local_path = './hub_pytest/test_deeplake_vectorstore/test_multiple_embeddings'

    @pytest.mark.slow
    def test_multiple_embeddings(local_path):
        vector_store = DeepLakeVectorStore(
            path=local_path,
            overwrite=True,
            tensor_params=[
                {
                    "name": "text",
                    "htype": "text",
                },
                {
                    "name": "embedding_1",
                    "htype": "embedding",
                },
                {
                    "name": "embedding_2",
                    "htype": "embedding",
                },
            ],
        )
    
        with pytest.raises(AssertionError):
            vector_store.add(
                text=texts,
                embedding_function=[embedding_fn, embedding_fn],
                embedding_data=[texts],
                embedding_tensor=["embedding_1", "embedding_2"],
            )
    
        with pytest.raises(AssertionError):
            vector_store.add(
                text=texts,
                embedding_function=[embedding_fn, embedding_fn],
                embedding_data=[texts, texts],
                embedding_tensor=["embedding_1"],
            )
    
        with pytest.raises(AssertionError):
            vector_store.add(
                text=texts,
                embedding_function=[embedding_fn],
                embedding_data=[texts, texts],
                embedding_tensor=["embedding_1", "embedding_2"],
            )
    
        vector_store.add(
            text=texts,
            embedding_function=[embedding_fn, embedding_fn],
            embedding_data=[texts, texts],
            embedding_tensor=["embedding_1", "embedding_2"],
        )
    
        vector_store.add(
            text=texts, embedding_1=(embedding_fn, texts), embedding_2=(embedding_fn, texts)
        )
    
        vector_store.add(
            text=texts,
            embedding_function=embedding_fn,
            embedding_data=[texts, texts],
            embedding_tensor=["embedding_1", "embedding_2"],
        )
    
        # test with initial embedding function
        vector_store.embedding_function = embedding_fn
        vector_store.add(
            text=texts,
            embedding_data=[texts, texts],
            embedding_tensor=["embedding_1", "embedding_2"],
        )
    
        number_of_data = 1000
        _texts, embeddings, ids, metadatas, _ = utils.create_data(
            number_of_data=number_of_data, embedding_dim=EMBEDDING_DIM
        )
        vector_store.add(
            text=25 * _texts,
            embedding_function=[embedding_fn3, embedding_fn3],
            embedding_data=[25 * _texts, 25 * _texts],
            embedding_tensor=["embedding_1", "embedding_2"],
        )
>       vector_store.add(
            text=25 * _texts,
            embedding_1=(embedding_fn3, 25 * _texts),
            embedding_2=(embedding_fn3, 25 * _texts),
        )

deeplake/core/vectorstore/test_deeplake_vectorstore.py:1697: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/core/vectorstore/deeplake_vectorstore.py:277: in add
    dataset_utils.extend_or_ingest_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:460: in extend_or_ingest_dataset
    extend(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:445: in extend
    dataset.extend(processed_tensors)
deeplake/core/dataset/dataset.py:3131: in extend
    self.append(
deeplake/util/invalid_view_op.py:22: in inner
    return callable(x, *args, **kwargs)
deeplake/core/dataset/dataset.py:3173: in append
    self._append_or_extend(
deeplake/core/dataset/dataset.py:3047: in _append_or_extend
    tensor.append(v)
deeplake/util/invalid_view_op.py:22: in inner
    return callable(x, *args, **kwargs)
deeplake/core/tensor.py:404: in append
    self.extend([sample], progressbar=False)
deeplake/util/invalid_view_op.py:22: in inner
    return callable(x, *args, **kwargs)
deeplake/core/tensor.py:316: in extend
    self.chunk_engine.extend(
deeplake/core/chunk_engine.py:1080: in extend
    self._extend(samples, progressbar, pg_callback=pg_callback)
deeplake/core/chunk_engine.py:1015: in _extend
    start_chunk=self.last_appended_chunk(allow_copy=False),
deeplake/core/chunk_engine.py:555: in last_appended_chunk
    chunk = self.get_chunk(chunk_key)
deeplake/core/chunk_engine.py:577: in get_chunk
    if not partial_chunk_bytes and isinstance(chunk.data_bytes, PartialReader):
deeplake/core/chunk/chunk_compressed_chunk.py:571: in data_bytes
    self._compress()
deeplake/core/chunk/chunk_compressed_chunk.py:562: in _compress
    self._data_bytes = compress_bytes(self.decompressed_bytes, self.compression)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

buffer = b'0163c264-4c33-11ee-af92-7715898d59320163c265-4c33-11ee-af92-7715898d59320163c266-4c33-11ee-af92-7715898d59320163c267...15898d593257c12881-4c33-11ee-af92-7715898d593257c12882-4c33-11ee-af92-7715898d593257c12883-4c33-11ee-af92-7715898d5932'
compression = 'lz4'

    def compress_bytes(
        buffer: Union[bytes, memoryview], compression: Optional[str]
    ) -> bytes:
        if not buffer:
            return b""
        if compression == "lz4":
            if not buffer:
                return b""
>           return numcodecs.lz4.compress(buffer)
E           Failed: Timeout >300.0s

deeplake/core/compression.py:162: Failed