# OpenAI Java API Library

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The OpenAI Java SDK provides convenient access to the [OpenAI REST API](https://platform.openai.com/docs) from applications written in Java.

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The REST API documentation can be found on [platform.openai.com](https://platform.openai.com/docs). Javadocs are available on [javadoc.io](https://javadoc.io/doc/com.openai/openai-java/2.7.0).

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## Installation

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### Gradle

```kotlin
implementation("com.openai:openai-java:2.7.0")
```

### Maven

```xml
<dependency>
  <groupId>com.openai</groupId>
  <artifactId>openai-java</artifactId>
  <version>2.7.0</version>
</dependency>
```

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## Requirements

This library requires Java 8 or later.

## Usage

See the [`openai-java-example`](openai-java-example/src/main/java/com/openai/example) directory for complete and runnable examples.

The primary API for interacting with OpenAI models is the [Responses API](https://platform.openai.com/docs/api-reference/responses). You can generate text from the model with the code below.

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

// Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID` and `OPENAI_PROJECT_ID` environment variables
OpenAIClient client = OpenAIOkHttpClient.fromEnv();

ResponseCreateParams params = ResponseCreateParams.builder()
        .input("Say this is a test")
        .model(ChatModel.GPT_4_1)
        .build();
Response response = client.responses().create(params);
```

The previous standard (supported indefinitely) for generating text is the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). You can use that API to generate text from the model with the code below.

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

// Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID` and `OPENAI_BASE_URL` environment variables
OpenAIClient client = OpenAIOkHttpClient.fromEnv();

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .addUserMessage("Say this is a test")
    .model(ChatModel.GPT_4_1)
    .build();
ChatCompletion chatCompletion = client.chat().completions().create(params);
```

## Client configuration

Configure the client using environment variables:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

// Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID` and `OPENAI_BASE_URL` environment variables
OpenAIClient client = OpenAIOkHttpClient.fromEnv();
```

Or manually:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .apiKey("My API Key")
    .build();
```

Or using a combination of the two approaches:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

OpenAIClient client = OpenAIOkHttpClient.builder()
    // Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID` and `OPENAI_BASE_URL` environment variables
    .fromEnv()
    .apiKey("My API Key")
    .build();
```

See this table for the available options:

| Setter         | Environment variable | Required | Default value                 |
| -------------- | -------------------- | -------- | ----------------------------- |
| `apiKey`       | `OPENAI_API_KEY`     | true     | -                             |
| `organization` | `OPENAI_ORG_ID`      | false    | -                             |
| `project`      | `OPENAI_PROJECT_ID`  | false    | -                             |
| `baseUrl`      | `OPENAI_BASE_URL`    | true     | `"https://api.openai.com/v1"` |

> [!TIP]
> Don't create more than one client in the same application. Each client has a connection pool and
> thread pools, which are more efficient to share between requests.

## Requests and responses

To send a request to the OpenAI API, build an instance of some `Params` class and pass it to the corresponding client method. When the response is received, it will be deserialized into an instance of a Java class.

For example, `client.chat().completions().create(...)` should be called with an instance of `ChatCompletionCreateParams`, and it will return an instance of `ChatCompletion`.

## Immutability

Each class in the SDK has an associated [builder](https://blogs.oracle.com/javamagazine/post/exploring-joshua-blochs-builder-design-pattern-in-java) or factory method for constructing it.

Each class is [immutable](https://docs.oracle.com/javase/tutorial/essential/concurrency/immutable.html) once constructed. If the class has an associated builder, then it has a `toBuilder()` method, which can be used to convert it back to a builder for making a modified copy.

Because each class is immutable, builder modification will _never_ affect already built class instances.

## Asynchronous execution

The default client is synchronous. To switch to asynchronous execution, call the `async()` method:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.concurrent.CompletableFuture;

// Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID` and `OPENAI_BASE_URL` environment variables
OpenAIClient client = OpenAIOkHttpClient.fromEnv();

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .addUserMessage("Say this is a test")
    .model(ChatModel.GPT_4_1)
    .build();
CompletableFuture<ChatCompletion> chatCompletion = client.async().chat().completions().create(params);
```

Or create an asynchronous client from the beginning:

```java
import com.openai.client.OpenAIClientAsync;
import com.openai.client.okhttp.OpenAIOkHttpClientAsync;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.concurrent.CompletableFuture;

// Configures using the `OPENAI_API_KEY`, `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID` and `OPENAI_BASE_URL` environment variables
OpenAIClientAsync client = OpenAIOkHttpClientAsync.fromEnv();

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .addUserMessage("Say this is a test")
    .model(ChatModel.GPT_4_1)
    .build();
CompletableFuture<ChatCompletion> chatCompletion = client.chat().completions().create(params);
```

The asynchronous client supports the same options as the synchronous one, except most methods return `CompletableFuture`s.

## Streaming

The SDK defines methods that return response "chunk" streams, where each chunk can be individually processed as soon as it arrives instead of waiting on the full response. Streaming methods generally correspond to [SSE](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events) or [JSONL](https://jsonlines.org) responses.

Some of these methods may have streaming and non-streaming variants, but a streaming method will always have a `Streaming` suffix in its name, even if it doesn't have a non-streaming variant.

These streaming methods return [`StreamResponse`](openai-java-core/src/main/kotlin/com/openai/core/http/StreamResponse.kt) for synchronous clients:

```java
import com.openai.core.http.StreamResponse;
import com.openai.models.chat.completions.ChatCompletionChunk;

try (StreamResponse<ChatCompletionChunk> streamResponse = client.chat().completions().createStreaming(params)) {
    streamResponse.stream().forEach(chunk -> {
        System.out.println(chunk);
    });
    System.out.println("No more chunks!");
}
```

