chore: import upstream snapshot with attribution

examples/flows/evaluation/eval-perceived-intelligence-maf/test_eval.py

@@ -0,0 +1,47 @@
+import asyncio
+import json
+from pathlib import Path
+from workflow import EvalInput, create_workflow
+
+
+async def test_single_row():
+    """Smoke test - requires Azure OpenAI credentials in .env"""
+    wf = create_workflow()
+    result = await wf.run(EvalInput(
+        question="What is deep learning?",
+        answer="Deep learning is a subset of machine learning.",
+        context="Deep learning uses neural networks with many layers.",
+    ))
+    score = result.get_outputs()[0]
+    assert isinstance(score, float)
+    print(f"PASS: test_single_row (score={score})")
+
+
+async def test_data_jsonl():
+    """Run eval on every row in data.jsonl"""
+    data_path = Path(__file__).parent / "data.jsonl"
+    rows = [json.loads(line) for line in data_path.read_text(encoding="utf-8").splitlines() if line.strip()]
+    wf = create_workflow()
+    for i, row in enumerate(rows):
+        context = row["context"]
+        if isinstance(context, list):
+            context = "\n".join(context)
+        result = await wf.run(EvalInput(
+            question=row["question"],
+            answer=row["answer"],
+            context=context,
+        ))
+        score = result.get_outputs()[0]
+        assert isinstance(score, float), f"Row {i}: expected float, got {type(score)}"
+        print(f"  Row {i}: score={score}")
+    print(f"PASS: test_data_jsonl ({len(rows)} rows)")
+
+
+async def main():
+    await test_single_row()
+    await test_data_jsonl()
+    print("\nAll tests passed!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())