test: bunch of tests

Written with AI, still need to check them

ref: N25B-449

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -14,6 +14,7 @@ from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
 from control_backend.schemas.program import (
+    BaseGoal,  # Changed from Goal
     ConditionalNorm,
     KeywordBelief,
     LLMAction,
@@ -28,7 +29,8 @@ from control_backend.schemas.program import (
 @pytest.fixture
 def llm():
     llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
-    llm._query_llm = AsyncMock()
+    # We must ensure _query_llm returns a dictionary so iterating it doesn't fail
+    llm._query_llm = AsyncMock(return_value={})
     return llm
 
 
@@ -374,3 +376,155 @@ async def test_llm_failure_handling(agent, llm, sample_program):
 
     assert len(belief_changes.true) == 0
     assert len(belief_changes.false) == 0
+
+
+def test_belief_state_bool():
+    # Empty
+    bs = BeliefState()
+    assert not bs
+
+    # True set
+    bs_true = BeliefState(true={InternalBelief(name="a", arguments=None)})
+    assert bs_true
+
+    # False set
+    bs_false = BeliefState(false={InternalBelief(name="a", arguments=None)})
+    assert bs_false
+
+
+@pytest.mark.asyncio
+async def test_handle_beliefs_message_validation_error(agent, mock_settings):
+    # Invalid JSON
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="beliefs",
+        body="invalid json",
+    )
+    # Should log warning and return
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+    # Invalid Model
+    msg.body = json.dumps({"beliefs": [{"invalid": "obj"}]})
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_goals_message_validation_error(agent, mock_settings):
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="goals",
+        body="invalid json",
+    )
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_goal_achieved_message_validation_error(agent, mock_settings):
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="achieved_goals",
+        body="invalid json",
+    )
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_goal_inferrer_infer_from_conversation(agent, llm):
+    # Setup goals
+    # Use BaseGoal object as typically received by the extractor
+    g1 = BaseGoal(id=uuid.uuid4(), name="g1", description="desc", can_fail=True)
+
+    # Use real GoalAchievementInferrer
+    from control_backend.agents.bdi.text_belief_extractor_agent import GoalAchievementInferrer
+
+    inferrer = GoalAchievementInferrer(llm)
+    inferrer.goals = {g1}
+
+    # Mock LLM response
+    llm._query_llm.return_value = True
+
+    completions = await inferrer.infer_from_conversation(ChatHistory(messages=[]))
+    assert completions
+    # slugify uses slugify library, hard to predict exact string without it,
+    # but we can check values
+    assert list(completions.values())[0] is True
+
+
+def test_apply_conversation_message_limit(agent):
+    with patch("control_backend.agents.bdi.text_belief_extractor_agent.settings") as mock_s:
+        mock_s.behaviour_settings.conversation_history_length_limit = 2
+        agent.conversation.messages = []
+
+        agent._apply_conversation_message(ChatMessage(role="user", content="1"))
+        agent._apply_conversation_message(ChatMessage(role="assistant", content="2"))
+        agent._apply_conversation_message(ChatMessage(role="user", content="3"))
+
+        assert len(agent.conversation.messages) == 2
+        assert agent.conversation.messages[0].content == "2"
+        assert agent.conversation.messages[1].content == "3"
+
+
+@pytest.mark.asyncio
+async def test_handle_program_manager_reset(agent):
+    with patch("control_backend.agents.bdi.text_belief_extractor_agent.settings") as mock_s:
+        mock_s.agent_settings.bdi_program_manager_name = "pm"
+        agent.conversation.messages = [ChatMessage(role="user", content="hi")]
+        agent.belief_inferrer.available_beliefs = [
+            SemanticBelief(id=uuid.uuid4(), name="b", description="d")
+        ]
+
+        msg = InternalMessage(to="me", sender="pm", thread="conversation_history", body="reset")
+        await agent.handle_message(msg)
+
+        assert len(agent.conversation.messages) == 0
+        assert len(agent.belief_inferrer.available_beliefs) == 0
+
+
+def test_split_into_chunks():
+    from control_backend.agents.bdi.text_belief_extractor_agent import SemanticBeliefInferrer
+
+    items = [1, 2, 3, 4, 5]
+    chunks = SemanticBeliefInferrer._split_into_chunks(items, 2)
+    assert len(chunks) == 2
+    assert len(chunks[0]) + len(chunks[1]) == 5
+
+
+@pytest.mark.asyncio
+async def test_infer_beliefs_call(agent, llm):
+    from control_backend.agents.bdi.text_belief_extractor_agent import SemanticBeliefInferrer
+
+    inferrer = SemanticBeliefInferrer(llm)
+    sb = SemanticBelief(id=uuid.uuid4(), name="is_happy", description="User is happy")
+
+    llm.query = AsyncMock(return_value={"is_happy": True})
+
+    res = await inferrer._infer_beliefs(ChatHistory(messages=[]), [sb])
+    assert res == {"is_happy": True}
+    llm.query.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_infer_goal_call(agent, llm):
+    from control_backend.agents.bdi.text_belief_extractor_agent import GoalAchievementInferrer
+
+    inferrer = GoalAchievementInferrer(llm)
+    goal = BaseGoal(id=uuid.uuid4(), name="g1", description="d")
+
+    llm.query = AsyncMock(return_value=True)
+
+    res = await inferrer._infer_goal(ChatHistory(messages=[]), goal)
+    assert res is True
+    llm.query.assert_called_once()