langchain/libs/community/langchain_community/tools/searx_search/tool.py
Erick Friis 600b7bdd61
all: test 3.13 ci (#27197)
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
2024-10-25 12:56:58 -07:00

86 lines
2.5 KiB
Python

"""Tool for the SearxNG search API."""
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, ConfigDict, Field
from langchain_community.utilities.searx_search import SearxSearchWrapper
class SearxSearchQueryInput(BaseModel):
"""Input for the SearxSearch tool."""
query: str = Field(description="query to look up on searx")
class SearxSearchRun(BaseTool): # type: ignore[override, override]
"""Tool that queries a Searx instance."""
name: str = "searx_search"
description: str = (
"A meta search engine."
"Useful for when you need to answer questions about current events."
"Input should be a search query."
)
wrapper: SearxSearchWrapper
kwargs: dict = Field(default_factory=dict)
args_schema: Type[BaseModel] = SearxSearchQueryInput
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.wrapper.run(query, **self.kwargs)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return await self.wrapper.arun(query, **self.kwargs)
class SearxSearchResults(BaseTool): # type: ignore[override, override]
"""Tool that queries a Searx instance and gets back json."""
name: str = "searx_search_results"
description: str = (
"A meta search engine."
"Useful for when you need to answer questions about current events."
"Input should be a search query. Output is a JSON array of the query results"
)
wrapper: SearxSearchWrapper
num_results: int = 4
kwargs: dict = Field(default_factory=dict)
args_schema: Type[BaseModel] = SearxSearchQueryInput
model_config = ConfigDict(
extra="allow",
)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.wrapper.results(query, self.num_results, **self.kwargs))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return (
await self.wrapper.aresults(query, self.num_results, **self.kwargs)
).__str__()