Or [`AsyncStreamResponse`](openai-java-core/src/main/kotlin/com/openai/core/http/AsyncStreamResponse.kt) for asynchronous clients:

```java
import com.openai.core.http.AsyncStreamResponse;
import com.openai.models.chat.completions.ChatCompletionChunk;
import java.util.Optional;

client.async().chat().completions().createStreaming(params).subscribe(chunk -> {
    System.out.println(chunk);
});

// If you need to handle errors or completion of the stream
client.async().chat().completions().createStreaming(params).subscribe(new AsyncStreamResponse.Handler<>() {
    @Override
    public void onNext(ChatCompletionChunk chunk) {
        System.out.println(chunk);
    }

    @Override
    public void onComplete(Optional<Throwable> error) {
        if (error.isPresent()) {
            System.out.println("Something went wrong!");
            throw new RuntimeException(error.get());
        } else {
            System.out.println("No more chunks!");
        }
    }
});

// Or use futures
client.async().chat().completions().createStreaming(params)
    .subscribe(chunk -> {
        System.out.println(chunk);
    })
    .onCompleteFuture();
    .whenComplete((unused, error) -> {
        if (error != null) {
            System.out.println("Something went wrong!");
            throw new RuntimeException(error);
        } else {
            System.out.println("No more chunks!");
        }
    });
```

Async streaming uses a dedicated per-client cached thread pool [`Executor`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/Executor.html) to stream without blocking the current thread. This default is suitable for most purposes.

To use a different `Executor`, configure the subscription using the `executor` parameter:

```java
import java.util.concurrent.Executor;
import java.util.concurrent.Executors;

Executor executor = Executors.newFixedThreadPool(4);
client.async().chat().completions().createStreaming(params).subscribe(
    chunk -> System.out.println(chunk), executor
);
```

Or configure the client globally using the `streamHandlerExecutor` method:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import java.util.concurrent.Executors;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .fromEnv()
    .streamHandlerExecutor(Executors.newFixedThreadPool(4))
    .build();
```

### Streaming helpers

The SDK provides conveniences for streamed chat completions. A
[`ChatCompletionAccumulator`](openai-java-core/src/main/kotlin/com/openai/helpers/ChatCompletionAccumulator.kt)
can record the stream of chat completion chunks in the response as they are processed and accumulate
a [`ChatCompletion`](openai-java-core/src/main/kotlin/com/openai/models/chat/completions/ChatCompletion.kt)
object similar to that which would have been returned by the non-streaming API.

For a synchronous response add a
[`Stream.peek()`](https://docs.oracle.com/javase/8/docs/api/java/util/stream/Stream.html#peek-java.util.function.Consumer-)
call to the stream pipeline to accumulate each chunk:

```java
import com.openai.core.http.StreamResponse;
import com.openai.helpers.ChatCompletionAccumulator;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionChunk;

ChatCompletionAccumulator chatCompletionAccumulator = ChatCompletionAccumulator.create();

try (StreamResponse<ChatCompletionChunk> streamResponse =
        client.chat().completions().createStreaming(createParams)) {
    streamResponse.stream()
            .peek(chatCompletionAccumulator::accumulate)
            .flatMap(completion -> completion.choices().stream())
            .flatMap(choice -> choice.delta().content().stream())
            .forEach(System.out::print);
}

ChatCompletion chatCompletion = chatCompletionAccumulator.chatCompletion();
```

For an asynchronous response, add the `ChatCompletionAccumulator` to the `subscribe()` call:

```java
import com.openai.helpers.ChatCompletionAccumulator;
import com.openai.models.chat.completions.ChatCompletion;

ChatCompletionAccumulator chatCompletionAccumulator = ChatCompletionAccumulator.create();

client.chat()
        .completions()
        .createStreaming(createParams)
        .subscribe(chunk -> chatCompletionAccumulator.accumulate(chunk).choices().stream()
                .flatMap(choice -> choice.delta().content().stream())
                .forEach(System.out::print))
        .onCompleteFuture()
        .join();

ChatCompletion chatCompletion = chatCompletionAccumulator.chatCompletion();
```

## Structured outputs with JSON schemas

Open AI [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs?api-mode=chat)
is a feature that ensures that the model will always generate responses that adhere to a supplied
[JSON schema](https://json-schema.org/overview/what-is-jsonschema).

A JSON schema can be defined by creating a
[`ResponseFormatJsonSchema`](openai-java-core/src/main/kotlin/com/openai/models/ResponseFormatJsonSchema.kt)
and setting it on the input parameters. However, for greater convenience, a JSON schema can instead
be derived automatically from the structure of an arbitrary Java class. The JSON content from the
response will then be converted automatically to an instance of that Java class. A full, working
example of the use of Structured Outputs with arbitrary Java classes can be seen in
[`StructuredOutputsExample`](openai-java-example/src/main/java/com/openai/example/StructuredOutputsExample.java).

Java classes can contain fields declared to be instances of other classes and can use collections:

```java
class Person {
    public String name;
    public int birthYear;
}

class Book {
    public String title;
    public Person author;
    public int publicationYear;
}

class BookList {
    public List<Book> books;
}
```

Pass the top-level class—`BookList` in this example—to `responseFormat(Class<T>)` when building the
parameters and then access an instance of `BookList` from the generated message content in the
response:

```java
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import com.openai.models.chat.completions.StructuredChatCompletionCreateParams;

StructuredChatCompletionCreateParams<BookList> params = ChatCompletionCreateParams.builder()
        .addUserMessage("List some famous late twentieth century novels.")
        .model(ChatModel.GPT_4_1)
        .responseFormat(BookList.class)
        .build();

client.chat().completions().create(params).choices().stream()
        .flatMap(choice -> choice.message().content().stream())
        .flatMap(bookList -> bookList.books.stream())
        .forEach(book -> System.out.println(book.title + " by " + book.author.name));
```

You can start building the parameters with an instance of
[`ChatCompletionCreateParams.Builder`](openai-java-core/src/main/kotlin/com/openai/models/chat/completions/ChatCompletionCreateParams.kt)
or
[`StructuredChatCompletionCreateParams.Builder`](openai-java-core/src/main/kotlin/com/openai/models/chat/completions/StructuredChatCompletionCreateParams.kt).
If you start with the former (which allows for more compact code) the builder type will change to
the latter when `ChatCompletionCreateParams.Builder.responseFormat(Class<T>)` is called.

If a field in a class is optional and does not require a defined value, you can represent this using
the [`java.util.Optional`](https://docs.oracle.com/javase/8/docs/api/java/util/Optional.html) class.
It is up to the AI model to decide whether to provide a value for that field or leave it empty.

