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langchain/libs/langchain/tests/integration_tests/cache/test_opensearch_cache.py

60 lines
2.0 KiB
Python

from langchain_community.cache import OpenSearchSemanticCache
from langchain_core.outputs import Generation
from langchain.globals import get_llm_cache, set_llm_cache
from tests.integration_tests.cache.fake_embeddings import (
FakeEmbeddings,
)
from tests.unit_tests.llms.fake_llm import FakeLLM
DEFAULT_OPENSEARCH_URL = "http://localhost:9200"
def test_opensearch_semantic_cache() -> None:
"""Test opensearch semantic cache functionality."""
set_llm_cache(
OpenSearchSemanticCache(
embedding=FakeEmbeddings(),
opensearch_url=DEFAULT_OPENSEARCH_URL,
score_threshold=0.0,
)
)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
cache_output = get_llm_cache().lookup("bar", llm_string)
assert cache_output == [Generation(text="fizz")]
get_llm_cache().clear(llm_string=llm_string)
output = get_llm_cache().lookup("bar", llm_string)
assert output != [Generation(text="fizz")]
def test_opensearch_semantic_cache_multi() -> None:
set_llm_cache(
OpenSearchSemanticCache(
embedding=FakeEmbeddings(),
opensearch_url=DEFAULT_OPENSEARCH_URL,
score_threshold=0.0,
)
)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update(
"foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
)
# foo and bar will have the same embedding produced by FakeEmbeddings
cache_output = get_llm_cache().lookup("bar", llm_string)
assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]
# clear the cache
get_llm_cache().clear(llm_string=llm_string)
output = get_llm_cache().lookup("bar", llm_string)
assert output != [Generation(text="fizz"), Generation(text="Buzz")]