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);

OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY")) .organization("org-zweLLamVlP6c5n66zY334ivs") .project(System.getenv("PROJECT_ID")) .build();

OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY")) .baseUrl("https://api.foobar.com/v1/") .build();

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();

OpenAI openAI = OpenAI.newBuilder(System.getenv("OPENAI_API_KEY")) .requestTimeout(Duration.ofSeconds(10)) .build();

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);

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() { // ... } });

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);

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);

AudioClient audioClient = openAI.audioClient(); TranslationRequest request = TranslationRequest.newBuilder() .model(OpenAIModel.WHISPER_1) .file(Paths.get("/tmp/german.m4a")) .build(); String translatedText = audioClient.createTranslation(request);

ModelsClient modelsClient = openAI.modelsClient(); List<Model> models = modelsClient.listModels();

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();

// 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()); 

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();

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();

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();

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

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();

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();