Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/lambdua/openai4j
Java client library for OpenAI API.Full support for all OpenAI API models including Completions, Chat, Edits, Embeddings, Audio, Files, Assistants-v2, Images, Moderations, Batch, and Fine-tuning.
https://github.com/lambdua/openai4j
assistants-api chatgpt chatgpt4 gpt-4o gpt-4o-api gpt-vision gpt4 openai openai-api openai-api-chatbot openai-assistant-api openai-images openai-whisper
Last synced: about 1 month ago
JSON representation
Java client library for OpenAI API.Full support for all OpenAI API models including Completions, Chat, Edits, Embeddings, Audio, Files, Assistants-v2, Images, Moderations, Batch, and Fine-tuning.
- Host: GitHub
- URL: https://github.com/lambdua/openai4j
- Owner: Lambdua
- License: mit
- Created: 2024-03-21T08:34:48.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-30T09:47:40.000Z (about 2 months ago)
- Last Synced: 2024-07-30T12:58:14.724Z (about 2 months ago)
- Topics: assistants-api, chatgpt, chatgpt4, gpt-4o, gpt-4o-api, gpt-vision, gpt4, openai, openai-api, openai-api-chatbot, openai-assistant-api, openai-images, openai-whisper
- Language: Java
- Homepage:
- Size: 1.08 MB
- Stars: 240
- Watchers: 6
- Forks: 13
- Open Issues: 3
-
Metadata Files:
- Readme: README-zh.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - openai4j - Java client library for OpenAI API.Full support for all OpenAI API models including Completions, Chat, Edits, Embeddings, Audio, Files, Assistants-v2, Images, Moderations, Batch, and Fine-tuning. (CLIs)
README
# OpenAi4J
OpenAi4J是一个非官方的Java库，旨在帮助java开发者与OpenAI的GPT模型和相关api交互，支持包括最新添加的gpt4 turbo vision。
该库最初从TheoKanning/openai-java派生而来，在最初项目的维护中断后，该库继续开发，以包含最新的 openAi API功能。
## 特性
- 支持所有OpenAI的最新API，包括Completions、Chat、Edits、Embeddings、Audio、Files、Assistants-v2、Images、Moderations、Batch和Fine-tuning。
Images, Moderations, Batch, and Fine-tuning.
- 易于使用的客户端设置，可立即进行API交互。
- 大量的示例和文档可帮助您快速入门,具体参考example包下的代码。
- API密钥和基本URL的环境变量集成的可定制设置。
- 支持同步和异步、流式API调用。
这个库旨在为Java开发人员提供一个强大的工具，将OpenAI的强大功能毫不费力地集成到他们的应用程序中。
# 快速开始
## 导入依赖
### Gradle
`implementation 'io.github.lambdua::0.21.3'`
### Maven
```xml
io.github.lambdua
service
0.21.3
```
## 和OpenAi模型聊天
```java
static void simpleChat() {
//从环境变量OPENAI_api_key获取api-key
OpenAiService service = new OpenAiService(Duration.ofSeconds(30));
List messages = new ArrayList<>();
ChatMessage systemMessage = new SystemMessage("你是一只可爱的猫，会这样说话。");
messages.add(systemMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.n(1)
.maxTokens(50)
.build();
ChatCompletionResult chatCompletion = service.createChatCompletion(chatCompletionRequest);
System.out.println(chatCompletion.getChoices().get(0).getMessage().getContent());
}
```
# 单纯使用POJO
如果您希望开发自己的客户端，只需从api模块导入POJO即可,api模块提供了OpenAi API的接口对象，您可以直接使用这些对象进行开发。
```xml
io.github.lambdua
api
0.21.3
```
# 更多使用示例
示例代码都在`example`包下，包括了大部分功能的使用。
您可以参考example包下的代码。以下是一些常用功能的使用示例。
gpt-vision 图像识别
```java
static void gptVision() {
OpenAiService service = new OpenAiService(Duration.ofSeconds(20));
final List messages = new ArrayList<>();
final ChatMessage systemMessage = new SystemMessage("You are a helpful assistant.");
//Here, the imageMessage is intended for image recognition
final ChatMessage imageMessage = UserMessage.buildImageMessage("What's in this image?",
"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg");
messages.add(systemMessage);
