检索增强生成(Retrieval-Augmented Generation, RAG)是当前AI工程师在实际应用中面临的重要技术挑战之一。从理论角度来看,RAG的工作原理相对直观:从自定义数据源中检索相关上下文,然后基于这些上下文让大语言模型生成对应的回答。

在实际部署过程中,开发者往往需要处理大量格式混乱的异构数据,并经历反复的系统调优过程,包括分块策略的优化、嵌入模型的选择、检索器的配置、排序器的微调以及提示工程等多个环节。即便如此,系统仍可能出现信息检索不足或生成虚假信息的问题。

RAG系统包含多个相互关联的组件,其中文本分块策略是决定整个系统性能的关键因素。不同的数据类型、文件格式、内容结构、文档长度和应用场景都需要采用相应的分块策略。分块策略的选择不当会直接影响检索质量和生成效果。

本文将系统介绍21种文本分块策略,从基础方法到高级技术,并详细分析每种策略的适用场景,以帮助开发者构建更加可靠的RAG系统。

1. 基础分块(换行符分割)

基础分块是最基础的文本分割方法,按照换行符对文本进行简单分割。

基础分块示例

适用场景:该方法适用于结构相对规整且以换行符均匀分隔的文本数据,包括笔记文档、项目列表、FAQ文档、聊天记录或转录文本等,特别是当每行文本都包含完整语义单元的情况。

技术要点:需要注意文本行长度的平衡。过长的文本行可能超出大语言模型的token限制,而过短的文本行则可能导致上下文信息不足,影响模型的理解和生成质量。

代码实现:

 def naive_chunking(text):
    """按换行符进行基础分块"""
    chunks = text.split('\n')
    # 过滤空行
    chunks = [chunk.strip() for chunk in chunks if chunk.strip()]
    return chunks

# 使用示例
text = """这是第一行内容
这是第二行内容
这是第三行内容"""

chunks = naive_chunking(text)
 print(f"分块结果: {chunks}")

2. 固定大小分块

固定大小分块方法按照预设的词数或字符数将文本分割为等长片段,不考虑语义边界。

固定大小分块示例

适用场景:该方法主要用于处理结构化程度较低的原始文本数据,如扫描文档的OCR输出、质量较差的语音转录文本,或缺乏标点符号、标题等结构标记的大型文本文件。

代码实现:

 def fixed_size_chunking(text, chunk_size=100, overlap=0):
    """按固定大小进行文本分块
    
    Args:
        text: 输入文本
        chunk_size: 每个分块的字符数
        overlap: 重叠字符数
    """
    chunks = []
    start = 0
    text_length = len(text)
    
    while start < text_length:
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end - overlap
    
    return chunks

# 使用示例
text = "这是一段很长的文本内容,需要按照固定大小进行分块处理。" * 10
chunks = fixed_size_chunking(text, chunk_size=50, overlap=10)
print(f"分块数量: {len(chunks)}")
 print(f"第一个分块: {chunks[0]}")

3. 滑动窗口分块

滑动窗口分块在固定大小分块的基础上引入了重叠机制,相邻分块之间保持一定的内容重叠,以维持跨分块的上下文连续性。

滑动窗口分块示例(与固定窗口分块比较)

适用场景:该方法特别适用于语义信息跨越较长文本段落的内容,如学术论文、叙述性报告、自由格式写作等。与固定窗口分块类似,它也能处理缺乏明确结构标记的文本,但需要在token使用效率和上下文完整性之间进行权衡。

代码实现:

 def sliding_window_chunking(text, window_size=200, step_size=150):
    """滑动窗口分块
    
    Args:
        text: 输入文本
        window_size: 窗口大小(字符数)
        step_size: 步长(字符数),小于window_size时产生重叠
    """
    chunks = []
    start = 0
    text_length = len(text)
    
    while start < text_length:
        end = min(start + window_size, text_length)
        chunk = text[start:end]
        chunks.append(chunk)
        
        # 如果已经到达文本末尾,停止
        if end == text_length:
            break
            
        start += step_size
    
    return chunks

# 使用示例
text = "人工智能技术的发展日新月异。深度学习模型在各个领域都取得了突破性进展。" * 5
chunks = sliding_window_chunking(text, window_size=100, step_size=80)
print(f"分块数量: {len(chunks)}")
for i, chunk in enumerate(chunks[:3]):
     print(f"分块 {i+1}: {chunk}")

