= OpenAI Chat

Spring AI supports the various AI language models from OpenAI, the company behind ChatGPT, which has been instrumental in sparking interest in AI-driven text generation thanks to its creation of industry-leading text generation models and embeddings.

== Prerequisites

You will need to create an API with OpenAI to access ChatGPT 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.

You can set this configuration property in your `application.properties` file:

[source,properties]
----
spring.ai.openai.api-key=<your-openai-api-key>
----

For enhanced security when handling sensitive information like API keys, you can use Spring Expression Language (SpEL) to reference a custom environment variable:

[source,yaml]
----
# In application.yml
spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
----

[source,bash]
----
# In your environment or .env file
export OPENAI_API_KEY=<your-openai-api-key>
----

You can also set this configuration programmatically in your application code:

[source,java]
----
// Retrieve API key from a secure source or environment variable
String apiKey = System.getenv("OPENAI_API_KEY");
----

=== Add Repositories and BOM

Spring AI artifacts are published in Maven Central and Spring Snapshot repositories.
Refer to the xref:getting-started.adoc#artifact-repositories[Artifact 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

[NOTE]
====
There has been a significant change in the Spring AI auto-configuration, starter modules' artifact names.
Please refer to the https://docs.spring.io/spring-ai/reference/upgrade-notes.html[upgrade notes] for more information.
====

Spring AI provides Spring Boot auto-configuration for the OpenAI Chat Client.
To enable it add the following dependency to your project's Maven `pom.xml` or Gradle `build.gradle` build files:

[tabs]
======
Maven::
+
[source, xml]
----
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>
----

Gradle::
+
[source,groovy]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-starter-model-openai'
}
----
======

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

=== Chat Properties

==== Retry Properties

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

[cols="3,5,1", stripes=even]
|====
| 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", stripes=even]
|====
| 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           |  -
| spring.ai.openai.organization-id | Optionally, you can specify which organization to use for an API request. |  -
| spring.ai.openai.project-id      | Optionally, you can specify which project to use for an API request. |  -
|====

TIP: For users that belong to multiple organizations (or are accessing their projects through their legacy user API key), you can optionally specify which organization and project is used for an API request.
Usage from these API requests will count as usage for the specified organization and project.

==== Configuration Properties

[NOTE]
====
Enabling and disabling of the chat auto-configurations are now configured via top level properties with the prefix `spring.ai.model.chat`.

To enable, spring.ai.model.chat=openai (It is enabled by default)

To disable, spring.ai.model.chat=none (or any value which doesn't match openai)

This change is done to allow configuration of multiple models.
====

The prefix `spring.ai.openai.chat` is the property prefix that lets you configure the chat model implementation for OpenAI.

