chore: import upstream snapshot with attribution

backend/test/unit/knowledge/eval/test_evaluator.py

@@ -0,0 +1,39 @@
+import os
+
+os.environ.setdefault("OPENAI_API_KEY", "test-key")
+
+from yuxi.knowledge.eval.evaluator import aggregate_metrics, build_answer_prompt, normalize_query_result
+
+
+def test_normalize_query_result_supports_dict_and_list():
+    answer, chunks = normalize_query_result({"answer": "A", "retrieved_chunks": [{"content": "C"}]})
+    assert answer == "A"
+    assert chunks == [{"content": "C"}]
+
+    answer, chunks = normalize_query_result([{"content": "C"}])
+    assert answer == ""
+    assert chunks == [{"content": "C"}]
+
+
+def test_build_answer_prompt_uses_first_five_non_empty_chunks():
+    chunks = [{"content": f"内容{i}"} for i in range(6)] + [{"content": ""}]
+
+    prompt = build_answer_prompt("问题", chunks)
+
+    assert "用户问题：问题" in prompt
+    assert "内容0" in prompt
+    assert "内容4" in prompt
+    assert "内容5" not in prompt
+
+
+def test_aggregate_metrics_matches_service_output_shape():
+    metrics, overall_score = aggregate_metrics(
+        [{"recall@1": 1.0, "f1@1": 0.0}, {"recall@1": 0.0, "f1@1": 1.0}],
+        [{"score": 1.0}, {"score": 0.0}],
+        include_overall_score=True,
+    )
+
+    assert metrics["recall@1"] == 0.5
+    assert metrics["f1@1"] == 0.5
+    assert metrics["answer_correctness"] == 0.5
+    assert metrics["overall_score"] == overall_score