All URIs are relative to https://api.inda.ai
| Method | HTTP request | Description |
|---|---|---|
| anonymize_cv_post | POST /hr/v2/parse/resume/anonymize/ | Anonymize CV |
| bulk_map_entity_post | POST /hr/v2/keywords/bulk/map/entity/ | Bulk Map Entity |
| map_entity_post | POST /hr/v2/keywords/map/entity/ | Map Entity |
| parse_resume_post | POST /hr/v2/parse/resume/data/ | Parse Resume |
| text_extraction_post | POST /hr/v2/parse/resume/text/ | Text Extraction |
DocumentAnonymizationResponse anonymize_cv_post(base_file_doc)
Anonymize CV
This method allows you to anonymize a CV/resume (only textual documents, not scanned documents or images) by covering the main sensitive information in the document. Output is always a PDF file containing an anonymized copy of the source document. Masked entities are: given name, family name, birthdate, telephone numbers, emails, links, gender, nationality, profile picture. Supported extensions: pdf, doc, docx, odt, txt, html, pptx, rtf.
- Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import resume_parsing_api
from inda_hr.model.document_anonymization_response import DocumentAnonymizationResponse
from inda_hr.model.error_model import ErrorModel
from inda_hr.model.base_file_doc import BaseFileDoc
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 = resume_parsing_api.ResumeParsingApi(api_client)
base_file_doc = BaseFileDoc(
file=open('/path/to/file', 'rb'),
file_ext="file_ext_example",
) # BaseFileDoc |
src_lang = "it" # str | Language to use to interpret the text. If missing, language detection is performed. (optional)
# example passing only required values which don't have defaults set
try:
# Anonymize CV
api_response = api_instance.anonymize_cv_post(base_file_doc)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->anonymize_cv_post: %s\n" % e)
# example passing only required values which don't have defaults set
# and optional values
try:
# Anonymize CV
api_response = api_instance.anonymize_cv_post(base_file_doc, src_lang=src_lang)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->anonymize_cv_post: %s\n" % e)| Name | Type | Description | Notes |
|---|---|---|---|
| base_file_doc | BaseFileDoc | ||
| src_lang | str | Language to use to interpret the text. If missing, language detection is performed. | [optional] |
- Content-Type: application/json
- Accept: application/json
| Status code | Description | Response headers |
|---|---|---|
| 200 | Document Successfully Processed | - |
| 422 | Unprocessable Entity | - |
| 415 | Unsupported Media Type | - |
| 500 | Internal Server Error | - |
| 503 | Service Unavailable | - |
| 400 | Bad Request | - |
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MultiEntityMapping bulk_map_entity_post(multi_entity_input)
Bulk Map Entity
This method wraps the Map Entity method and allows a user to send all the entities to be mapped in one API call, e.g., for pagination purposes. Note that the request does not raise any Validation Error on the input data but instead it returns all the errors in the response.
- Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import resume_parsing_api
from inda_hr.model.multi_entity_mapping import MultiEntityMapping
from inda_hr.model.http_validation_error import HTTPValidationError
from inda_hr.model.multi_entity_input import MultiEntityInput
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 = resume_parsing_api.ResumeParsingApi(api_client)
multi_entity_input = MultiEntityInput(
entities=[
IdEntityInput(
entity_id="entity_id_example",
entity=EntityInput(
input_language="it",
output_language="it",
entity_type="entity_type_example",
allowed_outputs=[
AllowedOutput(
id="id_example",
value="value_example",
),
],
input_string="input_string_example",
severity=0.5,
),
),
],
) # MultiEntityInput |
# example passing only required values which don't have defaults set
try:
# Bulk Map Entity
api_response = api_instance.bulk_map_entity_post(multi_entity_input)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->bulk_map_entity_post: %s\n" % e)| Name | Type | Description | Notes |
|---|---|---|---|
| multi_entity_input | MultiEntityInput |
- Content-Type: application/json
- Accept: application/json
| Status code | Description | Response headers |
|---|---|---|
| 200 | Entities Successfully Mapped | - |
| 422 | Validation Error | - |
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EntityMapping map_entity_post(entity_input)
Map Entity
This method maps an input string (ideally an entity extracted with the Parse Resume method) to the most similar string among a list of strings chosen by the user (AllowedOutputs). Note that, after a best match has been identified, it is actually presented as output only when it passes a similarity check, which takes into account the similarity of this best match with the input string, but also the similarity of the other elements of allowed outputs with the input string (namely, if many allowed outputs have a comparable similarity level, the similarity check is not passed, because there is not a clear winner). When the severity check is passed, the id of the best match is given in output (see the payload and response examples on the right); otherwise, an empty string is returned instead of the id. The severity of the similarity check can be controlled via the severity parameter, which takes value between 0 and 1: a user who prefers to obtain the best match also when there is not guarantee it actually corresponds to the input string should set severity to a low value (i.e., close to 0); vice versa, a user who prefers to have a response only when there is a high confidence in the correspondence should set severity to a large value (i.e., close to 1); an intermediate value (such as the default value 0.5) is appropriate in many situations. The method has been specialized for different entity types, and for each of them it performs an analysis optimized over the specific type. The currently supported entity types are Data.EducationExperiences.Organization.OrganizationName and Data.EducationExperiences.EducationTitle. If the entity type is a string that does not match any of the supported entity types, the mapping is performed using a non-specialized method.
- Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import resume_parsing_api
from inda_hr.model.error_model import ErrorModel
from inda_hr.model.entity_input import EntityInput
from inda_hr.model.entity_mapping import EntityMapping
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 = resume_parsing_api.ResumeParsingApi(api_client)
entity_input = EntityInput(
input_language="it",
output_language="it",
entity_type="entity_type_example",
allowed_outputs=[
AllowedOutput(
id="id_example",
value="value_example",
),
],
input_string="input_string_example",
severity=0.5,
) # EntityInput |
# example passing only required values which don't have defaults set
try:
# Map Entity
api_response = api_instance.map_entity_post(entity_input)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->map_entity_post: %s\n" % e)| Name | Type | Description | Notes |
|---|---|---|---|
| entity_input | EntityInput |
- Content-Type: application/json
- Accept: application/json
| Status code | Description | Response headers |
|---|---|---|
| 200 | Entity Successfully Mapped | - |
| 400 | Bad Request | - |
| 422 | Validation Error | - |
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ExtractionItem parse_resume_post(base_file)
Parse Resume
This method performs the Information Extraction from a resume, i.e., it recognizes the named entities contained in a given resume and reconstructs the relations between them, returning a structured information in the form of a json file. The method requires in input the binary and the extension of the file and automatically performs many steps: (i) Document Layout Analysis, (ii) Optical Character Recognition (if the input document is an image), (iii) Text Extraction, (iv) Named Entity Recognition, (v) Relation Extraction, and, finally, (vi) Face Recognition (which is carried out to identify, if present, the candidate photo). The information provided in the output (see the schema below and the example on the right) can be used as structured data input for the Add Resume method (some adjustments may be required). The allowed file extensions are pdf, doc, docx, odt, txt, html, pptx, rtf, jpg, jpeg, png, tif, tiff . Please consider to use the Info Extraction Feedback to inform us about differences between user's expectations and the actual data provided as output by INDA engine. It is very useful to improve our algorithms' performances.
- Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import resume_parsing_api
from inda_hr.model.extraction_item import ExtractionItem
from inda_hr.model.error_model import ErrorModel
from inda_hr.model.base_file import BaseFile
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 = resume_parsing_api.ResumeParsingApi(api_client)
base_file = BaseFile(
file=open('/path/to/file', 'rb'),
file_ext="file_ext_example",
) # BaseFile |
src_lang = "it" # str | Optional. Language to use to extract data from the *Attachment.CV.File*.If missing, the detected language from the input file text is assumed as `src_lang`. (optional)
dst_lang = "dst_lang_example" # str | Optional. Destination language in which the following *Data* entities are translated: *Skills*, *WorkExperiences.Skills*, *JobTitles*, *WorkExperiences.PositionTitle* and *Languages*.If missing, the input or detected `src_lang` is assumed as `dst_lang`. (optional)
graphics = False # bool | Whether to read skill graphs such as bars, pie charts, and symbols. (optional) if omitted the server will use the default value of False
# example passing only required values which don't have defaults set
try:
# Parse Resume
api_response = api_instance.parse_resume_post(base_file)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->parse_resume_post: %s\n" % e)
# example passing only required values which don't have defaults set
# and optional values
try:
# Parse Resume
api_response = api_instance.parse_resume_post(base_file, src_lang=src_lang, dst_lang=dst_lang, graphics=graphics)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->parse_resume_post: %s\n" % e)| Name | Type | Description | Notes |
|---|---|---|---|
| base_file | BaseFile | ||
| src_lang | str | Optional. Language to use to extract data from the Attachment.CV.File.If missing, the detected language from the input file text is assumed as `src_lang`. | [optional] |
| dst_lang | str | Optional. Destination language in which the following Data entities are translated: Skills, WorkExperiences.Skills, JobTitles, WorkExperiences.PositionTitle and Languages.If missing, the input or detected `src_lang` is assumed as `dst_lang`. | [optional] |
| graphics | bool | Whether to read skill graphs such as bars, pie charts, and symbols. | [optional] if omitted the server will use the default value of False |
- Content-Type: application/json
- Accept: application/json
| Status code | Description | Response headers |
|---|---|---|
| 200 | Document Successfully Processed | - |
| 422 | Unprocessable Entity | - |
| 415 | Unsupported Media Type | - |
| 500 | Internal Server Error | - |
| 503 | Service Unavailable | - |
| 400 | Bad Request | - |
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TextResponse text_extraction_post(base_file)
Text Extraction
This method extracts the text from a resume by performing the first steps of Parse Resume. In particular, the method requires in input the binary and the extension of the file and automatically performs (i) Document Layout Analysis, (ii) Optical Character Recognition (if the input document is an image), and (iii) Text Extraction. The allowed file extensions are pdf, doc, docx, odt, txt, html, pptx, rtf, jpg, jpeg, png, tif, tiff .
- Bearer Authentication (APIKey):
import time
import inda_hr
from inda_hr.api import resume_parsing_api
from inda_hr.model.text_response import TextResponse
from inda_hr.model.error_model import ErrorModel
from inda_hr.model.base_file import BaseFile
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 = resume_parsing_api.ResumeParsingApi(api_client)
base_file = BaseFile(
file=open('/path/to/file', 'rb'),
file_ext="file_ext_example",
) # BaseFile |
# example passing only required values which don't have defaults set
try:
# Text Extraction
api_response = api_instance.text_extraction_post(base_file)
pprint(api_response)
except inda_hr.ApiException as e:
print("Exception when calling ResumeParsingApi->text_extraction_post: %s\n" % e)| Name | Type | Description | Notes |
|---|---|---|---|
| base_file | BaseFile |
- Content-Type: application/json
- Accept: application/json
| Status code | Description | Response headers |
|---|---|---|
| 200 | Successfully Parsed Text | - |
| 422 | Unprocessable Entity | - |
| 415 | Unsupported Media Type | - |
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