import abc
from _typeshed import Incomplete
from abc import ABC, abstractmethod
class ChunkingStrategy(ABC, metaclass=abc.ABCMeta):
@abstractmethod
def chunk(self, text: str) -> list: ...
class IdentityChunking(ChunkingStrategy):
def chunk(self, text: str) -> list: ...
class RegexChunking(ChunkingStrategy):
patterns: Incomplete
def __init__(self, patterns=None, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
class NlpSentenceChunking(ChunkingStrategy):
def __init__(self, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
class TopicSegmentationChunking(ChunkingStrategy):
tokenizer: Incomplete
num_keywords: Incomplete
def __init__(self, num_keywords: int = 3, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
def extract_keywords(self, text: str) -> list: ...
def chunk_with_topics(self, text: str) -> list: ...
class FixedLengthWordChunking(ChunkingStrategy):
chunk_size: Incomplete
def __init__(self, chunk_size: int = 100, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
class SlidingWindowChunking(ChunkingStrategy):
window_size: Incomplete
step: Incomplete
def __init__(self, window_size: int = 100, step: int = 50, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
class OverlappingWindowChunking(ChunkingStrategy):
window_size: Incomplete
overlap: Incomplete
def __init__(self, window_size: int = 1000, overlap: int = 100, **kwargs) -> None: ...
def chunk(self, text: str) -> list: ...
