langchain/libs/community/langchain_community/tools/searx_search/tool.py

"""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):
    """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):
    """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__()