diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
index 1a38b9f1a8bd13e257151d89929a31741e470ba4..b88aeed10c2c1c241dec6672eb31012f6138023b 100644
--- a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
+++ b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
@@ -27,6 +27,7 @@ class NomicEmbedding(BaseEmbedding):
     # Instance variables initialized via Pydantic's mechanism
     query_task_type: Optional[str] = Field(description="Query Embedding prefix")
     document_task_type: Optional[str] = Field(description="Document Embedding prefix")
+    dimensionality: Optional[int] = Field(description="Dimension of the Embedding")
     model_name: str = Field(description="Embedding model name")
     _model: Any = PrivateAttr()
 
@@ -38,6 +39,7 @@ class NomicEmbedding(BaseEmbedding):
         callback_manager: Optional[CallbackManager] = None,
         query_task_type: Optional[str] = "search_query",
         document_task_type: Optional[str] = "search_document",
+        dimensionality: Optional[int] = 768,
         **kwargs: Any,
     ) -> None:
         if query_task_type not in TASK_TYPES or document_task_type not in TASK_TYPES:
@@ -63,12 +65,14 @@ class NomicEmbedding(BaseEmbedding):
             _model=embed,
             query_task_type=query_task_type,
             document_task_type=document_task_type,
+            dimensionality=dimensionality,
             **kwargs,
         )
         self._model = embed
         self.model_name = model_name
         self.query_task_type = query_task_type
         self.document_task_type = document_task_type
+        self.dimensionality = dimensionality
 
     @classmethod
     def class_name(cls) -> str:
@@ -78,7 +82,12 @@ class NomicEmbedding(BaseEmbedding):
         self, texts: List[str], task_type: Optional[str] = None
     ) -> List[List[float]]:
         """Embed sentences using NomicAI."""
-        result = self._model.text(texts, model=self.model_name, task_type=task_type)
+        result = self._model.text(
+            texts,
+            model=self.model_name,
+            task_type=task_type,
+            dimensionality=self.dimensionality,
+        )
         return result["embeddings"]
 
     def _get_query_embedding(self, query: str) -> List[float]: