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80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
# coding=utf-8
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"""
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@project: maxkb
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@Author：虎
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@file： pg_vector.py
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@date：2023/10/19 15:28
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@desc:
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"""
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import uuid
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from typing import Dict, List
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from django.db.models import QuerySet
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from langchain.embeddings import HuggingFaceEmbeddings
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from embedding.models import Embedding, SourceType
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from embedding.vector.base_vector import BaseVectorStore
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class PGVector(BaseVectorStore):
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def vector_is_create(self) -> bool:
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# 项目启动默认是创建好的 不需要再创建
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return True
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def vector_create(self):
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return True
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def _save(self, text, source_type: SourceType, dataset_id: str, document_id: str, paragraph_id: str, source_id: str,
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is_active: bool,
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embedding: HuggingFaceEmbeddings):
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text_embedding = embedding.embed_query(text)
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embedding = Embedding(id=uuid.uuid1(),
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dataset_id=dataset_id,
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document_id=document_id,
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is_active=is_active,
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paragraph_id=paragraph_id,
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source_id=source_id,
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embedding=text_embedding,
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source_type=source_type,
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)
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embedding.save()
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return True
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def _batch_save(self, text_list: List[Dict], embedding: HuggingFaceEmbeddings):
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texts = [row.get('text') for row in text_list]
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embeddings = embedding.embed_documents(texts)
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QuerySet(Embedding).bulk_create([Embedding(id=uuid.uuid1(),
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document_id=text_list[index].get('document_id'),
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paragraph_id=text_list[index].get('paragraph_id'),
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dataset_id=text_list[index].get('dataset_id'),
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is_active=text_list[index].get('is_active', True),
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source_id=text_list[index].get('source_id'),
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source_type=text_list[index].get('source_type'),
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embedding=embeddings[index]) for index in
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range(0, len(text_list))]) if len(text_list) > 0 else None
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return True
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def search(self, query_text, dataset_id_list: list[str], is_active: bool, embedding: HuggingFaceEmbeddings):
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pass
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def update_by_source_id(self, source_id: str, instance: Dict):
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QuerySet(Embedding).filter(source_id=source_id).update(**instance)
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def update_by_paragraph_id(self, paragraph_id: str, instance: Dict):
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QuerySet(Embedding).filter(paragraph_id=paragraph_id).update(**instance)
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def delete_by_dataset_id(self, dataset_id: str):
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QuerySet(Embedding).filter(dataset_id=dataset_id).delete()
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def delete_by_document_id(self, document_id: str):
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QuerySet(Embedding).filter(document_id=document_id).delete()
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return True
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def delete_by_source_id(self, source_id: str, source_type: str):
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QuerySet(Embedding).filter(source_id=source_id, source_type=source_type).delete()
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return True
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def delete_by_paragraph_id(self, paragraph_id: str):
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QuerySet(Embedding).filter(paragraph_id=paragraph_id).delete()
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