"""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",
allow_dangerous_code: bool = False,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a Pandas agent from an LLM and dataframe(s).
Security Notice:
This agent relies on access to a python repl tool which can execute
arbitrary code. This can be dangerous and requires a specially sandboxed
environment to be safely used. Failure to run this code in a properly
sandboxed environment can lead to arbitrary code execution vulnerabilities,
which can lead to data breaches, data loss, or other security incidents.
Do not use this code with untrusted inputs, with elevated permissions,
or without consulting your security team about proper sandboxing!
You must opt-in to use this functionality by setting allow_dangerous_code=True.
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".
allow_dangerous_code: bool, default False
This agent relies on access to a python repl tool which can execute
arbitrary code. This can be dangerous and requires a specially sandboxed
environment to be safely used.
Failure to properly sandbox this class can lead to arbitrary code execution
vulnerabilities, which can lead to data breaches, data loss, or
other security incidents.
You must opt in to use this functionality by setting
allow_dangerous_code=True.
**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
)
"""
if not allow_dangerous_code:
raise ValueError(
"This agent relies on access to a python repl tool which can execute "
"arbitrary code. This can be dangerous and requires a specially sandboxed "
"environment to be safely used. Please read the security notice in the "
"doc-string of this function. You must opt-in to use this functionality "
"by setting allow_dangerous_code=True."
"For general security guidelines, please see: "
"https://python.langchain.com/v0.2/docs/security/"
)
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 {}),
)
