Analyze text with Azure Language in Foundry Tools | AI-103 | Episode 15

Need to identify a document’s language, extract people and places, or remove personal data? Azure Language in Foundry Tools provides purpose-built NLP capabilities without requiring an LLM.

Core capabilities to know

Key decision: prefer these specialized tools when you need predictable, focused text analysis. Compared with general-purpose LLMs, they can also be more appropriate where cost and latency matter. An LLM is better suited when the task requires broader reasoning or flexible natural-language generation.

Python mental model

from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics import TextAnalyticsClient

client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
)

docs = ["Alex visited Seattle last week."]

language = client.detect_language(docs)
entities = client.recognize_entities(docs)
pii = client.recognize_pii_entities(docs)

print(language[0].primary_language.name)
print([(e.text, e.category) for e in entities[0].entities])
print(pii[0].redacted_text)

Remember: endpoint + credential → TextAnalyticsClient → analysis method → structured result. Authentication can use credentials such as Microsoft Entra ID; document operations can also be performed in batches.

Scenario clues: “extract people, places and dates” → NER. “Remove customer details before publishing” → PII detection/redaction.