MiniMax Chat

Spring AI supports the various AI language models from MiniMax. You can interact with MiniMax language models and create a multilingual conversational assistant based on MiniMax models.

Prerequisites

You will need to create an API with MiniMax to access MiniMax language models.

Create an account at MiniMax registration page and generate the token on the API Keys page. The Spring AI project defines a configuration property named spring.ai.minimax.api-key that you should set to the value of the API Key obtained from API Keys page. Exporting an environment variable is one way to set that configuration property:

export SPRING_AI_MINIMAX_API_KEY=<INSERT KEY HERE>

Add Repositories and BOM

Spring AI artifacts are published in Spring Milestone and Snapshot repositories. Refer to the Repositories section to add these repositories to your build system.

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the Dependency Management section to add the Spring AI BOM to your build system.

Auto-configuration

Spring AI provides Spring Boot auto-configuration for the MiniMax Chat Client. To enable it add the following dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-minimax-spring-boot-starter</artifactId>
</dependency>

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-minimax-spring-boot-starter'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Chat Properties

Retry Properties

The prefix spring.ai.retry is used as the property prefix that lets you configure the retry mechanism for the MiniMax chat model.

Property Description Default

spring.ai.retry.max-attempts

Maximum number of retry attempts.

10

spring.ai.retry.backoff.initial-interval

Initial sleep duration for the exponential backoff policy.

2 sec.

spring.ai.retry.backoff.multiplier

Backoff interval multiplier.

5

spring.ai.retry.backoff.max-interval

Maximum backoff duration.

3 min.

spring.ai.retry.on-client-errors

If false, throw a NonTransientAiException, and do not attempt retry for 4xx client error codes

false

spring.ai.retry.exclude-on-http-codes

List of HTTP status codes that should not trigger a retry (e.g. to throw NonTransientAiException).

empty

spring.ai.retry.on-http-codes

List of HTTP status codes that should trigger a retry (e.g. to throw TransientAiException).

empty

Connection Properties

The prefix spring.ai.minimax is used as the property prefix that lets you connect to MiniMax.

Property Description Default

spring.ai.minimax.base-url

The URL to connect to

api.minimax.chat

spring.ai.minimax.api-key

The API Key

-

Configuration Properties

The prefix spring.ai.minimax.chat is the property prefix that lets you configure the chat model implementation for MiniMax.

Property Description Default

spring.ai.minimax.chat.enabled

Enable MiniMax chat model.

true

spring.ai.minimax.chat.base-url

Optional overrides the spring.ai.minimax.base-url to provide chat specific url

api.minimax.chat

spring.ai.minimax.chat.api-key

Optional overrides the spring.ai.minimax.api-key to provide chat specific api-key

-

spring.ai.minimax.chat.options.model

This is the MiniMax Chat model to use

abab6.5g-chat (the abab5.5-chat, abab5.5s-chat, abab6.5-chat, abab6.5g-chat, abab6.5t-chat and abab6.5s-chat point to the latest model versions)

spring.ai.minimax.chat.options.maxTokens

The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model’s context length.

-

spring.ai.minimax.chat.options.temperature

The sampling temperature to use that controls the apparent creativity of generated completions. Higher values will make output more random while lower values will make results more focused and deterministic. It is not recommended to modify temperature and top_p for the same completions request as the interaction of these two settings is difficult to predict.

0.7

spring.ai.minimax.chat.options.topP

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

1.0

spring.ai.minimax.chat.options.n

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Default value is 1 and cannot be greater than 5. Specifically, when the temperature is very small and close to 0, we can only return 1 result. If n is already set and>1 at this time, service will return an illegal input parameter (invalid_request_error)

1

spring.ai.minimax.chat.options.presencePenalty

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

0.0f

spring.ai.minimax.chat.options.frequencyPenalty

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

0.0f

spring.ai.minimax.chat.options.stop

The model will stop generating characters specified by stop, and currently only supports a single stop word in the format of ["stop_word1"]

-
You can override the common spring.ai.minimax.base-url and spring.ai.minimax.api-key for the ChatModel implementations. The spring.ai.minimax.chat.base-url and spring.ai.minimax.chat.api-key properties if set take precedence over the common properties. This is useful if you want to use different MiniMax accounts for different models and different model endpoints.
All properties prefixed with spring.ai.minimax.chat.options can be overridden at runtime by adding a request specific Runtime Options to the Prompt call.

Runtime Options

The MiniMaxChatOptions.java provides model configurations, such as the model to use, the temperature, the frequency penalty, etc.

