Source code for langchain_community.tools.plugin
from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.tools import BaseTool
[docs]class ApiConfig(BaseModel):
"""API Configuration."""
type: str
url: str
has_user_authentication: Optional[bool] = False
[docs]class AIPlugin(BaseModel):
"""AI Plugin Definition."""
schema_version: str
name_for_model: str
name_for_human: str
description_for_model: str
description_for_human: str
auth: Optional[dict] = None
api: ApiConfig
logo_url: Optional[str]
contact_email: Optional[str]
legal_info_url: Optional[str]
[docs] @classmethod
def from_url(cls, url: str) -> AIPlugin:
"""Instantiate AIPlugin from a URL."""
response = requests.get(url).json()
return cls(**response)
[docs]def marshal_spec(txt: str) -> dict:
"""Convert the yaml or json serialized spec to a dict.
Args:
txt: The yaml or json serialized spec.
Returns:
dict: The spec as a dict.
"""
try:
return json.loads(txt)
except json.JSONDecodeError:
return yaml.safe_load(txt)
[docs]class AIPluginToolSchema(BaseModel):
"""Schema for AIPluginTool."""
tool_input: Optional[str] = ""
[docs]class AIPluginTool(BaseTool):
"""Tool for getting the OpenAPI spec for an AI Plugin."""
plugin: AIPlugin
api_spec: str
args_schema: Type[AIPluginToolSchema] = AIPluginToolSchema
[docs] @classmethod
def from_plugin_url(cls, url: str) -> AIPluginTool:
plugin = AIPlugin.from_url(url)
description = (
f"Call this tool to get the OpenAPI spec (and usage guide) "
f"for interacting with the {plugin.name_for_human} API. "
f"You should only call this ONCE! What is the "
f"{plugin.name_for_human} API useful for? "
) + plugin.description_for_human
open_api_spec_str = requests.get(plugin.api.url).text
open_api_spec = marshal_spec(open_api_spec_str)
api_spec = (
f"Usage Guide: {plugin.description_for_model}\n\n"
f"OpenAPI Spec: {open_api_spec}"
)
return cls(
name=plugin.name_for_model,
description=description,
plugin=plugin,
api_spec=api_spec,
)
def _run(
self,
tool_input: Optional[str] = "",
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.api_spec
async def _arun(
self,
tool_input: Optional[str] = None,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return self.api_spec