diff --git a/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py b/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
index 1ba37dfb3..b2fbfced0 100644
--- a/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
+++ b/api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
@@ -1,10 +1,12 @@
 import copy
 import json
 import logging
+import time
 from typing import Any, Optional
 
 from opensearchpy import OpenSearch
 from pydantic import BaseModel, model_validator
+from tenacity import retry, stop_after_attempt, wait_exponential
 
 from configs import dify_config
 from core.rag.datasource.vdb.field import Field
@@ -77,31 +79,74 @@ class LindormVectorStore(BaseVector):
     def refresh(self):
         self._client.indices.refresh(index=self._collection_name)
 
-    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
-        actions = []
+    def add_texts(
+        self,
+        documents: list[Document],
+        embeddings: list[list[float]],
+        batch_size: int = 64,
+        timeout: int = 60,
+        **kwargs,
+    ):
+        logger.info(f"Total documents to add: {len(documents)}")
         uuids = self._get_uuids(documents)
-        for i in range(len(documents)):
-            action_header = {
-                "index": {
-                    "_index": self.collection_name.lower(),
-                    "_id": uuids[i],
+
+        total_docs = len(documents)
+        num_batches = (total_docs + batch_size - 1) // batch_size
+
+        @retry(
+            stop=stop_after_attempt(3),
+            wait=wait_exponential(multiplier=1, min=4, max=10),
+        )
+        def _bulk_with_retry(actions):
+            try:
+                response = self._client.bulk(actions, timeout=timeout)
+                if response["errors"]:
+                    error_items = [item for item in response["items"] if "error" in item["index"]]
+                    error_msg = f"Bulk indexing had {len(error_items)} errors"
+                    logger.exception(error_msg)
+                    raise Exception(error_msg)
+                return response
+            except Exception:
+                logger.exception("Bulk indexing error")
+                raise
+
+        for batch_num in range(num_batches):
+            start_idx = batch_num * batch_size
+            end_idx = min((batch_num + 1) * batch_size, total_docs)
+
+            actions = []
+            for i in range(start_idx, end_idx):
+                action_header = {
+                    "index": {
+                        "_index": self.collection_name.lower(),
+                        "_id": uuids[i],
+                    }
                 }
-            }
-            action_values: dict[str, Any] = {
-                Field.CONTENT_KEY.value: documents[i].page_content,
-                Field.VECTOR.value: embeddings[i],  # Make sure you pass an array here
-                Field.METADATA_KEY.value: documents[i].metadata,
-            }
-            if self._using_ugc:
-                action_header["index"]["routing"] = self._routing
-                if self._routing_field is not None:
-                    action_values[self._routing_field] = self._routing
-            actions.append(action_header)
-            actions.append(action_values)
-        response = self._client.bulk(actions)
-        if response["errors"]:
-            for item in response["items"]:
-                print(f"{item['index']['status']}: {item['index']['error']['type']}")
+                action_values: dict[str, Any] = {
+                    Field.CONTENT_KEY.value: documents[i].page_content,
+                    Field.VECTOR.value: embeddings[i],
+                    Field.METADATA_KEY.value: documents[i].metadata,
+                }
+                if self._using_ugc:
+                    action_header["index"]["routing"] = self._routing
+                    if self._routing_field is not None:
+                        action_values[self._routing_field] = self._routing
+
+                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})")
+
+            try:
+                _bulk_with_retry(actions)
+                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)
+
+            except Exception:
+                logger.exception(f"Failed to process batch {batch_num + 1}")
+                raise
 
     def get_ids_by_metadata_field(self, key: str, value: str):
         query: dict[str, Any] = {
@@ -130,7 +175,6 @@ class LindormVectorStore(BaseVector):
                 if self._using_ugc:
                     params["routing"] = self._routing
                 self._client.delete(index=self._collection_name, id=id, params=params)
-                self.refresh()
             else:
                 logger.warning(f"DELETE BY ID: ID {id} does not exist in the index.")