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lunaticbum 2026-04-15 10:53:40 +09:00
parent dd1f961d57
commit 68a7a05961
1 changed files with 30 additions and 21 deletions
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51
src/main/kotlin/network/RagService.kt
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@ -19,6 +19,7 @@ import dev.langchain4j.model.openai.OpenAiEmbeddingModel
import dev.langchain4j.service.AiServices import dev.langchain4j.service.AiServices
import dev.langchain4j.service.SystemMessage import dev.langchain4j.service.SystemMessage
import dev.langchain4j.store.embedding.EmbeddingSearchRequest import dev.langchain4j.store.embedding.EmbeddingSearchRequest
import dev.langchain4j.store.embedding.EmbeddingSearchResult
import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder
import kotlinx.coroutines.Dispatchers import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.async import kotlinx.coroutines.async
@ -298,20 +299,23 @@ object RagService {
val question = "$stockName 실적 및 향후 전망" val question = "$stockName 실적 및 향후 전망"
val questionEmbedding = embeddingModel.embed(question).content() val questionEmbedding = embeddingModel.embed(question).content()
var finalSearchResult : EmbeddingSearchResult<TextSegment>? = null
try {
finalSearchResult = embeddingStore.search(
EmbeddingSearchRequest.builder()
.queryEmbedding(questionEmbedding)
.filter(MetadataFilterBuilder.metadataKey("stockCode").isEqualTo(stockCode))
.maxResults(10) // 최신 뉴스 3개 적정
.minScore(0.2)
.build()
)
} catch (e: Exception) {}
val finalSearchResult = embeddingStore.search(
EmbeddingSearchRequest.builder()
.queryEmbedding(questionEmbedding)
.filter(MetadataFilterBuilder.metadataKey("stockCode").isEqualTo(stockCode))
.maxResults(10) // 최신 뉴스 3개 적정
.minScore(0.2)
.build()
)
// 3. 검색된 내용을 하나의 문자열로 합쳐서 전달 // 3. 검색된 내용을 하나의 문자열로 합쳐서 전달
tradingDecision.newsContext = finalSearchResult.matches().distinct() // 중복 제거 tradingDecision.newsContext = finalSearchResult?.matches()?.distinct() // 중복 제거
.take(4) // 10개에서 4개로 축소 ?.take(4) // 10개에서 4개로 축소
.joinToString("\n\n") { ?.joinToString("\n\n") {
it.embedded().text() it.embedded().text()
} }
@ -336,20 +340,25 @@ object RagService {
val questionEmbedding = embeddingModel.embed(question).content() val questionEmbedding = embeddingModel.embed(question).content()
// 1. 벡터 DB에서 해당 종목의 뉴스 검색 // 1. 벡터 DB에서 해당 종목의 뉴스 검색
val searchResult = embeddingStore.search( var searchResult : EmbeddingSearchResult<TextSegment>? = null
EmbeddingSearchRequest.builder() try {
.queryEmbedding(questionEmbedding) searchResult = embeddingStore.search(
.filter(MetadataFilterBuilder.metadataKey("stockCode").isEqualTo(stockCode)) EmbeddingSearchRequest.builder()
.maxResults(10) .queryEmbedding(questionEmbedding)
.minScore(0.2) .filter(MetadataFilterBuilder.metadataKey("stockCode").isEqualTo(stockCode))
.build() .maxResults(10)
) .minScore(0.2)
.build()
)
} catch (e: Exception) {
}
// 2. 검색된 뉴스 중 1시간 이내(Very Recent) 데이터가 있는지 확인 // 2. 검색된 뉴스 중 1시간 이내(Very Recent) 데이터가 있는지 확인
val hasHotNews = searchResult.matches().any { match -> val hasHotNews = searchResult?.matches()?.any { match ->
val pubDate = match.embedded().metadata().getString("date") val pubDate = match.embedded().metadata().getString("date")
isVeryRecentNews(pubDate, maxHours = 1) isVeryRecentNews(pubDate, maxHours = 1)
} } ?: false
// 3. 최신 뉴스가 없다면 네이버 API 및 Playwright 스크래핑 가동 // 3. 최신 뉴스가 없다면 네이버 API 및 Playwright 스크래핑 가동
if (!hasHotNews) { if (!hasHotNews) {