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73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
"""Standard LangChain interface tests."""
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from typing import Literal
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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_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 TestGroq(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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@property
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def chat_model_params(self) -> dict:
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return {"model": "llama-3.3-70b-versatile", "rate_limiter": rate_limiter}
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@pytest.mark.xfail(
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reason="Groq models have inconsistent tool calling performance. See: "
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"https://github.com/langchain-ai/langchain/discussions/19990"
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)
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def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
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super().test_bind_runnables_as_tools(model)
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@pytest.mark.xfail(reason="Retry flaky tool calling behavior")
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@pytest.mark.retry(count=3, delay=1)
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def test_tool_calling(self, model: BaseChatModel) -> None:
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super().test_tool_calling(model)
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@pytest.mark.xfail(reason="Retry flaky tool calling behavior")
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@pytest.mark.retry(count=3, delay=1)
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async def test_tool_calling_async(self, model: BaseChatModel) -> None:
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await super().test_tool_calling_async(model)
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@pytest.mark.xfail(reason="Retry flaky tool calling behavior")
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@pytest.mark.retry(count=3, delay=1)
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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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@property
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def supports_json_mode(self) -> bool:
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return True
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@pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
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def test_json_schema(
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schema_type: Literal["pydantic", "typeddict", "json_schema"],
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) -> None:
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class JsonSchemaTests(ChatModelIntegrationTests):
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@property
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def chat_model_class(self) -> type[ChatGroq]:
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return ChatGroq
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@property
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def chat_model_params(self) -> dict:
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return {"model": "openai/gpt-oss-120b", "rate_limiter": rate_limiter}
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@property
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def structured_output_kwargs(self) -> dict:
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return {"method": "json_schema"}
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test_instance = JsonSchemaTests()
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model = test_instance.chat_model_class(**test_instance.chat_model_params)
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JsonSchemaTests().test_structured_output(model, schema_type)
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