mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-08 08:38:48 +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()
```
126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
"""Utilities to init Vertex AI."""
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from importlib import metadata
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from typing import TYPE_CHECKING, Any, Callable, 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.llms import BaseLLM, create_base_retry_decorator
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if TYPE_CHECKING:
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from google.api_core.gapic_v1.client_info import ClientInfo
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from google.auth.credentials import Credentials
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from vertexai.preview.generative_models import Image
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def create_retry_decorator(
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llm: BaseLLM,
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*,
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max_retries: int = 1,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Create a retry decorator for Vertex / Palm LLMs."""
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import google.api_core
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errors = [
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google.api_core.exceptions.ResourceExhausted,
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google.api_core.exceptions.ServiceUnavailable,
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google.api_core.exceptions.Aborted,
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google.api_core.exceptions.DeadlineExceeded,
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google.api_core.exceptions.GoogleAPIError,
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]
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decorator = create_base_retry_decorator(
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error_types=errors, max_retries=max_retries, run_manager=run_manager
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)
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return decorator
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def raise_vertex_import_error(minimum_expected_version: str = "1.38.0") -> None:
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"""Raise ImportError related to Vertex SDK being not available.
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Args:
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minimum_expected_version: The lowest expected version of the SDK.
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Raises:
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ImportError: an ImportError that mentions a required version of the SDK.
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"""
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raise ImportError(
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"Please, install or upgrade the google-cloud-aiplatform library: "
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f"pip install google-cloud-aiplatform>={minimum_expected_version}"
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)
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def init_vertexai(
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project: Optional[str] = None,
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location: Optional[str] = None,
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credentials: Optional["Credentials"] = None,
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) -> None:
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"""Init Vertex AI.
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Args:
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project: The default GCP project to use when making Vertex API calls.
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location: The default location to use when making API calls.
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credentials: The default custom
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credentials to use when making API calls. If not provided credentials
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will be ascertained from the environment.
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Raises:
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ImportError: If importing vertexai SDK did not succeed.
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"""
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try:
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import vertexai
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except ImportError:
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raise_vertex_import_error()
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vertexai.init(
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project=project,
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location=location,
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credentials=credentials,
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)
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def get_client_info(module: Optional[str] = None) -> "ClientInfo":
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r"""Return a custom user agent header.
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Args:
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module (Optional[str]):
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Optional. The module for a custom user agent header.
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Returns:
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google.api_core.gapic_v1.client_info.ClientInfo
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"""
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try:
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from google.api_core.gapic_v1.client_info import ClientInfo
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except ImportError as exc:
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raise ImportError(
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"Could not import ClientInfo. Please, install it with "
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"pip install google-api-core"
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) from exc
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langchain_version = metadata.version("langchain")
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client_library_version = (
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f"{langchain_version}-{module}" if module else langchain_version
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)
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return ClientInfo(
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client_library_version=client_library_version,
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user_agent=f"langchain/{client_library_version}",
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)
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def load_image_from_gcs(path: str, project: Optional[str] = None) -> "Image":
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"""Load an image from Google Cloud Storage."""
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try:
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from google.cloud import storage
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except ImportError:
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raise ImportError("Could not import google-cloud-storage python package.")
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from vertexai.preview.generative_models import Image
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gcs_client = storage.Client(project=project)
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pieces = path.split("/")
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blobs = list(gcs_client.list_blobs(pieces[2], prefix="/".join(pieces[3:])))
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if len(blobs) > 1:
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raise ValueError(f"Found more than one candidate for {path}!")
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return Image.from_bytes(blobs[0].download_as_bytes())
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