mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
"""Test LLM chain."""
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from tempfile import TemporaryDirectory
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from typing import Dict, List, Union
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from unittest.mock import patch
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import pytest
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from langchain.chains.llm import LLMChain
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from langchain.chains.loading import load_chain
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import BaseOutputParser
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from tests.unit_tests.llms.fake_llm import FakeLLM
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class FakeOutputParser(BaseOutputParser):
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"""Fake output parser class for testing."""
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def parse(self, text: str) -> Union[str, List[str], Dict[str, str]]:
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"""Parse by splitting."""
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return text.split()
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@pytest.fixture
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def fake_llm_chain() -> LLMChain:
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"""Fake LLM chain for testing purposes."""
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prompt = PromptTemplate(input_variables=["bar"], template="This is a {bar}:")
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return LLMChain(prompt=prompt, llm=FakeLLM(), output_key="text1")
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@patch("langchain.llms.loading.type_to_cls_dict", {"fake": FakeLLM})
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def test_serialization(fake_llm_chain: LLMChain) -> None:
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"""Test serialization."""
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with TemporaryDirectory() as temp_dir:
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file = temp_dir + "/llm.json"
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fake_llm_chain.save(file)
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loaded_chain = load_chain(file)
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assert loaded_chain == fake_llm_chain
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def test_missing_inputs(fake_llm_chain: LLMChain) -> None:
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"""Test error is raised if inputs are missing."""
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with pytest.raises(ValueError):
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fake_llm_chain({"foo": "bar"})
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def test_valid_call(fake_llm_chain: LLMChain) -> None:
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"""Test valid call of LLM chain."""
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output = fake_llm_chain({"bar": "baz"})
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assert output == {"bar": "baz", "text1": "foo"}
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# Test with stop words.
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output = fake_llm_chain({"bar": "baz", "stop": ["foo"]})
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# Response should be `bar` now.
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assert output == {"bar": "baz", "stop": ["foo"], "text1": "bar"}
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def test_predict_method(fake_llm_chain: LLMChain) -> None:
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"""Test predict method works."""
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output = fake_llm_chain.predict(bar="baz")
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assert output == "foo"
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def test_predict_and_parse() -> None:
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"""Test parsing ability."""
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prompt = PromptTemplate(
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input_variables=["foo"], template="{foo}", output_parser=FakeOutputParser()
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)
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llm = FakeLLM(queries={"foo": "foo bar"})
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chain = LLMChain(prompt=prompt, llm=llm)
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output = chain.predict_and_parse(foo="foo")
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assert output == ["foo", "bar"]
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