f5c3dea833
## 변경 사항 ### app/services/search/retrieval_service.py - **_QUERY_EMBED_CACHE**: 모듈 레벨 LRU (maxsize=500, TTL=24h) - sha256(text|bge-m3) 키. fixed query 재호출 시 vector_ms 절반 감소. - **_get_query_embedding(client, text)**: cache-first helper. 기존 search_vector()도 이를 사용하도록 교체. - **search_vector_multilingual(session, normalized_queries, limit)**: 신규 - normalized_queries 각 언어별 embedding 병렬 생성 (cache hit 활용) - 각 embedding에 대해 docs+chunks hybrid retrieval 병렬 - weight 기반 score 누적 merge (lang_weight 이미 1.0 정규화) - match_reason에 "ml_ko+en" 등 언어 병합 표시 - 호출 조건 문서화 — cache hit + analyzer_tier=analyzed 시에만 ### app/api/search.py - use_multilingual 결정 로직: - analyzer_cache_hit == True - analyzer_tier == "analyzed" (confidence >= 0.85) - normalized_queries >= 2 (다언어 버전 실제 존재) - 위 3조건 모두 만족할 때만 search_vector_multilingual 호출 - 그 외 모든 경로 (cache miss, low conf, single lang)는 기존 search_vector 그대로 사용 (회귀 0 보장) - notes에 `multilingual langs=[ko, en, ...]` 기록 ## 기대 효과 - crosslingual_ko_en NDCG 0.53 → 0.65+ (Phase 2 목표) - 기존 경로 완전 불변 → 회귀 0 - Phase 2.1 async 구조와 결합해 "cache hit일 때만 활성" 조건 준수 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
495 lines
19 KiB
Python
495 lines
19 KiB
Python
"""검색 후보 수집 서비스 (Phase 1.2 + Phase 2.2 multilingual).
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text(documents FTS + trigram) + vector(documents.embedding + chunks.embedding hybrid) 후보를
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SearchResult 리스트로 반환.
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Phase 1.1a: search.py의 _search_text/_search_vector를 이전 (ILIKE 그대로).
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Phase 1.2-B: ILIKE → trigram `%` + `similarity()`. ILIKE 풀 스캔 제거.
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Phase 1.2-C: vector retrieval을 document_chunks 테이블로 전환 → catastrophic recall 손실.
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Phase 1.2-G: doc + chunks hybrid retrieval 보강.
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- documents.embedding (recall robust, 자연어 매칭 강함)
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- document_chunks.embedding (precision, segment 매칭)
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- 두 SQL 동시 호출 후 doc_id 기준 merge (chunk 가중치 1.2, doc 1.0)
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Phase 2.2 추가:
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- _QUERY_EMBED_CACHE: bge-m3 query embedding 캐시 (모듈 레벨 LRU, TTL 24h)
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- search_vector_multilingual: normalized_queries (lang별 쿼리) 배열 지원
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QueryAnalyzer cache hit + analyzer_tier >= merge 일 때만 호출.
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- crosslingual_ko_en NDCG 0.53 → 0.65+ 목표
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"""
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from __future__ import annotations
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import asyncio
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import hashlib
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import time
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from typing import TYPE_CHECKING, Any
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
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from ai.client import AIClient
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from core.database import engine
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from core.utils import setup_logger
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if TYPE_CHECKING:
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from api.search import SearchResult
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logger = setup_logger("retrieval_service")
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# Hybrid merge 가중치 (1.2-G)
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DOC_VECTOR_WEIGHT = 1.0
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CHUNK_VECTOR_WEIGHT = 1.2
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# ─── Phase 2.2: Query embedding cache ───────────────────
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# bge-m3 호출 비용 절반 감소 (동일 normalized_query 재호출 방지)
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_QUERY_EMBED_CACHE: dict[str, dict[str, Any]] = {}
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QUERY_EMBED_TTL = 86400 # 24h
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QUERY_EMBED_MAXSIZE = 500
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def _query_embed_key(text_: str) -> str:
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return hashlib.sha256(f"{text_}|bge-m3".encode("utf-8")).hexdigest()
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async def _get_query_embedding(
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client: AIClient, text_: str
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) -> list[float] | None:
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"""Query embedding with in-memory cache.
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동일 텍스트 재호출 시 bge-m3 skip. fixed query 회귀 시 vector_ms 대폭 감소.
