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inda_hr.JobAdKnowledgeExtractionApi

All URIs are relative to https://api.inda.ai

Method HTTP request Description
extract_jobtitles_from_jobad_post POST /hr/v2/parse/jobad/jobtitles/ Extract JobTitles from JobAd
extract_languages_from_jobad_post POST /hr/v2/parse/jobad/languages/ Extract Languages from JobAd
extract_skills_from_jobad_post POST /hr/v2/parse/jobad/skills/ Extract Skills from JobAd

extract_jobtitles_from_jobad_post

JobAdJobTitlesResponse extract_jobtitles_from_jobad_post(job_ad_job_description_request)

Extract JobTitles from JobAd

This method extract job titles that are semantically related with a job advert. The input is a json containing the structure of the advert, as described in the schema below and in the example on the right. The field sections in the body contains a list of documents, which correspond to distinct sections of the advert (e.g., company description, job description, requirements); in each document, the field content contains the text of the section, while the field weight (a number between 0 and 1) can be used to give different weights to the different sections in the skill extraction (e.g., a section with the requirements is probably much more relevant for the skill extraction than a section with the company description); in the absence of the field value, the maximum value (i.e., weight = 1) will be assumed. The field header contains the information about the job title.

Example

  • Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import job_ad_knowledge_extraction_api
from inda_hr.model.job_ad_job_description_request import JobAdJobDescriptionRequest
from inda_hr.model.job_ad_job_titles_response import JobAdJobTitlesResponse
from inda_hr.model.http_validation_error import HTTPValidationError
from pprint import pprint
# Defining the host is optional and defaults to https://api.inda.ai
# See configuration.py for a list of all supported configuration parameters.
configuration = inda_hr.Configuration(
    host = "https://api.inda.ai"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: APIKey
configuration = inda_hr.Configuration(
    access_token = 'YOUR_BEARER_TOKEN'
)

# Enter a context with an instance of the API client
with inda_hr.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = job_ad_knowledge_extraction_api.JobAdKnowledgeExtractionApi(api_client)
    job_ad_job_description_request = JobAdJobDescriptionRequest(
        data=SlimData(
            job_title=JobTitleHeader(
                details=JobTitleHeaderDetails(
                    text_positions=[
                        TextPosition(
                            start=1,
                            end=1,
                        ),
                    ],
                    raw_value="raw_value_example",
                    raw_values=[
                        TextDetails(
                            text_positions=[
                                TextPosition(
                                    start=1,
                                    end=1,
                                ),
                            ],
                            raw_value="raw_value_example",
                        ),
                    ],
                    is_validated=False,
                    entity_type="entity_type_example",
                    proficiency_level="proficiency_level_example",
                    category="category_example",
                    code=JobAdJobTitleCode(
                        key="key_example",
                    ),
                    weight=0.8,
                ),
                value="value_example",
            ),
            job_description=JobDescription(
                company_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_requirements=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                additional_information=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
            ),
        ),
        metadata=OptionalMetadata(
            language="it",
        ),
    ) # JobAdJobDescriptionRequest | 
    src_lang = "it" # str | Job Description language. If left empty each section's language will detected automatically. (optional)
    dst_lang = "it" # str | Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. (optional)
    size = 10 # int | Number of job titles to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. (optional) if omitted the server will use the default value of 10
    min_score = 0.2 # float | Minimum score for the proposed job titles. The job titles with a score lower than this value will be neglected. (optional) if omitted the server will use the default value of 0.2

    # example passing only required values which don't have defaults set
    try:
        # Extract JobTitles from JobAd
        api_response = api_instance.extract_jobtitles_from_jobad_post(job_ad_job_description_request)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_jobtitles_from_jobad_post: %s\n" % e)

    # example passing only required values which don't have defaults set
    # and optional values
    try:
        # Extract JobTitles from JobAd
        api_response = api_instance.extract_jobtitles_from_jobad_post(job_ad_job_description_request, src_lang=src_lang, dst_lang=dst_lang, size=size, min_score=min_score)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_jobtitles_from_jobad_post: %s\n" % e)

Parameters

Name Type Description Notes
job_ad_job_description_request JobAdJobDescriptionRequest
src_lang str Job Description language. If left empty each section's language will detected automatically. [optional]
dst_lang str Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. [optional]
size int Number of job titles to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. [optional] if omitted the server will use the default value of 10
min_score float Minimum score for the proposed job titles. The job titles with a score lower than this value will be neglected. [optional] if omitted the server will use the default value of 0.2

