那我們接續昨天OpenAI API的介紹與API KEY的申請後，今天就直接切入正題吧 !!

安裝

application.properties新增以下設定

openai.model=gpt-3.5-turbo
openai.api.url=https://api.openai.com/v1/chat/completions
openai.api.key={YOUR OPENAI API KEY}

OpenAI API Sample

我們從底下的範例就可以看到，我們如果需要發Request去問這支API的時候，需要利用Bearer的方式帶上自己的OPENAI_API_KEY

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
     "model": "gpt-3.5-turbo",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

新增Config

由上面範例可以看到我們需要戴上OPENAI_API_KEY去發送請求，所以我們就來新增一個Config來加上我們的金鑰

• @Value去讀到application.properties設定的openai.api.key
• @Qualifier可以用來指定注入的@Bean的名字，可以避免同時有兩個同樣類型的Bean存在的問題

import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.web.client.RestTemplate;

@Configuration
public class ChatGPTConfig {

    @Value("${openai.api.key}")
    private String apiKey;

    @Bean
    @Qualifier("openaiRestTemplate")
    RestTemplate restTemplate() {

        RestTemplate restTemplate = new RestTemplate();

        restTemplate.getInterceptors().add((request, body, execution) -> {
            request.getHeaders().add("Authorization", "Bearer " + apiKey);
            return execution.execute(request, body);
        });

        return restTemplate;
    }

新增ChatRequest / ChatReponse Model

我們依照官網教學的範例，來新增對應的Model來去接資料

{
    "id": "chatcmpl-abc123",
    "object": "chat.completion",
    "created": 1677858242,
    "model": "gpt-3.5-turbo",
    "usage": {
        "prompt_tokens": 13,
        "completion_tokens": 7,
        "total_tokens": 20,
        "completion_tokens_details": {
            "reasoning_tokens": 0
        }
    },
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "\n\nThis is a test!"
            },
            "logprobs": null,
            "finish_reason": "stop",
            "index": 0
        }
    ]
}

@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class ChatRequest {

    private String model;
    private List<Message> messages;
    private int n;
    private double temperature;

    public ChatRequest(String model, String prompt) {
        this.model = model;

        this.messages = new ArrayList<>();
        this.messages.add(new Message("user", prompt));
    }
}
-----------------------------------------------------------
@Data
@AllArgsConstructor
@NoArgsConstructor
public class ChatResponse {

    private List<Choice> choices;

    @Data
    @AllArgsConstructor
    @NoArgsConstructor
    public static class Choice {
        private int index;
        private Message message;
    }
}
----------------------------------------------------------
@Data
@NoArgsConstructor
@AllArgsConstructor
public class Message {

    private String role;
    private String content;

}

新增Controller

1. 我們跟上面一樣的方式用@Value來讀取設定值，另外注入我們在config設定的openaiRestTemplate來藉此發送請求
2. 宣告一個Method，用POST的方式來傳送請求
3. 底下透過restTemplate.postForObject(apiUrl, request.build(), ChatResponse.class);來發送請求跟接回Response

@RestController
@RequestMapping("/api/v1")
@Slf4j
public class ChatAPIController {
    @Qualifier("openaiRestTemplate")
    @Autowired
    private RestTemplate restTemplate;

    @Value("${openai.model}")
    private String model;

    @Value("${openai.max-completions}")
    private int maxCompletions;

    @Value("${openai.temperature}")
    private double temperature;

    @Value("${openai.api.url}")
    private String apiUrl;

    @PostMapping("/chat")
    public ChatResponse chat(@RequestParam("prompt") String prompt) {
        /* Initialize variables */
        ChatResponse chatResponse = null;
        List<Message> ChatMessages = new ArrayList<>();
        ChatRequest.ChatRequestBuilder request = ChatRequest.builder();
        try {
            /* Add user prompt to chat messages */
            ChatMessages.add(new Message("user", prompt));
            /* Build chat request */
            request
                    .model(model)
                    .messages(ChatMessages)
                    .n(maxCompletions)
                    .temperature(temperature);

            /* Send chat request */
            chatResponse = restTemplate.postForObject(apiUrl, request.build(), ChatResponse.class);
        } catch (Exception e) {
            log.error("error : {}", e.getMessage());
        }
        return chatResponse;
    }

}

接著我們就來用PostMan試試看吧 !!!

結論

我們整合了Spring boot來使用OpenAI API，讓我們讓自己撰寫的應用程式更輕鬆的使用AI功能，今天的範例是使用Chat相關的API，大家有機會不仿也可以試試看Image API ....相關應用吧

參考資源

• openai官網
• Integrating OpenAI API with Spring Boot and Spring AI Framework