test(cache): add cache miss test cases for safety checks

Author: Pouyanpi

tests/test_content_safety_cache.py

@@ -279,3 +279,46 @@ async def test_content_safety_check_output_cache_hit(
 
     llm_call_info = llm_call_info_var.get()
     assert llm_call_info.from_cache is True
+
+
+@pytest.mark.asyncio
+async def test_content_safety_check_output_cache_miss(
+    fake_llm_with_stats, mock_task_manager
+):
+    cache = LFUCache(maxsize=10)
+
+    cache_entry = {
+        "result": {"allowed": True, "policy_violations": []},
+        "llm_stats": {
+            "total_tokens": 50,
+            "prompt_tokens": 40,
+            "completion_tokens": 10,
+        },
+        "llm_metadata": None,
+    }
+    cache_key = create_normalized_cache_key("different prompt")
+    cache.put(cache_key, cache_entry)
+
+    mock_task_manager.render_task_prompt.return_value = "new output prompt"
+    mock_task_manager.parse_task_output.return_value = [True, "policy2"]
+
+    llm_stats = LLMStats()
+    llm_stats_var.set(llm_stats)
+
+    llm_call_info = LLMCallInfo(task="content_safety_check_output $model=test_model")
+    llm_call_info_var.set(llm_call_info)
+
+    result = await content_safety_check_output(
+        llms=fake_llm_with_stats,
+        llm_task_manager=mock_task_manager,
+        model_name="test_model",
+        context={"user_message": "new user input", "bot_message": "new bot response"},
+        model_caches={"test_model": cache},
+    )
+
+    assert result["allowed"] is True
+    assert result["policy_violations"] == ["policy2"]
+    assert cache.size() == 2
+
+    llm_call_info = llm_call_info_var.get()
+    assert llm_call_info.from_cache is False

tests/test_topic_safety_cache.py

@@ -108,6 +108,48 @@ async def test_topic_safety_cache_hit(fake_llm_topic, mock_task_manager):
     assert llm_call_info.from_cache is True
 
 
+@pytest.mark.asyncio
+async def test_topic_safety_cache_miss(fake_llm_topic, mock_task_manager):
+    cache = LFUCache(maxsize=10)
+
+    different_messages = [
+        {"type": "system", "content": "Different prompt"},
+        {"type": "user", "content": "Different question"},
+    ]
+    cache_entry = {
+        "result": {"on_topic": True},
+        "llm_stats": {
+            "total_tokens": 30,
+            "prompt_tokens": 25,
+            "completion_tokens": 5,
+        },
+        "llm_metadata": None,
+    }
+    cache_key = create_normalized_cache_key(different_messages)
+    cache.put(cache_key, cache_entry)
+
+    llm_stats = LLMStats()
+    llm_stats_var.set(llm_stats)
+
+    llm_call_info = LLMCallInfo(task="topic_safety_check_input $model=test_model")
+    llm_call_info_var.set(llm_call_info)
+
+    result = await topic_safety_check_input(
+        llms=fake_llm_topic,
+        llm_task_manager=mock_task_manager,
+        model_name="test_model",
+        context={"user_message": "What is machine learning?"},
+        events=[],
+        model_caches={"test_model": cache},
+    )
+
+    assert result["on_topic"] is True
+    assert cache.size() == 2
+
+    llm_call_info = llm_call_info_var.get()
+    assert llm_call_info.from_cache is False
+
+
 @pytest.mark.asyncio
 async def test_topic_safety_without_cache(fake_llm_topic, mock_task_manager):
     result = await topic_safety_check_input(