mirror of
https://github.com/1Panel-dev/MaxKB.git
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219 lines
9.9 KiB
Python
219 lines
9.9 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 json
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import os
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import uuid
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from abc import ABC, abstractmethod
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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_community.embeddings import HuggingFaceEmbeddings
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from common.config.embedding_config import EmbeddingModel
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from common.db.search import generate_sql_by_query_dict
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from common.db.sql_execute import select_list
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from common.util.file_util import get_file_content
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from common.util.ts_vecto_util import to_ts_vector, to_query
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from embedding.models import Embedding, SourceType, SearchMode
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from embedding.vector.base_vector import BaseVectorStore
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from smartdoc.conf import PROJECT_DIR
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class PGVector(BaseVectorStore):
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def delete_by_source_ids(self, source_ids: List[str], source_type: str):
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QuerySet(Embedding).filter(source_id__in=source_ids, source_type=source_type).delete()
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def update_by_source_ids(self, source_ids: List[str], instance: Dict):
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QuerySet(Embedding).filter(source_id__in=source_ids).update(**instance)
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def embed_documents(self, text_list: List[str]):
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embedding = EmbeddingModel.get_embedding_model()
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return embedding.embed_documents(text_list)
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def embed_query(self, text: str):
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embedding = EmbeddingModel.get_embedding_model()
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return embedding.embed_query(text)
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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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search_vector=to_ts_vector(text))
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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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embedding_list = [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],
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search_vector=to_ts_vector(text_list[index]['text'])) for index in
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range(0, len(text_list))]
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QuerySet(Embedding).bulk_create(embedding_list) if len(embedding_list) > 0 else None
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return True
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def hit_test(self, query_text, dataset_id_list: list[str], exclude_document_id_list: list[str], top_number: int,
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similarity: float,
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search_mode: SearchMode,
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embedding: HuggingFaceEmbeddings):
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if dataset_id_list is None or len(dataset_id_list) == 0:
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return []
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exclude_dict = {}
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embedding_query = embedding.embed_query(query_text)
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query_set = QuerySet(Embedding).filter(dataset_id__in=dataset_id_list, is_active=True)
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if exclude_document_id_list is not None and len(exclude_document_id_list) > 0:
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exclude_dict.__setitem__('document_id__in', exclude_document_id_list)
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query_set = query_set.exclude(**exclude_dict)
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for search_handle in search_handle_list:
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if search_handle.support(search_mode):
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return search_handle.handle(query_set, query_text, embedding_query, top_number, similarity, search_mode)
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def query(self, query_text: str, query_embedding: List[float], dataset_id_list: list[str],
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exclude_document_id_list: list[str],
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exclude_paragraph_list: list[str], is_active: bool, top_n: int, similarity: float,
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search_mode: SearchMode):
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exclude_dict = {}
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if dataset_id_list is None or len(dataset_id_list) == 0:
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return []
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query_set = QuerySet(Embedding).filter(dataset_id__in=dataset_id_list, is_active=is_active)
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if exclude_document_id_list is not None and len(exclude_document_id_list) > 0:
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exclude_dict.__setitem__('document_id__in', exclude_document_id_list)
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if exclude_paragraph_list is not None and len(exclude_paragraph_list) > 0:
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exclude_dict.__setitem__('paragraph_id__in', exclude_paragraph_list)
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query_set = query_set.exclude(**exclude_dict)
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for search_handle in search_handle_list:
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if search_handle.support(search_mode):
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return search_handle.handle(query_set, query_text, query_embedding, top_n, similarity, search_mode)
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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_dataset_id_list(self, dataset_id_list: List[str]):
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QuerySet(Embedding).filter(dataset_id__in=dataset_id_list).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_bu_document_id_list(self, document_id_list: List[str]):
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return QuerySet(Embedding).filter(document_id__in=document_id_list).delete()
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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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class ISearch(ABC):
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@abstractmethod
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def support(self, search_mode: SearchMode):
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pass
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@abstractmethod
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def handle(self, query_set, query_text, query_embedding, top_number: int,
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similarity: float, search_mode: SearchMode):
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pass
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class EmbeddingSearch(ISearch):
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def handle(self,
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query_set,
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query_text,
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query_embedding,
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top_number: int,
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similarity: float,
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search_mode: SearchMode):
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exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
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select_string=get_file_content(
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os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
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'embedding_search.sql')),
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with_table_name=True)
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embedding_model = select_list(exec_sql,
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[json.dumps(query_embedding), *exec_params, similarity, top_number])
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return embedding_model
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def support(self, search_mode: SearchMode):
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return search_mode.value == SearchMode.embedding.value
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class KeywordsSearch(ISearch):
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def handle(self,
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query_set,
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query_text,
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query_embedding,
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top_number: int,
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similarity: float,
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search_mode: SearchMode):
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exec_sql, exec_params = generate_sql_by_query_dict({'keywords_query': query_set},
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select_string=get_file_content(
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os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
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'keywords_search.sql')),
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with_table_name=True)
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embedding_model = select_list(exec_sql,
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[to_query(query_text), *exec_params, similarity, top_number])
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return embedding_model
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def support(self, search_mode: SearchMode):
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return search_mode.value == SearchMode.keywords.value
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class BlendSearch(ISearch):
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def handle(self,
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query_set,
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query_text,
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query_embedding,
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top_number: int,
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similarity: float,
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search_mode: SearchMode):
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exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
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select_string=get_file_content(
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os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
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'blend_search.sql')),
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with_table_name=True)
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embedding_model = select_list(exec_sql,
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[json.dumps(query_embedding), to_query(query_text), *exec_params, similarity,
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top_number])
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return embedding_model
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def support(self, search_mode: SearchMode):
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return search_mode.value == SearchMode.blend.value
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search_handle_list = [EmbeddingSearch(), KeywordsSearch(), BlendSearch()]
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