Return type

JobAdJobTitlesResponse

Authorization

APIKey

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 Document Successfully Processed -
422 Validation Error -

[Back to top] [Back to API list] [Back to Model list] [Back to README]

extract_languages_from_jobad_post

JobAdLanguagesResponse extract_languages_from_jobad_post(job_ad_job_description_request)

Extract Languages from JobAd

This method extract job titles that are semantically related with a job advert. The input is a json containing the structure of the advert, as described in the schema below and in the example on the right. The field sections in the body contains a list of documents, which correspond to distinct sections of the advert (e.g., company description, job description, requirements); in each document, the field content contains the text of the section, while the field weight (a number between 0 and 1) can be used to give different weights to the different sections in the skill extraction (e.g., a section with the requirements is probably much more relevant for the skill extraction than a section with the company description); in the absence of the field value, the maximum value (i.e., weight = 1) will be assumed. The field header contains the information about the job title.

Example

  • Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import job_ad_knowledge_extraction_api
from inda_hr.model.job_ad_job_description_request import JobAdJobDescriptionRequest
from inda_hr.model.job_ad_languages_response import JobAdLanguagesResponse
from inda_hr.model.http_validation_error import HTTPValidationError
from pprint import pprint
# Defining the host is optional and defaults to https://api.inda.ai
# See configuration.py for a list of all supported configuration parameters.
configuration = inda_hr.Configuration(
    host = "https://api.inda.ai"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: APIKey
configuration = inda_hr.Configuration(
    access_token = 'YOUR_BEARER_TOKEN'
)

# Enter a context with an instance of the API client
with inda_hr.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = job_ad_knowledge_extraction_api.JobAdKnowledgeExtractionApi(api_client)
    job_ad_job_description_request = JobAdJobDescriptionRequest(
        data=SlimData(
            job_title=JobTitleHeader(
                details=JobTitleHeaderDetails(
                    text_positions=[
                        TextPosition(
                            start=1,
                            end=1,
                        ),
                    ],
                    raw_value="raw_value_example",
                    raw_values=[
                        TextDetails(
                            text_positions=[
                                TextPosition(
                                    start=1,
                                    end=1,
                                ),
                            ],
                            raw_value="raw_value_example",
                        ),
                    ],
                    is_validated=False,
                    entity_type="entity_type_example",
                    proficiency_level="proficiency_level_example",
                    category="category_example",
                    code=JobAdJobTitleCode(
                        key="key_example",
                    ),
                    weight=0.8,
                ),
                value="value_example",
            ),
            job_description=JobDescription(
                company_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_requirements=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                additional_information=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
            ),
        ),
        metadata=OptionalMetadata(
            language="it",
        ),
    ) # JobAdJobDescriptionRequest | 
    src_lang = "it" # str | Job Description language. If left empty each section's language will detected automatically. (optional)
    dst_lang = "it" # str | Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. (optional)
    size = 10 # int | Number of languages to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. (optional) if omitted the server will use the default value of 10
    min_score = 0.2 # float | Minimum score for the proposed languages. The languages with a score lower than this value will be neglected. (optional) if omitted the server will use the default value of 0.2

    # example passing only required values which don't have defaults set
    try:
        # Extract Languages from JobAd
        api_response = api_instance.extract_languages_from_jobad_post(job_ad_job_description_request)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_languages_from_jobad_post: %s\n" % e)

    # example passing only required values which don't have defaults set
    # and optional values
    try:
        # Extract Languages from JobAd
        api_response = api_instance.extract_languages_from_jobad_post(job_ad_job_description_request, src_lang=src_lang, dst_lang=dst_lang, size=size, min_score=min_score)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_languages_from_jobad_post: %s\n" % e)

Parameters

Name Type Description Notes
job_ad_job_description_request JobAdJobDescriptionRequest
src_lang str Job Description language. If left empty each section's language will detected automatically. [optional]
dst_lang str Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. [optional]
size int Number of languages to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. [optional] if omitted the server will use the default value of 10
min_score float Minimum score for the proposed languages. The languages with a score lower than this value will be neglected. [optional] if omitted the server will use the default value of 0.2

Return type

JobAdLanguagesResponse

Authorization

APIKey

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 Document Successfully Processed -
422 Validation Error -

[Back to top] [Back to API list] [Back to Model list] [Back to README]

extract_skills_from_jobad_post

JobAdSkillsResponse extract_skills_from_jobad_post(job_ad_job_description_request)

Extract Skills from JobAd

This method extract job skills (both hard and soft skills) that are semantically related with a job advert. The input is a json containing the structure of the advert, as described in the schema below and in the example on the right. The field sections in the body contains a list of documents, which correspond to distinct sections of the advert (e.g., company description, job description, requirements); in each document, the field content contains the text of the section, while the field weight (a number between 0 and 1) can be used to give different weights to the different sections in the skill extraction (e.g., a section with the requirements is probably much more relevant for the skill extraction than a section with the company description); in the absence of the field value, the maximum value (i.e., weight = 1) will be assumed. The field header contains the information about the job title.

