Example 1: As a python module

You can run the uploader as a python module and pass the name as a keyword argument.

$ export ENTITYSVC_BASE_URL='http://localhost.newsdev.net:8000'
$ python
Python 3.6.1 (default, Apr  4 2017, 09:40:21)
[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.38)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from entity_uploader import UploadEntity
>>> e = UploadEntity(name="Bank of America")
>>> e.to_dict()
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 0, 'created': True}

Example 2: Running example.py

example.py is a sample implementation that reads a list of entity names from example_entities.txt.

$ export ENTITYSVC_BASE_URL='http://localhost.newsdev.net:8000'
$ python example.py

{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 0, 'created': True}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 95, 'created': False}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 100, 'created': False}
{'name': "banque d'amerique", 'uuid': 'cb626971-1989-4d78-870d-e6835017c936', 'score': 62, 'created': True}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 95, 'created': False}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 90, 'created': False}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 86, 'created': False}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 86, 'created': False}
{'name': 'Bank of America', 'uuid': 'f514b0e1-eea5-4676-aed2-2f9ee501cd5e', 'score': 86, 'created': False}

In this example, the default create_if_below score is 80. The first entity, Bank of America is created. The next entity, Bank of America, N.A. is not created because it has a similarity score of 95. The UUID of the matching entity, Bank of America, is returned. The same is true for the next entity, BANK OF AMERICA, which has an even higher score of 100. The next entity, banque d'amerique, is created as a new entity because it has a matching score of 62, which is lower than the default create_if_below score of 80. The last few entities in example_entities.txt match the first entity with varying degrees of closeness.