Source code for langchain.agents.agent_toolkits.pandas.base

"""Agent for working with pandas objects."""
from typing import Any, List, Optional

from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.tools.python.tool import PythonAstREPLTool


[docs]def create_pandas_dataframe_agent( llm: BaseLLM, df: Any, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = PREFIX, suffix: str = SUFFIX, 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", **kwargs: Any, ) -> AgentExecutor: """Construct a pandas agent from an LLM and dataframe.""" import pandas as pd if not isinstance(df, pd.DataFrame): raise ValueError(f"Expected pandas object, got {type(df)}") if input_variables is None: input_variables = ["df", "input", "agent_scratchpad"] tools = [PythonAstREPLTool(locals={"df": df})] prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix, input_variables=input_variables ) partial_prompt = prompt.partial(df=str(df.head())) 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, verbose=verbose, return_intermediate_steps=return_intermediate_steps, max_iterations=max_iterations, max_execution_time=max_execution_time, early_stopping_method=early_stopping_method, callback_manager=callback_manager, )