chore: import upstream snapshot with attribution

python/packages/azure-contentunderstanding/samples/02-devui/01-multimodal_agent/README.md

@@ -0,0 +1,33 @@
+# DevUI Multi-Modal Agent
+
+Interactive web UI for uploading and chatting with documents, images, audio, and video using Azure Content Understanding.
+
+## Setup
+
+1. Set environment variables (or create a `.env` file in `python/`):
+   ```bash
+   FOUNDRY_PROJECT_ENDPOINT=https://your-project.api.azureml.ms
+   AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=gpt-4.1
+   AZURE_CONTENTUNDERSTANDING_ENDPOINT=https://your-cu-resource.cognitiveservices.azure.com/
+   ```
+
+2. Log in with Azure CLI:
+   ```bash
+   az login
+   ```
+
+3. Run with DevUI:
+   ```bash
+   uv run poe devui --agent packages/azure-contentunderstanding/samples/devui_multimodal_agent
+   ```
+
+4. Open the DevUI URL in your browser and start uploading files.
+
+## What You Can Do
+
+- **Upload PDFs** — including scanned/image-based PDFs that LLM vision struggles with
+- **Upload images** — handwritten notes, infographics, charts
+- **Upload audio** — meeting recordings, call center calls (transcription with speaker ID)
+- **Upload video** — product demos, training videos (frame extraction + transcription)
+- **Ask questions** across all uploaded documents
+- **Check status** — "which documents are ready?" uses the auto-registered `list_documents()` tool

python/packages/azure-contentunderstanding/samples/02-devui/01-multimodal_agent/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft. All rights reserved.
+"""DevUI Multi-Modal Agent with Azure Content Understanding."""
+
+from .agent import agent
+
+__all__ = ["agent"]

python/packages/azure-contentunderstanding/samples/02-devui/01-multimodal_agent/agent.py

@@ -0,0 +1,66 @@
+# Copyright (c) Microsoft. All rights reserved.
+"""DevUI Multi-Modal Agent — file upload + CU-powered analysis.
+
+This agent uses Azure Content Understanding to analyze uploaded files
+(PDFs, scanned documents, handwritten images, audio recordings, video)
+and answer questions about them through the DevUI web interface.
+
+Unlike the standard azure_responses_agent which sends files directly to the LLM,
+this agent uses CU for structured extraction — superior for scanned PDFs,
+handwritten content, audio transcription, and video analysis.
+
+Required environment variables:
+  FOUNDRY_PROJECT_ENDPOINT                 — Azure AI Foundry project endpoint
+  FOUNDRY_MODEL                            — Model deployment name (e.g. gpt-4.1)
+  AZURE_CONTENTUNDERSTANDING_ENDPOINT      — CU endpoint URL
+
+Run with DevUI:
+  uv run poe devui --agent packages/azure-contentunderstanding/samples/devui_multimodal_agent
+"""
+
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import ContentUnderstandingContextProvider, FoundryChatClient
+from azure.core.credentials import AzureKeyCredential
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# --- Auth ---
+_credential = AzureCliCredential()
+_cu_api_key = os.environ.get("AZURE_CONTENTUNDERSTANDING_API_KEY")
+_cu_credential = AzureKeyCredential(_cu_api_key) if _cu_api_key else _credential
+
+cu = ContentUnderstandingContextProvider(
+    endpoint=os.environ["AZURE_CONTENTUNDERSTANDING_ENDPOINT"],
+    credential=_cu_credential,
+    # max_wait controls how long before_run() waits for CU analysis before
+    # deferring to background.  For interactive DevUI use, a short timeout
+    # (e.g. 5s) keeps the chat responsive — the agent tells the user the
+    # file is still being analyzed and resolves it on the next turn.
+    # Use max_wait=None to always wait for analysis to complete.
+    max_wait=5.0,
+)
+
+client = FoundryChatClient(
+    project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+    model=os.environ["FOUNDRY_MODEL"],
+    credential=_credential,
+)
+
+agent = Agent(
+    client=client,
+    name="MultiModalDocAgent",
+    instructions=(
+        "You are a helpful document analysis assistant. "
+        "When a user uploads files, they are automatically analyzed using Azure Content Understanding. "
+        "Use list_documents() to check which documents are ready, pending, or failed "
+        "and to see which files are available for answering questions. "
+        "Tell the user if any documents are still being analyzed. "
+        "You can process PDFs, scanned documents, handwritten images, audio recordings, and video files. "
+        "When answering, cite specific content from the documents."
+    ),
+    context_providers=[cu],
+)