test_qdrant_from_texts_stores_embeddings_as_named_vectors() — langchain Function Reference

Source Code

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py lines 81–110

def test_qdrant_from_texts_stores_embeddings_as_named_vectors(
    location: str,
    retrieval_mode: RetrievalMode,
    vector_name: str,
    sparse_vector_name: str,
) -> None:
    """Test end to end Qdrant.from_texts stores named vectors if name is provided."""
    collection_name = uuid.uuid4().hex
    vec_store = QdrantVectorStore.from_texts(
        ["lorem", "ipsum", "dolor", "sit", "amet"],
        ConsistentFakeEmbeddings(),
        collection_name=collection_name,
        location=location,
        vector_name=vector_name,
        retrieval_mode=retrieval_mode,
        sparse_vector_name=sparse_vector_name,
        sparse_embedding=ConsistentFakeSparseEmbeddings(),
    )

    assert vec_store.client.count(collection_name).count == 5
    if retrieval_mode in retrieval_modes(sparse=False):
        assert all(
            (vector_name in point.vector or isinstance(point.vector, list))  # type: ignore[operator]
            for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
        )
    if retrieval_mode in retrieval_modes(dense=False):
        assert all(
            sparse_vector_name in point.vector  # type: ignore[operator]
            for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
        )