messages.add(imageMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
.model("gpt-4-turbo")
.messages(messages)
.n(1)
.maxTokens(200)
.build();
ChatCompletionChoice choice = service.createChatCompletion(chatCompletionRequest).getChoices().get(0);
System.out.println(choice.getMessage().getContent());
}
```
自定义 OpenAiService
OpenAiService的设置选项多样，可以根据需要进行设置，如下所示：
```java
//0 使用默认配置读取环境变量OPENAI-API_KEY、OPENAI-API _BASE-URL作为默认API_KEY和BASE-URL，
//鼓励使用环境变量来加载OpenAI API密钥
OpenAiService openAiService0 = new OpenAiService();
//1.使用默认的baseUrl,默认配置service,这里会默认先从环境变量中获取BaseURL(key:OPENAI_API_BASE_URL),如果没有则使用默认的"https://api.openai.com/v1/";
OpenAiService openAiService = new OpenAiService(API_KEY);
//2. 使用自定义的baseUrl,默认配置配置service
OpenAiService openAiService1 = new OpenAiService(API_KEY, BASE_URL);
//3.自定义过期时间
OpenAiService openAiService2 = new OpenAiService(API_KEY, Duration.ofSeconds(10));
//4. 更灵活的自定义
//4.1. 自定义okHttpClient
OkHttpClient client = new OkHttpClient.Builder()
//连接池
.connectionPool(new ConnectionPool(Runtime.getRuntime().availableProcessors() * 2, 30, TimeUnit.SECONDS))
//自定义的拦截器,如重试拦截器,日志拦截器,负载均衡拦截器等
// .addInterceptor(new RetryInterceptor())
// .addInterceptor(new LogInterceptor())
// .addInterceptor(new LoadBalanceInterceptor())
//添加代理
// .proxy(new Proxy(Proxy.Type.HTTP, new InetSocketAddress("proxyHost", 8080)))
.connectTimeout(2, TimeUnit.SECONDS)
.writeTimeout(3, TimeUnit.SECONDS)
.readTimeout(10, TimeUnit.SECONDS)
.protocols(Arrays.asList(Protocol.HTTP_2, Protocol.HTTP_1_1))
.build();
//4.2 自定义Retorfit配置
Retrofit retrofit = OpenAiService.defaultRetrofit(client, OpenAiService.defaultObjectMapper(), BASE_URL);
OpenAiApi openAiApi = retrofit.create(OpenAiApi.class);
OpenAiService openAiService3 = new OpenAiService(openAiApi);
```
流式对话
```java
static void streamChat() {
//从环境变量OPENAI_API_KEY获取
OpenAiService service = new OpenAiService(Duration.ofSeconds(30));
List messages = new ArrayList<>();
ChatMessage systemMessage = new SystemMessage("You are a cute cat and will speak as such.");
messages.add(systemMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.n(1)
.maxTokens(50)
.build();
service.streamChatCompletion(chatCompletionRequest).blockingForEach(System.out::println);
}
```
Tools 使用
本库支持过时的function调用方法和当前基于tool的方法。
首先,我们定义一个function对象,定义function对象的方式很灵活,你可以使用pojo定义(json schema自动序列化)
,也可以使用如`map`,`ChatFunctionDynamic`方式去定义,可以参考example包下的代码,这里我们定义一个天气查询的function对象:
```java
public class Weather {
@JsonPropertyDescription("City and state, for example: León, Guanajuato")
public String location;
@JsonPropertyDescription("The temperature unit, can be 'celsius' or 'fahrenheit'")
@JsonProperty(required = true)
public WeatherUnit unit;
}
public enum WeatherUnit {
CELSIUS, FAHRENHEIT;
}
public static class WeatherResponse {
public String location;
public WeatherUnit unit;
public int temperature;
public String description;
// constructor
}
```
接下来，我们声明该函数并将其与执行器相关联，在这里模拟API响应：
```java
//First, a function to fetch the weather
public static FunctionDefinition weatherFunction() {
return FunctionDefinition.builder()
.name("get_weather")
.description("Get the current weather in a given location")
.parametersDefinitionByClass(Weather.class)
//The executor here is a lambda expression that accepts a Weather object and returns a Weather Response object
.executor(w -> new WeatherResponse(w.location, w.unit, 25, "sunny"))
.build();
}
```
然后，该服务用于聊天完成请求，包含以下工具：
```java
static void toolChat() {
