"""OpenAPI spec agent."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_community.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
if TYPE_CHECKING:
from langchain.agents.agent import AgentExecutor
[docs]
def create_openapi_agent(
llm: BaseLanguageModel,
toolkit: OpenAPIToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = OPENAPI_PREFIX,
suffix: str = OPENAPI_SUFFIX,
format_instructions: Optional[str] = None,
input_variables: Optional[List[str]] = None,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
return_intermediate_steps: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Construct an OpenAPI agent from an LLM and tools.
*Security Note*: When creating an OpenAPI agent, check the permissions
and capabilities of the underlying toolkit.
For example, if the default implementation of OpenAPIToolkit
uses the RequestsToolkit which contains tools to make arbitrary
network requests against any URL (e.g., GET, POST, PATCH, PUT, DELETE),
Control access to who can submit issue requests using this toolkit and
what network access it has.
See https://python.langchain.com/docs/security for more information.
Args:
llm: The language model to use.
toolkit: The OpenAPI toolkit.
callback_manager: Optional. The callback manager. Default is None.
prefix: Optional. The prefix for the prompt. Default is OPENAPI_PREFIX.
suffix: Optional. The suffix for the prompt. Default is OPENAPI_SUFFIX.
format_instructions: Optional. The format instructions for the prompt.
Default is None.
input_variables: Optional. The input variables for the prompt. Default is None.
max_iterations: Optional. The maximum number of iterations. Default is 15.
max_execution_time: Optional. The maximum execution time. Default is None.
early_stopping_method: Optional. The early stopping method. Default is "force".
verbose: Optional. Whether to print verbose output. Default is False.
return_intermediate_steps: Optional. Whether to return intermediate steps.
Default is False.
agent_executor_kwargs: Optional. Additional keyword arguments
for the agent executor.
kwargs: Additional arguments.
Returns:
The agent executor.
"""
from langchain.agents.agent import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain
tools = toolkit.get_tools()
prompt_params = (
{"format_instructions": format_instructions}
if format_instructions is not None
else {}
)
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
**prompt_params,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
)
