mirror of
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135 lines
3.5 KiB
Python
135 lines
3.5 KiB
Python
from enum import Enum
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from typing import List
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class Scene:
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def __init__(
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self,
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code,
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name,
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describe,
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param_types: List = [],
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is_inner: bool = False,
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show_disable=False,
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prepare_scene_code: str = None,
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):
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self.code = code
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self.name = name
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self.describe = describe
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self.param_types = param_types
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self.is_inner = is_inner
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self.show_disable = show_disable
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self.prepare_scene_code = prepare_scene_code
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class ChatScene(Enum):
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ChatWithDbExecute = Scene(
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code="chat_with_db_execute",
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name="Chat DB",
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describe="Dialogue with your private databse data through natural language.",
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param_types=["DB Select"],
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)
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ExcelLearning = Scene(
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code="excel_learning",
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name="Excel Learning",
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describe="Analyze and summarize your excel files.",
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is_inner=True,
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)
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ChatExcel = Scene(
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code="chat_excel",
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name="Chat Excel",
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describe="Dialogue with your excel, use natural language.",
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param_types=["File Select"],
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prepare_scene_code="excel_learning",
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)
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ChatWithDbQA = Scene(
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code="chat_with_db_qa",
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name="Chat DB",
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describe="Have a Professional Conversation with Metadata.",
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param_types=["DB Select"],
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)
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ChatExecution = Scene(
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code="chat_execution",
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name="Use Plugin",
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describe="Use tools through dialogue to accomplish your goals.",
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param_types=["Plugin Select"],
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)
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ChatAgent = Scene(
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code="chat_agent",
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name="Agent Chat",
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describe="Use tools through dialogue to accomplish your goals.",
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param_types=["Plugin Select"],
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)
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InnerChatDBSummary = Scene(
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"inner_chat_db_summary", "DB Summary", "Db Summary.", True
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)
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ChatNormal = Scene(
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"chat_normal", "Chat Normal", "Native LLM large model AI dialogue."
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)
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ChatDashboard = Scene(
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"chat_dashboard",
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"Dashboard",
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"Provide you with professional analysis reports through natural language.",
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["DB Select"],
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)
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ChatKnowledge = Scene(
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"chat_knowledge",
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"Chat Knowledge",
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"Dialogue through natural language and private documents and knowledge bases.",
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["Knowledge Space Select"],
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)
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ExtractTriplet = Scene(
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"extract_triplet",
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"Extract Triplet",
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"Extract Triplet",
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["Extract Select"],
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True,
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)
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ExtractSummary = Scene(
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"extract_summary",
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"Extract Summary",
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"Extract Summary",
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["Extract Select"],
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True,
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)
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ExtractRefineSummary = Scene(
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"extract_refine_summary",
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"Extract Summary",
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"Extract Summary",
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["Extract Select"],
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True,
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)
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ExtractEntity = Scene(
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"extract_entity", "Extract Entity", "Extract Entity", ["Extract Select"], True
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)
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@staticmethod
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def of_mode(mode):
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return [x for x in ChatScene._value_ if x.code == mode][0]
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@staticmethod
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def is_valid_mode(mode):
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return any(mode == item.value() for item in ChatScene)
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def value(self):
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return self._value_.code
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def scene_name(self):
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return self._value_.name
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def describe(self):
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return self._value_.describe
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def param_types(self):
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return self._value_.param_types
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def show_disable(self):
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return self._value_.show_disable
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def is_inner(self):
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return self._value_.is_inner
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