Azure Ai Contentunderstanding Py

---
name: azure-ai-contentunderstanding-py
description: Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. 
category: Document Processing
source: antigravity
tags: [python, pdf, markdown, ai, workflow, document, presentation, image, azure, rag]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/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

```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

| 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

```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
print