4. 基于句子的分块

基于句子的分块方法以句子为基本单位进行文本分割,通常以句号、问号或感叹号作为分割标记。

基于句子的分块示例

适用场景:该方法适用于结构良好、语言规范的文本内容,其中每个句子都包含相对完整的语义信息,如技术博客、文档总结或产品说明等。此外,句子级分块还可以作为预处理步骤,为后续更复杂的分块策略提供基础数据单元。

代码实现:

 import re

def sentence_based_chunking(text, sentences_per_chunk=3):
    """基于句子的分块
    
    Args:
        text: 输入文本
        sentences_per_chunk: 每个分块包含的句子数
    """
    # 使用正则表达式分割句子
    sentences = re.split(r'[.!?。!?]+', text)
    sentences = [s.strip() for s in sentences if s.strip()]
    
    chunks = []
    for i in range(0, len(sentences), sentences_per_chunk):
        chunk_sentences = sentences[i:i + sentences_per_chunk]
        chunk = '。'.join(chunk_sentences)
        if chunk:  # 确保分块不为空
            chunks.append(chunk + '。')
    
    return chunks

# 使用示例
text = """机器学习是人工智能的一个重要分支。它通过算法让计算机从数据中学习模式。
深度学习是机器学习的子集。它使用神经网络来模拟人脑的工作方式。
目前深度学习在图像识别和自然语言处理领域取得了巨大成功。"""

chunks = sentence_based_chunking(text, sentences_per_chunk=2)
for i, chunk in enumerate(chunks):
     print(f"分块 {i+1}: {chunk}")

5. 基于段落的分块

基于段落的分块方法以段落为单位进行文本分割,通常通过识别双换行符来确定段落边界,确保每个分块包含完整的主题或思想单元。

基于段落的分块示例

适用场景:当句子级分块提供的上下文信息不足时,段落级分块能够提供更丰富的语义环境。该方法特别适用于已经按照段落结构良好组织的文档,如学术文章、博客文章或技术报告等。

代码实现:

 def paragraph_based_chunking(text, max_paragraphs_per_chunk=2):
    """基于段落的分块
    
    Args:
        text: 输入文本
        max_paragraphs_per_chunk: 每个分块最大段落数
    """
    # 按双换行符分割段落
    paragraphs = text.split('\n\n')
    paragraphs = [p.strip() for p in paragraphs if p.strip()]
    
    chunks = []
    for i in range(0, len(paragraphs), max_paragraphs_per_chunk):
        chunk_paragraphs = paragraphs[i:i + max_paragraphs_per_chunk]
        chunk = '\n\n'.join(chunk_paragraphs)
        chunks.append(chunk)
    
    return chunks

# 使用示例
text = """人工智能的发展经历了多个阶段。从最初的符号主义到现在的深度学习,每个阶段都有其独特的特点和贡献。

机器学习作为人工智能的核心技术,为各行各业带来了革命性的变化。它不仅改变了我们处理数据的方式,也为未来的技术发展奠定了基础。

深度学习的出现标志着人工智能进入了新的时代。通过模拟人脑神经网络的工作原理,深度学习在图像识别、语音处理等领域取得了突破性进展。"""

chunks = paragraph_based_chunking(text, max_paragraphs_per_chunk=1)
for i, chunk in enumerate(chunks):
     print(f"分块 {i+1}:\n{chunk}\n{'-'*50}")