OpenAiService service = new OpenAiService(Duration.ofSeconds(30));
final ChatTool tool = new ChatTool(ToolUtil.weatherFunction());
final List messages = new ArrayList<>();
final ChatMessage systemMessage = new SystemMessage("You are a helpful assistant.");
final ChatMessage userMessage = new UserMessage("What is the weather in BeiJin?");
messages.add(systemMessage);
messages.add(userMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
//Tools is a list; multiple tools can be included
.tools(Collections.singletonList(tool))
.toolChoice(ToolChoice.AUTO)
.n(1)
.maxTokens(100)
.build();
//Request is sent
ChatCompletionChoice choice = service.createChatCompletion(chatCompletionRequest).getChoices().get(0);
AssistantMessage toolCallMsg = choice.getMessage();
ChatToolCall toolCall = toolCallMsg.getToolCalls().get(0);
System.out.println(toolCall.getFunction());
messages.add(toolCallMsg);
messages.add(new ToolMessage("the weather is fine today.", toolCall.getId()));
//submit tool call
ChatCompletionRequest toolCallRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.n(1)
.maxTokens(100)
.build();
ChatCompletionChoice toolCallChoice = service.createChatCompletion(toolCallRequest).getChoices().get(0);
System.out.println(toolCallChoice.getMessage().getContent());
}
```
function(deprecated)
```java
static void functionChat() {
OpenAiService service = new OpenAiService(Duration.ofSeconds(30));
final List messages = new ArrayList<>();
final ChatMessage systemMessage = new SystemMessage("You are a helpful assistant.");
final ChatMessage userMessage = new UserMessage("What is the weather in BeiJin?");
messages.add(systemMessage);
messages.add(userMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.functions(Collections.singletonList(ToolUtil.weatherFunction()))
.functionCall("auto")
.n(1)
.maxTokens(100)
.build();
//Request is sent
ChatCompletionChoice choice = service.createChatCompletion(chatCompletionRequest).getChoices().get(0);
AssistantMessage functionCallMsg = choice.getMessage();
ChatFunctionCall functionCall = functionCallMsg.getFunctionCall();
System.out.println(functionCall);
messages.add(functionCallMsg);
messages.add(new FunctionMessage("the weather is fine today.", "get_weather"));
//submit tool call
ChatCompletionRequest toolCallRequest = ChatCompletionRequest.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.n(1)
.maxTokens(100)
.build();
ChatCompletionChoice toolCallChoice = service.createChatCompletion(toolCallRequest).getChoices().get(0);
System.out.println(toolCallChoice.getMessage().getContent());
}
```
流式对话中调用tool(支持同时调用多个tool)
```java
void streamChatMultipleToolCalls() {
final List functions = Arrays.asList(
//1. weather query
FunctionDefinition.builder()
.name("get_weather")
.description("Get the current weather in a given location")
.parametersDefinitionByClass(ToolUtil.Weather.class)
.executor( w -> {
switch (w.location) {
case "tokyo":
return new ToolUtil.WeatherResponse(w.location, w.unit, 10, "cloudy");
case "san francisco":
return new ToolUtil.WeatherResponse(w.location, w.unit, 72, "sunny");
case "paris":
return new ToolUtil.WeatherResponse(w.location, w.unit, 22, "sunny");
default:
return new ToolUtil.WeatherResponse(w.location, w.unit, 0, "unknown");
}
}).build(),
//2. city query
FunctionDefinition.builder().name("getCities").description("Get a list of cities by time").parametersDefinitionByClass(ToolUtil.City.class).executor(v -> Arrays.asList("tokyo", "paris")).build()
