This project is a Java library for GPT API, designed to make it easier
for Java developers to use GPT API.
This is a community maintained project （an unofficial library）
<dependency>
<groupId>io.github.whitemagic2014</groupId>
<artifactId>gpt-magic</artifactId>
<version>version</version>
</dependency>
implementation group: 'io.github.whitemagic2014', name: 'gpt-magic', version: 'version'
short
implementation 'io.github.whitemagic2014:gpt-magic:version'
- This project is lightweight as almost all the code is written in original Java, which will not add any extra weight to your project.
- Of course, it includes interfaces. If you need higher performance, you can override the interfaces yourself.
- The project is more like a utility tool, allowing you to manage your API key by yourself. You can set your key in environment variables such as application.properties/application.yml in a Spring project.
You can set the global key and proxy server as follows:
System.setProperty("OPENAI_API_KEY",""); // no system default value
System.setProperty("OPENAI_API_ORG","");// no system default value
System.setProperty("OPENAI_API_SERVER",""); // default = https://api.openai.com
Alternatively, you can configure them independently for each request.
new CreateChatCompletionRequest()
.key("your api key")
.server("default is https://api.openai.com")
.organization("optional")
.addMessage(ChatMessage.userMessage("prompt"));
.send();
- Audio
- Chat
Completions(legacy)- Embeddings
- Fine-tuning
- Batch
- Files
- Images
- Models
- Moderations
- Assistants
- Threads
- Messages
- Runs
System.setProperty("OPENAI_API_KEY", "");
//System.setProperty("OPENAI_API_SERVER", "");// default is https://api.openai.com
CreateChatCompletionRequest demo = new CreateChatCompletionRequest()
.model(GptModel.gpt_3p5_turbo)
.addMessage(ChatMessage.systemMessage("system set"))
.addMessage(ChatMessage.userMessage("prompt"));
// send with stream model
String result1 = RequestUtil.streamRequest(demo);
// send without stream model
String result2 = demo2.sendForChoices().get(0).getMessage().getContent();
// ...
// See the code for more details
- Update: Enhanced the GptModel and removed some models that are no longer in use.
- New: Support Batch Apis
- Update: All POJOs have implemented the Serializable interface.
- Optimize: The assignment method for the purpose field in UploadFileRequest has been optimized.
- Update: In RetrieveFileContentRequest, the send method is no longer recommended. Instead, use the sendForContent method to directly obtain the file content.
- Add new models in GptModel. (gpt-4o)
- Update: The CreateChatCompletionRequest now includes a new method, sendForResponse, which returns a CreateChatCompletionResponse. This object not only contains the choice list returned by the sendForChoices method, but also includes additional information such as the token usage for this request.
- Update models in GptModel. (0125 -> gpt-4-turbo-2024-04-09)
- Vision requests can now use JSON mode and function calling. see more
- Update models in GptModel. (1106 -> 0125)
- Fixed the bug that occurred when specifying the calling method using toolChoiceFunction in CreateChatCompletionRequest.
- Update: RequestUtil now includes a new method called streamRequestV2, which is an extended version of streamRequest and provides enhanced support for the function mode.
- Update: update models in GptModel.class
- Update: Renamed ChatTool(function) to FunctionTool and introduced new tools: CodeInterpreterTool and RetrievalTool.
- Update: Enhanced CreateFineTuningJobRequest with two additional parameters: BatchSize and LearningRateMultiplier.
- New: Introduced a new request "ListFineTuningJobsRequest" to the file-tuning feature.
- New: Assistants Apis
- New: Threads Apis
- New: Messages
- New: Runs
// Assistant simple demo
// https://platform.openai.com/docs/assistants/overview/agents
// Step 1: Create an Assistant
JSONObject json1 = new CreateAssistantRequest()
.name("Math Tutor")
.instructions("You are a personal math tutor. Write and run code to answer math questions.")
.model(GptModel.gpt_4_turbo)
.tools(Arrays.asList(new CodeInterpreterTool()))
.send();
System.out.println(json1);
String assistantId = "assistantId";
// Step 2: Create a Thread
JSONObject json2 = new CreateThreadRequest().send();
System.out.println(json2);
String threadId = "threadId";
// Step 3: Add a Message to a Thread
JSONObject json3 = new CreateMessageRequest()
.threadId(threadId)
.content("I need to solve the equation `3x + 11 = 14`. Can you help me?")
.send();
System.out.println(json3);
// Step 4: Run the Assistant
JSONObject json4 = new CreateRunRequest()
.assistantId(assistantId)
.threadId(threadId)
.instructions("Please address the user as WhiteMagic2014. The user has a premium account")
.send();
System.out.println(json4);
String runId = "runId";
// Step 5: Check the Run status
JSONObject json5 = new RetrieveRunRequest().threadId(threadId).runId(runId).send();
System.out.println(json5);
// Step 6: Display the Assistant's Response
JSONObject json6 = new ListMessagesRequest().threadId(threadId).send();
System.out.println(json6);
- Deprecated: Removed outdated methods. (FileTunes,Edit)
- Update: CreateImageRequest now support dall-e-3.
new CreateImageRequest()
.prompt("xxxxxx")
.model(GptModel.Dall_E_3)
.styleVivid()
.size1024x1024()
.formatUrl()
.sendForImages()
.stream()
.forEach(System.out::println);
- New: CreateSpeechRequest is available. It can trans text to speech.
