`@requests` methods#

Methods of Executor can be named and written freely.

Methods decorated with @requests are mapped to network endpoints while serving.

Decorator#

Executor methods decorated with requests are bound to specific network requests, and respond to network queries.

Both def or async def function can be decorated with requests.

You can import the @requests decorator via

from jina import requests

requests is a decorator that takes an optional parameter: on=. It binds the decorated method of the Executor to the specified route.

from jina import Executor, requests
import asyncio


class RequestExecutor(Executor):
    @requests(
        on=['/index', '/search']
    )  # foo will be bound to `/index` and `/search` endpoints
    def foo(self, **kwargs):
        print(f'Calling foo')

    @requests(on='/other')  # bar will be bound to `/other` endpoint
    async def bar(self, **kwargs):
        await asyncio.sleep(1.0)
        print(f'Calling bar')

from jina import Flow

f = Flow().add(uses=RequestExecutor)

with f:
    f.post(on='/index', inputs=[])
    f.post(on='/other', inputs=[])
    f.post(on='/search', inputs=[])

           Flow@18048[I]:🎉 Flow is ready to use!                                                   
	🔗 Protocol: 		GRPC
	🏠 Local access:	0.0.0.0:52255
	🔒 Private network:	192.168.1.187:52255
	🌐 Public address:	212.231.186.65:52255
Calling foo
Calling bar
Calling foo

Default binding#

A class method decorated with plain @requests (without on=) is the default handler for all endpoints. That means it is the fallback handler for endpoints that are not found. f.post(on='/blah', ...) will invoke MyExecutor.foo.

from jina import Executor, requests
import asyncio


class MyExecutor(Executor):
    @requests
    def foo(self, **kwargs):
        print(kwargs)

    @requests(on='/index')
    async def bar(self, **kwargs):
        await asyncio.sleep(1.0)
        print(f'Calling bar')

No binding#

A class with no @requests binding plays no part in the Flow. The request will simply pass through without any processing.

Arguments#

All Executor methods decorated by @requests need to follow the signature below in order to be usable as a microservice inside a Flow. The async definition is optional.

from typing import Dict, Union, List
from jina import Executor, requests, DocumentArray


class MyExecutor(Executor):
    @requests
    async def foo(
        self, docs: DocumentArray, parameters: Dict, docs_matrix: List[DocumentArray]
    ) -> Union[DocumentArray, Dict, None]:
        pass

    @requests
    def bar(
        self, docs: DocumentArray, parameters: Dict, docs_matrix: List[DocumentArray]
    ) -> Union[DocumentArray, Dict, None]:
        pass

Let’s take a look at all these arguments:

docs: A DocumentArray that is part of the request. Since the nature of Executor is to wrap functionality related to DocumentArray, it is usually the main processing unit inside Executor methods. It is important to notice that these docs can be also changed in place, just like it could happen with any other list-like object in a Python function.
parameters: A Dict object that can be used to pass extra parameters to the Executor functions.
docs_matrix: This is the least common parameter to be used for an Executor. This argument is needed when an Executor is used inside a Flow to merge or reduce the output of more than one other Executor.

Hint

If you don’t need some arguments, you can suppress them into **kwargs. For example:

from jina import Executor, requests


class MyExecutor(Executor):

    @requests
    def foo_using_docs_arg(self, docs, **kwargs):
        print(docs)

    @requests
    def foo_using_docs_parameters_arg(self, docs, parameters, **kwargs):
        print(docs)
        print(parameters)

    @requests
    def foo_using_no_arg(self, **kwargs):
        # the args are suppressed into kwargs
        print(kwargs['docs_matrix'])

Multiple DocumentArrays as input argument#

You have seen that Executor methods can receive three types of parameters: docs, parameters and docs_matrix.

docs_matrix is a parameter that is only used in some special cases.

One case is when an Executor receives messages from more than one upstream Executor in the Flow.

Let’s see an example:

from jina import Flow, Executor, requests, Document, DocumentArray


class Exec1(Executor):
    @requests
    def foo(self, docs, **kwargs):
        for doc in docs:
            doc.text = 'Exec1'


class Exec2(Executor):
    @requests
    def foo(self, docs, **kwargs):
        for doc in docs:
            doc.text = 'Exec2'


class MergeExec(Executor):
    @requests
    def foo(self, docs_matrix, **kwargs):
        documents_to_return = DocumentArray()
        for doc1, doc2 in zip(*docs_matrix):
            print(
                f'MergeExec processing pairs of Documents "{doc1.text}" and "{doc2.text}"'
            )
            documents_to_return.append(
                Document(text=f'Document merging from "{doc1.text}" and "{doc2.text}"')
            )
        return documents_to_return


f = (
    Flow()
    .add(uses=Exec1, name='exec1')
    .add(uses=Exec2, name='exec2')
    .add(uses=MergeExec, needs=['exec1', 'exec2'], disable_reduce=True)
)

with f:
    returned_docs = f.post(on='/', Document())

print(f'Resulting documents {returned_docs[0].text}')

           Flow@1244[I]:🎉 Flow is ready to use!
	🔗 Protocol: 		GRPC
	🏠 Local access:	0.0.0.0:54550
	🔒 Private network:	192.168.1.187:54550
	🌐 Public address:	212.231.186.65:54550
MergeExec processing pairs of Documents "Exec1" and "Exec2"
Resulting documents Document merging from "Exec1" and "Exec2"

Async coroutines#

You can naturally call async coroutines within Executor’s, allowing you to leverage the power of asynchronous Python to write concurrent code.

from jina import Executor, requests, Flow


class MyExecutor(Executor):
    @requests
    async def encode(self, docs, *kwargs):
        await some_coroutines()

In this example we have a heavy lifting API for which we want to call several times, and we want to leverage the async Python features to speed up the Executor’s call by calling the API multiples times concurrently. As a counterpart, in an example without using coroutines, all of the 50 API calls will be queued and nothing will be done concurrently.

