diff --git a/docetl/operations/clustering_utils.py b/docetl/operations/clustering_utils.py
index 05fed867..7663b892 100644
--- a/docetl/operations/clustering_utils.py
+++ b/docetl/operations/clustering_utils.py
@@ -60,8 +60,8 @@ def get_embeddings_for_clustering_with_st(
     return embeddings, 0
 
 
-def cluster_items(
-    items: List[Dict],
+def cluster_documents(
+    documents: List[Dict],
     sampling_config: Dict,
     sample_size: int,
     api_wrapper: APIWrapper,
@@ -70,7 +70,7 @@ def cluster_items(
     Cluster documents using KMeans clustering algorithm.
 
     Args:
-        items (List[Dict]): The list of documents to cluster.
+        documents (List[Dict]): The list of documents to cluster.
         sampling_config (Dict): The sampling configuration. Must contain embedding_model. If embedding_keys is not specified, it will use all keys in the document. If embedding_model is not specified, it will use text-embedding-3-small. If embedding_model is sentence-transformer, it will use all-MiniLM-L6-v2.
         sample_size (int): The number of clusters to create.
         api_wrapper (APIWrapper): The API wrapper to use for embedding.
@@ -78,17 +78,17 @@ def cluster_items(
         Dict[int, List[Dict]]: A dictionary of clusters, where each cluster is a list of documents.
     """
     embeddings, cost = get_embeddings_for_clustering(
-        items, sampling_config, api_wrapper
+        documents, sampling_config, api_wrapper
     )
 
     from sklearn.cluster import KMeans
 
-    num_clusters = min(sample_size, len(items))
+    num_clusters = min(sample_size, len(documents))
     kmeans = KMeans(n_clusters=num_clusters, random_state=42)
     cluster_labels = kmeans.fit_predict(embeddings)
 
     clusters = {i: [] for i in range(num_clusters)}
     for idx, label in enumerate(cluster_labels):
-        clusters[label].append(items[idx])
+        clusters[label].append(documents[idx])
 
     return clusters, cost
diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py
index 5d7f5012..cd3cee78 100644
--- a/docetl/operations/reduce.py
+++ b/docetl/operations/reduce.py
@@ -20,7 +20,7 @@
 
 from docetl.operations.base import BaseOperation
 from docetl.operations.clustering_utils import (
-    cluster_items,
+    cluster_documents,
     get_embeddings_for_clustering,
 )
 from docetl.operations.utils import rich_as_completed
@@ -428,7 +428,7 @@ def _cluster_based_sampling(
         if sample_size >= len(group_list):
             return group_list, 0
 
-        clusters, cost = cluster_items(
+        clusters, cost = cluster_documents(
             group_list, value_sampling, sample_size, self.runner.api
         )