Merge branch 'main' of https://github.com/microsoft/TechExcel-Integra…

…ting-Azure-PaaS-and-AI-Services-for-AI-Design-Wins
microsoft · Aug 31, 2024 · 3dd051f · 3dd051f
2 parents 5b9bbda + 04b7273
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diff --git a/.vscode/extensions.json b/.vscode/extensions.json
@@ -0,0 +1,6 @@
+{
+  "recommendations": [
+    "ms-azuretools.vscode-azurefunctions",
+    "ms-dotnettools.csharp"
+  ]
+}
diff --git a/.vscode/launch.json b/.vscode/launch.json
@@ -0,0 +1,11 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Attach to .NET Functions",
+            "type": "coreclr",
+            "request": "attach",
+            "processId": "${command:azureFunctions.pickProcess}"
+        }
+    ]
+}
diff --git a/.vscode/tasks.json b/.vscode/tasks.json
@@ -0,0 +1,81 @@
+{
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"label": "clean (functions)",
+			"command": "dotnet",
+			"args": [
+				"clean",
+				"/property:GenerateFullPaths=true",
+				"/consoleloggerparameters:NoSummary"
+			],
+			"type": "process",
+			"problemMatcher": "$msCompile",
+			"options": {
+				"cwd": "${workspaceFolder}/src\\ContosoSuitesVectorizationFunction"
+			}
+		},
+		{
+			"label": "build (functions)",
+			"command": "dotnet",
+			"args": [
+				"build",
+				"/property:GenerateFullPaths=true",
+				"/consoleloggerparameters:NoSummary"
+			],
+			"type": "process",
+			"dependsOn": "clean (functions)",
+			"group": {
+				"kind": "build",
+				"isDefault": true
+			},
+			"problemMatcher": "$msCompile",
+			"options": {
+				"cwd": "${workspaceFolder}/src\\ContosoSuitesVectorizationFunction"
+			}
+		},
+		{
+			"label": "clean release (functions)",
+			"command": "dotnet",
+			"args": [
+				"clean",
+				"--configuration",
+				"Release",
+				"/property:GenerateFullPaths=true",
+				"/consoleloggerparameters:NoSummary"
+			],
+			"type": "process",
+			"problemMatcher": "$msCompile",
+			"options": {
+				"cwd": "${workspaceFolder}/src\\ContosoSuitesVectorizationFunction"
+			}
+		},
+		{
+			"label": "publish (functions)",
+			"command": "dotnet",
+			"args": [
+				"publish",
+				"--configuration",
+				"Release",
+				"/property:GenerateFullPaths=true",
+				"/consoleloggerparameters:NoSummary"
+			],
+			"type": "process",
+			"dependsOn": "clean release (functions)",
+			"problemMatcher": "$msCompile",
+			"options": {
+				"cwd": "${workspaceFolder}/src\\ContosoSuitesVectorizationFunction"
+			}
+		},
+		{
+			"type": "func",
+			"dependsOn": "build (functions)",
+			"options": {
+				"cwd": "${workspaceFolder}/src\\ContosoSuitesVectorizationFunction/bin/Debug/net8.0"
+			},
+			"command": "host start",
+			"isBackground": true,
+			"problemMatcher": "$func-dotnet-watch"
+		}
+	]
+}
diff --git a/docs/02_implement_vector_search_in_cosmos_db_nosql/0201.md b/docs/02_implement_vector_search_in_cosmos_db_nosql/0201.md
@@ -11,29 +11,23 @@ parent: 'Exercise 02: Implement contextual grounding using vector search in Azur
 
 Vector search is a technique that allows items to be found based on their data characteristics instead of exact matches on a specific property field. Instead of requiring exact matches, vector search enables you to find items based on their vector representations. This technique is advantageous when performing similarity searches and is particularly valuable in applications that need to search for information within large blocks of text, such as Consoso Suites' applications.
 
