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了解文本切分策略

了解为何以及如何将大型文档切分为更小且有意义的文本块，以优化检索和上下文窗口的使用

了解文本切分策略 是 CoddyKit 上的免费 LangChain / RAG / Vector DBs 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 LangChain / RAG / Vector DBs 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 LangChain / RAG / Vector DBs 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Why Split Documents?

Large Language Models (LLMs) have a 'context window' – a limit on how much text they can process at once. If you feed them a document that's too long, they simply can't handle it all.

Text splitting is the process of breaking down large documents into smaller, manageable chunks. This makes them suitable for LLMs and helps retrieval systems find more precise information.

The Context Window Limit

Imagine an LLM as a very smart person with a short-term memory limit. The context window is like that limit. If you give it too much information, it might forget the beginning or get confused.

  • LLMs can only process a certain number of tokens (words or sub-words).
  • Going over this limit means information is truncated or ignored.
  • Smaller chunks ensure all relevant information fits and is processed effectively.

Basic Splitting: By Character

One of the most straightforward ways to split text is using a CharacterTextSplitter. It simply breaks text based on a specified separator, usually a newline character (\n).

It's like cutting a long rope into smaller pieces at every knot you find. This method is easy to understand but can sometimes break sentences or paragraphs in awkward places.

Code: Simple Character Split

Try running this example. Notice how the CharacterTextSplitter breaks the text primarily at each newline character.

from langchain_text_splitters import CharacterTextSplitter

text = "Hello world.\nThis is a test.\nAnother line here." 

# Initialize the splitter
text_splitter = CharacterTextSplitter(
    separator="\n",
    chunk_size=20, # Max characters per chunk
    chunk_overlap=0, # No overlap for simplicity
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Understanding Chunk Size

The chunk_size parameter determines the maximum length of each piece of text. If a piece of text (before splitting by a separator) exceeds this size, the splitter will try to break it further.

Choosing the right size is crucial:

  • Too small: Context might be lost across multiple chunks, making it harder for the LLM to understand the full picture.
  • Too large: Might still exceed the LLM's context window or contain too much irrelevant information, diluting the focus.

The Role of Overlap

chunk_overlap specifies how many characters (or tokens) each chunk shares with the previous one. This is vital to maintain continuity and prevent loss of context at the boundaries of chunks.

Imagine a sentence that gets split perfectly in half across two chunks. Without overlap, the LLM might miss the connection between the two halves. Overlap ensures that key phrases or ideas aren't cut off abruptly, providing a smoother flow of information.

Recursive Character Splitting

The RecursiveCharacterTextSplitter is often preferred for general-purpose documents. Instead of just one separator, it tries a list of separators in order of preference (e.g., ["\n\n", "\n", " ", ""]).

It first tries to split by the largest, most semantically meaningful separator (like a double newline for paragraphs). If a chunk is still too big, it then tries the next smaller separator (like a single newline), and so on. This creates more semantically coherent chunks.

Code: Recursive Split in Action

This example uses a recursive splitter. Notice how it prioritizes paragraph breaks (double newlines) to keep related sentences together.

from langchain_text_splitters import RecursiveCharacterTextSplitter

text = """
LangChain is a framework for developing applications powered by language models.
It enables applications that are:
1. Data-aware: connect a language model to other sources of data.
2. Agentic: allow a language model to interact with its environment.

This framework provides tools and components to build complex LLM workflows.
"""

# Initialize the recursive splitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=100, # Max characters per chunk
    chunk_overlap=20, # Overlap to maintain context
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Choosing the Right Strategy

When should you use which splitter?

  • CharacterTextSplitter: Good for simple, highly structured text where you know the exact delimiters (e.g., CSV files, specific log formats).
  • RecursiveCharacterTextSplitter: Generally the default and best choice for most general-purpose documents (like articles, reports), as it aims for more logical and semantically coherent breaks.
  • Other splitters: LangChain offers specialized splitters for code, Markdown, and even semantic content. We'll touch on these in future lessons!

Text Splitting Challenge

You have a long article and need to split it into smaller chunks for an LLM. You decide to use a chunk_size of 500 and a chunk_overlap of 50.

Recap: Text Splitting Fundamentals

Great job! You've learned the fundamental concepts behind text splitting, a crucial step for preparing documents for LLMs.

  • We split text due to LLM context window limits and for more effective retrieval.
  • The CharacterTextSplitter provides basic splitting using a single separator.
  • The RecursiveCharacterTextSplitter offers a smarter, hierarchical approach for general text.
  • chunk_size controls the maximum length of your chunks.
  • chunk_overlap preserves context by sharing text between adjacent chunks.

Next, we'll dive deeper into customizing splitting strategies for specific content types!

常见问题解答

「了解文本切分策略」课时是免费的吗？

是的 — 「了解文本切分策略」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 LangChain / RAG / Vector DBs 课程的其余内容，请升级到 CoddyKit PRO。 LangChain / RAG / Vector DBs 课程共包含 4 节课。

「了解文本切分策略」这节课中我会学到什么？

了解为何以及如何将大型文档切分为更小且有意义的文本块，以优化检索和上下文窗口的使用 你通过在浏览器中直接运行的动手代码来练习 LangChain / RAG / Vector DBs，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 LangChain / RAG / Vector DBs 需要有经验吗？

无需任何先前经验。CoddyKit 上的 LangChain / RAG / Vector DBs 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。

「了解文本切分策略」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 LangChain / RAG / Vector DBs 课中编写并运行代码吗？

能。每节 LangChain / RAG / Vector DBs 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 加载多种文档类型
  2. 了解文本切分策略
  3. 自定义文档切分
  4. 处理文档元数据与筛选
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