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91 lines
2.1 KiB
Python
91 lines
2.1 KiB
Python
from typing import Literal
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from pydantic import BaseModel, field_validator
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from core.rag.entities import KeywordSetting, VectorSetting
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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class IconInfo(BaseModel):
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icon: str
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icon_background: str | None = None
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icon_type: str | None = None
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icon_url: str | None = None
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class PipelineTemplateInfoEntity(BaseModel):
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name: str
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description: str
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icon_info: IconInfo
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class RagPipelineDatasetCreateEntity(BaseModel):
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name: str
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description: str
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icon_info: IconInfo
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permission: str
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partial_member_list: list[dict[str, str]] | None = None
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yaml_content: str | None = None
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class RerankingModelConfig(BaseModel):
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"""
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Reranking Model Config.
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"""
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reranking_provider_name: str | None = ""
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reranking_model_name: str | None = ""
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class WeightedScoreConfig(BaseModel):
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"""
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Weighted score Config.
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"""
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vector_setting: VectorSetting | None
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keyword_setting: KeywordSetting | None
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class RetrievalSetting(BaseModel):
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"""
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Retrieval Setting.
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"""
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search_method: RetrievalMethod
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top_k: int
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score_threshold: float | None = 0.5
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score_threshold_enabled: bool = False
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reranking_mode: str | None = "reranking_model"
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reranking_enable: bool | None = True
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reranking_model: RerankingModelConfig | None = None
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weights: WeightedScoreConfig | None = None
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class KnowledgeConfiguration(BaseModel):
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"""
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Knowledge Base Configuration.
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"""
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chunk_structure: str
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indexing_technique: Literal["high_quality", "economy"]
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embedding_model_provider: str = ""
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embedding_model: str = ""
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keyword_number: int | None = 10
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retrieval_model: RetrievalSetting
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# add summary index setting
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summary_index_setting: dict | None = None
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@field_validator("embedding_model_provider", mode="before")
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@classmethod
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def validate_embedding_model_provider(cls, v):
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if v is None:
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return ""
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return v
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@field_validator("embedding_model", mode="before")
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@classmethod
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def validate_embedding_model(cls, v):
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if v is None:
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return ""
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return v
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