System.out.println(new CreateSpeechRequest().filePath("/path/to/save/file")
.input("CreateSpeechRequest is available. It can trans text to speech.")
.voice(AudioVoiceType.shimmer)
.formatMp3()
.send());
- Update: CreateChatCompletionRequest support image input.
// You can use base64 or URL to upload one or more images.
System.out.println(new CreateChatCompletionRequest()
.model(GptModel.gpt_4_vision_preview)
// 2 url pictures
.addMessage(ChatMessage.userMessageWithImageUrl("Which animal in these two pictures is stronger?",
Arrays.asList("https://pic1/url","https://pic2/url")))
.maxTokens(300)
.sendForChoices().get(0).getMessage().getContent());
System.out.println(new CreateChatCompletionRequest()
.model(GptModel.gpt_4_vision_preview)
// 1 base64 pictures
.addMessage(ChatMessage.userMessageWithImageFilePath("What does this picture describe",
Arrays.asList("/path/to/image/file.jpg")))
.maxTokens(300)
.sendForChoices().get(0).getMessage().getContent());
- Update and Deprecated: The function in ChatMessage and CreateChatCompletionRequest is deprecated, please use the tool now, you can refer to the code in demo.class.
- Fixed the issue where DefaultGptHttpUtil sometimes fails to correctly throw RuntimeException.
- Please read gpt-3-5-turbo-fine-tuning-and-api-updates
- Deprecated: Requests under the FineTunes package have been replaced by the FineTuning requests.
- New: RequestUtil.streamRequest method (Get String result content with stream model)
- Fix the wrong name in GptModel.class
- Update: update models in GptModel.class
- Deprecated: CreateCompletionRequest will be Deprecated
- Optimize:Now, with the use of System.setProperty, you can easily set properties without the need to manually set them each time a request is created.
Explicitly setting in the request > Setting with System.setProperty > System default value.
System.setProperty("OPENAI_API_KEY",""); // no system default value
System.setProperty("OPENAI_API_ORG","");// no system default value
System.setProperty("OPENAI_API_SERVER",""); // default = https://api.openai.com
- New: The CreateChatCompletionRequest function can now call other functions. For more information on how to call functions, please refer to here
- New: Two new models, gpt_3p5_turbo_0613 and gpt_3p5_turbo_16k, have been added to the GptModel.class
- New: The ChatMessage.class now includes a message function, and a new ChatFunction.class has been added for calling functions within the model.
/**
* your custom function
* Get the current weather in a given location
*/
public JSONObject getCurrentWeather(String location, String unit) {
JSONObject fakeResult = new JSONObject();
fakeResult.put("location", location);
if ("celsius".equals(unit)) {
fakeResult.put("temperature", "22~31 C°");
} else if ("fahrenheit".equals(unit)) {
fakeResult.put("temperature", "71.6~87.8 F°");
}
fakeResult.put("forecast", Arrays.asList("sunny", "windy"));
fakeResult.put("unit", unit);
return fakeResult;
}
// A sample for function call
// first make a function object from your custom function method
ChatFunction function = JSONObject.parseObject("{\n" +
" \"name\":\"getCurrentWeather\",\n" +
" \"description\":\"Get the current weather in a given location\",\n" +
" \"parameters\":{\n" +
" \"type\":\"object\",\n" +
" \"required\":[\"location\",\"unit\"],\n" +
" \"properties\":{\n" +
" \"unit\":{\n" +
" \"default\":\"celsius\",\n" +
" \"type\":\"string\",\n" +
" \"enum\":[\"celsius\",\"fahrenheit\"]\n" +
" },\n" +
" \"location\":{\n" +
" \"description\":\"The city and state\",\n" +
" \"type\":\"string\"\n" +
" }\n" +
" }\n" +
" }\n" +
"}", ChatFunction.class);
// second send a CreateChatCompletionRequest with your Function
ChatMessage functionResult1 = new CreateChatCompletionRequest()
.key(key)
.addFunction(function)
.model(GptModel.gpt_3p5_turbo_0613)
.addMessage(ChatMessage.userMessage("What's the weather like in ShangHai today?"))
.functionCallAuto() //.functionCallName("getCurrentWeather")
.sendForChoices()
.get(0)
.getMessage();
// When using functionCallName or functionCallAuto to enable GPT to call a function,
// please note that the resulting output is temporary and not suitable for end users.
// To perform the desired action, you will need to call your custom function.
System.out.println("temp result:");
System.out.println(functionResult1);
// result eg:
// ChatMessage{role='assistant', content='null', name='null', function_call={"name":"getCurrentWeather","arguments":"{\n \"location\": \"Shanghai\"\n}"}}
// If your function returns any information, you should call GPT again to obtain the final result for end users.
