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jina.helloworld.fashion.my_executors module

class jina.helloworld.fashion.my_executors.MyIndexer(**kwargs)

Bases: BaseExecutor

Executor with basic exact search using cosine distance

metas and requests are always auto-filled with values from YAML config.

Parameters

• metas – a dict of metas fields

• requests – a dict of endpoint-function mapping

• runtime_args – a dict of arguments injected from Runtime during runtime

• kwargs – additional extra keyword arguments to avoid failing when extra params ara passed that are not expected

index(docs, **kwargs)

Extend self._docs

Parameters

• docs (DocumentArray) – DocumentArray containing Documents

• kwargs – other keyword arguments

search(docs, parameters, **kwargs)

Append best matches to each document in docs

Parameters

• docs (DocumentArray) – documents that are searched

• parameters (Dict) – dictionary of pairs (parameter,value)

• kwargs – other keyword arguments

close()

Stores the DocumentArray to disk

requests = {'/eval': <function MyIndexer.search>, '/index': <function MyIndexer.index>, '/search': <function MyIndexer.search>}

class jina.helloworld.fashion.my_executors.MyEncoder(**kwargs)

Bases: BaseExecutor

Encode data using SVD decomposition

metas and requests are always auto-filled with values from YAML config.

Parameters

• metas – a dict of metas fields

• requests – a dict of endpoint-function mapping

• runtime_args – a dict of arguments injected from Runtime during runtime

• kwargs – additional extra keyword arguments to avoid failing when extra params ara passed that are not expected

encode(docs, **kwargs)

Encode the data using an SVD decomposition

Parameters

• docs (DocumentArray) – input documents to update with an embedding

• kwargs – other keyword arguments

requests = {'/default': <function MyEncoder.encode>}

class jina.helloworld.fashion.my_executors.MyConverter(metas=None, requests=None, runtime_args=None, **kwargs)

Bases: BaseExecutor

Convert DocumentArrays removing tensor and reshaping tensor as image

metas and requests are always auto-filled with values from YAML config.

Parameters

• metas (Optional[Dict]) – a dict of metas fields

• requests (Optional[Dict]) – a dict of endpoint-function mapping

• runtime_args (Optional[Dict]) – a dict of arguments injected from Runtime during runtime

• kwargs – additional extra keyword arguments to avoid failing when extra params ara passed that are not expected

convert(docs, **kwargs)

Remove tensor and reshape documents as squared images :type docs: DocumentArray :param docs: documents to modify :param kwargs: other keyword arguments

requests = {'/default': <function MyConverter.convert>}