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1"""Document chunking strategies for RAG pipelines. 

2 

3This module provides various chunking strategies to split documents into 

4smaller pieces suitable for embedding and retrieval. 

5 

6Example: 

7 >>> from lexigram.ai.rag.chunking import FixedSizeChunker 

8 >>> chunker = FixedSizeChunker(chunk_size=500, overlap=50) 

9 >>> chunks = chunker.chunk("Long document text...") 

10""" 

11 

12from __future__ import annotations 

13 

14from lexigram.ai.rag.chunking.base import AbstractChunker 

15from lexigram.ai.rag.chunking.factory import create_chunker 

16from lexigram.ai.rag.chunking.strategies.custom import CustomChunker 

17from lexigram.ai.rag.chunking.strategies.fixed_size import FixedSizeChunker 

18from lexigram.ai.rag.chunking.strategies.recursive import RecursiveChunker 

19from lexigram.ai.rag.chunking.strategies.semantic import SemanticChunker 

20from lexigram.ai.rag.chunking.strategies.sliding_window import SlidingWindowChunker 

21from lexigram.ai.rag.chunking.strategies.token import TokenChunker 

22from lexigram.ai.rag.chunking.strategy_registry import ChunkingStrategyRegistry 

23from lexigram.ai.rag.chunking.types import Chunk, ChunkingConfig, ChunkingStrategy 

24 

25__all__ = [ 

26 "AbstractChunker", 

27 "Chunk", 

28 "ChunkingConfig", 

29 "ChunkingStrategy", 

30 "ChunkingStrategyRegistry", 

31 "CustomChunker", 

32 "FixedSizeChunker", 

33 "RecursiveChunker", 

34 "SemanticChunker", 

35 "SlidingWindowChunker", 

36 "TokenChunker", 

37 "create_chunker", 

38]