feat(report): 일간 리포트 생성 모델 50대50 랜덤 배정

src/main/java/com/devkor/ifive/nadab/domain/dailyreport/application/DailyReportService.java

@@ -5,11 +5,13 @@
 import com.devkor.ifive.nadab.domain.dailyreport.api.dto.response.CreateAnswerImageUploadUrlResponse;
 import com.devkor.ifive.nadab.domain.dailyreport.api.dto.response.CreateDailyReportResponse;
 import com.devkor.ifive.nadab.domain.dailyreport.api.dto.response.ImageStatusResponse;
+import com.devkor.ifive.nadab.domain.dailyreport.application.helper.DailyReportModelSelector;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.ConfirmDailyAndRewardDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.PrepareDailyResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.AiDailyReportResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.AnswerEntry;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.ImageStatus;
+import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties.ModelCandidate;
 import com.devkor.ifive.nadab.domain.dailyreport.infra.DailyReportLlmClient;
 import com.devkor.ifive.nadab.domain.question.core.entity.DailyQuestion;
 import com.devkor.ifive.nadab.domain.question.core.entity.UserDailyQuestion;
@@ -48,6 +50,7 @@ public class DailyReportService {
     private final DailyReportTxService dailyReportTxService;
     private final ProfileImageService profileImageService;
 
+    private final DailyReportModelSelector dailyReportModelSelector;
     private final DailyReportLlmClient dailyReportLlmClient;
     private final ReportGenerationLogRecorder reportGenerationLogRecorder;
 
@@ -88,19 +91,20 @@ public CreateDailyReportResponse generateDailyReport(Long userId, DailyReportReq
         PrepareDailyResultDto prep = dailyReportTxService.prepareDaily(user, question, request.answer(), isDayPassed, request.objectKey());
 
         AnswerEntry answerEntry = prep.entry();
+        ModelCandidate modelCandidate = dailyReportModelSelector.select();
         Long generationLogId = reportGenerationLogRecorder.start(
                 userId,
                 ReportGenerationType.DAILY,
                 prep.reportId(),
                 ReportGenerationStep.DAILY_GENERATE,
                 LlmProvider.OPENAI,
-                dailyReportLlmClient.model()
+                modelCandidate.getModel()
         );
 
         AiDailyReportResultDto dto;
         try {
             LlmGenerationResult<AiDailyReportResultDto> generationResult =
-                    dailyReportLlmClient.generate(question.getQuestionText(), answerEntry);
+                    dailyReportLlmClient.generate(question.getQuestionText(), answerEntry, modelCandidate);
             dto = generationResult.content();
             LlmTokenUsage tokenUsage = generationResult.tokenUsage();
             reportGenerationLogRecorder.recordTokenUsage(

src/main/java/com/devkor/ifive/nadab/domain/dailyreport/application/helper/DailyReportModelSelector.java

@@ -0,0 +1,45 @@
+package com.devkor.ifive.nadab.domain.dailyreport.application.helper;
+
+import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties;
+import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties.ModelCandidate;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Component;
+
+import java.util.concurrent.ThreadLocalRandom;
+import java.util.function.IntUnaryOperator;
+
+@Component
+public class DailyReportModelSelector {
+
+    private static final int TOTAL_WEIGHT = 100;
+
+    private final DailyReportLlmProperties properties;
+    private final IntUnaryOperator randomValueGenerator;
+
+    @Autowired
+    public DailyReportModelSelector(DailyReportLlmProperties properties) {
+        this(properties, bound -> ThreadLocalRandom.current().nextInt(bound));
+    }
+
+    DailyReportModelSelector(
+            DailyReportLlmProperties properties,
+            IntUnaryOperator randomValueGenerator
+    ) {
+        this.properties = properties;
+        this.randomValueGenerator = randomValueGenerator;
+    }
+
+    public ModelCandidate select() {
+        int randomValue = randomValueGenerator.applyAsInt(TOTAL_WEIGHT);
+        int cumulativeWeight = 0;
+
+        for (ModelCandidate candidate : properties.getCandidates()) {
+            cumulativeWeight += candidate.getWeight();
+            if (randomValue < cumulativeWeight) {
+                return candidate;
+            }
+        }
+
+        throw new IllegalStateException("Failed to select a DailyReport LLM model candidate");
+    }
+}

src/main/java/com/devkor/ifive/nadab/domain/dailyreport/core/properties/DailyReportLlmProperties.java

@@ -1,37 +1,86 @@
 package com.devkor.ifive.nadab.domain.dailyreport.core.properties;
 
