sickn33/antigravity-awesome-skills/azure-ai-contentunderstanding-py

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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.

Install

npx vskill@latest install sickn33/antigravity-awesome-skills/azure-ai-contentunderstanding-py

SKILL.md

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

bash
pip install azure-ai-contentunderstanding

Environment Variables

bash
CONTENTUNDERSTANDING_ENDPOINT=https://<resource>.cognitiveservices.azure.com/

Authentication

python
import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.identity import DefaultAzureCredential
endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
credential = DefaultAzureCredential()
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

AnalyzerContent TypePurpose
prebuilt-documentSearchDocumentsExtract markdown for RAG applications
prebuilt-imageSearchImagesExtract content from images
prebuilt-audioSearchAudioTranscribe audio with timing
prebuilt-videoSearchVideoExtract frames, transcripts, summaries
prebuilt-invoiceDocumentsExtract invoice fields

Analyze Document

python
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

python
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

python
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

python
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

python
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:

python
# 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
for content in result.contents or []:
    fields = content.fields or {}
    print(fields.get("vendor_name"))
    print(fields.get("invoice_total"))

Analyzer Management

python
# 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

python
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

ClassForProvides
DocumentContentPDF, images, Office docsPages, tables, figures, paragraphs
AudioVisualContentAudio, video filesTranscript phrases, timing, key frames

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

Model Imports

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

Client Types

ClientPurpose
ContentUnderstandingClientSync client for all operations
ContentUnderstandingClient (aio)Async client for all operations

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

SDK/service versions differ: verify analyzer schemas and result fields against the pinned package; guard empty contents before indexing and missing extracted fields before use. These are fragments, not a full analyzer lifecycle. Close async clients and credentials with context managers, bound polling and report failures. Documents/audio/video leave the local environment; authorize sensitive transfer, redact extracted fields and validate outputs before financial/legal decisions. See official SDK guide.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

See the full security report

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

Popularity

Repository stars
26,125
Repository forks
4,452
Repository npm downloads, 7 days
11,772
7-day trend score
72

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 20 Mar 2026, 7 months ago.

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