Source code for langchain_community.agent_toolkits.powerbi.base

"""Power BI agent."""
from __future__ import annotations

from typing import TYPE_CHECKING, Any, Dict, List, Optional

from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel

from langchain_community.agent_toolkits.powerbi.prompt import (
    POWERBI_PREFIX,
    POWERBI_SUFFIX,
)
from langchain_community.agent_toolkits.powerbi.toolkit import PowerBIToolkit
from langchain_community.utilities.powerbi import PowerBIDataset

if TYPE_CHECKING:
    from langchain.agents import AgentExecutor


[docs]def create_pbi_agent( llm: BaseLanguageModel, toolkit: Optional[PowerBIToolkit] = None, powerbi: Optional[PowerBIDataset] = None, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = POWERBI_PREFIX, suffix: str = POWERBI_SUFFIX, format_instructions: Optional[str] = None, examples: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: int = 10, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> AgentExecutor: """Construct a Power BI agent from an LLM and tools.""" from langchain.agents import AgentExecutor from langchain.agents.mrkl.base import ZeroShotAgent from langchain.chains.llm import LLMChain if toolkit is None: if powerbi is None: raise ValueError("Must provide either a toolkit or powerbi dataset") toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples) tools = toolkit.get_tools() tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names prompt_params = ( {"format_instructions": format_instructions} if format_instructions is not None else {} ) agent = ZeroShotAgent( llm_chain=LLMChain( llm=llm, prompt=ZeroShotAgent.create_prompt( tools, prefix=prefix.format(top_k=top_k).format(tables=tables), suffix=suffix, input_variables=input_variables, **prompt_params, ), callback_manager=callback_manager, # type: ignore verbose=verbose, ), allowed_tools=[tool.name for tool in tools], **kwargs, ) return AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, callback_manager=callback_manager, verbose=verbose, **(agent_executor_kwargs or {}), )