+import jakarta.validation.Valid;
+import jakarta.validation.constraints.AssertTrue;
 import jakarta.validation.constraints.DecimalMax;
 import jakarta.validation.constraints.DecimalMin;
+import jakarta.validation.constraints.Max;
 import jakarta.validation.constraints.Min;
 import jakarta.validation.constraints.NotBlank;
+import jakarta.validation.constraints.NotEmpty;
 import jakarta.validation.constraints.NotNull;
 import lombok.Getter;
 import lombok.Setter;
 import org.springframework.boot.context.properties.ConfigurationProperties;
 import org.springframework.stereotype.Component;
 import org.springframework.validation.annotation.Validated;
 
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Objects;
+
 @Component
 @Getter
 @Setter
 @Validated
 @ConfigurationProperties(prefix = "daily-report.llm")
 public class DailyReportLlmProperties {
 
-    @NotBlank
-    private String model = "gpt-4o-mini";
+    @Valid
+    @NotEmpty
+    private List<@NotNull ModelCandidate> candidates = new ArrayList<>();
+
+    @AssertTrue(message = "daily-report.llm.candidates weights must total 100")
+    public boolean isCandidateWeightTotalValid() {
+        if (candidates == null || candidates.isEmpty()) {
+            return true;
+        }
+
+        return candidates.stream()
+                .filter(Objects::nonNull)
+                .mapToInt(ModelCandidate::getWeight)
+                .sum() == 100;
+    }
+
+    @AssertTrue(message = "daily-report.llm.candidates models must be unique")
+    public boolean isCandidateModelUnique() {
+        if (candidates == null || candidates.isEmpty()) {
+            return true;
+        }
+
+        List<String> models = candidates.stream()
+                .filter(Objects::nonNull)
+                .map(ModelCandidate::getModel)
+                .filter(Objects::nonNull)
+                .toList();
 
-    @DecimalMin("0.0")
-    @DecimalMax("2.0")
-    private double temperature = 0.3;
+        return new HashSet<>(models).size() == models.size();
+    }
+
+    @Getter
+    @Setter
+    public static class ModelCandidate {
+
+        @NotBlank
+        private String model;
 
-    @Min(1)
-    private int maxOutputTokens = 512;
+        @Min(1)
+        @Max(100)
+        private int weight;
 
-    @NotNull
-    private TokenLimitParameter tokenLimitParameter = TokenLimitParameter.MAX_TOKENS;
+        @DecimalMin("0.0")
+        @DecimalMax("2.0")
+        private double temperature;
 
-    private String reasoningEffort;
+        @Min(1)
+        private int maxOutputTokens;
+
+        @NotNull
+        private TokenLimitParameter tokenLimitParameter;
+
+        private String reasoningEffort;
+    }
 
     public enum TokenLimitParameter {
         MAX_TOKENS,

src/main/java/com/devkor/ifive/nadab/domain/dailyreport/infra/DailyReportLlmClient.java

@@ -3,7 +3,7 @@
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.AiDailyReportResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.LlmDailyResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.AnswerEntry;
-import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties;
+import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties.ModelCandidate;
 import com.devkor.ifive.nadab.domain.user.infra.ProfileImageUrlBuilder;
 import com.devkor.ifive.nadab.global.core.prompt.daily.DailyReportPromptLoader;
 import com.devkor.ifive.nadab.global.core.response.ErrorCode;
@@ -37,11 +37,14 @@ public class DailyReportLlmClient {
     private final ObjectMapper objectMapper;
     private final LlmRouter llmRouter;
     private final ProfileImageUrlBuilder profileImageUrlBuilder;
-    private final DailyReportLlmProperties properties;
 
     private final LlmProvider provider = LlmProvider.OPENAI;
 
-    public LlmGenerationResult<AiDailyReportResultDto> generate(String question, AnswerEntry answerEntry) {
+    public LlmGenerationResult<AiDailyReportResultDto> generate(
+            String question,
+            AnswerEntry answerEntry,
+            ModelCandidate modelCandidate
+    ) {
 
         String answer = answerEntry.getContent();
 
@@ -55,7 +58,7 @@ public LlmGenerationResult<AiDailyReportResultDto> generate(String question, Ans
 
         ChatClient chatClient = llmRouter.route(provider);
 
-        OpenAiChatOptions options = buildOptions();
+        OpenAiChatOptions options = buildOptions(modelCandidate);
 
         UserMessage userMessage = buildUserMessage(prompt, withImagePrompt,answerEntry);
 
