mem0/tests/llm/test_gpt4all.py

import pytest
from langchain_community.llms.gpt4all import GPT4All as LangchainGPT4All

from embedchain.config import BaseLlmConfig
from embedchain.llm.gpt4all import GPT4ALLLlm


@pytest.fixture
def config():
    config = BaseLlmConfig(
        temperature=0.7,
        max_tokens=50,
        top_p=0.8,
        stream=False,
        system_prompt="System prompt",
        model="orca-mini-3b-gguf2-q4_0.gguf",
    )
    yield config


@pytest.fixture
def gpt4all_with_config(config):
    return GPT4ALLLlm(config=config)


@pytest.fixture
def gpt4all_without_config():
    return GPT4ALLLlm()


def test_gpt4all_init_with_config(config, gpt4all_with_config):
    assert gpt4all_with_config.config.temperature == config.temperature
    assert gpt4all_with_config.config.max_tokens == config.max_tokens
    assert gpt4all_with_config.config.top_p == config.top_p
    assert gpt4all_with_config.config.stream == config.stream
    assert gpt4all_with_config.config.system_prompt == config.system_prompt
    assert gpt4all_with_config.config.model == config.model

    assert isinstance(gpt4all_with_config.instance, LangchainGPT4All)


def test_gpt4all_init_without_config(gpt4all_without_config):
    assert gpt4all_without_config.config.model == "orca-mini-3b-gguf2-q4_0.gguf"
    assert isinstance(gpt4all_without_config.instance, LangchainGPT4All)


def test_get_llm_model_answer(mocker, gpt4all_with_config):
    test_query = "Test query"
    test_answer = "Test answer"

    mocked_get_answer = mocker.patch("embedchain.llm.gpt4all.GPT4ALLLlm._get_answer", return_value=test_answer)
    answer = gpt4all_with_config.get_llm_model_answer(test_query)

    assert answer == test_answer
    mocked_get_answer.assert_called_once_with(prompt=test_query, config=gpt4all_with_config.config)


def test_gpt4all_model_switching(gpt4all_with_config):
    with pytest.raises(RuntimeError, match="GPT4ALLLlm does not support switching models at runtime."):
        gpt4all_with_config._get_answer("Test prompt", BaseLlmConfig(model="new_model"))