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"""Test caching for LLMs and ChatModels."""
import sqlite3
from typing import Dict, Generator, List, Union
import pytest
from _pytest.fixtures import FixtureRequest
from langchain_core.caches import InMemoryCache
from langchain_core.language_models import FakeListChatModel, FakeListLLM
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.language_models.llms import BaseLLM
from langchain_core.load import dumps
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration
from sqlalchemy import Column, Integer, Sequence, String, create_engine
from sqlalchemy.orm import Session
try:
from sqlalchemy.orm import declarative_base
except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from langchain.globals import get_llm_cache, set_llm_cache
from langchain_core.outputs import Generation, LLMResult
from langchain_community.cache import SQLAlchemyCache
from tests.unit_tests.llms.fake_llm import FakeLLM
def get_sqlite_cache() -> SQLAlchemyCache:
return SQLAlchemyCache(
engine=create_engine(
"sqlite://", creator=lambda: sqlite3.connect("file::memory:?cache=shared")
)
)
CACHE_OPTIONS = [
InMemoryCache,
get_sqlite_cache,
]
@pytest.fixture(autouse=True, params=CACHE_OPTIONS)
def set_cache_and_teardown(request: FixtureRequest) -> Generator[None, None, None]:
# Will be run before each test
cache_instance = request.param
set_llm_cache(cache_instance())
if llm_cache := get_llm_cache():
llm_cache.clear()
else:
raise ValueError("Cache not set. This should never happen.")
yield
# Will be run after each test
if llm_cache:
llm_cache.clear()
set_llm_cache(None)
else:
raise ValueError("Cache not set. This should never happen.")
async def test_llm_caching() -> None:
prompt = "How are you?"
response = "Test response"
cached_response = "Cached test response"
llm = FakeListLLM(responses=[response])
if llm_cache := get_llm_cache():
# sync test
llm_cache.update(
prompt=prompt,
llm_string=create_llm_string(llm),
return_val=[Generation(text=cached_response)],
)
assert llm.invoke(prompt) == cached_response
# async test
await llm_cache.aupdate(
prompt=prompt,
llm_string=create_llm_string(llm),
return_val=[Generation(text=cached_response)],
)
assert await llm.ainvoke(prompt) == cached_response
else:
raise ValueError(
"The cache not set. This should never happen, as the pytest fixture "
"`set_cache_and_teardown` always sets the cache."
)
def test_old_sqlite_llm_caching() -> None:
llm_cache = get_llm_cache()
if isinstance(llm_cache, SQLAlchemyCache):
prompt = "How are you?"
response = "Test response"
cached_response = "Cached test response"
llm = FakeListLLM(responses=[response])
items = [
llm_cache.cache_schema(
prompt=prompt,
llm=create_llm_string(llm),
response=cached_response,
idx=0,
)
]
with Session(llm_cache.engine) as session, session.begin():
for item in items:
session.merge(item)
assert llm.invoke(prompt) == cached_response
async def test_chat_model_caching() -> None:
prompt: List[BaseMessage] = [HumanMessage(content="How are you?")]
response = "Test response"
cached_response = "Cached test response"
cached_message = AIMessage(content=cached_response)
llm = FakeListChatModel(responses=[response])
if llm_cache := get_llm_cache():
# sync test
llm_cache.update(
prompt=dumps(prompt),
llm_string=llm._get_llm_string(),
return_val=[ChatGeneration(message=cached_message)],
)
result = llm.invoke(prompt)
assert isinstance(result, AIMessage)
assert result.content == cached_response
# async test
await llm_cache.aupdate(
prompt=dumps(prompt),
llm_string=llm._get_llm_string(),
return_val=[ChatGeneration(message=cached_message)],
)
result = await llm.ainvoke(prompt)
assert isinstance(result, AIMessage)
assert result.content == cached_response
else:
raise ValueError(
"The cache not set. This should never happen, as the pytest fixture "
"`set_cache_and_teardown` always sets the cache."
)
async def test_chat_model_caching_params() -> None:
prompt: List[BaseMessage] = [HumanMessage(content="How are you?")]
response = "Test response"
cached_response = "Cached test response"
cached_message = AIMessage(content=cached_response)
llm = FakeListChatModel(responses=[response])
if llm_cache := get_llm_cache():
# sync test
llm_cache.update(
prompt=dumps(prompt),
llm_string=llm._get_llm_string(functions=[]),
return_val=[ChatGeneration(message=cached_message)],
)
result = llm.invoke(prompt, functions=[])
result_no_params = llm.invoke(prompt)
assert isinstance(result, AIMessage)
assert result.content == cached_response
assert isinstance(result_no_params, AIMessage)
assert result_no_params.content == response
# async test
await llm_cache.aupdate(
prompt=dumps(prompt),
llm_string=llm._get_llm_string(functions=[]),
return_val=[ChatGeneration(message=cached_message)],
)
result = await llm.ainvoke(prompt, functions=[])
result_no_params = await llm.ainvoke(prompt)
assert isinstance(result, AIMessage)
assert result.content == cached_response
assert isinstance(result_no_params, AIMessage)
assert result_no_params.content == response
else:
raise ValueError(
"The cache not set. This should never happen, as the pytest fixture "
"`set_cache_and_teardown` always sets the cache."
)
async def test_llm_cache_clear() -> None:
prompt = "How are you?"
expected_response = "Test response"
cached_response = "Cached test response"
llm = FakeListLLM(responses=[expected_response])
if llm_cache := get_llm_cache():
# sync test
llm_cache.update(
prompt=prompt,
llm_string=create_llm_string(llm),
return_val=[Generation(text=cached_response)],
)
llm_cache.clear()
response = llm.invoke(prompt)
assert response == expected_response
# async test
await llm_cache.aupdate(
prompt=prompt,
llm_string=create_llm_string(llm),
return_val=[Generation(text=cached_response)],
)
await llm_cache.aclear()
response = await llm.ainvoke(prompt)
assert response == expected_response
else:
raise ValueError(
"The cache not set. This should never happen, as the pytest fixture "
"`set_cache_and_teardown` always sets the cache."
)
def create_llm_string(llm: Union[BaseLLM, BaseChatModel]) -> str:
_dict: Dict = llm.dict()
_dict["stop"] = None
return str(sorted([(k, v) for k, v in _dict.items()]))
def test_sql_alchemy_cache() -> None:
"""Test custom_caching behavior."""
Base = declarative_base()
class FulltextLLMCache(Base): # type: ignore
"""Postgres table for fulltext-indexed LLM Cache."""
__tablename__ = "llm_cache_fulltext"
id = Column(Integer, Sequence("cache_id"), primary_key=True)
prompt = Column(String, nullable=False)
llm = Column(String, nullable=False)
idx = Column(Integer)
response = Column(String)
engine = create_engine("sqlite://")
from langchain_community.cache import SQLAlchemyCache
set_llm_cache(SQLAlchemyCache(engine, FulltextLLMCache))
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")])
output = llm.generate(["foo", "bar", "foo"])
expected_cache_output = [Generation(text="foo")]
cache_output = get_llm_cache().lookup("bar", llm_string)
assert cache_output == expected_cache_output
set_llm_cache(None)
expected_generations = [
[Generation(text="fizz")],
[Generation(text="foo")],
[Generation(text="fizz")],
]
expected_output = LLMResult(
generations=expected_generations,
llm_output=None,
)
assert output == expected_output
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