Azure AI Contentunderstanding Py

Azure AI Content Understanding SDK for Python

Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows.

Installation

pip install azure-ai-contentunderstanding

Environment Variables

CONTENTUNDERSTANDING_ENDPOINT=https://<resource>.cognitiveservices.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication

import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
client = ContentUnderstandingClient(endpoint=endpoint, credential=credential)

Core Workflow

Content Understanding operations are asynchronous long-running operations:

1. Begin Analysis — Start the analysis operation with begin_analyze() (returns a poller)
2. Poll for Results — Poll until analysis completes (SDK handles this with .result())
3. Process Results — Extract structured results from AnalyzeResult.contents

Prebuilt Analyzers

Analyze Document

import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalyzeInput
from azure.identity import DefaultAzureCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
client = ContentUnderstandingClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
)

# Analyze document from URL
poller = client.begin_analyze(
    analyzer_id="prebuilt-documentSearch",
    inputs=[AnalyzeInput(url="https://example.com/document.pdf")]
)

result = poller.result()

# Access markdown content (contents is a list)
content = result.contents[0]
print(content.markdown)

Access Document Content Details

from azure.ai.contentunderstanding.models import MediaContentKind, DocumentContent

content = result.contents[0]
if content.kind == MediaContentKind.DOCUMENT:
    document_content: DocumentContent = content  # type: ignore
    print(document_content.start_page_number)

Analyze Image

from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-imageSearch",
    inputs=[AnalyzeInput(url="https://example.com/image.jpg")]
)
result = poller.result()
content = result.contents[0]
print(content.markdown)

Analyze Video

from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-videoSearch",
    inputs=[AnalyzeInput(url="https://example.com/video.mp4")]
)

result = poller.result()

# Access video content (AudioVisualContent)
content = result.contents[0]

# Get transcript phrases with timing
for phrase in content.transcript_phrases:
    print(f"[{phrase.start_time} - {phrase.end_time}]: {phrase.text}")

# Get key frames (for video)
for frame in content.key_frames:
    print(f"Frame at {frame.time}: {frame.description}")

Analyze Audio

from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-audioSearch",
    inputs=[AnalyzeInput(url="https://example.com/audio.mp3")]
)

result = poller.result()

# Access audio transcript
content = result.contents[0]
for phrase in content.transcript_phrases:
    print(f"[{phrase.start_time}] {phrase.text}")

Custom Analyzers

Create custom analyzers with field schemas for specialized extraction:

# Create custom analyzer
analyzer = client.create_analyzer(
    analyzer_id="my-invoice-analyzer",
    analyzer={
        "description": "Custom invoice analyzer",
        "base_analyzer_id": "prebuilt-documentSearch",
        "field_schema": {
            "fields": {
                "vendor_name": {"type": "string"},
                "invoice_total": {"type": "number"},
                "line_items": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "description": {"type": "string"},
                            "amount": {"type": "number"}
                        }
                    }
                }
            }
        }
    }
)

# Use custom analyzer
from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="my-invoice-analyzer",
    inputs=[AnalyzeInput(url="https://example.com/invoice.pdf")]
)

result = poller.result()

# Access extracted fields
print(result.fields["vendor_name"])
print(result.fields["invoice_total"])

Analyzer Management

# List all analyzers
analyzers = client.list_analyzers()
for analyzer in analyzers:
    print(f"{analyzer.analyzer_id}: {analyzer.description}")

# Get specific analyzer
analyzer = client.get_analyzer("prebuilt-documentSearch")

# Delete custom analyzer
client.delete_analyzer("my-custom-analyzer")

Async Client

import asyncio
import os
from azure.ai.contentunderstanding.aio import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalyzeInput
from azure.identity.aio import DefaultAzureCredential

async def analyze_document():
    endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
    credential = DefaultAzureCredential()
    
    async with ContentUnderstandingClient(
        endpoint=endpoint,
        credential=credential
    ) as client:
        poller = await client.begin_analyze(
            analyzer_id="prebuilt-documentSearch",
            inputs=[AnalyzeInput(url="https://example.com/doc.pdf")]
        )
        result = await poller.result()
        content = result.contents[0]
        return content.markdown

asyncio.run(analyze_document())

Content Types

Both derive from MediaContent which provides basic info and markdown representation.

Model Imports

from azure.ai.contentunderstanding.models import (
    AnalyzeInput,
    AnalyzeResult,
    MediaContentKind,
    DocumentContent,
    AudioVisualContent,
)

Client Types

Best Practices

1. Use begin_analyze with AnalyzeInput — this is the correct method signature
2. Access results via result.contents[0] — results are returned as a list
3. Use prebuilt analyzers for common scenarios (document/image/audio/video search)
4. Create custom analyzers only for domain-specific field extraction
5. Use async client for high-throughput scenarios with azure.identity.aio credentials
6. Handle long-running operations — video/audio analysis can take minutes
7. Use URL sources when possible to avoid upload overhead