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987099cfcd
Added missed docstrings. Formatted docstrings to the consistent form. --------- Co-authored-by: ccurme <chester.curme@gmail.com>
92 lines
3.6 KiB
Python
92 lines
3.6 KiB
Python
"""Power BI agent."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_community.agent_toolkits.powerbi.prompt import (
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POWERBI_CHAT_PREFIX,
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POWERBI_CHAT_SUFFIX,
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)
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from langchain_community.agent_toolkits.powerbi.toolkit import PowerBIToolkit
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from langchain_community.utilities.powerbi import PowerBIDataset
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if TYPE_CHECKING:
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from langchain.agents import AgentExecutor
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from langchain.agents.agent import AgentOutputParser
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from langchain.memory.chat_memory import BaseChatMemory
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def create_pbi_chat_agent(
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llm: BaseChatModel,
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toolkit: Optional[PowerBIToolkit] = None,
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powerbi: Optional[PowerBIDataset] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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output_parser: Optional[AgentOutputParser] = None,
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prefix: str = POWERBI_CHAT_PREFIX,
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suffix: str = POWERBI_CHAT_SUFFIX,
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examples: Optional[str] = None,
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input_variables: Optional[List[str]] = None,
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memory: Optional[BaseChatMemory] = None,
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top_k: int = 10,
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verbose: bool = False,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct a Power BI agent from a Chat LLM and tools.
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If you supply only a toolkit and no Power BI dataset, the same LLM is used for both.
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Args:
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llm: The language model to use.
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toolkit: Optional. The Power BI toolkit. Default is None.
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powerbi: Optional. The Power BI dataset. Default is None.
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callback_manager: Optional. The callback manager. Default is None.
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output_parser: Optional. The output parser. Default is None.
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prefix: Optional. The prefix for the prompt. Default is POWERBI_CHAT_PREFIX.
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suffix: Optional. The suffix for the prompt. Default is POWERBI_CHAT_SUFFIX.
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examples: Optional. The examples for the prompt. Default is None.
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input_variables: Optional. The input variables for the prompt. Default is None.
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memory: Optional. The memory. Default is None.
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top_k: Optional. The top k for the prompt. Default is 10.
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verbose: Optional. Whether to print verbose output. Default is False.
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agent_executor_kwargs: Optional. The agent executor kwargs. Default is None.
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kwargs: Any. Additional keyword arguments.
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Returns:
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The agent executor.
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"""
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from langchain.agents import AgentExecutor
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from langchain.agents.conversational_chat.base import ConversationalChatAgent
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from langchain.memory import ConversationBufferMemory
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if toolkit is None:
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if powerbi is None:
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raise ValueError("Must provide either a toolkit or powerbi dataset")
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toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
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tools = toolkit.get_tools()
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tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
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agent = ConversationalChatAgent.from_llm_and_tools(
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llm=llm,
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tools=tools,
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system_message=prefix.format(top_k=top_k).format(tables=tables),
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human_message=suffix,
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input_variables=input_variables,
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callback_manager=callback_manager,
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output_parser=output_parser,
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verbose=verbose,
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**kwargs,
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)
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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memory=memory
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or ConversationBufferMemory(memory_key="chat_history", return_messages=True),
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verbose=verbose,
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**(agent_executor_kwargs or {}),
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)
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