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feat: OpenAI 챗봇 API 구현
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‎src/main/kotlin/com/example/spring_ai_tutorial/config/OpenApiConfig.kt‎

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package com.example.spring_ai_tutorial.config
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import io.swagger.v3.oas.models.OpenAPI
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import io.swagger.v3.oas.models.info.Info
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import org.springframework.context.annotation.Bean
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import org.springframework.context.annotation.Configuration
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@Configuration
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class OpenApiConfig {
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@Bean
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fun springOpenAPI(): OpenAPI {
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return OpenAPI()
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.info(
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Info()
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.title("Spring AI Tutorial API")
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.version("1.0")
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.description("Spring AI를 활용한 챗봇 API")
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)
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}
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}

‎src/main/kotlin/com/example/spring_ai_tutorial/controller/ChatController.kt‎

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package com.example.spring_ai_tutorial.controller
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import com.example.spring_ai_tutorial.service.ChatService
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import io.github.oshai.kotlinlogging.KotlinLogging
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import io.swagger.v3.oas.annotations.Operation
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import io.swagger.v3.oas.annotations.Parameter
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import io.swagger.v3.oas.annotations.media.Content
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import io.swagger.v3.oas.annotations.media.Schema
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import io.swagger.v3.oas.annotations.responses.ApiResponse as SwaggerResponse
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import io.swagger.v3.oas.annotations.tags.Tag
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import org.springframework.http.HttpStatus
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import org.springframework.http.ResponseEntity
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import org.springframework.web.bind.annotation.*
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/**
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* Chat API 컨트롤러
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*
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* LLM API를 통해 채팅 기능을 제공합니다.
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*/
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@RestController
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@RequestMapping("/api/v1/chat")
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@Tag(name = "Chat API", description = "OpenAI API를 통한 채팅 기능")
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class ChatController(
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private val chatService: ChatService
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) {
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private val logger = KotlinLogging.logger {}
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/**
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* 사용자의 메시지를 받아 OpenAI API로 응답 생성
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*/
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@Operation(
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summary = "LLM 채팅 메시지 전송",
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description = "사용자의 메시지를 받아 OpenAI API를 통해 응답을 생성합니다."
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)
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@SwaggerResponse(
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responseCode = "200",
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description = "LLM 응답 성공",
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content = [Content(schema = Schema(implementation = ApiResponse::class))]
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)
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@SwaggerResponse(responseCode = "400", description = "잘못된 요청")
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@SwaggerResponse(responseCode = "500", description = "서버 오류")
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@PostMapping("/query")
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suspend fun sendMessage(
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@Parameter(description = "채팅 요청 객체", required = true)
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@RequestBody request: ChatRequest
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): ResponseEntity<ApiResponse<Map<String, Any>>> {
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logger.info { "Chat API 요청 받음: model=${request.model}" }
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// 유효성 검사
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if (request.query.isBlank()) {
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logger.warn { "빈 질의가 요청됨" }
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return ResponseEntity.badRequest().body(
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ApiResponse(success = false, error = "질의가 비어있습니다.")
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)
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}
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return try {
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// 시스템 프롬프트 지정
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val systemMessage = "You are a helpful AI assistant."
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// AI 응답 생성
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val response = chatService.openAiChat(
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userInput = request.query,
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systemMessage = systemMessage,
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model = request.model
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)
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logger.debug { "LLM 응답 생성: $response" }
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response?.let { chatResponse ->
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ResponseEntity.ok(
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ApiResponse(
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success = true,
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data = mapOf("answer" to chatResponse.result.output.text)
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)
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)
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} ?: run {
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logger.error { "LLM 응답 생성 실패" }
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ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body(
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ApiResponse(
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success = false,
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error = "LLM 응답 생성 중 오류 발생"
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)
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)
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}
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} catch (e: Exception) {
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logger.error(e) { "Chat API 처리 중 오류 발생" }
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ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body(
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ApiResponse(
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success = false,
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error = e.message ?: "알 수 없는 오류 발생"
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)
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)
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}
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}
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}
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@Schema(description = "채팅 요청 데이터 모델")
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data class ChatRequest(
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@Schema(description = "사용자 질문", example = "안녕하세요")
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val query: String,
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@Schema(description = "사용할 LLM 모델", example = "gpt-3.5-turbo", defaultValue = "gpt-3.5-turbo")
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val model: String = "gpt-3.5-turbo"
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)
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@Schema(description = "API 응답 포맷")
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data class ApiResponse<T>(
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@Schema(description = "요청 처리 성공 여부")
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val success: Boolean,
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@Schema(description = "성공 응답 데이터")
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val data: T? = null,
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@Schema(description = "실패 오류 메시지")
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val error: String? = null
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)

‎src/main/kotlin/com/example/spring_ai_tutorial/service/ChatService.kt‎

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package com.example.spring_ai_tutorial.service
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import io.github.oshai.kotlinlogging.KotlinLogging
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import kotlinx.coroutines.Dispatchers
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import kotlinx.coroutines.withContext
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import org.springframework.ai.chat.model.ChatResponse
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import org.springframework.ai.openai.api.OpenAiApi
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import org.springframework.stereotype.Service
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/**
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* OpenAI API를 사용하여 질의응답을 수행하는 서비스
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*/
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@Service
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class ChatService(
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private val openAiApi: OpenAiApi
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) {
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private val logger = KotlinLogging.logger {}
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/**
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* OpenAI 챗 API를 이용하여 응답을 생성합니다.
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*
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* @param userInput 사용자 입력 메시지
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* @param systemMessage 시스템 프롬프트
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* @param model 사용할 LLM 모델명
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* @return 챗 응답 객체, 오류 시 null
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*/
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suspend fun openAiChat(
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userInput: String,
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systemMessage: String,
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model: String = "gpt-3.5-turbo"
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): ChatResponse? = withContext(Dispatchers.IO) {
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logger.debug { "OpenAI 챗 호출 시작 - 모델: $model" }
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try {
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// 메시지 구성
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// 챗 옵션 설정
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// 프롬프트 생성
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// 챗 모델 생성 및 호출
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return@withContext TODO("응답 생성 로직을 작성하세요")
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} catch (e: Exception) {
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logger.error(e) { "OpenAI 챗 호출 중 오류 발생: ${e.message}" }
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return@withContext null
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}
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}
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}

‎src/main/resources/application.properties‎

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# OpenAI API Configuration
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spring.ai.openai.api-key=${OPENAI_API_KEY}
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# File Upload Settings
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spring.servlet.multipart.max-file-size=20MB
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spring.servlet.multipart.max-request-size=20MB
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# Swagger/OpenAPI Configuration
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springdoc.api-docs.path=/api-docs

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