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

"""Agent for working with pandas objects."""
import warnings
from typing import Any, Dict, List, Literal, Optional, Sequence, Union, cast

from langchain.agents import (
    AgentType,
    create_openai_tools_agent,
    create_react_agent,
    create_tool_calling_agent,
)
from langchain.agents.agent import (
    AgentExecutor,
    BaseMultiActionAgent,
    BaseSingleActionAgent,
    RunnableAgent,
    RunnableMultiActionAgent,
)
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.agents.openai_functions_agent.base import (
    OpenAIFunctionsAgent,
    create_openai_functions_agent,
)
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel, LanguageModelLike
from langchain_core.messages import SystemMessage
from langchain_core.prompts import (
    BasePromptTemplate,
    ChatPromptTemplate,
    PromptTemplate,
)
from langchain_core.tools import BaseTool
from langchain_core.utils.interactive_env import is_interactive_env

from langchain_experimental.agents.agent_toolkits.pandas.prompt import (
    FUNCTIONS_WITH_DF,
    FUNCTIONS_WITH_MULTI_DF,
    MULTI_DF_PREFIX,
    MULTI_DF_PREFIX_FUNCTIONS,
    PREFIX,
    PREFIX_FUNCTIONS,
    SUFFIX_NO_DF,
    SUFFIX_WITH_DF,
    SUFFIX_WITH_MULTI_DF,
)
from langchain_experimental.tools.python.tool import PythonAstREPLTool


def _get_multi_prompt(
    dfs: List[Any],
    *,
    prefix: Optional[str] = None,
    suffix: Optional[str] = None,
    include_df_in_prompt: Optional[bool] = True,
    number_of_head_rows: int = 5,
) -> BasePromptTemplate:
    if suffix is not None:
        suffix_to_use = suffix
    elif include_df_in_prompt:
        suffix_to_use = SUFFIX_WITH_MULTI_DF
    else:
        suffix_to_use = SUFFIX_NO_DF
    prefix = prefix if prefix is not None else MULTI_DF_PREFIX

    template = "\n\n".join([prefix, "{tools}", FORMAT_INSTRUCTIONS, suffix_to_use])
    prompt = PromptTemplate.from_template(template)
    partial_prompt = prompt.partial()
    if "dfs_head" in partial_prompt.input_variables:
        dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs])
        partial_prompt = partial_prompt.partial(dfs_head=dfs_head)
    if "num_dfs" in partial_prompt.input_variables:
        partial_prompt = partial_prompt.partial(num_dfs=str(len(dfs)))
    return partial_prompt


def _get_single_prompt(
    df: Any,
    *,
    prefix: Optional[str] = None,
    suffix: Optional[str] = None,
    include_df_in_prompt: Optional[bool] = True,
    number_of_head_rows: int = 5,
) -> BasePromptTemplate:
    if suffix is not None:
        suffix_to_use = suffix
    elif include_df_in_prompt:
        suffix_to_use = SUFFIX_WITH_DF
    else:
        suffix_to_use = SUFFIX_NO_DF
    prefix = prefix if prefix is not None else PREFIX

    template = "\n\n".join([prefix, "{tools}", FORMAT_INSTRUCTIONS, suffix_to_use])
    prompt = PromptTemplate.from_template(template)

    partial_prompt = prompt.partial()
    if "df_head" in partial_prompt.input_variables:
        df_head = str(df.head(number_of_head_rows).to_markdown())
        partial_prompt = partial_prompt.partial(df_head=df_head)
    return partial_prompt


def _get_prompt(df: Any, **kwargs: Any) -> BasePromptTemplate:
    return (
        _get_multi_prompt(df, **kwargs)
        if isinstance(df, list)
        else _get_single_prompt(df, **kwargs)
    )


def _get_functions_single_prompt(
    df: Any,
    *,
    prefix: Optional[str] = None,
    suffix: str = "",
    include_df_in_prompt: Optional[bool] = True,
    number_of_head_rows: int = 5,
) -> ChatPromptTemplate:
    if include_df_in_prompt:
        df_head = str(df.head(number_of_head_rows).to_markdown())
        suffix = (suffix or FUNCTIONS_WITH_DF).format(df_head=df_head)
    prefix = prefix if prefix is not None else PREFIX_FUNCTIONS
    system_message = SystemMessage(content=prefix + suffix)
    prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
    return prompt


def _get_functions_multi_prompt(
    dfs: Any,
    *,
    prefix: str = "",
    suffix: str = "",
    include_df_in_prompt: Optional[bool] = True,
    number_of_head_rows: int = 5,
) -> ChatPromptTemplate:
    if include_df_in_prompt:
        dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs])
        suffix = (suffix or FUNCTIONS_WITH_MULTI_DF).format(dfs_head=dfs_head)
    prefix = (prefix or MULTI_DF_PREFIX_FUNCTIONS).format(num_dfs=str(len(dfs)))
    system_message = SystemMessage(content=prefix + suffix)
    prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
    return prompt


def _get_functions_prompt(df: Any, **kwargs: Any) -> ChatPromptTemplate:
    return (
        _get_functions_multi_prompt(df, **kwargs)
        if isinstance(df, list)
        else _get_functions_single_prompt(df, **kwargs)
    )