Async coroutines

import asyncio

from jina import Flow, Executor, requests, Document, DocumentArray


class DummyAsyncExecutor(Executor):
    @requests
    async def process(self, docs: DocumentArray, **kwargs):
        await asyncio.sleep(1)
        for doc in docs:
            doc.text = doc.text.upper()


f = Flow().add(uses=DummyAsyncExecutor)

with f:
    f.index(
        inputs=DocumentArray([Document(text="hello") for _ in range(50)]),
        request_size=1,
        show_progress=True,
    )

           Flow@20588[I]:🎉 Flow is ready to use!
	🔗 Protocol: 		GRPC
	🏠 Local access:	0.0.0.0:62598
	🔒 Private network:	192.168.1.187:62598
	🌐 Public address:	212.231.186.65:62598
⠙       DONE ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:01 100% ETA: 0 seconds 41 steps done in 1 second

Sync version

import time

from jina import Flow, Executor, requests, DocumentArray, Document


class DummyExecutor(Executor):
    @requests
    def process(self, docs: DocumentArray, **kwargs):
        time.sleep(1)
        for doc in docs:
            doc.text = doc.text.upper()


f = Flow().add(uses=DummyExecutor)

with f:
    f.index(
        inputs=DocumentArray([Document(text="hello") for _ in range(50)]),
        request_size=1,
        show_progress=True,
    )

           Flow@20394[I]:🎉 Flow is ready to use!
	🔗 Protocol: 		GRPC
	🏠 Local access:	0.0.0.0:52592
	🔒 Private network:	192.168.1.187:52592
	🌐 Public address:	212.231.186.65:52592
⠏       DONE ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:50 100% ETA: 0 seconds 41 steps done in 50 seconds

The processing of the data is 50 faster when using coroutines because it happens concurrently.

Call another Jina Flow#

To call other another Jina Flow using Client from an Executor, you will also need to use async def and async Client.

from jina import Client, Executor, requests, DocumentArray


class DummyExecutor(Executor):

    c = Client(host='grpc://0.0.0.0:51234', asyncio=True)

    @requests
    async def process(self, docs: DocumentArray, **kwargs):
        self.c.post('/', docs)

Returns#

Every Executor method can return in 3 ways:

If you return a DocumentArray object, then it will be sent over to the next Executor.
If you return None or if you don’t have a return in your method, then the original doc object (potentially mutated by your function) will be sent over to the next Executor.
If you return a dict object, then it will be considered as a result and passed on behind parameters['__results__']. The original doc object (potentially mutated by your function) will be sent over to the next Executor.

from jina import requests, Executor, Flow


class MyExec(Executor):
    @requests(on='/status')
    def status(self, **kwargs):
        return {'internal_parameter': 20}


f = Flow().add(uses=MyExec, name='my_executor')

with f:
    print(f.post(on='/status').to_dict()["parameters"])

{"__results__": {"my_executor/rep-0": {"internal_parameter": 20.0}}}

Exception handling#

Exceptions raised inside @requests-decorated functions can simply be raised. The Flow will handle it.

from jina import Executor, requests


class MyExecutor(Executor):
    @requests
    def foo(self, **kwargs):
        raise NotImplementedError('no time for it')

Example#

Let’s understand how Executor’s process DocumentArray’s inside a Flow, and how the changes are chained and applied, affecting downstream Executors in the Flow.

from jina import Executor, requests, Flow, DocumentArray, Document


class PrintDocuments(Executor):
    @requests
    def foo(self, docs, **kwargs):
        for doc in docs:
            print(f' PrintExecutor: received document with text: "{doc.text}"')


class ProcessDocuments(Executor):
    @requests(on='/change_in_place')
    def in_place(self, docs, **kwargs):
        # This executor will only work on `docs` and will not consider any other arguments
        for doc in docs:
            print(f' ProcessDocuments: received document with text "{doc.text}"')
            doc.text = 'I changed the executor in place'

    @requests(on='/return_different_docarray')
    def ret_docs(self, docs, **kwargs):
        # This executor will only work on `docs` and will not consider any other arguments
        ret = DocumentArray()
        for doc in docs:
            print(f' ProcessDocuments: received document with text: "{doc.text}"')
            ret.append(Document(text='I returned a different Document'))
        return ret


f = Flow().add(uses=ProcessDocuments).add(uses=PrintDocuments)

with f:
    f.post(on='/change_in_place', inputs=DocumentArray(Document(text='request')))
    f.post(
        on='/return_different_docarray', inputs=DocumentArray(Document(text='request'))
    )

           Flow@23300[I]:🎉 Flow is ready to use!                                                   
	🔗 Protocol: 		GRPC
	🏠 Local access:	0.0.0.0:61855
	🔒 Private network:	192.168.1.187:61855
	🌐 Public address:	212.231.186.65:61855
 ProcessDocuments: received document with text "request1"
 PrintExecutor: received document with text: "I changed the executor in place"
 ProcessDocuments: received document with text: "request2"
 PrintExecutor: received document with text: "I returned a different Document"