Source code for langchain_community.agent_toolkits.load_tools

# flake8: noqa
"""工具提供访问各种资源和服务。

LangChain具有与各种外部资源集成的庞大生态系统，如本地和远程文件系统、API和数据库。

这些集成使开发人员能够创建结合LLM的强大功能与访问、交互和操作外部资源的能力的多功能应用程序。

在开发应用程序时，开发人员应检查给定代理工具包的工具的功能和权限，并确定给定工具包的权限是否适合该应用程序。

有关更多信息，请参见[安全性](https://python.langchain.com/docs/security)。
"""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple

from mypy_extensions import Arg, KwArg

from langchain_community.tools.arxiv.tool import ArxivQueryRun
from langchain_community.tools.bing_search.tool import BingSearchRun
from langchain_community.tools.dataforseo_api_search import DataForSeoAPISearchResults
from langchain_community.tools.dataforseo_api_search import DataForSeoAPISearchRun
from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchRun
from langchain_community.tools.eleven_labs.text2speech import ElevenLabsText2SpeechTool
from langchain_community.tools.file_management import ReadFileTool
from langchain_community.tools.golden_query.tool import GoldenQueryRun
from langchain_community.tools.google_cloud.texttospeech import (
    GoogleCloudTextToSpeechTool,
)
from langchain_community.tools.google_finance.tool import GoogleFinanceQueryRun
from langchain_community.tools.google_jobs.tool import GoogleJobsQueryRun
from langchain_community.tools.google_lens.tool import GoogleLensQueryRun
from langchain_community.tools.google_scholar.tool import GoogleScholarQueryRun
from langchain_community.tools.google_search.tool import (
    GoogleSearchResults,
    GoogleSearchRun,
)
from langchain_community.tools.google_serper.tool import (
    GoogleSerperResults,
    GoogleSerperRun,
)
from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
from langchain_community.tools.graphql.tool import BaseGraphQLTool
from langchain_community.tools.human.tool import HumanInputRun
from langchain_community.tools.memorize.tool import Memorize
from langchain_community.tools.merriam_webster.tool import MerriamWebsterQueryRun
from langchain_community.tools.metaphor_search.tool import MetaphorSearchResults
from langchain_community.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain_community.tools.pubmed.tool import PubmedQueryRun
from langchain_community.tools.reddit_search.tool import RedditSearchRun
from langchain_community.tools.requests.tool import (
    RequestsDeleteTool,
    RequestsGetTool,
    RequestsPatchTool,
    RequestsPostTool,
    RequestsPutTool,
)
from langchain_community.tools.scenexplain.tool import SceneXplainTool
from langchain_community.tools.searchapi.tool import SearchAPIResults, SearchAPIRun
from langchain_community.tools.searx_search.tool import (
    SearxSearchResults,
    SearxSearchRun,
)
from langchain_community.tools.shell.tool import ShellTool
from langchain_community.tools.sleep.tool import SleepTool
from langchain_community.tools.stackexchange.tool import StackExchangeTool
from langchain_community.tools.wikipedia.tool import WikipediaQueryRun
from langchain_community.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain_community.utilities.arxiv import ArxivAPIWrapper
from langchain_community.utilities.awslambda import LambdaWrapper
from langchain_community.utilities.bing_search import BingSearchAPIWrapper
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
from langchain_community.utilities.dataforseo_api_search import DataForSeoAPIWrapper
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain_community.utilities.golden_query import GoldenQueryAPIWrapper
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper
from langchain_community.utilities.google_lens import GoogleLensAPIWrapper
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
from langchain_community.utilities.graphql import GraphQLAPIWrapper
from langchain_community.utilities.merriam_webster import MerriamWebsterAPIWrapper
from langchain_community.utilities.metaphor_search import MetaphorSearchAPIWrapper
from langchain_community.utilities.openweathermap import OpenWeatherMapAPIWrapper
from langchain_community.utilities.pubmed import PubMedAPIWrapper
from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
from langchain_community.utilities.searx_search import SearxSearchWrapper
from langchain_community.utilities.serpapi import SerpAPIWrapper
from langchain_community.utilities.stackexchange import StackExchangeAPIWrapper
from langchain_community.utilities.twilio import TwilioAPIWrapper
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain_core.tools import Tool