```java
import java.util.Optional;

class Book {
    public String title;
    public Person author;
    public int publicationYear;
    public Optional<String> isbn;
}
```

Generic type information for fields is retained in the class's metadata, but _generic type erasure_
applies in other scopes. While, for example, a JSON schema defining an array of books can be derived
from the `BookList.books` field with type `List<Book>`, a valid JSON schema cannot be derived from a
local variable of that same type, so the following will _not_ work:

```java
List<Book> books = new ArrayList<>();

StructuredChatCompletionCreateParams<List<Book>> params = ChatCompletionCreateParams.builder()
        .responseFormat(books.getClass())
        // ...
        .build();
```

If an error occurs while converting a JSON response to an instance of a Java class, the error
message will include the JSON response to assist in diagnosis. For instance, if the response is
truncated, the JSON data will be incomplete and cannot be converted to a class instance. If your
JSON response may contain sensitive information, avoid logging it directly, or ensure that you
redact any sensitive details from the error message.

### Local JSON schema validation

Structured Outputs supports a
[subset](https://platform.openai.com/docs/guides/structured-outputs#supported-schemas) of the JSON
Schema language. Schemas are generated automatically from classes to align with this subset.
However, due to the inherent structure of the classes, the generated schema may still violate
certain OpenAI schema restrictions, such as exceeding the maximum nesting depth or utilizing
unsupported data types.

To facilitate compliance, the method `responseFormat(Class<T>)` performs a validation check on the
schema derived from the specified class. This validation ensures that all restrictions are adhered
to. If any issues are detected, an exception will be thrown, providing a detailed message outlining
the reasons for the validation failure.

- **Local Validation**: The validation process occurs locally, meaning no requests are sent to the
remote AI model. If the schema passes local validation, it is likely to pass remote validation as
well.
- **Remote Validation**: The remote AI model will conduct its own validation upon receiving the JSON
schema in the request.
- **Version Compatibility**: There may be instances where local validation fails while remote
validation succeeds. This can occur if the SDK version is outdated compared to the restrictions
enforced by the remote AI model.
- **Disabling Local Validation**: If you encounter compatibility issues and wish to bypass local
validation, you can disable it by passing
[`JsonSchemaLocalValidation.NO`](openai-java-core/src/main/kotlin/com/openai/core/JsonSchemaLocalValidation.kt)
to the `responseFormat(Class<T>, JsonSchemaLocalValidation)` method when building the parameters.
(The default value for this parameter is `JsonSchemaLocalValidation.YES`.)

```java
import com.openai.core.JsonSchemaLocalValidation;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import com.openai.models.chat.completions.StructuredChatCompletionCreateParams;

StructuredChatCompletionCreateParams<BookList> params = ChatCompletionCreateParams.builder()
        .addUserMessage("List some famous late twentieth century novels.")
        .model(ChatModel.GPT_4_1)
        .responseFormat(BookList.class, JsonSchemaLocalValidation.NO)
        .build();
```

By following these guidelines, you can ensure that your structured outputs conform to the necessary
schema requirements and minimize the risk of remote validation errors.

### Usage with the Responses API

_Structured Outputs_ are also supported for the Responses API. The usage is the same as described
except where the Responses API differs slightly from the Chat Completions API. Pass the top-level
class to `text(Class<T>)` when building the parameters and then access an instance of the class from
the generated message content in the response.

You can start building the parameters with an instance of
[`ResponseCreateParams.Builder`](openai-java-core/src/main/kotlin/com/openai/models/responses/ResponseCreateParams.kt)
or
[`StructuredResponseCreateParams.Builder`](openai-java-core/src/main/kotlin/com/openai/models/responses/StructuredResponseCreateParams.kt).
If you start with the former (which allows for more compact code) the builder type will change to
the latter when `ResponseCreateParams.Builder.text(Class<T>)` is called.

For a full example of the usage of _Structured Outputs_ with the Responses API, see
[`ResponsesStructuredOutputsExample`](openai-java-example/src/main/java/com/openai/example/ResponsesStructuredOutputsExample.java).

### Annotating classes and JSON schemas

You can use annotations to add further information to the JSON schema derived from your Java
classes, or to exclude individual fields from the schema. Details from annotations captured in the
JSON schema may be used by the AI model to improve its response. The SDK supports the use of
[Jackson Databind](https://github.com/FasterXML/jackson-databind) annotations.

```java
import com.fasterxml.jackson.annotation.JsonClassDescription;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonPropertyDescription;

class Person {
    @JsonPropertyDescription("The first name and surname of the person")
    public String name;
    public int birthYear;
    @JsonPropertyDescription("The year the person died, or 'present' if the person is living.")
    public String deathYear;
}

@JsonClassDescription("The details of one published book")
class Book {
    public String title;
    public Person author;
    @JsonPropertyDescription("The year in which the book was first published.")
    public int publicationYear;
    @JsonIgnore public String genre;
}

