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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_analyze_actions_async.py
DESCRIPTION:
This sample demonstrates running multiple **text analysis actions** in a single job (async):
- Named Entity Recognition
- Key Phrase Extraction
USAGE:
python sample_analyze_actions_async.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 analyze_async]
import os
import asyncio
from azure.identity.aio import DefaultAzureCredential
from azure.ai.textanalytics.aio import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageTextInput,
MultiLanguageInput,
EntitiesLROTask,
KeyPhraseLROTask,
EntityRecognitionOperationResult,
KeyPhraseExtractionOperationResult,
EntityTag,
)
async def sample_analyze_actions_async():
# get settings
endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
credential = DefaultAzureCredential()
async with TextAnalysisClient(endpoint, credential=credential) as client:
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."
)
text_b = "Sentences in different languages."
text_c = (
"That was the best day of my life! We went on a 4 day trip where we stayed at Hotel Foo. They had"
" great amenities that included an indoor pool, a spa, and a bar. The spa offered couples massages"
" which were really good. The spa was clean and felt very peaceful. Overall the whole experience was"
" great. We will definitely come back."
)
text_d = "" # intentionally empty
# Batch documents
text_input = MultiLanguageTextInput(
multi_language_inputs=[
MultiLanguageInput(id="A", text=text_a, language="en"),
MultiLanguageInput(id="B", text=text_b, language="es"),
MultiLanguageInput(id="C", text=text_c, language="en"),
MultiLanguageInput(id="D", text=text_d), # auto-detect language
]
)
actions = [
EntitiesLROTask(name="EntitiesOperationActionSample"),
KeyPhraseLROTask(name="KeyPhraseOperationActionSample"),
]
# Submit multi-action job (LRO)
poller = await client.begin_analyze_text_job(text_input=text_input, actions=actions)
paged_actions = await poller.result()
# Iterate each action result
async for action_result in paged_actions:
print() # spacing
# --- Entities ---
if isinstance(action_result, EntityRecognitionOperationResult):
print("=== Entity Recognition Results ===")
for ent_doc in action_result.results.documents:
print(f'Result for document with Id = "{ent_doc.id}":')
print(f" Recognized {len(ent_doc.entities)} entities:")
for entity in ent_doc.entities:
print(f" Text: {entity.text}")
print(f" Offset: {entity.offset}")
print(f" Length: {entity.length}")
print(f" Category: {entity.category}")
if hasattr(entity, "type") and entity.type is not None:
print(f" Type: {entity.type}")
if hasattr(entity, "subcategory") and entity.subcategory:
print(f" Subcategory: {entity.subcategory}")
if hasattr(entity, "tags") and entity.tags:
print(" Tags:")
for tag in entity.tags:
if isinstance(tag, EntityTag):
print(f" TagName: {tag.name}")
print(f" TagConfidenceScore: {tag.confidence_score}")
print(f" Confidence score: {entity.confidence_score}")
print()
for err in action_result.results.errors:
print(f" Error in document: {err.id}!")
print(f" Document error: {err.error}")
# --- Key Phrases ---
elif isinstance(action_result, KeyPhraseExtractionOperationResult):
print("=== Key Phrase Extraction Results ===")
for kp_doc in action_result.results.documents:
print(f'Result for document with Id = "{kp_doc.id}":')
for kp in kp_doc.key_phrases:
print(f" {kp}")
print()
for err in action_result.results.errors:
print(f" Error in document: {err.id}!")
print(f" Document error: {err.error}")
# [END analyze_async]
async def main():
await sample_analyze_actions_async()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
|
