Quick fixes

microsoft · Nov 21, 2024 · d96720c · d96720c
1 parent 942432b
commit d96720c
Show file tree

Hide file tree

Showing 2 changed files with 38 additions and 38 deletions.
diff --git a/docs/03_implement_vector_search_in_cosmos_db_nosql/03_02.md b/docs/03_implement_vector_search_in_cosmos_db_nosql/03_02.md
@@ -311,40 +311,40 @@ Use the Data Explorer to populate the `MaintenanceRequests` container with data
 1. Observe the files being processed in the Log Stream and review documents in the Cosmos DB Data Explorer to confirm the function correctly populated the `request_vector` field for newly added documents.
 2. Execute a query to find documents similar to "air conditioning malfunction" using the `VectorDistance()` function via Cosmos DB Data Explorer. You can find the vector embeddings for "air conditioning malfunction" in the `Query_Vector.txt` file in the [src\data folder](https://github.com/microsoft/TechExcel-Integrating-Azure-PaaS-and-AI-Services-for-AI-Design-Wins/tree/main/src/data).
 
-    <details markdown="block">
-    <summary><strong>Expand this section to view the solution</strong></summary>
-
-    To execute a query using the `VectorDistance()` function for "air conditioning malfunction":
-
-    1. In the [Azure portal](https://portal.azure.com), navigate to your Cosmos DB resource and select **Data Explorer** in the left-hand menu.
-    2. In the Data Explorer, expand the **ContosoSuites** database and the **MaintenanceRequests** container, then select **Items**.
-    3. On the toolbar, select **New SQL Query**.
-
-        ![The New SQL Query button on the Data Explorer Items toolbar is highlighted.](../../media/Solution/0302-azure-cosmos-db-data-explorer-items-new-sql-query.png)
-
-    4. In the new query window, paste in the following query:
-
-        ```sql
-        SELECT c.hotel_id, c.hotel, c.details, VectorDistance(c.request_vector, <QUERY_VECTOR>) AS SimilarityScore
-        FROM c
-        ```
-
-    5. Replace the `<QUERY_VECTOR>` token in the query with the vectorized representation of "air conditioning malfunction," which you can find in the `Query_Vector.txt` file in the [src\data folder](https://github.com/microsoft/TechExcel-Integrating-Azure-PaaS-and-AI-Services-for-AI-Design-Wins/tree/main/src/data). Copy the entire contents of the file, and paste it into the query in place of `<QUERY_VECTOR>`.
-    6. Select **Execute Query** on the toolbar and observe the output in the **Results** panel. You should see a list of results similar to the following (abbreviated for brevity):
-
-        ```json
-        {
-            "hotel_id": 1,
-            "hotel": "Oceanview Inn",
-            "details": "The air conditioning (A/C) unit in room 227 is malfunctioning and making a loud noise. Customer will be out of the room between 5:00 and 8:30 PM this evening. This needs immediate maintenance attention. If the issue cannot be resolved, we will need to move the customer to a new room.",
-            "SimilarityScore": 0.847799148429741
-        },
-        {
-            "hotel_id": 12,
-            "hotel": "The Downtown Plaza Hotel",
-            "details": "The thermostat does not appear to be working in room 328, and the air conditioning (A/C) unit will not turn on to cool down the room.",
-            "SimilarityScore": 0.838297398035295
-        }
-        ```
-
-    </details>
+<details markdown="block">
+<summary><strong>Expand this section to view the solution</strong></summary>
+
+To execute a query using the `VectorDistance()` function for "air conditioning malfunction":
+
+1. In the [Azure portal](https://portal.azure.com), navigate to your Cosmos DB resource and select **Data Explorer** in the left-hand menu.
+2. In the Data Explorer, expand the **ContosoSuites** database and the **MaintenanceRequests** container, then select **Items**.
+3. On the toolbar, select **New SQL Query**.
+
+    ![The New SQL Query button on the Data Explorer Items toolbar is highlighted.](../../media/Solution/0302-azure-cosmos-db-data-explorer-items-new-sql-query.png)
+
+4. In the new query window, paste in the following query:
+
+    ```sql
+    SELECT c.hotel_id, c.hotel, c.details, VectorDistance(c.request_vector, <QUERY_VECTOR>) AS SimilarityScore
+    FROM c
+    ```
+
+5. Replace the `<QUERY_VECTOR>` token in the query with the vectorized representation of "air conditioning malfunction," which you can find in the `Query_Vector.txt` file in the [src\data folder](https://github.com/microsoft/TechExcel-Integrating-Azure-PaaS-and-AI-Services-for-AI-Design-Wins/tree/main/src/data). Copy the entire contents of the file, and paste it into the query in place of `<QUERY_VECTOR>`.
+6. Select **Execute Query** on the toolbar and observe the output in the **Results** panel. You should see a list of results similar to the following (abbreviated for brevity):
+
+    ```json
+    {
+        "hotel_id": 1,
+        "hotel": "Oceanview Inn",
+        "details": "The air conditioning (A/C) unit in room 227 is malfunctioning and making a loud noise. Customer will be out of the room between 5:00 and 8:30 PM this evening. This needs immediate maintenance attention. If the issue cannot be resolved, we will need to move the customer to a new room.",
+        "SimilarityScore": 0.847799148429741
+    },
+    {
+        "hotel_id": 12,
+        "hotel": "The Downtown Plaza Hotel",
+        "details": "The thermostat does not appear to be working in room 328, and the air conditioning (A/C) unit will not turn on to cool down the room.",
+        "SimilarityScore": 0.838297398035295
+    }
+    ```
+
+</details>
diff --git a/docs/06_add_genai_gateway_capabilities_with_api_management/06_02.md b/docs/06_add_genai_gateway_capabilities_with_api_management/06_02.md
@@ -238,7 +238,7 @@ To inspect the token consumption metrics emitted by the APIM policy in Applicati
 
 </details>
 
-## Bonus Task (if time allows)
+## Bonus Tasks (if time allows)
 
 This bonus task allows you to explore API Management's support of backend pools when you want to implement multiple backends for an API and load-balance requests across those backends. You will implement a load-balanced pool to distribute the LLM workload across two instances of Azure OpenAI.