chore: import upstream snapshot with attribution

python/packages/azure-contentunderstanding/samples/01-get-started/03_multimodal_chat.py

@@ -0,0 +1,186 @@
+# Copyright (c) Microsoft. All rights reserved.
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-azure-contentunderstanding",
+#     "agent-framework-foundry",
+#     "azure-identity",
+# ]
+# ///
+# Run with: uv run packages/azure-contentunderstanding/samples/01-get-started/03_multimodal_chat.py
+
+
+import asyncio
+import os
+import time
+from pathlib import Path
+
+from agent_framework import Agent, AgentSession, Content, Message
+from agent_framework.foundry import ContentUnderstandingContextProvider, FoundryChatClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+load_dotenv()
+
+"""
+Multi-Modal Chat — PDF, audio, and video in a single turn
+
+This sample demonstrates CU's multi-modal capability: upload a PDF invoice,
+an audio call recording, and a video file all at once. The provider analyzes
+all three in parallel using the right CU analyzer for each media type.
+
+The provider auto-detects the media type and selects the right CU analyzer:
+  - PDF/images  → prebuilt-documentSearch
+  - Audio       → prebuilt-audioSearch
+  - Video       → prebuilt-videoSearch
+
+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
+"""
+
+# Local PDF from package assets
+SAMPLE_PDF = Path(__file__).resolve().parents[1] / "shared" / "sample_assets" / "invoice.pdf"
+
+# Public audio/video from Azure CU samples repo (raw GitHub URLs)
+_CU_ASSETS = "https://raw.githubusercontent.com/Azure-Samples/azure-ai-content-understanding-assets/main"
+AUDIO_URL = f"{_CU_ASSETS}/audio/callCenterRecording.mp3"
+VIDEO_URL = f"{_CU_ASSETS}/videos/sdk_samples/FlightSimulator.mp4"
+
+
+async def main() -> None:
+    # 1. Set up credentials and CU context provider
+    credential = AzureCliCredential()
+
+    # No analyzer_id specified — the provider auto-detects from media type:
+    #   PDF/images → prebuilt-documentSearch
+    #   Audio      → prebuilt-audioSearch
+    #   Video      → prebuilt-videoSearch
+    cu = ContentUnderstandingContextProvider(
+        endpoint=os.environ["AZURE_CONTENTUNDERSTANDING_ENDPOINT"],
+        credential=credential,
+        max_wait=None,  # wait until each analysis finishes
+    )
+
+    # 2. Set up the LLM client
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=credential,
+    )
+
+    # 3. Create agent and session
+    async with cu:
+        agent = Agent(
+            client=client,
+            name="MultiModalAgent",
+            instructions=(
+                "You are a helpful assistant that can analyze documents, audio, "
+                "and video files. Answer questions using the extracted content."
+            ),
+            context_providers=[cu],
+        )
+
+        session = AgentSession()
+
+        # --- Turn 1: Upload all 3 modalities at once ---
+        # The provider analyzes all files in parallel using the appropriate
+        # CU analyzer for each media type. All results are injected into
+        # the same context so the agent can answer about all of them.
+        turn1_prompt = (
+            "I'm uploading three files: an invoice PDF, a call center "
+            "audio recording, and a flight simulator video. "
+            "Give a brief summary of each file."
+        )
+        print("--- Turn 1: Upload PDF + audio + video (parallel analysis) ---")
+        print("  (CU analysis may take a few minutes for these audio/video files...)")
+        print(f"User: {turn1_prompt}")
+        t0 = time.perf_counter()
+        response = await agent.run(
+            Message(
+                role="user",
+                contents=[
+                    Content.from_text(turn1_prompt),
+                    Content.from_data(
+                        SAMPLE_PDF.read_bytes(),
+                        "application/pdf",
+                        additional_properties={"filename": "invoice.pdf"},
+                    ),
+                    Content.from_uri(
+                        AUDIO_URL,
+                        media_type="audio/mp3",
+                        additional_properties={"filename": "callCenterRecording.mp3"},
+                    ),
+                    Content.from_uri(
+                        VIDEO_URL,
+                        media_type="video/mp4",
+                        additional_properties={"filename": "FlightSimulator.mp4"},
+                    ),
+                ],
+            ),
+            session=session,
+        )
+        elapsed = time.perf_counter() - t0
+        usage = response.usage_details or {}
+        print(f"  [Analyzed in {elapsed:.1f}s | Input tokens: {usage.get('input_token_count', 'N/A')}]")
+        print(f"Agent: {response}\n")
+
+        # --- Turn 2: Detail question about the PDF ---
+        turn2_prompt = "What are the line items and their amounts on the invoice?"
+        print("--- Turn 2: PDF detail ---")
+        print(f"User: {turn2_prompt}")
+        response = await agent.run(turn2_prompt, session=session)
+        usage = response.usage_details or {}
+        print(f"  [Input tokens: {usage.get('input_token_count', 'N/A')}]")
+        print(f"Agent: {response}\n")
+
+        # --- Turn 3: Detail question about the audio ---
+        turn3_prompt = "What was the customer's issue in the call recording?"
+        print("--- Turn 3: Audio detail ---")
+        print(f"User: {turn3_prompt}")
+        response = await agent.run(turn3_prompt, session=session)
+        usage = response.usage_details or {}
+        print(f"  [Input tokens: {usage.get('input_token_count', 'N/A')}]")
+        print(f"Agent: {response}\n")
+
+        # --- Turn 4: Detail question about the video ---
+        turn4_prompt = "What key scenes or actions are shown in the flight simulator video?"
+        print("--- Turn 4: Video detail ---")
+        print(f"User: {turn4_prompt}")
+        response = await agent.run(turn4_prompt, session=session)
+        usage = response.usage_details or {}
+        print(f"  [Input tokens: {usage.get('input_token_count', 'N/A')}]")
+        print(f"Agent: {response}\n")
+
+        # --- Turn 5: Cross-document question ---
+        turn5_prompt = (
+            "Across all three files, which one contains financial data, "
+            "which one involves a customer interaction, and which one is "
+            "a visual demonstration?"
+        )
+        print("--- Turn 5: Cross-document question ---")
+        print(f"User: {turn5_prompt}")
+        response = await agent.run(turn5_prompt, session=session)
+        usage = response.usage_details or {}
+        print(f"  [Input tokens: {usage.get('input_token_count', 'N/A')}]")
+        print(f"Agent: {response}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+
+--- Turn 1: Upload PDF + audio + video (parallel analysis) ---
+User: I'm uploading three files...
+  (CU analysis may take 1-2 minutes for audio/video files...)
+  [Analyzed in ~94s | Input tokens: ~2939]
+Agent: ### invoice.pdf: An invoice from CONTOSO LTD. to MICROSOFT CORPORATION...
+       ### callCenterRecording.mp3: A customer service call about point balance...
+       ### FlightSimulator.mp4: A clip discussing neural text-to-speech...
+
+--- Turn 2-5: Detail and cross-document questions ---
+(Agent answers from conversation history without re-analysis)
+"""