);
final FunctionExecutorManager toolExecutor = new FunctionExecutorManager(functions);
List tools = new ArrayList<>();
tools.add(new ChatTool(functions.get(0)));
tools.add(new ChatTool(functions.get(1)));
final List messages = new ArrayList<>();
final ChatMessage systemMessage = new SystemMessage("You are a helpful assistant.");
final ChatMessage userMessage = new UserMessage("What is the weather like in cities with weather on 2022-12-01 ?");
messages.add(systemMessage);
messages.add(userMessage);
ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest
.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.tools(tools)
.toolChoice(ToolChoice.AUTO)
.n(1)
.maxTokens(200)
.build();
AssistantMessage accumulatedMessage = service.mapStreamToAccumulator(service.streamChatCompletion(chatCompletionRequest))
.blockingLast()
.getAccumulatedMessage();
List toolCalls = accumulatedMessage.getToolCalls();
ChatToolCall toolCall = toolCalls.get(0);
ChatFunctionCall function = toolCall.getFunction();
JsonNode jsonNode = toolExecutor.executeAndConvertToJson(function.getName(), function.getArguments());
ToolMessage toolMessage = toolExecutor.executeAndConvertToChatMessage(function.getName(),function.getArguments(), toolCall.getId());
messages.add(accumulatedMessage);
messages.add(toolMessage);
ChatCompletionRequest chatCompletionRequest2 = ChatCompletionRequest
.builder()
//3.5 there may be logical issues
.model("gpt-3.5-turbo-0125")
.messages(messages)
.tools(tools)
.toolChoice(ToolChoice.AUTO)
.n(1)
.maxTokens(100)
.logitBias(new HashMap<>())
.build();
// ChatCompletionChoice choice2 = service.createChatCompletion(chatCompletionRequest2).getChoices().get(0);
AssistantMessage accumulatedMessage2 = service.mapStreamToAccumulator(service.streamChatCompletion(chatCompletionRequest2))
.blockingLast()
.getAccumulatedMessage();
messages.add(accumulatedMessage2);
for (ChatToolCall weatherToolCall : accumulatedMessage2.getToolCalls()) {
ChatFunctionCall call2 = weatherToolCall.getFunction();
Object itemResult = toolExecutor.execute(call2.getName(), call2.getArguments());
messages.add(toolExecutor.executeAndConvertToChatMessage(call2.getName(),call2.getArguments(), weatherToolCall.getId()));
}
ChatCompletionRequest chatCompletionRequest3 = ChatCompletionRequest
.builder()
.model("gpt-3.5-turbo")
.messages(messages)
.tools(tools)
.toolChoice(ToolChoice.AUTO)
.n(1)
.maxTokens(100)
.logitBias(new HashMap<>())
.build();
AssistantMessage accumulatedMessage3 = service.mapStreamToAccumulator(service.streamChatCompletion(chatCompletionRequest3))
.blockingLast()
.getAccumulatedMessage();
}
```
Token使用的计算
```java
public static void main(String... args) {
List messages = new ArrayList<>();
messages.add(new SystemMessage("Hello OpenAI 1."));
messages.add(new SystemMessage("Hello OpenAI 2. "));
messages.add(new UserMessage(Arrays.asList(new ImageContent("text", "", new ImageUrl("dddd")))));
int tokens_1 = TikTokensUtil.tokens(TikTokensUtil.ModelEnum.GPT_3_5_TURBO.getName(), messages);
int tokens_2 = TikTokensUtil.tokens(TikTokensUtil.ModelEnum.GPT_3_5_TURBO.getName(), "Hello OpenAI 1.");
int tokens_3 = TikTokensUtil.tokens(TikTokensUtil.ModelEnum.GPT_4_TURBO.getName(), messages);
}
```
Assistant Tool Call
```java
static void assistantToolCall() {
OpenAiService service = new OpenAiService();
FunctionExecutorManager executor = new FunctionExecutorManager(Collections.singletonList(ToolUtil.weatherFunction()));
