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如何按字符递归分割文本

这个文本分割器是推荐用于通用文本的。它通过一个字符列表进行参数化。它会尝试按顺序在这些字符上分割,直到块足够小。默认列表是["\n\n", "\n", " ", ""]。这样做的效果是尽可能保持所有段落(然后是句子,然后是单词)在一起,因为这些通常似乎是语义上最强的文本片段。

  1. 文本如何分割:按字符列表。
  2. 块大小的测量方式:按字符数。

下面我们展示示例用法。

要直接获取字符串内容,请使用.split_text

要创建LangChain Document 对象(例如,用于下游任务),请使用 .create_documents

%pip install -qU langchain-text-splitters
from langchain_text_splitters import RecursiveCharacterTextSplitter

# Load example document
with open("state_of_the_union.txt") as f:
state_of_the_union = f.read()

text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size=100,
chunk_overlap=20,
length_function=len,
is_separator_regex=False,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
print(texts[1])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and'
page_content='of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.'
text_splitter.split_text(state_of_the_union)[:2]
['Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and',
'of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.']

让我们来看看上面为RecursiveCharacterTextSplitter设置的参数:

  • chunk_size: 块的最大大小,大小由length_function决定。
  • chunk_overlap: 块之间的目标重叠。重叠的块有助于在上下文被分割时减少信息丢失。
  • length_function: 确定块大小的函数。
  • is_separator_regex: 分隔符列表(默认为 ["\n\n", "\n", " ", ""])是否应被解释为正则表达式。

从没有词边界的语言中分割文本

一些书写系统没有词边界,例如中文、日文和泰文。使用默认的分隔符列表["\n\n", "\n", " ", ""]来分割文本可能会导致单词在块之间被分割。为了保持单词的完整性,你可以覆盖分隔符列表以包含额外的标点符号:

  • 添加ASCII句号".",Unicode全角句号""(用于中文文本),以及表意句号""(用于日语和中文)
  • 添加零宽空格用于泰语、缅甸语、高棉语和日语。
  • 添加ASCII逗号 ",",Unicode全角逗号 "",和Unicode表意逗号 ""
text_splitter = RecursiveCharacterTextSplitter(
separators=[
"\n\n",
"\n",
" ",
".",
",",
"\u200b", # Zero-width space
"\uff0c", # Fullwidth comma
"\u3001", # Ideographic comma
"\uff0e", # Fullwidth full stop
"\u3002", # Ideographic full stop
"",
],
# Existing args
)

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