Feature/rag quality improvements#61
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분석/검색 RAG 파이프라인의 정밀도를 끌어올린다. AI 서버: - 임베딩 task_type 분리: 검색 쿼리는 RETRIEVAL_QUERY, 인덱싱은 RETRIEVAL_DOCUMENT - 구조 인식 청킹: 마크다운 헤딩 단위 분할 + 코드블록 보존, heading_path 보존 - Contextual Retrieval: 청크 임베딩 전 [문서요약 > 헤딩경로] 프리픽스 부착 (LLM 호출 0) - LLM 리랭커: 하이브리드 후보 N개 → flash 모델로 재정렬 → top_k (실패 시 검색순 폴백) - core client: search_embeddings 에 queryText 추가 (하이브리드 트리거) - 질문/꼬리질문/피드백 consumer: 후보 검색 → 리랭크 → top_k 주입 Core 서버: - V8 마이그레이션: chunk_text_tsv(tsvector, simple) + GIN, ivfflat→HNSW 교체 - 검색에 queryText 옵션 추가 → 벡터 + BM25 를 RRF(k=60) 로 융합한 하이브리드 검색 (queryText 없으면 기존 벡터-only 하위호환) - openapi.json 갱신 테스트: AI 190 passed, backend compile/test-compile OK, openapi 재생성. 로컬 e2e(V8 적용·하이브리드 SQL)는 Docker 환경에서 후속 검증. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
백엔드 SearchRequest 에 queryText(하이브리드 검색) 추가됨에 따라 generated.ts 재생성. 프론트 빌드(tsc+vite) 통과 확인. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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