ChatGPT Spring Boot Starter

Spring Boot ChatGPT starter with ChatGPT chat and functions support.

Features

• Base on Spring Boot 3.0+
• Async with Spring Webflux
• Support ChatGPT Chat Stream
• Support ChatGPT functions: @GPTFunction annotation
• Support structured output: @StructuredOutput annotation for record
• Prompt Management: load prompt templates from prompt.properties with @PropertyKey, and friendly with IntelliJ IDEA
• Prompt as Lambda: convert prompt template to lambda expression and call it with FP style
• ChatGPT interface: Declare ChatGPT service interface with @ChatGPTExchange and @ChatCompletion annotations.
• No third-party library: base on Spring 6 HTTP interface
• GraalVM native image support
• Azure OpenAI support

Get Started

Add dependency

Add chatgpt-spring-boot-starter dependency in your pom.xml.

<dependency>
    <groupId>org.mvnsearch</groupId>
    <artifactId>chatgpt-spring-boot-starter</artifactId>
    <version>0.8.0</version>
</dependency>

Adjust configuration

Add openai.api.key in application.properties:

# OpenAI API Token, or you can set environment variable OPENAI_API_KEY
openai.api.key=sk-proj-xxxx

If you want to use Azure OpenAI, you can add openai.api.url in application.properties:

openai.api.key=1138xxxx9037
openai.api.url=https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2023-05-15

Call ChatGPT Service

@RestController
public class ChatRobotController {
    @Autowired
    private ChatGPTService chatGPTService;

    @PostMapping("/chat")
    public Mono<String> chat(@RequestBody String content) {
        return chatGPTService.chat(ChatCompletionRequest.of(content))
                .map(ChatCompletionResponse::getReplyText);
    }

    @GetMapping("/stream-chat")
    public Flux<String> streamChat(@RequestParam String content) {
        return chatGPTService.stream(ChatCompletionRequest.of(content))
                .map(ChatCompletionResponse::getReplyText);
    }
}

ChatGPT Service Interface

ChatGPT service interface is almost like Spring 6 HTTP Interface. You can declare a ChatGPT service interface with @ChatGPTExchange annotation, and declare completion methods with @ChatCompletion annotation, then you just call service interface directly.

@GPTExchange
public interface GPTHelloService {

    @ChatCompletion("You are a language translator, please translate the below text to Chinese.\n")
    Mono<String> translateIntoChinese(String text);

    @ChatCompletion("You are a language translator, please translate the below text from {0} to {1}.\n {2}")
    Mono<String> translate(String sourceLanguage, String targetLanguage, String text);

    @Completion("please complete poem: {0}")
    Mono<String> completePoem(String text);

}

Create ChatGPT interface service bean:

    @Bean
    public GPTHelloService gptHelloService(ChatGPTServiceProxyFactory proxyFactory) {
        return proxyFactory.createClient(GPTHelloService.class);
    }

ChatGPT functions

• Create a Spring Bean with @Component. Annotate GPT functions with @GPTFunction annotation, and annotate function parameters with @Parameter annotation. @Nonnull means that the parameter is required.

import jakarta.annotation.Nonnull;

@Component
public class GPTFunctions {

    public record SendEmailRequest(
            @Nonnull @Parameter("Recipients of email") List<String> recipients,
            @Nonnull @Parameter("Subject of email") String subject,
            @Parameter("Content of email") String content) {
    }

    @GPTFunction(name = "send_email", value = "Send email to receiver")
    public String sendEmail(SendEmailRequest request) {
        System.out.println("Recipients: " + String.join(",", request.recipients));
        System.out.println("Subject: " + request.subject);
        System.out.println("Content:\n" + request.content);
        return "Email sent to " + String.join(",", request.recipients) + " successfully!";
    }

    public record SQLQueryRequest(
            @Parameter(required = true, value = "SQL to query") String sql) {
    }