class BookList {
    public List<Book> books;
}
```

- Use `@JsonClassDescription` to add a detailed description to a class.
- Use `@JsonPropertyDescription` to add a detailed description to a field of a class.
- Use `@JsonIgnore` to omit a field of a class from the generated JSON schema.

If you use `@JsonProperty(required = false)`, the `false` value will be ignored. OpenAI JSON schemas
must mark all properties as _required_, so the schema generated from your Java classes will respect
that restriction and ignore any annotation that would violate it.

## Function calling with JSON schemas

OpenAI [Function Calling](https://platform.openai.com/docs/guides/function-calling?api-mode=chat)
lets you integrate external functions directly into the language model's responses. Instead of
producing plain text, the model can output instructions (with parameters) for calling a function
when appropriate. You define a [JSON schema](https://json-schema.org/overview/what-is-jsonschema)
for functions, and the model uses it to decide when and how to trigger these calls, enabling more
interactive, data-driven applications.

A JSON schema describing a function's parameters can be defined via the API by building a
[`ChatCompletionTool`](openai-java-core/src/main/kotlin/com/openai/models/chat/completions/ChatCompletionTool.kt)
containing a
[`FunctionDefinition`](openai-java-core/src/main/kotlin/com/openai/models/FunctionDefinition.kt)
and then using `addTool` to set it on the input parameters. The response from the AI model may then
contain requests to call your functions, detailing the functions' names and their parameter values
as JSON data that conforms to the JSON schema from the function definition. You can then parse the
parameter values from this JSON, invoke your functions, and pass your functions' results back to the
AI model. A full, working example of _Function Calling_ using the low-level API can be seen in
[`FunctionCallingRawExample`](openai-java-example/src/main/java/com/openai/example/FunctionCallingRawExample.java).

However, for greater convenience, the SDK can derive a function and its parameters automatically
from the structure of an arbitrary Java class: the class's name provides the function name, and the
class's fields define the function's parameters. When the AI model responds with the parameter
values in JSON form, you can then easily convert that JSON to an instance of your Java class and
use the parameter values to invoke your custom function. A full, working example of the use of
_Function Calling_ with Java classes to define function parameters can be seen in
[`FunctionCallingExample`](openai-java-example/src/main/java/com/openai/example/FunctionCallingExample.java).

Like for [Structured Outputs](#structured-outputs-with-json-schemas), Java classes can contain
fields declared to be instances of other classes and can use collections. Optionally, annotations
can be used to set the descriptions of the function (class) and its parameters (fields) to assist
the AI model in understanding the purpose of the function and the possible values of its parameters.

```java
import com.fasterxml.jackson.annotation.JsonClassDescription;
import com.fasterxml.jackson.annotation.JsonPropertyDescription;

@JsonClassDescription("Gets the quality of the given SDK.")
static class GetSdkQuality {
    @JsonPropertyDescription("The name of the SDK.")
    public String name;

    public SdkQuality execute() {
        return new SdkQuality(
                name, name.contains("OpenAI") ? "It's robust and polished!" : "*shrug*");
    }
}

static class SdkQuality {
    public String quality;

    public SdkQuality(String name, String evaluation) {
        quality = name + ": " + evaluation;
    }
}

@JsonClassDescription("Gets the review score (out of 10) for the named SDK.")
static class GetSdkScore {
  public String name;

  public int execute() {
    return name.contains("OpenAI") ? 10 : 3;
  }
}
```

When your functions are defined, add them to the input parameters using `addTool(Class<T>)` and then
call them if requested to do so in the AI model's response. `Function.argments(Class<T>)` can be
used to parse a function's parameters in JSON form to an instance of your function-defining class.
The fields of that instance will be set to the values of the parameters to the function call.

After calling the function, use `ChatCompletionToolMessageParam.Builder.contentAsJson(Object)` to
pass the function's result back to the AI model. The method will convert the result to JSON form
for consumption by the model. The `Object` can be any object, including simple `String` instances
and boxed primitive types.

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.*;
import java.util.Collection;

OpenAIClient client = OpenAIOkHttpClient.fromEnv();

ChatCompletionCreateParams.Builder createParamsBuilder = ChatCompletionCreateParams.builder()
        .model(ChatModel.GPT_3_5_TURBO)
        .maxCompletionTokens(2048)
        .addTool(GetSdkQuality.class)
        .addTool(GetSdkScore.class)
        .addUserMessage("How good are the following SDKs and what do reviewers say: "
                + "OpenAI Java SDK, Unknown Company SDK.");

client.chat().completions().create(createParamsBuilder.build()).choices().stream()
        .map(ChatCompletion.Choice::message)
        // Add each assistant message onto the builder so that we keep track of the
        // conversation for asking a follow-up question later.
        .peek(createParamsBuilder::addMessage)
        .flatMap(message -> {
            message.content().ifPresent(System.out::println);
            return message.toolCalls().stream().flatMap(Collection::stream);
        })
        .forEach(toolCall -> {
            Object result = callFunction(toolCall.function());
            // Add the tool call result to the conversation.
            createParamsBuilder.addMessage(ChatCompletionToolMessageParam.builder()
                    .toolCallId(toolCall.id())
                    .contentAsJson(result)
                    .build());
        });

// Ask a follow-up question about the function call result.
createParamsBuilder.addUserMessage("Why do you say that?");
client.chat().completions().create(createParamsBuilder.build()).choices().stream()
        .flatMap(choice -> choice.message().content().stream())
        .forEach(System.out::println);

static Object callFunction(ChatCompletionMessageToolCall.Function function) {
  switch (function.name()) {
    case "GetSdkQuality":
      return function.arguments(GetSdkQuality.class).execute();
    case "GetSdkScore":
      return function.arguments(GetSdkScore.class).execute();
    default:
      throw new IllegalArgumentException("Unknown function: " + function.name());
  }
}
```

In the code above, an `execute()` method encapsulates each function's logic. However, there is no
requirement to follow that pattern. You are free to implement your function's logic in any way that
best suits your use case. The pattern above is only intended to _suggest_ that a suitable pattern
may make the process of function calling simpler to understand and implement.

### Usage with the Responses API

_Function Calling_ is also supported for the Responses API. The usage is the same as described
except where the Responses API differs slightly from the Chat Completions API. Pass the top-level
class to `addTool(Class<T>)` when building the parameters. In the response, look for
[`RepoonseOutputItem`](openai-java-core/src/main/kotlin/com/openai/models/responses/ResponseOutputItem.kt)
instances that are function calls. Parse the parameters to each function call to an instance of the
class using
[`ResponseFunctionToolCall.arguments(Class<T>)`](openai-java-core/src/main/kotlin/com/openai/models/responses/ResponseFunctionToolCall.kt).
Finally, pass the result of each call back to the model.

For a full example of the usage of _Function Calling_ with the Responses API using the low-level
API to define and parse function parameters, see
[`ResponsesFunctionCallingRawExample`](openai-java-example/src/main/java/com/openai/example/ResponsesFunctionCallingRawExample.java).

For a full example of the usage of _Function Calling_ with the Responses API using Java classes to
define and parse function parameters, see
[`ResponsesFunctionCallingExample`](openai-java-example/src/main/java/com/openai/example/ResponsesFunctionCallingExample.java).

