Method test_with_dense_vector_fields

python/tests/test_convert.py:133–182

(self)

            assert results[i] == doc.field("results")[i]

    def test_with_dense_vector_fields(self):
        schema = CollectionSchema(
            name="test_collection",
            vectors=[
                VectorSchema(
                    name="embedding",
                    data_type=DataType.VECTOR_FP16,
                    dimension=4,
                ),
                VectorSchema(
                    name="image",
                    data_type=DataType.VECTOR_FP32,
                    dimension=8,
                ),
                VectorSchema(
                    name="text",
                    data_type=DataType.VECTOR_INT8,
                    dimension=32,
                ),
            ],
        )

        doc = Doc(
            id="1",
            vectors={
                "embedding": [1.1] * 4,
                "image": [2.2] * 8,
                "text": [4] * 32,
            },
        )
        cpp_doc = convert_to_cpp_doc(doc, collection_schema=schema)
        assert cpp_doc is not None
        assert cpp_doc.pk() == doc.id

        embedding_vector = cpp_doc.get_any("embedding", DataType.VECTOR_FP16)
        assert len(embedding_vector) == 4
        for i in range(4):
            assert math.isclose(
                embedding_vector[i], doc.vector("embedding")[i], rel_tol=1e-1
            )

        image_vector = cpp_doc.get_any("image", DataType.VECTOR_FP32)
        assert len(image_vector) == 8
        for i in range(8):
            assert math.isclose(image_vector[i], doc.vector("image")[i], rel_tol=1e-1)

        text_vector = cpp_doc.get_any("text", DataType.VECTOR_INT8)
        assert len(text_vector) == 32
        for i in range(32):
            assert text_vector[i] == doc.vectors["text"][i]

    def test_with_sparse_vector_fields(self):
        schema = CollectionSchema(