-The Azure Cosmos DB for NoSQL API includes a vector search feature that provides a robust method for managing and querying high-dimensional vectors. This capability is essential for AI-driven applications requiring an integrated vector search capability. It allows you to store vectors alongside traditional schema-free data within your documents, streamlining data management and significantly enhancing the efficiency of vector operations. Keeping all relevant data in a single logical unit simplifies your data architecture, making it easy to understand and manage.
+The Azure Cosmos DB for NoSQL API includes a vector search feature that provides a robust method for managing and querying high-dimensional vectors. This capability is essential for AI-driven applications requiring an integrated vector search capability.
 
 ## Description
 
-In this task, you will enable the Vector Search feature in your Azure Cosmos DB for NoSQL database, define container vector policies, and specify indexing policies on Cosmos DB containers you will create for storing user reviews and property maintenance data.
-
-You will conclude by uploading data supplied by the Contoso Suites staff into the containers you created in Cosmos DB. These JSON data files contain user reviews and maintenance tasks for several hotels on their resorts. They offer an example of the types of data the company believes can benefit from the similarity search capabilities provided by Vector Search in Cosmos DB, so they would like you to incorporate this data into the proof of concept.
+In this task, you will enable the Vector Search feature in your Azure Cosmos DB for NoSQL database, then define a container vector policy and specify an indexing policy on a Cosmos DB container you will create for storing property maintenance request data.
 
 The key tasks are as follows:
 
 1. Enable the Vector Search feature in Azure Cosmos DB for NoSQL.
-2. Create containers named "UserReviews" and "MaintenanceTasks" in the `ContosoSuites` database, using the `hotel_id` field as the partition key.
-3. During container creation, define a container vector policy using the `cosine` distance function and assign an appropriate vector index type. Name the vector field `content_vector` for each.
-4. Populate the containers with data from files in the [/src/data folder](https://github.com/microsoft/TechExcel-Integrating-Azure-PaaS-and-AI-Services-for-AI-Design-Wins/tree/main/src/data) of the repository.
-   1. The `UserReviews` container should be populated from the `UserReviews.json` file.
-   2. Populate the `MaintenanceTasks` container with data from the file named `PropertyMaintenance.json`.
+2. Create a container named "MaintenanceRequests" in the `ContosoSuites` database, using the `hotel_id` field as the partition key.
+3. During container creation, define a container vector policy using the `cosine` distance function and assign an appropriate vector index type. Name the vector field `request_vector`.
 
 ## Success Criteria
 
 - You have enabled the Vector Search for NoSQL API feature in your Azure Cosmos DB instance.
-- You have defined a vector policy for the `UserReviews` and `MaintenanceTasks` containers in the `ContosoSuites` database in Azure Cosmos DB.
-- You have set appropriate vector indexing policies on both containers to improve similarity search efficiency.
-- You have populated both containers with data.
+- You have defined a vector policy for the `MaintenanceRequests` container in the `ContosoSuites` database in Azure Cosmos DB.
+- You have specified an appropriate vector indexing policy on the container to improve similarity search efficiency.
 
 ## Learning Resources
 
@@ -42,14 +36,13 @@ The key tasks are as follows:
 - [Vector Search in Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search)
 - [Vector indexes](https://learn.microsoft.com/azure/cosmos-db/index-policy#vector-indexes)
 - [What are distance functions?](https://learn.microsoft.com/azure/cosmos-db/gen-ai/distance-functions)
-- [What are vector embeddings?](https://learn.microsoft.com/azure/cosmos-db/gen-ai/vector-embeddings)
 
 ## Solution
 
 <details markdown="block">
 <summary>Expand this section to view the solution</summary>
 
-- Enabling the Vector Search for NoSQL API feature in Azure Cosmos DB, can be done via the Azure portal or the Azure CLI. The steps for each technique are listed below. Note that enabling the registration may take several minutes to take effect.
+- Enabling the Vector Search for NoSQL API feature in Azure Cosmos DB can be done via the [Azure portal](https://portal.azure.com) or the Azure CLI. The steps for each technique are listed below. Note that enabling the feature may take several minutes to take effect.
   - The steps for enabling the feature in the Azure portal are as follows:
     1. Navigate to your Azure Cosmos DB for NoSQL resource in the [Azure portal](https://portal.azure.com).
     2. Expand the **Settings** item in the left-hand menu, select **Features**, and on the **Features** page, select **Vector Search for NoSQL API**.
@@ -63,7 +56,9 @@ The key tasks are as follows:
     4. Wait for the notification that the feature was successfully enabled.
 
