data chunking properties of the Text Split skill. |
 | [**Import and vectorize data**](search-get-started-portal-import-vectors.md) | Azure portal | A wizard that creates a full indexing pipeline that includes data chunking and vectorization. The wizard creates all of the objects and configuration settings. | Available on all search services, in all regions. |

articles/search/search-traffic-analytics.md

@@ -8,14 +8,14 @@ ms.author: heidist
 
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 06/19/2024
 ---
 
 # Collect telemetry data for search traffic analytics
 
 Search traffic analytics is a pattern for collecting telemetry about user interactions with your Azure AI Search application, such as user-initiated click events and keyboard inputs. Using this information, you can determine the effectiveness of your search solution, including popular search terms, clickthrough rate, and which query inputs yield zero results.
 
-This pattern takes a dependency on [Application Insights](../azure-monitor/app/app-insights-overview.md) (a feature of [Azure Monitor](../azure-monitor/index.yml)) to collect user data. It requires that you add instrumentation to your client code, as described in this article. Finally, you need a reporting mechanism to analyze the data. We recommend Power BI, but you can use the Application Dashboard or any tool that connects to Application Insights.
+This pattern takes a dependency on [Application Insights](../azure-monitor/app/app-insights-overview.md) (a feature of [Azure Monitor](../azure-monitor/index.yml)) to collect user data. It requires that you add instrumentation to your client code, as described in this article. Finally, you need a reporting mechanism to analyze the data. We recommend Power BI, but you can use any visualization tool that connects to Application Insights.
 
 > [!NOTE]
 > The pattern described in this article is for advanced scenarios and clickstream data generated by code you add to your client. In contrast, service logs are easy to set up, provide a range of metrics, and can be done in the portal with no code required. Enabling logging is recommended for all scenarios. For more information, see [Collect and analyze log data](monitor-azure-cognitive-search.md).
@@ -172,9 +172,9 @@ Metrics included the following items:
 + Search volume and most popular term-document pairs: terms that result in the same document clicked, ordered by clicks.
 + Searches without clicks: terms for top queries that register no clicks
 
-The following screenshot shows what a built-in report might look like if you have used all of the schema elements.
+The following screenshot shows the data elements that your report might contain.
 
-:::image type="content" source="media/search-traffic-analytics/azuresearch-powerbi-dashboard.png" alt-text="Screenshot showing the built-in report using all available schema elements. ":::
+:::image type="content" source="media/search-traffic-analytics/azuresearch-powerbi-dashboard.png" alt-text="Screenshot showing the available schema elements in the data catalog. ":::
 
 ## Next steps