mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-20 20:48:16 +00:00
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
179 lines
5.9 KiB
Python
179 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["CASSANDRA_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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