Azure AI Contentunderstanding Py

MicrosoftOfficialCreated by Microsoft

Skill
Tool integration
Data and AI
MIT
Low risk

From the author

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Azure AI Content Understanding SDK for Python provides a programmatic interface for extracting semantic content from various media types. It supports prebuilt analyzers for common tasks like document search, audio transcription, and video frame analysis, as well as custom analyzers for domain-specific field extraction. The SDK is designed for integration into RAG applications and automated workflows, offering both synchronous and asynchronous client implementations.

What it does

Extracts structured data and markdown from documents, images, audio, and video files.
Provides prebuilt analyzers for document search, invoice processing, and media transcription.
Supports the creation of custom analyzers with defined field schemas for specialized extraction.
Handles long-running analysis operations via polling mechanisms.
Offers both synchronous and asynchronous clients for flexible integration.
Integrates with Azure Identity for secure authentication using DefaultAzureCredential.

When to use it

When building RAG applications that require structured content from diverse media sources.
When automating the extraction of specific fields from documents like invoices.
When you need to transcribe audio or extract key frames and summaries from video files.
When you need to perform multimodal analysis within a Python-based automated workflow.

Advantages

Supports a wide range of media formats including documents, images, audio, and video.
Enables custom schema definition for tailored data extraction requirements.
Provides consistent patterns for both sync and async development.
Facilitates secure credential management through Azure Identity integration.

Usage examples

“Analyze a PDF document using the prebuilt document search analyzer.”: The agent uses the ContentUnderstandingClient to trigger a document analysis operation, polls for the result, and prints the extracted markdown content.
“Extract transcript phrases and key frames from a video file.”: The agent processes the video using the prebuilt video search analyzer and iterates through the resulting transcript phrases and key frame descriptions.
“Create a custom analyzer for invoice processing.”: The agent defines a custom analyzer with a specific field schema for vendor names and invoice totals, then uses it to extract data from an invoice document.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/azure-ai-contentunderstanding-py/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip azure-ai-contentunderstanding-py.zip -d .agents/skills

Documentation by Microsoft

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 & Lifecycle

🔑 Two rules apply to every code sample below:
Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
Local dev: DefaultAzureCredential works as-is.
Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
Sync: with <Client>(...) as client:
Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)
Snippets may abbreviate this setup, but production code should always follow both rules.

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()
with ContentUnderstandingClient(endpoint=endpoint, credential=credential) as client:
    analyzers = list(client.list_analyzers())

Core Workflow

Content Understanding operations are asynchronous long-running operations:

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

Prebuilt Analyzers

Analyzer	Content Type	Purpose
`prebuilt-documentSearch`	Documents	Extract markdown for RAG applications
`prebuilt-imageSearch`	Images	Extract content from images
`prebuilt-audioSearch`	Audio	Transcribe audio with timing
`prebuilt-videoSearch`	Video	Extract frames, transcripts, summaries
`prebuilt-invoice`	Documents	Extract invoice fields

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"]
with ContentUnderstandingClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
) as client:
    # 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"]
    async with DefaultAzureCredential() as credential:
        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

Class	For	Provides
`DocumentContent`	PDF, images, Office docs	Pages, tables, figures, paragraphs
`AudioVisualContent`	Audio, video files	Transcript phrases, timing, key frames

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

Client	Purpose
`ContentUnderstandingClient`	Sync client for all operations
`ContentUnderstandingClient` (aio)	Async client for all operations

Best Practices

Pick sync OR async and stay consistent. Do not mix azure.ai.contentunderstanding sync clients with azure.ai.contentunderstanding.aio async clients in the same call path. Choose one mode per module.
Always use context managers for clients and async credentials. Wrap every client in with ContentUnderstandingClient(...) as client: (sync) or async with ContentUnderstandingClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
Use begin_analyze with AnalyzeInput — this is the correct method signature
Access results via result.contents[0] — results are returned as a list
Use prebuilt analyzers for common scenarios (document/image/audio/video search)
Create custom analyzers only for domain-specific field extraction
Use async client for high-throughput scenarios with azure.identity.aio credentials
Handle long-running operations — video/audio analysis can take minutes
Use URL sources when possible to avoid upload overhead

Reference Files

File	Contents
references/capabilities.md	Additional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.md	Dedicated non-hero examples for secondary/advanced scenarios.

Files

ATTRIBUTION.mdAdded by AutoGineer1007 B
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md10.3 KiB
references/capabilities.md2.3 KiB
references/non-hero-scenarios.md5.0 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-ai-contentunderstanding-py.zip