Function Calling in Java and Spring AI Using the Mistral AI API

Function Calling is a powerful feature in modern AI integration that allows language models to interact dynamically with external tools and APIs. This feature facilitates tasks such as retrieving real-time information or executing operations directly from the model. Spring AI is an application framework designed to seamlessly integrate AI functionalities with Spring’s portability and modularity design principles. This article will guide you through implementing function calling in Java using the Mistral AI API, a tool that simplifies integrating advanced AI models into your applications.

What is Mistral AI API?

The Mistral AI API enables developers to integrate Mistral's advanced models into their applications with minimal code. Accessible via La Plateforme, the API requires activation of payments to obtain an API key, which is necessary for accessing the chat platform and other functionalities.

Steps to Implement Function Calling in Java and Spring AI Using the Mistral AI API

Step 1: Obtain the API Key

To access the Mistral API, you must first obtain an API key. Follow these steps:

• Sign Up/Log In: Access your Mistral AI account.
• Navigate to API Keys: Go to the API section in your dashboard.
• Generate API Key: Create a new API key and copy it for use in your application.

To use the Mistral AI API, you first need to obtain an API key. Navigate to the administration console for API keys, activate payments, and retrieve your API key.

Step 2: Add Dependencies

Add this Spring Milestones and Spring Snapshots dependencies to your pom.xml file.

  <repositories>
    <repository>
      <id>spring-milestones</id>
      <name>Spring Milestones</name>
      <url>https://repo.spring.io/milestone</url>
      <snapshots>
        <enabled>false</enabled>
      </snapshots>
    </repository>
    <repository>
      <id>spring-snapshots</id>
      <name>Spring Snapshots</name>
      <url>https://repo.spring.io/snapshot</url>
      <releases>
        <enabled>false</enabled>
      </releases>
    </repository>
  </repositories>

Step 3: Add the API key

Store your Mistral API key and base URL in the application.properties file.

# Base URL for Mistral AI API
ai.api.base-url=https://api.mistral.ai

# API key for authentication
ai.api.token=YOUR_API_KEY

Note: Replace YOUR_API_KEY with the actual API key you obtained.

Step 4: Create a Spring Service to Call Mistral AI API

Create a Spring service to handle communication with the Mistral AI API. This service will use RestTemplate to make HTTP requests.

import org.springframework.beans.factory.annotation.Value;
import org.springframework.http.*;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;
import org.springframework.web.util.UriComponentsBuilder;

@Service
public class MistralAIService {

    @Value("${ai.api.base-url}")
    private String baseUrl;

    @Value("${ai.api.token}")
    private String apiToken;

    private final RestTemplate restTemplate;

    public MistralAIService(RestTemplate restTemplate) {
        this.restTemplate = restTemplate;
    }

    public String postRequestToAI(String endpoint, String payload) {
        try {
            String fullUrl = UriComponentsBuilder.fromHttpUrl(baseUrl)
                                              .pathSegment(endpoint)
                                              .toUriString();
            HttpHeaders headers = new HttpHeaders();
            headers.setBearerAuth(apiToken);
            headers.setContentType(MediaType.APPLICATION_JSON);

            HttpEntity<String> requestEntity = new HttpEntity<>(payload, headers);

            ResponseEntity<String> response = restTemplate.exchange(fullUrl, HttpMethod.POST, requestEntity, String.class);
            if (response.getStatusCode() == HttpStatus.OK) {
                return response.getBody();
            } else {
                // Handle non-OK responses
                return "Error: " + response.getStatusCode();
            }
        } catch (Exception e) {
            // Handle exceptions
            return "Exception: " + e.getMessage();
        }
    }
}

• RestTemplate: A Spring class for making HTTP requests, included via the constructor as a necessary dependency.
• UriComponentsBuilder: Adds the endpoint to the base URL and creates a new URL from it.
• response.getBody(): Retrieves the HTTP response body returned by the postRequestToAI method.

Step 5: Create a Configuration Class

Define a configuration class to set up a RestTemplate bean:

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class AppConfig {

    @Bean
    public RestTemplate restTemplate() {
        return new RestTemplate();
    }
}

Step 6: Test the Integration

Create a test class to ensure that the service works as expected:

import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.http.HttpStatus;
import org.springframework.http.ResponseEntity;
import org.springframework.web.client.RestTemplate;

import static org.assertj.core.api.Assertions.assertThat;
import static org.mockito.Mockito.*;

@SpringBootTest
public class MistralAIServiceTest {

    @Autowired
    private MistralAIService mistralAIService;

    @Test
    void whenPostRequestToAI_thenExpectedResponseIsReturned() {
        RestTemplate mockRestTemplate = mock(RestTemplate.class);
        mistralAIService = new MistralAIService(mockRestTemplate);

        String mockResponse = "Hello, Mistral!";
        when(mockRestTemplate.exchange(anyString(), any(), any(), eq(String.class)))
            .thenReturn(new ResponseEntity<>(mockResponse, HttpStatus.OK));

        String response = mistralAIService.postRequestToAI("/test-endpoint", "{}");
        assertThat(response).isEqualTo(mockResponse);
    }
}

Conclusion

In this article, we have demonstrated how to integrate the Mistral AI API with a Java application using Spring Boot. We covered setting up dependencies, configuring properties, creating a service for API communication, and testing the integration. This setup will enable you to leverage advanced AI models in your applications efficiently.