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 OpenAI signup page and generate the token on the 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:

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 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 Dependency Management section to add the Spring AI BOM to your build system.

Auto-configuration

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:

  • Maven

  • Gradle

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
dependencies {
    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
}
Refer to the 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.

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.

Property Description Default

spring.ai.openai.base-url

The URL to connect to

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.

-
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

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

Property Description Default

spring.ai.openai.chat.enabled

Enable OpenAI chat model.

true

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 models page for more information.

gpt-4o

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.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 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 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
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.
All properties prefixed with spring.ai.openai.chat.options can be overridden at runtime by adding request-specific Runtime Options to the Prompt call.

Runtime Options

The 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:

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        OpenAiChatOptions.builder()
            .model("gpt-4-o")
            .temperature(0.4)
        .build()
    ));
In addition to the model specific OpenAiChatOptions you can use a portable ChatOptions instance, created with ChatOptionsBuilder#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 OpenAI Function 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 Vision guide for more information.

The OpenAI User Message API can incorporate a list of base64-encoded images or image urls with the message. Spring AI’s Message interface facilitates multimodal AI models by introducing the 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 OpenAiChatModelIT.java, illustrating the fusion of user text with an image using the gpt-4o model.

var imageResource = new ClassPathResource("/multimodal.test.png");

var userMessage = new UserMessage("Explain what do you see on this picture?",
        new Media(MimeTypeUtils.IMAGE_PNG, this.imageResource));

ChatResponse response = chatModel.call(new Prompt(this.userMessage,
        OpenAiChatOptions.builder().model(OpenAiApi.ChatModel.GPT_4_O.getValue()).build()));
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 here

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

var userMessage = new UserMessage("Explain what do you see on this picture?",
        new Media(MimeTypeUtils.IMAGE_PNG,
                "https://docs.spring.io/spring-ai/reference/_images/multimodal.test.png"));

ChatResponse response = chatModel.call(new Prompt(this.userMessage,
        OpenAiChatOptions.builder().model(OpenAiApi.ChatModel.GPT_4_O.getValue()).build()));
You can pass multiple images as well.

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

Multimodal Test Image

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 Audio guide for more information.

The OpenAI User Message API can incorporate a list of base64-encoded audio files with the message. Spring AI’s Message interface facilitates multimodal AI models by introducing the 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 OpenAiChatModelIT.java, illustrating the fusion of user text with an audio file using the gpt-4o-audio-preview model.

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()));
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 Audio guide for more information.

The OpenAI Assystant Message API can contain a list of base64-encoded audio files with the message. Spring AI’s Message interface facilitates multimodal AI models by introducing the 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:

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 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 Structured Output Converter, these APIs offer enhanced control and precision.

Currently, OpenAI supports a 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:

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);
Adhere to the OpenAI subset of the JSON Schema language format.

Integrating with BeanOutputConverter Utilities

You can leverage existing BeanOutputConverter utilities to automatically generate the JSON Schema from your domain objects and later convert the structured response into domain-specific instances:

  • Java

  • Kotlin

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);
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)
Although this is optional for JSON Schema, OpenAI 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 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:

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

Create a new Spring Boot project and add the spring-ai-openai-spring-boot-starter 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:

spring.ai.openai.api-key=YOUR_API_KEY
spring.ai.openai.chat.options.model=gpt-4o
spring.ai.openai.chat.options.temperature=0.7
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.

@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 OpenAiChatModel implements the ChatModel and StreamingChatModel and uses the Low-level OpenAiApi Client to connect to the OpenAI service.

Add the spring-ai-openai dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai</artifactId>
</dependency>

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-openai'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

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

var openAiApi = new OpenAiApi(System.getenv("OPENAI_API_KEY"));
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 OpenAiChatOptions.Builder is a fluent options-builder.

Low-level OpenAiApi Client

The OpenAiApi provides is lightweight Java client for OpenAI Chat API OpenAI Chat API.

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

OpenAiApi Chat API Diagram

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

OpenAiApi openAiApi =
    new OpenAiApi(System.getenv("OPENAI_API_KEY"));

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 OpenAiApi.java's JavaDoc for further information.

Low-level API Examples