### Local function JSON schema validation

Like for _Structured Outputs_, you can perform local validation to check that the JSON schema
derived from your function class respects the restrictions imposed by OpenAI on such schemas. Local
validation is enabled by default, but it can be disabled by adding `JsonSchemaLocalValidation.NO` to
the call to `addTool`.

```java
ChatCompletionCreateParams.Builder createParamsBuilder = ChatCompletionCreateParams.builder()
        .model(ChatModel.GPT_3_5_TURBO)
        .maxCompletionTokens(2048)
        .addTool(GetSdkQuality.class, JsonSchemaLocalValidation.NO)
        .addTool(GetSdkScore.class, JsonSchemaLocalValidation.NO)
        .addUserMessage("How good are the following SDKs and what do reviewers say: "
                + "OpenAI Java SDK, Unknown Company SDK.");
```

See [Local JSON schema validation](#local-json-schema-validation) for more details on local schema
validation and under what circumstances you might want to disable it.

### Annotating function classes

You can use annotations to add further information about functions to the JSON schemas that are
derived from your function classes, or to exclude individual fields from the parameters to the
function. Details from annotations captured in the JSON schema may be used by the AI model to
improve its response. The SDK supports the use of
[Jackson Databind](https://github.com/FasterXML/jackson-databind) annotations.

- Use `@JsonClassDescription` to add a description to a function class detailing when and how to use
  that function.
- Use `@JsonTypeName` to set the function name to something other than the simple name of the class,
  which is used by default.
- Use `@JsonPropertyDescription` to add a detailed description to function parameter (a field of
  a function class).
- Use `@JsonIgnore` to omit a field of a class from the generated JSON schema for a function's
  parameters.

OpenAI provides some
[Best practices for defining functions](https://platform.openai.com/docs/guides/function-calling#best-practices-for-defining-functions)
that may help you to understand how to use the above annotations effectively for your functions.

## File uploads

The SDK defines methods that accept files.

To upload a file, pass a [`Path`](https://docs.oracle.com/javase/8/docs/api/java/nio/file/Path.html):

```java
import com.openai.models.files.FileCreateParams;
import com.openai.models.files.FileObject;
import com.openai.models.files.FilePurpose;
import java.nio.file.Paths;

FileCreateParams params = FileCreateParams.builder()
    .purpose(FilePurpose.FINE_TUNE)
    .file(Paths.get("input.jsonl"))
    .build();
FileObject fileObject = client.files().create(params);
```

Or an arbitrary [`InputStream`](https://docs.oracle.com/javase/8/docs/api/java/io/InputStream.html):

```java
import com.openai.models.files.FileCreateParams;
import com.openai.models.files.FileObject;
import com.openai.models.files.FilePurpose;
import java.net.URL;

FileCreateParams params = FileCreateParams.builder()
    .purpose(FilePurpose.FINE_TUNE)
    .file(new URL("https://example.com/input.jsonl").openStream())
    .build();
FileObject fileObject = client.files().create(params);
```

Or a `byte[]` array:

```java
import com.openai.models.files.FileCreateParams;
import com.openai.models.files.FileObject;
import com.openai.models.files.FilePurpose;

FileCreateParams params = FileCreateParams.builder()
    .purpose(FilePurpose.FINE_TUNE)
    .file("content".getBytes())
    .build();
FileObject fileObject = client.files().create(params);
```

Note that when passing a non-`Path` its filename is unknown so it will not be included in the request. To manually set a filename, pass a [`MultipartField`](openai-java-core/src/main/kotlin/com/openai/core/Values.kt):

```java
import com.openai.core.MultipartField;
import com.openai.models.files.FileCreateParams;
import com.openai.models.files.FileObject;
import com.openai.models.files.FilePurpose;
import java.io.InputStream;
import java.net.URL;

FileCreateParams params = FileCreateParams.builder()
    .purpose(FilePurpose.FINE_TUNE)
    .file(MultipartField.<InputStream>builder()
        .value(new URL("https://example.com/input.jsonl").openStream())
        .filename("input.jsonl")
        .build())
    .build();
FileObject fileObject = client.files().create(params);
```

## Binary responses

The SDK defines methods that return binary responses, which are used for API responses that shouldn't necessarily be parsed, like non-JSON data.

These methods return [`HttpResponse`](openai-java-core/src/main/kotlin/com/openai/core/http/HttpResponse.kt):

```java
import com.openai.core.http.HttpResponse;
import com.openai.models.files.FileContentParams;

HttpResponse response = client.files().content("file_id");
```

To save the response content to a file, use the [`Files.copy(...)`](https://docs.oracle.com/javase/8/docs/api/java/nio/file/Files.html#copy-java.io.InputStream-java.nio.file.Path-java.nio.file.CopyOption...-) method:

```java
import com.openai.core.http.HttpResponse;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.nio.file.StandardCopyOption;

try (HttpResponse response = client.files().content(params)) {
    Files.copy(
        response.body(),
        Paths.get(path),
        StandardCopyOption.REPLACE_EXISTING
    );
} catch (Exception e) {
    System.out.println("Something went wrong!");
    throw new RuntimeException(e);
}
```

Or transfer the response content to any [`OutputStream`](https://docs.oracle.com/javase/8/docs/api/java/io/OutputStream.html):

```java
import com.openai.core.http.HttpResponse;
import java.nio.file.Files;
import java.nio.file.Paths;

try (HttpResponse response = client.files().content(params)) {
    response.body().transferTo(Files.newOutputStream(Paths.get(path)));
} catch (Exception e) {
    System.out.println("Something went wrong!");
    throw new RuntimeException(e);
}
```

## Raw responses

The SDK defines methods that deserialize responses into instances of Java classes. However, these methods don't provide access to the response headers, status code, or the raw response body.

To access this data, prefix any HTTP method call on a client or service with `withRawResponse()`:

```java
import com.openai.core.http.Headers;
import com.openai.core.http.HttpResponseFor;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .addUserMessage("Say this is a test")
    .model(ChatModel.GPT_4_1)
    .build();
HttpResponseFor<ChatCompletion> chatCompletion = client.chat().completions().withRawResponse().create(params);

int statusCode = chatCompletion.statusCode();
Headers headers = chatCompletion.headers();
```

You can still deserialize the response into an instance of a Java class if needed:

```java
import com.openai.models.chat.completions.ChatCompletion;

ChatCompletion parsedChatCompletion = chatCompletion.parse();
```

### Request IDs

> For more information on debugging requests, see [the API docs](https://platform.openai.com/docs/api-reference/debugging-requests).

