prepare tests for multimodality

core/tests/looking_glass/test_stray_cat.py

@@ -26,7 +26,7 @@ def test_stray_nlp(stray):
     assert isinstance(embedding[0][0], float)
 
 
-def test_stray_call(stray):
+def test_stray_call_with_text(stray):
     msg = {"text": "Where do I go?", "user_id": "Alice"}
 
     reply = stray.__call__(msg)
@@ -38,6 +38,21 @@ def test_stray_call(stray):
     assert isinstance(reply.why, MessageWhy)
 
 
+def test_stray_call_with_text_and_image(stray):
+    msg = {
+        "text": "Where do I go?",
+        "user_id": "Alice",
+        "image": "https://raw.githubusercontent.com/cheshire-cat-ai/core/refs/heads/main/readme/cheshire-cat.jpeg",
+    }
+
+    reply = stray.__call__(msg)
+
+    assert isinstance(reply, CatMessage)
+    assert "You did not configure" in reply.text
+    assert reply.user_id == "Alice"
+    assert reply.type == "chat"
+    assert isinstance(reply.why, MessageWhy)
+
 # TODO: update these tests once we have a real LLM in tests
 def test_stray_classify(stray):
     label = stray.classify("I feel good", labels=["positive", "negative"])

core/tests/mocks/mock_plugin/mock_llm.py

@@ -2,15 +2,16 @@
 from typing import List, Type
 from pydantic import ConfigDict
 
-from langchain_community.llms.fake import FakeListLLM
+from langchain_community.chat_models.fake import FakeListChatModel
 
 from cat.mad_hatter.decorators import hook
 from cat.factory.llm import LLMSettings
 
 class FakeLLMConfig(LLMSettings):
     """Fake LLM for testing purposes."""
 
-    _pyclass: Type = FakeListLLM
+    responses: List[str] = ["I'm a fake LLM!"]
+    _pyclass: Type = FakeListChatModel
 
     model_config = ConfigDict(
         json_schema_extra={