photools3/test/test_llm_session_mocked.py

from unittest.mock import MagicMock, patch

import pytest

from photools3.llm_session import LlmSession


def test_llm_session_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 = '{"text": "some text"}'
        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 LlmSession instance that will use the mocked OpenAI client
        mocked_session = LlmSession(system_prompt="You are a helper.")

        # Run the test
        result = mocked_session.call(
            prompt="keywords for the attached picture", photos=[test_photo]
        )

        # Verify the results
        assert result.text == "some text"

        # Verify that the mock was enough
        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": ["architektur"]}'
        )
        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 "architektur" in result.tags

        # Verify that the mock was called
        assert mock_client.chat.completions.create.called


def test_llm_session_retries_from_original_request_without_accumulating_messages():
    with patch("photools3.llm_session.OpenAI") as mocked_openai:
        mock_client = MagicMock()
        mocked_openai.return_value = mock_client
        mock_model = MagicMock()
        mock_model.id = "gpt-4o"
        mock_client.models.list.return_value.data = [mock_model]

        def response(content):
            message = MagicMock()
            message.content = content
            choice = MagicMock()
            choice.message = message
            result = MagicMock()
            result.choices = [choice]
            return result

        mock_client.chat.completions.create.side_effect = [
            response('{"wrong": "first"}'),
            response('{"wrong": "second"}'),
            response('{"text": "correct"}'),
        ]

        session = LlmSession(system_prompt="You are a helper.")
        result = session.call(photos=[], prompt="Return JSON")

    assert result.text == "correct"
    requests = mock_client.chat.completions.create.call_args_list
    messages_per_attempt = [call.kwargs["messages"] for call in requests]
    assert [len(messages) for messages in messages_per_attempt] == [2, 3, 3]
    final_correction = messages_per_attempt[2][-1]["content"]
    assert '"wrong": "second"' in final_correction
    assert '"wrong": "first"' not in final_correction


def test_llm_session_uses_configured_retry_count(monkeypatch):
    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 1)

    with patch("photools3.llm_session.OpenAI") as mocked_openai:
        mock_client = MagicMock()
        mocked_openai.return_value = mock_client
        mock_model = MagicMock()
        mock_model.id = "gpt-4o"
        mock_client.models.list.return_value.data = [mock_model]

        def response(content):
            message = MagicMock()
            message.content = content
            choice = MagicMock()
            choice.message = message
            result = MagicMock()
            result.choices = [choice]
            return result

        mock_client.chat.completions.create.side_effect = [
            response('{"wrong": "first"}'),
            response('{"wrong": "second"}'),
            response('{"text": "would be a third call"}'),
        ]
        session = LlmSession(system_prompt="You are a helper.")

        with pytest.raises(ValueError, match="after 1 retries"):
            session.call(photos=[])

    assert mock_client.chat.completions.create.call_count == 2