A minimalistic unofficial OpenAI API client for the JVM, written in Java. The only dependency used is Jackson for JSON parsing.
Java 17+ is a prerequisite
implementation("io.github.stefanbratanov:jvm-openai:${version}")
<dependency>
<groupId>io.github.stefanbratanov</groupId>
<artifactId>jvm-openai</artifactId>
<version>${version}</version>
</dependency>
OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY")).build();
ChatClient chatClient = openAI.chatClient();
CreateChatCompletionRequest createChatCompletionRequest = CreateChatCompletionRequest.newBuilder()
.model(OpenAIModel.GPT_3_5_TURBO)
.message(ChatMessage.userMessage("Who won the world series in 2020?"))
.build();
ChatCompletion chatCompletion = chatClient.createChatCompletion(createChatCompletionRequest);
API | Status |
---|---|
Audio | ✔️ |
Chat | ✔️ |
Embeddings | ✔️ |
Fine-tuning | ✔️ |
Batch | ✔️ |
Files | ✔️ |
Uploads | ✔️ |
Images | ✔️ |
Models | ✔️ |
Moderations | ✔️ |
API | Status |
---|---|
Assistants | ✔️ |
Threads | ✔️ |
Messages | ✔️ |
Runs | ✔️ |
Run Steps | ✔️ |
Vector Stores | ✔️ |
Vector Store Files | ✔️ |
Vector Store File Batches | ✔️ |
API | Status |
---|---|
Invites | ✔️ |
Users | ✔️ |
Projects | ✔️ |
Project Users | ✔️ |
Project Service Accounts | ✔️ |
Project API Keys | ✔️ |
Audit Logs | ✔️ |
NOTE: Legacy APIs are not supported
- Configure an organization and project
OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY"))
.organization("org-zweLLamVlP6c5n66zY334ivs")
.project(System.getenv("PROJECT_ID"))
.build();
- Configure a custom base url
OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY"))
.baseUrl("https://api.foobar.com/v1/")
.build();
- Configure a custom Java's
HttpClient
HttpClient httpClient = HttpClient.newBuilder()
.connectTimeout(Duration.ofSeconds(20))
.executor(Executors.newFixedThreadPool(3))
.proxy(ProxySelector.of(new InetSocketAddress("proxy.example.com", 80)))
.build();
OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY"))
.httpClient(httpClient)
.build();
- Configure a timeout for all requests
OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY"))
.requestTimeout(Duration.ofSeconds(10))
.build();
- Create chat completion async
ChatClient chatClient = openAI.chatClient();
CreateChatCompletionRequest request = CreateChatCompletionRequest.newBuilder()
.model(OpenAIModel.GPT_3_5_TURBO)
.message(ChatMessage.userMessage("Who won the world series in 2020?"))
.build();
CompletableFuture<ChatCompletion> chatCompletion = chatClient.createChatCompletionAsync(request);
chatCompletion.thenAccept(System.out::println);
- Streaming
ChatClient chatClient = openAI.chatClient();
CreateChatCompletionRequest request = CreateChatCompletionRequest.newBuilder()
.message(ChatMessage.userMessage("Who won the world series in 2020?"))
.stream(true)
.build();
// with java.util.stream.Stream
chatClient.streamChatCompletion(request).forEach(System.out::println);
// with subscriber
chatClient.streamChatCompletion(request, new ChatCompletionStreamSubscriber() {
@Override
public void onChunk(ChatCompletionChunk chunk) {
System.out.println(chunk);
}
@Override
public void onException(Throwable ex) {
// ...
}
@Override
public void onComplete() {
// ...
}
});
- Create image
ImagesClient imagesClient = openAI.imagesClient();
CreateImageRequest createImageRequest = CreateImageRequest.newBuilder()
.model(OpenAIModel.DALL_E_3)
.prompt("A cute baby sea otter")
.build();
Images images = imagesClient.createImage(createImageRequest);
- Create speech
AudioClient audioClient = openAI.audioClient();
SpeechRequest request = SpeechRequest.newBuilder()
.model(OpenAIModel.TTS_1)
.input("The quick brown fox jumped over the lazy dog.")
.voice(Voice.ALLOY)
.build();
Path output = Paths.get("/tmp/speech.mp3");
audioClient.createSpeech(request, output);
- Create translation
AudioClient audioClient = openAI.audioClient();
TranslationRequest request = TranslationRequest.newBuilder()
.model(OpenAIModel.WHISPER_1)
.file(Paths.get("/tmp/german.m4a"))
.build();
String translatedText = audioClient.createTranslation(request);
- List models
ModelsClient modelsClient = openAI.modelsClient();
List<Model> models = modelsClient.listModels();
- Classify if text violates OpenAI's Content Policy
ModerationsClient moderationsClient = openAI.moderationsClient();
ModerationRequest request = ModerationRequest.newBuilder()
.input("I want to bake a cake.")
