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feat(core): APP use new SDK component (#1050)
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@@ -1,5 +1,9 @@
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from enum import Enum
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from typing import List
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from typing import List, Optional
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from dbgpt._private.pydantic import BaseModel, Field
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from dbgpt.core import BaseOutputParser, ChatPromptTemplate
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from dbgpt.core._private.example_base import ExampleSelector
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class Scene:
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@@ -135,3 +139,49 @@ class ChatScene(Enum):
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def is_inner(self):
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return self._value_.is_inner
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class AppScenePromptTemplateAdapter(BaseModel):
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"""The template of the scene.
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Include some fields that in :class:`dbgpt.core.PromptTemplate`
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"""
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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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prompt: ChatPromptTemplate = Field(..., description="The prompt of this scene")
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template_scene: Optional[str] = Field(
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default=None, description="The scene of this template"
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)
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template_is_strict: Optional[bool] = Field(
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default=True, description="Whether strict"
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)
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output_parser: Optional[BaseOutputParser] = Field(
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default=None, description="The output parser of this scene"
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)
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sep: Optional[str] = Field(
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default="###", description="The default separator of this scene"
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)
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stream_out: Optional[bool] = Field(
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default=True, description="Whether to stream out"
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)
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example_selector: Optional[ExampleSelector] = Field(
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default=None, description="Example selector"
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)
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need_historical_messages: Optional[bool] = Field(
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default=False, description="Whether to need historical messages"
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)
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temperature: Optional[float] = Field(
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default=0.6, description="The default temperature of this scene"
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)
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max_new_tokens: Optional[int] = Field(
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default=1024, description="The default max new tokens of this scene"
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)
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str_history: Optional[bool] = Field(
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default=False, description="Whether transform history to str"
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)
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