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

| spring.ai.openai.chat.enabled (Removed and no longer valid) | Enable OpenAI chat model.  | true
| spring.ai.model.chat | Enable OpenAI chat model.  | openai
| spring.ai.openai.chat.base-url   | Optional override for the `spring.ai.openai.base-url` property to provide a chat-specific URL. |  -
| spring.ai.openai.chat.completions-path   | The path to append to the base URL. |  `/v1/chat/completions`
| spring.ai.openai.chat.api-key   | Optional override for the `spring.ai.openai.api-key` to provide a chat-specific API Key. |  -
| spring.ai.openai.chat.organization-id | Optionally, you can specify which organization to use for an API request. |  -
| spring.ai.openai.chat.project-id      | Optionally, you can specify which project to use for an API request. |  -
| spring.ai.openai.chat.options.model | Name of the OpenAI chat model to use. You can select between models such as: `gpt-4o`, `gpt-4o-mini`, `gpt-4-turbo`, `gpt-3.5-turbo`, and more. See the https://platform.openai.com/docs/models[models] page for more information. | `gpt-4o-mini`
| spring.ai.openai.chat.options.temperature | The sampling temperature to use that controls the apparent creativity of generated completions. Higher values will make output more random while lower values will make results more focused and deterministic. It is not recommended to modify `temperature` and `top_p` for the same completions request as the interaction of these two settings is difficult to predict. | 0.8
| spring.ai.openai.chat.options.frequencyPenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. | 0.0f
| spring.ai.openai.chat.options.logitBias | Modify the likelihood of specified tokens appearing in the completion. | -
| spring.ai.openai.chat.options.maxTokens | (Deprecated in favour of `maxCompletionTokens`) The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. | -
| spring.ai.openai.chat.options.maxCompletionTokens | An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens. | -
| spring.ai.openai.chat.options.n | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as 1 to minimize costs. | 1
| spring.ai.openai.chat.options.store | Whether to store the output of this chat completion request for use in our model | false
| spring.ai.openai.chat.options.metadata | Developer-defined tags and values used for filtering completions in the chat completion dashboard | empty map
| spring.ai.openai.chat.options.output-modalities | Output types that you would like the model to generate for this request. Most models are capable of generating text, which is the default.
The `gpt-4o-audio-preview` model can also be used to generate audio. To request that this model generate both text and audio responses,
you can use: `text`, `audio`. Not supported for streaming. | -
| spring.ai.openai.chat.options.output-audio | Audio parameters for the audio generation. Required when audio output is requested with `output-modalities`: `audio`.
Requires the `gpt-4o-audio-preview` model and is is not supported for streaming completions. | -
| spring.ai.openai.chat.options.presencePenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. | -
| spring.ai.openai.chat.options.responseFormat.type | Compatible with `GPT-4o`, `GPT-4o mini`, `GPT-4 Turbo` and all `GPT-3.5 Turbo` models newer than `gpt-3.5-turbo-1106`. The `JSON_OBJECT` type enables JSON mode, which guarantees the message the model generates is valid JSON.
The `JSON_SCHEMA` type enables link:https://platform.openai.com/docs/guides/structured-outputs[Structured Outputs] which guarantees the model will match your supplied JSON schema. The JSON_SCHEMA type requires setting the `responseFormat.schema` property as well. | -
| spring.ai.openai.chat.options.responseFormat.name | Response format schema name. Applicable only for `responseFormat.type=JSON_SCHEMA` | custom_schema
| spring.ai.openai.chat.options.responseFormat.schema | Response format JSON schema. Applicable only for `responseFormat.type=JSON_SCHEMA` | -
| spring.ai.openai.chat.options.responseFormat.strict | Response format JSON schema adherence strictness. Applicable only for `responseFormat.type=JSON_SCHEMA` | -
| spring.ai.openai.chat.options.seed | This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. | -
| spring.ai.openai.chat.options.stop | Up to 4 sequences where the API will stop generating further tokens. | -
| spring.ai.openai.chat.options.topP | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both. | -
| spring.ai.openai.chat.options.tools | A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. | -
| spring.ai.openai.chat.options.toolChoice | Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"type: "function", "function": {"name": "my_function"}}` forces the model to call that function. `none` is the default when no functions are present. `auto` is the default if functions are present. | -
| spring.ai.openai.chat.options.user | A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. | -
| spring.ai.openai.chat.options.functions | List of functions, identified by their names, to enable for function calling in a single prompt requests. Functions with those names must exist in the `functionCallbacks` registry. | -
| spring.ai.openai.chat.options.stream-usage | (For streaming only) Set to add an additional chunk with token usage statistics for the entire request. The `choices` field for this chunk is an empty array and all other chunks will also include a usage field, but with a null value. | false
| spring.ai.openai.chat.options.parallel-tool-calls | Whether to enable link:https://platform.openai.com/docs/guides/function-calling/parallel-function-calling[parallel function calling] during tool use. | true
| spring.ai.openai.chat.options.http-headers | Optional HTTP headers to be added to the chat completion request. To override the `api-key` you need to use an `Authorization` header key, and you have to prefix the key value with the `Bearer` prefix. | -
| spring.ai.openai.chat.options.proxy-tool-calls | If true, the Spring AI will not handle the function calls internally, but will proxy them to the client. Then is the client's responsibility to handle the function calls, dispatch them to the appropriate function, and return the results. If false (the default), the Spring AI will handle the function calls internally. Applicable only for chat models with function calling support | false
|====

NOTE: You can override the common `spring.ai.openai.base-url` and `spring.ai.openai.api-key` for the `ChatModel` and `EmbeddingModel` implementations.
The `spring.ai.openai.chat.base-url` and `spring.ai.openai.chat.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.chat.options` can be overridden at runtime by adding request-specific <<chat-options>> to the `Prompt` call.

== Runtime Options [[chat-options]]

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java[OpenAiChatOptions.java] class provides model configurations such as the model to use, the temperature, the frequency penalty, etc.

