diff --git a/generative_ai/embedding.py b/generative_ai/embedding.py
index 4a004d2c7166..133275b24d9c 100644
--- a/generative_ai/embedding.py
+++ b/generative_ai/embedding.py
@@ -21,13 +21,15 @@
 def embed_text(
     texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
     task: str = "RETRIEVAL_DOCUMENT",
-    model_name: str = "textembedding-gecko@003"
+    model_name: str = "textembedding-gecko@003",
 ) -> List[List[float]]:
     """Embeds texts with a pre-trained, foundational model."""
     model = TextEmbeddingModel.from_pretrained(model_name)
     inputs = [TextEmbeddingInput(text, task) for text in texts]
     embeddings = model.get_embeddings(inputs)
     return [embedding.values for embedding in embeddings]
+
+
 # [END aiplatform_sdk_embedding]
 
 
diff --git a/generative_ai/embedding_model_tuning.py b/generative_ai/embedding_model_tuning.py
index 21f357cffed1..55f58d179437 100644
--- a/generative_ai/embedding_model_tuning.py
+++ b/generative_ai/embedding_model_tuning.py
@@ -32,7 +32,7 @@ def tune_embedding_model(
     train_label_path: str = "gs://embedding-customization-pipeline/dataset/train.tsv",
     test_label_path: str = "gs://embedding-customization-pipeline/dataset/test.tsv",
     batch_size: int = 50,
-    iterations: int = 300
+    iterations: int = 300,
 ) -> pipeline_jobs.PipelineJob:
     match = re.search(r"(.+)(-autopush|-staging)?-aiplatform.+", api_endpoint)
     location = match.group(1) if match else "us-central1"
@@ -50,7 +50,8 @@ def tune_embedding_model(
             train_label_path=train_label_path,
             test_label_path=test_label_path,
             batch_size=batch_size,
-            iterations=iterations)
+            iterations=iterations,
+        ),
     )
     job.submit()
     return job
@@ -58,6 +59,8 @@ def tune_embedding_model(
 
 # [END aiplatform_sdk_embedding]
 if __name__ == "__main__":
-    tune_embedding_model(aiplatform_init.global_config.api_endpoint,
-                         aiplatform_init.global_config.project,
-                         aiplatform_init.global_config.staging_bucket)
+    tune_embedding_model(
+        aiplatform_init.global_config.api_endpoint,
+        aiplatform_init.global_config.project,
+        aiplatform_init.global_config.staging_bucket,
+    )
diff --git a/generative_ai/embedding_model_tuning_test.py b/generative_ai/embedding_model_tuning_test.py
index 09eeea756471..7ddb6154fd45 100644
--- a/generative_ai/embedding_model_tuning_test.py
+++ b/generative_ai/embedding_model_tuning_test.py
@@ -35,16 +35,19 @@ def dispose(job: pipeline_jobs.PipelineJob) -> None:
 
 def test_tune_embedding_model() -> None:
     credentials, _ = google.auth.default(  # Set explicit credentials with Oauth scopes.
-        scopes=["https://www.googleapis.com/auth/cloud-platform"])
+        scopes=["https://www.googleapis.com/auth/cloud-platform"]
+    )
     aiplatform.init(
         api_endpoint="us-central1-aiplatform.googleapis.com:443",
         project=os.getenv("GOOGLE_CLOUD_PROJECT"),
         staging_bucket="gs://ucaip-samples-us-central1/training_pipeline_output",
-        credentials=credentials)
+        credentials=credentials,
+    )
     job = embedding_model_tuning.tune_embedding_model(
         aiplatform_init.global_config.api_endpoint,
         aiplatform_init.global_config.project,
-        aiplatform_init.global_config.staging_bucket)
+        aiplatform_init.global_config.staging_bucket,
+    )
     try:
         assert job.state != "PIPELINE_STATE_FAILED"
     finally:
diff --git a/generative_ai/embedding_preview.py b/generative_ai/embedding_preview.py
index d66af5001188..5ea285fb7a6b 100644
--- a/generative_ai/embedding_preview.py
+++ b/generative_ai/embedding_preview.py
@@ -22,7 +22,7 @@ def embed_text(
     texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
     task: str = "QUESTION_ANSWERING",
     model_name: str = "text-embedding-preview-0409",
-    dimensionality: Optional[int] = 256
+    dimensionality: Optional[int] = 256,
 ) -> List[List[float]]:
     """Embeds texts with a pre-trained, foundational model."""
     model = TextEmbeddingModel.from_pretrained(model_name)
@@ -30,6 +30,8 @@ def embed_text(
     kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
     embeddings = model.get_embeddings(inputs, **kwargs)
     return [embedding.values for embedding in embeddings]
+
+
 # [END generativeaionvertexai_sdk_embedding]
 
 
diff --git a/generative_ai/requirements.txt b/generative_ai/requirements.txt
index b143b8465fb6..29c26ed4fb87 100644
--- a/generative_ai/requirements.txt
+++ b/generative_ai/requirements.txt
@@ -1,7 +1,7 @@
 pandas==1.3.5; python_version == '3.7'
 pandas==2.0.1; python_version > '3.7'
 pillow==9.5.0; python_version < '3.8'
-pillow==10.0.1; python_version >= '3.8'
+pillow==10.3.0; python_version >= '3.8'
 google-cloud-aiplatform[pipelines]==1.46.0
 google-auth==2.17.3
 anthropic[vertex]==0.21.3