b80116243f
retrieval_service.search_vector를 documents.embedding → document_chunks.embedding로 전환. fetch_limit = limit*5로 raw chunks를 넓게 가져온 후 doc 기준 압축. 신규: compress_chunks_to_docs(chunks, limit) → (doc_results, chunks_by_doc) - doc_id 별 best score chunk만 doc_results (fusion 입력) - 모든 raw chunks는 chunks_by_doc dict에 보존 (Phase 1.3 reranker용) - '같은 doc 중복으로 RRF가 false boost' 방지 SearchResult: chunk_id / chunk_index / section_title optional 필드 추가. - text 검색 결과는 None (doc-level) - vector 검색 결과는 채워짐 (chunk-level) search.py 흐름: 1. raw_chunks = await search_vector(...) 2. vector_results, chunks_by_doc = compress_chunks_to_docs(raw_chunks, limit) 3. fusion(text_results, vector_results) — doc 기준 4. (Phase 1.3) chunks_by_doc → reranker — chunk 기준 debug notes: raw=N compressed=M unique_docs=K로 흐름 검증. 데이터 의존: 재인덱싱(reindex_all_chunks.py 진행 중) 완료 후 평가셋으로 검증.
204 lines
8.7 KiB
Python
204 lines
8.7 KiB
Python
"""검색 후보 수집 서비스 (Phase 1.2).
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text(documents FTS + trigram) + vector(documents.embedding → chunks) 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-B 이후: vector retrieval을 document_chunks 테이블 기반으로 전환.
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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from ai.client import AIClient
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if TYPE_CHECKING:
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from api.search import SearchResult
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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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"""벡터 유사도 검색 — chunk-level (Phase 1.2-C).
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document_chunks 테이블에서 cosine similarity로 raw chunks 반환.
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같은 doc에서 여러 chunks가 들어올 수 있음 (압축 안 함).
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fusion 직전에 compress_chunks_to_docs() helper로 doc 기준 압축 필요.
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Phase 1.3 reranker는 raw chunks를 그대로 활용.
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SearchResult.id = doc_id (fusion 호환)
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SearchResult.chunk_id / chunk_index / section_title = chunk 메타
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snippet = chunk의 text 앞 200자
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"""
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from api.search import SearchResult # 순환 import 회피
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try:
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client = AIClient()
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query_embedding = await client.embed(query)
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await client.close()
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except Exception:
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return []
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# raw chunks를 doc 메타와 join. limit * 5 정도 넓게 → 압축 후 doc 다양성.
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fetch_limit = limit * 5
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result = await session.execute(
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text("""
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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 - (c.embedding <=> cast(:embedding AS vector))) AS score,
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left(c.text, 200) AS snippet,
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'vector' AS match_reason,
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c.id AS chunk_id,
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c.chunk_index AS chunk_index,
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c.section_title AS section_title
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FROM document_chunks c
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JOIN documents d ON d.id = c.doc_id
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WHERE c.embedding IS NOT NULL AND d.deleted_at IS NULL
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ORDER BY c.embedding <=> cast(:embedding AS vector)
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LIMIT :limit
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"""),
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{"embedding": str(query_embedding), "limit": fetch_limit},
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)
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return [SearchResult(**row._mapping) for row in result]
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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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