AssistantRequest assistantRequest = AssistantRequest.builder()
.model("gpt-3.5-turbo").name("weather assistant")
.instructions("You are a weather assistant responsible for calling the weather API to return weather information based on the location entered by the user")
.tools(Collections.singletonList(new FunctionTool(ToolUtil.weatherFunction())))
.temperature(0D)
.build();
Assistant assistant = service.createAssistant(assistantRequest);
String assistantId = assistant.getId();
ThreadRequest threadRequest = ThreadRequest.builder().build();
Thread thread = service.createThread(threadRequest);
String threadId = thread.getId();
MessageRequest messageRequest = MessageRequest.builder()
.content("What's the weather of Xiamen?")
.build();
//add message to thread
service.createMessage(threadId, messageRequest);
RunCreateRequest runCreateRequest = RunCreateRequest.builder().assistantId(assistantId).build();
Run run = service.createRun(threadId, runCreateRequest);
Run retrievedRun = service.retrieveRun(threadId, run.getId());
while (!(retrievedRun.getStatus().equals("completed"))
&& !(retrievedRun.getStatus().equals("failed"))
&& !(retrievedRun.getStatus().equals("expired"))
&& !(retrievedRun.getStatus().equals("incomplete"))
&& !(retrievedRun.getStatus().equals("requires_action"))) {
retrievedRun = service.retrieveRun(threadId, run.getId());
}
System.out.println(retrievedRun);
RequiredAction requiredAction = retrievedRun.getRequiredAction();
List toolCalls = requiredAction.getSubmitToolOutputs().getToolCalls();
ToolCall toolCall = toolCalls.get(0);
ToolCallFunction function = toolCall.getFunction();
String toolCallId = toolCall.getId();
SubmitToolOutputsRequest submitToolOutputsRequest = SubmitToolOutputsRequest.ofSingletonToolOutput(toolCallId, executor.executeAndConvertToJson(function.getName(),function.getArguments()).toPrettyString());
retrievedRun = service.submitToolOutputs(threadId, retrievedRun.getId(), submitToolOutputsRequest);
while (!(retrievedRun.getStatus().equals("completed"))
&& !(retrievedRun.getStatus().equals("failed"))
&& !(retrievedRun.getStatus().equals("expired"))
&& !(retrievedRun.getStatus().equals("incomplete"))
&& !(retrievedRun.getStatus().equals("requires_action"))) {
retrievedRun = service.retrieveRun(threadId, run.getId());
}
System.out.println(retrievedRun);
OpenAiResponse response = service.listMessages(threadId, MessageListSearchParameters.builder()
.runId(retrievedRun.getId()).build());
List messages = response.getData();
messages.forEach(message -> {
System.out.println(message.getContent());
});
}
```
Assistant Stream Manager
通过使用`AssistantEventHandler`类和`AssistantStreamManager`
类，可以更容易地管理Assistant的流式调用。 `AssistantEventHandler`包含了所有的Assistant stream
事件回调钩子,你可以根据需要实现不同的event:
```java
/**
* You can implement various event callbacks for Assistant Event Handlers according to your own needs, making it convenient for you to handle various events related to Assistant
*/
private static class LogHandler implements AssistantEventHandler {
@Override
public void onEvent(AssistantSSE sse) {
//每一个事件都会调用这个方法
}
@Override
public void onRunCreated(Run run) {
System.out.println("start run: " + run.getId());
}
@Override
public void onEnd() {
System.out.println("stream end");
}
@Override
public void onMessageDelta(MessageDelta messageDelta) {
System.out.println(messageDelta.getDelta().getContent().get(0).getText());
