[chore] 안 쓰는 Hugging face, Vector DB 관련 코드 제거

.env.default

@@ -7,11 +7,6 @@ MYSQL_PASSWORD=
 
 # AI 서비스 설정
 AI_OPENAI_API_KEY=
-AI_HUGGINGFACE_API_KEY=
-
-# 벡터 데이터베이스 설정
-PINECONE_API_KEY=
-PINECONE_INDEX_NAME=
 
 # 이메일 인증
 GMAIL_SENDER_EMAIL=
@@ -25,10 +20,16 @@ AWS_S3_BUCKET=
 # Unsplash API
 UNSPLASH_ACCESS_KEY=
 
+# Pixabay API
+PIXABAY_ACCESS_KEY=
+
 # Google Custom Search API
 GOOGLE_API_KEY=
 GOOGLE_CX_ID=
 
+# Google Cloud 인증 (Base64 인코딩된 JSON)
+GOOGLE_CLOUD_CREDENTIALS_JSON=
+
 # OAuth2
 KAKAO_CLIENT_ID=
 NAVER_CLIENT_ID=

build.gradle.kts

@@ -91,9 +91,6 @@ dependencies {
     // Spring AI
     implementation(platform("org.springframework.ai:spring-ai-bom:1.1.0"))
     implementation("org.springframework.ai:spring-ai-starter-model-openai")
-    implementation("org.springframework.ai:spring-ai-starter-model-huggingface")
-    // Vector DB
-    implementation("org.springframework.ai:spring-ai-starter-vector-store-pinecone")
     // Spring WebFlux (WebClient)
     implementation("org.springframework.boot:spring-boot-starter-webflux")
     // QueryDSL

src/main/java/com/back/domain/ai/ai/config/ChatClientConfig.java

@@ -1,7 +1,6 @@
 package com.back.domain.ai.ai.config;
 
 import org.springframework.ai.chat.client.ChatClient;
-import org.springframework.ai.huggingface.HuggingfaceChatModel;
 import org.springframework.ai.openai.OpenAiChatModel;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
@@ -14,8 +13,4 @@ public ChatClient openAiChatClient(OpenAiChatModel chatModel) {
         return ChatClient.create(chatModel);
     }
 
-    @Bean
-    public ChatClient huggingfaceChatClient(HuggingfaceChatModel chatModel) {
-        return ChatClient.create(chatModel);
-    }
 }

src/main/java/com/back/domain/ai/ai/controller/ApiV1AiController.java

@@ -4,7 +4,6 @@
 import com.back.domain.ai.ai.dto.AiGenerateRequest;
 import com.back.domain.ai.ai.service.AiChatService;
 import com.back.domain.ai.ai.service.AiGenerateService;
-import com.back.domain.ai.ai.service.AiIndexService;
 import com.back.domain.ai.ai.util.AiChatHttpUtil;
 import com.back.domain.ai.model.exception.ModelUsageExceededException;
 import com.back.domain.ai.model.service.ModelUsageService;
@@ -34,7 +33,6 @@
 @Tag(name = "AI API", description = "AI 관련 API")
 public class ApiV1AiController {
     private final AiGenerateService aiGenerateService;
-    private final AiIndexService aiIndexService;
     private final AiChatService aiChatService;
     private final ModelUsageService modelUsageService;
 

src/main/java/com/back/domain/ai/ai/service/AiChatService.java

@@ -8,24 +8,17 @@
 import org.springframework.ai.chat.messages.SystemMessage;
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.prompt.Prompt;
-import org.springframework.ai.chat.prompt.PromptTemplate;
-import org.springframework.ai.document.Document;
 import org.springframework.ai.openai.OpenAiChatOptions;
-import org.springframework.ai.vectorstore.SearchRequest;
-import org.springframework.ai.vectorstore.VectorStore;
 import org.springframework.stereotype.Service;
 import reactor.core.publisher.Flux;
 
 import java.util.List;
-import java.util.Map;
-import java.util.stream.Collectors;
 
 @Service
 @RequiredArgsConstructor
 @Slf4j
 public class AiChatService {
     private final ChatClient openAiChatClient;
-    private final VectorStore vectorStore;
 
     private static final String SYSTEM_DETAIL_PROMPT = """
             * 전문적이면서도 친근한 톤을 유지합니다.
@@ -114,59 +107,4 @@ private OpenAiChatOptions buildOptions(AiModel model) {
 
         return optionBuilder.build();
     }
-
-    /**
-     * RAG 기반으로 사용자 질문에 답변합니다.
-     */
-    public String chatWithRag(Integer id, String message) {
-        List<Document> similarDocuments = searchRelevantDocuments(id, message);
-
-        String context = similarDocuments.stream()
-                .map(Document::getText)
-                .collect(Collectors.joining("\n---\n"));
-
-        SystemMessage systemMessage = SystemMessage.builder()
-                .text(AiGenerateService.SYSTEM_BASE_PROMPT)
-                .text(SYSTEM_DETAIL_PROMPT)
-                .build();
-
-        PromptTemplate promptTemplate = PromptTemplate.builder()
-                .template("""
-                        [Context]를 바탕으로 [질문]에 답변하세요.
-
-                        [질문]
-                        {message}
-
-                        [Context]
-                        {context}
-                        """)
-                .build();
-
-        UserMessage userMessage = UserMessage.builder()
-                .text(
-                        promptTemplate.render(Map.of(
-                                "message", message,
-                                "context", context
-                        ))
-                )
-                .build();
-
-        return openAiChatClient.prompt()
-                .messages(
-                        systemMessage,
-                        userMessage
-                )
-                .call()
-                .content();
-    }
-
-    private List<Document> searchRelevantDocuments(Integer blogId, String query) {
-        SearchRequest searchRequest = SearchRequest.builder()
-                .query(query)
-                .topK(3)
-                .filterExpression("blogId == %d".formatted(blogId))
-                .build();
-
-        return vectorStore.similaritySearch(searchRequest);
-    }
 }