[docs]def create_pandas_dataframe_agent( llm: LanguageModelLike, df: Any, agent_type: Union[ AgentType, Literal["openai-tools", "tool-calling"] ] = AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[BaseCallbackManager] = None, prefix: Optional[str] = None, suffix: Optional[str] = None, 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, include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, extra_tools: Sequence[BaseTool] = (), engine: Literal["pandas", "modin"] = "pandas", **kwargs: Any, ) -> AgentExecutor: """Construct a Pandas agent from an LLM and dataframe(s). Args: llm: Language model to use for the agent. If agent_type is "tool-calling" then llm is expected to support tool calling. df: Pandas dataframe or list of Pandas dataframes. agent_type: One of "tool-calling", "openai-tools", "openai-functions", or "zero-shot-react-description". Defaults to "zero-shot-react-description". "tool-calling" is recommended over the legacy "openai-tools" and "openai-functions" types. callback_manager: DEPRECATED. Pass "callbacks" key into 'agent_executor_kwargs' instead to pass constructor callbacks to AgentExecutor. prefix: Prompt prefix string. suffix: Prompt suffix string. input_variables: DEPRECATED. Input variables automatically inferred from constructed prompt. verbose: AgentExecutor verbosity. return_intermediate_steps: Passed to AgentExecutor init. max_iterations: Passed to AgentExecutor init. max_execution_time: Passed to AgentExecutor init. early_stopping_method: Passed to AgentExecutor init. agent_executor_kwargs: Arbitrary additional AgentExecutor args. include_df_in_prompt: Whether to include the first number_of_head_rows in the prompt. Must be None if suffix is not None. number_of_head_rows: Number of initial rows to include in prompt if include_df_in_prompt is True. extra_tools: Additional tools to give to agent on top of a PythonAstREPLTool. engine: One of "modin" or "pandas". Defaults to "pandas". **kwargs: DEPRECATED. Not used, kept for backwards compatibility. Returns: An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the DataFrame(s) and any user-provided extra_tools. Example: .. code-block:: python from langchain_openai import ChatOpenAI from langchain_experimental.agents import create_pandas_dataframe_agent import pandas as pd df = pd.read_csv("titanic.csv") llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) agent_executor = create_pandas_dataframe_agent( llm, df, agent_type="tool-calling", verbose=True ) """ try: if engine == "modin": import modin.pandas as pd elif engine == "pandas": import pandas as pd else: raise ValueError( f"Unsupported engine {engine}. It must be one of 'modin' or 'pandas'." ) except ImportError as e: raise ImportError( f"`{engine}` package not found, please install with `pip install {engine}`" ) from e if is_interactive_env(): pd.set_option("display.max_columns", None) for _df in df if isinstance(df, list) else [df]: if not isinstance(_df, pd.DataFrame): raise ValueError(f"Expected pandas DataFrame, got {type(_df)}") if input_variables: kwargs = kwargs or {} kwargs["input_variables"] = input_variables if kwargs: warnings.warn( f"Received additional kwargs {kwargs} which are no longer supported." ) df_locals = {} if isinstance(df, list): for i, dataframe in enumerate(df): df_locals[f"df{i + 1}"] = dataframe else: df_locals["df"] = df tools = [PythonAstREPLTool(locals=df_locals)] + list(extra_tools) if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION: if include_df_in_prompt is not None and suffix is not None: raise ValueError( "If suffix is specified, include_df_in_prompt should not be." ) prompt = _get_prompt( df, prefix=prefix, suffix=suffix, include_df_in_prompt=include_df_in_prompt, number_of_head_rows=number_of_head_rows, ) agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] = RunnableAgent( runnable=create_react_agent(llm, tools, prompt), # type: ignore input_keys_arg=["input"], return_keys_arg=["output"], ) elif agent_type in (AgentType.OPENAI_FUNCTIONS, "openai-tools", "tool-calling"): prompt = _get_functions_prompt( df, prefix=prefix, suffix=suffix, include_df_in_prompt=include_df_in_prompt, number_of_head_rows=number_of_head_rows, ) if agent_type == AgentType.OPENAI_FUNCTIONS: runnable = create_openai_functions_agent( cast(BaseLanguageModel, llm), tools, prompt ) agent = RunnableAgent( runnable=runnable, input_keys_arg=["input"], return_keys_arg=["output"], ) else: if agent_type == "openai-tools": runnable = create_openai_tools_agent( cast(BaseLanguageModel, llm), tools, prompt ) else: runnable = create_tool_calling_agent( cast(BaseLanguageModel, llm), tools, prompt ) agent = RunnableMultiActionAgent( runnable=runnable, input_keys_arg=["input"], return_keys_arg=["output"], ) else: raise ValueError( f"Agent type {agent_type} not supported at the moment. Must be one of " "'tool-calling', 'openai-tools', 'openai-functions', or " "'zero-shot-react-description'." ) return AgentExecutor( 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 {}), )