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>
178 lines
5.9 KiB
Python
178 lines
5.9 KiB
Python
"""Test Cassandra caches. Requires a running vector-capable Cassandra cluster."""
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import asyncio
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import os
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import time
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from typing import Any, Iterator, Tuple
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import pytest
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from langchain.globals import get_llm_cache, set_llm_cache
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from langchain_core.outputs import Generation, LLMResult
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from langchain_community.cache import CassandraCache, CassandraSemanticCache
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from langchain_community.utilities.cassandra import SetupMode
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from tests.integration_tests.cache.fake_embeddings import FakeEmbeddings
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from tests.unit_tests.llms.fake_llm import FakeLLM
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@pytest.fixture(scope="module")
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def cassandra_connection() -> Iterator[Tuple[Any, str]]:
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from cassandra.cluster import Cluster
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keyspace = "langchain_cache_test_keyspace"
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# get db connection
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if "CASSANDRA_CONTACT_POINTS" in os.environ:
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contact_points = os.environ["CONTACT_POINTS"].split(",")
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cluster = Cluster(contact_points)
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else:
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cluster = Cluster()
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#
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session = cluster.connect()
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# ensure keyspace exists
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session.execute(
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(
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f"CREATE KEYSPACE IF NOT EXISTS {keyspace} "
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f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
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)
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)
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yield (session, keyspace)
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def test_cassandra_cache(cassandra_connection: Tuple[Any, str]) -> None:
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session, keyspace = cassandra_connection
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cache = CassandraCache(session=session, keyspace=keyspace)
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set_llm_cache(cache)
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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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output = llm.generate(["foo"])
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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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cache.clear()
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async def test_cassandra_cache_async(cassandra_connection: Tuple[Any, str]) -> None:
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session, keyspace = cassandra_connection
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cache = CassandraCache(
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session=session, keyspace=keyspace, setup_mode=SetupMode.ASYNC
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)
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set_llm_cache(cache)
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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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await get_llm_cache().aupdate("foo", llm_string, [Generation(text="fizz")])
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output = await llm.agenerate(["foo"])
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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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await cache.aclear()
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def test_cassandra_cache_ttl(cassandra_connection: Tuple[Any, str]) -> None:
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session, keyspace = cassandra_connection
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cache = CassandraCache(session=session, keyspace=keyspace, ttl_seconds=2)
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set_llm_cache(cache)
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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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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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output = llm.generate(["foo"])
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assert output == expected_output
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time.sleep(2.5)
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# entry has expired away.
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output = llm.generate(["foo"])
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assert output != expected_output
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cache.clear()
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async def test_cassandra_cache_ttl_async(cassandra_connection: Tuple[Any, str]) -> None:
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session, keyspace = cassandra_connection
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cache = CassandraCache(
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session=session, keyspace=keyspace, ttl_seconds=2, setup_mode=SetupMode.ASYNC
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)
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set_llm_cache(cache)
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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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await get_llm_cache().aupdate("foo", llm_string, [Generation(text="fizz")])
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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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output = await llm.agenerate(["foo"])
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assert output == expected_output
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await asyncio.sleep(2.5)
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# entry has expired away.
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output = await llm.agenerate(["foo"])
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assert output != expected_output
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await cache.aclear()
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def test_cassandra_semantic_cache(cassandra_connection: Tuple[Any, str]) -> None:
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session, keyspace = cassandra_connection
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sem_cache = CassandraSemanticCache(
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session=session,
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keyspace=keyspace,
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embedding=FakeEmbeddings(),
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)
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set_llm_cache(sem_cache)
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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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output = llm.generate(["bar"]) # same embedding as 'foo'
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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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sem_cache.clear()
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output = llm.generate(["bar"]) # 'fizz' is erased away now
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assert output != expected_output
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sem_cache.clear()
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async def test_cassandra_semantic_cache_async(
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cassandra_connection: Tuple[Any, str],
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) -> None:
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session, keyspace = cassandra_connection
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sem_cache = CassandraSemanticCache(
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session=session,
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keyspace=keyspace,
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embedding=FakeEmbeddings(),
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setup_mode=SetupMode.ASYNC,
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)
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set_llm_cache(sem_cache)
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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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await get_llm_cache().aupdate("foo", llm_string, [Generation(text="fizz")])
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output = await llm.agenerate(["bar"]) # same embedding as 'foo'
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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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await sem_cache.aclear()
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output = await llm.agenerate(["bar"]) # 'fizz' is erased away now
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assert output != expected_output
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await sem_cache.aclear()
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