= OpenAI Embeddings

Spring AI supports the OpenAI's text embeddings models.
OpenAI’s text embeddings measure the relatedness of text strings.
An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

== Prerequisites

You will need to create an API with OpenAI to access OpenAI embeddings models.

Create an account at https://platform.openai.com/signup[OpenAI signup page] and generate the token on the https://platform.openai.com/account/api-keys[API Keys page].
The Spring AI project defines a configuration property named `spring.ai.openai.api-key` that you should set to the value of the `API Key` obtained from openai.com.
Exporting an environment variable is one way to set that configuration property:

[source,shell]
----
export SPRING_AI_OPENAI_API_KEY=<INSERT KEY 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-openai-spring-boot-starter</artifactId>
</dependency>
----

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

[source,groovy]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-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

==== Retry Properties

The prefix `spring.ai.retry` is used as the property prefix that lets you configure the retry mechanism for the OpenAI Embedding client.

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

| spring.ai.retry.max-attempts   | Maximum number of retry attempts. |  10
| spring.ai.retry.backoff.initial-interval | Initial sleep duration for the exponential backoff policy. |  2 sec.
| spring.ai.retry.backoff.multiplier | Backoff interval multiplier. |  5
| spring.ai.retry.backoff.max-interval | Maximum backoff duration. |  3 min.
| spring.ai.retry.on-client-errors | If false, throw a NonTransientAiException, and do not attempt retry for `4xx` client error codes | false
| spring.ai.retry.exclude-on-http-codes | List of HTTP status codes that should not trigger a retry (e.g. to throw NonTransientAiException). | empty
| spring.ai.retry.on-http-codes | List of HTTP status codes that should trigger a retry (e.g. to throw TransientAiException). | empty
|====

==== Connection Properties

The prefix `spring.ai.openai` is used as the property prefix that lets you connect to OpenAI.

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

| spring.ai.openai.base-url   | The URL to connect to |  https://api.openai.com
| spring.ai.openai.api-key    | The API Key           |  -
|====

==== Configuration Properties

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

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

| spring.ai.openai.embedding.enabled | Enable OpenAI embedding client.  | true
| spring.ai.openai.embedding.base-url   | Optional overrides the spring.ai.openai.base-url to provide embedding specific url | -
| spring.ai.openai.embedding.api-key    | Optional overrides the spring.ai.openai.api-key to provide embedding specific api-key  | -
| spring.ai.openai.embedding.metadata-mode      | Document content extraction mode.      | EMBED
| spring.ai.openai.embedding.options.model      | The model to use      | text-embedding-ada-002 (other options: text-embedding-3-large, text-embedding-3-small)
| spring.ai.openai.embedding.options.encodingFormat   | The format to return the embeddings in. Can be either float or base64.  | -
| spring.ai.openai.embedding.options.user   | A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.  | -
| spring.ai.openai.embedding.options.dimensions   | The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.  | -
|====

NOTE: You can override the common `spring.ai.openai.base-url` and `spring.ai.openai.api-key` for the `ModelCall` and `EmbeddingClient` implementations.
The `spring.ai.openai.embedding.base-url` and `spring.ai.openai.embedding.api-key` properties if set take precedence over the common properties.
Similarly, the `spring.ai.openai.embedding.base-url` and `spring.ai.openai.embedding.api-key` properties if set take precedence over the common properties.
This is useful if you want to use different OpenAI accounts for different models and different model endpoints.

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

== Runtime Options [[embedding-options]]

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiEmbeddingOptions.java[OpenAiEmbeddingOptions.java] provides the OpenAI configurations, such as the model to use and etc.

The default options can be configured using the `spring.ai.openai.embedding.options` properties as well.

At start-time use the `OpenAiEmbeddingClient` constructor to set the  default options used for all embedding requests.
At run-time you can override the default options, using a `OpenAiEmbeddingOptions` instance as part of your `EmbeddingRequest`.

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"),
        OpenAiEmbeddingOptions.builder()
            .withModel("Different-Embedding-Model-Deployment-Name")
        .build()));
----

== Sample Controller

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.openai.api-key=YOUR_API_KEY
spring.ai.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 are not using Spring Boot, you can manually configure the OpenAI Embedding Client.
For this add the `spring-ai-openai` dependency to your project's Maven `pom.xml` file:
[source, xml]
----
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai</artifactId>
</dependency>
----

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

[source,groovy]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-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-openai` dependency provides access also to the `OpenAiModelCall`.
For more information about the `OpenAiModelCall` refer to the link:../chat/openai-chat.html[OpenAI Chat Client] section.

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

[source,java]
----
var openAiApi = new OpenAiApi(System.getenv("OPENAI_API_KEY"));

var embeddingClient = new OpenAiEmbeddingClient(
        openAiApi,
        MetadataMode.EMBED,
        OpenAiEmbeddingOptions.builder()
                .withModel("text-embedding-ada-002")
                .withUser("user-6")
                .build(),
        RetryUtils.DEFAULT_RETRY_TEMPLATE);

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

The `OpenAiEmbeddingOptions` provides the configuration information for the embedding requests.
The options class offers a `builder()` for easy options creation.


