Regtransfers/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/

@ Authentication

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

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"]
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

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

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.
• never 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.