String functionName = functionResult1.getFunction_call().getString("name");
String location = functionResult1.getFunction_call().getJSONObject("arguments").getString("location");
String unit = functionResult1.getFunction_call().getJSONObject("arguments").getString("unit");
JSONObject weatherInfo = getCurrentWeather(location, unit);
// finally
ChatMessage functionResult2 = new CreateChatCompletionRequest()
.key(key)
.addFunction(function)
.model(GptModel.gpt_3p5_turbo_0613)
.addMessage(ChatMessage.userMessage("What's the weather like in ShangHai today?"))
.addMessage(functionResult1)// gpt result
.addMessage(ChatMessage.functionMessage(functionName, weatherInfo.toString())) // send a function message with function_name and custom result
.sendForChoices()
.get(0)
.getMessage();
System.out.println("final result:");
System.out.println(functionResult2);
// result eg:
//ChatMessage{role='assistant', content='The weather in Shanghai today is sunny and windy, with a temperature ranging from 22 to 31 degrees Celsius.', name='null', function_call=null}
- Optimize: CreateChatCompletionRequest.addMessage can now directly accept ChatMessage as a parameter.
// Now there are two ways to add a message.
new CreateChatCompletionRequest().addMessage("user","hello");
new CreateChatCompletionRequest().addMessage(ChatMessage.userMessage("hello"));
- New: When using CreateEmbeddingsRequest, if base64Embedding is set to 'true', the returned embedding data format will be in base64.
- New: EmbeddingUtil has been added to convert base64 string embeddings to numerical embeddings.
- Optimize:Optimize ChatMessage, now providing several convenient instantiation methods.
ChatMessage.systemMessage("content");
ChatMessage.userMessage("content");
ChatMessage.assistantMessage("content");
- Fix bug:Fixed the issue of mismatched error message for CreateEmbeddingsRequest due to incorrect input settings.
- Fix bug:Fix the issue of incorrect formatting of the returned result when using CreateChatCompletionRequest and CreateCompletionRequest with stream set to true.
- Optimize:In order to facilitate users who need to use proxies, all requests now support self-specifying a server address.
List<ChatCompletionChoice> demo8 = new CreateChatCompletionRequest()
.server("https://Your.Proxy.Server/servername")
.key(key)
.addMessage("system", "You are a helpful assistant.")
.addMessage("user", "Who won the world series in 2020?")
.addMessage("assistant", "The Los Angeles Dodgers won the World Series in 2020.")
.addMessage("user", "Where was it played?")
.sendForChoices();
- New: When using CreateChatCompletionRequest and CreateCompletionRequest, if the stream mode is set to 'true', an OutputStream can be provided to receive the returned stream data.
// CreateChatCompletionRequest with stream mode
// If you are providing a web service, you can offer HttpServletResponse.getOutputStream()
// so that you can provide your callers with OpenAI's streaming return, allowing them to easily achieve a typewriter effect similar to the OpenAI official page.
ByteArrayOutputStream baos = new ByteArrayOutputStream();
new Thread(() -> {
new CreateChatCompletionRequest()
.key(key)
.addMessage("user", "Can you recommend some science fiction novels to me?")
.stream(true)
.outputStream(baos) // You need to set an OutputStream to receive the returned stream
.send();
}).start();
// a sample to use the streaming return
boolean flag = true;
while (flag) {
byte[] data = baos.toByteArray();
if (data.length > 0) {
String result = new String(data);
baos.reset();
String str = "[" + result.replace("data: [DONE]", "").replace("data:", ",") + "]";
JSONArray jsonArray;
try {
jsonArray = JSON.parseArray(str);
} catch (Exception e) {
System.out.println(str);
throw new RuntimeException(e);
}
for (int i = 0; i < jsonArray.size(); i++) {
JSONObject choice = jsonArray.getJSONObject(i).getJSONArray("choices").getJSONObject(0);
if ("stop".equals(choice.getString("finish_reason"))) {
flag = false;
break;
}
JSONObject delta = choice.getJSONObject("delta");
if (delta.containsKey("content")) {
System.out.print(delta.getString("content"));
}
}
}
try {
// wait for a while
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
- Optimize: CreateEmbeddingsRequest now retrieves embeddings as double instead of float.
- New: Add Distance.class. Calculate cosine distance for embeddings.
- Optimize: Distinguish between sending failure and parsing failure in GptRequest.class
- Optimize: Add GptModel.class list some available models for request; You can choose models instead of type the model name。
version before 1.3.1
new CreateCompletionRequest().model("text-davinci-003");
version after 1.3.1
new CreateCompletionRequest().model(GptModel.text_davinci_003);
- New: chat apis
- New: audio apis
- Optimize: Image apis added image verification(square,4mb,png)
- Optimize: When the http code is not 200, the error message will be thrown through a RuntimeException
- Fix bug: Fix the problem of garbled code when using Chinese
This project is an open-sourced software licensed under the MIT license.