   - To enable Vector Search via the Azure CLI:
-    1. Execute the following command from the Azure Cloud Shell. Ensure you replace the `<resource-group-name>` and `<account-name>` tokens with the values from your deployed resource group.
+    1. From the toolbar in the [Azure portal](https://portal.azure.com), open the Azure Cloud Shell.
+    2. Ensure the cloud shell is using the subscription you are using for this exercise by running `az account set -s <YOUR_SUBSCRIPTION_ID>`, replacing the `<YOUR_SUBSCRIPTION_ID>` token with the id of the subscription you are using for this exercise.
+    3. Execute the following command from the Azure Cloud Shell. Ensure you replace the `<resource-group-name>` and `<account-name>` tokens with the appropriate values from your deployed resource group.
 
         ```azurecli
         az cosmosdb update \
@@ -72,39 +67,22 @@ The key tasks are as follows:
             --capabilities EnableNoSQLVectorSearch
         ```
 
-    2. Wait for the command to run successfully before leaving the Azure Cloud Shell.
+    4. Wait for the command to run successfully before leaving the Azure Cloud Shell.
 
 - Container vector policies and vector indexing policies must be defined at the time of container creation.
   - In the [Azure portal](https://portal.azure.com), navigate to your Cosmos DB resource.
   - Select **Data Explorer** in the left-hand menu.
   - On the **Data Explorer** page, select **New Container**
   - In the **New Container** dialog:
     - Select **Use existing** under **Database id** and select the **ContosoSuites** database from the dropdown list.
-    - Enter "UserReviews" into the **Container id** box.
-    - Enter "/hotel" into the **Partition key** box.
+    - Enter "MaintenanceRequests" into the **Container id** box.
+    - Enter "/hotel_id" into the **Partition key** box.
     - Expand the **Container Vectory Policy** section of the dialog and select **Add vector embedding**.
-      - Path: Enter "/content_vector"
+      - Path: Enter **"/request_vector"**.
       - Data type: Select **float32**.
       - Distance function: Select **cosine**.
       - Dimensions: Enter **1536**. This is based on the number of dimensions generated by the `ada-text-embedding-002` model in Azure OpenAI.
-      - Index type: Select **quantizedFlat**. Given the number of dimensions being specified, 1536, the `flat` index type would not be appropriate, as it only supports a maximum of 505 dimensions for vectors. The `diskANN` index could also be used here, but is only available in a limited preview at this time.
+      - Index type: Select **quantizedFlat**. Given the number of dimensions being specified, 1536, the `flat` index type will not work, as it only supports a maximum of 505 dimensions for vectors. The `diskANN` index could also be used here.
     - Select **OK** to create the container.
-  - Repeat the above steps to create a second container named "MaintenanceTasks."
-
-- The Azure Cosmos DB Data Explorer can be used to upload the data files provided by Contoso Suites.
-  - In the [Azure portal](https://portal.azure.com), navigate to your Cosmos DB resource and select **Data Explorer** in the left-hand menu.
-  - In the Data Explorer, expand the **ContosoSuites** database and the **UserReviews** container, then select **Items**.
-
-    ![Data Explorer is highlighted in the left-hand menu. The expand icon is highlighted for the database and UserReviews containers. Items is highlighted.](../../media/Solution/0201-azure-cosmos-db-data-explorer-user-reviews-items.png)
-
-  - Select **Upload Item** on the toolbar.
-
-    ![The Upload Item button on the Azure Cosmos DB toolbar is highlighted.](../../media/Solution/0201-azure-cosmos-db-toolbar-upload-item.png)
-
-  - In the **Upload Items** dialog, select the browse button and navigate to the `UserReviews.json` file in the `/src/data` directory in the location where cloned the repository, then select **Upload** to import the data in the file.
-
-    ![The Upload Items dialog is displayed with the browse and Upload buttons highlighted. UserReviews.json appears in the Select JSON files box.](../../media/Solution/0201-upload-items-user-reviews.png)
-
-  - Repeat the above steps, this time uploading data into the `MaintenanceTasks` container from the `PropertyMaintenance.json` file.
 
 </details>