}
@Override
public void onMessageCompleted(Message message) {
System.out.println("message completed");
}
@Override
public void onMessageInComplete(Message message) {
System.out.println("message in complete");
}
@Override
public void onError(Throwable error) {
System.out.println("error:" + error.getMessage());
}
}
```
`AssistantStreamManager`
对stream中的各个事件进行编排管理,支持同步/异步获取stream中的内容,可以通过manager获取.下面是一个使用样例,更多样例可以参考`AssistantStreamManagerTest.java`
```java
static void streamTest() {
OpenAiService service = new OpenAiService();
//1. create assistant
AssistantRequest assistantRequest = AssistantRequest.builder()
.model("gpt-3.5-turbo").name("weather assistant")
.instructions("You are a weather assistant responsible for calling the weather API to return weather information based on the location entered by the user")
.tools(Collections.singletonList(new FunctionTool(ToolUtil.weatherFunction())))
.temperature(0D)
.build();
Assistant assistant = service.createAssistant(assistantRequest);
String assistantId = assistant.getId();
System.out.println("assistantId:" + assistantId);
ThreadRequest threadRequest = ThreadRequest.builder()
.build();
Thread thread = service.createThread(threadRequest);
String threadId = thread.getId();
System.out.println("threadId:" + threadId);
MessageRequest messageRequest = MessageRequest.builder()
.content("What can you help me with?")
.build();
service.createMessage(threadId, messageRequest);
RunCreateRequest runCreateRequest = RunCreateRequest.builder()
.assistantId(assistantId)
.toolChoice(ToolChoice.AUTO)
.build();
//blocking
// AssistantStreamManager blockedManagere = AssistantStreamManager.syncStart(service.createRunStream(threadId, runCreateRequest), new LogHandler());
//async
AssistantStreamManager streamManager = AssistantStreamManager.start(service.createRunStream(threadId, runCreateRequest), new LogHandler());
//Other operations can be performed here...
boolean completed = streamManager.isCompleted();
// you can shut down the streamManager if you want to stop the stream
streamManager.shutDown();
//waiting for completion
streamManager.waitForCompletion();
// all of flowable events
List eventMsgsHolder = streamManager.getEventMsgsHolder();
Optional currentRun = streamManager.getCurrentRun();
// get the accumulated message
streamManager.getAccumulatedMsg().ifPresent(msg -> {
System.out.println("accumulatedMsg:" + msg);
});
service.deleteAssistant(assistantId);
service.deleteThread(threadId);
}
```
Assistant Stream
```java
static void assistantStream() throws JsonProcessingException {
OpenAiService service = new OpenAiService();
String assistantId;
String threadId;
AssistantRequest assistantRequest = AssistantRequest.builder()
.model("gpt-3.5-turbo").name("weather assistant")
.instructions("You are a weather assistant responsible for calling the weather API to return weather information based on the location entered by the user")
.tools(Collections.singletonList(new FunctionTool(ToolUtil.weatherFunction())))
.temperature(0D)
.build();
Assistant assistant = service.createAssistant(assistantRequest);
assistantId = assistant.getId();
//一般响应
Flowable threadAndRunStream = service.createThreadAndRunStream(
CreateThreadAndRunRequest.builder()
.assistantId(assistantId)
//这里不使用任何工具
.toolChoice(ToolChoice.NONE)
.thread(ThreadRequest.builder()
.messages(Collections.singletonList(
MessageRequest.builder()
.content("你好,你可以帮助我做什么?")
