jvm openai

jvm-openai

用於 JVM 的簡約非官方 OpenAI API 客戶端，用 Java 編寫。唯一使用的依賴項是用於 JSON 解析的 Jackson。

新增依賴項

Java 17+ 是先決條件

搖籃

implementation( " io.github.stefanbratanov:jvm-openai: ${ version } " )

梅文

< dependency >
    < groupId >io.github.stefanbratanovgroupId >
    < artifactId >jvm-openaiartifactId >
    < version >${version}version >
dependency >

最小的例子

 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 );

支援的API

端點

應用程式介面	地位
聲音的	✔️
聊天	✔️
嵌入	✔️
微調	✔️
批次	✔️
文件	✔️
上傳	✔️
圖片	✔️
型號	✔️
節制	✔️

助理（測試版）

應用程式介面	地位
助理	✔️
執行緒數	✔️
留言	✔️
跑步	✔️
運行步驟	✔️
向量商店	✔️
向量儲存文件	✔️
向量存儲文件批次	✔️

行政

應用程式介面	地位
邀請	✔️
使用者	✔️
專案	✔️
專案用戶	✔️
項目服務帳戶	✔️
專案 API 金鑰	✔️
審核日誌	✔️

注意：不支援舊版 API

更多範例

• 配置組織和項目

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

• 配置自訂基本 url

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

• 配置自訂 Java 的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 ();

• 為所有請求配置逾時

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

• 建立聊天完成異步

chatCompletion = chatClient.createChatCompletionAsync(request); chatCompletion.thenAccept(System.out::println);">

 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 );

• 串流媒體

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

• 創建圖像

 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 );

• 創建演講

 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 );

• 創建翻譯

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

• 列出型號

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

• 如果文字違反 OpenAI 的內容政策，則進行分類

 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 ();

• 建立並執行批次處理

 // 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 ());      

• 分多個部分上傳大文件

 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 ();

• 打造AI助手

messages = paginatedMessages.data();">

 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 ();

• 建立啟用文件搜尋的 AI 助手

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 ();

• 建立運行並串流執行運行的結果（助手串流）

{ 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">

 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

• 列出組織中的所有使用者。

users = usersClient.listUsers(Optional.empty(), Optional.empty()).data();">

 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 ();

• 列出組織內的使用者操作和配置更改

auditLogs = auditLogsClient.listAuditLogs(queryParameters).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 ();