.build();
Moderation moderation = moderationsClient.createModeration(request);
boolean violence = moderation.results().get(0).categories().violence();
- Create and execute a batch
// Upload JSONL file containing requests for the batch
FilesClient filesClient = openAI.filesClient();
UploadFileRequest uploadInputFileRequest = UploadFileRequest.newBuilder()
.file(Paths.get("/tmp/batch-requests.jsonl"))
.purpose(Purpose.BATCH)
.build();
File inputFile = filesClient.uploadFile(uploadInputFileRequest);
BatchClient batchClient = openAI.batchClient();
CreateBatchRequest request = CreateBatchRequest.newBuilder()
.inputFileId(inputFile.id())
.endpoint("/v1/chat/completions")
.completionWindow("24h")
.build();
Batch batch = batchClient.createBatch(request);
// check status of the batch
Batch retrievedBatch = batchClient.retrieveBatch(batch.id());
System.out.println(retrievedBatch.status());
- Upload large file in multiple parts
UploadsClient uploadsClient = openAI.uploadsClient();
CreateUploadRequest createUploadRequest = CreateUploadRequest.newBuilder()
.filename("training_examples.jsonl")
.purpose(Purpose.FINE_TUNE)
.bytes(2147483648)
.mimeType("text/jsonl")
.build();
Upload upload = uploadsClient.createUpload(createUploadRequest);
UploadPart part1 = uploadsClient.addUploadPart(upload.id(), Paths.get("/tmp/part1.jsonl"));
UploadPart part2 = uploadsClient.addUploadPart(upload.id(), Paths.get("/tmp/part2.jsonl"));
CompleteUploadRequest completeUploadRequest = CompleteUploadRequest.newBuilder()
.partIds(List.of(part1.id(), part2.id()))
.build();
Upload completedUpload = uploadsClient.completeUpload(upload.id(), completeUploadRequest);
// the created usable File object
File file = completedUpload.file();
- Build AI Assistant
AssistantsClient assistantsClient = openAI.assistantsClient();
ThreadsClient threadsClient = openAI.threadsClient();
MessagesClient messagesClient = openAI.messagesClient();
RunsClient runsClient = openAI.runsClient();
// Step 1: Create an Assistant
CreateAssistantRequest createAssistantRequest = CreateAssistantRequest.newBuilder()
.name("Math Tutor")
.model(OpenAIModel.GPT_3_5_TURBO_1106)
.instructions("You are a personal math tutor. Write and run code to answer math questions.")
.tool(Tool.codeInterpreterTool())
.build();
Assistant assistant = assistantsClient.createAssistant(createAssistantRequest);
// Step 2: Create a Thread
CreateThreadRequest createThreadRequest = CreateThreadRequest.newBuilder().build();
Thread thread = threadsClient.createThread(createThreadRequest);
// Step 3: Add a Message to a Thread
CreateMessageRequest createMessageRequest = CreateMessageRequest.newBuilder()
.role(Role.USER)
.content("I need to solve the equation `3x + 11 = 14`. Can you help me?")
.build();
ThreadMessage message = messagesClient.createMessage(thread.id(), createMessageRequest);
// Step 4: Run the Assistant
CreateRunRequest createRunRequest = CreateRunRequest.newBuilder()
.assistantId(assistant.id())
.instructions("Please address the user as Jane Doe. The user has a premium account.")
.build();
ThreadRun run = runsClient.createRun(thread.id(), createRunRequest);
// Step 5: Check the Run status
ThreadRun retrievedRun = runsClient.retrieveRun(thread.id(), run.id());
String status = retrievedRun.status();
// Step 6: Display the Assistant's Response
MessagesClient.PaginatedThreadMessages paginatedMessages = messagesClient.listMessages(thread.id(), PaginationQueryParameters.none(), Optional.empty());
List<ThreadMessage> messages = paginatedMessages.data();
- Build AI Assistant with File Search Enabled
AssistantsClient assistantsClient = openAI.assistantsClient();
ThreadsClient threadsClient = openAI.threadsClient();
MessagesClient messagesClient = openAI.messagesClient();
RunsClient runsClient = openAI.runsClient();
VectorStoresClient vectorStoresClient = openAI.vectorStoresClient();
FilesClient filesClient = openAI.filesClient();
VectorStoreFileBatchesClient vectorStoreFileBatchesClient = openAI.vectorStoreFileBatchesClient();
// Step 1: Create a new Assistant with File Search Enabled
CreateAssistantRequest createAssistantRequest = CreateAssistantRequest.newBuilder()
.name("Financial Analyst Assistant")
.model(OpenAIModel.GPT_4_TURBO)
.instructions("You are an expert financial analyst. Use you knowledge base to answer questions about audited financial statements.")
