2023-08-24 17:01:34 +00:00
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from datetime import datetime
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from pathlib import Path
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2023-08-14 11:37:20 +00:00
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from unittest.mock import patch
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2023-08-24 17:01:34 +00:00
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from zoneinfo import ZoneInfo
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2023-08-14 11:37:20 +00:00
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2023-08-24 14:23:32 +00:00
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import pytest
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2023-08-24 17:01:34 +00:00
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from llm_chat.chat import Chat, save_conversation
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from llm_chat.models import Conversation, Message, Role
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2023-08-24 14:23:32 +00:00
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from llm_chat.settings import Model, OpenAISettings
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2023-08-14 11:37:20 +00:00
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2023-08-24 17:01:34 +00:00
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def test_save_conversation(tmp_path: Path) -> None:
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conversation = Conversation(
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messages=[
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Message(role=Role.SYSTEM, content="Hello!"),
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Message(role=Role.USER, content="Hi!"),
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Message(role=Role.ASSISTANT, content="How are you?"),
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],
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model=Model.GPT3,
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temperature=0.5,
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completion_tokens=10,
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prompt_tokens=15,
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cost=0.000043,
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)
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path = tmp_path / ".history"
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expected_file_path = path / "20210101120000.json"
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dt = datetime(2021, 1, 1, 12, 0, 0, tzinfo=ZoneInfo("UTC"))
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assert not path.exists()
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save_conversation(
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conversation=conversation,
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history_dir=path,
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dt=dt,
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)
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assert path.exists()
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assert path.is_dir()
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assert expected_file_path in path.iterdir()
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with expected_file_path.open() as f:
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conversation_from_file = Conversation.model_validate_json(f.read())
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assert conversation == conversation_from_file
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2023-08-16 09:10:06 +00:00
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def test_send_message() -> None:
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2023-08-14 11:37:20 +00:00
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with patch("llm_chat.chat.Chat._make_request") as mock_make_request:
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2023-08-16 09:10:06 +00:00
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mock_make_request.return_value = {
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2023-08-24 14:23:32 +00:00
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"choices": [{"message": {"content": "Hello!"}}],
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"usage": {"completion_tokens": 1, "prompt_tokens": 1},
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2023-08-16 09:10:06 +00:00
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}
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2023-08-14 11:37:20 +00:00
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conversation = Chat()
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response = conversation.send_message("Hello")
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assert isinstance(response, str)
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assert response == "Hello!"
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2023-08-24 14:23:32 +00:00
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@pytest.mark.parametrize("model,cost", [(Model.GPT3, 0.000043), (Model.GPT4, 0.00105)])
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def test_calculate_cost(model: Model, cost: float) -> None:
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with patch("llm_chat.chat.Chat._make_request") as mock_make_request:
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mock_make_request.return_value = {
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"choices": [{"message": {"content": "Hello!"}}],
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"usage": {"completion_tokens": 10, "prompt_tokens": 15},
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}
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settings = OpenAISettings(model=model)
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conversation = Chat(settings=settings)
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_ = conversation.send_message("Hello")
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assert conversation.cost == cost
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