mirror of
https://github.com/hwchase17/langchain
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56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
"""Test Redis cache functionality."""
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import redis
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import langchain
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from langchain.cache import RedisCache, RedisSemanticCache
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from langchain.schema import Generation, LLMResult
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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from tests.unit_tests.llms.fake_llm import FakeLLM
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REDIS_TEST_URL = "redis://localhost:6379"
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def test_redis_cache() -> None:
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langchain.llm_cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))
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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"])
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print(output)
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expected_output = LLMResult(
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generations=[[Generation(text="fizz")]],
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llm_output={},
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)
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print(expected_output)
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assert output == expected_output
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langchain.llm_cache.redis.flushall()
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def test_redis_semantic_cache() -> None:
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langchain.llm_cache = RedisSemanticCache(
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embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1
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)
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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(
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["bar"]
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) # foo and bar will have the same embedding produced by FakeEmbeddings
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expected_output = LLMResult(
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generations=[[Generation(text="fizz")]],
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llm_output={},
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)
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assert output == expected_output
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# clear the cache
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langchain.llm_cache.clear(llm_string=llm_string)
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output = llm.generate(
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["bar"]
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) # foo and bar will have the same embedding produced by FakeEmbeddings
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# expect different output now without cached result
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assert output != expected_output
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langchain.llm_cache.clear(llm_string=llm_string)
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