On start-up, the default options can be configured with the `OpenAiChatModel(api, options)` constructor or the `spring.ai.openai.chat.options.*` properties.

At run-time, you can override the default options by adding new, request-specific options to the `Prompt` call.
For example, to override the default model and temperature for a specific request:

[source,java]
----
ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        OpenAiChatOptions.builder()
            .model("gpt-4o")
            .temperature(0.4)
        .build()
    ));
----

TIP: In addition to the model specific https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java[OpenAiChatOptions] you can use a portable link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java[ChatOptions] instance, created with link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/prompt/DefaultChatOptionsBuilder.java[ChatOptions#builder()].

== Function Calling

You can register custom Java functions with the `OpenAiChatModel` and have the OpenAI model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
This is a powerful technique to connect the LLM capabilities with external tools and APIs.
Read more about xref:api/tools.adoc[Tool Calling].

== Multimodal

Multimodality refers to a model's ability to simultaneously understand and process information from various sources, including text, images, audio, and other data formats.
OpenAI supports text, vision, and audio input modalities.

=== Vision

OpenAI models that offer vision multimodal support include `gpt-4`, `gpt-4o`, and `gpt-4o-mini`.
Refer to the link:https://platform.openai.com/docs/guides/vision[Vision] guide for more information.

The OpenAI link:https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages[User Message API] can incorporate a list of base64-encoded images or image urls with the message.
Spring AI’s link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/messages/Message.java[Message] interface facilitates multimodal AI models by introducing the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-commons/src/main/java/org/springframework/ai/content/Media.java[Media] type.
This type encompasses data and details regarding media attachments in messages, utilizing Spring’s `org.springframework.util.MimeType` and a `org.springframework.core.io.Resource` for the raw media data.

Below is a code example excerpted from link:https://github.com/spring-projects/spring-ai/blob/v1.0.0/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatModelIT.java#L469[OpenAiChatModelIT.java], illustrating the fusion of user text with an image using the `gpt-4o` model.

[source,java]
----
var imageData = new ClassPathResource("/test.png");

var userMessage = UserMessage.builder()
    .text("Explain what do you see on this picture?")
    .media(List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)))
    .build();

var response = this.chatModel
    .call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().model(modelName).build()));
----

TIP: GPT_4_VISION_PREVIEW will continue to be available only to existing users of this model starting June 17, 2024. If you are not an existing user, please use the GPT_4_O or GPT_4_TURBO models. More details https://platform.openai.com/docs/deprecations/2024-06-06-gpt-4-32k-and-vision-preview-models[here]

or the image URL equivalent using the `gpt-4o` model:

[source,java]
----
var userMessage = UserMessage.builder()
    .text("Explain what do you see on this picture?")
    .media(List.of(Media.builder()
        .mimeType(MimeTypeUtils.IMAGE_PNG)
        .data(URI.create("https://docs.spring.io/spring-ai/reference/_images/multimodal.test.png"))
        .build()))
    .build();

ChatResponse response = this.chatModel
    .call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().model(modelName).build()));
----

TIP: You can pass multiple images as well.

The example shows a model taking as an input the `multimodal.test.png` image:

image::multimodal.test.png[Multimodal Test Image, 200, 200, align="left"]

along with the text message "Explain what do you see on this picture?", and generating a response like this:

----
This is an image of a fruit bowl with a simple design. The bowl is made of metal with curved wire edges that
create an open structure, allowing the fruit to be visible from all angles. Inside the bowl, there are two
yellow bananas resting on top of what appears to be a red apple. The bananas are slightly overripe, as
indicated by the brown spots on their peels. The bowl has a metal ring at the top, likely to serve as a handle
for carrying. The bowl is placed on a flat surface with a neutral-colored background that provides a clear
view of the fruit inside.
----

=== Audio

OpenAI models that offer input audio multimodal support include `gpt-4o-audio-preview`.
Refer to the link:https://platform.openai.com/docs/guides/audio[Audio] guide for more information.

The OpenAI link:https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages[User Message API] can incorporate a list of base64-encoded audio files with the message.
Spring AI’s link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/messages/Message.java[Message] interface facilitates multimodal AI models by introducing the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-commons/src/main/java/org/springframework/ai/content/Media.java[Media] type.
This type encompasses data and details regarding media attachments in messages, utilizing Spring’s `org.springframework.util.MimeType` and a `org.springframework.core.io.Resource` for the raw media data.
Currently, OpenAI support only the following media types: `audio/mp3` and `audio/wav`.

