"""用于处理 xorbits 对象的代理。"""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain
from langchain_core.callbacks.base import BaseCallbackManager
from langchain_core.language_models import BaseLLM
from langchain_experimental.agents.agent_toolkits.xorbits.prompt import (
NP_PREFIX,
NP_SUFFIX,
PD_PREFIX,
PD_SUFFIX,
)
from langchain_experimental.tools.python.tool import PythonAstREPLTool
[docs]def create_xorbits_agent(
llm: BaseLLM,
data: Any,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = "",
suffix: str = "",
input_variables: Optional[List[str]] = None,
verbose: bool = False,
return_intermediate_steps: bool = False,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""从LML和数据框构建一个xorbits代理。"""
try:
from xorbits import numpy as np
from xorbits import pandas as pd
except ImportError:
raise ImportError(
"Xorbits package not installed, please install with `pip install xorbits`"
)
if not isinstance(data, (pd.DataFrame, np.ndarray)):
raise ValueError(
f"Expected Xorbits DataFrame or ndarray object, got {type(data)}"
)
if input_variables is None:
input_variables = ["data", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"data": data})]
prompt, partial_input = None, None
if isinstance(data, pd.DataFrame):
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=PD_PREFIX if prefix == "" else prefix,
suffix=PD_SUFFIX if suffix == "" else suffix,
input_variables=input_variables,
)
partial_input = str(data.head())
else:
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=NP_PREFIX if prefix == "" else prefix,
suffix=NP_SUFFIX if suffix == "" else suffix,
input_variables=input_variables,
)
partial_input = str(data[: len(data) // 2])
partial_prompt = prompt.partial(data=partial_input)
llm_chain = LLMChain(
llm=llm,
prompt=partial_prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**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 {}),
)