6. 基于页面的分块

基于页面的分块方法将每个物理页面视为一个独立的文本分块。

基于页面的分块示例

适用场景:该方法主要用于处理具有分页结构的文档,如PDF扫描件、演示文稿或图书等。在需要保持页面布局信息或在检索过程中需要引用页码的应用场景中特别有用。

代码实现:

 import PyPDF2
from io import BytesIO

def page_based_chunking_pdf(pdf_path):
    """基于页面的PDF分块
    
    Args:
        pdf_path: PDF文件路径
    """
    chunks = []
    
    try:
        with open(pdf_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            
            for page_num, page in enumerate(pdf_reader.pages):
                text = page.extract_text()
                if text.strip():  # 确保页面有内容
                    chunk = {
                        'page_number': page_num + 1,
                        'content': text.strip()
                    }
                    chunks.append(chunk)
    
    except Exception as e:
        print(f"PDF处理错误: {e}")
    
    return chunks

# 简化版本:模拟页面分块
def simulate_page_chunking(text, chars_per_page=500):
    """模拟基于页面的分块
    
    Args:
        text: 输入文本
        chars_per_page: 每页字符数
    """
    chunks = []
    page_num = 1
    
    for i in range(0, len(text), chars_per_page):
        page_content = text[i:i + chars_per_page]
        chunk = {
            'page_number': page_num,
            'content': page_content
        }
        chunks.append(chunk)
        page_num += 1
    
    return chunks

# 使用示例
text = "这是一个长文档的内容。" * 100
chunks = simulate_page_chunking(text, chars_per_page=200)
print(f"总页数: {len(chunks)}")
 print(f"第一页内容: {chunks[0]['content'][:50]}...")

7. 结构化分块

结构化分块方法基于文档的已知结构特征进行分割,如日志条目、数据库模式字段、HTML标签或Markdown元素等。

结构化分块示例

适用场景:该方法适用于具有明确结构标记的数据格式,包括系统日志、JSON记录、CSV文件或HTML文档等结构化或半结构化数据。

代码实现:

 import json
import re
from bs4 import BeautifulSoup

def json_structured_chunking(json_data):
    """JSON数据结构化分块"""
    chunks = []
    
    if isinstance(json_data, list):
        for i, item in enumerate(json_data):
            chunk = {
                'type': 'json_item',
                'index': i,
                'content': json.dumps(item, ensure_ascii=False, indent=2)
            }
            chunks.append(chunk)
    elif isinstance(json_data, dict):
        for key, value in json_data.items():
            chunk = {
                'type': 'json_field',
                'key': key,
                'content': json.dumps({key: value}, ensure_ascii=False, indent=2)
            }
            chunks.append(chunk)
    
    return chunks

def html_structured_chunking(html_content):
    """HTML内容结构化分块"""
    soup = BeautifulSoup(html_content, 'html.parser')
    chunks = []
    
    # 按段落分块
    for i, p in enumerate(soup.find_all('p')):
        if p.get_text().strip():
            chunk = {
                'type': 'paragraph',
                'index': i,
                'content': p.get_text().strip()
            }
            chunks.append(chunk)
    
    # 按标题分块
    for level in range(1, 7):  # h1-h6
        for i, h in enumerate(soup.find_all(f'h{level}')):
            chunk = {
                'type': f'heading_{level}',
                'index': i,
                'content': h.get_text().strip()
            }
            chunks.append(chunk)
    
    return chunks

# 使用示例
json_data = [
    {"name": "张三", "age": 25, "department": "技术部"},
    {"name": "李四", "age": 30, "department": "产品部"}
]

chunks = json_structured_chunking(json_data)
for chunk in chunks:
     print(f"类型: {chunk['type']}, 内容: {chunk['content'][:50]}...")

8. 基于文档结构的分块

基于文档结构的分块方法利用文档的自然层次结构进行分割,如标题、副标题和章节等组织元素。

基于文档结构的分块示例

适用场景:该方法适用于具有清晰层次结构的文档,如技术文章、操作手册、教科书或研究论文等。同时,它也可以作为更高级分块策略(如层次分块)的预处理步骤。

9. 基于关键词的分块

基于关键词的分块方法通过预定义的关键词来识别分割点,将关键词的出现位置作为逻辑分段标记。

基于关键词的分块示例(关键词是'Note')

适用场景:当文档缺乏明确的标题层次结构,但包含能够标识主题转换的特定关键词或短语时,该方法能够有效地进行主题分割。

10. 基于实体的分块

基于实体的分块方法使用命名实体识别(Named Entity Recognition, NER)技术来检测文本中的特定实体(如人名、地名、产品名等),然后围绕这些实体组织相关文本内容。

基于实体的分块示例(关键词是'Note')

