azure-ai-textanalytics-py

# Azure AI Text Analytics SDK for Python Client library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more. ## Installation ```bash pip install azure-ai-textanalytics ``` ## Environment Variables ```bash AZURE_LANGUAGE_ENDPOINT=https://<resource>.cognitiveservices.azure.com AZURE_LANGUAGE_KEY=<your-api-key> # If using API key ``` ## Authentication ### API Key ```python import os from azure.core.credentials import AzureKeyCredential from azure.ai.textanalytics import TextAnalyticsClient endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"] key = os.environ["AZURE_LANGUAGE_KEY"] client = TextAnalyticsClient(endpoint, AzureKeyCredential(key)) ``` ### Entra ID (Recommended) ```python from azure.ai.textanalytics import TextAnalyticsClient from azure.identity import DefaultAzureCredential client = TextAnalyticsClient( endpoint=os.environ["AZURE_LANGUAGE_ENDPOINT"], credential=DefaultAzureCredential() ) ``` ## Sentiment Analysis ```python documents = [ "I had a wonderful trip to Seattle last week!", "The food was terrible and the service was slow." ] result = client.analyze_sentiment(documents, show_opinion_mining=True) for doc in result: if not doc.is_error: print(f"Sentiment: {doc.sentiment}") print(f"Scores: pos={doc.confidence_scores.positive:.2f}, " f"neg={doc.confidence_scores.negative:.2f}, " f"neu={doc.confidence_scores.neutral:.2f}") # Opinion mining (aspect-based sentiment) for sentence in doc.sentences: for opinion in sentence.mined_opinions: target = opinion.target print(f" Target: '{target.text}' - {target.sentiment}") for assessment in opinion.assessments: print(f" Assessment: '{assessment.text}' - {assessment.sentiment}") ``` ## Entity Recognition ```python documents = ["Microsoft was founded by Bill Gates and Paul Allen in Albuquerque."] result = client.recognize_entities(documents) for doc in result: if not doc.is_error: for entity in doc.entities: print(f"Entity: {entity.text}") print(f" Category: {entity.category}") print(f" Subcategory: {entity.subcategory}") print(f" Confidence: {entity.confidence_score:.2f}") ``` ## PII Detection ```python documents = ["My SSN is 123-45-6789 and my email is john@example.com"] result = client.recognize_pii_entities(documents) for doc in result: if not doc.is_error: print(f"Redacted: {doc.redacted_text}") for entity in doc.entities: print(f"PII: {entity.text} ({entity.category})") ``` ## Key Phrase Extraction ```python documents = ["Azure AI provides powerful machine learning capabilities for developers."] result = client.extract_key_phrases(documents) for doc in result: if not doc.is_error: print(f"Key phrases: {doc.key_phrases}") ``` ## Language Detection ```python documents = ["Ce document est en francais.", "This is written in English."] result = client.detect_language(documents) for doc in result: if not doc.is_error: print(f"Language: {doc.primary_language.name} ({doc.primary_language.iso6391_name})") print(f"Confidence: {doc.primary_language.confidence_score:.2f}") ``` ## Healthcare Text Analytics ```python documents = ["Patient has diabetes and was prescribed metformin 500mg twice daily."] poller = client.begin_analyze_healthcare_entities(documents) result = poller.result() for doc in result: if not doc.is_error: for entity in doc.entities: print(f"Entity: {entity.text}") print(f" Category: {entity.category}") print(f" Normalized: {entity.normalized_text}") # Entity links (UMLS, etc.) for link in entity.data_sources: print(f" Link: {link.name} - {link.entity_id}") ``` ## Multiple Analysis (Batch) ```python from azure.ai.textanalytics import ( RecognizeEntitiesAction, ExtractKeyPhrasesAction, AnalyzeSentimentAction ) documents = ["Microsoft announced new Azure AI features at Build conference."] poller = client.begin_analyze_actions( documents, actions=[ RecognizeEntitiesAction(), ExtractKeyPhrasesAction(), AnalyzeSentimentAction() ] ) results = poller.result() for doc_results in results: for result in doc_results: if result.kind == "EntityRecognition": print(f"Entities: {[e.text for e in result.entities]}") elif result.kind == "KeyPhraseExtraction": print(f"Key phrases: {result.key_phrases}") elif result.kind == "SentimentAnalysis": print(f"Sentiment: {result.sentiment}") ``` ## Async Client ```python from azure.ai.textanalytics.aio import TextAnalyticsClient from azure.identity.aio import DefaultAzureCredential async def analyze(): async with TextAnalyticsClient( endpoint=endpoint, credential=DefaultAzureCredential() ) as client: result = await client.analyze_sentiment(documents) # Process results... ``` ## Client Types | Client | Purpose | |--------|---------| | `TextAnalyticsClient` | All text analytics operations | | `TextAnalyticsClient` (aio) | Async version | ## Available Operations | Method | Description | |--------|-------------| | `analyze_sentiment` | Sentiment analysis with opinion mining | | `recognize_entities` | Named entity recognition | | `recognize_pii_entities` | PII detection and redaction | | `recognize_linked_entities` | Entity linking to Wikipedia | | `extract_key_phrases` | Key phrase extraction | | `detect_language` | Language detection | | `begin_analyze_healthcare_entities` | Healthcare NLP (long-running) | | `begin_analyze_actions` | Multiple analyses in batch | ## Best Practices 1. **Use batch operations** for multiple documents (up to 10 per request) 2. **Enable opinion mining** for detailed aspect-based sentiment 3. **Use async client** for high-throughput scenarios 4. **Handle document errors** — results list may contain errors for some docs 5. **Specify language** when known to improve accuracy 6. **Use context manager** or close client explicitly ## When to Use This skill is applicable to execute the workflow or actions described in the overview. ## Diff History - **v00.33.0**: Ingested from antigravity-awesome-skills community repo --- ## Why This Skill Exists Implement — Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, <!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. --> ## What If Fails - condition: Código não disponível para análise <!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->