Below is a code example excerpted from link:https://github.com/spring-projects/spring-ai/blob/c9a3e66f90187ce7eae7eb78c462ec622685de6c/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatModelIT.java#L442[OpenAiChatModelIT.java], illustrating the fusion of user text with an audio file using the `gpt-4o-audio-preview` model.

[source,java]
----
var audioResource = new ClassPathResource("speech1.mp3");

var userMessage = new UserMessage("What is this recording about?",
        List.of(new Media(MimeTypeUtils.parseMimeType("audio/mp3"), audioResource)));

ChatResponse response = chatModel.call(new Prompt(List.of(userMessage),
        OpenAiChatOptions.builder().model(OpenAiApi.ChatModel.GPT_4_O_AUDIO_PREVIEW).build()));
----

TIP: You can pass multiple audio files as well.

=== Output Audio

OpenAI models that offer input audio multimodal support include `gpt-4o-audio-preview`.
Refer to the link:https://platform.openai.com/docs/guides/audio[Audio] guide for more information.

The OpenAI link:https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages[Assistant Message API] can contain a list of base64-encoded audio files with the message.
Spring AI’s link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/messages/Message.java[Message] interface facilitates multimodal AI models by introducing the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-commons/src/main/java/org/springframework/ai/content/Media.java[Media] type.
This type encompasses data and details regarding media attachments in messages, utilizing Spring’s `org.springframework.util.MimeType` and a `org.springframework.core.io.Resource` for the raw media data.
Currently, OpenAI support only the following audio types: `audio/mp3` and `audio/wav`.

Below is a code example, illustrating the response of user text along with an audio byte array, using the `gpt-4o-audio-preview` model:

[source,java]
----
var userMessage = new UserMessage("Tell me joke about Spring Framework");

ChatResponse response = chatModel.call(new Prompt(List.of(userMessage),
        OpenAiChatOptions.builder()
            .model(OpenAiApi.ChatModel.GPT_4_O_AUDIO_PREVIEW)
            .outputModalities(List.of("text", "audio"))
            .outputAudio(new AudioParameters(Voice.ALLOY, AudioResponseFormat.WAV))
            .build()));

String text = response.getResult().getOutput().getContent(); // audio transcript

byte[] waveAudio = response.getResult().getOutput().getMedia().get(0).getDataAsByteArray(); // audio data
----

You have to specify an `audio` modality in the `OpenAiChatOptions` to generate audio output.
The `AudioParameters` class provides the voice and audio format for the audio output.

== Structured Outputs

OpenAI provides custom https://platform.openai.com/docs/guides/structured-outputs[Structured Outputs] APIs that ensure your model generates responses conforming strictly to your provided `JSON Schema`.
In addition to the existing Spring AI model-agnostic xref::api/structured-output-converter.adoc[Structured Output Converter], these APIs offer enhanced control and precision.

NOTE: Currently, OpenAI supports a link:https://platform.openai.com/docs/guides/structured-outputs/supported-schemas[subset of the JSON Schema language] format.

=== Configuration

Spring AI allows you to configure your response format either programmatically using the `OpenAiChatOptions` builder or through application properties.

==== Using the Chat Options Builder

You can set the response format programmatically with the `OpenAiChatOptions` builder as shown below:

[source,java]
----
String jsonSchema = """
        {
            "type": "object",
            "properties": {
                "steps": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "explanation": { "type": "string" },
                            "output": { "type": "string" }
                        },
                        "required": ["explanation", "output"],
                        "additionalProperties": false
                    }
                },
                "final_answer": { "type": "string" }
            },
            "required": ["steps", "final_answer"],
            "additionalProperties": false
        }
        """;

Prompt prompt = new Prompt("how can I solve 8x + 7 = -23",
        OpenAiChatOptions.builder()
            .model(ChatModel.GPT_4_O_MINI)
            .responseFormat(new ResponseFormat(ResponseFormat.Type.JSON_SCHEMA, this.jsonSchema))
            .build());

ChatResponse response = this.openAiChatModel.call(this.prompt);
----

NOTE: Adhere to the OpenAI link:https://platform.openai.com/docs/guides/structured-outputs/supported-schemas[subset of the JSON Schema language] format.

