Quickstart: Vectorize text and images by using the Azure portal

Important

The Import and vectorize data wizard is in public preview under Supplemental Terms of Use. By default, it targets the 2024-05-01-Preview REST API.

This quickstart helps you get started with integrated vectorization (preview) by using the Import and vectorize data wizard in the Azure portal. This wizard calls a user-specified embedding model to vectorize content during indexing and for queries.

Preview limitations

• Source data is either Azure Blob Storage or OneLake files and shortcuts, using the default parsing mode (one search document per blob or file).

• The index schema is nonconfigurable. Source fields include content (chunked and vectorized), metadata_storage_name for the title, and metadata_storage_path for the document key. This key is represented as parent_id in the index.

• Chunking is nonconfigurable. The effective settings are:

  textSplitMode: "pages",
  maximumPageLength: 2000,
  pageOverlapLength: 500
  

For fewer limitations or more data source options, try a code-base approach. For more information, see the integrated vectorization sample.

Prerequisites

• An Azure subscription. Create one for free.

• For data, either Azure Blob Storage or a OneLake lakehouse.

  Azure Storage must be a standard performance (general-purpose v2) account. Access tiers can be hot, cool, and cold.

  Don't use Azure Data Lake Storage Gen2 (a storage account with a hierarchical namespace). This version of the wizard doesn't support Data Lake Storage Gen2.

• For vectorization, an Azure AI services multiservice account or Azure OpenAI Service endpoint with deployments.

  For multimodal with Azure AI Vision, create an Azure AI service in SwedenCentral, EastUS, NorthEurope, WestEurope, WestUS, SoutheastAsia, KoreaCentral, FranceCentral, AustraliaEast, WestUS2, SwitzerlandNorth, or JapanEast. Check the documentation for an updated list.

  You can also use an Azure AI Studio model catalog (and hub and project) with model deployments.

• For indexing and queries, Azure AI Search. It must be in the same region as your Azure AI service. We recommend the Basic tier or higher.

• Role assignments or API keys for connections to embedding models and data sources. This article provides instructions for role-based access control (RBAC).

All of the preceding resources must have public access enabled so that the portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see Secure connections in the import wizards.

If private endpoints are already present and you can't disable them, the alternative option is to run the respective end-to-end flow from a script or program on a virtual machine. The virtual machine must be on the same virtual network as the private endpoint. Here's a Python code sample for integrated vectorization. The same GitHub repo has samples in other programming languages.

A free search service supports RBAC on connections to Azure AI Search, but it doesn't support managed identities on outbound connections to Azure Storage or Azure AI Vision. This level of support means you must use key-based authentication on connections between a free search service and other Azure services. For connections that are more secure:

• Use the Basic tier or higher.
• Configure a managed identity and role assignments to admit requests from Azure AI Search on other Azure services.

Note

If you can't progress through the wizard because options aren't available (for example, you can't select a data source or an embedding model), revisit the role assignments. Error messages indicate that models or deployments don't exist, when in fact the real problem is that the search service doesn't have permission to access them.

Check for space

If you're starting with the free service, you're limited to three indexes, three data sources, three skillsets, and three indexers. Make sure you have room for extra items before you begin. This quickstart creates one of each object.

Check for service identity

We recommend role assignments for search service connections to other resources.

1. On Azure AI Search, enable RBAC.

2. Configure your search service to use a system-assigned or user-assigned managed identity.

In the following sections, you can assign the search service's managed identity to roles in other services. The sections provide steps for role assignments where applicable.

Check for semantic ranking

The wizard supports semantic ranking, but only on the Basic tier and higher, and only if semantic ranking is already enabled on your search service. If you're using a billable tier, check whether semantic ranking is enabled.

Prepare sample data

This section points you to data that works for this quickstart.

Azure Storage

1. Sign in to the Azure portal with your Azure account, and go to your Azure Storage account.

2. On the left pane, under Data Storage, select Containers.

3. Create a new container and then upload the health-plan PDF documents used for this quickstart.

4. On Access control, assign the Storage Blob Data Reader role on the container to the search service identity. Or, get a connection string to the storage account from the Access keys page.

OneLake

1. Sign in to Power BI and create a workspace.

2. In Power BI, select Workspaces on the left menu and open the workspace that you created.

3. Assign permissions at the workspace level:

   1. On the upper-right menu, select Manage access.

   2. Select Add people or groups.

   3. Enter the name of your search service. For example, if the URL is https://my-demo-service.search.windows.net, the search service name is my-demo-service.