def _get_tools_requests_get() -> BaseTool:
    # Dangerous requests are allowed here, because there's another flag that the user
    # has to provide in order to actually opt in.
    # This is a private function and should not be used directly.
    return RequestsGetTool(
        requests_wrapper=TextRequestsWrapper(), allow_dangerous_requests=True
    )


def _get_tools_requests_post() -> BaseTool:
    # Dangerous requests are allowed here, because there's another flag that the user
    # has to provide in order to actually opt in.
    # This is a private function and should not be used directly.
    return RequestsPostTool(
        requests_wrapper=TextRequestsWrapper(), allow_dangerous_requests=True
    )


def _get_tools_requests_patch() -> BaseTool:
    # Dangerous requests are allowed here, because there's another flag that the user
    # has to provide in order to actually opt in.
    # This is a private function and should not be used directly.
    return RequestsPatchTool(
        requests_wrapper=TextRequestsWrapper(), allow_dangerous_requests=True
    )


def _get_tools_requests_put() -> BaseTool:
    # Dangerous requests are allowed here, because there's another flag that the user
    # has to provide in order to actually opt in.
    # This is a private function and should not be used directly.
    return RequestsPutTool(
        requests_wrapper=TextRequestsWrapper(), allow_dangerous_requests=True
    )


def _get_tools_requests_delete() -> BaseTool:
    # Dangerous requests are allowed here, because there's another flag that the user
    # has to provide in order to actually opt in.
    # This is a private function and should not be used directly.
    return RequestsDeleteTool(
        requests_wrapper=TextRequestsWrapper(), allow_dangerous_requests=True
    )


def _get_terminal() -> BaseTool:
    return ShellTool()


def _get_sleep() -> BaseTool:
    return SleepTool()


_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
    "sleep": _get_sleep,
}

DANGEROUS_TOOLS = {
    # Tools that contain some level of risk.
    # Please use with caution and read the documentation of these tools
    # to understand the risks and how to mitigate them.
    # Refer to https://python.langchain.com/docs/security
    # for more information.
    "requests": _get_tools_requests_get,  # preserved for backwards compatibility
    "requests_get": _get_tools_requests_get,
    "requests_post": _get_tools_requests_post,
    "requests_patch": _get_tools_requests_patch,
    "requests_put": _get_tools_requests_put,
    "requests_delete": _get_tools_requests_delete,
    "terminal": _get_terminal,
}


def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
    try:
        from langchain.chains.llm_math.base import LLMMathChain
    except ImportError:
        raise ImportError(
            "LLM Math tools require the library `langchain` to be installed."
            " Please install it with `pip install langchain`."
        )
    return Tool(
        name="Calculator",
        description="Useful for when you need to answer questions about math.",
        func=LLMMathChain.from_llm(llm=llm).run,
        coroutine=LLMMathChain.from_llm(llm=llm).arun,
    )


def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
    try:
        from langchain.chains.api.base import APIChain
        from langchain.chains.api import (
            open_meteo_docs,
        )
    except ImportError:
        raise ImportError(
            "API tools require the library `langchain` to be installed."
            " Please install it with `pip install langchain`."
        )
    chain = APIChain.from_llm_and_api_docs(
        llm,
        open_meteo_docs.OPEN_METEO_DOCS,
        limit_to_domains=["https://api.open-meteo.com/"],
    )
    return Tool(
        name="Open-Meteo-API",
        description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )


_LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] = {
    "llm-math": _get_llm_math,
    "open-meteo-api": _get_open_meteo_api,
}


def _get_news_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
    news_api_key = kwargs["news_api_key"]
    try:
        from langchain.chains.api.base import APIChain
        from langchain.chains.api import (
            news_docs,
        )
    except ImportError:
        raise ImportError(
            "API tools require the library `langchain` to be installed."
            " Please install it with `pip install langchain`."
        )
    chain = APIChain.from_llm_and_api_docs(
        llm,
        news_docs.NEWS_DOCS,
        headers={"X-Api-Key": news_api_key},
        limit_to_domains=["https://newsapi.org/"],
    )
    return Tool(
        name="News-API",
        description="Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )


