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library.qai.utilities
v2! A REST server and helper functions and classes for interacting with the rest of the Qordoba platform.
See GitHub history for <2.0 docs.
Things to know
See The Changelog for details.
Required "conventions"
All projects must have a config.json
, and that config must specify SUPPORTED_LANGUAGES
, which is either a string or list of strings, of the form "en"
or ["en", "de", "zh"]
(the prefix of the ISO code). QAI will not let your service start unless it thinks you have a valid SUPPORTED_LANGUAGES
field. By default, QAI will look for this in conf/config.json
. This is overridable. Here is the minimal config:
{
"SUPPORTED_LANGUAGES": "en"
}
You can specify the service name in the config file with
{
"SUPPORTED_LANGUAGES": "en",
"SERVICE_NAME": "hey look at me service",
}
To change the config path to, for example, ./my_config_dir/a_sub_dir/my_wacky_config.json
:
QRest(analyzer,
category='service name, e.g. formality',
white_lister=white_lister,
config_path=['my_config_dir', 'a_sub_dir', 'my_wacky_config.json'])
Usage
You can explicitly create a REST connection like this:
from app import Analyzer, whitelist
from qai.qconnect.qrest import QRest
SERVICE_NAME = 'service_name'
host = '0.0.0.0'
port = 5000
if __name__ == '__main__':
analyzer = Analyzer()
rest_connection = QRest(analyzer,
category=category,
white_lister=white_lister,
host=host,
port=port)
# create a blocking connection:
rest_connection.connect()
The above will create as many workers as you have cores. This is great, unless you are using AutoML. There is a known bug where AutoML crashes if you are using more than one worker.
So if you're using AutoML, the above would look like:
from app import Analyzer, whitelist
from qai.qconnect.qrest import QRest
SERVICE_NAME = 'service_name'
host = '0.0.0.0'
port = 5000
workers = 1
if __name__ == '__main__':
analyzer = Analyzer()
rest_connection = QRest(analyzer,
category=category,
white_lister=white_lister,
host=host,
port=port,
workers=workers)
# create a blocking connection:
rest_connection.connect()
There is also a helper class for turning spaCy Span
s into issues the rest of the platform can process:
from spacy.tokens import Span
from app.factor import SpacyFactor
SOV = SpacyFactor(
"subject_object_verb_spacing",
"Keep the subject, verb, and object of a sentence close together to help the reader understand the sentence."
)
Span.set_extension("score", default=0)
Span.set_extension("suggestions", default=[])
doc = nlp("Holders of the Class A and Class B-1 certificates will be entitled to receive on each Payment Date, to the extent monies are available therefor (but not more than the Class A Certificate Balance or Class B-1 Certificate Balance then outstanding), a distribution.")
score = analyze(doc)
if score is not None:
span = Span(doc, 0, len(doc)) # or whichever TOKENS are the issue (don't have to worry about character indexes)
span._.score = score
span._.suggestions = get_suggestions(doc)
issues = SOV(span)
Installation
pip?
Testing
See Confluence for docs on input format expectations.
scripts/test_qai.sh
has some helpful testing functions.
Development
Source of truth is VERSION
file, read by setup.py
and Jenkinsfile
. When you run python setup.py sdist/bdist
, this creates qai/version.py
, which is read in qai/__init__.py
. This was done for reasons having to do with python's module system being frustrating. It allows one to not have to know the absolute path of a file at runtime, which is a big bonus in Python. Anyway, that means VERSION
is the source of truth.
License
This software is not licensed. If you do not work at Qordoba, you are not legally allowed to use it. Also, it's just helper functions that really won't help you. If something in it does look interesting, and you would like access, open an issue.
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