diff --git a/libs/langchain/langchain/embeddings/embaas.py b/libs/langchain/langchain/embeddings/embaas.py
index df7696467..4d8e682ef 100644
--- a/libs/langchain/langchain/embeddings/embaas.py
+++ b/libs/langchain/langchain/embeddings/embaas.py
@@ -1,10 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+from requests.adapters import HTTPAdapter, Retry
 from typing_extensions import NotRequired, TypedDict
 
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain.schema.embeddings import Embeddings
 from langchain.utils import get_from_dict_or_env
 
 # Currently supported maximum batch size for embedding requests
@@ -51,6 +52,10 @@ class EmbaasEmbeddings(BaseModel, Embeddings):
     api_url: str = EMBAAS_API_URL
     """The URL for the embaas embeddings API."""
     embaas_api_key: Optional[str] = None
+    """max number of retries for requests"""
+    max_retries: Optional[int] = 3
+    """request timeout in seconds"""
+    timeout: Optional[int] = 30
 
     class Config:
         """Configuration for this pydantic object."""
@@ -85,8 +90,22 @@ class EmbaasEmbeddings(BaseModel, Embeddings):
             "Content-Type": "application/json",
         }
 
-        response = requests.post(self.api_url, headers=headers, json=payload)
-        response.raise_for_status()
+        session = requests.Session()
+        retries = Retry(
+            total=self.max_retries,
+            backoff_factor=0.5,
+            allowed_methods=["POST"],
+            raise_on_status=True,
+        )
+
+        session.mount("http://", HTTPAdapter(max_retries=retries))
+        session.mount("https://", HTTPAdapter(max_retries=retries))
+        response = session.post(
+            self.api_url,
+            headers=headers,
+            json=payload,
+            timeout=self.timeout,
+        )
 
         parsed_response = response.json()
         embeddings = [item["embedding"] for item in parsed_response["data"]]