@@ -103,22 +106,18 @@ public LlmGenerationResult<AiDailyReportResultDto> generate(String question, Ans
         }
     }
 
-    public String model() {
-        return properties.getModel();
-    }
-
-    private OpenAiChatOptions buildOptions() {
+    private OpenAiChatOptions buildOptions(ModelCandidate modelCandidate) {
         var builder = OpenAiChatOptions.builder()
-                .model(properties.getModel())
-                .temperature(properties.getTemperature());
+                .model(modelCandidate.getModel())
+                .temperature(modelCandidate.getTemperature());
 
-        switch (properties.getTokenLimitParameter()) {
-            case MAX_TOKENS -> builder.maxTokens(properties.getMaxOutputTokens());
-            case MAX_COMPLETION_TOKENS -> builder.maxCompletionTokens(properties.getMaxOutputTokens());
+        switch (modelCandidate.getTokenLimitParameter()) {
+            case MAX_TOKENS -> builder.maxTokens(modelCandidate.getMaxOutputTokens());
+            case MAX_COMPLETION_TOKENS -> builder.maxCompletionTokens(modelCandidate.getMaxOutputTokens());
         }
 
-        if (!isBlank(properties.getReasoningEffort())) {
-            builder.reasoningEffort(properties.getReasoningEffort());
+        if (!isBlank(modelCandidate.getReasoningEffort())) {
+            builder.reasoningEffort(modelCandidate.getReasoningEffort());
         }
 
         return builder.build();

src/main/resources/application-dev.yml

@@ -15,14 +15,6 @@ spring:
         ddl-auto: validate
     show-sql: false
 
-daily-report:
-  llm:
-    model: gpt-5.6-luna
-    temperature: 1.0
-    max-output-tokens: 512
-    token-limit-parameter: MAX_COMPLETION_TOKENS
-    reasoning-effort: none
-
 springdoc:
   api-docs:
     enabled: true

src/main/resources/application.yml

@@ -97,10 +97,18 @@ api_prefix: /api/v1
 
 daily-report:
   llm:
-    model: gpt-4o-mini
-    temperature: 0.3
-    max-output-tokens: 512
-    token-limit-parameter: MAX_TOKENS
+    candidates:
+      - model: gpt-4o-mini
+        weight: 50
+        temperature: 0.3
+        max-output-tokens: 512
+        token-limit-parameter: MAX_TOKENS
+      - model: gpt-5.6-luna
+        weight: 50
+        temperature: 1.0
+        max-output-tokens: 512
+        token-limit-parameter: MAX_COMPLETION_TOKENS
+        reasoning-effort: none
 
 ask-chat:
   answer:

src/test/java/com/devkor/ifive/nadab/domain/dailyreport/application/DailyReportServiceTest.java

@@ -2,12 +2,14 @@
 
 import com.devkor.ifive.nadab.domain.dailyreport.api.dto.request.DailyReportRequest;
 import com.devkor.ifive.nadab.domain.dailyreport.api.dto.response.CreateDailyReportResponse;
+import com.devkor.ifive.nadab.domain.dailyreport.application.helper.DailyReportModelSelector;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.AiDailyReportResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.ConfirmDailyAndRewardDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.dto.PrepareDailyResultDto;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.AnswerEntry;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.Emotion;
 import com.devkor.ifive.nadab.domain.dailyreport.core.entity.EmotionCode;
+import com.devkor.ifive.nadab.domain.dailyreport.core.properties.DailyReportLlmProperties.ModelCandidate;
 import com.devkor.ifive.nadab.domain.dailyreport.infra.DailyReportLlmClient;
 import com.devkor.ifive.nadab.domain.question.core.entity.DailyQuestion;
 import com.devkor.ifive.nadab.domain.question.core.entity.UserDailyQuestion;
@@ -61,6 +63,9 @@ class DailyReportServiceTest {
     @Mock
     ProfileImageService profileImageService;
 
+    @Mock
+    DailyReportModelSelector dailyReportModelSelector;
+
     @Mock
     DailyReportLlmClient dailyReportLlmClient;
 
