fix(retrieval): 修复路径逃逸、部分提交、失效向量与分页问题

- 路径逃逸：清洗 folder（拒绝 ..、绝对路径/盘符），_abs_path 增加 Vault 边界校验
- 部分提交：create/update 索引失败时回滚文件
- 失效向量残留：replace_note_metadata 返回旧 id，index_note 清理旧向量
- 分页不完整：fts_count 返回真实命中总数，候选池覆盖 offset+limit

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/retrieval/engine.py

@@ -27,6 +27,8 @@ from app.textutils import make_snippet, match_query
 
 # 每个通道的候选池大小；真实规模上来后按 Retrieval Config 调整
 CANDIDATE_POOL = 50
+# 分页窗口上限：候选池至少覆盖 offset+limit，但设上限防止超大 offset 撑爆内存
+MAX_CANDIDATE_POOL = 200
 
 
 class RetrievalEngine:
@@ -41,23 +43,30 @@ class RetrievalEngine:
         self.vector_store = vector_store
 
     async def search(self, request: SearchRequest) -> SearchResponse:
+        # 候选池至少覆盖本次请求的 offset+limit，保证分页能取到目标页；设上限防内存失控
+        window = min(request.offset + request.limit, MAX_CANDIDATE_POOL)
+        pool_size = max(CANDIDATE_POOL, window)
+
         # 1. 按模式收集候选（FTS 与 Vector 各产出「按相关性降序」的 block_id 列表）
         fts_ranked: list[str] = []
         vec_ranked: list[str] = []
         fts_scores: dict[str, float] = {}
         vec_scores: dict[str, float] = {}
+        fts_total = 0
 
         if request.mode in (SearchMode.fts, SearchMode.hybrid):
             match = match_query(request.query)
             if match:
-                fts_hits = repository.fts_search(match, CANDIDATE_POOL)
+                fts_hits = repository.fts_search(match, pool_size)
                 fts_ranked = [h.block_id for h in fts_hits]
                 # bm25 越小越相关，取反后统一为「越大越相关」
                 fts_scores = {h.block_id: -h.bm25 for h in fts_hits}
+                if request.mode == SearchMode.fts:
+                    fts_total = repository.fts_count(match)
 
         if request.mode in (SearchMode.vector, SearchMode.hybrid):
             query_vec = await self.embedding.embed_query(request.query)
-            vec_hits = await self.vector_store.search(query_vec, top_k=CANDIDATE_POOL)
+            vec_hits = await self.vector_store.search(query_vec, top_k=pool_size)
             vec_ranked = [v.id for v in vec_hits]
             vec_scores = {v.id: v.score for v in vec_hits}
 
@@ -95,8 +104,11 @@ class RetrievalEngine:
 
         ordered = normalize_scores(ordered)
 
-        # 5. 分页
-        total = len(ordered)
+        # 5. 分页：fts 用真实命中总数；vector/hybrid 为 KNN 候选集，无全局 total
+        if request.mode == SearchMode.fts:
+            total = fts_total
+        else:
+            total = len(ordered)
         page = ordered[request.offset : request.offset + request.limit]
         items = [self._build_result(hits[block_id], request, score) for block_id, score in page]
         return SearchResponse(