mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-28 23:07:11 +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()
```
186 lines
6.8 KiB
Python
186 lines
6.8 KiB
Python
"""Util that calls Tavily Search API.
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In order to set this up, follow instructions at:
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https://docs.tavily.com/docs/tavily-api/introduction
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"""
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import json
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from typing import Dict, List, Optional
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import aiohttp
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import requests
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from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
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from langchain_core.utils import get_from_dict_or_env
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TAVILY_API_URL = "https://api.tavily.com"
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class TavilySearchAPIWrapper(BaseModel):
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"""Wrapper for Tavily Search API."""
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tavily_api_key: SecretStr
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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 endpoint exists in environment."""
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tavily_api_key = get_from_dict_or_env(
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values, "tavily_api_key", "TAVILY_API_KEY"
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)
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values["tavily_api_key"] = tavily_api_key
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return values
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def raw_results(
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self,
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query: str,
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max_results: Optional[int] = 5,
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search_depth: Optional[str] = "advanced",
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include_domains: Optional[List[str]] = [],
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exclude_domains: Optional[List[str]] = [],
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include_answer: Optional[bool] = False,
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include_raw_content: Optional[bool] = False,
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include_images: Optional[bool] = False,
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) -> Dict:
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params = {
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"api_key": self.tavily_api_key.get_secret_value(),
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"query": query,
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"max_results": max_results,
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"search_depth": search_depth,
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"include_domains": include_domains,
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"exclude_domains": exclude_domains,
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"include_answer": include_answer,
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"include_raw_content": include_raw_content,
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"include_images": include_images,
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}
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response = requests.post(
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# type: ignore
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f"{TAVILY_API_URL}/search",
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json=params,
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)
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response.raise_for_status()
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return response.json()
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def results(
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self,
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query: str,
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max_results: Optional[int] = 5,
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search_depth: Optional[str] = "advanced",
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include_domains: Optional[List[str]] = [],
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exclude_domains: Optional[List[str]] = [],
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include_answer: Optional[bool] = False,
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include_raw_content: Optional[bool] = False,
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include_images: Optional[bool] = False,
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) -> List[Dict]:
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"""Run query through Tavily Search and return metadata.
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Args:
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query: The query to search for.
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max_results: The maximum number of results to return.
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search_depth: The depth of the search. Can be "basic" or "advanced".
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include_domains: A list of domains to include in the search.
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exclude_domains: A list of domains to exclude from the search.
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include_answer: Whether to include the answer in the results.
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include_raw_content: Whether to include the raw content in the results.
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include_images: Whether to include images in the results.
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Returns:
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query: The query that was searched for.
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follow_up_questions: A list of follow up questions.
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response_time: The response time of the query.
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answer: The answer to the query.
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images: A list of images.
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results: A list of dictionaries containing the results:
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title: The title of the result.
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url: The url of the result.
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content: The content of the result.
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score: The score of the result.
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raw_content: The raw content of the result.
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"""
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raw_search_results = self.raw_results(
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query,
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max_results=max_results,
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search_depth=search_depth,
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include_domains=include_domains,
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exclude_domains=exclude_domains,
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include_answer=include_answer,
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include_raw_content=include_raw_content,
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include_images=include_images,
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)
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return self.clean_results(raw_search_results["results"])
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async def raw_results_async(
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self,
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query: str,
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max_results: Optional[int] = 5,
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search_depth: Optional[str] = "advanced",
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include_domains: Optional[List[str]] = [],
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exclude_domains: Optional[List[str]] = [],
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include_answer: Optional[bool] = False,
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include_raw_content: Optional[bool] = False,
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include_images: Optional[bool] = False,
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) -> Dict:
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"""Get results from the Tavily Search API asynchronously."""
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# Function to perform the API call
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async def fetch() -> str:
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params = {
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"api_key": self.tavily_api_key.get_secret_value(),
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"query": query,
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"max_results": max_results,
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"search_depth": search_depth,
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"include_domains": include_domains,
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"exclude_domains": exclude_domains,
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"include_answer": include_answer,
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"include_raw_content": include_raw_content,
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"include_images": include_images,
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{TAVILY_API_URL}/search", json=params) as res:
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if res.status == 200:
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data = await res.text()
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return data
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else:
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raise Exception(f"Error {res.status}: {res.reason}")
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results_json_str = await fetch()
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return json.loads(results_json_str)
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async def results_async(
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self,
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query: str,
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max_results: Optional[int] = 5,
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search_depth: Optional[str] = "advanced",
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include_domains: Optional[List[str]] = [],
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exclude_domains: Optional[List[str]] = [],
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include_answer: Optional[bool] = False,
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include_raw_content: Optional[bool] = False,
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include_images: Optional[bool] = False,
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) -> List[Dict]:
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results_json = await self.raw_results_async(
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query=query,
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max_results=max_results,
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search_depth=search_depth,
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include_domains=include_domains,
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exclude_domains=exclude_domains,
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include_answer=include_answer,
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include_raw_content=include_raw_content,
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include_images=include_images,
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)
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return self.clean_results(results_json["results"])
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def clean_results(self, results: List[Dict]) -> List[Dict]:
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"""Clean results from Tavily Search API."""
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clean_results = []
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for result in results:
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clean_results.append(
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{
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"url": result["url"],
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"content": result["content"],
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}
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)
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return clean_results
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