mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
e81ddb32a6
Fix: 
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
"""OpenAPI spec agent."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models import BaseLanguageModel
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from langchain_community.agent_toolkits.openapi.prompt import (
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OPENAPI_PREFIX,
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OPENAPI_SUFFIX,
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)
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from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
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if TYPE_CHECKING:
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from langchain.agents.agent import AgentExecutor
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def create_openapi_agent(
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llm: BaseLanguageModel,
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toolkit: OpenAPIToolkit,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = OPENAPI_PREFIX,
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suffix: str = OPENAPI_SUFFIX,
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format_instructions: Optional[str] = None,
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input_variables: Optional[List[str]] = None,
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max_iterations: Optional[int] = 15,
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max_execution_time: Optional[float] = None,
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early_stopping_method: str = "force",
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verbose: bool = False,
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return_intermediate_steps: bool = False,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct an OpenAPI agent from an LLM and tools.
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*Security Note*: When creating an OpenAPI agent, check the permissions
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and capabilities of the underlying toolkit.
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For example, if the default implementation of OpenAPIToolkit
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uses the RequestsToolkit which contains tools to make arbitrary
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network requests against any URL (e.g., GET, POST, PATCH, PUT, DELETE),
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Control access to who can submit issue requests using this toolkit and
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what network access it has.
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See https://python.langchain.com/docs/security for more information.
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Args:
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llm: The language model to use.
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toolkit: The OpenAPI toolkit.
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callback_manager: Optional. The callback manager. Default is None.
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prefix: Optional. The prefix for the prompt. Default is OPENAPI_PREFIX.
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suffix: Optional. The suffix for the prompt. Default is OPENAPI_SUFFIX.
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format_instructions: Optional. The format instructions for the prompt.
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Default is None.
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input_variables: Optional. The input variables for the prompt. Default is None.
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max_iterations: Optional. The maximum number of iterations. Default is 15.
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max_execution_time: Optional. The maximum execution time. Default is None.
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early_stopping_method: Optional. The early stopping method. Default is "force".
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verbose: Optional. Whether to print verbose output. Default is False.
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return_intermediate_steps: Optional. Whether to return intermediate steps.
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Default is False.
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agent_executor_kwargs: Optional. Additional keyword arguments
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for the agent executor.
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kwargs: Additional arguments.
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Returns:
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The agent executor.
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"""
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.chains.llm import LLMChain
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tools = toolkit.get_tools()
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prompt_params = (
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{"format_instructions": format_instructions}
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if format_instructions is not None
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else {}
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)
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prompt = ZeroShotAgent.create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix,
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input_variables=input_variables,
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**prompt_params,
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)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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verbose=verbose,
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return_intermediate_steps=return_intermediate_steps,
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max_iterations=max_iterations,
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max_execution_time=max_execution_time,
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early_stopping_method=early_stopping_method,
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**(agent_executor_kwargs or {}),
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)
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