   4. Select a role. The default is Viewer, but you need Contributor to pull data into a search index.

4. Load the sample data:

   1. From the Power BI switcher on the lower left, select Data Engineering.

   2. On the Data Engineering pane, select Lakehouse to create a lakehouse.

   3. Provide a name, and then select Create to create and open the new lakehouse.

   4. Select Upload files, and then upload the health-plan PDF documents used for this quickstart.

5. Before you leave the lakehouse, copy the URL, or get the workspace and lakehouse IDs, so that you can specify the lakehouse in the wizard. The URL is in this format: https://msit.powerbi.com/groups/00000000-0000-0000-0000-000000000000/lakehouses/11111111-1111-1111-1111-111111111111?experience=data-engineering.

Set up embedding models

Integrated vectorization and the Import and vectorize data wizard tap into deployed embedding models during indexing to convert text and images into vectors.

You can use embedding models deployed in Azure OpenAI, in Azure AI Vision for multimodal embeddings, or in the model catalog in Azure AI Studio.

Azure OpenAI

Import and vectorize data supports text-embedding-ada-002, text-embedding-3-large, and text-embedding-3-small. Internally, the wizard uses the AzureOpenAIEmbedding skill to connect to Azure OpenAI.

Use these instructions to assign permissions or get an API key for search service connection to Azure OpenAI. You should set up permissions or have connection information available before you run the wizard.

1. Sign in to the Azure portal with your Azure account, and go to your Azure OpenAI resource.

2. Set up permissions:

   1. On the left menu, select Access control.

   2. Select Add, and then select Add role assignment.

   3. Under Job function roles, select Cognitive Services OpenAI User, and then select Next.

   4. Under Members, select Managed identity, and then select Members.

   5. Filter by subscription and resource type (search services), and then select the managed identity of your search service.

   6. Select Review + assign.

3. On the Overview page, select Click here to view endpoints or Click here to manage keys if you need to copy an endpoint or API key. You can paste these values into the wizard if you're using an Azure OpenAI resource with key-based authentication.

4. Under Resource Management and Model deployments, select Manage Deployments to open Azure AI Studio.

5. Copy the deployment name of text-embedding-ada-002 or another supported embedding model. If you don't have an embedding model, deploy one now.

Azure AI Vision

Import and vectorize data supports Azure AI Vision image retrieval through multimodal embeddings (version 4.0). Internally, the wizard uses the multimodal embeddings skill to connect to Azure AI Vision.

1. Create an Azure AI Vision service in a supported region.

2. Make sure your Azure AI Search service is in the same region.

3. After the service is deployed, go to the resource and select Access control to assign the Cognitive Services OpenAI User role to your search service's managed identity. Optionally, you can use key-based authentication for the connection.

After you finish these steps, you should be able to select the Azure AI Vision vectorizer in the Import and vectorize data wizard.

Note

If you can't select an Azure AI Vision vectorizer, make sure you have an Azure AI Vision resource in a supported region. Also make sure that your search service's managed identity has Cognitive Services OpenAI User permissions.

Azure AI Studio model catalog

Import and vectorize data supports Azure, Cohere, and Facebook embedding models in the Azure AI Studio model catalog, but it doesn't currently support the OpenAI CLIP model. Internally, the wizard uses the AML skill to connect to the catalog.

Use these instructions to assign permissions or get an API key for search service connection to Azure OpenAI. You should set up permissions or have connection information available before you run the wizard.

1. For the model catalog, you should have an Azure OpenAI resource, a hub in Azure AI Studio, and a project. Hubs and projects that have the same name can share connection information and permissions.

2. Deploy a supported embedding model to the model catalog in your project.

3. For RBAC, create two role assignments: one for Azure AI Search, and another for the AI Studio project. Assign the Cognitive Services OpenAI User role for embeddings and vectorization.

Start the wizard

1. Sign in to the Azure portal with your Azure account, and go to your Azure AI Search service.

2. On the Overview page, select Import and vectorize data.

   

Connect to your data

The next step is to connect to a data source to use for the search index.

1. In the Import and vectorize data wizard, on the Set up your data connection page, select Azure Blob Storage or OneLake.

