mirror of
https://github.com/hwchase17/langchain
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76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
"""Test base LLM functionality."""
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from sqlalchemy import Column, Integer, Sequence, String, create_engine
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try:
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from sqlalchemy.orm import declarative_base
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except ImportError:
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from sqlalchemy.ext.declarative import declarative_base
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import langchain
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from langchain.cache import InMemoryCache, SQLAlchemyCache
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from langchain.schema import Generation, LLMResult
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from tests.unit_tests.llms.fake_llm import FakeLLM
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def test_caching() -> None:
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"""Test caching behavior."""
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langchain.llm_cache = InMemoryCache()
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llm = FakeLLM()
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params = llm.dict()
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params["stop"] = None
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llm_string = str(sorted([(k, v) for k, v in params.items()]))
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langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")])
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output = llm.generate(["foo", "bar", "foo"])
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expected_cache_output = [Generation(text="foo")]
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cache_output = langchain.llm_cache.lookup("bar", llm_string)
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assert cache_output == expected_cache_output
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langchain.llm_cache = None
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expected_generations = [
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[Generation(text="fizz")],
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[Generation(text="foo")],
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[Generation(text="fizz")],
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]
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expected_output = LLMResult(
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generations=expected_generations,
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llm_output=None,
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)
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assert output == expected_output
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def test_custom_caching() -> None:
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"""Test custom_caching behavior."""
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Base = declarative_base()
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class FulltextLLMCache(Base): # type: ignore
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"""Postgres table for fulltext-indexed LLM Cache."""
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__tablename__ = "llm_cache_fulltext"
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id = Column(Integer, Sequence("cache_id"), primary_key=True)
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prompt = Column(String, nullable=False)
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llm = Column(String, nullable=False)
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idx = Column(Integer)
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response = Column(String)
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engine = create_engine("sqlite://")
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langchain.llm_cache = SQLAlchemyCache(engine, FulltextLLMCache)
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llm = FakeLLM()
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params = llm.dict()
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params["stop"] = None
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llm_string = str(sorted([(k, v) for k, v in params.items()]))
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langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")])
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output = llm.generate(["foo", "bar", "foo"])
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expected_cache_output = [Generation(text="foo")]
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cache_output = langchain.llm_cache.lookup("bar", llm_string)
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assert cache_output == expected_cache_output
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langchain.llm_cache = None
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expected_generations = [
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[Generation(text="fizz")],
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[Generation(text="foo")],
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[Generation(text="fizz")],
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]
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expected_output = LLMResult(
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generations=expected_generations,
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llm_output=None,
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)
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assert output == expected_output
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