==== Integrating with BeanOutputConverter Utilities

You can leverage existing xref::api/structured-output-converter.adoc#_bean_output_converter[BeanOutputConverter] utilities to automatically generate the JSON Schema from your domain objects and later convert the structured response into domain-specific instances:

--
[tabs]
======
Java::
+
[source,java]
----
record MathReasoning(
    @JsonProperty(required = true, value = "steps") Steps steps,
    @JsonProperty(required = true, value = "final_answer") String finalAnswer) {

    record Steps(
        @JsonProperty(required = true, value = "items") Items[] items) {

        record Items(
            @JsonProperty(required = true, value = "explanation") String explanation,
            @JsonProperty(required = true, value = "output") String output) {
        }
    }
}

var outputConverter = new BeanOutputConverter<>(MathReasoning.class);

var jsonSchema = this.outputConverter.getJsonSchema();

Prompt prompt = new Prompt("how can I solve 8x + 7 = -23",
        OpenAiChatOptions.builder()
            .model(ChatModel.GPT_4_O_MINI)
            .responseFormat(new ResponseFormat(ResponseFormat.Type.JSON_SCHEMA, this.jsonSchema))
            .build());

ChatResponse response = this.openAiChatModel.call(this.prompt);
String content = this.response.getResult().getOutput().getContent();

MathReasoning mathReasoning = this.outputConverter.convert(this.content);
----
Kotlin::
+
[source,kotlin]
----
data class MathReasoning(
	val steps: Steps,
	@get:JsonProperty(value = "final_answer") val finalAnswer: String) {

	data class Steps(val items: Array<Items>) {

		data class Items(
			val explanation: String,
			val output: String)
	}
}

val outputConverter = BeanOutputConverter(MathReasoning::class.java)

val jsonSchema = outputConverter.jsonSchema;

val prompt = Prompt("how can I solve 8x + 7 = -23",
	OpenAiChatOptions.builder()
		.model(ChatModel.GPT_4_O_MINI)
		.responseFormat(ResponseFormat(ResponseFormat.Type.JSON_SCHEMA, jsonSchema))
		.build())

val response = openAiChatModel.call(prompt)
val content = response.getResult().getOutput().getContent()

val mathReasoning = outputConverter.convert(content)
----
======
--

NOTE: Although this is optional for JSON Schema, OpenAI link:https://platform.openai.com/docs/guides/structured-outputs/all-fields-must-be-required#all-fields-must-be-required[mandates] required fields for the structured response to function correctly. Kotlin reflection is used to infer which property are required or not based on the nullability of types and default values of parameters, so for most use case `@get:JsonProperty(required = true)` is not needed. `@get:JsonProperty(value = "custom_name")` can be useful to customize the property name. Make sure to generate the annotation on the related getters with this `@get:` syntax, see link:https://kotlinlang.org/docs/annotations.html#annotation-use-site-targets[related documentation].

==== Configuring via Application Properties

Alternatively, when using the OpenAI auto-configuration, you can configure the desired response format through the following application properties:

[source,application.properties]
----
spring.ai.openai.api-key=YOUR_API_KEY
spring.ai.openai.chat.options.model=gpt-4o-mini

spring.ai.openai.chat.options.response-format.type=JSON_SCHEMA
spring.ai.openai.chat.options.response-format.name=MySchemaName
spring.ai.openai.chat.options.response-format.schema={"type":"object","properties":{"steps":{"type":"array","items":{"type":"object","properties":{"explanation":{"type":"string"},"output":{"type":"string"}},"required":["explanation","output"],"additionalProperties":false}},"final_answer":{"type":"string"}},"required":["steps","final_answer"],"additionalProperties":false}
spring.ai.openai.chat.options.response-format.strict=true
----

== Sample Controller

https://start.spring.io/[Create] a new Spring Boot project and add the `spring-ai-starter-model-openai` to your pom (or gradle) dependencies.

Add an `application.properties` file under the `src/main/resources` directory to enable and configure the OpenAi chat model:

[source,application.properties]
----
spring.ai.openai.api-key=YOUR_API_KEY
spring.ai.openai.chat.options.model=gpt-4o
spring.ai.openai.chat.options.temperature=0.7
----

TIP: Replace the `api-key` with your OpenAI credentials.

This will create an `OpenAiChatModel` implementation that you can inject into your classes.
Here is an example of a simple `@RestController` class that uses the chat model for text generations.

