Hybrid RAG: BM25+Dense (sqlite-vec/BGE-M3) + cross-encoder reranker (bge-reranker-v2-m3)

src/kpaa/cli.py

@@ -106,6 +106,31 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     p_build_guides.add_argument("--rebuild", action="store_true", default=True)
 
+    p_build_embed = sub.add_parser(
+        "build-embeddings",
+        help="data/guide/chunks/*.jsonl + cases.sqlite → embeddings.sqlite (sqlite-vec, BGE-M3)",
+    )
+    p_build_embed.add_argument(
+        "--source", choices=["guide", "case", "all"], default="all",
+        help="인덱싱 대상 (기본: all)",
+    )
+    p_build_embed.add_argument(
+        "--force", action="store_true",
+        help="기존 embeddings.sqlite 삭제 후 재빌드",
+    )
+    p_build_embed.add_argument(
+        "--batch", type=int, default=32,
+        help="임베딩 batch size (기본 32)",
+    )
+
+    p_embed_search = sub.add_parser(
+        "embed-search",
+        help="dense 검색만 단독 테스트 (BM25/RRF/reranker 미적용)",
+    )
+    p_embed_search.add_argument("query", help="검색 질의")
+    p_embed_search.add_argument("--source", choices=["guide", "case"], default="guide")
+    p_embed_search.add_argument("-k", type=int, default=10)
+
     sub.add_parser(
         "refresh-related-laws",
         help="privacy.go.kr 개인정보 관련 법령·행정규칙 목록 재스크래이프 (data/related_laws.yaml)",
@@ -407,6 +432,49 @@ def main(argv: list[str] | None = None) -> int:
         build()
         return 0
 
+    if args.cmd == "build-embeddings":
+        import logging as _logging
+        _logging.basicConfig(level=_logging.INFO, format="%(message)s")
+        from kpaa.embeddings.index import build_embed_index, stats
+
+        n = build_embed_index(source=args.source, force=args.force, batch=args.batch)
+        s = stats()
+        print(f"새로 인덱싱: {n}건 / 합계: {s.get('by_source', {})}", file=sys.stderr)
+        return 0
+
+    if args.cmd == "embed-search":
+        from kpaa.embeddings.index import search_embed
+        from kpaa.guides.index import list_all_chunks
+
+        # guide chunks 로 chunk_id → section/body 미리 로드 (case 는 cases.sqlite 직접)
+        hits = search_embed(args.query, source_type=args.source, k=args.k)
+        if args.source == "guide":
+            chunks_by_id = {c.chunk_id: c for c in list_all_chunks()}
+            for h in hits:
+                c = chunks_by_id.get(h.chunk_id)
+                if c is None:
+                    print(f"  {h.distance:.4f}  {h.chunk_id} (chunks 에 없음)", file=sys.stderr)
+                    continue
+                print(
+                    f"  {h.distance:.4f}  [{c.section[:60]}] {c.body[:80]}...",
+                    file=sys.stderr,
+                )
+        else:
+            import sqlite3
+            from kpaa.cases.index import default_db_path as case_db_path
+            con = sqlite3.connect(case_db_path()); con.row_factory = sqlite3.Row
+            for h in hits:
+                ntt_id = h.chunk_id.removeprefix("case_")
+                row = con.execute("SELECT title, body, summary FROM cases WHERE ntt_id=?", (ntt_id,)).fetchone()
+                if row is None:
+                    print(f"  {h.distance:.4f}  {h.chunk_id} (cases 에 없음)", file=sys.stderr)
+                    continue
+                title = row["title"] or ""
+                body = (row["body"] or row["summary"] or "")[:80]
+                print(f"  {h.distance:.4f}  [{title[:50]}] {body}...", file=sys.stderr)
+            con.close()
+        return 0
+
     if args.cmd == "refresh-related-laws":
         from kpaa.related_laws import refresh as refresh_rl