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chore: import upstream snapshot with attribution
2026-07-13 12:38:54 +08:00

166 lines
4.4 KiB
Python

"""
Copyright 2024, Zep Software, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from abc import ABC, abstractmethod
from typing import Any
from graphiti_core.driver.query_executor import QueryExecutor
from graphiti_core.edges import EntityEdge
from graphiti_core.nodes import CommunityNode, EntityNode, EpisodicNode
from graphiti_core.search.search_filters import SearchFilters
class SearchOperations(ABC):
# Node search
@abstractmethod
async def node_fulltext_search(
self,
executor: QueryExecutor,
query: str,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[EntityNode]: ...
@abstractmethod
async def node_similarity_search(
self,
executor: QueryExecutor,
search_vector: list[float],
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
min_score: float = 0.6,
) -> list[EntityNode]: ...
@abstractmethod
async def node_bfs_search(
self,
executor: QueryExecutor,
origin_uuids: list[str],
search_filter: SearchFilters,
max_depth: int,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[EntityNode]: ...
# Edge search
@abstractmethod
async def edge_fulltext_search(
self,
executor: QueryExecutor,
query: str,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[EntityEdge]: ...
@abstractmethod
async def edge_similarity_search(
self,
executor: QueryExecutor,
search_vector: list[float],
source_node_uuid: str | None,
target_node_uuid: str | None,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
min_score: float = 0.6,
) -> list[EntityEdge]: ...
@abstractmethod
async def edge_bfs_search(
self,
executor: QueryExecutor,
origin_uuids: list[str],
max_depth: int,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[EntityEdge]: ...
# Episode search
@abstractmethod
async def episode_fulltext_search(
self,
executor: QueryExecutor,
query: str,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[EpisodicNode]: ...
# Community search
@abstractmethod
async def community_fulltext_search(
self,
executor: QueryExecutor,
query: str,
group_ids: list[str] | None = None,
limit: int = 10,
) -> list[CommunityNode]: ...
@abstractmethod
async def community_similarity_search(
self,
executor: QueryExecutor,
search_vector: list[float],
group_ids: list[str] | None = None,
limit: int = 10,
min_score: float = 0.6,
) -> list[CommunityNode]: ...
# Rerankers
@abstractmethod
async def node_distance_reranker(
self,
executor: QueryExecutor,
node_uuids: list[str],
center_node_uuid: str,
min_score: float = 0,
) -> list[EntityNode]: ...
@abstractmethod
async def episode_mentions_reranker(
self,
executor: QueryExecutor,
node_uuids: list[str],
min_score: float = 0,
) -> list[EntityNode]: ...
# Filter builders (sync)
@abstractmethod
def build_node_search_filters(self, search_filters: SearchFilters) -> Any: ...
@abstractmethod
def build_edge_search_filters(self, search_filters: SearchFilters) -> Any: ...
# Fulltext query builder
@abstractmethod
def build_fulltext_query(
self,
query: str,
group_ids: list[str] | None = None,
max_query_length: int = 8000,
) -> str: ...