chore: import upstream snapshot with attribution

python/packages/azure-contentunderstanding/samples/01-get-started/04_invoice_processing.py

@@ -0,0 +1,193 @@
+# Copyright (c) Microsoft. All rights reserved.
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-azure-contentunderstanding",
+#     "agent-framework-foundry",
+#     "azure-identity",
+#     "pydantic",
+# ]
+# ///
+# Run with: uv run packages/azure-contentunderstanding/samples/01-get-started/04_invoice_processing.py
+
+
+import asyncio
+import os
+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
+from pydantic import BaseModel, Field
+
+load_dotenv()
+
+"""
+Invoice Processing — Structured output with prebuilt-invoice analyzer
+
+This sample demonstrates CU's structured field extraction combined with
+LLM structured output (Pydantic model). The prebuilt-invoice analyzer extracts
+typed fields (VendorName, InvoiceTotal, DueDate, LineItems, etc.) with
+confidence scores. We use output_sections=["fields"] only (no markdown needed)
+since we want the LLM to produce a structured JSON response from the extracted
+fields, not summarize document text.
+
+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
+"""
+
+SAMPLE_PDF_PATH = Path(__file__).resolve().parents[1] / "shared" / "sample_assets" / "invoice.pdf"
+
+
+# Structured output model — the LLM will return JSON matching this schema
+# Structured output models — the LLM returns JSON matching this schema.
+#
+# Note: the prebuilt-invoice analyzer extracts an extensive set of fields
+# (VendorName, BillingAddress, ShippingAddress, TaxDetails, PONumber, etc.).
+# This sample defines a simplified schema to extract only the fields of
+# interest to the caller. The LLM maps the full CU field output to this
+# subset automatically.
+# Learn more about prebuilt analyzers: https://learn.microsoft.com/azure/ai-services/content-understanding/concepts/prebuilt-analyzers
+
+
+class LineItem(BaseModel):
+    description: str
+    quantity: float | None = None
+    unit_price: float | None = None
+    amount: float | None = None
+
+
+class LowConfidenceField(BaseModel):
+    field_name: str
+    confidence: float
+
+
+class InvoiceResult(BaseModel):
+    vendor_name: str
+    total_amount: float | None = None
+    currency: str = "USD"
+    due_date: str | None = None
+    line_items: list[LineItem] = Field(default_factory=list)
+    low_confidence_fields: list[LowConfidenceField] = Field(
+        default_factory=list,
+        description="Fields with confidence < 0.8, including their confidence score",
+    )
+
+
+async def main() -> None:
+    # 1. Set up credentials and CU context provider
+    credential = AzureCliCredential()
+
+    # Default analyzer is prebuilt-documentSearch (RAG-optimized).
+    # Per-file override via additional_properties["analyzer_id"] lets us
+    # use prebuilt-invoice for structured field extraction on specific files.
+    #
+    # Only request "fields" (not "markdown") — we want the extracted typed
+    # fields for structured output, not the raw document text.
+    cu = ContentUnderstandingContextProvider(
+        endpoint=os.environ["AZURE_CONTENTUNDERSTANDING_ENDPOINT"],
+        credential=credential,
+        analyzer_id="prebuilt-documentSearch",  # default for all files
+        max_wait=None,  # wait until CU analysis finishes
+        output_sections=["fields"],  # fields only — structured output doesn't need markdown
+    )
+
+    # 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="InvoiceProcessor",
+            instructions=(
+                "You are an invoice processing assistant. Extract invoice data from "
+                "the provided CU fields (JSON with confidence scores). Return structured "
+                "output matching the requested schema. Flag fields with confidence < 0.8 "
+                "in the low_confidence_fields list."
+            ),
+            context_providers=[cu],
+        )
+
+        session = AgentSession()
+
+        # 4. Upload an invoice PDF — uses structured output (Pydantic model)
+        print("--- Upload Invoice (Structured Output) ---")
+
+        pdf_bytes = SAMPLE_PDF_PATH.read_bytes()
+
+        response = await agent.run(
+            Message(
+                role="user",
+                contents=[
+                    Content.from_text(
+                        "Process this invoice. Extract the vendor name, total amount, due date, and all line items."
+                    ),
+                    Content.from_data(
+                        pdf_bytes,
+                        "application/pdf",
+                        # Per-file analyzer override: use prebuilt-invoice for
+                        # structured field extraction (VendorName, InvoiceTotal, etc.)
+                        # instead of the provider default (prebuilt-documentSearch).
+                        additional_properties={
+                            "filename": SAMPLE_PDF_PATH.name,
+                            "analyzer_id": "prebuilt-invoice",
+                        },
+                    ),
+                ],
+            ),
+            session=session,
+            options={"response_format": InvoiceResult},
+        )
+
+        # Parse the structured output from JSON text
+        try:
+            invoice = InvoiceResult.model_validate_json(response.text)
+            print(f"Vendor: {invoice.vendor_name}")
+            print(f"Total: {invoice.currency} {invoice.total_amount}")
+            print(f"Due date: {invoice.due_date}")
+            print(f"Line items ({len(invoice.line_items)}):")
+            for item in invoice.line_items:
+                print(f"  - {item.description}: {item.amount}")
+            if invoice.low_confidence_fields:
+                print("⚠ Low confidence fields:")
+                for f in invoice.low_confidence_fields:
+                    print(f"  - {f.field_name}: {f.confidence:.3f}")
+        except Exception:
+            print(f"Agent (raw): {response.text}\n")
+
+        # 5. Follow-up: free-text question about the invoice
+        print("\n--- Follow-up (Free Text) ---")
+        response = await agent.run(
+            "What is the payment term? Are there any fields with low confidence?",
+            session=session,
+        )
+        print(f"Agent: {response}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+
+--- Upload Invoice (Structured Output) ---
+Vendor: CONTOSO LTD.
+Total: USD 110.0
+Due date: 2019-12-15
+Line items (3):
+  - Consulting Services: 60.0
+  - Document Fee: 30.0
+  - Printing Fee: 10.0
+⚠ Low confidence: VendorName, CustomerName
+
+--- Follow-up (Free Text) ---
+Agent: The payment terms are not explicitly stated on the invoice...
+"""