= Azure OpenAI Embeddings

Azure's OpenAI extends the OpenAI capabilities, offering safe text generation and Embeddings computation models for various task:

- Similarity embeddings are good at capturing semantic similarity between two or more pieces of text.
- Text search embeddings help measure whether long documents are relevant to a short query.
- Code search embeddings are useful for embedding code snippets and embedding natural language search queries.

The Azure OpenAI embeddings rely on `cosine similarity` to compute similarity between documents and a query.

== Prerequisites

Obtain your Azure OpenAI `endpoint` and `api-key` from the Azure OpenAI Service section on the link:https://portal.azure.com[Azure Portal].

Spring AI defines a configuration property named `spring.ai.azure.openai.api-key` that you should set to the value of the `API Key` obtained from Azure.
There is also a configuration property named `spring.ai.azure.openai.endpoint` that you should set to the endpoint URL obtained when provisioning your model in Azure.

Exporting environment variables is one way to set these configuration properties:
[source,shell]
----
export SPRING_AI_AZURE_OPENAI_API_KEY=<INSERT KEY HERE>
export SPRING_AI_AZURE_OPENAI_ENDPOINT=<INSERT ENDPOINT URL HERE>
----

=== Add Repositories and BOM

Spring AI artifacts are published in Spring Milestone and Snapshot repositories.   Refer to the xref:getting-started.adoc#repositories[Repositories] section to add these repositories to your build system.

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build system.


== Auto-configuration

Spring AI provides Spring Boot auto-configuration for the Azure OpenAI Embedding Client.
To enable it add the following dependency to your project's Maven `pom.xml` file:

[source, xml]
----
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-openai-spring-boot-starter</artifactId>
</dependency>
----

or to your Gradle `build.gradle` build file.

[source,groovy]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-azure-openai-spring-boot-starter'
}
----

TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.

=== Embedding Properties

The prefix `spring.ai.azure.openai` is the property prefix to configure the connection to Azure OpenAI.

[cols="3,5,1"]
|====
| Property | Description | Default

| spring.ai.azure.openai.api-key |  The Key from Azure AI OpenAI `Keys and Endpoint` section under `Resource Management`  | -
| spring.ai.azure.openai.endpoint | The endpoint from the Azure AI OpenAI `Keys and Endpoint` section under `Resource Management` | -
|====


The prefix `spring.ai.azure.openai.embedding` is the property prefix that configures the `EmbeddingClient` implementation for Azure OpenAI

[cols="3,5,1"]
|====
| Property | Description | Default

| spring.ai.azure.openai.embedding.enabled | Enable Azure OpenAI embedding client.  | true
| spring.ai.azure.openai.embedding.metadata-mode | Document content extraction mode    | EMBED
| spring.ai.azure.openai.embedding.options.deployment-name | This is the value of the 'Deployment Name' as presented in the Azure AI Portal | text-embedding-ada-002
| spring.ai.azure.openai.embedding.options.user | An identifier for the caller or end user of the operation. This may be used for tracking or rate-limiting purposes. | -
|====

TIP: All properties prefixed with `spring.ai.azure.openai.embedding.options` can be overridden at runtime by adding a request specific <<embedding-options>> to the `EmbeddingRequest` call.

== Runtime Options [[embedding-options]]

The `AzureOpenAiEmbeddingOptions` provides the configuration information for the embedding requests.
The `AzureOpenAiEmbeddingOptions` offers a builder to create the options.

At start time use the `AzureOpenAiEmbeddingClient` constructor to set the  default options used for all embedding requests.
At run-time you can override the default options, by passing a `AzureOpenAiEmbeddingOptions` instance with your to the  `EmbeddingRequest` request.

For example to override the default model name for a specific request:

[source,java]
----
EmbeddingResponse embeddingResponse = embeddingClient.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        AzureOpenAiEmbeddingOptions.builder()
        .withModel("Different-Embedding-Model-Deployment-Name")
        .build()));
----


== Sample Code

This will create a `EmbeddingClient` implementation that you can inject into your class.
Here is an example of a simple `@Controller` class that uses the `EmbeddingClient` implementation.

[source,application.properties]
----
spring.ai.azure.openai.api-key=YOUR_API_KEY
spring.ai.azure.openai.endpoint=YOUR_ENDPOINT
spring.ai.azure.openai.embedding.options.model=text-embedding-ada-002
----

[source,java]
----
@RestController
public class EmbeddingController {

    private final EmbeddingClient embeddingClient;

    @Autowired
    public EmbeddingController(EmbeddingClient embeddingClient) {
        this.embeddingClient = embeddingClient;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
        return Map.of("embedding", embeddingResponse);
    }
}
----

== Manual Configuration

If you prefer not to use the Spring Boot auto-configuration, you can manually configure the `AzureOpenAiEmbeddingClient` in your application.
For this add the `spring-ai-azure-openai` dependency to your project's Maven `pom.xml` file:
[source, xml]
----
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-openai</artifactId>
</dependency>
----

or to your Gradle `build.gradle` build file.

[source,gradle]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-azure-openai'
}
----

TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.

NOTE: The `spring-ai-azure-openai` dependency also provide the access to the `AzureOpenAiEmbeddingClient`. For more information about the `AzureOpenAiModelCall` refer to the link:../embeddings/azure-openai-embeddings.html[Azure OpenAI Embeddings] section.

Next, create an `AzureOpenAiEmbeddingClient` instance and use it to compute the similarity between two input texts:

[source,java]
----
var openAIClient = OpenAIClientBuilder()
        .credential(new AzureKeyCredential(System.getenv("AZURE_OPENAI_API_KEY")))
		.endpoint(System.getenv("AZURE_OPENAI_ENDPOINT"))
		.buildClient();

var embeddingClient = new AzureOpenAiEmbeddingClient(openAIClient)
    .withDefaultOptions(AzureOpenAiEmbeddingOptions.builder()
        .withModel("text-embedding-ada-002")
        .withUser("user-6")
        .build());

EmbeddingResponse embeddingResponse = embeddingClient
	.embedForResponse(List.of("Hello World", "World is big and salvation is near"));
----

NOTE: the `text-embedding-ada-002` is actually the `Deployment Name` as presented in the Azure AI Portal.

