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[LLM] gemini rerank 추가 #33

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20 changes: 20 additions & 0 deletions src/main/java/io/github/petty/llm/dto/GeminiRerankRequestDTO.java
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package io.github.petty.llm.dto;

import java.util.List;

public record GeminiRerankRequestDTO(
String userPrompt,
List<RecommendPlace> place
) {
public record RecommendPlace(
String contentId,
String title,
String addr,
String description,
String acmpyTypeCd,
String acmpyPsblCpam,
String acmpyNeedMtr
) {}
}


12 changes: 12 additions & 0 deletions src/main/java/io/github/petty/llm/dto/GeminiRerankResponseDTO.java
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package io.github.petty.llm.dto;

import java.util.List;

public record GeminiRerankResponseDTO(
List<RerankedPlace> rankedPlaces
) {
public record RerankedPlace(
String contentId,
String recommendReason
) {}
}
4 changes: 3 additions & 1 deletion src/main/java/io/github/petty/llm/dto/RecommendResponseDTO.java
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Expand Up @@ -14,5 +14,7 @@ public record PlaceRecommend (
String imageUrl,
String acmpyTypeCd,
String acmpyPsblCpam,
String acmpyNeedMtr ) {}
String acmpyNeedMtr,
String recommendReason
) {}
}
164 changes: 164 additions & 0 deletions src/main/java/io/github/petty/llm/service/GeminiRerankingService.java
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package io.github.petty.llm.service;

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import io.github.petty.llm.dto.GeminiRequestDTO;
import io.github.petty.llm.dto.GeminiRerankResponseDTO;
import io.github.petty.llm.dto.GeminiResponseDTO;
import io.github.petty.llm.dto.RecommendResponseDTO;
import io.qdrant.client.grpc.Points;
import lombok.RequiredArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.List;


@Slf4j
@Service
@RequiredArgsConstructor
public class GeminiRerankingService {
@Value("${gemini.api.key}")
private String apiKey;

private final ObjectMapper objectMapper;

public GeminiRerankResponseDTO rerankGemini(String userPrompt, List<RecommendResponseDTO.PlaceRecommend> candidates) {
log.info("GeminiReranking 프롬프트 실행");
String prompt = buildRerankingPrompt(userPrompt, candidates);
String response = null;
try {
response = callGemini(prompt);
} catch (Exception e) {
throw new RuntimeException(e);
}
return parseGemini(response);
}

private String buildRerankingPrompt(String userPrompt, List<RecommendResponseDTO.PlaceRecommend> candidates) {
StringBuilder sb = new StringBuilder();

sb.append("당신은 반려동물 동반 여행지 추천 전문가입니다. 사용자의 요청과 반려동물 정보를 바탕으로 다음 후보지들을 평가하고 순위를 매겨주세요.\n\n");

sb.append("사용자 요청: \n");
sb.append(userPrompt).append("\n");

sb.append("후보 장소들:\n");
for (int i = 0; i < candidates.size(); i++) {
var place = candidates.get(i);
sb.append(String.format("%d. %s\n", i + 1, place.title()));
sb.append(String.format(" - 주소: %s\n", place.addr()));
sb.append(String.format(" - 설명: %s\n", place.description()));
sb.append(String.format(" - 동반 유형: %s\n", place.acmpyTypeCd()));
sb.append(String.format(" - 동반 가능: %s\n", place.acmpyPsblCpam()));
sb.append(String.format(" - 준비사항: %s\n", place.acmpyNeedMtr()));
sb.append(String.format(" - contentId: %s\n\n", place.contentId()));
}

sb.append("평가 기준:\n");
sb.append("1. 사용자의 반려동물 정보(견종, 몸무게, 맹견 여부)와 장소의 동반 조건 일치도\n");
sb.append("2. 사용자가 원하는 지역과의 근접성\n");
sb.append("3. 사용자의 추가 요구사항 부합도\n");

sb.append("요구사항:\n");
sb.append("1. 사용자 요청사항과 평가 기준에 가장 적합한 순서로 정렬해주세요\n");
sb.append("2. 각 장소별로 사용자 요청에 맞는 구체적인 추천 이유를 50자 이내로 작성해주세요\n");
sb.append("3. 반려동물 동반이 불가능하거나 사용자 조건에 맞지 않는 경우 제외해주세요\n");
sb.append("4. 반드시 아래 JSON 형식으로만 응답해주세요:\n\n");


