diff --git a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
index cefbb6b..83d9394 100644
--- a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
+++ b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
@@ -219,7 +219,6 @@ def __init__(
             text_key: MongoDB field that will contain the text for each document
             index_name: Existing Atlas Vector Search Index
             embedding_key: Field that will contain the embedding for each document
-            vector_index_name: Name of the Atlas Vector Search index
             relevance_score_fn: The similarity score used for the index
                 Currently supported: 'euclidean', 'cosine', and 'dotProduct'
             dimensions: Number of dimensions in embedding.  If the value is set and
diff --git a/libs/langchain-mongodb/tests/unit_tests/test_vectorstores.py b/libs/langchain-mongodb/tests/unit_tests/test_vectorstores.py
index b1962e7..de68463 100644
--- a/libs/langchain-mongodb/tests/unit_tests/test_vectorstores.py
+++ b/libs/langchain-mongodb/tests/unit_tests/test_vectorstores.py
@@ -96,7 +96,7 @@ def test_from_documents(
             documents,
             embedding_openai,
             collection=collection,
-            vector_index_name=INDEX_NAME,
+            index_name=INDEX_NAME,
         )
         self._validate_search(
             vectorstore, collection, metadata=documents[2].metadata["c"]
@@ -117,7 +117,7 @@ def test_from_texts(
             texts,
             embedding_openai,
             collection=collection,
-            vector_index_name=INDEX_NAME,
+            index_name=INDEX_NAME,
         )
         self._validate_search(vectorstore, collection, metadata=None)
 
@@ -136,7 +136,7 @@ def test_from_texts_with_metadatas(
             embedding_openai,
             metadatas=metadatas,
             collection=collection,
-            vector_index_name=INDEX_NAME,
+            index_name=INDEX_NAME,
         )
         self._validate_search(vectorstore, collection, metadata=metadatas[2]["c"])
 
@@ -155,7 +155,7 @@ def test_from_texts_with_metadatas_and_pre_filter(
             embedding_openai,
             metadatas=metadatas,
             collection=collection,
-            vector_index_name=INDEX_NAME,
+            index_name=INDEX_NAME,
         )
         collection._aggregate_result = list(
             filter(
@@ -177,7 +177,7 @@ def test_mmr(
             texts,
             embedding=embedding_openai,
             collection=collection,
-            vector_index_name=INDEX_NAME,
+            index_name=INDEX_NAME,
         )
         query = "foo"
         self._validate_search(