oxapi 0.0.7

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OxAPI Python Library

The OxAPI Python library provides simplified access to the OxAPI from applications written in the Python language.

Documentation

See the OxAPI documentation . Access to repo doc: http://github-oxapi-python-doc.s3-website.eu-central-1.amazonaws.com

Installation

You don't need this source code unless you want to modify the package. If you just want to use the package, just run:

pip install --upgrade oxapi-python

Install from source with:

python setup.py install

Package structure

├── oxapi
│   ├── abstract                
│   │   └── api.py              # Non-instantiable, super classes for API calls
│   ├── nlp                     
│   │   ├── classification.py   # NLP Classification package
│   │   ├── completion.py       # NLP Completion package
│   │   ├── encoding.py         # NLP Encoding package
│   │   ├── pipeline.py         # NLP Pipeline package
│   │   └── transformation.py   # NLP Transformation package
│   ├── utils.py                # General utilities
│   ├── asynch.py               # package for asynchronous API calls
│   └── error.py                # Custom exceptions module
├── tests                       # Tests
└── docs_src                    # Documentation source files

Usage

API key

The library needs to be configured with your account's secret key. Either set it as the OXAPI_KEY environment variable before using the library:

export OXAPI_KEY='sk-...'

Or set oxapi.api_key to its value:

import oxapi
oxapi.api_key = "sk-..."

Completion

from oxapi.nlp.completion import Completion

# Performing API call

completion = Completion.create(
    model="gpt-neo-2-7b", 
    prompt="My name is Tim.\nSentiment: Neutral\nIt is such a lovely day.\nSentiment: Positive\nAlthough I am in a bad mood\nSentiment:",
    max_length=2, 
    do_sample=False, 
    eos_words=["\n"]
)

# Fetching result

res = completion.format_result(result_format="str")

print(completion.result)

Output:

{'results': ['Neutral\n']}

Classification

from oxapi.nlp.classification import Classification

# Performing API call

classification = Classification.create(
    model="dialog-content-filter", 
    texts=["I want to kill myself.", "I want to kill myself.<sep>You should not do that!", "I want to kill myself.<sep>Do it!"]
)

# Fetching result

res = classification.format_result(result_format="pd")

print(res)

Output:

                                                text label          confidence_score
0                             I want to kill myself.  unsafe      0.9772329101403038
1  I want to kill myself.<sep>You should not do t...  safe        0.9736578740966625
2                  I want to kill myself.<sep>Do it!  unsafe      0.9266854663680397

Encoding

from oxapi.nlp.encoding import Encoding

# Performing API call

encoding = Encoding.create(
    model="mpnet-base-v2",
    texts=["Hello", "How are you?"]
)

# Fetching result

print(encoding.result)

Output:

{'results': [[
   -0.017791748046875,
   -2.980232238769531e-07,
   -0.022003173828125,
   0.02105712890625,
   -0.06695556640625,
   -0.02435302734375,
   -0.0174713134765625,
   ...
    -0.0011529922485351562]]
}

Transformation

from oxapi.nlp.transformation import Transformation

# Performing API call

transformation = Transformation.create(
    model="punctuation-imputation", 
    texts=["hello my name is tim i just came back from nyc how are you doing"]
)

# Fetching result

print(transformation.result)

Output:

{'results': ['Hello my name is Tim. I just came back from NYC. How are you doing?']}

Pipeline

from oxapi.nlp.pipeline import Pipeline

# Performing API call

pipeline = Pipeline.create(
    model="en-core-web-lg",
    texts=["Hi there!"]
)

# Fetching result

print(pipeline.result)

Output:

{'results': [{'text': 'Hi there!',
   'ents': [],
   'sents': [{'start': 0, 'end': 9}],
   'tokens': [{'id': 0,
     'start': 0,
     'end': 2,
     'tag': 'UH',
     'pos': 'INTJ',
     'morph': '',
     'lemma': 'hi',
     'dep': 'ROOT',
     'head': 0},
    {'id': 1,
     'start': 3,
     'end': 8,
     'tag': 'RB',
     'pos': 'ADV',
     'morph': 'PronType=Dem',
     'lemma': 'there',
     'dep': 'advmod',
     'head': 0},
    {'id': 2,
     'start': 8,
     'end': 9,
     'tag': '.',
     'pos': 'PUNCT',
     'morph': 'PunctType=Peri',
     'lemma': '!',
     'dep': 'punct',
     'head': 0}],
   'sents_text': ['Hi there!']}]
}

Asynchronous call pipeline

With oxapi-python package is possible to make calls to OxAPI in parallel. The AsyncCallPipe class takes as input a list of API calls each set through the prepare function to be executed by the pipeline.

from oxapi.asynch import AsyncCallPipe

from oxapi.nlp.completion import Completion
from oxapi.nlp.classification import Classification
from oxapi.nlp.transformation import Transformation
from oxapi.nlp.pipeline import Pipeline

# Set up API calls

cl = Classification.prepare(model="dialog-content-filter", texts=["I want to kill myself."])
cm = Completion.prepare(model="gpt-neo-2-7b", prompt="Hello there, ", max_length=25, do_sample=True, eos_words=["\n"])
tr = Transformation.prepare(model="punctuation-imputation", texts=["hello my name is tim i just came back from nyc how are you doing"])
pl = Pipeline.prepare(model="en-core-web-lg", texts=["Hi there!"])

# Building and running the asynchronous pipe

asy = AsyncCallPipe([cl, cm, tr, pl])
res = asy.run()

# Fetching the result of the first call in the list

print(res[0].format_result(result_format="pd"))

Output:

                                                text label          confidence_score
0                             I want to kill myself.  unsafe      0.9772329101403038

It is possible to add API calls to the asynchronous pipe even after its instantiation though the add function. There's also the flush function to clear the list in the pipe.

from oxapi.asynch import AsyncCallPipe
from oxapi.nlp.encoding import Encoding

# Instantiate an empty asynchornous pipe

asy = AsyncCallPipe()

# Set up API call and add it to the pipe

en = Encoding.prepare(model="mpnet-base-v2", texts=["Hello", "How are you?"])
asy.add(en)

# running the asynchronous pipe

res = asy.run()

Credit