mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
25fbe356b4
This PR upgrades community to a recent version of mypy. It inserts type: ignore on all existing failures.
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
"""Toolkit for interacting with a Power BI dataset."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, List, Optional, Union
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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_core.language_models.chat_models import BaseChatModel
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from langchain_core.prompts import PromptTemplate
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from langchain_core.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain_core.pydantic_v1 import Field
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from langchain_core.tools import BaseToolkit
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from langchain_community.tools import BaseTool
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from langchain_community.tools.powerbi.prompt import (
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QUESTION_TO_QUERY_BASE,
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SINGLE_QUESTION_TO_QUERY,
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USER_INPUT,
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)
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from langchain_community.tools.powerbi.tool import (
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InfoPowerBITool,
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ListPowerBITool,
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QueryPowerBITool,
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)
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from langchain_community.utilities.powerbi import PowerBIDataset
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if TYPE_CHECKING:
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from langchain.chains.llm import LLMChain
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class PowerBIToolkit(BaseToolkit):
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"""Toolkit for interacting with Power BI dataset.
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*Security Note*: This toolkit interacts with an external service.
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Control access to who can use this toolkit.
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Make sure that the capabilities given by this toolkit to the calling
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code are appropriately scoped to the application.
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See https://python.langchain.com/docs/security for more information.
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"""
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powerbi: PowerBIDataset = Field(exclude=True)
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llm: Union[BaseLanguageModel, BaseChatModel] = Field(exclude=True)
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examples: Optional[str] = None
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max_iterations: int = 5
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callback_manager: Optional[BaseCallbackManager] = None
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output_token_limit: Optional[int] = None
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tiktoken_model_name: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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return [
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QueryPowerBITool(
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llm_chain=self._get_chain(),
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powerbi=self.powerbi,
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examples=self.examples,
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max_iterations=self.max_iterations,
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output_token_limit=self.output_token_limit, # type: ignore[arg-type]
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tiktoken_model_name=self.tiktoken_model_name,
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),
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InfoPowerBITool(powerbi=self.powerbi),
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ListPowerBITool(powerbi=self.powerbi),
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]
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def _get_chain(self) -> LLMChain:
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"""Construct the chain based on the callback manager and model type."""
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from langchain.chains.llm import LLMChain
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if isinstance(self.llm, BaseLanguageModel):
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return LLMChain(
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llm=self.llm,
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callback_manager=self.callback_manager
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if self.callback_manager
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else None,
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prompt=PromptTemplate(
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template=SINGLE_QUESTION_TO_QUERY,
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input_variables=["tool_input", "tables", "schemas", "examples"],
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),
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)
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system_prompt = SystemMessagePromptTemplate(
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prompt=PromptTemplate(
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template=QUESTION_TO_QUERY_BASE,
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input_variables=["tables", "schemas", "examples"],
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)
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)
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human_prompt = HumanMessagePromptTemplate(
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prompt=PromptTemplate(
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template=USER_INPUT,
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input_variables=["tool_input"],
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)
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)
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return LLMChain(
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llm=self.llm,
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callback_manager=self.callback_manager if self.callback_manager else None,
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prompt=ChatPromptTemplate.from_messages([system_prompt, human_prompt]),
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)
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