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https://github.com/hwchase17/langchain
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0d294760e7
## Description Fuse HuggingFace Endpoint-related classes into one: - [HuggingFaceHub](5ceaf784f3/libs/community/langchain_community/llms/huggingface_hub.py) - [HuggingFaceTextGenInference](5ceaf784f3/libs/community/langchain_community/llms/huggingface_text_gen_inference.py) - and [HuggingFaceEndpoint](5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py) Are fused into - HuggingFaceEndpoint ## Issue The deduplication of classes was creating a lack of clarity, and additional effort to develop classes leads to issues like [this hack](5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py (L159)). ## Dependancies None, this removes dependancies. ## Twitter handle If you want to post about this: @AymericRoucher --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
"""Test HuggingFace Endpoints."""
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from pathlib import Path
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import pytest
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from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
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from langchain_community.llms.loading import load_llm
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from tests.integration_tests.llms.utils import assert_llm_equality
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def test_huggingface_endpoint_call_error() -> None:
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"""Test valid call to HuggingFace that errors."""
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llm = HuggingFaceEndpoint(endpoint_url="", model_kwargs={"max_new_tokens": -1})
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with pytest.raises(ValueError):
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llm("Say foo:")
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def test_saving_loading_endpoint_llm(tmp_path: Path) -> None:
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"""Test saving/loading an HuggingFaceHub LLM."""
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llm = HuggingFaceEndpoint(
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endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10}
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)
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llm.save(file_path=tmp_path / "hf.yaml")
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loaded_llm = load_llm(tmp_path / "hf.yaml")
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assert_llm_equality(llm, loaded_llm)
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def test_huggingface_text_generation() -> None:
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"""Test valid call to HuggingFace text generation model."""
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llm = HuggingFaceEndpoint(repo_id="gpt2", model_kwargs={"max_new_tokens": 10})
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output = llm("Say foo:")
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print(output) # noqa: T201
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assert isinstance(output, str)
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def test_huggingface_text2text_generation() -> None:
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"""Test valid call to HuggingFace text2text model."""
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llm = HuggingFaceEndpoint(repo_id="google/flan-t5-xl")
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output = llm("The capital of New York is")
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assert output == "Albany"
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def test_huggingface_summarization() -> None:
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"""Test valid call to HuggingFace summarization model."""
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llm = HuggingFaceEndpoint(repo_id="facebook/bart-large-cnn")
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_call_error() -> None:
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"""Test valid call to HuggingFace that errors."""
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llm = HuggingFaceEndpoint(repo_id="gpt2", model_kwargs={"max_new_tokens": -1})
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with pytest.raises(ValueError):
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llm("Say foo:")
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def test_saving_loading_llm(tmp_path: Path) -> None:
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"""Test saving/loading an HuggingFaceEndpoint LLM."""
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llm = HuggingFaceEndpoint(repo_id="gpt2", model_kwargs={"max_new_tokens": 10})
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llm.save(file_path=tmp_path / "hf.yaml")
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loaded_llm = load_llm(tmp_path / "hf.yaml")
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assert_llm_equality(llm, loaded_llm)
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def test_invocation_params_stop_sequences() -> None:
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llm = HuggingFaceEndpoint()
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assert llm._default_params["stop_sequences"] == []
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runtime_stop = None
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assert llm._invocation_params(runtime_stop)["stop_sequences"] == []
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assert llm._default_params["stop_sequences"] == []
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runtime_stop = ["stop"]
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assert llm._invocation_params(runtime_stop)["stop_sequences"] == ["stop"]
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assert llm._default_params["stop_sequences"] == []
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llm = HuggingFaceEndpoint(stop_sequences=["."])
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runtime_stop = ["stop"]
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assert llm._invocation_params(runtime_stop)["stop_sequences"] == [".", "stop"]
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assert llm._default_params["stop_sequences"] == ["."]
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