/
load_tools.py
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/
load_tools.py
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# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.schema.language_model import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import Callbacks
from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
from langchain.chains.api.base import APIChain
from langchain.chains.llm_math.base import LLMMathChain
from langchain.utilities.dalle_image_generator import DallEAPIWrapper
from langchain.utilities.requests import TextRequestsWrapper
from langchain.tools.arxiv.tool import ArxivQueryRun
from langchain.tools.golden_query.tool import GoldenQueryRun
from langchain.tools.pubmed.tool import PubmedQueryRun
from langchain.tools.base import BaseTool
from langchain.tools.bing_search.tool import BingSearchRun
from langchain.tools.ddg_search.tool import DuckDuckGoSearchRun
from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
from langchain.tools.metaphor_search.tool import MetaphorSearchResults
from langchain.tools.google_serper.tool import GoogleSerperResults, GoogleSerperRun
from langchain.tools.graphql.tool import BaseGraphQLTool
from langchain.tools.human.tool import HumanInputRun
from langchain.tools.python.tool import PythonREPLTool
from langchain.tools.requests.tool import (
RequestsDeleteTool,
RequestsGetTool,
RequestsPatchTool,
RequestsPostTool,
RequestsPutTool,
)
from langchain.tools.scenexplain.tool import SceneXplainTool
from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun
from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.tools.dataforseo_api_search import DataForSeoAPISearchRun
from langchain.tools.dataforseo_api_search import DataForSeoAPISearchResults
from langchain.utilities import ArxivAPIWrapper
from langchain.utilities import GoldenQueryAPIWrapper
from langchain.utilities import PubMedAPIWrapper
from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.metaphor_search import MetaphorSearchAPIWrapper
from langchain.utilities.awslambda import LambdaWrapper
from langchain.utilities.graphql import GraphQLAPIWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.twilio import TwilioAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
def _get_python_repl() -> BaseTool:
return PythonREPLTool()
def _get_tools_requests_get() -> BaseTool:
return RequestsGetTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_post() -> BaseTool:
return RequestsPostTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_patch() -> BaseTool:
return RequestsPatchTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_put() -> BaseTool:
return RequestsPutTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"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,
"sleep": _get_sleep,
}
def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
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:
chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
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"]
chain = APIChain.from_llm_and_api_docs(
llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
)
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"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
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"]
chain = APIChain.from_llm_and_api_docs(
llm,
podcast_docs.PODCAST_DOCS,
headers={"X-ListenAPI-Key": listen_api_key},
)
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_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_serper(**kwargs: Any) -> BaseTool:
return GoogleSerperRun(api_wrapper=GoogleSerperAPIWrapper(**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_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_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:
graphql_endpoint = kwargs["graphql_endpoint"]
wrapper = GraphQLAPIWrapper(graphql_endpoint=graphql_endpoint)
return BaseGraphQLTool(graphql_wrapper=wrapper)
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))
_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"]),
}
_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-serper": (_get_google_serper, ["serper_api_key", "aiosession"]),
"google-serper-results-json": (
_get_google_serper_results_json,
["serper_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"]),
"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"],
),
"sceneXplain": (_get_scenexplain, []),
"graphql": (_get_graphql_tool, ["graphql_endpoint"]),
"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"],
),
}
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:
"""Loads a tool from the HuggingFace Hub.
Args:
task_or_repo_id: Task or model repo id.
model_repo_id: Optional model repo id.
token: Optional token.
remote: Optional remote. Defaults to False.
**kwargs:
Returns:
A tool.
"""
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,
**kwargs: Any,
) -> List[BaseTool]:
"""Load tools based on their name.
Args:
tool_names: name of tools to load.
llm: An optional language model, may be needed to initialize certain tools.
callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
"""
tools = []
callbacks = _handle_callbacks(
callback_manager=kwargs.get("callback_manager"), callbacks=callbacks
)
for name in tool_names:
if name == "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 _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]:
"""Get a list of all possible tool names."""
return (
list(_BASE_TOOLS)
+ list(_EXTRA_OPTIONAL_TOOLS)
+ list(_EXTRA_LLM_TOOLS)
+ list(_LLM_TOOLS)
)