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

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