perf: batch metadata query in KB retrieval to fix N+1 problem (#5463)

* perf: batch metadata query in KB retrieval to fix N+1 problem

Replace N sequential get_document_with_metadata() calls with a single
get_documents_with_metadata_batch() call using SQL IN clause.

Benchmark results (local SQLite):
- 10 docs: 10.67ms → 1.47ms (7.3x faster)
- 20 docs: 26.00ms → 2.68ms (9.7x faster)
- 50 docs: 63.87ms → 2.79ms (22.9x faster)

* refactor: use set[str] param type and chunk IN clause for SQLite safety

Address review feedback:
- Change doc_ids param from list[str] to set[str] to avoid unnecessary conversion
- Chunk IN clause into batches of 900 to stay under SQLite's 999 parameter limit
- Remove list() wrapping at call site, pass set directly

astrbot/core/knowledge_base/kb_db_sqlite.py

@@ -256,6 +256,46 @@ class KBSQLiteDatabase:
                 "knowledge_base": row[1],
             }
 
+    async def get_documents_with_metadata_batch(
+        self, doc_ids: set[str]
+    ) -> dict[str, dict]:
+        """批量获取文档及其所属知识库元数据
+
+        Args:
+            doc_ids: 文档 ID 集合
+
+        Returns:
+            dict: doc_id -> {"document": KBDocument, "knowledge_base": KnowledgeBase}
+
+        """
+        if not doc_ids:
+            return {}
+
+        metadata_map: dict[str, dict] = {}
+        # SQLite 参数上限为 999，分片查询避免超限
+        chunk_size = 900
+        doc_id_list = list(doc_ids)
+
+        async with self.get_db() as session:
+            for i in range(0, len(doc_id_list), chunk_size):
+                chunk = doc_id_list[i : i + chunk_size]
+                stmt = (
+                    select(KBDocument, KnowledgeBase)
+                    .join(
+                        KnowledgeBase,
+                        col(KBDocument.kb_id) == col(KnowledgeBase.kb_id),
+                    )
+                    .where(col(KBDocument.doc_id).in_(chunk))
+                )
+                result = await session.execute(stmt)
+                for row in result.all():
+                    metadata_map[row[0].doc_id] = {
+                        "document": row[0],
+                        "knowledge_base": row[1],
+                    }
+
+        return metadata_map
+
     async def delete_document_by_id(self, doc_id: str, vec_db: FaissVecDB) -> None:
         """删除单个文档及其相关数据"""
         # 在知识库表中删除

astrbot/core/knowledge_base/retrieval/manager.py

@@ -142,10 +142,13 @@ class RetrievalManager:
             f"Rank fusion took {time_end - time_start:.2f}s and returned {len(fused_results)} results.",
         )
 
-        # 4. 转换为 RetrievalResult (获取元数据)
+        # 4. 转换为 RetrievalResult (批量获取元数据)
+        doc_ids = {fr.doc_id for fr in fused_results}
+        metadata_map = await self.kb_db.get_documents_with_metadata_batch(doc_ids)
+
         retrieval_results = []
         for fr in fused_results:
-            metadata_dict = await self.kb_db.get_document_with_metadata(fr.doc_id)
+            metadata_dict = metadata_map.get(fr.doc_id)
             if metadata_dict:
                 retrieval_results.append(
                     RetrievalResult(