DB-GPT/dbgpt/storage/full_text/opensearch.py
M1n9X 759f7d99cc
feat(GraphRAG): enhance GraphRAG by graph community summary (#1801)
Co-authored-by: Florian <fanzhidongyzby@163.com>
Co-authored-by: KingSkyLi <15566300566@163.com>
Co-authored-by: aries_ckt <916701291@qq.com>
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>
Co-authored-by: yvonneyx <zhuyuxin0627@gmail.com>
2024-08-30 21:59:44 +08:00

64 lines
1.6 KiB
Python

"""OpenSearch index store."""
from typing import List, Optional
from dbgpt.core import Chunk
from dbgpt.storage.full_text.base import FullTextStoreBase
from dbgpt.storage.vector_store.filters import MetadataFilters
class OpenSearch(FullTextStoreBase):
"""OpenSearch index store."""
def load_document(self, chunks: List[Chunk]) -> List[str]:
"""Load document in index database.
Args:
chunks(List[Chunk]): document chunks.
Return:
List[str]: chunk ids.
"""
pass
def aload_document(self, chunks: List[Chunk]) -> List[str]:
"""Async load document in index database.
Args:
chunks(List[Chunk]): document chunks.
Return:
List[str]: chunk ids.
"""
pass
def similar_search_with_scores(
self,
text,
topk,
score_threshold: float,
filters: Optional[MetadataFilters] = None,
) -> List[Chunk]:
"""Similar search with scores in index database.
Args:
text(str): The query text.
topk(int): The number of similar documents to return.
score_threshold(int): score_threshold: Optional, a floating point value
between 0 to 1
filters(Optional[MetadataFilters]): metadata filters.
Return:
List[Chunk]: The similar documents.
"""
pass
def delete_by_ids(self, ids: str):
"""Delete docs.
Args:
ids(str): The vector ids to delete, separated by comma.
"""
pass
def delete_vector_name(self, index_name: str):
"""Delete name."""
pass