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Here we allow standard tests to specify a value for `tool_choice` via a `tool_choice_value` property, which defaults to None. Chat models [available in Together](https://docs.together.ai/docs/chat-models) have issues passing standard tool calling tests: - llama 3.1 models currently [appear to rely on user-side parsing](https://docs.together.ai/docs/llama-3-function-calling) in Together; - Mixtral-8x7B and Mistral-7B (currently tested) consistently do not call tools in some tests. Specifying tool_choice also lets us remove an existing `xfail` and use a smaller model in Groq tests.
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
"""Standard LangChain interface tests"""
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from typing import Optional, Type
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import pytest
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from langchain_core.language_models import BaseChatModel
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from langchain_core.rate_limiters import InMemoryRateLimiter
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from langchain_standard_tests.integration_tests import (
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ChatModelIntegrationTests,
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)
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from langchain_groq import ChatGroq
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rate_limiter = InMemoryRateLimiter(requests_per_second=0.2)
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class BaseTestGroq(ChatModelIntegrationTests):
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@property
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def chat_model_class(self) -> Type[BaseChatModel]:
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return ChatGroq
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@pytest.mark.xfail(reason="Not yet implemented.")
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def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None:
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super().test_tool_message_histories_list_content(model)
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class TestGroqLlama(BaseTestGroq):
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@property
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def chat_model_params(self) -> dict:
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return {
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"model": "llama-3.1-8b-instant",
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"temperature": 0,
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"rate_limiter": rate_limiter,
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}
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@property
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def tool_choice_value(self) -> Optional[str]:
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"""Value to use for tool choice when used in tests."""
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return "any"
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@pytest.mark.xfail(
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reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
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)
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def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
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super().test_tool_calling_with_no_arguments(model)
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@pytest.mark.xfail(
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reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
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)
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def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
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super().test_tool_message_histories_string_content(model)
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