sb.append("{\n");
sb.append(" \"rankedPlaces\": [\n");
sb.append(" {\n");
sb.append(" \"contentId\": \"장소ID\",\n");
sb.append(" \"recommendReason\": \"추천 이유 (50자 이내)\"\n");
sb.append(" }\n");
sb.append(" ]\n");
sb.append("}\n\n");

sb.append("다른 설명이나 마크다운 없이 오직 JSON만 응답하세요!");

return sb.toString();
}

private String callGemini(String prompt) throws Exception {
HttpClient httpClient = HttpClient.newHttpClient();
String url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=%s"
.formatted(apiKey);

log.info("[Gemini 리랭킹] 프롬프트 전송 중...");

HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(url))
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(
objectMapper.writeValueAsString(
new GeminiRequestDTO(List.of(
new GeminiRequestDTO.Content(List.of(
new GeminiRequestDTO.Part(prompt)
))
))
)
))
.build();

HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
log.info("Gemini API 응답 상태코드: {}", response.statusCode());
log.info("Gemini API 응답 본문: {}", response.body()); // 이 줄 추가

if (response.statusCode() != 200) {
throw new RuntimeException("Gemini API 호출 실패: " + response.statusCode());
}

if (response.statusCode() != 200) {
throw new RuntimeException("Gemini API 호출 실패: " + response.statusCode());
}

GeminiResponseDTO resp = objectMapper.readValue(response.body(), GeminiResponseDTO.class);
return resp.candidates().get(0).content().parts().get(0).text();
}


/**
* Gemini 응답 파싱
*/
private GeminiRerankResponseDTO parseGemini(String response) {
try {
// JSON 부분만 추출 (혹시 다른 텍스트가 포함되어 있을 경우)
String jsonPart = extractJsonFromResponse(response);
return objectMapper.readValue(jsonPart, GeminiRerankResponseDTO.class);
} catch (JsonProcessingException e) {
log.error("Gemini 응답 파싱 실패: {}", response, e);
throw new RuntimeException("Gemini 응답 파싱 실패", e);
}
}

/**
* 응답에서 JSON 부분만 추출
*/
private String extractJsonFromResponse(String response) {
// 1. 먼저 코드 블록 제거
response = response.replaceAll("```json\\s*", "").replaceAll("```\\s*", "");

// 2. 앞뒤 공백 제거
response = response.trim();

// 3. JSON 시작과 끝 찾기
int start = response.indexOf('{');
int end = response.lastIndexOf('}');

if (start != -1 && end != -1 && end > start) {
String jsonResult = response.substring(start, end + 1);
log.info("추출된 JSON: {}", jsonResult); // 디버깅용
return jsonResult;
}

log.error("JSON 추출 실패. 원본 응답: {}", response);
throw new RuntimeException("유효한 JSON 형식을 찾을 수 없습니다: " + response);
}
}
75 changes: 66 additions & 9 deletions src/main/java/io/github/petty/llm/service/RecommendServiceImpl.java
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@@ -1,5 +1,7 @@
package io.github.petty.llm.service;

import io.github.petty.llm.dto.GeminiRerankRequestDTO;
import io.github.petty.llm.dto.GeminiRerankResponseDTO;
import lombok.extern.slf4j.Slf4j;
import io.github.petty.llm.common.AreaCode;
import io.github.petty.llm.dto.RecommendResponseDTO;
Expand All @@ -10,10 +12,8 @@
import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
import org.springframework.stereotype.Service;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Optional;
import java.util.*;
import java.util.stream.Collectors;

@Slf4j
@Service
Expand All @@ -22,6 +22,7 @@ public class RecommendServiceImpl implements RecommendService {
// RecommendController를 RecommendService로 분리
private final VectorStoreService vectorStoreService;
private final ContentService contentService;
private final GeminiRerankingService geminiRerankingService;

@Override
public RecommendResponseDTO recommend(Map<String, String> promptMap) {
Expand All @@ -41,8 +42,16 @@ public RecommendResponseDTO recommend(Map<String, String> promptMap) {
return new RecommendResponseDTO(new ArrayList<>());
}

