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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
169 lines
3.8 KiB
Python
169 lines
3.8 KiB
Python
from enum import StrEnum
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from typing import Literal
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from pydantic import BaseModel
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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class ParentMode(StrEnum):
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FULL_DOC = "full-doc"
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PARAGRAPH = "paragraph"
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class NotionIcon(BaseModel):
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type: str
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url: str | None = None
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emoji: str | None = None
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class NotionPage(BaseModel):
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page_id: str
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page_name: str
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page_icon: NotionIcon | None = None
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type: str
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class NotionInfo(BaseModel):
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credential_id: str
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workspace_id: str
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pages: list[NotionPage]
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class WebsiteInfo(BaseModel):
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provider: str
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job_id: str
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urls: list[str]
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only_main_content: bool = True
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class FileInfo(BaseModel):
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file_ids: list[str]
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class InfoList(BaseModel):
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data_source_type: Literal["upload_file", "notion_import", "website_crawl"]
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notion_info_list: list[NotionInfo] | None = None
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file_info_list: FileInfo | None = None
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website_info_list: WebsiteInfo | None = None
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class DataSource(BaseModel):
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info_list: InfoList
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class PreProcessingRule(BaseModel):
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id: str
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enabled: bool
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class Segmentation(BaseModel):
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separator: str = "\n"
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max_tokens: int
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chunk_overlap: int = 0
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class Rule(BaseModel):
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pre_processing_rules: list[PreProcessingRule] | None = None
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segmentation: Segmentation | None = None
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parent_mode: Literal["full-doc", "paragraph"] | None = None
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subchunk_segmentation: Segmentation | None = None
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class ProcessRule(BaseModel):
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mode: Literal["automatic", "custom", "hierarchical"]
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rules: Rule | None = None
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class RerankingModel(BaseModel):
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reranking_provider_name: str | None = None
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reranking_model_name: str | None = None
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class WeightVectorSetting(BaseModel):
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vector_weight: float
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embedding_provider_name: str
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embedding_model_name: str
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class WeightKeywordSetting(BaseModel):
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keyword_weight: float
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class WeightModel(BaseModel):
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weight_type: Literal["semantic_first", "keyword_first", "customized"] | None = None
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vector_setting: WeightVectorSetting | None = None
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keyword_setting: WeightKeywordSetting | None = None
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class RetrievalModel(BaseModel):
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search_method: RetrievalMethod
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reranking_enable: bool
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reranking_model: RerankingModel | None = None
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reranking_mode: str | None = None
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top_k: int
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score_threshold_enabled: bool
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score_threshold: float | None = None
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weights: WeightModel | None = None
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class MetaDataConfig(BaseModel):
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doc_type: str
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doc_metadata: dict
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class KnowledgeConfig(BaseModel):
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original_document_id: str | None = None
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duplicate: bool = True
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indexing_technique: Literal["high_quality", "economy"]
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data_source: DataSource | None = None
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process_rule: ProcessRule | None = None
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retrieval_model: RetrievalModel | None = None
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doc_form: str = "text_model"
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doc_language: str = "English"
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embedding_model: str | None = None
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embedding_model_provider: str | None = None
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name: str | None = None
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class SegmentUpdateArgs(BaseModel):
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content: str | None = None
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answer: str | None = None
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keywords: list[str] | None = None
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regenerate_child_chunks: bool = False
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enabled: bool | None = None
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class ChildChunkUpdateArgs(BaseModel):
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id: str | None = None
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content: str
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class MetadataArgs(BaseModel):
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type: Literal["string", "number", "time"]
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name: str
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class MetadataUpdateArgs(BaseModel):
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name: str
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value: str | int | float | None = None
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class MetadataDetail(BaseModel):
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id: str
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name: str
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value: str | int | float | None = None
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class DocumentMetadataOperation(BaseModel):
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document_id: str
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metadata_list: list[MetadataDetail]
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class MetadataOperationData(BaseModel):
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"""
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Metadata operation data
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"""
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operation_data: list[DocumentMetadataOperation]
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