fix: handle missing queried knowledge in model strategy

rdagent/components/coder/data_science/model/__init__.py

@@ -46,7 +46,7 @@ def implement_one_task(
         queried_former_failed_knowledge = (
             queried_knowledge.task_to_former_failed_traces[model_information_str]
             if queried_knowledge is not None
-            else []
+            else ([], None)
         )
         queried_former_failed_knowledge = (
             [

test/scenarios/data_science/test_model_strategy_none_knowledge.py

@@ -0,0 +1,57 @@
+from types import SimpleNamespace
+
+import pytest
+
+import rdagent.components.coder.data_science.model as model_module
+from rdagent.components.coder.data_science.model import (
+    ModelMultiProcessEvolvingStrategy,
+)
+
+pytestmark = pytest.mark.offline
+
+
+def test_model_strategy_accepts_none_queried_knowledge(monkeypatch):
+    class FakeTemplate:
+        def r(self, *args, **kwargs):
+            return "prompt"
+
+    monkeypatch.setattr(model_module, "T", lambda *args, **kwargs: FakeTemplate())
+    monkeypatch.setattr(model_module.DS_RD_SETTING, "spec_enabled", True)
+    monkeypatch.setattr(
+        model_module.PythonBatchEditOut,
+        "extract_output",
+        lambda *args, **kwargs: {"model_generated.py": "new code"},
+    )
+
+    class FakeBackend:
+        def build_messages_and_create_chat_completion(self, **kwargs):
+            return "response"
+
+    monkeypatch.setattr(model_module, "APIBackend", FakeBackend)
+
+    strategy = object.__new__(ModelMultiProcessEvolvingStrategy)
+    strategy.scen = SimpleNamespace(get_scenario_all_desc=lambda eda_output=None: "scenario")
+
+    target_task = SimpleNamespace(
+        name="model_1",
+        get_task_information=lambda: "model task",
+    )
+
+    class FakeWorkspace:
+        file_dict = {
+            "model_1.py": "old code",
+            "feature.py": "feature code",
+            "load_data.py": "loader code",
+            "spec/model.md": "model spec",
+        }
+
+        def get_codes(self, pattern):
+            return {"model_1.py": "old code"}
+
+    result = strategy.implement_one_task(
+        target_task=target_task,
+        queried_knowledge=None,
+        workspace=FakeWorkspace(),
+    )
+
+    assert result == {"model_1.py": "new code"}