mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-27 11:41:51 +00:00
a0c2281540
```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()
```
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
"""Fake ChatModel for testing purposes."""
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import asyncio
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import time
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
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from langchain_core.messages import AIMessageChunk, BaseMessage
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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class FakeMessagesListChatModel(BaseChatModel):
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"""Fake ChatModel for testing purposes."""
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responses: List[BaseMessage]
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sleep: Optional[float] = None
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i: int = 0
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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response = self.responses[self.i]
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if self.i < len(self.responses) - 1:
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self.i += 1
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else:
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self.i = 0
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generation = ChatGeneration(message=response)
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return ChatResult(generations=[generation])
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@property
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def _llm_type(self) -> str:
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return "fake-messages-list-chat-model"
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class FakeListChatModel(SimpleChatModel):
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"""Fake ChatModel for testing purposes."""
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responses: List
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sleep: Optional[float] = None
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i: int = 0
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@property
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def _llm_type(self) -> str:
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return "fake-list-chat-model"
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def _call(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""First try to lookup in queries, else return 'foo' or 'bar'."""
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response = self.responses[self.i]
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if self.i < len(self.responses) - 1:
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self.i += 1
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else:
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self.i = 0
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return response
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Union[List[str], None] = None,
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run_manager: Union[CallbackManagerForLLMRun, None] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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response = self.responses[self.i]
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if self.i < len(self.responses) - 1:
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self.i += 1
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else:
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self.i = 0
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for c in response:
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if self.sleep is not None:
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time.sleep(self.sleep)
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yield ChatGenerationChunk(message=AIMessageChunk(content=c))
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Union[List[str], None] = None,
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run_manager: Union[AsyncCallbackManagerForLLMRun, None] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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response = self.responses[self.i]
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if self.i < len(self.responses) - 1:
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self.i += 1
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else:
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self.i = 0
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for c in response:
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if self.sleep is not None:
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await asyncio.sleep(self.sleep)
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yield ChatGenerationChunk(message=AIMessageChunk(content=c))
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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return {"responses": self.responses}
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