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db28894443
1.create knowledge space 2.list knowledge space 3.create knowledge document 4.list knowledge document 5.save document chunks 6.sync embedding document
75 lines
1.6 KiB
Python
75 lines
1.6 KiB
Python
from typing import List
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from pydantic import BaseModel
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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
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"""doc_type: doc type"""
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doc_type: str
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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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"""doc_ids: doc ids"""
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doc_ids: List
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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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