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116 lines
2.4 KiB
Python
116 lines
2.4 KiB
Python
from typing import List, Optional
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from pydantic import BaseModel
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from fastapi import UploadFile
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class KnowledgeQueryRequest(BaseModel):
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"""query: knowledge query"""
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query: str
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"""top_k: return topK documents"""
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top_k: int
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class KnowledgeSpaceRequest(BaseModel):
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"""name: knowledge space name"""
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name: str = None
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"""vector_type: vector type"""
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vector_type: str = None
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"""desc: description"""
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desc: str = None
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"""owner: owner"""
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owner: str = None
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class KnowledgeDocumentRequest(BaseModel):
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"""doc_name: doc path"""
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doc_name: str = None
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"""doc_type: doc type"""
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doc_type: str = None
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"""content: content"""
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content: str = None
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"""content: content"""
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source: str = None
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"""text_chunk_size: text_chunk_size"""
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# text_chunk_size: int
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class DocumentQueryRequest(BaseModel):
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"""doc_name: doc path"""
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doc_name: str = None
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"""doc_type: doc type"""
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doc_type: str = None
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"""status: status"""
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status: str = None
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"""page: page"""
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page: int = 1
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"""page_size: page size"""
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page_size: int = 20
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class DocumentSyncRequest(BaseModel):
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"""Sync request"""
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"""doc_ids: doc ids"""
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doc_ids: List
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model_name: Optional[str] = None
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"""Preseparator, this separator is used for pre-splitting before the document is actually split by the text splitter.
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Preseparator are not included in the vectorized text.
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"""
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pre_separator: Optional[str] = None
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"""Custom separators"""
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separators: Optional[List[str]] = None
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"""Custom chunk size"""
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chunk_size: Optional[int] = None
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"""Custom chunk overlap"""
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chunk_overlap: Optional[int] = None
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class ChunkQueryRequest(BaseModel):
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"""id: id"""
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id: int = None
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"""document_id: doc id"""
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document_id: int = None
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"""doc_name: doc path"""
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doc_name: str = None
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"""doc_type: doc type"""
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doc_type: str = None
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"""page: page"""
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page: int = 1
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"""page_size: page size"""
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page_size: int = 20
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class KnowledgeQueryResponse:
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"""source: knowledge reference source"""
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source: str
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"""score: knowledge vector query similarity score"""
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score: float = 0.0
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"""text: raw text info"""
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text: str
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class SpaceArgumentRequest(BaseModel):
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"""argument: argument"""
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argument: str
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class EntityExtractRequest(BaseModel):
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"""argument: argument"""
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text: str
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model_name: str
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