When using raw responses, you can access the `x-request-id` response header using the `requestId()` method:

```java
import com.openai.core.http.HttpResponseFor;
import com.openai.models.chat.completions.ChatCompletion;
import java.util.Optional;

HttpResponseFor<ChatCompletion> chatCompletion = client.chat().completions().withRawResponse().create(params);
Optional<String> requestId = chatCompletion.requestId();
```

This can be used to quickly log failing requests and report them back to OpenAI.

## Error handling

The SDK throws custom unchecked exception types:

- [`OpenAIServiceException`](openai-java-core/src/main/kotlin/com/openai/errors/OpenAIServiceException.kt): Base class for HTTP errors. See this table for which exception subclass is thrown for each HTTP status code:

  | Status | Exception                                                                                                              |
  | ------ | ---------------------------------------------------------------------------------------------------------------------- |
  | 400    | [`BadRequestException`](openai-java-core/src/main/kotlin/com/openai/errors/BadRequestException.kt)                     |
  | 401    | [`UnauthorizedException`](openai-java-core/src/main/kotlin/com/openai/errors/UnauthorizedException.kt)                 |
  | 403    | [`PermissionDeniedException`](openai-java-core/src/main/kotlin/com/openai/errors/PermissionDeniedException.kt)         |
  | 404    | [`NotFoundException`](openai-java-core/src/main/kotlin/com/openai/errors/NotFoundException.kt)                         |
  | 422    | [`UnprocessableEntityException`](openai-java-core/src/main/kotlin/com/openai/errors/UnprocessableEntityException.kt)   |
  | 429    | [`RateLimitException`](openai-java-core/src/main/kotlin/com/openai/errors/RateLimitException.kt)                       |
  | 5xx    | [`InternalServerException`](openai-java-core/src/main/kotlin/com/openai/errors/InternalServerException.kt)             |
  | others | [`UnexpectedStatusCodeException`](openai-java-core/src/main/kotlin/com/openai/errors/UnexpectedStatusCodeException.kt) |

  [`SseException`](openai-java-core/src/main/kotlin/com/openai/errors/SseException.kt) is thrown for errors encountered during [SSE streaming](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events) after a successful initial HTTP response.

- [`OpenAIIoException`](openai-java-core/src/main/kotlin/com/openai/errors/OpenAIIoException.kt): I/O networking errors.

- [`OpenAIInvalidDataException`](openai-java-core/src/main/kotlin/com/openai/errors/OpenAIInvalidDataException.kt): Failure to interpret successfully parsed data. For example, when accessing a property that's supposed to be required, but the API unexpectedly omitted it from the response.

- [`OpenAIException`](openai-java-core/src/main/kotlin/com/openai/errors/OpenAIException.kt): Base class for all exceptions. Most errors will result in one of the previously mentioned ones, but completely generic errors may be thrown using the base class.

## Pagination

The SDK defines methods that return a paginated lists of results. It provides convenient ways to access the results either one page at a time or item-by-item across all pages.

### Auto-pagination

To iterate through all results across all pages, use the `autoPager()` method, which automatically fetches more pages as needed.

When using the synchronous client, the method returns an [`Iterable`](https://docs.oracle.com/javase/8/docs/api/java/lang/Iterable.html)

```java
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobListPage;

JobListPage page = client.fineTuning().jobs().list();

// Process as an Iterable
for (FineTuningJob job : page.autoPager()) {
    System.out.println(job);
}

// Process as a Stream
page.autoPager()
    .stream()
    .limit(50)
    .forEach(job -> System.out.println(job));
```

When using the asynchronous client, the method returns an [`AsyncStreamResponse`](openai-java-core/src/main/kotlin/com/openai/core/http/AsyncStreamResponse.kt):

```java
import com.openai.core.http.AsyncStreamResponse;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobListPageAsync;
import java.util.Optional;
import java.util.concurrent.CompletableFuture;

CompletableFuture<JobListPageAsync> pageFuture = client.async().fineTuning().jobs().list();

pageFuture.thenRun(page -> page.autoPager().subscribe(job -> {
    System.out.println(job);
}));

// If you need to handle errors or completion of the stream
pageFuture.thenRun(page -> page.autoPager().subscribe(new AsyncStreamResponse.Handler<>() {
    @Override
    public void onNext(FineTuningJob job) {
        System.out.println(job);
    }

    @Override
    public void onComplete(Optional<Throwable> error) {
        if (error.isPresent()) {
            System.out.println("Something went wrong!");
            throw new RuntimeException(error.get());
        } else {
            System.out.println("No more!");
        }
    }
}));

// Or use futures
pageFuture.thenRun(page -> page.autoPager()
    .subscribe(job -> {
        System.out.println(job);
    })
    .onCompleteFuture()
    .whenComplete((unused, error) -> {
        if (error != null) {
            System.out.println("Something went wrong!");
            throw new RuntimeException(error);
        } else {
            System.out.println("No more!");
        }
    }));
```

### Manual pagination

To access individual page items and manually request the next page, use the `items()`,
`hasNextPage()`, and `nextPage()` methods:

```java
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobListPage;

JobListPage page = client.fineTuning().jobs().list();
while (true) {
    for (FineTuningJob job : page.items()) {
        System.out.println(job);
    }

    if (!page.hasNextPage()) {
        break;
    }

    page = page.nextPage();
}
```

## Logging

The SDK uses the standard [OkHttp logging interceptor](https://github.com/square/okhttp/tree/master/okhttp-logging-interceptor).

Enable logging by setting the `OPENAI_LOG` environment variable to `info`:

```sh
$ export OPENAI_LOG=info
```

Or to `debug` for more verbose logging:

```sh
$ export OPENAI_LOG=debug
```

## Jackson

The SDK depends on [Jackson](https://github.com/FasterXML/jackson) for JSON serialization/deserialization. It is compatible with version 2.13.4 or higher, but depends on version 2.18.2 by default.

