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[Fix] 검색 E2E가 최소 유사도 가드레일을 검증하도록 재작성 #238

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Merged
Gimini-3 merged 1 commit into from
Aug 18, 2026
Merged

[Fix] 검색 E2E가 최소 유사도 가드레일을 검증하도록 재작성 #238

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58 changes: 35 additions & 23 deletions backend/src/test/java/com/opensource/docgrid/e2e/LocalDocumentIndexingE2ETest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -141,8 +141,8 @@ void upload_indexesDocumentsAndSearchesRelatedContent() throws Exception {
assertIndexedInvariants(pdfUpload, true);
assertIndexedInvariants(docxUpload, false);

// 5. 실제 단건 Query Embedding과 pgvector 코사인 검색에서 관련 PDF가 먼저 반환된다.
assertSemanticSearchRanksPdfFirst(pdfUpload, docxUpload);
// 5. 실제 단건 Query Embedding과 pgvector 코사인 검색이 질의별로 관련 문서만 반환한다.
assertSemanticSearchAppliesRelevanceGuardrail(pdfUpload, docxUpload);

// 6. 같은 ADMIN JWT로 실제 관리자 조회와 OpenAPI 노출·민감정보 제거 계약을 검증한다.
assertAdminHttpContracts(accessToken, pdfUpload);
Expand Down Expand Up @@ -240,37 +240,49 @@ private void assertEventOrder(Long jobId) {
);
}

private void assertSemanticSearchRanksPdfFirst(
/**
* 질의와 관련된 문서만 검색되고, 관련 없는 문서는 최소 유사도에서 제외되는지 확인한다.
*
* <p>두 질의를 함께 검증한다. 한 질의만 보면 문서가 빠진 이유가 관련성 판정 때문인지
* 인덱싱 누락 때문인지 구분할 수 없기 때문이다.
*/
private void assertSemanticSearchAppliesRelevanceGuardrail(
UploadedDocument pdfUpload,
UploadedDocument docxUpload
) {
float[] queryVector = embeddingClient.embed(
"How does pgvector HNSW improve cosine similarity search for embeddings?"
);
assertThat(queryVector).hasSize(1024);
for (float value : queryVector) {
assertThat(Float.isFinite(value)).isTrue();
}
Long modelId = queryLong(
"SELECT id FROM embedding_models WHERE is_active = TRUE AND is_searchable = TRUE"
);
List<Long> permittedIds = List.of(pdfUpload.documentId(), docxUpload.documentId());

List<VectorSearchCandidate> candidates = vectorSearchQueryService.search(
queryVector,
// 1. pgvector 질의에는 관련 PDF만 남고 무관한 제빵 DOCX는 최소 유사도에서 제외된다.
List<VectorSearchCandidate> vectorQueryHits = searchByQuery(
"How does pgvector HNSW improve cosine similarity search for embeddings?",
modelId,
List.of(pdfUpload.documentId(), docxUpload.documentId()),
10
permittedIds
);
assertThat(candidates).isNotEmpty();
assertThat(candidates.get(0).documentId()).isEqualTo(pdfUpload.documentId());
assertThat(candidates).anyMatch(candidate -> candidate.documentId().equals(docxUpload.documentId()));
assertThat(candidates.get(0).similarityScore()).isGreaterThan(
candidates.stream()
.filter(candidate -> candidate.documentId().equals(docxUpload.documentId()))
.findFirst()
.orElseThrow()
.similarityScore()
assertThat(vectorQueryHits).isNotEmpty();
assertThat(vectorQueryHits.get(0).documentId()).isEqualTo(pdfUpload.documentId());
assertThat(vectorQueryHits)
.allMatch(candidate -> candidate.documentId().equals(pdfUpload.documentId()));

// 2. 같은 DOCX도 관련 질의에는 검색된다. 1의 제외가 인덱싱 누락이 아니라 관련성 판정임을 확인한다.
List<VectorSearchCandidate> bakingQueryHits = searchByQuery(
"How is bread dough fermented with yeast before baking?",
modelId,
permittedIds
);
assertThat(bakingQueryHits).isNotEmpty();
assertThat(bakingQueryHits.get(0).documentId()).isEqualTo(docxUpload.documentId());
}

private List<VectorSearchCandidate> searchByQuery(String query, Long modelId, List<Long> permittedIds) {
float[] queryVector = embeddingClient.embed(query);
assertThat(queryVector).hasSize(1024);
for (float value : queryVector) {
assertThat(Float.isFinite(value)).isTrue();
}
return vectorSearchQueryService.search(queryVector, modelId, permittedIds, 10);
}

private void assertAdminHttpContracts(String accessToken, UploadedDocument upload) {
Expand Down