mirror of
https://github.com/hwchase17/langchain
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f92006de3c
0.2rc migrations - [x] Move memory - [x] Move remaining retrievers - [x] graph_qa chains - [x] some dependency from evaluation code potentially on math utils - [x] Move openapi chain from `langchain.chains.api.openapi` to `langchain_community.chains.openapi` - [x] Migrate `langchain.chains.ernie_functions` to `langchain_community.chains.ernie_functions` - [x] migrate `langchain/chains/llm_requests.py` to `langchain_community.chains.llm_requests` - [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder` -> `langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder` (namespace not ideal, but it needs to be moved to `langchain` to avoid circular deps) - [x] unit tests langchain -- add pytest.mark.community to some unit tests that will stay in langchain - [x] unit tests community -- move unit tests that depend on community to community - [x] mv integration tests that depend on community to community - [x] mypy checks Other todo - [x] Make deprecation warnings not noisy (need to use warn deprecated and check that things are implemented properly) - [x] Update deprecation messages with timeline for code removal (likely we actually won't be removing things until 0.4 release) -- will give people more time to transition their code. - [ ] Add information to deprecation warning to show users how to migrate their code base using langchain-cli - [ ] Remove any unnecessary requirements in langchain (e.g., is SQLALchemy required?) --------- Co-authored-by: Erick Friis <erick@langchain.dev>
60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
from langchain.globals import get_llm_cache, set_llm_cache
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from langchain_core.outputs import Generation
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from langchain_community.cache import OpenSearchSemanticCache
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from tests.integration_tests.cache.fake_embeddings import (
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FakeEmbeddings,
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)
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from tests.unit_tests.llms.fake_llm import FakeLLM
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DEFAULT_OPENSEARCH_URL = "http://localhost:9200"
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def test_opensearch_semantic_cache() -> None:
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"""Test opensearch semantic cache functionality."""
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set_llm_cache(
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OpenSearchSemanticCache(
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embedding=FakeEmbeddings(),
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opensearch_url=DEFAULT_OPENSEARCH_URL,
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score_threshold=0.0,
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)
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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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get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
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cache_output = get_llm_cache().lookup("bar", llm_string)
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assert cache_output == [Generation(text="fizz")]
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get_llm_cache().clear(llm_string=llm_string)
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output = get_llm_cache().lookup("bar", llm_string)
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assert output != [Generation(text="fizz")]
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def test_opensearch_semantic_cache_multi() -> None:
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set_llm_cache(
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OpenSearchSemanticCache(
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embedding=FakeEmbeddings(),
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opensearch_url=DEFAULT_OPENSEARCH_URL,
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score_threshold=0.0,
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)
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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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get_llm_cache().update(
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"foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
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)
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# foo and bar will have the same embedding produced by FakeEmbeddings
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cache_output = get_llm_cache().lookup("bar", llm_string)
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assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]
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# clear the cache
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get_llm_cache().clear(llm_string=llm_string)
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output = get_llm_cache().lookup("bar", llm_string)
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assert output != [Generation(text="fizz"), Generation(text="Buzz")]
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