The SDK throws an exception if it detects an incompatible Jackson version at runtime (e.g. if the default version was overridden in your Maven or Gradle config).

If the SDK threw an exception, but you're _certain_ the version is compatible, then disable the version check using the `checkJacksonVersionCompatibility` on [`OpenAIOkHttpClient`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClient.kt) or [`OpenAIOkHttpClientAsync`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClientAsync.kt).

> [!CAUTION]
> We make no guarantee that the SDK works correctly when the Jackson version check is disabled.

## Microsoft Azure

To use this library with [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview), use the same
OpenAI client builder but with the Azure-specific configuration.

```java
OpenAIClient client = OpenAIOkHttpClient.builder()
        // Gets the API key and endpoint from the `AZURE_OPENAI_KEY` and `OPENAI_BASE_URL` environment variables, respectively
        .fromEnv()
        // Set the Azure Entra ID
        .credential(BearerTokenCredential.create(AuthenticationUtil.getBearerTokenSupplier(
                new DefaultAzureCredentialBuilder().build(), "https://cognitiveservices.azure.com/.default")))
        .build();
```

See the complete Azure OpenAI example in the [`openai-java-example`](openai-java-example/src/main/java/com/openai/example/AzureEntraIdExample.java) directory. The other examples in the directory also work with Azure as long as the client is configured to use it.

## Network options

### Retries

The SDK automatically retries 2 times by default, with a short exponential backoff.

Only the following error types are retried:

- Connection errors (for example, due to a network connectivity problem)
- 408 Request Timeout
- 409 Conflict
- 429 Rate Limit
- 5xx Internal

The API may also explicitly instruct the SDK to retry or not retry a response.

To set a custom number of retries, configure the client using the `maxRetries` method:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .fromEnv()
    .maxRetries(4)
    .build();
```

### Timeouts

Requests time out after 10 minutes by default.

To set a custom timeout, configure the method call using the `timeout` method:

```java
import com.openai.models.chat.completions.ChatCompletion;

ChatCompletion chatCompletion = client.chat().completions().create(
  params, RequestOptions.builder().timeout(Duration.ofSeconds(30)).build()
);
```

Or configure the default for all method calls at the client level:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import java.time.Duration;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .fromEnv()
    .timeout(Duration.ofSeconds(30))
    .build();
```

### Proxies

To route requests through a proxy, configure the client using the `proxy` method:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import java.net.InetSocketAddress;
import java.net.Proxy;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .fromEnv()
    .proxy(new Proxy(
      Proxy.Type.HTTP, new InetSocketAddress(
        "https://example.com", 8080
      )
    ))
    .build();
```

### Custom HTTP client

The SDK consists of three artifacts:

- `openai-java-core`
  - Contains core SDK logic
  - Does not depend on [OkHttp](https://square.github.io/okhttp)
  - Exposes [`OpenAIClient`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClient.kt), [`OpenAIClientAsync`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientAsync.kt), [`OpenAIClientImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientImpl.kt), and [`OpenAIClientAsyncImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientAsyncImpl.kt), all of which can work with any HTTP client
- `openai-java-client-okhttp`
  - Depends on [OkHttp](https://square.github.io/okhttp)
  - Exposes [`OpenAIOkHttpClient`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClient.kt) and [`OpenAIOkHttpClientAsync`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClientAsync.kt), which provide a way to construct [`OpenAIClientImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientImpl.kt) and [`OpenAIClientAsyncImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientAsyncImpl.kt), respectively, using OkHttp
- `openai-java`
  - Depends on and exposes the APIs of both `openai-java-core` and `openai-java-client-okhttp`
  - Does not have its own logic

This structure allows replacing the SDK's default HTTP client without pulling in unnecessary dependencies.

#### Customized [`OkHttpClient`](https://square.github.io/okhttp/3.x/okhttp/okhttp3/OkHttpClient.html)

> [!TIP]
> Try the available [network options](#network-options) before replacing the default client.

To use a customized `OkHttpClient`:

1. Replace your [`openai-java` dependency](#installation) with `openai-java-core`
2. Copy `openai-java-client-okhttp`'s [`OkHttpClient`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OkHttpClient.kt) class into your code and customize it
3. Construct [`OpenAIClientImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientImpl.kt) or [`OpenAIClientAsyncImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientAsyncImpl.kt), similarly to [`OpenAIOkHttpClient`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClient.kt) or [`OpenAIOkHttpClientAsync`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClientAsync.kt), using your customized client

### Completely custom HTTP client

To use a completely custom HTTP client:

1. Replace your [`openai-java` dependency](#installation) with `openai-java-core`
2. Write a class that implements the [`HttpClient`](openai-java-core/src/main/kotlin/com/openai/core/http/HttpClient.kt) interface
3. Construct [`OpenAIClientImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientImpl.kt) or [`OpenAIClientAsyncImpl`](openai-java-core/src/main/kotlin/com/openai/client/OpenAIClientAsyncImpl.kt), similarly to [`OpenAIOkHttpClient`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClient.kt) or [`OpenAIOkHttpClientAsync`](openai-java-client-okhttp/src/main/kotlin/com/openai/client/okhttp/OpenAIOkHttpClientAsync.kt), using your new client class

## Undocumented API functionality

The SDK is typed for convenient usage of the documented API. However, it also supports working with undocumented or not yet supported parts of the API.

### Parameters

To set undocumented parameters, call the `putAdditionalHeader`, `putAdditionalQueryParam`, or `putAdditionalBodyProperty` methods on any `Params` class:

```java
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .putAdditionalHeader("Secret-Header", "42")
    .putAdditionalQueryParam("secret_query_param", "42")
    .putAdditionalBodyProperty("secretProperty", JsonValue.from("42"))
    .build();
```

These can be accessed on the built object later using the `_additionalHeaders()`, `_additionalQueryParams()`, and `_additionalBodyProperties()` methods.

To set undocumented parameters on _nested_ headers, query params, or body classes, call the `putAdditionalProperty` method on the nested class:

