diff --git a/libs/langchain/langchain/vectorstores/pgvector.py b/libs/langchain/langchain/vectorstores/pgvector.py index e1a58e81af..c10686bf2b 100644 --- a/libs/langchain/langchain/vectorstores/pgvector.py +++ b/libs/langchain/langchain/vectorstores/pgvector.py @@ -434,16 +434,24 @@ class PGVector(VectorStore): if filter is not None: filter_clauses = [] + IN, NIN = "in", "nin" for key, value in filter.items(): - IN = "in" - if isinstance(value, dict) and IN in map(str.lower, value): + if isinstance(value, dict): value_case_insensitive = { k.lower(): v for k, v in value.items() } - filter_by_metadata = self.EmbeddingStore.cmetadata[ - key - ].astext.in_(value_case_insensitive[IN]) - filter_clauses.append(filter_by_metadata) + if IN in map(str.lower, value): + filter_by_metadata = self.EmbeddingStore.cmetadata[ + key + ].astext.in_(value_case_insensitive[IN]) + elif NIN in map(str.lower, value): + filter_by_metadata = self.EmbeddingStore.cmetadata[ + key + ].astext.not_in(value_case_insensitive[NIN]) + else: + filter_by_metadata = None + if filter_by_metadata is not None: + filter_clauses.append(filter_by_metadata) else: filter_by_metadata = self.EmbeddingStore.cmetadata[ key diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_pgvector.py b/libs/langchain/tests/integration_tests/vectorstores/test_pgvector.py index 6fe9fc6cb7..e799b42712 100644 --- a/libs/langchain/tests/integration_tests/vectorstores/test_pgvector.py +++ b/libs/langchain/tests/integration_tests/vectorstores/test_pgvector.py @@ -17,7 +17,6 @@ CONNECTION_STRING = PGVector.connection_string_from_db_params( password=os.environ.get("TEST_PGVECTOR_PASSWORD", "postgres"), ) - ADA_TOKEN_COUNT = 1536 @@ -186,6 +185,27 @@ def test_pgvector_with_filter_in_set() -> None: ] +def test_pgvector_with_filter_nin_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": {"NIN": ["1"]}} + ) + assert output == [ + (Document(page_content="foo", metadata={"page": "0"}), 0.0), + (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406), + ] + + def test_pgvector_delete_docs() -> None: """Add and delete documents.""" texts = ["foo", "bar", "baz"]