def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
    tmdb_bearer_token = kwargs["tmdb_bearer_token"]
    try:
        from langchain.chains.api.base import APIChain
        from langchain.chains.api import (
            tmdb_docs,
        )
    except ImportError:
        raise ImportError(
            "API tools require the library `langchain` to be installed."
            " Please install it with `pip install langchain`."
        )
    chain = APIChain.from_llm_and_api_docs(
        llm,
        tmdb_docs.TMDB_DOCS,
        headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
        limit_to_domains=["https://api.themoviedb.org/"],
    )
    return Tool(
        name="TMDB-API",
        description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )


def _get_podcast_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
    listen_api_key = kwargs["listen_api_key"]
    try:
        from langchain.chains.api.base import APIChain
        from langchain.chains.api import (
            podcast_docs,
        )
    except ImportError:
        raise ImportError(
            "API tools require the library `langchain` to be installed."
            " Please install it with `pip install langchain`."
        )
    chain = APIChain.from_llm_and_api_docs(
        llm,
        podcast_docs.PODCAST_DOCS,
        headers={"X-ListenAPI-Key": listen_api_key},
        limit_to_domains=["https://listen-api.listennotes.com/"],
    )
    return Tool(
        name="Podcast-API",
        description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )


def _get_lambda_api(**kwargs: Any) -> BaseTool:
    return Tool(
        name=kwargs["awslambda_tool_name"],
        description=kwargs["awslambda_tool_description"],
        func=LambdaWrapper(**kwargs).run,
    )


def _get_wolfram_alpha(**kwargs: Any) -> BaseTool:
    return WolframAlphaQueryRun(api_wrapper=WolframAlphaAPIWrapper(**kwargs))


def _get_google_search(**kwargs: Any) -> BaseTool:
    return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))


def _get_merriam_webster(**kwargs: Any) -> BaseTool:
    return MerriamWebsterQueryRun(api_wrapper=MerriamWebsterAPIWrapper(**kwargs))


def _get_wikipedia(**kwargs: Any) -> BaseTool:
    return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))


def _get_arxiv(**kwargs: Any) -> BaseTool:
    return ArxivQueryRun(api_wrapper=ArxivAPIWrapper(**kwargs))


def _get_golden_query(**kwargs: Any) -> BaseTool:
    return GoldenQueryRun(api_wrapper=GoldenQueryAPIWrapper(**kwargs))


def _get_pubmed(**kwargs: Any) -> BaseTool:
    return PubmedQueryRun(api_wrapper=PubMedAPIWrapper(**kwargs))


def _get_google_jobs(**kwargs: Any) -> BaseTool:
    return GoogleJobsQueryRun(api_wrapper=GoogleJobsAPIWrapper(**kwargs))


def _get_google_lens(**kwargs: Any) -> BaseTool:
    return GoogleLensQueryRun(api_wrapper=GoogleLensAPIWrapper(**kwargs))


def _get_google_serper(**kwargs: Any) -> BaseTool:
    return GoogleSerperRun(api_wrapper=GoogleSerperAPIWrapper(**kwargs))


def _get_google_scholar(**kwargs: Any) -> BaseTool:
    return GoogleScholarQueryRun(api_wrapper=GoogleScholarAPIWrapper(**kwargs))


def _get_google_finance(**kwargs: Any) -> BaseTool:
    return GoogleFinanceQueryRun(api_wrapper=GoogleFinanceAPIWrapper(**kwargs))


def _get_google_trends(**kwargs: Any) -> BaseTool:
    return GoogleTrendsQueryRun(api_wrapper=GoogleTrendsAPIWrapper(**kwargs))


def _get_google_serper_results_json(**kwargs: Any) -> BaseTool:
    return GoogleSerperResults(api_wrapper=GoogleSerperAPIWrapper(**kwargs))


def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
    return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))


def _get_searchapi(**kwargs: Any) -> BaseTool:
    return SearchAPIRun(api_wrapper=SearchApiAPIWrapper(**kwargs))


def _get_searchapi_results_json(**kwargs: Any) -> BaseTool:
    return SearchAPIResults(api_wrapper=SearchApiAPIWrapper(**kwargs))


