Claude
7f55cdd204
Covers every module in src/ with unit tests:
- tests/conftest.py shared fixtures (complete_registration, fresh_state, …)
- tests/test_models.py RegistrationData.is_complete(), to_dict/from_dict round-trips
- tests/test_storage.py normalize_email, _diff_registrations, ConversationStore CRUD,
registration versioning
- tests/test_llm.py litellm wrapper — message construction, model passthrough,
error propagation
- tests/test_agent.py EmailAgent — new/existing conversations, registration
completion, admin notification, fallback on LLM error,
JSON parsing, _apply_updates
- tests/test_notifier.py AdminNotifier routing, fee calculation, SMTP dispatch
- tests/test_knowledge_base.py KnowledgeBase loading and reload
All external I/O (litellm, SMTP, filesystem) is mocked. Tests run fast (~6s)
with no network access required.
https://claude.ai/code/session_01HaUFs7SaLD5SoiuGCY27Tw
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
"""Tests for the litellm wrapper in src/llm.py."""
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import pytest
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from src import llm
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from src.models.conversation import ChatMessage
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class TestLlmComplete:
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def test_returns_model_reply(self, mocker):
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mock_response = mocker.MagicMock()
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mock_response.choices[0].message.content = "Hallo! Wie heisst dein Kind?"
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mocker.patch("litellm.completion", return_value=mock_response)
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result = llm.complete("anthropic/claude-opus-4-6", "system prompt", [])
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assert result == "Hallo! Wie heisst dein Kind?"
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def test_passes_model_to_litellm(self, mocker):
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mock_response = mocker.MagicMock()
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mock_response.choices[0].message.content = "ok"
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mock_completion = mocker.patch("litellm.completion", return_value=mock_response)
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llm.complete("openai/gpt-4o", "system", [])
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call_kwargs = mock_completion.call_args.kwargs
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assert call_kwargs["model"] == "openai/gpt-4o"
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def test_system_prompt_prepended_as_system_message(self, mocker):
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mock_response = mocker.MagicMock()
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mock_response.choices[0].message.content = "ok"
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mock_completion = mocker.patch("litellm.completion", return_value=mock_response)
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llm.complete("anthropic/claude-opus-4-6", "You are helpful.", [])
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messages = mock_completion.call_args.kwargs["messages"]
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assert messages[0] == {"role": "system", "content": "You are helpful."}
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def test_chat_messages_appended_after_system(self, mocker):
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mock_response = mocker.MagicMock()
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mock_response.choices[0].message.content = "ok"
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mock_completion = mocker.patch("litellm.completion", return_value=mock_response)
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chat = [
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ChatMessage(role="user", content="Hallo"),
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ChatMessage(role="assistant", content="Guten Tag"),
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]
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llm.complete("anthropic/claude-opus-4-6", "system", chat)
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messages = mock_completion.call_args.kwargs["messages"]
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assert messages[1] == {"role": "user", "content": "Hallo"}
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assert messages[2] == {"role": "assistant", "content": "Guten Tag"}
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def test_max_tokens_passed(self, mocker):
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mock_response = mocker.MagicMock()
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mock_response.choices[0].message.content = "ok"
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mock_completion = mocker.patch("litellm.completion", return_value=mock_response)
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llm.complete("anthropic/claude-opus-4-6", "system", [])
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assert mock_completion.call_args.kwargs["max_tokens"] == 2048
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def test_litellm_exception_propagates(self, mocker):
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mocker.patch("litellm.completion", side_effect=RuntimeError("API error"))
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with pytest.raises(RuntimeError, match="API error"):
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llm.complete("anthropic/claude-opus-4-6", "system", [])
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