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"""
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if not text_:
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return None
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key = _query_embed_key(text_)
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entry = _QUERY_EMBED_CACHE.get(key)
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if entry and time.time() - entry["ts"] < QUERY_EMBED_TTL:
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return entry["emb"]
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try:
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emb = await client.embed(text_)
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except Exception as exc:
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logger.warning("query embed failed text=%r err=%r", text_[:40], exc)
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return None
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if len(_QUERY_EMBED_CACHE) >= QUERY_EMBED_MAXSIZE:
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try:
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oldest = next(iter(_QUERY_EMBED_CACHE))
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_QUERY_EMBED_CACHE.pop(oldest, None)
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except StopIteration:
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pass
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_QUERY_EMBED_CACHE[key] = {"emb": emb, "ts": time.time()}
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return emb
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def query_embed_cache_stats() -> dict[str, int]:
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return {"size": len(_QUERY_EMBED_CACHE), "maxsize": QUERY_EMBED_MAXSIZE}
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async def search_text(
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session: AsyncSession, query: str, limit: int
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) -> list["SearchResult"]:
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"""FTS + trigram 필드별 가중치 검색 (Phase 1.2-B UNION 분해).
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Phase 1.2-B 진단:
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OR로 묶은 단일 SELECT는 PostgreSQL planner가 OR 결합 인덱스를 못 만들고
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Seq Scan을 선택 (small table 765 docs). EXPLAIN으로 측정 시 525ms.
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→ CTE + UNION으로 분해하면 각 branch가 자기 인덱스 활용 → 26ms (95% 감소).
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구조:
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candidates CTE
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├─ title % → idx_documents_title_trgm
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├─ ai_summary % → idx_documents_ai_summary_trgm
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│ (length > 0 partial index 매치 조건 포함)
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└─ FTS @@ plainto_tsquery → idx_documents_fts_full
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JOIN documents d ON d.id = c.id
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ORDER BY 5컬럼 similarity 가중 합산 + ts_rank * 2.0
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가중치: title 3.0 / ai_tags 2.5 / user_note 2.0 / ai_summary 1.5 / extracted_text 1.0
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threshold:
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pg_trgm.similarity_threshold default = 0.3
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→ multi-token 한국어 뉴스 쿼리(예: "이란 미국 전쟁 글로벌 반응")에서
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candidates를 못 모음 → recall 감소 (0.788 → 0.750)
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→ set_limit(0.15)으로 낮춰 recall 회복. precision은 ORDER BY similarity 합산이 보정.
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"""
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from api.search import SearchResult # 순환 import 회피
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# trigram threshold를 0.15로 낮춰 multi-token query recall 회복
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# SQLAlchemy async session 내 두 execute는 같은 connection 사용
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await session.execute(text("SELECT set_limit(0.15)"))
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result = await session.execute(
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text("""
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WITH candidates AS (
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-- title trigram (idx_documents_title_trgm)
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SELECT id FROM documents
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WHERE deleted_at IS NULL AND title % :q
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UNION
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-- ai_summary trigram (idx_documents_ai_summary_trgm 부분 인덱스 매치)
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SELECT id FROM documents
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WHERE deleted_at IS NULL
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AND ai_summary IS NOT NULL
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AND length(ai_summary) > 0
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AND ai_summary % :q
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UNION
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-- FTS 통합 인덱스 (idx_documents_fts_full)
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SELECT id FROM documents
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WHERE deleted_at IS NULL
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AND to_tsvector('simple',
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coalesce(title, '') || ' ' ||
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coalesce(ai_tags::text, '') || ' ' ||
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coalesce(ai_summary, '') || ' ' ||
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coalesce(user_note, '') || ' ' ||
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coalesce(extracted_text, '')
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) @@ plainto_tsquery('simple', :q)
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)
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SELECT d.id, d.title, d.ai_domain, d.ai_summary, d.file_format,
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left(d.extracted_text, 200) AS snippet,
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(
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-- 컬럼별 trigram similarity 가중 합산
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similarity(coalesce(d.title, ''), :q) * 3.0
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+ similarity(coalesce(d.ai_tags::text, ''), :q) * 2.5
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+ similarity(coalesce(d.user_note, ''), :q) * 2.0
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+ similarity(coalesce(d.ai_summary, ''), :q) * 1.5
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+ similarity(coalesce(d.extracted_text, ''), :q) * 1.0
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-- FTS 보너스 (idx_documents_fts_full 활용)
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+ coalesce(ts_rank(
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to_tsvector('simple',
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coalesce(d.title, '') || ' ' ||
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coalesce(d.ai_tags::text, '') || ' ' ||
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coalesce(d.ai_summary, '') || ' ' ||
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coalesce(d.user_note, '') || ' ' ||
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coalesce(d.extracted_text, '')
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),
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plainto_tsquery('simple', :q)
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), 0) * 2.0
