diff --git a/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py b/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
index b2fbfced0a..643ac2df4e 100644
--- a/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
+++ b/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
@@ -4,7 +4,8 @@ import logging
 import time
 from typing import Any, Optional
 
-from opensearchpy import OpenSearch
+from opensearchpy import OpenSearch, helpers
+from opensearchpy.helpers import BulkIndexError
 from pydantic import BaseModel, model_validator
 from tenacity import retry, stop_after_attempt, wait_exponential
 
@@ -135,14 +136,14 @@ class LindormVectorStore(BaseVector):
                 actions.append(action_header)
                 actions.append(action_values)
 
-            logger.info(f"Processing batch {batch_num + 1}/{num_batches} (documents {start_idx + 1} to {end_idx})")
+            # logger.info(f"Processing batch {batch_num + 1}/{num_batches} (documents {start_idx + 1} to {end_idx})")
 
             try:
                 _bulk_with_retry(actions)
-                logger.info(f"Successfully processed batch {batch_num + 1}")
+                # logger.info(f"Successfully processed batch {batch_num + 1}")
                 # simple latency to avoid too many requests in a short time
                 if batch_num < num_batches - 1:
-                    time.sleep(1)
+                    time.sleep(0.5)
 
             except Exception:
                 logger.exception(f"Failed to process batch {batch_num + 1}")
@@ -166,18 +167,51 @@ class LindormVectorStore(BaseVector):
             self.delete_by_ids(ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
-        params = {}
-        if self._using_ugc:
-            params["routing"] = self._routing
+        """Delete documents by their IDs in batch.
+
+        Args:
+            ids: List of document IDs to delete
+        """
+        if not ids:
+            return
+
+        params = {"routing": self._routing} if self._using_ugc else {}
+
+        # 1. First check if collection exists
+        if not self._client.indices.exists(index=self._collection_name):
+            logger.warning(f"Collection {self._collection_name} does not exist")
+            return
+
+        # 2. Batch process deletions
+        actions = []
         for id in ids:
             if self._client.exists(index=self._collection_name, id=id, params=params):
-                params = {}
-                if self._using_ugc:
-                    params["routing"] = self._routing
-                self._client.delete(index=self._collection_name, id=id, params=params)
+                actions.append(
+                    {
+                        "_op_type": "delete",
+                        "_index": self._collection_name,
+                        "_id": id,
+                        **params,  # Include routing if using UGC
+                    }
+                )
             else:
                 logger.warning(f"DELETE BY ID: ID {id} does not exist in the index.")
 
+        # 3. Perform bulk deletion if there are valid documents to delete
+        if actions:
+            try:
+                helpers.bulk(self._client, actions)
+            except BulkIndexError as e:
+                for error in e.errors:
+                    delete_error = error.get("delete", {})
+                    status = delete_error.get("status")
+                    doc_id = delete_error.get("_id")
+
+                    if status == 404:
+                        logger.warning(f"Document not found for deletion: {doc_id}")
+                    else:
+                        logger.exception(f"Error deleting document: {error}")
+
     def delete(self) -> None:
         if self._using_ugc:
             routing_filter_query = {
@@ -213,7 +247,7 @@ class LindormVectorStore(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filters = []
         if document_ids_filter:
-            filters.append({"terms": {"metadata.document_id": document_ids_filter}})
+            filters.append({"terms": {"metadata.document_id.keyword": document_ids_filter}})
         query = default_vector_search_query(query_vector=query_vector, k=top_k, filters=filters, **kwargs)
 
         try:
@@ -256,7 +290,7 @@ class LindormVectorStore(BaseVector):
         filters = kwargs.get("filter", [])
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            filters.append({"terms": {"metadata.document_id": document_ids_filter}})
+            filters.append({"terms": {"metadata.document_id.keyword": document_ids_filter}})
         routing = self._routing
         full_text_query = default_text_search_query(
             query_text=query,
@@ -270,6 +304,7 @@ class LindormVectorStore(BaseVector):
             routing=routing,
             routing_field=self._routing_field,
         )
+
         response = self._client.search(index=self._collection_name, body=full_text_query)
         docs = []
         for hit in response["hits"]["hits"]:
@@ -479,7 +514,7 @@ def default_vector_search_query(
     **kwargs,
 ) -> dict:
     if filters is not None:
-        filter_type = "post_filter" if filter_type is None else filter_type
+        filter_type = "pre_filter" if filter_type is None else filter_type
         if not isinstance(filters, list):
             raise RuntimeError(f"unexpected filter with {type(filters)}")
     final_ext: dict[str, Any] = {"lvector": {}}