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feat : 분석api 개발

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lsh9672 2025-06-16 16:40:32 +09:00
parent 988b30c7be
commit addee4a50c
1 changed files with 53 additions and 0 deletions
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53
analytics/src/main/java/com/ktds/hi/analytics/biz/service/AnalyticsService.java
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@ -1,7 +1,9 @@
package com.ktds.hi.analytics.biz.service;
import com.ktds.hi.analytics.biz.domain.ActionPlan;
import com.ktds.hi.analytics.biz.domain.Analytics;
import com.ktds.hi.analytics.biz.domain.AiFeedback;
import com.ktds.hi.analytics.biz.domain.PlanStatus;
import com.ktds.hi.analytics.biz.usecase.in.AnalyticsUseCase;
import com.ktds.hi.analytics.biz.usecase.out.*;
import com.ktds.hi.analytics.infra.dto.*;
@ -32,6 +34,7 @@ public class AnalyticsService implements AnalyticsUseCase {
private final OrderDataPort orderDataPort;
private final CachePort cachePort;
private final EventPort eventPort;
private final ActionPlanPort actionPlanPort; // 추가된 의존성
@Override
// @Cacheable(value = "storeAnalytics", key = "#storeId")
@ -481,9 +484,14 @@ public class AnalyticsService implements AnalyticsUseCase {
throw new RuntimeException("AI 피드백을 찾을 수 없습니다: " + feedbackId);
}
AiFeedback feedback = aiFeedback.get();
// 2. 기존 AIServicePort.generateActionPlan 메서드 활용
List<String> actionPlans = aiServicePort.generateActionPlan(aiFeedback.get());
// 3. DB에 실행계획 저장
saveGeneratedActionPlansToDatabase(feedback, actionPlans);
log.info("실행계획 생성 완료: feedbackId={}, planCount={}", feedbackId, actionPlans.size());
return actionPlans;
@ -565,4 +573,49 @@ public class AnalyticsService implements AnalyticsUseCase {
.build();
}
/**
* 생성된 실행계획을 데이터베이스에 저장하는 메서드
* AI 피드백 기반으로 생성된 실행계획들을 ActionPlan 테이블에 저장
*/
private void saveGeneratedActionPlansToDatabase(AiFeedback feedback, List<String> actionPlans) {
if (actionPlans.isEmpty()) {
log.info("저장할 실행계획이 없습니다: storeId={}", feedback.getStoreId());
return;
}
log.info("실행계획 DB 저장 시작: storeId={}, feedbackId={}, planCount={}",
feedback.getStoreId(), feedback.getId(), actionPlans.size());
for (int i = 0; i < actionPlans.size(); i++) {
String planContent = actionPlans.get(i);
// ActionPlan 도메인 객체 생성 (기존 ActionPlanService의 패턴과 동일하게)
ActionPlan actionPlan = ActionPlan.builder()
.storeId(feedback.getStoreId())
.userId(null) // AI가 생성한 계획이므로 userId는 null
.title("AI 추천 실행계획 " + (i + 1))
.description(planContent)
.period("1개월") // 기본 실행 기간
.status(PlanStatus.PLANNED)
.tasks(List.of(planContent)) // 생성된 계획을 tasks로 설정
.note("AI 피드백(ID: " + feedback.getId() + ")을 기반으로 자동 생성된 실행계획")
.createdAt(LocalDateTime.now())
.build();
try {
// ActionPlan 저장 (기존 ActionPlanPort 활용)
ActionPlan savedPlan = actionPlanPort.saveActionPlan(actionPlan);
log.info("실행계획 저장 완료: storeId={}, planId={}, title={}",
feedback.getStoreId(), savedPlan.getId(), savedPlan.getTitle());
} catch (Exception e) {
log.error("실행계획 저장 실패: storeId={}, title={}",
feedback.getStoreId(), actionPlan.getTitle(), e);
// 개별 저장 실패 시에도 다음 계획은 계속 저장 시도
}
}
log.info("실행계획 DB 저장 완료: storeId={}, 총 {}개 계획 저장",
feedback.getStoreId(), actionPlans.size());
}
}