Learning by Patrik

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

Capability	Use it for
Language Detection	Identify the primary language of text
Named Entity Recognition (NER)	Extract and classify people, organizations, locations, dates, etc.
PII Detection	Find sensitive personal information and produce redacted text

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.

Azure

Language

NLP

NER

PII