mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
74 lines
2.4 KiB
Python
74 lines
2.4 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 import BaseLanguageModel
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from langchain_community.agent_toolkits.powerbi.prompt import (
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POWERBI_PREFIX,
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POWERBI_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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def create_pbi_agent(
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llm: BaseLanguageModel,
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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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prefix: str = POWERBI_PREFIX,
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suffix: str = POWERBI_SUFFIX,
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format_instructions: Optional[str] = None,
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examples: Optional[str] = None,
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input_variables: Optional[List[str]] = 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 an LLM and tools."""
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from langchain.agents import AgentExecutor
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.chains.llm import LLMChain
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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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prompt_params = (
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{"format_instructions": format_instructions}
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if format_instructions is not None
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else {}
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)
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agent = ZeroShotAgent(
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llm_chain=LLMChain(
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llm=llm,
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prompt=ZeroShotAgent.create_prompt(
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tools,
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prefix=prefix.format(top_k=top_k).format(tables=tables),
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suffix=suffix,
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input_variables=input_variables,
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**prompt_params,
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),
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callback_manager=callback_manager,
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verbose=verbose,
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),
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allowed_tools=[tool.name for tool in tools],
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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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verbose=verbose,
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**(agent_executor_kwargs or {}),
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)
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