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 the API Keys page.

You can set this configuration property in your application.properties file:

spring.ai.minimax.api-key=<your-minimax-api-key>

For enhanced security when handling sensitive information like API keys, you can use Spring Expression Language (SpEL) to reference an environment variable:

# In application.yml
spring:
  ai:
    minimax:
      api-key: ${MINIMAX_API_KEY}

# In your environment or .env file
export MINIMAX_API_KEY=<your-minimax-api-key>

You can also set this configuration programmatically in your application code:

// Retrieve API key from a secure source or environment variable
String apiKey = System.getenv("MINIMAX_API_KEY");

Add Repositories and BOM

Spring AI artifacts are published in Maven Central and Spring Snapshot repositories. Refer to the Artifact 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 Azure MiniMax Embedding Model. To enable it add the following dependency to your project’s Maven pom.xml file:

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

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-starter-model-minimax'
}

Embedding Properties

Retry Properties

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

Connection Properties

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

Configuration Properties

Enabling and disabling of the embedding auto-configurations are now configured via top level properties with the prefix spring.ai.model.embedding.

To enable, spring.ai.model.embedding=minimax (It is enabled by default)

To disable, spring.ai.model.embedding=none (or any value which doesn’t match minimax)

This change is done to allow configuration of multiple models.

The prefix spring.ai.minimax.embedding is property prefix that configures the EmbeddingModel implementation for MiniMax.

Runtime Options

The MiniMaxEmbeddingOptions.java provides the MiniMax configurations, such as the model to use and etc.

The default options can be configured using the spring.ai.minimax.embedding.options properties as well.

At start-time use the MiniMaxEmbeddingModel constructor to set the default options used for all embedding requests. At run-time you can override the default options, using a MiniMaxEmbeddingOptions instance as part of your EmbeddingRequest.

For example to override the default model name for a specific request:

EmbeddingResponse embeddingResponse = embeddingModel.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        MiniMaxEmbeddingOptions.builder()
            .model("Different-Embedding-Model-Deployment-Name")
        .build()));

Sample Controller

This will create a EmbeddingModel implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the EmbeddingC implementation.

spring.ai.minimax.api-key=YOUR_API_KEY
spring.ai.minimax.embedding.options.model=embo-01

@RestController
public class EmbeddingController {

    private final EmbeddingModel embeddingModel;

    @Autowired
    public EmbeddingController(EmbeddingModel embeddingModel) {
        this.embeddingModel = embeddingModel;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingModel.embedForResponse(List.of(message));
        return Map.of("embedding", embeddingResponse);
    }
}

Manual Configuration

If you are not using Spring Boot, you can manually configure the MiniMax Embedding Model. For this 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'
}

Next, create an MiniMaxEmbeddingModel instance and use it to compute the similarity between two input texts:

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

var embeddingModel = new MiniMaxEmbeddingModel(minimaxApi, MetadataMode.EMBED,
MiniMaxEmbeddingOptions.builder().model("embo-01").build());

EmbeddingResponse embeddingResponse = this.embeddingModel
    .embedForResponse(List.of("Hello World", "World is big and salvation is near"));

The MiniMaxEmbeddingOptions provides the configuration information for the embedding requests. The options class offers a builder() for easy options creation.