langchain_experimental.agents.agent_toolkits.csv.base

from __future__ import annotations

from io import IOBase
from typing import TYPE_CHECKING, Any, List, Optional, Union

from langchain_experimental.agents.agent_toolkits.pandas.base import (
    create_pandas_dataframe_agent,
)

if TYPE_CHECKING:
    from langchain.agents.agent import AgentExecutor
    from langchain_core.language_models import LanguageModelLike


[docs]def create_csv_agent(
    llm: LanguageModelLike,
    path: Union[str, IOBase, List[Union[str, IOBase]]],
    pandas_kwargs: Optional[dict] = None,
    **kwargs: Any,
) -> AgentExecutor:
    """创建pandas dataframe代理，通过加载csv到数据框中。

参数:
    llm: 用于代理的语言模型。
    path: 字符串路径、类文件对象或字符串路径/类文件对象列表，可以通过pd.read_csv()读取为pandas DataFrames。
    pandas_kwargs: 传递给pd.read_csv()的命名参数。
    **kwargs: 传递给langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent()的额外kwargs。

返回:
    具有指定agent_type代理和访问已加载的DataFrame(s)以及任何用户提供的extra_tools的AgentExecutor。

示例:
    .. code-block:: python

        from langchain_openai import ChatOpenAI
        from langchain_experimental.agents import create_csv_agent

        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        agent_executor = create_pandas_dataframe_agent(
            llm,
            "titanic.csv",
            agent_type="openai-tools",
            verbose=True
        )
"""  # noqa: E501
    try:
        import pandas as pd
    except ImportError:
        raise ImportError(
            "pandas package not found, please install with `pip install pandas`."
        )

    _kwargs = pandas_kwargs or {}
    if isinstance(path, (str, IOBase)):
        df = pd.read_csv(path, **_kwargs)
    elif isinstance(path, list):
        df = []
        for item in path:
            if not isinstance(item, (str, IOBase)):
                raise ValueError(f"Expected str or file-like object, got {type(path)}")
            df.append(pd.read_csv(item, **_kwargs))
    else:
        raise ValueError(f"Expected str, list, or file-like object, got {type(path)}")
    return create_pandas_dataframe_agent(llm, df, **kwargs)
Source code for langchain_experimental.agents.agent_toolkits.csv.base
