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multiple: langchain 0.2 in master (#21191)
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>
This commit is contained in:
Eugene Yurtsev
GitHub
319
libs/community/tests/integration_tests/cache/test_redis_cache.py
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319
libs/community/tests/integration_tests/cache/test_redis_cache.py
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"""Test Redis cache functionality."""
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import uuid
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from contextlib import asynccontextmanager, contextmanager
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from typing import AsyncGenerator, Generator, List, Optional, cast
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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.embeddings import Embeddings
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from langchain_core.load.dump import dumps
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
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from langchain_core.outputs import ChatGeneration, Generation, LLMResult
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from langchain_community.cache import AsyncRedisCache, RedisCache, RedisSemanticCache
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from tests.integration_tests.cache.fake_embeddings import (
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ConsistentFakeEmbeddings,
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FakeEmbeddings,
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)
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from tests.unit_tests.llms.fake_chat_model import FakeChatModel
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from tests.unit_tests.llms.fake_llm import FakeLLM
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# Using a non-standard port to avoid conflicts with potentially local running
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# redis instances
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# You can spin up a local redis using docker compose
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# cd [repository-root]/docker
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# docker-compose up redis
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REDIS_TEST_URL = "redis://localhost:6020"
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def random_string() -> str:
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return str(uuid.uuid4())
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@contextmanager
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def get_sync_redis(*, ttl: Optional[int] = 1) -> Generator[RedisCache, None, None]:
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"""Get a sync RedisCache instance."""
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import redis
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cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=ttl)
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try:
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yield cache
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finally:
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cache.clear()
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@asynccontextmanager
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async def get_async_redis(
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*, ttl: Optional[int] = 1
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) -> AsyncGenerator[AsyncRedisCache, None]:
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"""Get an async RedisCache instance."""
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from redis.asyncio import Redis
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cache = AsyncRedisCache(redis_=Redis.from_url(REDIS_TEST_URL), ttl=ttl)
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try:
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yield cache
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finally:
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await cache.aclear()
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def test_redis_cache_ttl() -> None:
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from redis import Redis
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with get_sync_redis() as llm_cache:
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set_llm_cache(llm_cache)
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llm_cache.update("foo", "bar", [Generation(text="fizz")])
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key = llm_cache._key("foo", "bar")
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assert isinstance(llm_cache.redis, Redis)
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assert llm_cache.redis.pttl(key) > 0
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async def test_async_redis_cache_ttl() -> None:
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from redis.asyncio import Redis as AsyncRedis
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async with get_async_redis() as redis_cache:
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set_llm_cache(redis_cache)
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llm_cache = cast(RedisCache, get_llm_cache())
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await llm_cache.aupdate("foo", "bar", [Generation(text="fizz")])
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key = llm_cache._key("foo", "bar")
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assert isinstance(llm_cache.redis, AsyncRedis)
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assert await llm_cache.redis.pttl(key) > 0
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def test_sync_redis_cache() -> None:
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with get_sync_redis() as llm_cache:
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set_llm_cache(llm_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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llm_cache.update("prompt", llm_string, [Generation(text="fizz0")])
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output = llm.generate(["prompt"])
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expected_output = LLMResult(
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generations=[[Generation(text="fizz0")]],
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llm_output={},
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)
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assert output == expected_output
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async def test_sync_in_async_redis_cache() -> None:
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"""Test the sync RedisCache invoked with async methods"""
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with get_sync_redis() as llm_cache:
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set_llm_cache(llm_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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# llm_cache.update("meow", llm_string, [Generation(text="meow")])
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await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz1")])
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output = await llm.agenerate(["prompt"])
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expected_output = LLMResult(
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generations=[[Generation(text="fizz1")]],
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llm_output={},
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)
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assert output == expected_output
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async def test_async_redis_cache() -> None:
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async with get_async_redis() as redis_cache:
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set_llm_cache(redis_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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llm_cache = cast(RedisCache, get_llm_cache())
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await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz2")])
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output = await llm.agenerate(["prompt"])
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expected_output = LLMResult(
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generations=[[Generation(text="fizz2")]],
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llm_output={},
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)
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assert output == expected_output
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async def test_async_in_sync_redis_cache() -> None:
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async with get_async_redis() as redis_cache:
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set_llm_cache(redis_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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llm_cache = cast(RedisCache, get_llm_cache())
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with pytest.raises(NotImplementedError):
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llm_cache.update("foo", llm_string, [Generation(text="fizz")])
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def test_redis_cache_chat() -> None:
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with get_sync_redis() as redis_cache:
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set_llm_cache(redis_cache)
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llm = FakeChatModel()
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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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prompt: List[BaseMessage] = [HumanMessage(content="foo")]
