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MachineLearning API

All URIs are relative to http://localhost:1000

MethodHTTP requestDescription
personification_technical_language_generationPOST /machine_learning/text/technical_language/generators/personification/machine_learning/text/technical_language/generators/personification [GET]
segment_technical_languagePOST /machine_learning/text/technical_language/parsers/segmentation/machine_learning/text/technical_language/parsers/segmentation [POST]

personification_technical_language_generation

OnboardedPersonaDetails personification_technical_language_generation(preonboarded_persona_details=preonboarded_persona_details)

/machine_learning/text/technical_language/generators/personification [GET]

This is going to take in some personification details ie languages & personas. and will return generated Seeds that can be used as snippets post/pre onboarding.

Example

import pieces_os_client
from pieces_os_client.models.onboarded_persona_details import OnboardedPersonaDetails
from pieces_os_client.models.preonboarded_persona_details import PreonboardedPersonaDetails
from pieces_os_client.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to http://localhost:1000
# See configuration.py for a list of all supported configuration parameters.
configuration = pieces_os_client.Configuration(
host="http://localhost:1000"
)


# Enter a context with an instance of the API client
with pieces_os_client.ApiClient(configuration) as api_client:
# Create an instance of the API class
api_instance = pieces_os_client.MachineLearningApi(api_client)
preonboarded_persona_details = pieces_os_client.PreonboardedPersonaDetails() # PreonboardedPersonaDetails | (optional)

try:
# /machine_learning/text/technical_language/generators/personification [GET]
api_response = api_instance.personification_technical_language_generation(preonboarded_persona_details=preonboarded_persona_details)
print("The response of MachineLearningApi->personification_technical_language_generation:\n")
pprint(api_response)
except Exception as e:
print("Exception when calling MachineLearningApi->personification_technical_language_generation: %s\n" % e)

Parameters

NameTypeDescriptionNotes
preonboarded_persona_detailsPreonboardedPersonaDetails[optional]

Return type

OnboardedPersonaDetails

Authorization

No authorization required

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json, text/plain

HTTP response details

Status codeDescriptionResponse headers
200OK-
500Internal Server Error-

segment_technical_language

SegmentedTechnicalLanguage segment_technical_language(classify=classify, unsegmented_technical_language=unsegmented_technical_language)

/machine_learning/text/technical_language/parsers/segmentation [POST]

This is a functional endpoint that will parse a message or text in to text or code. if the optional query param is passed along 'classify' then we will optionally classify the just the code that is segmented.

Example

import pieces_os_client
from pieces_os_client.models.segmented_technical_language import SegmentedTechnicalLanguage
from pieces_os_client.models.unsegmented_technical_language import UnsegmentedTechnicalLanguage
from pieces_os_client.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to http://localhost:1000
# See configuration.py for a list of all supported configuration parameters.
configuration = pieces_os_client.Configuration(
host="http://localhost:1000"
)


# Enter a context with an instance of the API client
with pieces_os_client.ApiClient(configuration) as api_client:
# Create an instance of the API class
api_instance = pieces_os_client.MachineLearningApi(api_client)
classify = True # bool | This will let us know if you want us to classifiy your code, this is default to false. (optional)
unsegmented_technical_language = pieces_os_client.UnsegmentedTechnicalLanguage() # UnsegmentedTechnicalLanguage | (optional)

try:
# /machine_learning/text/technical_language/parsers/segmentation [POST]
api_response = api_instance.segment_technical_language(classify=classify, unsegmented_technical_language=unsegmented_technical_language)
print("The response of MachineLearningApi->segment_technical_language:\n")
pprint(api_response)
except Exception as e:
print("Exception when calling MachineLearningApi->segment_technical_language: %s\n" % e)

Parameters

NameTypeDescriptionNotes
classifyboolThis will let us know if you want us to classifiy your code, this is default to false.[optional]
unsegmented_technical_languageUnsegmentedTechnicalLanguage[optional]

Return type

SegmentedTechnicalLanguage

Authorization

No authorization required

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json, text/plain

HTTP response details

Status codeDescriptionResponse headers
200OK-
500Internal Server Error-