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) AS score,
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-- match_reason: similarity 가장 큰 컬럼 또는 FTS
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CASE
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WHEN similarity(coalesce(d.title, ''), :q) >= 0.3 THEN 'title'
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WHEN similarity(coalesce(d.ai_tags::text, ''), :q) >= 0.3 THEN 'tags'
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WHEN similarity(coalesce(d.user_note, ''), :q) >= 0.3 THEN 'note'
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WHEN similarity(coalesce(d.ai_summary, ''), :q) >= 0.3 THEN 'summary'
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WHEN similarity(coalesce(d.extracted_text, ''), :q) >= 0.3 THEN 'content'
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ELSE 'fts'
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END AS match_reason
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FROM documents d
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JOIN candidates c ON d.id = c.id
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ORDER BY score DESC
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LIMIT :limit
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"""),
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{"q": query, "limit": limit},
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)
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return [SearchResult(**row._mapping) for row in result]
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async def search_vector(
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session: AsyncSession, query: str, limit: int
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) -> list["SearchResult"]:
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"""Hybrid 벡터 검색 — doc + chunks 동시 retrieval (Phase 1.2-G).
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Phase 1.2-C 진단:
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chunks-only는 segment 의미 손실로 자연어 query에서 catastrophic recall.
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doc embedding은 전체 본문 평균 → recall robust.
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→ 두 retrieval 동시 사용이 정석.
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데이터 흐름:
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1. query embedding 1번 (bge-m3)
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2. asyncio.gather로 두 SQL 동시 호출:
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- _search_vector_docs: documents.embedding cosine top N
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- _search_vector_chunks: document_chunks.embedding window partition (doc당 top 2)
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3. _merge_doc_and_chunk_vectors로 가중치 + dedup:
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- chunk score * 1.2 (precision)
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- doc score * 1.0 (recall)
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- doc_id 기준 dedup, chunks 우선
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Returns:
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list[SearchResult] — doc_id 중복 제거됨. compress_chunks_to_docs는 그대로 동작.
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chunks_by_doc은 search.py에서 group_by_doc으로 보존.
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"""
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client = AIClient()
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try:
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query_embedding = await _get_query_embedding(client, query)
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finally:
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try:
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await client.close()
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except Exception:
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pass
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if query_embedding is None:
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return []
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embedding_str = str(query_embedding)
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# 두 SQL 병렬 호출 — 각각 별도 session 사용 (asyncpg connection은 statement 단위 직렬)
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Session = async_sessionmaker(engine)
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async def _docs_call() -> list["SearchResult"]:
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async with Session() as s:
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return await _search_vector_docs(s, embedding_str, limit * 4)
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async def _chunks_call() -> list["SearchResult"]:
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async with Session() as s:
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return await _search_vector_chunks(s, embedding_str, limit * 4)
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doc_results, chunk_results = await asyncio.gather(_docs_call(), _chunks_call())
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return _merge_doc_and_chunk_vectors(doc_results, chunk_results)
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async def _search_vector_docs(
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session: AsyncSession, embedding_str: str, limit: int
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) -> list["SearchResult"]:
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"""documents.embedding 직접 검색 — recall robust (자연어 매칭).
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chunks가 없는 doc도 매칭 가능. score는 cosine similarity (1 - distance).
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chunk_id/chunk_index/section_title은 None.
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"""
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from api.search import SearchResult # 순환 import 회피
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result = await session.execute(
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text("""
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SELECT
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id,
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title,
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ai_domain,
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ai_summary,
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file_format,
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(1 - (embedding <=> cast(:embedding AS vector))) AS score,
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left(extracted_text, 200) AS snippet,
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'vector_doc' AS match_reason,
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NULL::bigint AS chunk_id,
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NULL::integer AS chunk_index,
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NULL::text AS section_title
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FROM documents
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WHERE embedding IS NOT NULL AND deleted_at IS NULL
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ORDER BY embedding <=> cast(:embedding AS vector)
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LIMIT :limit
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"""),
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{"embedding": embedding_str, "limit": limit},
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)
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return [SearchResult(**row._mapping) for row in result]
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async def _search_vector_chunks(
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session: AsyncSession, embedding_str: str, limit: int
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) -> list["SearchResult"]:
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"""document_chunks.embedding 검색 + window partition (doc당 top 2 chunks).
