The 5 levels of text splitting for retrieval

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okay, let's dive deep into the world of text splitting for retrieval-augmented generation (rag) and other natural language processing (nlp) tasks. we'll cover the 5 levels of text splitting, their strengths, weaknesses, and provide code examples using python and langchain.

*understanding text splitting and its importance*

before we jump into the levels, let's quickly recap why text splitting is so crucial:

*context window limits:* large language models (llms) have limited input lengths (context windows). if you try to feed an entire book or a long document, it will likely exceed that limit.
*relevance and efficiency:* passing irrelevant information to an llm dilutes the signal and increases computation time. we want to provide only the most relevant parts of a document.
*retrieval accuracy:* when using retrieval-augmented generation (rag), you want to retrieve the most semantically meaningful chunks of text to provide context to the llm. splitting in the right way significantly impacts retrieval accuracy.
*semantic coherence:* splitting must be done strategically so that resulting chunks remain semantically meaningful. cutting a sentence in half or splitting a paragraph in the middle of its explanation usually defeats the purpose.

*the 5 levels of text splitting (and beyond)*

here's a breakdown of the different levels, ranging from simple to more sophisticated techniques:

1. *character splitting*
2. *recursive character splitting*
3. *token splitting*
4. *semantic splitting*
5. *agentic splitting*

let's explore each of these with examples:

*level 1: character splitting*

*concept:* splitting the text based on a fixed number of characters. this is the most basic approach.

*pros:* simple to implement.

*cons:* often leads to semantic breaks (splitting sentences or paragraphs mid-way), making the resulting chunks less meaningful. poor for retrieval accuracy.

*when to use:* when sem ...

#TextSplitting #InformationRetrieval #numpy
text splitting
retrieval techniques
information retrieval
data segmentation
document processing
text analysis
content categorization
query optimization
semantic search
indexing strategies
retrieval models
natural language processing
machine learning
data extraction
user interaction