diff --git a/libs/community/langchain_community/embeddings/oci_generative_ai.py b/libs/community/langchain_community/embeddings/oci_generative_ai.py
index b3e428fe2f..dcfa38f648 100644
--- a/libs/community/langchain_community/embeddings/oci_generative_ai.py
+++ b/libs/community/langchain_community/embeddings/oci_generative_ai.py
@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Dict, Iterator, List, Mapping, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
@@ -80,6 +80,10 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
     truncate: Optional[str] = "END"
     """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
 
+    batch_size: int = 96
+    """Batch size of OCI GenAI embedding requests. OCI GenAI may handle up to 96 texts
+     per request"""
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -182,16 +186,23 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
         else:
             serving_mode = models.OnDemandServingMode(model_id=self.model_id)
 
-        invocation_obj = models.EmbedTextDetails(
-            serving_mode=serving_mode,
-            compartment_id=self.compartment_id,
-            truncate=self.truncate,
-            inputs=texts,
-        )
+        embeddings = []
+
+        def split_texts() -> Iterator[List[str]]:
+            for i in range(0, len(texts), self.batch_size):
+                yield texts[i : i + self.batch_size]
 
-        response = self.client.embed_text(invocation_obj)
+        for chunk in split_texts():
+            invocation_obj = models.EmbedTextDetails(
+                serving_mode=serving_mode,
+                compartment_id=self.compartment_id,
+                truncate=self.truncate,
+                inputs=chunk,
+            )
+            response = self.client.embed_text(invocation_obj)
+            embeddings.extend(response.data.embeddings)
 
-        return response.data.embeddings
+        return embeddings
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OCIGenAI's embedding endpoint.