mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-19 05:51:16 +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()
```
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
"""Test GradientAI API wrapper.
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In order to run this test, you need to have an GradientAI api key.
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You can get it by registering for free at https://gradient.ai/.
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You'll then need to set:
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- `GRADIENT_ACCESS_TOKEN` environment variable to your api key.
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- `GRADIENT_WORKSPACE_ID` environment variable to your workspace id.
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- `GRADIENT_MODEL` environment variable to your workspace id.
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"""
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import os
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from langchain_community.llms import GradientLLM
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def test_gradient_acall() -> None:
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"""Test simple call to gradient.ai."""
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model = os.environ["GRADIENT_MODEL"]
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gradient_access_token = os.environ["GRADIENT_ACCESS_TOKEN"]
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gradient_workspace_id = os.environ["GRADIENT_WORKSPACE_ID"]
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llm = GradientLLM(
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model=model,
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gradient_access_token=gradient_access_token,
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gradient_workspace_id=gradient_workspace_id,
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)
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output = llm.invoke("Say hello:", temperature=0.2, max_tokens=250)
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assert llm._llm_type == "gradient"
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assert isinstance(output, str)
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assert len(output)
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async def test_gradientai_acall() -> None:
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"""Test async call to gradient.ai."""
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model = os.environ["GRADIENT_MODEL"]
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gradient_access_token = os.environ["GRADIENT_ACCESS_TOKEN"]
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gradient_workspace_id = os.environ["GRADIENT_WORKSPACE_ID"]
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llm = GradientLLM(
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model=model,
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gradient_access_token=gradient_access_token,
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gradient_workspace_id=gradient_workspace_id,
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)
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output = await llm.agenerate(["Say hello:"], temperature=0.2, max_tokens=250)
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assert llm._llm_type == "gradient"
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assert isinstance(output, str)
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assert len(output)
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