2. Specify the Azure subscription.

3. For OneLake, specify the lakehouse URL, or provide the workspace and lakehouse IDs.

   For Azure Storage, select the account and container that provide the data.

4. Specify whether you want deletion detection.

5. Select Next.

Vectorize your text

In this step, specify the embedding model for vectorizing chunked data.

1. On the Vectorize your text page, specify whether deployed models are on Azure OpenAI, the Azure AI Studio model catalog, or an existing Azure AI Vision multimodal resource in the same region as Azure AI Search.

2. Specify the Azure subscription.

3. Make selections according to the resource:

   1. For Azure OpenAI, select the service, model deployment, and authentication type.

   2. For AI Studio catalog, select the project, model deployment, and authentication type.

   3. For AI Vision vectorization, select the account.

   For more information, see Set up embedding models earlier in this article.

4. Select the checkbox that acknowledges the billing impact of using these resources.

5. Select Next.

Vectorize and enrich your images

If your content includes images, you can apply AI in two ways:

• Use a supported image embedding model from the catalog, or choose the Azure AI Vision multimodal embeddings API to vectorize images.
• Use optical character recognition (OCR) to recognize text in images.

Azure AI Search and your Azure AI resource must be in the same region.

1. On the Vectorize your images page, specify the kind of connection the wizard should make. For image vectorization, the wizard can connect to embedding models in Azure AI Studio or Azure AI Vision.

2. Specify the subscription.

3. For the Azure AI Studio model catalog, specify the project and deployment. For more information, see Set up embedding models earlier in this article.

4. Optionally, you can crack binary images (for example, scanned document files) and use OCR to recognize text.

5. Select the checkbox that acknowledges the billing impact of using these resources.

6. Select Next.

Choose advanced settings

1. On the Advanced settings page, you can optionally add semantic ranking to rerank results at the end of query execution. Reranking promotes the most semantically relevant matches to the top.

2. Optionally, specify a run schedule for the indexer.

3. Select Next.

Finish the wizard

1. On the Review your configuration page, specify a prefix for the objects that the wizard will create. A common prefix helps you stay organized.

2. Select Create.

When the wizard completes the configuration, it creates the following objects:

• Data source connection.

• Index with vector fields, vectorizers, vector profiles, and vector algorithms. You can't design or modify the default index during the wizard workflow. Indexes conform to the 2024-05-01-preview REST API.

• Skillset with the Text Split skill for chunking and an embedding skill for vectorization. The embedding skill is either the AzureOpenAIEmbeddingModel skill for Azure OpenAI or the AML skill for the Azure AI Studio model catalog.

• Indexer with field mappings and output field mappings (if applicable).

Check results

Search Explorer accepts text strings as input and then vectorizes the text for vector query execution.

1. In the Azure portal, go to Search Management > Indexes, and then select the index that you created.

2. Optionally, select Query options and hide vector values in search results. This step makes your search results easier to read.

   

3. On the View menu, select JSON view so that you can enter text for your vector query in the text vector query parameter.

   

   The wizard offers a default query that issues a vector query on the vector field and returns the five nearest neighbors. If you opted to hide vector values, your default query includes a select statement that excludes the vector field from search results.

   {
      "select": "chunk_id,parent_id,chunk,title",
      "vectorQueries": [
          {
             "kind": "text",
             "text": "*",
             "k": 5,
             "fields": "vector"
          }
       ]
   }
   

4. For the text value, replace the asterisk (*) with a question related to health plans, such as Which plan has the lowest deductible?.

5. Select Search to run the query.

   

   Five matches should appear. Each document is a chunk of the original PDF. The title field shows which PDF the chunk comes from.

6. To see all of the chunks from a specific document, add a filter for the title field for a specific PDF:

   {
      "select": "chunk_id,parent_id,chunk,title",
      "filter": "title eq 'Benefit_Options.pdf'",
      "count": true,
      "vectorQueries": [
          {
             "kind": "text",
             "text": "*",
             "k": 5,
             "fields": "vector"
          }
       ]
   }
   
   

Clean up

Azure AI Search is a billable resource. If you no longer need it, delete it from your subscription to avoid charges.

Next step

This quickstart introduced you to the Import and vectorize data wizard that creates all of the necessary objects for integrated vectorization. If you want to explore each step in detail, try an integrated vectorization sample.