[docs]classAsyncFlow(AsyncPostMixin,Flow):""" :class:`AsyncFlow` is the asynchronous version of the :class:`Flow`. They share the same interface, except in :class:`AsyncFlow` :meth:`train`, :meth:`index`, :meth:`search` methods are coroutines (i.e. declared with the async/await syntax), simply calling them will not schedule them to be executed. To actually run a coroutine, user need to put them in an eventloop, e.g. via ``asyncio.run()``, ``asyncio.create_task()``. :class:`AsyncFlow` can be very useful in the integration settings, where Jina/Jina Flow is NOT the main logic, but rather served as a part of other program. In this case, users often do not want to let Jina control the ``asyncio.eventloop``. On contrary, :class:`Flow` is controlling and wrapping the eventloop internally, making the Flow looks synchronous from outside. In particular, :class:`AsyncFlow` makes Jina usage in Jupyter Notebook more natural and reliable. For example, the following code will use the eventloop that already spawned in Jupyter/ipython to run Jina Flow (instead of creating a new one). .. highlight:: python .. code-block:: python from jina import AsyncFlow from jina.types.document.generators import from_ndarray import numpy as np with AsyncFlow().add() as f: await f.index(from_ndarray(np.random.random([5, 4])), on_done=print) Notice that the above code will NOT work in standard Python REPL, as only Jupyter/ipython implements "autoawait". .. seealso:: Asynchronous in REPL: Autoawait https://ipython.readthedocs.io/en/stable/interactive/autoawait.html Another example is when using Jina as an integration. Say you have another IO-bounded job ``heavylifting()``, you can use this feature to schedule Jina ``index()`` and ``heavylifting()`` concurrently. One can think of :class:`Flow` as Jina-managed eventloop, whereas :class:`AsyncFlow` is self-managed eventloop. """