feersum_nlu 2.0.20

FeersumNLU API

FeersumNLU API Wrappers

This git repo hosts Python and other language wrappers for the FeersumNLU RESTful HTTP API. FeersumNLU is the natural language understanding component of Feersum Engine https://www.feersum.io .

The HTTP API is designed such that a user may apply multi-lingual Natural Language Understanding (NLU) to their application domain without requiring a deep understanding of NLU theory. The examples in this repo demonstrate commonly required NLU functions and how to access them. There are also some curl examples of direct HTTP access in the examples_curl folder.

These language wrappers and examples are for version 2.0.20 of the HTTP API. The language wrappers are auto-generated from a Swagger spec of the API available at https://nlu.playground.feersum.io:443/nlu/v2/swagger.json

(Note: This repo is currently being updated with more examples)

Overview

FeersumNLU provides natural language understanding solutions for chat-based interactions, where messages from the end-user may be long or very short. Our product is also designed to be language-agnostic, which means it can be scaled to work with any language, even in markets where large bodies of labelled data do not exist. Our Natural Language Understanding models are developed locally, and can be modified to provide solutions for specialised industries like finance or health.

Features

FeersumNLU makes use of open-source building blocks like NLTK, sklearn, PyTorch and Duckling, as well as algorithms we’ve developed in-house. We build on all elements to support a growing list of local and international languages.

Current features include Natural Language FAQ’s, detection of the user’s intent and sentiment, information extraction, entity extraction, and text-based language identification.

Installing

Please have a look at the examples in the repo. It is also recommended that you create a Python virtual environment and then follow one of the install options below to run the examples. The examples were tested with Python 3.5 and 3.6.

Creating a Python Virtual Environment

To get started with the Python wrappers install Python 3.5 (or 3.6) and pip. Then do:

$ virtualenv -p /usr/local/bin/python3.5 .pyenv
$ source .pyenv/bin/activate
$ pip install pip-tools
$ pip install appdirs

If you don’t have virtualenv installed first run:

$ pip install virtualenv
$ sudo /usr/bin/easy_install virtualenv

Install Option 1 - Using make

Clone the repo and then to install the dependencies required to run the module’s examples run:

$ make requirements
$ make deps

Install Option 2 - Using setuptools

Alternatively clone the repo and then install the feersum_nlu wrapper module into your Python environment using setuptools:

$ make requirements
$ make deps
$ python setup.py install

The benefit of installing the feersum_nlu module into you Python environment is that you can more easily use it in your own projects.

Install Option 3 - Using pip

The feersum_nlu wrapper module is also available from the Python Package Index https://pypi.python.org/pypi/feersum_nlu. To install it using pip run:

$ pip install feersum_nlu

The benefit of installing the feersum_nlu module into you Python environment is that you can more easily use it in your own projects.

Running the Examples

If you use an IDE like PyCharm you can simply open the folder you cloned the source to (e.g. feersum-nlu-api-wrappers) with the IDE.

Or to start executing example notebooks install Jupyter with

$ pip install jupyter

and then run:

$ jupyter notebook

The notebooks are all in the example_notebooks folder.

Alternatively run the example python scripts in the examples folder from the terminal e.g.:

$ PYTHONPATH=. python examples/faq_matcher.py

Remember to set your API token in the example scripts and notebooks.

The Playground Instance

A FeersumNLU playground instance of the RESTful web service is hosted at nlu.playground.feersum.io:443/nlu/v2 You may use this URL to run the examples given in this repo.

You’ll need an authentication token to access the service, so email us at [e-mail] and we’ll send you a token to use. Some autogenerated API documentation is available at https://nlu.playground.feersum.io:443/nlu/v2/ui/

Note that this is just a playground instance. Please contact us at [e-mail] if you need a production quality instance of the service.

Changelog

Version 2.0.20

• Changed name of similar_entity_extractor to sim_word_entity_extractor.

• Removed the api hit counts from the dashboard.

• Added a trashed flag to each model in the dashboard.

• Removed the immediate_mode arguments in the train operations.

• The spec and Python API wrapper have been updated to rather return lists of python objects as opposed to lists of json objects.

• Changes to LR4 to have a load from store that works like all of the other names.

Version 2.0.18

• Added a ‘long_name’ attribute to all models. May be used as a ‘pretty’ formatted model name while the previous model name is really a slug used in urls, etc.

• Addition of params end point to all models. Used to update and get model attributes like desc, long_name and threshold.

• Updated LID LR4 to load from store.

Version 2.0.16

• Added model delete endpoints.

• Expose experimental person name entity extractor on the http API.

• Exposed the reference_time attribute to the Duckling entity extractor.

• Moved the examples’ and tests’ auth token and host config to a central location in the __init.py__

• Added this changelog.