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llm_cache = cast(RedisCache, get_llm_cache())
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llm_cache.update(
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dumps(prompt),
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llm_string,
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[ChatGeneration(message=AIMessage(content="fizz"))],
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)
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output = llm.generate([prompt])
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expected_output = LLMResult(
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generations=[[ChatGeneration(message=AIMessage(content="fizz"))]],
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llm_output={},
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)
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assert output == expected_output
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async def test_async_redis_cache_chat() -> None:
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async with get_async_redis() as redis_cache:
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set_llm_cache(redis_cache)
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llm = FakeChatModel()
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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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prompt: List[BaseMessage] = [HumanMessage(content="foo")]
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llm_cache = cast(RedisCache, get_llm_cache())
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await llm_cache.aupdate(
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dumps(prompt),
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llm_string,
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[ChatGeneration(message=AIMessage(content="fizz"))],
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)
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output = await llm.agenerate([prompt])
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expected_output = LLMResult(
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generations=[[ChatGeneration(message=AIMessage(content="fizz"))]],
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llm_output={},
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)
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assert output == expected_output
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def test_redis_semantic_cache() -> None:
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"""Test redis semantic cache functionality."""
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set_llm_cache(
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RedisSemanticCache(
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embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1
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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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llm_cache = cast(RedisSemanticCache, get_llm_cache())
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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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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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llm_cache.clear(llm_string=llm_string)
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def test_redis_semantic_cache_multi() -> None:
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set_llm_cache(
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RedisSemanticCache(
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embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1
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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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llm_cache = cast(RedisSemanticCache, get_llm_cache())
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llm_cache.update(
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"foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
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)
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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"), Generation(text="Buzz")]],
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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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llm_cache.clear(llm_string=llm_string)
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def test_redis_semantic_cache_chat() -> None:
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set_llm_cache(
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RedisSemanticCache(
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embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1
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)
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)
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llm = FakeChatModel()
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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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prompt: List[BaseMessage] = [HumanMessage(content="foo")]
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llm_cache = cast(RedisSemanticCache, get_llm_cache())
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llm_cache.update(
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dumps(prompt), llm_string, [ChatGeneration(message=AIMessage(content="fizz"))]
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)
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output = llm.generate([prompt])
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expected_output = LLMResult(
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generations=[[ChatGeneration(message=AIMessage(content="fizz"))]],
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llm_output={},
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)
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assert output == expected_output
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llm_cache.clear(llm_string=llm_string)
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@pytest.mark.parametrize("embedding", [ConsistentFakeEmbeddings()])
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@pytest.mark.parametrize(
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"prompts, generations",
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[
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# Single prompt, single generation
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([random_string()], [[random_string()]]),
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# Single prompt, multiple generations
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([random_string()], [[random_string(), random_string()]]),
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# Single prompt, multiple generations
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([random_string()], [[random_string(), random_string(), random_string()]]),
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# Multiple prompts, multiple generations
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(
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[random_string(), random_string()],
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[[random_string()], [random_string(), random_string()]],
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),
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],
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ids=[
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"single_prompt_single_generation",
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"single_prompt_multiple_generations",
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"single_prompt_multiple_generations",
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"multiple_prompts_multiple_generations",
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],
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)
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def test_redis_semantic_cache_hit(
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embedding: Embeddings, prompts: List[str], generations: List[List[str]]
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) -> None:
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set_llm_cache(RedisSemanticCache(embedding=embedding, redis_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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llm_generations = [
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[
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Generation(text=generation, generation_info=params)
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for generation in prompt_i_generations
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]
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for prompt_i_generations in generations
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]
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llm_cache = cast(RedisSemanticCache, get_llm_cache())
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for prompt_i, llm_generations_i in zip(prompts, llm_generations):
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print(prompt_i) # noqa: T201
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print(llm_generations_i) # noqa: T201
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llm_cache.update(prompt_i, llm_string, llm_generations_i)
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llm.generate(prompts)
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assert llm.generate(prompts) == LLMResult(
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generations=llm_generations, llm_output={}
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)
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