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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_recognize_entities.py
DESCRIPTION:
This sample demonstrates how to run **entity recognition** over text.
USAGE:
python sample_recognize_entities.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_TEXT_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_TEXT_ENDPOINT
- AZURE_TEXT_KEY
"""
# [START recognize_entities]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageTextInput,
MultiLanguageInput,
TextEntityRecognitionInput,
EntitiesActionContent,
AnalyzeTextEntitiesResult,
)
def sample_recognize_entities():
# settings
endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
credential = DefaultAzureCredential()
client = TextAnalysisClient(endpoint, credential=credential)
# input
text_a = (
"We love this trail and make the trip every year. The views are breathtaking and well worth the hike! "
"Yesterday was foggy though, so we missed the spectacular views. We tried again today and it was "
"amazing. Everyone in my family liked the trail although it was too challenging for the less "
"athletic among us. Not necessarily recommended for small children. A hotel close to the trail "
"offers services for childcare in case you want that."
)
body = TextEntityRecognitionInput(
text_input=MultiLanguageTextInput(
multi_language_inputs=[MultiLanguageInput(id="A", text=text_a, language="en")]
),
action_content=EntitiesActionContent(model_version="latest"),
)
result = client.analyze_text(body=body)
# Print results
if isinstance(result, AnalyzeTextEntitiesResult) and result.results and result.results.documents:
for doc in result.results.documents:
print(f"\nDocument ID: {doc.id}")
if doc.entities:
print("Entities:")
for entity in doc.entities:
print(f" Text: {entity.text}")
print(f" Category: {entity.category}")
if entity.subcategory:
print(f" Subcategory: {entity.subcategory}")
print(f" Offset: {entity.offset}")
print(f" Length: {entity.length}")
print(f" Confidence score: {entity.confidence_score}\n")
else:
print("No entities found for this document.")
else:
print("No documents in the response or unexpected result type.")
# [END recognize_entities]
def main():
sample_recognize_entities()
if __name__ == "__main__":
main()
|