Example

  • Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import job_ad_knowledge_extraction_api
from inda_hr.model.job_ad_job_description_request import JobAdJobDescriptionRequest
from inda_hr.model.http_validation_error import HTTPValidationError
from inda_hr.model.job_ad_skills_response import JobAdSkillsResponse
from pprint import pprint
# Defining the host is optional and defaults to https://api.inda.ai
# See configuration.py for a list of all supported configuration parameters.
configuration = inda_hr.Configuration(
    host = "https://api.inda.ai"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: APIKey
configuration = inda_hr.Configuration(
    access_token = 'YOUR_BEARER_TOKEN'
)

# Enter a context with an instance of the API client
with inda_hr.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = job_ad_knowledge_extraction_api.JobAdKnowledgeExtractionApi(api_client)
    job_ad_job_description_request = JobAdJobDescriptionRequest(
        data=SlimData(
            job_title=JobTitleHeader(
                details=JobTitleHeaderDetails(
                    text_positions=[
                        TextPosition(
                            start=1,
                            end=1,
                        ),
                    ],
                    raw_value="raw_value_example",
                    raw_values=[
                        TextDetails(
                            text_positions=[
                                TextPosition(
                                    start=1,
                                    end=1,
                                ),
                            ],
                            raw_value="raw_value_example",
                        ),
                    ],
                    is_validated=False,
                    entity_type="entity_type_example",
                    proficiency_level="proficiency_level_example",
                    category="category_example",
                    code=JobAdJobTitleCode(
                        key="key_example",
                    ),
                    weight=0.8,
                ),
                value="value_example",
            ),
            job_description=JobDescription(
                company_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_description=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                position_requirements=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
                additional_information=Section(
                    details=SectionDetails(
                        language="de",
                        weight=0.8,
                    ),
                    title=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                    content=BaseBenefitsValueModelStrictStr(
                        value="value_example",
                    ),
                ),
            ),
        ),
        metadata=OptionalMetadata(
            language="it",
        ),
    ) # JobAdJobDescriptionRequest | 
    src_lang = "it" # str | Job Description language. If left empty each section's language will detected automatically. (optional)
    dst_lang = "it" # str | Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. (optional)
    size = 10 # int | Number of skills to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. (optional) if omitted the server will use the default value of 10
    min_score = 0.2 # float | Minimum score for the proposed skills. The skills with a score lower than this value will be neglected. (optional) if omitted the server will use the default value of 0.2

    # example passing only required values which don't have defaults set
    try:
        # Extract Skills from JobAd
        api_response = api_instance.extract_skills_from_jobad_post(job_ad_job_description_request)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_skills_from_jobad_post: %s\n" % e)

    # example passing only required values which don't have defaults set
    # and optional values
    try:
        # Extract Skills from JobAd
        api_response = api_instance.extract_skills_from_jobad_post(job_ad_job_description_request, src_lang=src_lang, dst_lang=dst_lang, size=size, min_score=min_score)
        pprint(api_response)
    except inda_hr.ApiException as e:
        print("Exception when calling JobAdKnowledgeExtractionApi->extract_skills_from_jobad_post: %s\n" % e)

Parameters

Name Type Description Notes
job_ad_job_description_request JobAdJobDescriptionRequest
src_lang str Job Description language. If left empty each section's language will detected automatically. [optional]
dst_lang str Extracted entities destination language. If left empty is assumed to be equal to the Job Description language. [optional]
size int Number of skills to be returned, must be greater than <code style='color: #333333; opacity: 0.9'>0</code> and smaller or equal to <code style='color: #333333; opacity: 0.9'>20</code>. [optional] if omitted the server will use the default value of 10
min_score float Minimum score for the proposed skills. The skills with a score lower than this value will be neglected. [optional] if omitted the server will use the default value of 0.2

Return type

JobAdSkillsResponse

Authorization

APIKey

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 Document Successfully Processed -
422 Validation Error -

[Back to top] [Back to API list] [Back to Model list] [Back to README]