适用场景:该方法适用于实体信息具有重要意义的文档类型,如新闻报道、法律合同、案例研究或影视剧本等,能够确保与特定实体相关的信息被完整保留在同一分块中。

11. 基于Token的分块

基于Token的分块方法使用分词器(tokenizer)按照token数量进行文本分割。

适用场景:该方法主要用于处理缺乏标题或段落结构的非结构化文档,以及需要严格控制输入长度的低token限制大语言模型场景。为了避免在句子中间进行分割而破坏语义完整性,通常建议将该技术与句子级分块相结合。

代码实现:

 from transformers import AutoTokenizer
import tiktoken

def token_based_chunking_transformers(text, model_name="bert-base-chinese", max_tokens=512):
    """使用Transformers分词器进行基于Token的分块"""
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    # 对整个文本进行分词
    tokens = tokenizer.tokenize(text)
    chunks = []
    
    for i in range(0, len(tokens), max_tokens):
        chunk_tokens = tokens[i:i + max_tokens]
        # 将tokens转换回文本
        chunk_text = tokenizer.convert_tokens_to_string(chunk_tokens)
        chunks.append(chunk_text)
    
    return chunks

def token_based_chunking_tiktoken(text, model="gpt-3.5-turbo", max_tokens=1000):
    """使用tiktoken进行基于Token的分块(适用于OpenAI模型)"""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        encoding = tiktoken.get_encoding("cl100k_base")
    
    # 编码文本
    tokens = encoding.encode(text)
    chunks = []
    
    for i in range(0, len(tokens), max_tokens):
        chunk_tokens = tokens[i:i + max_tokens]
        # 解码回文本
        chunk_text = encoding.decode(chunk_tokens)
        chunks.append(chunk_text)
    
    return chunks

# 简化版本:字符近似token分块
def simple_token_chunking(text, max_tokens=1000, chars_per_token=4):
    """简化的基于Token的分块(字符数近似)"""
    max_chars = max_tokens * chars_per_token
    chunks = []
    
    for i in range(0, len(text), max_chars):
        chunk = text[i:i + max_chars]
        chunks.append(chunk)
    
    return chunks

# 使用示例
text = "人工智能技术的发展正在改变我们的生活方式。" * 50
chunks = simple_token_chunking(text, max_tokens=100, chars_per_token=2)
print(f"分块数量: {len(chunks)}")
 print(f"第一个分块长度: {len(chunks[0])} 字符")

12. 基于主题的分块

基于主题的分块方法通过主题建模或聚类技术来识别主题边界。该过程首先将文本分割为较小的单元(如句子或段落),然后使用机器学习方法将语义相关的片段聚合为单一分块。

通过聚类进行基于主题的分块示例

https://github.com/usuzifo-pie/ou/issues/401
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https://github.com/qaoocif/po/issues/1823
https://github.com/qaoocif/po/issues/1824
https://github.com/qaoocif/po/issues/1825
https://github.com/qaoocif/po/issues/1826
https://github.com/qaoocif/po/issues/1827
https://github.com/qaoocif/po/issues/1828
https://github.com/qaoocif/po/issues/1829
https://github.com/qaoocif/po/issues/1830
https://github.com/qaoocif/po/issues/1831
https://github.com/qaoocif/po/issues/1832
https://github.com/qaoocif/po/issues/1833
https://github.com/qaoocif/po/issues/1834
https://github.com/qaoocif/po/issues/1835
https://github.com/qaoocif/po/issues/1836
https://github.com/qaoocif/po/issues/1837
https://github.com/qaoocif/po/issues/1838
https://github.com/qaoocif/po/issues/1839
https://github.com/qaoocif/po/issues/1840
https://github.com/qaoocif/po/issues/1841
https://github.com/qaoocif/po/issues/1842
https://github.com/qaoocif/po/issues/1843
https://github.com/qaoocif/po/issues/1844
https://github.com/qaoocif/po/issues/1845
https://github.com/qaoocif/po/issues/1846
 

适用场景:该方法适用于涵盖多个主题的文档,能够确保每个分块专注于单一主题,特别是在主题转换较为渐进且缺乏明确标题或关键词标记的文本中表现良好。

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