    @GPTFunction(name = "execute_sql_query", value = "Execute SQL query and return the result set")
    public String executeSQLQuery(SQLQueryRequest request) {
        System.out.println("Execute SQL: " + request.sql);
        return "id, name, salary\n1,Jackie,8000\n2,Libing,78000\n3,Sam,7500";
    }
}

• Call GPT function by response.getReplyCombinedText() or chatMessage.getFunctionCall().getFunctionStub().call():

public class ChatGPTServiceImplTest {
    @Test
    public void testChatWithFunctions() throws Exception {
        final String prompt = "Hi Jackie, could you write an email to Libing(libing.chen@gmail.com) and Sam(linux_china@hotmail.com) and invite them to join Mike's birthday party at 4 pm tomorrow? Thanks!";
        final ChatCompletionRequest request = ChatCompletionRequest.functions(prompt, List.of("send_email"));
        final ChatCompletionResponse response = chatGPTService.chat(request).block();
        // display reply combined text with function call
        System.out.println(response.getReplyCombinedText());
        // call function manually
        for (ChatMessage chatMessage : response.getReply()) {
            final FunctionCall functionCall = chatMessage.getFunctionCall();
            if (functionCall != null) {
                final Object result = functionCall.getFunctionStub().call();
                System.out.println(result);
            }
        }
    }

    @Test
    public void testExecuteSQLQuery() {
        String context = "You are SQL developer. Write SQL according to requirements, and execute it in MySQL database.";
        final String prompt = "Query all employees whose salary is greater than the average.";
        final ChatCompletionRequest request = ChatCompletionRequest.functions(prompt, List.of("execute_sql_query"));
        // add prompt context as system message
        request.addMessage(ChatMessage.systemMessage(context));
        final ChatCompletionResponse response = chatGPTService.chat(request).block();
        System.out.println(response.getReplyCombinedText());
    }
}

Note: @GPTExchange and @ChatCompletion has functions built-in, so you just need to fill functions parameters.

ChatGPT Functions use cases:

• Structure Output: such as SQL, JSON, CSV, YAML etc., then delegate functions to process them.
• Commands: such as send_email, post on Twitter.
• DevOps: such as generate K8S yaml file, then call K8S functions to deploy it.
• Search Matching: bind search with functions, such as search for a book, then call function to show it.
• Spam detection: email spam, advertisement spam etc
• PipeLine: you can think function as a node in pipeline. After process by function, and you can pass it to ChatGPT again.
• Data types supported: string, number, integer, array. Nested object not supported now!

If you want to have a simple test for ChatGPT functions, you can install ChatGPT with Markdown JetBrains IDE Plugin, and take a look at chat.gpt file.

Structured Output

Please refer OpenAI Structured Outputs for detail.

First you need to define record for structured output:

@StructuredOutput(name = "java_example")
public record JavaExample(@Nonnull @Parameter("explanation") String explanation,
                          @Nonnull @Parameter("answer") String answer,
                          @Nonnull @Parameter("code") String code,
                          @Nonnull @Parameter("dependencies") List<String> dependencies) {
}

Then you can use structured output record as return type as following:

@ChatCompletion(system = "You are a helpful Java language assistant.")
Mono<JavaExample> generateJavaExample(String question);

@ChatCompletion(system = "You are a helpful assistant.", user = "Say hello to {0}!")
Mono<String> hello(String word);

Attention: if the return type is not Mono<String>, and it means structured output.

Prompt Templates

How to manage prompts in Java? Now ChatGPT starter adopts prompts.properties to save prompt templates, and uses MessageFormat to format template value.PromptPropertiesStoreImpl will load all prompts.properties files from classpath. You can extend PromptStore to load prompts from database or other sources.

You can load prompt template by PromptManager.