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SQL 흐름:
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1. inner CTE topk: ivfflat 인덱스로 top-K chunks 추출
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2. ranked CTE: doc_id PARTITION + ROW_NUMBER (score 내림차순)
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3. outer: rn <= 2 (doc당 max 2 chunks) + JOIN documents
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"""
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from api.search import SearchResult # 순환 import 회피
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inner_k = max(limit * 5, 500)
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result = await session.execute(
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text("""
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WITH topk AS (
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SELECT
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c.id AS chunk_id,
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c.doc_id,
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c.chunk_index,
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c.section_title,
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c.text,
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c.embedding <=> cast(:embedding AS vector) AS dist
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FROM document_chunks c
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WHERE c.embedding IS NOT NULL
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ORDER BY c.embedding <=> cast(:embedding AS vector)
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LIMIT :inner_k
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),
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ranked AS (
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SELECT
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chunk_id, doc_id, chunk_index, section_title, text, dist,
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ROW_NUMBER() OVER (PARTITION BY doc_id ORDER BY dist ASC) AS rn
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FROM topk
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)
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SELECT
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d.id AS id,
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d.title AS title,
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d.ai_domain AS ai_domain,
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d.ai_summary AS ai_summary,
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d.file_format AS file_format,
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(1 - r.dist) AS score,
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left(r.text, 200) AS snippet,
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'vector_chunk' AS match_reason,
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r.chunk_id AS chunk_id,
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r.chunk_index AS chunk_index,
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r.section_title AS section_title
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FROM ranked r
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JOIN documents d ON d.id = r.doc_id
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WHERE r.rn <= 2 AND d.deleted_at IS NULL
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ORDER BY r.dist
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LIMIT :limit
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"""),
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{"embedding": embedding_str, "inner_k": inner_k, "limit": limit},
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)
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return [SearchResult(**row._mapping) for row in result]
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def _merge_doc_and_chunk_vectors(
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doc_results: list["SearchResult"],
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chunk_results: list["SearchResult"],
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) -> list["SearchResult"]:
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"""doc + chunks vector 결과 merge (Phase 1.2-G).
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가중치:
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- chunk score * 1.2 (segment 매칭이 더 정확)
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- doc score * 1.0 (전체 본문 평균, recall 보완)
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Dedup:
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- doc_id 기준
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- chunks가 있으면 chunks 우선 (segment 정보 + chunk_id 보존)
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- chunks에 없는 doc은 doc-wrap으로 추가
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Returns:
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score 내림차순 정렬된 SearchResult 리스트.
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chunk_id가 None이면 doc-wrap 결과(text-only 매치 doc 처리에 사용).
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"""
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by_doc_id: dict[int, "SearchResult"] = {}
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# chunks 먼저 (가중치 적용 + chunk_id 보존)
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for c in chunk_results:
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c.score = c.score * CHUNK_VECTOR_WEIGHT
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prev = by_doc_id.get(c.id)
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if prev is None or c.score > prev.score:
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by_doc_id[c.id] = c
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# doc 매치는 chunks에 없는 doc만 추가 (chunks 우선 원칙)
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for d in doc_results:
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d.score = d.score * DOC_VECTOR_WEIGHT
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if d.id not in by_doc_id:
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by_doc_id[d.id] = d
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# score 내림차순 정렬
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return sorted(by_doc_id.values(), key=lambda r: r.score, reverse=True)
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async def search_vector_multilingual(
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session: AsyncSession,
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normalized_queries: list[dict],
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limit: int,
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) -> list["SearchResult"]:
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"""Phase 2.2 — 다국어 normalized_queries 배열로 vector retrieval.
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각 language query에 대해 embedding을 병렬 생성(cache hit 활용),
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각 embedding에 대해 기존 docs+chunks hybrid 호출,
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결과를 weight 기반으로 merge.
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⚠️ 호출 조건:
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- QueryAnalyzer cache hit 이어야 함 (async-only 룰)
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- analyzer_confidence 높고 normalized_queries 존재해야 함
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- search.py에서만 호출. retrieval 경로 동기 LLM 호출 금지 룰 준수.
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Args:
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session: AsyncSession (호출자 관리, 본 함수 내부는 sessionmaker로 별도 연결 사용)
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normalized_queries: [{"lang": "ko", "text": "...", "weight": 0.56}, ...]
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weight는 _normalize_weights로 이미 합=1.0 정규화된 상태.
