forked from Archives/langchain
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
"""Agent for working with pandas objects."""
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from typing import Any, Dict, List, Optional
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains.llm import LLMChain
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from langchain.tools.python.tool import PythonAstREPLTool
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def create_pandas_dataframe_agent(
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llm: BaseLanguageModel,
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df: Any,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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input_variables: Optional[List[str]] = None,
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verbose: bool = False,
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return_intermediate_steps: bool = False,
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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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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Dict[str, Any],
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) -> AgentExecutor:
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"""Construct a pandas agent from an LLM and dataframe."""
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try:
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import pandas as pd
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except ImportError:
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raise ValueError(
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"pandas package not found, please install with `pip install pandas`"
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)
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if not isinstance(df, pd.DataFrame):
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raise ValueError(f"Expected pandas object, got {type(df)}")
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if input_variables is None:
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input_variables = ["df", "input", "agent_scratchpad"]
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tools = [PythonAstREPLTool(locals={"df": df})]
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prompt = ZeroShotAgent.create_prompt(
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tools, prefix=prefix, suffix=suffix, input_variables=input_variables
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)
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partial_prompt = prompt.partial(df=str(df.head().to_markdown()))
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llm_chain = LLMChain(
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llm=llm,
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prompt=partial_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(
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llm_chain=llm_chain,
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allowed_tools=tool_names,
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callback_manager=callback_manager,
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**kwargs,
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)
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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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