diff --git a/langchain/vectorstores/pgvector.py b/langchain/vectorstores/pgvector.py
index b29e5b3d4fc..81951f490a0 100644
--- a/langchain/vectorstores/pgvector.py
+++ b/langchain/vectorstores/pgvector.py
@@ -296,8 +296,18 @@ class PGVector(VectorStore):
         if filter is not None:
             filter_clauses = []
             for key, value in filter.items():
-                filter_by_metadata = EmbeddingStore.cmetadata[key].astext == str(value)
-                filter_clauses.append(filter_by_metadata)
+                IN = "in"
+                if isinstance(value, dict) and IN in map(str.lower, value):
+                    value_case_insensitive = {k.lower(): v for k, v in value.items()}
+                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext.in_(
+                        value_case_insensitive[IN]
+                    )
+                    filter_clauses.append(filter_by_metadata)
+                else:
+                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext == str(
+                        value
+                    )
+                    filter_clauses.append(filter_by_metadata)
 
             filter_by = sqlalchemy.and_(filter_by, *filter_clauses)
 
diff --git a/tests/integration_tests/vectorstores/test_pgvector.py b/tests/integration_tests/vectorstores/test_pgvector.py
index 3560bb591ef..8ad7f1bc147 100644
--- a/tests/integration_tests/vectorstores/test_pgvector.py
+++ b/tests/integration_tests/vectorstores/test_pgvector.py
@@ -147,3 +147,24 @@ def test_pgvector_collection_with_metadata() -> None:
     else:
         assert collection.name == "test_collection"
         assert collection.cmetadata == {"foo": "bar"}
+
+
+def test_pgvector_with_filter_in_set() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score(
+        "foo", k=2, filter={"page": {"IN": ["0", "2"]}}
+    )
+    assert output == [
+        (Document(page_content="foo", metadata={"page": "0"}), 0.0),
+        (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
+    ]