diff --git a/libs/community/langchain_community/embeddings/voyageai.py b/libs/community/langchain_community/embeddings/voyageai.py
index 93109d45c65b6..f8b1a4059e6d5 100644
--- a/libs/community/langchain_community/embeddings/voyageai.py
+++ b/libs/community/langchain_community/embeddings/voyageai.py
@@ -86,6 +86,15 @@ class VoyageEmbeddings(BaseModel, Embeddings):
     show_progress_bar: bool = False
     """Whether to show a progress bar when embedding. Must have tqdm installed if set 
         to True."""
+    truncation: Optional[bool] = None
+    """Whether to truncate the input texts to fit within the context length.
+    
+        If True, over-length input texts will be truncated to fit within the context 
+        length, before vectorized by the embedding model. If False, an error will be 
+        raised if any given text exceeds the context length. If not specified 
+        (defaults to None), we will truncate the input text before sending it to the 
+        embedding model if it slightly exceeds the context window length. If it 
+        significantly exceeds the context window length, an error will be raised."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -104,12 +113,14 @@ def _invocation_params(
         self, input: List[str], input_type: Optional[str] = None
     ) -> Dict:
         api_key = cast(SecretStr, self.voyage_api_key).get_secret_value()
-        params = {
+        params: Dict = {
             "url": self.voyage_api_base,
             "headers": {"Authorization": f"Bearer {api_key}"},
             "json": {"model": self.model, "input": input, "input_type": input_type},
             "timeout": self.request_timeout,
         }
+        if self.truncation is not None:
+            params["json"]["truncation"] = self.truncation
         return params
 
     def _get_embeddings(