def _get_serpapi(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Search",
        description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
        func=SerpAPIWrapper(**kwargs).run,
        coroutine=SerpAPIWrapper(**kwargs).arun,
    )


def _get_stackexchange(**kwargs: Any) -> BaseTool:
    return StackExchangeTool(api_wrapper=StackExchangeAPIWrapper(**kwargs))


def _get_dalle_image_generator(**kwargs: Any) -> Tool:
    return Tool(
        "Dall-E-Image-Generator",
        DallEAPIWrapper(**kwargs).run,
        "A wrapper around OpenAI DALL-E API. Useful for when you need to generate images from a text description. Input should be an image description.",
    )


def _get_twilio(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Text-Message",
        description="Useful for when you need to send a text message to a provided phone number.",
        func=TwilioAPIWrapper(**kwargs).run,
    )


def _get_searx_search(**kwargs: Any) -> BaseTool:
    return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))


def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
    wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
    return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapper_kwargs), **kwargs)


def _get_bing_search(**kwargs: Any) -> BaseTool:
    return BingSearchRun(api_wrapper=BingSearchAPIWrapper(**kwargs))


def _get_metaphor_search(**kwargs: Any) -> BaseTool:
    return MetaphorSearchResults(api_wrapper=MetaphorSearchAPIWrapper(**kwargs))


def _get_ddg_search(**kwargs: Any) -> BaseTool:
    return DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper(**kwargs))


def _get_human_tool(**kwargs: Any) -> BaseTool:
    return HumanInputRun(**kwargs)


def _get_scenexplain(**kwargs: Any) -> BaseTool:
    return SceneXplainTool(**kwargs)


def _get_graphql_tool(**kwargs: Any) -> BaseTool:
    return BaseGraphQLTool(graphql_wrapper=GraphQLAPIWrapper(**kwargs))


def _get_openweathermap(**kwargs: Any) -> BaseTool:
    return OpenWeatherMapQueryRun(api_wrapper=OpenWeatherMapAPIWrapper(**kwargs))


def _get_dataforseo_api_search(**kwargs: Any) -> BaseTool:
    return DataForSeoAPISearchRun(api_wrapper=DataForSeoAPIWrapper(**kwargs))


def _get_dataforseo_api_search_json(**kwargs: Any) -> BaseTool:
    return DataForSeoAPISearchResults(api_wrapper=DataForSeoAPIWrapper(**kwargs))


def _get_eleven_labs_text2speech(**kwargs: Any) -> BaseTool:
    return ElevenLabsText2SpeechTool(**kwargs)


def _get_memorize(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
    return Memorize(llm=llm)  # type: ignore[arg-type]


def _get_google_cloud_texttospeech(**kwargs: Any) -> BaseTool:
    return GoogleCloudTextToSpeechTool(**kwargs)


def _get_file_management_tool(**kwargs: Any) -> BaseTool:
    return ReadFileTool(**kwargs)


def _get_reddit_search(**kwargs: Any) -> BaseTool:
    return RedditSearchRun(api_wrapper=RedditSearchAPIWrapper(**kwargs))


