181 lines
5.5 KiB
Python
181 lines
5.5 KiB
Python
# 从dataclasses模块导入数据类相关工具
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from dataclasses import (
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dataclass, # 数据类装饰器，用于简化类的定义
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field # 字段函数，用于定义特殊的字段属性
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)
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# 从typing模块导入类型提示工具
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from typing import (
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TypedDict, # 类型化字典，用于定义具有特定类型的字典
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Union, # 联合类型，表示多个可能的类型之一
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Literal, # 字面量类型，用于限定特定的值
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Generic, # 泛型基类，用于创建泛型类
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TypeVar # 类型变量，用于泛型编程
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)
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# 导入numpy用于数值计算
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import numpy as np
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# 从本地utils模块导入嵌入函数类
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from .utils import EmbeddingFunc # 用于处理文本嵌入的函数类
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# 定义文本块的数据结构，包含令牌数、内容、完整文档ID和块序号
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TextChunkSchema = TypedDict(
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"TextChunkSchema",
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{"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int},
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)
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# 定义泛型类型变量
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T = TypeVar("T")
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@dataclass
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class QueryParam:
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"""查询参数配置类
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属性:
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mode: 查询模式，可选 'local'(局部)、'global'(全局)、'hybrid'(混合)或'naive'(朴素)
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only_need_context: 是否仅需要上下文
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response_type: 响应类型
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top_k: 检索的top-k项数量
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max_token_for_text_unit: 原始文本块的最大令牌数
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max_token_for_global_context: 关系描述的最大令牌数
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max_token_for_local_context: 实体描述的最大令牌数
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"""
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mode: Literal["local", "global", "hybrid", "naive"] = "global"
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only_need_context: bool = False
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response_type: str = "Multiple Paragraphs"
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top_k: int = 60
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max_token_for_text_unit: int = 4000
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max_token_for_global_context: int = 4000
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max_token_for_local_context: int = 4000
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@dataclass
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class StorageNameSpace:
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"""存储命名空间基类
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属性:
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namespace: 命名空间
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global_config: 全局配置字典
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"""
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namespace: str
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global_config: dict
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async def index_done_callback(self):
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"""索引完成后的回调函数，用于提交存储操作"""
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pass
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async def query_done_callback(self):
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"""查询完成后的回调函数，用于提交存储操作"""
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pass
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@dataclass
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class BaseVectorStorage(StorageNameSpace):
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"""向量存储基类
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属性:
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embedding_func: 嵌入函数
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meta_fields: 元数据字段集合
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"""
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embedding_func: EmbeddingFunc
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meta_fields: set = field(default_factory=set)
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async def query(self, query: str, top_k: int) -> list[dict]:
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"""查询接口"""
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raise NotImplementedError
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async def upsert(self, data: dict[str, dict]):
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"""更新或插入数据
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使用value中的'content'字段进行嵌入，使用key作为ID
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如果embedding_func为None，则使用value中的'embedding'字段
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"""
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raise NotImplementedError
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@dataclass
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class BaseKVStorage(Generic[T], StorageNameSpace):
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"""键值存储基类"""
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async def all_keys(self) -> list[str]:
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"""获取所有键"""
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raise NotImplementedError
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async def get_by_id(self, id: str) -> Union[T, None]:
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"""通过ID获取值"""
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raise NotImplementedError
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async def get_by_ids(
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self, ids: list[str], fields: Union[set[str], None] = None
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) -> list[Union[T, None]]:
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"""通过ID列表批量获取值"""
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raise NotImplementedError
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async def filter_keys(self, data: list[str]) -> set[str]:
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"""返回不存在的键集合"""
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raise NotImplementedError
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async def upsert(self, data: dict[str, T]):
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"""更新或插入数据"""
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raise NotImplementedError
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async def drop(self):
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"""删除存储"""
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raise NotImplementedError
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@dataclass
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class BaseGraphStorage(StorageNameSpace):
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"""图存储基类"""
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async def has_node(self, node_id: str) -> bool:
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"""检查节点是否存在"""
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raise NotImplementedError
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
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"""检查边是否存在"""
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raise NotImplementedError
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async def node_degree(self, node_id: str) -> int:
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"""获取节点的度"""
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raise NotImplementedError
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async def edge_degree(self, src_id: str, tgt_id: str) -> int:
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"""获取边的度"""
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raise NotImplementedError
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async def get_node(self, node_id: str) -> Union[dict, None]:
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"""获取节点信息"""
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raise NotImplementedError
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async def get_edge(
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self, source_node_id: str, target_node_id: str
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) -> Union[dict, None]:
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"""获取边信息"""
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raise NotImplementedError
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async def get_node_edges(
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self, source_node_id: str
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) -> Union[list[tuple[str, str]], None]:
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"""获取节点的所有边"""
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raise NotImplementedError
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async def upsert_node(self, node_id: str, node_data: dict[str, str]):
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"""更新或插入节点"""
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raise NotImplementedError
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async def upsert_edge(
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self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
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):
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"""更新或插入边"""
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raise NotImplementedError
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async def delete_node(self, node_id: str):
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"""删除节点"""
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raise NotImplementedError
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async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
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"""节点嵌入（在lightrag中未使用）"""
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raise NotImplementedError("Node embedding is not used in lightrag.")
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