Tips:

• Prompt template code completion: support by @PropertyKey(resourceBundle = PROMPTS_FQN)
• @ChatCompletion annotation has built-in prompt template support for user,system and assistant messages.
• Prompt value could be from classpath and URL: conversation=classpath:///conversation-prompt.txt or conversation=https://example.com/conversation-prompt.txt

Prompt Template as Lambda

For some case you want to use prompt template as lambda, such as translate first, then send it as email. You can declare prompt as function and chain them together.

public class PromptLambdaTest {
    @Test
    public void testPromptAsFunction() {
        Function<String, Mono<String>> translateIntoChineseFunction = chatGPTService.promptAsLambda("translate-into-chinese");
        Function<String, Mono<String>> sendEmailFunction = chatGPTService.promptAsLambda("send-email");
        String result = Mono.just("Hi Jackie, could you write an email to Libing(libing.chen@exaple.com) and Sam(linux_china@example.com) and invite them to join Mike's birthday party at 4 pm tomorrow? Thanks!")
                .flatMap(translateIntoChineseFunction)
                .flatMap(sendEmailFunction)
                .block();
        System.out.println(result);
    }
}

To keep placeholders safe in prompt template, you can use record as Lambda parameter.

public class PromptTest {
    public record TranslateRequest(String from, String to, String text) {
    }

    @Test
    public void testLambdaWithRecord() {
        Function<TranslateRequest, Mono<String>> translateFunction = chatGPTService.promptAsLambda("translate");
        String result = Mono.just(new TranslateRequest("Chinese", "English", "你好！"))
                .flatMap(translateFunction)
                .block();
        System.out.println(result);
    }
}

Batch API

• Convert multi requests to JSONL format
• Upload JSONL file to OpenAI
• Create batch with file id

	@Autowired
	private OpenAIFileAPI openAIFileAPI;

	@Autowired
	private OpenAIBatchAPI openAIBatchAPI;

	@Test
	public void testUpload() {
		String jsonl = Stream.of("What's Java Language?", "What's Kotlin Language?")
			.map(ChatCompletionRequest::of)
			.map(ChatCompletionBatchRequest::new)
			.map(this::toJson)
			.filter(Strings::isNotBlank)
			.collect(Collectors.joining("\n"));
		Resource resource = new ByteArrayResource(jsonl.getBytes());
		FileObject fileObject = openAIFileAPI.upload("batch", resource).block();
		CreateBatchRequest createBatchRequest = new CreateBatchRequest(fileObject.getId());
		BatchObject batchObject = openAIBatchAPI.create(createBatchRequest).block();
	}

After completion_window(24h), and you can call openAIBatchAPI.retrieve(batchId) to get the BatchObject. Get BatchObject.outputFileId and call OpenAIFileAPI.retrieve(outputFileId) to get jsonl response, and use follow code to parse every chat response.

List<String> lines = new BufferedReader(new InputStreamReader(inputStream)).lines().toList();
for (String line : lines) {
	if (line.startsWith("{")) {
		ChatCompletionBatchResponse response = objectMapper.readValue(line, ChatCompletionBatchResponse.class);
		System.out.println(response.getCustomId());
	}
}

FAQ

OpenAI REST API proxy

Please refer OpenAIProxyController.

@RestController
public class OpenAIProxyController {
    @Autowired
    private OpenAIChatAPI openAIChatAPI;

    @PostMapping("/v1/chat/completions")
    public Publisher<ChatCompletionResponse> completions(@RequestBody ChatCompletionRequest request) {
        return openAIChatAPI.proxy(request);
    }
}

Of course, you can use standard URL http://localhost:8080/v1/chat/completions to call Azure OpenAI API.

How to use ChatGPT with Spring Web?

Now ChatGPT starter use Reactive style API, and you know Reactive still hard to understand. Could ChatGPT starter work with Spring Web? Yes, you can use Mono or Flux with Spring Web and Virtual Threads, please refer Support for Virtual Threads on Spring Boot 3.2 for details.

Building the Code

The code uses the Spring Java Formatter Maven plugin, which keeps the code consistent. In order to build the code, run:

./mvnw spring-javaformat:apply

This will ensure that all contributions have the exact same code formatting, allowing us to focus on bigger issues, like functionality,

References

• OpenAI chat API
• Spring Boot Docs
• Spring Boot Webflux
• Spring 6 HTTP interface
• Properties File Format
• MessageFormat JavaDoc
• OpenAI Prompt engineering