.tool(Tool.fileSearchTool())
.build();
Assistant assistant = assistantsClient.createAssistant(createAssistantRequest);
// Step 2: Upload files and add them to a Vector Store
CreateVectorStoreRequest createVectorStoreRequest = CreateVectorStoreRequest.newBuilder()
.name("Financial Statements")
.build();
VectorStore vectorStore = vectorStoresClient.createVectorStore(createVectorStoreRequest);
UploadFileRequest uploadFileRequest1 = UploadFileRequest.newBuilder()
.file(Paths.get("edgar/goog-10k.pdf"))
.purpose(Purpose.ASSISTANTS)
.build();
File file1 = filesClient.uploadFile(uploadFileRequest1);
UploadFileRequest uploadFileRequest2 = UploadFileRequest.newBuilder()
.file(Paths.get("edgar/brka-10k.txt"))
.purpose(Purpose.ASSISTANTS)
.build();
File file2 = filesClient.uploadFile(uploadFileRequest2);
CreateVectorStoreFileBatchRequest createVectorStoreFileBatchRequest = CreateVectorStoreFileBatchRequest.newBuilder()
.fileIds(List.of(file1.id(), file2.id()))
.build();
VectorStoreFileBatch batch = vectorStoreFileBatchesClient.createVectorStoreFileBatch(vectorStore.id(), createVectorStoreFileBatchRequest);
// need to query the status of the file batch for completion
vectorStoreFileBatchesClient.retrieveVectorStoreFileBatch(vectorStore.id(), batch.id());
// Step 3: Update the assistant to use the new Vector Store
ModifyAssistantRequest modifyAssistantRequest = ModifyAssistantRequest.newBuilder()
.toolResources(ToolResources.fileSearchToolResources(vectorStore.id()))
.build();
assistantsClient.modifyAssistant(assistant.id(), modifyAssistantRequest);
// Step 4: Create a thread
CreateThreadRequest.Message message = CreateThreadRequest.Message.newBuilder()
.role(Role.USER)
.content("How many shares of AAPL were outstanding at the end of of October 2023?")
.build();
CreateThreadRequest createThreadRequest = CreateThreadRequest.newBuilder()
.message(message)
.build();
Thread thread = threadsClient.createThread(createThreadRequest);
// Step 5: Create a run and check the output
CreateRunRequest createRunRequest = CreateRunRequest.newBuilder()
.assistantId(assistant.id())
.instructions("Please address the user as Jane Doe. The user has a premium account.")
.build();
ThreadRun run = runsClient.createRun(thread.id(), createRunRequest);
// check the run status
ThreadRun retrievedRun = runsClient.retrieveRun(thread.id(), run.id());
String status = retrievedRun.status();
// display the Assistant's Response
MessagesClient.PaginatedThreadMessages paginatedMessages = messagesClient.listMessages(thread.id(), PaginationQueryParameters.none(), Optional.empty());
List<ThreadMessage> messages = paginatedMessages.data();
- Create a run and stream the result of executing the run (Assistants Streaming)
RunsClient runsClient = openAI.runsClient();
CreateRunRequest createRunRequest = CreateRunRequest.newBuilder()
.assistantId(assistant.id())
.instructions("Please address the user as Jane Doe. The user has a premium account.")
.stream(true)
.build();
// with java.util.stream.Stream
runsClient.createRunAndStream(thread.id(), createRunRequest).forEach(assistantStreamEvent -> {
System.out.println(assistantStreamEvent.event());
System.out.println(assistantStreamEvent.data());
});
// with subscriber
runsClient.createRunAndStream(thread.id(), createRunRequest, new AssistantStreamEventSubscriber() {
@Override
public void onThread(String event, Thread thread) {
// ...
}
@Override
public void onThreadRun(String event, ThreadRun threadRun) {
// ...
}
@Override
public void onThreadRunStep(String event, ThreadRunStep threadRunStep) {
// ...
}
@Override
public void onThreadRunStepDelta(String event, ThreadRunStepDelta threadRunStepDelta) {
// ...
}
@Override
public void onThreadMessage(String event, ThreadMessage threadMessage) {
// ...
}
@Override
public void onThreadMessageDelta(String event, ThreadMessageDelta threadMessageDelta) {
// ...
}
@Override
public void onUnknownEvent(String event, String data) {
// ...
}
@Override
public void onException(Throwable ex) {
// ...
}
@Override
public void onComplete() {
// ...
}
});
// "createThreadAndRunAndStream" and "submitToolOutputsAndStream" methods are also available
- List all the users in an organization.
OpenAI openAI = OpenAI.newBuilder()
.adminKey(System.getenv("OPENAI_ADMIN_KEY"))
.build();
UsersClient usersClient = openAI.usersClient();
List<User> users = usersClient.listUsers(Optional.empty(), Optional.empty()).data();
- List user actions and configuration changes within an organization
OpenAI openAI = OpenAI.newBuilder()
.adminKey(System.getenv("OPENAI_ADMIN_KEY"))
.build();
AuditLogsClient auditLogsClient = openAI.auditLogsClient();
ListAuditLogsQueryParameters queryParameters = ListAuditLogsQueryParameters.newBuilder()
.eventTypes(List.of("invite.sent", "invite.deleted"))
.build();
List<AuditLog> auditLogs = auditLogsClient.listAuditLogs(queryParameters).data();