On start-up, the default options can be configured with the MiniMaxChatModel(api, options) constructor or the spring.ai.minimax.chat.options.* properties.

At run-time you can override the default options by adding new, request specific, options to the Prompt call. For example to override the default model and temperature for a specific request:

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        MiniMaxChatOptions.builder()
            .withModel(MiniMaxApi.ChatModel.ABAB_6_5_S_Chat.getValue())
            .withTemperature(0.5)
        .build()
    ));
In addition to the model specific MiniMaxChatOptions you can use a portable ChatOptions instance, created with the ChatOptionsBuilder#builder().

Sample Controller

Create a new Spring Boot project and add the spring-ai-minimax-spring-boot-starter to your pom (or gradle) dependencies.

Add a application.properties file, under the src/main/resources directory, to enable and configure the MiniMax chat model:

spring.ai.minimax.api-key=YOUR_API_KEY
spring.ai.minimax.chat.options.model=abab6.5g-chat
spring.ai.minimax.chat.options.temperature=0.7
replace the api-key with your MiniMax credentials.

This will create a MiniMaxChatModel implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the chat model for text generations.

@RestController
public class ChatController {

    private final MiniMaxChatModel chatModel;

    @Autowired
    public ChatController(MiniMaxChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", this.chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        var prompt = new Prompt(new UserMessage(message));
        return this.chatModel.stream(prompt);
    }
}

Manual Configuration

The MiniMaxChatModel implements the ChatModel and StreamingChatModel and uses the Low-level MiniMaxApi Client to connect to the MiniMax service.

Add the spring-ai-minimax dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-minimax</artifactId>
</dependency>

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-minimax'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Next, create a MiniMaxChatModel and use it for text generations:

var miniMaxApi = new MiniMaxApi(System.getenv("MINIMAX_API_KEY"));

var chatModel = new MiniMaxChatModel(this.miniMaxApi, MiniMaxChatOptions.builder()
                .withModel(MiniMaxApi.ChatModel.ABAB_6_5_S_Chat.getValue())
                .withTemperature(0.4)
                .withMaxTokens(200)
                .build());

ChatResponse response = this.chatModel.call(
    new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> streamResponse = this.chatModel.stream(
    new Prompt("Generate the names of 5 famous pirates."));

The MiniMaxChatOptions provides the configuration information for the chat requests. The MiniMaxChatOptions.Builder is fluent options builder.

Low-level MiniMaxApi Client

The MiniMaxApi provides is lightweight Java client for MiniMax API.

Here is a simple snippet how to use the api programmatically:

MiniMaxApi miniMaxApi =
    new MiniMaxApi(System.getenv("MINIMAX_API_KEY"));

ChatCompletionMessage chatCompletionMessage =
    new ChatCompletionMessage("Hello world", Role.USER);

// Sync request
ResponseEntity<ChatCompletion> response = this.miniMaxApi.chatCompletionEntity(
    new ChatCompletionRequest(List.of(this.chatCompletionMessage), MiniMaxApi.ChatModel.ABAB_6_5_S_Chat.getValue(), 0.7f, false));

// Streaming request
Flux<ChatCompletionChunk> streamResponse = this.miniMaxApi.chatCompletionStream(
        new ChatCompletionRequest(List.of(this.chatCompletionMessage), MiniMaxApi.ChatModel.ABAB_6_5_S_Chat.getValue(), 0.7f, true));

Follow the MiniMaxApi.java's JavaDoc for further information.

The MiniMax model supported the web search feature. The web search feature allows you to search the web for information and return the results in the chat response.

About web search follow the MiniMax ChatCompletion for further information.

Here is a simple snippet how to use the web search:

UserMessage userMessage = new UserMessage(
        "How many gold medals has the United States won in total at the 2024 Olympics?");

List<Message> messages = new ArrayList<>(List.of(this.userMessage));

List<MiniMaxApi.FunctionTool> functionTool = List.of(MiniMaxApi.FunctionTool.webSearchFunctionTool());

MiniMaxChatOptions options = MiniMaxChatOptions.builder()
    .withModel(MiniMaxApi.ChatModel.ABAB_6_5_S_Chat.value)
    .withTools(this.functionTool)
    .build();


// Sync request
ChatResponse response = chatModel.call(new Prompt(this.messages, this.options));

// Streaming request
Flux<ChatResponse> streamResponse = chatModel.stream(new Prompt(this.messages, this.options));

MiniMaxApi Samples