Add tests for LlmSession and LlmSessionCaptionsAndKeywords in both integration and mocked scenarios

test/conftest.py

@@ -12,9 +12,11 @@ def test_photo():
 
 @pytest.fixture
 def llm_session():
-    return LlmSession(
-        system_prompt=(
-            "You are a helpful assistent to photographer. "
-            'Answer in JSON format: `{"caption": "...", "tags": ["tag1", "tag2", ...]}`'
-        )
-    )
+    return LlmSession(system_prompt="You are a helpful assistant.")
+
+
+@pytest.fixture
+def llm_session_captions_and_keywords():
+    from photools3.llm_session import LlmSessionCaptionsAndKeywords
+
+    return LlmSessionCaptionsAndKeywords()

test/test_llm_session_integration.py

@@ -7,7 +7,21 @@ from photools3.llm_session import LlmSession
     not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
 )
 def test_llm_session(llm_session, test_photo):
+    llm_session.system_prompt = "Return only JSON format: {'keywords': ['...']}"
     result = llm_session.call(
         prompt="keywords for the attached picture", photos=[test_photo]
     )
-    assert result
+    assert result is not None
+
+
+@pytest.mark.skipif(
+    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
+)
+def test_llm_session_captions_and_keywords(
+    llm_session_captions_and_keywords, test_photo
+):
+    result = llm_session_captions_and_keywords.call(photos=[test_photo])
+    assert "caption" in result
+    assert "tags" in result
+    assert isinstance(result["caption"], str)
+    assert isinstance(result["tags"], list)

test/test_llm_session_mocked.py

@@ -45,3 +45,44 @@ def test_llm_session_mocked(test_photo):
 
         # Verify that the mock was called
         assert mock_client.chat.completions.create.called
+
+
+def test_llm_session_captions_and_keywords_mocked(test_photo):
+    # We want to mock OpenAI client's chat.completions.create
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        # Setup the mock response structure
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+
+        # Mock the models list to return a proper object with an ID string
+        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        # Create a response object: response.choices[0].message.content
+        mock_response = MagicMock()
+        mock_choice = MagicMock()
+        mock_message = MagicMock()
+
+        mock_message.content = '{"caption": "A mocked caption", "tags": ["mocked_tag"]}'
+        mock_choice.message = mock_message
+        mock_response.choices = [mock_choice]
+
+        # Configure the client to return our mock response
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Create a new LlmSessionCaptionsAndKeywords instance that will use the mocked OpenAI client
+        from photools3.llm_session import LlmSessionCaptionsAndKeywords
+
+        mocked_session = LlmSessionCaptionsAndKeywords()
+
+        # Run the test
+        result = mocked_session.call(photos=[test_photo])
+
+        # Verify the results
+        assert result["caption"] == "A mocked caption"
+        assert "mocked_tag" in result["tags"]
+
+        # Verify that the mock was called
+        assert mock_client.chat.completions.create.called