mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-13 19:17:16 +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()
```
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
"""Tool for the SceneXplain API."""
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from typing import Optional
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from langchain_core.callbacks import CallbackManagerForToolRun
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.tools import BaseTool
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from langchain_community.utilities.scenexplain import SceneXplainAPIWrapper
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class SceneXplainInput(BaseModel):
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"""Input for SceneXplain."""
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query: str = Field(..., description="The link to the image to explain")
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class SceneXplainTool(BaseTool):
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"""Tool that explains images."""
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name: str = "image_explainer"
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description: str = (
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"An Image Captioning Tool: Use this tool to generate a detailed caption "
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"for an image. The input can be an image file of any format, and "
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"the output will be a text description that covers every detail of the image."
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)
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api_wrapper: SceneXplainAPIWrapper = Field(default_factory=SceneXplainAPIWrapper) # type: ignore[arg-type]
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def _run(
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self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None
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) -> str:
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"""Use the tool."""
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return self.api_wrapper.run(query)
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