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()
```
123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
"""Util that calls Google Scholar Search."""
|
|
|
|
from typing import Any, Dict, Optional, cast
|
|
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
|
|
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
|
|
|
|
|
|
class GoogleTrendsAPIWrapper(BaseModel):
|
|
"""Wrapper for SerpApi's Google Scholar API
|
|
|
|
You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
|
|
|
|
The wrapper uses the SerpApi.com python package:
|
|
https://serpapi.com/integrations/python
|
|
|
|
To use, you should have the environment variable ``SERPAPI_API_KEY``
|
|
set with your API key, or pass `serp_api_key` as a named parameter
|
|
to the constructor.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.utilities import GoogleTrendsAPIWrapper
|
|
google_trends = GoogleTrendsAPIWrapper()
|
|
google_trends.run('langchain')
|
|
"""
|
|
|
|
serp_search_engine: Any
|
|
serp_api_key: Optional[SecretStr] = None
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator(pre=True)
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
values["serp_api_key"] = convert_to_secret_str(
|
|
get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY")
|
|
)
|
|
|
|
try:
|
|
from serpapi import SerpApiClient
|
|
|
|
except ImportError:
|
|
raise ImportError(
|
|
"google-search-results is not installed. "
|
|
"Please install it with `pip install google-search-results"
|
|
">=2.4.2`"
|
|
)
|
|
serp_search_engine = SerpApiClient
|
|
values["serp_search_engine"] = serp_search_engine
|
|
|
|
return values
|
|
|
|
def run(self, query: str) -> str:
|
|
"""Run query through Google Trends with Serpapi"""
|
|
serpapi_api_key = cast(SecretStr, self.serp_api_key)
|
|
params = {
|
|
"engine": "google_trends",
|
|
"api_key": serpapi_api_key.get_secret_value(),
|
|
"q": query,
|
|
}
|
|
|
|
total_results = []
|
|
client = self.serp_search_engine(params)
|
|
client_dict = client.get_dict()
|
|
total_results = (
|
|
client_dict["interest_over_time"]["timeline_data"]
|
|
if "interest_over_time" in client_dict
|
|
else None
|
|
)
|
|
|
|
if not total_results:
|
|
return "No good Trend Result was found"
|
|
|
|
start_date = total_results[0]["date"].split()
|
|
end_date = total_results[-1]["date"].split()
|
|
values = [
|
|
results.get("values")[0].get("extracted_value") for results in total_results
|
|
]
|
|
min_value = min(values)
|
|
max_value = max(values)
|
|
avg_value = sum(values) / len(values)
|
|
percentage_change = (
|
|
(values[-1] - values[0])
|
|
/ (values[0] if values[0] != 0 else 1)
|
|
* (100 if values[0] != 0 else 1)
|
|
)
|
|
|
|
params = {
|
|
"engine": "google_trends",
|
|
"api_key": serpapi_api_key.get_secret_value(),
|
|
"data_type": "RELATED_QUERIES",
|
|
"q": query,
|
|
}
|
|
|
|
total_results2 = {}
|
|
client = self.serp_search_engine(params)
|
|
total_results2 = client.get_dict().get("related_queries", {})
|
|
rising = []
|
|
top = []
|
|
|
|
rising = [results.get("query") for results in total_results2.get("rising", [])]
|
|
top = [results.get("query") for results in total_results2.get("top", [])]
|
|
|
|
doc = [
|
|
f"Query: {query}\n"
|
|
f"Date From: {start_date[0]} {start_date[1]}, {start_date[-1]}\n"
|
|
f"Date To: {end_date[0]} {end_date[3]} {end_date[-1]}\n"
|
|
f"Min Value: {min_value}\n"
|
|
f"Max Value: {max_value}\n"
|
|
f"Average Value: {avg_value}\n"
|
|
f"Percent Change: {str(percentage_change) + '%'}\n"
|
|
f"Trend values: {', '.join([str(x) for x in values])}\n"
|
|
f"Rising Related Queries: {', '.join(rising)}\n"
|
|
f"Top Related Queries: {', '.join(top)}"
|
|
]
|
|
|
|
return "\n\n".join(doc)
|