# UNDF: UNDF-2026-000001116
--- a/libs/langchain/langchain_classic/retrievers/multi_vector.py
+++ b/libs/langchain/langchain_classic/retrievers/multi_vector.py
@@ -105,9 +105,10 @@ class MultiVectorRetriever(BaseRetriever):
             sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)

         # We do this to maintain the order of the IDs that are returned
-        ids = []
+        seen_ids: set = set()
+        ids = []
         for d in sub_docs:
-            if self.id_key in d.metadata and d.metadata[self.id_key] not in ids:
+            if self.id_key in d.metadata and d.metadata[self.id_key] not in seen_ids:
+                seen_ids.add(d.metadata[self.id_key])
                 ids.append(d.metadata[self.id_key])
         docs = self.docstore.mget(ids)
         return [d for d in docs if d is not None]
@@ -147,9 +148,10 @@ class MultiVectorRetriever(BaseRetriever):
             sub_docs = await self.vectorstore.asimilarity_search(
                 query, **self.search_kwargs
             )

         # We do this to maintain the order of the IDs that are returned
-        ids = []
+        seen_ids_async: set = set()
+        ids = []
         for d in sub_docs:
-            if self.id_key in d.metadata and d.metadata[self.id_key] not in ids:
+            if self.id_key in d.metadata and d.metadata[self.id_key] not in seen_ids_async:
+                seen_ids_async.add(d.metadata[self.id_key])
                 ids.append(d.metadata[self.id_key])
         docs = await self.docstore.amget(ids)
         return [d for d in docs if d is not None]