def test_a_taxonomies_request_is_executed(self):
"""
...then verify that whole flow works as expected
"""
response_json = {
"success": True,
"taxonomies": {
"iptc": {
"description": "IPTC Media Topics",
"languages": [
{"description": "English", "name": "en"},
{"description": "German", "name": "de"},
{"description": "Spanish", "name": "es"},
{"description": "French", "name": "fr"},
{"description": "Italian", "name": "it"},
],
}
},
}
response = MagicMock()
response.status_code = 200
response.ok = True
response.json.return_value = response_json
self.patched_get.return_value = response
client = ExpertAiClient()
dm = client.iptc_taxonomies()
self.assertEqual(
dm.iptc.languages[0].get_language_by_description, "en"
)
def test_a_request_is_created(self, patched_get_method_name_for_endpoint):
"""
...then the proper HTTP method should be set
"""
def fake_get_method(self):
return {url: "GET"}.get(url)
url = self.endpoint_path
expert_client = ExpertAiClient()
patched_get_method_name_for_endpoint.side_effect = fake_get_method
new_request = expert_client.create_request(self.endpoint_path)
self.assertEqual(new_request.string_method, "GET")
patched_get_method_name_for_endpoint.assert_called_once_with(
self.endpoint_path)
def test_create_request_method_is_called(self, patched_verify_request):
"""
...then the verify_request() should also be invoked with the
correct arguments
"""
expert_client = ExpertAiClient()
expert_client.create_request(
endpoint_path="resource_urlpath",
params={"language": "en"},
body={"text": "text"},
)
patched_verify_request.assert_called_with(
"resource_urlpath",
params={"language": "en"},
)
def test_a_bad_request_is_received(self, patched_object_mapper):
"""
...then the ObjectMapper should not be called
"""
def response_json():
return {
"errors": [{
"code": "PREPARE_DOCUMENT_FAILED",
"message": "missing layout key in json",
}],
"success":
False,
}
fake_response = MagicMock(status_code=constants.HTTP_SUCCESSFUL,
json=response_json)
expert_client = ExpertAiClient()
expert_client.process_response(fake_response)
patched_object_mapper.assert_not_called()
def setUp(self):
super().setUp()
self.expert_client = ExpertAiClient()
self.test_body = {"document": {"text": "text"}}
self.test_endpoint_path = "endpoint/{language}/{resource}"
class ExpertAiClientTestCase(ExpertAiTestCase):
def setUp(self):
super().setUp()
self.expert_client = ExpertAiClient()
self.test_body = {"document": {"text": "text"}}
self.test_endpoint_path = "endpoint/{language}/{resource}"
@patch(
"expertai.nlapi.v1.client.ExpertAiClient.get_method_name_for_endpoint")
def test_a_request_is_created(self, patched_get_method_name_for_endpoint):
"""
...then the proper HTTP method should be set
"""
def fake_get_method(self):
return {url: "GET"}.get(url)
url = self.endpoint_path
expert_client = ExpertAiClient()
patched_get_method_name_for_endpoint.side_effect = fake_get_method
new_request = expert_client.create_request(self.endpoint_path)
self.assertEqual(new_request.string_method, "GET")
patched_get_method_name_for_endpoint.assert_called_once_with(
self.endpoint_path)
@patch("expertai.nlapi.v1.validate.ExpertAiValidation.check_parameters")
def test_a_request_is_verified(self, patched_check_parameters):
"""
...then check_parameters method should be called
"""
self.expert_client.verify_request(endpoint_path="path/{language}",
params={"language": "en"})
patched_check_parameters.assert_called_once_with(
params={"language": "en"})
@patch("expertai.nlapi.v1.validate.ExpertAiValidation.check_parameters")
def test_parameters_are_not_required(self, patched_check_parameters):
"""
...then the check_parameters method should not be called
"""
self.expert_client.verify_request(endpoint_path="/path", params=None)
patched_check_parameters.assert_not_called()
def test_required_parameters_are_not_provided(self):
"""
...then an error should be raised, indicating which parameter
is missing
"""
self.assertRaises(
MissingParametersError,
self.expert_client.verify_request,
endpoint_path="path/{lang}",
)
def test_a_parameterized_urlpath(self):
"""
...then keywords should be extracted
"""
self.assertEqual(
self.expert_client.urlpath_keywords("path/{language}/{resource}"),
["language", "resource"],
)
@patch("expertai.nlapi.v1.client.ExpertAiClient.verify_request")
def test_create_request_method_is_called(self, patched_verify_request):
"""
...then the verify_request() should also be invoked with the
correct arguments
"""
expert_client = ExpertAiClient()
expert_client.create_request(
endpoint_path="resource_urlpath",
params={"language": "en"},
body={"text": "text"},
)
patched_verify_request.assert_called_with(
"resource_urlpath",
params={"language": "en"},
)
@patch("expertai.nlapi.v1.client.ObjectMapper")
def test_a_bad_request_is_received(self, patched_object_mapper):
"""
...then the ObjectMapper should not be called
"""
def response_json():
return {
"errors": [{
"code": "PREPARE_DOCUMENT_FAILED",
"message": "missing layout key in json",
}],
"success":
False,
}
fake_response = MagicMock(status_code=constants.HTTP_SUCCESSFUL,
json=response_json)
expert_client = ExpertAiClient()
expert_client.process_response(fake_response)
patched_object_mapper.assert_not_called()
def test_a_full_analysis_request_is_executed(self):
"""
...then verify that whole flow works as expected
"""
response_json = {
"success": True,
"data": {
"content":
"Facebook is looking at buying U.S. startup for $6 million",
"language":
"en",
"version":
"sensei: 3.1.0; disambiguator: 15.0-QNTX-2016",
"knowledge": [{
"label":
"organization.company",
"properties": [{
"type": "WikiDataId",
"value": "Q380"
}],
"syncon":
288110,
}],
"phrases": [
{
"tokens": [0],
"type": "PP",
"start": 54,
"end": 65
},
],
"tokens": [{
"syncon":
62653,
"start":
74,
"end":
83,
"type":
"NOU",
"lemma":
"long time",
"pos":
"NOUN",
"dependency": {
"id": 11,
"head": 7,
"label": "nmod"
},
"morphology":
"Number=Sing",
"paragraph":
0,
"sentence":
0,
"phrase":
4,
"atoms": [
{
"start": 74,
"end": 78,
"type": "ADJ",
"lemma": "long",
},
],
}],
"mainSentences": [],
"mainPhrases": [],
"mainLemmas": [],
"mainSyncons": [],
"entities": [],
"topics": [],
"sentences": [{
"phrases": [0],
"start": 0,
"end": 100
}],
"paragraphs": [],
},
}
response = MagicMock(text="e@i")
response.status_code = 200
response.json.return_value = response_json
self.patched_post.return_value = response
client = ExpertAiClient()
request_body = {"document": {"text": "text"}}
data_model = client.full_analysis(body=request_body,
params={"language": "es"})
# two POST requests are made, one for the token and one for analysis
self.assertEqual(self.patched_post.call_count, 2)
self.assertEqual(data_model.sentences[0].phrases[0].type_.key, "PP")