mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-05 06:33:20 +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()
```
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""Util that calls WolframAlpha."""
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from typing import Any, Dict, Optional
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from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class WolframAlphaAPIWrapper(BaseModel):
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"""Wrapper for Wolfram Alpha.
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Docs for using:
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1. Go to wolfram alpha and sign up for a developer account
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2. Create an app and get your APP ID
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3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
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4. pip install wolframalpha
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"""
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wolfram_client: Any #: :meta private:
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wolfram_alpha_appid: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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wolfram_alpha_appid = get_from_dict_or_env(
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values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
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)
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values["wolfram_alpha_appid"] = wolfram_alpha_appid
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try:
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import wolframalpha
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except ImportError:
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raise ImportError(
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"wolframalpha is not installed. "
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"Please install it with `pip install wolframalpha`"
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)
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client = wolframalpha.Client(wolfram_alpha_appid)
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values["wolfram_client"] = client
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return values
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def run(self, query: str) -> str:
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"""Run query through WolframAlpha and parse result."""
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res = self.wolfram_client.query(query)
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try:
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assumption = next(res.pods).text
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answer = next(res.results).text
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except StopIteration:
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return "Wolfram Alpha wasn't able to answer it"
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if answer is None or answer == "":
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# We don't want to return the assumption alone if answer is empty
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return "No good Wolfram Alpha Result was found"
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else:
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return f"Assumption: {assumption} \nAnswer: {answer}"
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