_EXTRA_LLM_TOOLS: Dict[
    str,
    Tuple[Callable[[Arg(BaseLanguageModel, "llm"), KwArg(Any)], BaseTool], List[str]],
] = {
    "news-api": (_get_news_api, ["news_api_key"]),
    "tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
    "podcast-api": (_get_podcast_api, ["listen_api_key"]),
    "memorize": (_get_memorize, []),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
    "wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
    "google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
    "google-search-results-json": (
        _get_google_search_results_json,
        ["google_api_key", "google_cse_id", "num_results"],
    ),
    "searx-search-results-json": (
        _get_searx_search_results_json,
        ["searx_host", "engines", "num_results", "aiosession"],
    ),
    "bing-search": (_get_bing_search, ["bing_subscription_key", "bing_search_url"]),
    "metaphor-search": (_get_metaphor_search, ["metaphor_api_key"]),
    "ddg-search": (_get_ddg_search, []),
    "google-lens": (_get_google_lens, ["serp_api_key"]),
    "google-serper": (_get_google_serper, ["serper_api_key", "aiosession"]),
    "google-scholar": (
        _get_google_scholar,
        ["top_k_results", "hl", "lr", "serp_api_key"],
    ),
    "google-finance": (
        _get_google_finance,
        ["serp_api_key"],
    ),
    "google-trends": (
        _get_google_trends,
        ["serp_api_key"],
    ),
    "google-jobs": (
        _get_google_jobs,
        ["serp_api_key"],
    ),
    "google-serper-results-json": (
        _get_google_serper_results_json,
        ["serper_api_key", "aiosession"],
    ),
    "searchapi": (_get_searchapi, ["searchapi_api_key", "aiosession"]),
    "searchapi-results-json": (
        _get_searchapi_results_json,
        ["searchapi_api_key", "aiosession"],
    ),
    "serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
    "dalle-image-generator": (_get_dalle_image_generator, ["openai_api_key"]),
    "twilio": (_get_twilio, ["account_sid", "auth_token", "from_number"]),
    "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
    "merriam-webster": (_get_merriam_webster, ["merriam_webster_api_key"]),
    "wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
    "arxiv": (
        _get_arxiv,
        ["top_k_results", "load_max_docs", "load_all_available_meta"],
    ),
    "golden-query": (_get_golden_query, ["golden_api_key"]),
    "pubmed": (_get_pubmed, ["top_k_results"]),
    "human": (_get_human_tool, ["prompt_func", "input_func"]),
    "awslambda": (
        _get_lambda_api,
        ["awslambda_tool_name", "awslambda_tool_description", "function_name"],
    ),
    "stackexchange": (_get_stackexchange, []),
    "sceneXplain": (_get_scenexplain, []),
    "graphql": (
        _get_graphql_tool,
        ["graphql_endpoint", "custom_headers", "fetch_schema_from_transport"],
    ),
    "openweathermap-api": (_get_openweathermap, ["openweathermap_api_key"]),
    "dataforseo-api-search": (
        _get_dataforseo_api_search,
        ["api_login", "api_password", "aiosession"],
    ),
    "dataforseo-api-search-json": (
        _get_dataforseo_api_search_json,
        ["api_login", "api_password", "aiosession"],
    ),
    "eleven_labs_text2speech": (_get_eleven_labs_text2speech, ["eleven_api_key"]),
    "google_cloud_texttospeech": (_get_google_cloud_texttospeech, []),
    "read_file": (_get_file_management_tool, []),
    "reddit_search": (
        _get_reddit_search,
        ["reddit_client_id", "reddit_client_secret", "reddit_user_agent"],
    ),
}


def _handle_callbacks(
    callback_manager: Optional[BaseCallbackManager], callbacks: Callbacks
) -> Callbacks:
    if callback_manager is not None:
        warnings.warn(
            "callback_manager is deprecated. Please use callbacks instead.",
            DeprecationWarning,
        )
        if callbacks is not None:
            raise ValueError(
                "Cannot specify both callback_manager and callbacks arguments."
            )
        return callback_manager
    return callbacks


def load_huggingface_tool(
    task_or_repo_id: str,
    model_repo_id: Optional[str] = None,
    token: Optional[str] = None,
    remote: bool = False,
    **kwargs: Any,
) -> BaseTool:
    """从HuggingFace Hub加载工具。

参数：
    task_or_repo_id：任务或模型存储库ID。
    model_repo_id：可选的模型存储库ID。
    token：可选的令牌。
    remote：可选的远程。默认为False。
    **kwargs：

返回：
    一个工具。
"""
    try:
        from transformers import load_tool
    except ImportError:
        raise ImportError(
            "HuggingFace tools require the libraries `transformers>=4.29.0`"
            " and `huggingface_hub>=0.14.1` to be installed."
            " Please install it with"
            " `pip install --upgrade transformers huggingface_hub`."
        )
    hf_tool = load_tool(
        task_or_repo_id,
        model_repo_id=model_repo_id,
        token=token,
        remote=remote,
        **kwargs,
    )
    outputs = hf_tool.outputs
    if set(outputs) != {"text"}:
        raise NotImplementedError("Multimodal outputs not supported yet.")
    inputs = hf_tool.inputs
    if set(inputs) != {"text"}:
        raise NotImplementedError("Multimodal inputs not supported yet.")
    return Tool.from_function(
        hf_tool.__call__, name=hf_tool.name, description=hf_tool.description
    )


def load_tools(
    tool_names: List[str],
    llm: Optional[BaseLanguageModel] = None,
    callbacks: Callbacks = None,
    allow_dangerous_tools: bool = False,
    **kwargs: Any,
) -> List[BaseTool]:
    """基于它们的名称加载工具。

