mirror of
https://github.com/HKUDS/VideoRAG.git
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150 lines
4.0 KiB
Python
Executable File
150 lines
4.0 KiB
Python
Executable File
from dataclasses import dataclass, field
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from typing import TypedDict, Union, Literal, Generic, TypeVar
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import numpy as np
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from ._utils import EmbeddingFunc
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@dataclass
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class QueryParam:
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mode: Literal["local", "global", "naive"] = "global"
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only_need_context: bool = False
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response_type: str = "Multiple Paragraphs"
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level: int = 2
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top_k: int = 20
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# naive search
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naive_max_token_for_text_unit = 12000
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# videorag search
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only_need_context: bool = False
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TextChunkSchema = TypedDict(
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"TextChunkSchema",
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{"tokens": int, "content": str, "video_segment_id": str, "chunk_order_index": int},
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)
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SingleCommunitySchema = TypedDict(
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"SingleCommunitySchema",
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{
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"level": int,
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"title": str,
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"edges": list[list[str, str]],
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"nodes": list[str],
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"chunk_ids": list[str],
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"occurrence": float,
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"sub_communities": list[str],
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},
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)
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class CommunitySchema(SingleCommunitySchema):
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report_string: str
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report_json: dict
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T = TypeVar("T")
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@dataclass
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class StorageNameSpace:
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namespace: str
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global_config: dict
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async def index_start_callback(self):
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"""commit the storage operations after indexing"""
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pass
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async def index_done_callback(self):
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"""commit the storage operations after indexing"""
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pass
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async def query_done_callback(self):
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"""commit the storage operations after querying"""
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pass
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@dataclass
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class BaseVectorStorage(StorageNameSpace):
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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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raise NotImplementedError
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async def upsert(self, data: dict[str, dict]):
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"""Use 'content' field from value for embedding, use key as id.
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If embedding_func is None, use 'embedding' field from value
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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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async def all_keys(self) -> list[str]:
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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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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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raise NotImplementedError
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async def filter_keys(self, data: list[str]) -> set[str]:
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"""return un-exist keys"""
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raise NotImplementedError
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async def upsert(self, data: dict[str, T]):
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raise NotImplementedError
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async def drop(self):
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raise NotImplementedError
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@dataclass
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class BaseGraphStorage(StorageNameSpace):
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async def has_node(self, node_id: str) -> bool:
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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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raise NotImplementedError
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async def node_degree(self, node_id: str) -> int:
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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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raise NotImplementedError
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async def get_node(self, node_id: str) -> Union[dict, None]:
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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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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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raise NotImplementedError
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async def upsert_node(self, node_id: str, node_data: dict[str, str]):
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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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raise NotImplementedError
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async def clustering(self, algorithm: str):
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raise NotImplementedError
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async def community_schema(self) -> dict[str, SingleCommunitySchema]:
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"""Return the community representation with report and nodes"""
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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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raise NotImplementedError("Node embedding is not used in nano-graphrag.")
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