```java
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .responseFormat(ChatCompletionCreateParams.ResponseFormat.builder()
        .putAdditionalProperty("secretProperty", JsonValue.from("42"))
        .build())
    .build();
```

These properties can be accessed on the nested built object later using the `_additionalProperties()` method.

To set a documented parameter or property to an undocumented or not yet supported _value_, pass a [`JsonValue`](openai-java-core/src/main/kotlin/com/openai/core/Values.kt) object to its setter:

```java
import com.openai.core.JsonValue;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .messages(JsonValue.from(42))
    .model(ChatModel.GPT_4_1)
    .build();
```

The most straightforward way to create a [`JsonValue`](openai-java-core/src/main/kotlin/com/openai/core/Values.kt) is using its `from(...)` method:

```java
import com.openai.core.JsonValue;
import java.util.List;
import java.util.Map;

// Create primitive JSON values
JsonValue nullValue = JsonValue.from(null);
JsonValue booleanValue = JsonValue.from(true);
JsonValue numberValue = JsonValue.from(42);
JsonValue stringValue = JsonValue.from("Hello World!");

// Create a JSON array value equivalent to `["Hello", "World"]`
JsonValue arrayValue = JsonValue.from(List.of(
  "Hello", "World"
));

// Create a JSON object value equivalent to `{ "a": 1, "b": 2 }`
JsonValue objectValue = JsonValue.from(Map.of(
  "a", 1,
  "b", 2
));

// Create an arbitrarily nested JSON equivalent to:
// {
//   "a": [1, 2],
//   "b": [3, 4]
// }
JsonValue complexValue = JsonValue.from(Map.of(
  "a", List.of(
    1, 2
  ),
  "b", List.of(
    3, 4
  )
));
```

Normally a `Builder` class's `build` method will throw [`IllegalStateException`](https://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html) if any required parameter or property is unset.

To forcibly omit a required parameter or property, pass [`JsonMissing`](openai-java-core/src/main/kotlin/com/openai/core/Values.kt):

```java
import com.openai.core.JsonMissing;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .model(ChatModel.GPT_4_1)
    .messages(JsonMissing.of())
    .build();
```

### Response properties

To access undocumented response properties, call the `_additionalProperties()` method:

```java
import com.openai.core.JsonValue;
import java.util.Map;

Map<String, JsonValue> additionalProperties = client.chat().completions().create(params)._additionalProperties();
JsonValue secretPropertyValue = additionalProperties.get("secretProperty");

String result = secretPropertyValue.accept(new JsonValue.Visitor<>() {
    @Override
    public String visitNull() {
        return "It's null!";
    }

    @Override
    public String visitBoolean(boolean value) {
        return "It's a boolean!";
    }

    @Override
    public String visitNumber(Number value) {
        return "It's a number!";
    }

    // Other methods include `visitMissing`, `visitString`, `visitArray`, and `visitObject`
    // The default implementation of each unimplemented method delegates to `visitDefault`, which throws by default, but can also be overridden
});
```

To access a property's raw JSON value, which may be undocumented, call its `_` prefixed method:

```java
import com.openai.core.JsonField;
import com.openai.models.chat.completions.ChatCompletionMessageParam;
import java.util.Optional;

JsonField<List<ChatCompletionMessageParam>> messages = client.chat().completions().create(params)._messages();

if (messages.isMissing()) {
  // The property is absent from the JSON response
} else if (messages.isNull()) {
  // The property was set to literal null
} else {
  // Check if value was provided as a string
  // Other methods include `asNumber()`, `asBoolean()`, etc.
  Optional<String> jsonString = messages.asString();

  // Try to deserialize into a custom type
  MyClass myObject = messages.asUnknown().orElseThrow().convert(MyClass.class);
}
```

### Response validation

In rare cases, the API may return a response that doesn't match the expected type. For example, the SDK may expect a property to contain a `String`, but the API could return something else.

By default, the SDK will not throw an exception in this case. It will throw [`OpenAIInvalidDataException`](openai-java-core/src/main/kotlin/com/openai/errors/OpenAIInvalidDataException.kt) only if you directly access the property.

If you would prefer to check that the response is completely well-typed upfront, then either call `validate()`:

```java
import com.openai.models.chat.completions.ChatCompletion;

ChatCompletion chatCompletion = client.chat().completions().create(params).validate();
```

Or configure the method call to validate the response using the `responseValidation` method:

```java
import com.openai.models.chat.completions.ChatCompletion;

ChatCompletion chatCompletion = client.chat().completions().create(
  params, RequestOptions.builder().responseValidation(true).build()
);
```

Or configure the default for all method calls at the client level:

```java
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .fromEnv()
    .responseValidation(true)
    .build();
```

## FAQ

### Why don't you use plain `enum` classes?

Java `enum` classes are not trivially [forwards compatible](https://www.stainless.com/blog/making-java-enums-forwards-compatible). Using them in the SDK could cause runtime exceptions if the API is updated to respond with a new enum value.

### Why do you represent fields using `JsonField<T>` instead of just plain `T`?

Using `JsonField<T>` enables a few features:

- Allowing usage of [undocumented API functionality](#undocumented-api-functionality)
- Lazily [validating the API response against the expected shape](#response-validation)
- Representing absent vs explicitly null values

### Why don't you use [`data` classes](https://kotlinlang.org/docs/data-classes.html)?

It is not [backwards compatible to add new fields to a data class](https://kotlinlang.org/docs/api-guidelines-backward-compatibility.html#avoid-using-data-classes-in-your-api) and we don't want to introduce a breaking change every time we add a field to a class.

### Why don't you use checked exceptions?

Checked exceptions are widely considered a mistake in the Java programming language. In fact, they were omitted from Kotlin for this reason.

Checked exceptions:

- Are verbose to handle
- Encourage error handling at the wrong level of abstraction, where nothing can be done about the error
- Are tedious to propagate due to the [function coloring problem](https://journal.stuffwithstuff.com/2015/02/01/what-color-is-your-function)
- Don't play well with lambdas (also due to the function coloring problem)

## Semantic versioning

This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals.)_
2. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an [issue](https://www.github.com/openai/openai-java/issues) with questions, bugs, or suggestions.