工具允许代理与各种资源和服务进行交互，如API、数据库、文件系统等。

请将每个工具的权限范围限制在应用程序所需的最低权限。

例如，如果一个应用程序只需要从数据库中读取数据，则不应该给予数据库工具写入权限。此外，考虑将权限范围限制在仅允许访问特定表格，并对资源使用施加用户级别的配额限制。

请阅读各个工具的API以确定它们支持哪些配置。

有关更多信息，请参阅[安全性](https://python.langchain.com/docs/security)。

参数：
    tool_names: 要加载的工具名称。
    llm: 可选的语言模型，可能需要初始化某些工具。
    callbacks: 可选的回调管理器或回调处理程序列表。
        如果未提供，默认将使用全局回调管理器。
    allow_dangerous_tools: 允许危险工具的可选标志。
        包含一定风险的工具。
        请谨慎使用，并阅读这些工具的文档以了解风险及如何减轻风险。
        有关更多信息，请参阅https://python.langchain.com/docs/security。
        请注意，此列表可能不是完全详尽的。
        您有责任了解您正在使用的工具及相关风险。

返回：
    工具列表。
"""
    tools = []
    callbacks = _handle_callbacks(
        callback_manager=kwargs.get("callback_manager"), callbacks=callbacks
    )
    for name in tool_names:
        if name in DANGEROUS_TOOLS and not allow_dangerous_tools:
            raise ValueError(
                f"{name} is a dangerous tool. You cannot use it without opting in "
                "by setting allow_dangerous_tools to True. "
                "Most tools have some inherit risk to them merely because they are "
                'allowed to interact with the "real world".'
                "Please refer to LangChain security guidelines "
                "to https://python.langchain.com/docs/security."
                "Some tools have been designated as dangerous because they pose "
                "risk that is not intuitively obvious. For example, a tool that "
                "allows an agent to make requests to the web, can also be used "
                "to make requests to a server that is only accessible from the "
                "server hosting the code."
                "Again, all tools carry some risk, and it's your responsibility to "
                "understand which tools you're using and the risks associated with "
                "them."
            )

        if name in {"requests"}:
            warnings.warn(
                "tool name `requests` is deprecated - "
                "please use `requests_all` or specify the requests method"
            )
        if name == "requests_all":
            # expand requests into various methods
            requests_method_tools = [
                _tool for _tool in _BASE_TOOLS if _tool.startswith("requests_")
            ]
            tool_names.extend(requests_method_tools)
        elif name in _BASE_TOOLS:
            tools.append(_BASE_TOOLS[name]())
        elif name in DANGEROUS_TOOLS:
            tools.append(DANGEROUS_TOOLS[name]())
        elif name in _LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            tool = _LLM_TOOLS[name](llm)
            tools.append(tool)
        elif name in _EXTRA_LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            _get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[name]
            missing_keys = set(extra_keys).difference(kwargs)
            if missing_keys:
                raise ValueError(
                    f"Tool {name} requires some parameters that were not "
                    f"provided: {missing_keys}"
                )
            sub_kwargs = {k: kwargs[k] for k in extra_keys}
            tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
            tools.append(tool)
        elif name in _EXTRA_OPTIONAL_TOOLS:
            _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
            sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
            tool = _get_tool_func(**sub_kwargs)
            tools.append(tool)
        else:
            raise ValueError(f"Got unknown tool {name}")
    if callbacks is not None:
        for tool in tools:
            tool.callbacks = callbacks
    return tools


def get_all_tool_names() -> List[str]:
    """获取所有可能的工具名称列表。"""
    return (
        list(_BASE_TOOLS)
        + list(_EXTRA_OPTIONAL_TOOLS)
        + list(_EXTRA_LLM_TOOLS)
        + list(_LLM_TOOLS)
        + list(DANGEROUS_TOOLS)
    )