Source code for langchain_community.agent_toolkits.powerbi.chat_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.chat_models import BaseChatModel

from langchain_community.agent_toolkits.powerbi.prompt import (
    POWERBI_CHAT_PREFIX,
    POWERBI_CHAT_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
    from langchain.agents.agent import AgentOutputParser
    from langchain.memory.chat_memory import BaseChatMemory


[docs] def create_pbi_chat_agent( llm: BaseChatModel, toolkit: Optional[PowerBIToolkit] = None, powerbi: Optional[PowerBIDataset] = None, callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, prefix: str = POWERBI_CHAT_PREFIX, suffix: str = POWERBI_CHAT_SUFFIX, examples: Optional[str] = None, input_variables: Optional[List[str]] = None, memory: Optional[BaseChatMemory] = 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 a Chat LLM and tools. If you supply only a toolkit and no Power BI dataset, the same LLM is used for both. Args: llm: The language model to use. toolkit: Optional. The Power BI toolkit. Default is None. powerbi: Optional. The Power BI dataset. Default is None. callback_manager: Optional. The callback manager. Default is None. output_parser: Optional. The output parser. Default is None. prefix: Optional. The prefix for the prompt. Default is POWERBI_CHAT_PREFIX. suffix: Optional. The suffix for the prompt. Default is POWERBI_CHAT_SUFFIX. examples: Optional. The examples for the prompt. Default is None. input_variables: Optional. The input variables for the prompt. Default is None. memory: Optional. The memory. Default is None. top_k: Optional. The top k for the prompt. Default is 10. verbose: Optional. Whether to print verbose output. Default is False. agent_executor_kwargs: Optional. The agent executor kwargs. Default is None. kwargs: Any. Additional keyword arguments. Returns: The agent executor. """ from langchain.agents import AgentExecutor from langchain.agents.conversational_chat.base import ConversationalChatAgent from langchain.memory import ConversationBufferMemory 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 agent = ConversationalChatAgent.from_llm_and_tools( llm=llm, tools=tools, system_message=prefix.format(top_k=top_k).format(tables=tables), human_message=suffix, input_variables=input_variables, callback_manager=callback_manager, output_parser=output_parser, verbose=verbose, **kwargs, ) return AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, callback_manager=callback_manager, memory=memory or ConversationBufferMemory(memory_key="chat_history", return_messages=True), verbose=verbose, **(agent_executor_kwargs or {}), )