diff --git a/libs/community/langchain_community/embeddings/deepinfra.py b/libs/community/langchain_community/embeddings/deepinfra.py
index 046e1e481f..4dbc0e4dad 100644
--- a/libs/community/langchain_community/embeddings/deepinfra.py
+++ b/libs/community/langchain_community/embeddings/deepinfra.py
@@ -6,6 +6,7 @@ from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain_core.utils import get_from_dict_or_env
 
 DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
+MAX_BATCH_SIZE = 1024
 
 
 class DeepInfraEmbeddings(BaseModel, Embeddings):
@@ -47,8 +48,11 @@ class DeepInfraEmbeddings(BaseModel, Embeddings):
     """Instruction used to embed the query."""
     model_kwargs: Optional[dict] = None
     """Other model keyword args"""
-
     deepinfra_api_token: Optional[str] = None
+    """API token for Deep Infra. If not provided, the token is 
+    fetched from the environment variable 'DEEPINFRA_API_TOKEN'."""
+    batch_size: int = MAX_BATCH_SIZE
+    """Batch size for embedding requests."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -103,6 +107,8 @@ class DeepInfraEmbeddings(BaseModel, Embeddings):
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Embed documents using a Deep Infra deployed embedding model.
+        For larger batches, the input list of texts is chunked into smaller
+        batches to avoid exceeding the maximum request size.
 
         Args:
             texts: The list of texts to embed.
@@ -110,8 +116,17 @@ class DeepInfraEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
+
+        embeddings = []
         instruction_pairs = [f"{self.embed_instruction}{text}" for text in texts]
-        embeddings = self._embed(instruction_pairs)
+
+        chunks = [
+            instruction_pairs[i : i + self.batch_size]
+            for i in range(0, len(instruction_pairs), self.batch_size)
+        ]
+        for chunk in chunks:
+            embeddings += self._embed(chunk)
+
         return embeddings
 
     def embed_query(self, text: str) -> List[float]:
diff --git a/libs/community/tests/integration_tests/embeddings/test_deepinfra.py b/libs/community/tests/integration_tests/embeddings/test_deepinfra.py
index f3a418ed23..8a72a5edcb 100644
--- a/libs/community/tests/integration_tests/embeddings/test_deepinfra.py
+++ b/libs/community/tests/integration_tests/embeddings/test_deepinfra.py
@@ -17,3 +17,13 @@ def test_deepinfra_call() -> None:
     assert len(r1[1]) == 768
     r2 = deepinfra_emb.embed_query("What is the third letter of Greek alphabet")
     assert len(r2) == 768
+
+
+def test_deepinfra_call_with_large_batch_size() -> None:
+    deepinfra_emb = DeepInfraEmbeddings(model_id="BAAI/bge-base-en-v1.5")
+    texts = 2000 * [
+        "Alpha is the first letter of Greek alphabet",
+    ]
+    r1 = deepinfra_emb.embed_documents(texts)
+    assert len(r1) == 2000
+    assert len(r1[0]) == 768