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aeb42dc900
- **Description:** Add to check pad_token_id and eos_token_id of model config. It seems that this is the same bug as the HuggingFace TGI bug. It's same bug as #29434 - **Issue:** #29431 - **Dependencies:** none - **Twitter handle:** tell14 Example code is followings: ```python from langchain_huggingface.llms import HuggingFacePipeline hf = HuggingFacePipeline.from_model_id( model_id="meta-llama/Llama-3.2-3B-Instruct", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}, ) from langchain_core.prompts import PromptTemplate template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate.from_template(template) chain = prompt | hf question = "What is electroencephalography?" print(chain.invoke({"question": question})) ```
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
"""Standard LangChain interface tests"""
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from typing import 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.tools import BaseTool
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from langchain_tests.integration_tests import ChatModelIntegrationTests
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from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
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class TestHuggingFaceEndpoint(ChatModelIntegrationTests):
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@property
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def chat_model_class(self) -> Type[BaseChatModel]:
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return ChatHuggingFace
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@property
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def chat_model_params(self) -> dict:
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return {}
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@pytest.fixture
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def model(self) -> BaseChatModel:
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llm = HuggingFaceEndpoint( # type: ignore[call-arg]
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repo_id="HuggingFaceH4/zephyr-7b-beta",
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task="text-generation",
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max_new_tokens=512,
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do_sample=False,
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repetition_penalty=1.03,
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)
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return self.chat_model_class(llm=llm) # type: ignore[call-arg]
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_stream(self, model: BaseChatModel) -> None:
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super().test_stream(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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async def test_astream(self, model: BaseChatModel) -> None:
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await super().test_astream(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_usage_metadata(self, model: BaseChatModel) -> None:
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super().test_usage_metadata(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
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super().test_usage_metadata_streaming(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_stop_sequence(self, model: BaseChatModel) -> None:
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super().test_stop_sequence(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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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=("Not implemented"))
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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=("Not implemented"))
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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(reason=("Not implemented"))
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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=("Not implemented"))
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def test_structured_output(self, model: BaseChatModel, schema_type: str) -> None:
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super().test_structured_output(model, schema_type)
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@pytest.mark.xfail(reason=("Not implemented"))
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async def test_structured_output_async(
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self, model: BaseChatModel, schema_type: str
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) -> None: # type: ignore[override]
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super().test_structured_output(model, schema_type)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None:
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super().test_structured_output_pydantic_2_v1(model)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_structured_output_optional_param(self, model: BaseChatModel) -> None:
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super().test_structured_output_optional_param(model)
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@pytest.mark.xfail(reason=("Not 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(
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model, my_adder_tool=my_adder_tool
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)
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@pytest.mark.xfail(reason=("Not implemented"))
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def test_structured_few_shot_examples(
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self, model: BaseChatModel, my_adder_tool: BaseTool
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) -> None:
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super().test_structured_few_shot_examples(model, my_adder_tool=my_adder_tool)
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