@@ -80,6 +85,7 @@ void setUp() {
                 userDailyQuestionRepository,
                 dailyReportTxService,
                 profileImageService,
+                dailyReportModelSelector,
                 dailyReportLlmClient,
                 reportGenerationLogRecorder,
                 profileImageUrlBuilder
@@ -99,14 +105,15 @@ void generate_daily_report_records_token_usage_before_succeeding_generation_log(
         AnswerEntry answerEntry = AnswerEntry.create(user, question, "answer", today, null);
         AiDailyReportResultDto aiResult = new AiDailyReportResultDto("message", "ACHIEVEMENT");
         Emotion emotion = emotion(EmotionCode.ACHIEVEMENT);
+        ModelCandidate modelCandidate = modelCandidate("gpt-5.6-luna");
 
         when(userRepository.findById(userId)).thenReturn(Optional.of(user));
         when(dailyQuestionRepository.findByIdWithInterest(20L)).thenReturn(Optional.of(question));
         when(userDailyQuestionRepository.findByUserIdAndDate(eq(userId), any(LocalDate.class)))
                 .thenReturn(Optional.of(UserDailyQuestion.create(user, today, question)));
         when(dailyReportTxService.prepareDaily(user, question, "answer", false, null))
                 .thenReturn(new PrepareDailyResultDto(answerEntry, reportId, userId));
-        when(dailyReportLlmClient.model()).thenReturn("gpt-5.6-luna");
+        when(dailyReportModelSelector.select()).thenReturn(modelCandidate);
         when(reportGenerationLogRecorder.start(
                 userId,
                 ReportGenerationType.DAILY,
@@ -115,7 +122,7 @@ void generate_daily_report_records_token_usage_before_succeeding_generation_log(
                 LlmProvider.OPENAI,
                 "gpt-5.6-luna"
         )).thenReturn(generationLogId);
-        when(dailyReportLlmClient.generate("question", answerEntry))
+        when(dailyReportLlmClient.generate("question", answerEntry, modelCandidate))
                 .thenReturn(new LlmGenerationResult<>(aiResult, new LlmTokenUsage(100L, 50L, 150L)));
         when(dailyReportTxService.confirmDailyAndReward(
                 any(PrepareDailyResultDto.class),
@@ -133,6 +140,8 @@ void generate_daily_report_records_token_usage_before_succeeding_generation_log(
         assertThat(response.reportId()).isEqualTo(reportId);
         assertThat(response.content()).isEqualTo("message");
         assertThat(response.balanceAfter()).isEqualTo(110L);
+        verify(dailyReportModelSelector).select();
+        verify(dailyReportLlmClient).generate("question", answerEntry, modelCandidate);
 
         InOrder inOrder = inOrder(reportGenerationLogRecorder);
         inOrder.verify(reportGenerationLogRecorder).recordTokenUsage(generationLogId, 100L, 50L, 150L, null);
@@ -151,16 +160,18 @@ void generate_daily_report_records_null_token_usage_when_usage_is_empty() {
         AnswerEntry answerEntry = AnswerEntry.create(user, question, "answer", today, null);
         AiDailyReportResultDto aiResult = new AiDailyReportResultDto("message", "ACHIEVEMENT");
         Emotion emotion = emotion(EmotionCode.ACHIEVEMENT);
+        ModelCandidate modelCandidate = modelCandidate("gpt-4o-mini");
 
         when(userRepository.findById(userId)).thenReturn(Optional.of(user));
         when(dailyQuestionRepository.findByIdWithInterest(20L)).thenReturn(Optional.of(question));
         when(userDailyQuestionRepository.findByUserIdAndDate(eq(userId), any(LocalDate.class)))
                 .thenReturn(Optional.of(UserDailyQuestion.create(user, today, question)));
         when(dailyReportTxService.prepareDaily(user, question, "answer", false, null))
                 .thenReturn(new PrepareDailyResultDto(answerEntry, reportId, userId));
+        when(dailyReportModelSelector.select()).thenReturn(modelCandidate);
         when(reportGenerationLogRecorder.start(any(), any(), any(), any(), any(), any()))
                 .thenReturn(generationLogId);
-        when(dailyReportLlmClient.generate("question", answerEntry))
+        when(dailyReportLlmClient.generate("question", answerEntry, modelCandidate))
                 .thenReturn(new LlmGenerationResult<>(aiResult, LlmTokenUsage.empty()));
         when(dailyReportTxService.confirmDailyAndReward(any(), eq(aiResult), eq(null)))
                 .thenReturn(new ConfirmDailyAndRewardDto(emotion, 110L));
@@ -190,4 +201,10 @@ private Emotion emotion(EmotionCode code) {
         when(emotion.getCode()).thenReturn(code);
         return emotion;
     }
+
+    private ModelCandidate modelCandidate(String model) {
+        ModelCandidate modelCandidate = new ModelCandidate();
+        modelCandidate.setModel(model);
+        return modelCandidate;
+    }
 }