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7becdc4f84
* new data pipiline * new data pipiline * shades and topic --------- Co-authored-by: yanmuyuan <2216646664@qq.com>
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
from typing import List
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from lpm_kernel.stage2.bio import Chunk
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import traceback
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import time
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from lpm_kernel.configs.logging import get_train_process_logger
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logger = get_train_process_logger()
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class DocumentChunker:
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def __init__(self, chunk_size: int = 1000, overlap: int = 200):
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self.chunk_size = chunk_size
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self.overlap = overlap
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self.text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=self.chunk_size,
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chunk_overlap=self.overlap,
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length_function=len,
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separators=["\n\n", "\n", "。", "！", "？", ".", "!", "?", " ", ""],
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)
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def split(self, content: str) -> List[Chunk]:
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try:
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if not content:
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logger.warning("Empty content provided")
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return []
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logger.info(f"Starting to split content of length {len(content)}")
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# use LangChain splitter
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texts = self.text_splitter.split_text(content)
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chunks = [
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Chunk(
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id=None,
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document_id=None,
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content=text,
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embedding=None,
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tags=None,
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topic=None,
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)
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for text in texts
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]
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logger.info(f"Split completed, created {len(chunks)} chunks")
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return chunks
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except Exception as e:
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logger.error(f"Error in split method: {str(e)}")
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logger.error(traceback.format_exc())
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raise
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