.build()
))
.build())
.build()
);
ObjectMapper objectMapper = new ObjectMapper();
TestSubscriber subscriber1 = new TestSubscriber<>();
threadAndRunStream
.doOnNext(System.out::println)
.blockingSubscribe(subscriber1);
Optional runStepCompletion = subscriber1.values().stream().filter(item -> item.getEvent().equals(StreamEvent.THREAD_RUN_STEP_COMPLETED)).findFirst();
RunStep runStep = objectMapper.readValue(runStepCompletion.get().getData(), RunStep.class);
System.out.println(runStep.getStepDetails());
// 函数调用 stream
threadId = runStep.getThreadId();
service.createMessage(threadId, MessageRequest.builder().content("请帮我查询北京天气").build());
Flowable getWeatherFlowable = service.createRunStream(threadId, RunCreateRequest.builder()
//这里强制使用get_weather函数
.assistantId(assistantId)
.toolChoice(new ToolChoice(new Function("get_weather")))
.build()
);
TestSubscriber subscriber2 = new TestSubscriber<>();
getWeatherFlowable
.doOnNext(System.out::println)
.blockingSubscribe(subscriber2);
AssistantSSE requireActionSse = subscriber2.values().get(subscriber2.values().size() - 2);
Run requireActionRun = objectMapper.readValue(requireActionSse.getData(), Run.class);
RequiredAction requiredAction = requireActionRun.getRequiredAction();
List toolCalls = requiredAction.getSubmitToolOutputs().getToolCalls();
ToolCall toolCall = toolCalls.get(0);
String callId = toolCall.getId();
System.out.println(toolCall.getFunction());
// 提交函数调用结果
Flowable toolCallResponseFlowable = service.submitToolOutputsStream(threadId, requireActionRun.getId(), SubmitToolOutputsRequest.ofSingletonToolOutput(callId, "北京的天气是晴天"));
TestSubscriber subscriber3 = new TestSubscriber<>();
toolCallResponseFlowable
.doOnNext(System.out::println)
.blockingSubscribe(subscriber3);
Optional msgSse = subscriber3.values().stream().filter(item -> StreamEvent.THREAD_MESSAGE_COMPLETED.equals(item.getEvent())).findFirst();
Message message = objectMapper.readValue(msgSse.get().getData(), Message.class);
String responseContent = message.getContent().get(0).getText().getValue();
System.out.println(responseContent);
}
```
# 常见问题
是否可以自定义OpenAI URL或使用代理URL？
是的，您可以在构建OpenAiService时指定一个URL，它将作为基本URL。但我们建议使用环境变量OPENAI_API_BASE_URL和OPENAI_PI_KEY来加载OPENAI API密钥。
为什么我遇到连接超时？
确保您的网络稳定，并且您的OpenAI服务器可以访问。如果您面临网络不稳定，请考虑增加超时时间.
# 为OpenAi4J做贡献
十分欢迎你对本仓库做出贡献，并一直在寻找使我们的项目变得更好的方法。如果你是有兴趣帮助改进OpenAi4J，以下是您可以贡献的一些方法：
## 提Issue
请使用GitHub Issue页面报告问题。尽可能具体地说明如何重现您的问题，包括操作系统、Java版本和任何相关日志跟踪等详细信息。
## 提交拉取请求
1. 分叉存储库并从`main`创建您的分支。
2. 如果您添加了应该进行测试的代码，请添加测试。
3. 确保您的代码符合现有的样式指南。
4. 为您的提交编写清晰的日志消息。一行消息对于小更改来说是可以的，但是对于更大的更改，应该有详细的描述。
5. 完成拉取请求表单，链接到您的PR地址的任何问题。
## 支持我
希望你觉得这个库有用！觉得还不错，可以给我一个star,我将十分感谢❤️! 您的支持帮助我保持活力并不断改进这个库。随时关注更新，以获取最新的功能和改进。
感谢您对OpenAi4J的支持！
# Contributors
# 许可证
Released under the MIT License