diff --git a/astrbot/core/db/vec_db/faiss_impl/embedding_storage.py b/astrbot/core/db/vec_db/faiss_impl/embedding_storage.py
index 581032aa7..2c0cc8dfe 100644
--- a/astrbot/core/db/vec_db/faiss_impl/embedding_storage.py
+++ b/astrbot/core/db/vec_db/faiss_impl/embedding_storage.py
@@ -51,6 +51,7 @@ class EmbeddingStorage:
                 f"向量维度不匹配, 期望: {self.dimension}, 实际: {vectors.shape[1]}"
             )
         self.index.add_with_ids(vectors, np.array(ids))
+        await self.save_index()
 
     async def search(self, vector: np.ndarray, k: int) -> tuple:
         """搜索最相似的向量
diff --git a/astrbot/core/knowledge_base/retrieval/sparse_retriever.py b/astrbot/core/knowledge_base/retrieval/sparse_retriever.py
index 4275c3ef4..75ed8738b 100644
--- a/astrbot/core/knowledge_base/retrieval/sparse_retriever.py
+++ b/astrbot/core/knowledge_base/retrieval/sparse_retriever.py
@@ -73,7 +73,9 @@ class SparseRetriever:
             vec_db: FaissVecDB = kb_options.get(kb_id, {}).get("vec_db")
             if not vec_db:
                 continue
-            result = await vec_db.document_storage.get_documents(metadata_filters={})
+            result = await vec_db.document_storage.get_documents(
+                metadata_filters={}, limit=None, offset=None
+            )
             chunk_mds = [json.loads(doc["metadata"]) for doc in result]
             result = [
                 {