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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
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正在显示 SKILL.md
| name | azure-ai-contentunderstanding-py |
| description | Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | cloud-infrastructure |
| category | azure |
| risk | unknown |
| source | community |
| tags | ["skill","cloud-infrastructure","azure","contentunderstanding"] |
Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows.
pip install azure-ai-contentunderstanding
CONTENTUNDERSTANDING_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
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)
Content Understanding operations are asynchronous long-running operations:
begin_analyze() (returns a poller).result())AnalyzeResult.contents| 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 |
import os
from azure.ai.contentunderstanding ContentUnderstandingClient
azure.ai.contentunderstanding.models AnalyzeInput
azure.identity DefaultAzureCredential
endpoint = os.environ[]
client = ContentUnderstandingClient(
endpoint=endpoint,
credential=DefaultAzureCredential()
)
poller = client.begin_analyze(
analyzer_id=,
inputs=[AnalyzeInput(url=)]
)
result = poller.result()
content = result.contents[]
(content.markdown)
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)
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)
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}")
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}")
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"])
# 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")
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())
| 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.
from azure.ai.contentunderstanding.models import (
AnalyzeInput,
AnalyzeResult,
MediaContentKind,
DocumentContent,
AudioVisualContent,
)
| Client | Purpose |
|---|---|
ContentUnderstandingClient | Sync client for all operations |
ContentUnderstandingClient (aio) | Async client for all operations |
begin_analyze with AnalyzeInput — this is the correct method signatureresult.contents[0] — results are returned as a listazure.identity.aio credentialsThis skill is applicable to execute the workflow or actions described in the overview.