llm-chat/tests/test_chat.py

from datetime import datetime
from pathlib import Path
from unittest.mock import patch
from zoneinfo import ZoneInfo

import pytest
from openai.types.chat import ChatCompletion, ChatCompletionMessage
from openai.types.chat.chat_completion import Choice
from openai.types.completion_usage import CompletionUsage

from llm_chat.chat import Chat, save_conversation
from llm_chat.models import Conversation, Message, Role
from llm_chat.settings import Model, OpenAISettings


@pytest.mark.parametrize(
    "name,expected_filename",
    [("", "20210101120000.json"), ("foo", "20210101120000-foo.json")],
)
def test_save_conversation(name: str, expected_filename: str, tmp_path: Path) -> None:
    conversation = Conversation(
        messages=[
            Message(role=Role.SYSTEM, content="Hello!"),
            Message(role=Role.USER, content="Hi!"),
            Message(role=Role.ASSISTANT, content="How are you?"),
        ],
        model=Model.GPT3,
        temperature=0.5,
        completion_tokens=10,
        prompt_tokens=15,
        cost=0.000043,
        name=name,
    )

    path = tmp_path / ".history"
    expected_file_path = path / expected_filename
    dt = datetime(2021, 1, 1, 12, 0, 0, tzinfo=ZoneInfo("UTC"))

    assert not path.exists()

    save_conversation(
        conversation=conversation,
        history_dir=path,
        dt=dt,
    )

    assert path.exists()
    assert path.is_dir()
    assert expected_file_path in path.iterdir()

    with expected_file_path.open() as f:
        conversation_from_file = Conversation.model_validate_json(f.read())

    assert conversation == conversation_from_file


def test_load(tmp_path: Path) -> None:
    # Create a conversation object to save
    conversation = Conversation(
        messages=[
            Message(role=Role.SYSTEM, content="Hello!"),
            Message(role=Role.USER, content="Hi!"),
            Message(role=Role.ASSISTANT, content="How are you?"),
        ],
        model=Model.GPT3,
        temperature=0.5,
        completion_tokens=10,
        prompt_tokens=15,
        cost=0.000043,
    )

    # Save the conversation to a file
    file_path = tmp_path / "conversation.json"
    with file_path.open("w") as f:
        f.write(conversation.model_dump_json())

    # Load the conversation from the file
    loaded_chat = Chat.load(file_path, api_key="foo", history_dir=tmp_path)

    # Check that the loaded conversation matches the original conversation
    assert loaded_chat.settings.model == conversation.model
    assert loaded_chat.settings.temperature == conversation.temperature
    assert loaded_chat.conversation.messages == conversation.messages
    assert loaded_chat.settings.api_key == "foo"
    assert loaded_chat.settings.history_dir == tmp_path

    # We don't want to load the tokens or cost from the previous session
    assert loaded_chat.conversation.completion_tokens == 0
    assert loaded_chat.conversation.prompt_tokens == 0
    assert loaded_chat.cost == 0


def test_send_message() -> None:
    with patch("llm_chat.chat.Chat._make_request") as mock_make_request:
        mock_make_request.return_value = ChatCompletion(
            choices=[
                Choice(
                    message=ChatCompletionMessage(content="Hello!", role="assistant"),
                    finish_reason="stop",
                    index=0,
                ),
            ],
            id="foo",
            created=0,
            model="gpt-3.5-turbo-0613",
            object="chat.completion",
            usage=CompletionUsage(
                completion_tokens=1,
                prompt_tokens=1,
                total_tokens=2,
            ),
        )
        conversation = Chat()
        response = conversation.send_message("Hello")
        assert isinstance(response, str)
        assert response == "Hello!"


@pytest.mark.parametrize("model,cost", [(Model.GPT3, 0.000043), (Model.GPT4, 0.00105)])
def test_calculate_cost(model: Model, cost: float) -> None:
    with patch("llm_chat.chat.Chat._make_request") as mock_make_request:
        mock_make_request.return_value = ChatCompletion(
            choices=[
                Choice(
                    message=ChatCompletionMessage(content="Hello!", role="assistant"),
                    finish_reason="stop",
                    index=0,
                ),
            ],
            id="foo",
            created=0,
            model="gpt-3.5-turbo-0613",
            object="chat.completion",
            usage=CompletionUsage(
                completion_tokens=10,
                prompt_tokens=15,
                total_tokens=25,
            ),
        )
        settings = OpenAISettings(model=model)
        conversation = Chat(settings=settings)
        _ = conversation.send_message("Hello")
        assert conversation.cost == cost