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limit: 상위 결과 개수
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Returns:
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list[SearchResult] — doc_id 중복 제거. merged score = sum(per-query score * lang_weight).
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"""
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if not normalized_queries:
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return []
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# 1. 각 lang별 embedding 병렬 (cache hit 활용)
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client = AIClient()
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try:
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embed_tasks = [
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_get_query_embedding(client, q["text"]) for q in normalized_queries
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]
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embeddings = await asyncio.gather(*embed_tasks)
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finally:
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try:
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await client.close()
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except Exception:
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pass
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# embedding 실패한 query는 skip (weight 재정규화 없이 조용히 drop)
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per_query_plan: list[tuple[dict, str]] = []
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for q, emb in zip(normalized_queries, embeddings):
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if emb is None:
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logger.warning("multilingual embed skipped lang=%s", q.get("lang"))
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continue
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per_query_plan.append((q, str(emb)))
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if not per_query_plan:
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return []
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# 2. 각 embedding에 대해 doc + chunks 병렬 retrieval
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Session = async_sessionmaker(engine)
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async def _one_query(q_meta: dict, embedding_str: str) -> list["SearchResult"]:
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async def _docs() -> list["SearchResult"]:
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async with Session() as s:
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return await _search_vector_docs(s, embedding_str, limit * 4)
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async def _chunks() -> list["SearchResult"]:
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async with Session() as s:
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return await _search_vector_chunks(s, embedding_str, limit * 4)
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doc_r, chunk_r = await asyncio.gather(_docs(), _chunks())
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return _merge_doc_and_chunk_vectors(doc_r, chunk_r)
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per_query_results = await asyncio.gather(
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*(_one_query(q, emb_str) for q, emb_str in per_query_plan)
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)
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# 3. weight 기반 merge — doc_id 중복 시 weighted score 합산
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merged: dict[int, "SearchResult"] = {}
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for (q_meta, _emb_str), results in zip(per_query_plan, per_query_results):
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weight = float(q_meta.get("weight", 1.0) or 1.0)
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for r in results:
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weighted = r.score * weight
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prev = merged.get(r.id)
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if prev is None:
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# 첫 방문: 원본을 shallow copy 대신 직접 wrap
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r.score = weighted
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r.match_reason = f"ml_{q_meta.get('lang', '?')}"
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merged[r.id] = r
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else:
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# 중복: score 누적, 가장 높은 weight 소스로 match_reason 표시
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prev.score += weighted
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# match_reason 병합 (가독성)
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if q_meta.get("lang") and q_meta.get("lang") not in (prev.match_reason or ""):
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prev.match_reason = (prev.match_reason or "ml") + f"+{q_meta['lang']}"
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sorted_results = sorted(merged.values(), key=lambda r: r.score, reverse=True)
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return sorted_results[: limit * 4] # rerank 후보로 넉넉히
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def compress_chunks_to_docs(
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chunks: list["SearchResult"], limit: int
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) -> tuple[list["SearchResult"], dict[int, list["SearchResult"]]]:
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"""chunk-level 결과를 doc-level로 압축하면서 raw chunks를 보존.
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fusion은 doc 기준이어야 하지만(같은 doc 중복 방지), Phase 1.3 reranker는
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chunk 기준 raw 데이터가 필요함. 따라서 압축본과 raw를 동시 반환.
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압축 규칙:
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- doc_id 별로 가장 score 높은 chunk만 doc_results에 추가
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- 같은 doc의 다른 chunks는 chunks_by_doc dict에 보존 (Phase 1.3 reranker용)
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- score 내림차순 정렬 후 limit개만 doc_results
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Returns:
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(doc_results, chunks_by_doc)
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- doc_results: list[SearchResult] — doc당 best chunk score, fusion 입력
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- chunks_by_doc: dict[doc_id, list[SearchResult]] — 모든 raw chunks 보존
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"""
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if not chunks:
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return [], {}
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chunks_by_doc: dict[int, list["SearchResult"]] = {}
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best_per_doc: dict[int, "SearchResult"] = {}
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for chunk in chunks:
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chunks_by_doc.setdefault(chunk.id, []).append(chunk)
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prev_best = best_per_doc.get(chunk.id)
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if prev_best is None or chunk.score > prev_best.score:
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best_per_doc[chunk.id] = chunk
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# doc 단위 best score 정렬, 상위 limit개
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doc_results = sorted(best_per_doc.values(), key=lambda r: r.score, reverse=True)
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return doc_results[:limit], chunks_by_doc
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