averbis-python-api 0.11.0

Description:

averbispythonapi 0.11.0

Averbis is a leading text mining and machine learning company in Healthcare and Life Sciences. We extract information from texts, automate intellectual processes and make meaningful predictions.
The Averbis Python API allows convenient access to the REST API of Averbis products. This includes in particular the ability to interact with the text mining pipelines offered by these products, e.g. to use these in data science environments such as Jupyter notebooks or for integration of the Averbis products in other enterprise systems.
Supported products are:

Health Discovery
Information Discovery
Patent Monitor


Status
The Averbis Python API is currently in an open alpha development stage. We try to keep breaking changes minimal, but they may happen on the way to the first stable release.


Features
Currently, supported features are:

Managing projects
Managing pipelines
Managing terminologies
Managing collection of documents
Managing pears
Analysing text using a server-side text mining pipeline
Classifying texts using a server-side classifier



Installation
The library can be installed easily via pip
pip install averbis-python-api


Documentation
To get an overview over the methods provided with the client and the corresponding documentation, we refer to our readthedocs API reference.
Moreover, we will provide a number of example Jupyter notebooks that showcase the usage of the client to solve different use cases in an upcoming release.
The usage for a selected number of API endpoints is given below.


Usage

Connecting the client to a platform
from averbis import Client
# Use existing API Token
client = Client('http://localhost:8400/health-discovery', api_token='YOUR_API_TOKEN')
# or generate new API Token based on your credentials (invalidates old API Token)
client = Client('http://localhost:8400/health-discovery', username = 'YOUR_USERNAME', password = 'YOUR_PASSWORD')


Connecting to a pipeline and assure that it is started
project = client.get_project('YOUR_PROJECT_NAME')
pipeline = project.get_pipeline('YOUR_PIPELINE_NAME')
pipeline.ensure_started()


Analysing a string
document = 'This is the string we want to analyse.'
annotations = pipeline.analyse_text(document, language='en')
for annotation in annotations:
print(annotation)


Analysing a text file
with open('/path/to/text_file.txt', 'rb') as document:
annotations = pipeline.analyse_text(document, language='en')
for annotation in annotations:
print(annotation)


Restricting returned annotation types
annotations = pipeline.analyse_text(document, language='en',
annotation_types='*Diagnosis') # will return only annotations that end with 'Diagnosis'


Upload documents, process them using a pipeline, and export results
In contrast to the simple text analysis endpoint above, one can also upload the documents into the product and create an analysis process there using experimental endpoints (may change soon). This has some advantages, namely that the results can be inspected in our product using the AnnotationViewer, and that the same document collection could be re-processed several times.
document_collection = project.create_document_collection("COLLECTION_NAME")

file_path = "path/to/text/file.txt"
with open(file_path, "r", encoding="UTF-8") as input_io:
document_collection.import_documents(input_io)
print(f"Number of documents: {document_collection.get_number_of_documents()}")

pipeline = project.get_pipeline("MY_PIPELINE_NAME")

# Using experimental endpoints to run the analysis and monitor the process state
process = document_collection.create_and_run_process(process_name="MY_PROCESS", pipeline=pipeline)
while process.get_process_state().state == "PROCESSING":
time.sleep(1)

results = process.export_text_analysis()
print(results)


Pear Management
A PEAR (Processing Engine ARchive) file is the UIMA standard packaging format for UIMA components like analysis engines (annotators) or CAS consumers. We provdie some (experimental - may change soon) endpoints to upload, delete and list PEARs.
project.list_pears()
pear = project.install_pear("path/to/mypear.pear")
print(pear.get_default_configuration())
pear.delete()


Connection profiles
To avoid storing API keys in the Python scripts or constantly re-generating them, it is
possible to store the keys for commonly used servers in a configuration file. This file
must be called client-settings.json and it must be located either in the working directory
of the script or in the user’s home folder in .averbis/client-settings.json.
Each profile has four settings:

url: the base URL of the server application
api-token: the API token
verify-ssl: the path to a PEM file used to validate the server certificate if SSL is used
timeout: An optional timeout parameter (in seconds)

Default settings which should be applied to all profiles can be stored in the special profile * (star).
{
"profiles": {
"*": {
"verify-ssl": "caRoot.pem"
},
"localhost-hd": {
"url": "https://localhost:8080/health-discovery",
"api-token": "dummy-token"
},
"localhost-id": {
"url": "https://localhost:8080/information-discovery",
"api-token": "dummy-token",
"verify-ssl": "id.pem"
}
}
}
An existing profile can then be loaded with
from averbis import Client
client = Client("localhost-id")



Development
To set up a local development environment, check out the repository, set up a virtual environment
and install the required dependencies (if --no-site-packages does not work on your system, omit it):
virtualenv venv --python=python3 --no-site-packages
source venv/bin/activate
pip install -e ".[test, dev, doc]"
To install the latest development version of the library directly from GitHub, you can use the following command:
$ pip install --force-reinstall --upgrade git+https://github.com/averbis/averbis-python-api.git@refs/heads/dev