List<RecommendResponseDTO.PlaceRecommend> recommendations = buildRecommendResponse(docs);

// Gemini 리랭킹
GeminiRerankResponseDTO rerank = geminiRerankingService.rerankGemini(userPrompt, recommendations);

List<RecommendResponseDTO.PlaceRecommend> finalRecommendations = applyRerankingResults(recommendations, rerank);
return new RecommendResponseDTO(finalRecommendations);

// 결과로 바로 dto
return buildRecommendResponse(docs);
// return buildRecommendResponse(docs);
} catch (Exception e) {
log.error("추천 처리 중 오류 발생: {}", e.getMessage(), e);
throw new RuntimeException("추천 생성 실패: " + e.getMessage(), e);
Expand Down Expand Up @@ -103,12 +112,21 @@ private Filter.Expression buildRegion(String location) {
return areaExpr.build();
}

// 유사도 검색으로 dto 바로 생성
private RecommendResponseDTO buildRecommendResponse(List<Document> docs) {
// 유사도 검색
private List<RecommendResponseDTO.PlaceRecommend> buildRecommendResponse(List<Document> docs) {
List<RecommendResponseDTO.PlaceRecommend> recommends = new ArrayList<>();
Set<String> checkId = new HashSet<>();

for (Document doc : docs) {
String contentId = (String) doc.getMetadata().get("contentId");

// 중복체크
if (checkId.contains(contentId)) {
log.debug("중복된 contentId 발견하여 건너뜀: {}", contentId);
continue;
}
checkId.add(contentId);

String title = (String) doc.getMetadata().get("title");
String addr = (String) doc.getMetadata().get("address");
String description = doc.getText();
Expand All @@ -133,10 +151,49 @@ private RecommendResponseDTO buildRecommendResponse(List<Document> docs) {

recommends.add(new RecommendResponseDTO.PlaceRecommend(
contentId, title, addr, description, imageUrl,
acmpyTypeCd, acmpyPsblCpam, acmpyNeedMtr
acmpyTypeCd, acmpyPsblCpam, acmpyNeedMtr, null
));
}
return recommends;
}

return new RecommendResponseDTO(recommends);

/**
* 리랭킹 결과를 원본 추천 리스트에 적용
*/
private List<RecommendResponseDTO.PlaceRecommend> applyRerankingResults(
List<RecommendResponseDTO.PlaceRecommend> initialRecommends,
GeminiRerankResponseDTO rerankResult) {

// contentId를 키로 하는 맵 생성
Map<String, RecommendResponseDTO.PlaceRecommend> recommendMap = initialRecommends.stream()
.collect(Collectors.toMap(
RecommendResponseDTO.PlaceRecommend::contentId,
recommend -> recommend
));

// 리랭킹 결과를 점수순으로 정렬하고 상위 10개만 선택
return rerankResult.rankedPlaces().stream()
.map(ranked -> {
RecommendResponseDTO.PlaceRecommend original = recommendMap.get(ranked.contentId());
if (original != null) {
// 추천 이유와 점수를 포함한 새로운 객체 생성
return new RecommendResponseDTO.PlaceRecommend(
original.contentId(),
original.title(),
original.addr(),
original.description(),
original.imageUrl(),
original.acmpyTypeCd(),
original.acmpyPsblCpam(),
original.acmpyNeedMtr(),
ranked.recommendReason()
);
}
return null;
})
.filter(java.util.Objects::nonNull)
.collect(Collectors.toList());
}

}
4 changes: 2 additions & 2 deletions src/main/java/io/github/petty/llm/service/VectorStoreService.java
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Expand Up @@ -83,8 +83,8 @@ public List<Document> findSimilarWithFilter(String query, int k, Filter.Expressi
log.info("유사 콘텐츠 검색 결과");
for (int i = 0; i < results.size(); i++) {
Document doc = results.get(i);
log.info("▶ 결과 {}: ID={}, Metadata={}, Content={}",
i + 1, doc.getId(), doc.getMetadata(), doc.getText());
log.info("▶ 결과 {}: ID={}, Metadata={}",
i + 1, doc.getId(), doc.getMetadata());
}
return results;
}
Expand Down