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

    private final OpenAiChatModel chatModel;

    @Autowired
    public ChatController(OpenAiChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map<String,String> generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", this.chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return this.chatModel.stream(prompt);
    }
}
----

== Manual Configuration

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatModel.java[OpenAiChatModel] implements the `ChatModel` and `StreamingChatModel` and uses the <<low-level-api>> to connect to the OpenAI service.

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.

Next, create an `OpenAiChatModel` and use it for text generations:

[source,java]
----
var openAiApi = OpenAiApi.builder()
            .apiKey(System.getenv("OPENAI_API_KEY"))
            .build();
var openAiChatOptions = OpenAiChatOptions.builder()
            .model("gpt-3.5-turbo")
            .temperature(0.4)
            .maxTokens(200)
            .build();
var chatModel = new OpenAiChatModel(this.openAiApi, this.openAiChatOptions);

ChatResponse response = this.chatModel.call(
    new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> response = this.chatModel.stream(
    new Prompt("Generate the names of 5 famous pirates."));
----

The `OpenAiChatOptions` provides the configuration information for the chat requests.
The `OpenAiApi.Builder` and `OpenAiChatOptions.Builder` are fluent options-builders for API client and chat config respectively.

== Low-level OpenAiApi Client [[low-level-api]]

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/api/OpenAiApi.java[OpenAiApi] provides is lightweight Java client for OpenAI Chat API link:https://platform.openai.com/docs/api-reference/chat[OpenAI Chat API].

Following class diagram illustrates the `OpenAiApi` chat interfaces and building blocks:

image::openai-chat-api.jpg[OpenAiApi Chat API Diagram, width=1000, align="center"]

Here is a simple snippet showing how to use the API programmatically:

[source,java]
----
OpenAiApi openAiApi = OpenAiApi.builder()
            .apiKey(System.getenv("OPENAI_API_KEY"))
            .build();

ChatCompletionMessage chatCompletionMessage =
    new ChatCompletionMessage("Hello world", Role.USER);

// Sync request
ResponseEntity<ChatCompletion> response = this.openAiApi.chatCompletionEntity(
    new ChatCompletionRequest(List.of(this.chatCompletionMessage), "gpt-3.5-turbo", 0.8, false));

// Streaming request
Flux<ChatCompletionChunk> streamResponse = this.openAiApi.chatCompletionStream(
        new ChatCompletionRequest(List.of(this.chatCompletionMessage), "gpt-3.5-turbo", 0.8, true));
----

Follow the https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/api/OpenAiApi.java[OpenAiApi.java]'s JavaDoc for further information.

=== Low-level API Examples

* The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/api/OpenAiApiIT.java[OpenAiApiIT.java] tests provide some general examples of how to use the lightweight library.

* The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/api/tool/OpenAiApiToolFunctionCallIT.java[OpenAiApiToolFunctionCallIT.java] tests show how to use the low-level API to call tool functions.
Based on the link:https://platform.openai.com/docs/guides/function-calling/parallel-function-calling[OpenAI Function Calling] tutorial.

== API Key Management

Spring AI provides flexible API key management through the `ApiKey` interface and its implementations. The default implementation, `SimpleApiKey`, is suitable for most use cases, but you can also create custom implementations for more complex scenarios.

=== Default Configuration

By default, Spring Boot auto-configuration will create an API key bean using the `spring.ai.openai.api-key` property:

[source,properties]
----
spring.ai.openai.api-key=your-api-key-here
----

=== Custom API Key Configuration

You can create a custom instance of `OpenAiApi` with your own `ApiKey` implementation using the builder pattern:

[source,java]
----
ApiKey customApiKey = new ApiKey() {
    @Override
    public String getValue() {
        // Custom logic to retrieve API key
        return "your-api-key-here";
    }
};

OpenAiApi openAiApi = OpenAiApi.builder()
    .apiKey(customApiKey)
    .build();

// Create a chat model with the custom OpenAiApi instance
OpenAiChatModel chatModel = OpenAiChatModel.builder()
    .openAiApi(openAiApi)
    .build();
// Build the ChatClient using the custom chat model
ChatClient openAiChatClient = ChatClient.builder(chatModel).build();
----

This is useful when you need to:

* Retrieve the API key from a secure key store
* Rotate API keys dynamically
* Implement custom API key selection logic

