diff --git a/src/backend/base/langflow/components/embeddings/google_generative_ai.py b/src/backend/base/langflow/components/embeddings/google_generative_ai.py
index 94a3c5a43..2284d4055 100644
--- a/src/backend/base/langflow/components/embeddings/google_generative_ai.py
+++ b/src/backend/base/langflow/components/embeddings/google_generative_ai.py
@@ -9,6 +9,13 @@ from langchain_google_genai._common import GoogleGenerativeAIError
 from langflow.custom import Component
 from langflow.io import MessageTextInput, Output, SecretStrInput
 
+MIN_DIMENSION_ERROR = "Output dimensionality must be at least 1"
+MAX_DIMENSION_ERROR = (
+    "Output dimensionality cannot exceed 768. Google's embedding models only support dimensions up to 768."
+)
+MAX_DIMENSION = 768
+MIN_DIMENSION = 1
+
 
 class GoogleGenerativeAIEmbeddingsComponent(Component):
     display_name = "Google Generative AI Embeddings"
@@ -62,6 +69,12 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
                 Returns:
                     List of embeddings, one for each text.
                 """
+                if output_dimensionality is not None and output_dimensionality < MIN_DIMENSION:
+                    raise ValueError(MIN_DIMENSION_ERROR)
+                if output_dimensionality is not None and output_dimensionality > MAX_DIMENSION:
+                    error_msg = MAX_DIMENSION_ERROR.format(output_dimensionality)
+                    raise ValueError(error_msg)
+
                 embeddings: list[list[float]] = []
                 batch_start_index = 0
                 for batch in GoogleGenerativeAIEmbeddings._prepare_batches(texts, batch_size):
@@ -111,6 +124,12 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
                 Returns:
                     Embedding for the text.
                 """
+                if output_dimensionality is not None and output_dimensionality < MIN_DIMENSION:
+                    raise ValueError(MIN_DIMENSION_ERROR)
+                if output_dimensionality is not None and output_dimensionality > MAX_DIMENSION:
+                    error_msg = MAX_DIMENSION_ERROR.format(output_dimensionality)
+                    raise ValueError(error_msg)
+
                 task_type = task_type or "RETRIEVAL_QUERY"
                 return self.embed_documents(
                     [text],