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8670b24c8e
Groq models have known issues with tool calling consistency, [particularly with complex tools derived from runnables](https://github.com/langchain-ai/langchain/discussions/19990). [(more)](https://github.com/langchain-ai/langchain/discussions/24309) xfail until we can dedicate time to wrangling their API/model handling
65 lines
2.1 KiB
Python
65 lines
2.1 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_core.tools import BaseTool
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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(reason="Not yet implemented.")
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def test_tool_message_histories_list_content(
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self, model: BaseChatModel, my_adder_tool: BaseTool
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) -> None:
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super().test_tool_message_histories_list_content(model, my_adder_tool)
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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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@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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