def update(self):
client = QnAMakerClient(endpoint=self.credential.app_host,
credentials=CognitiveServicesCredentials(
self.credential.app_key))
update_kb_operation_dto = UpdateKbOperationDTO(
add=UpdateKbOperationDTOAdd(qna_list=[
QnADTO(questions=self.questions, answer=self.answer)
]))
client.knowledgebase.update(kb_id=self.credential.app_id,
update_kb=update_kb_operation_dto)
time.sleep(5)
def main(myblob: func.InputStream):
logging.info(f"Python blob trigger function processed blob \n"
f"Name: {myblob.name}\n"
f"URI: {myblob.uri}\n"
f"Blob Size: {myblob.length} bytes")
subscription_key = os.environ["subscription_key"]
endpoint = os.environ["endpoint"]
kb_id = os.environ["kb_id"]
client = QnAMakerClient(
endpoint=endpoint,
credentials=CognitiveServicesCredentials(subscription_key))
update_kb(client=client, kb_id=kb_id, blob_url=myblob.uri)
publish_kb(client=client, kb_id=kb_id)
def knowledge_based_crud_sample(subscription_key):
"""KnowledgeBasedCRUDSample.
This will create, update, publish, download, then delete a knowledge base.
"""
def _create_sample_kb(client):
"""Helper function for knowledge_based_crud_sample.
This helper function takes in a QnAMakerClient and returns an operation of a created knowledge base.
"""
qna = QnADTO(
answer="You can use our REST APIs to manage your knowledge base.",
questions=["How do I manage my knowledgebase?"],
metadata=[MetadataDTO(name="Category", value="api")]
)
urls = [
"https://docs.microsoft.com/en-in/azure/cognitive-services/qnamaker/faqs"]
create_kb_dto = CreateKbDTO(
name="QnA Maker FAQ from quickstart",
qna_list=[qna],
urls=urls
)
create_op = client.knowledgebase.create(
create_kb_payload=create_kb_dto)
create_op = _monitor_operation(client=client, operation=create_op)
return create_op.resource_location.replace("/knowledgebases/", "")
def _monitor_operation(client, operation):
"""Helper function for knowledge_based_crud_sample.
This helper function takes in a QnAMakerClient and an operation, and loops until the operation has either succeeded
or failed and returns the operation.
"""
for i in range(20):
if operation.operation_state in [OperationStateType.not_started, OperationStateType.running]:
print("Waiting for operation: {} to complete.".format(
operation.operation_id))
time.sleep(5)
operation = client.operations.get_details(
operation_id=operation.operation_id)
else:
break
if operation.operation_state != OperationStateType.succeeded:
raise Exception("Operation {} failed to complete.".format(
operation.operation_id))
return operation
client = QnAMakerClient(endpoint=QNA_ENDPOINT), credentials=CognitiveServicesCredentials(subscription_key))
# Create a KB
print("Creating KB...")
kb_id = _create_sample_kb(client=client)
print("Created KB with ID: {}".format(kb_id))
# Update the KB
print("Updating KB...")
update_kb_operation_dto = UpdateKbOperationDTO(
add=UpdateKbOperationDTOAdd(
qna_list=[
QnADTO(questions=["bye"], answer="goodbye")
]
)
def publish(self):
client = QnAMakerClient(endpoint=self.credential.app_host,
credentials=CognitiveServicesCredentials(
self.credential.app_key))
client.knowledgebase.publish(kb_id=self.credential.app_id)
listSearchResults = client.knowledgebase.generate_answer(
kb_id, QueryDTO(question="How do I manage my knowledgebase?"))
for i in listSearchResults.answers:
print(f"Answer ID: {i.id}.")
print(f"Answer: {i.answer}.")
print(f"Answer score: {i.score}.")
# </GenerateAnswerPreview>
# <Main>
# <AuthorizationAuthor>
client = QnAMakerClient(
endpoint=endpoint,
credentials=CognitiveServicesCredentials(subscription_key))
# </AuthorizationAuthor>
kb_id = create_kb(client=client)
update_kb(client=client, kb_id=kb_id)
publish_kb(client=client, kb_id=kb_id)
download_kb(client=client, kb_id=kb_id)
queryRuntimeKey = getEndpointKeys_kb(client=client)
if (try_managed_preview):
generate_answer_preview(client=client, kb_id=kb_id)
else:
# <AuthorizationQuery>
runtimeClient = QnAMakerRuntimeClient(