chunking strategies for llm applications by f bio

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chunking strategies for llm applications: a comprehensive tutorial

large language models (llms) have limitations on the amount of text they can process at once, a constraint known as the **context window**. this window limits the model's ability to understand long documents or maintain coherent context over extended conversations. chunking is a crucial technique to overcome this limitation by breaking down large texts into smaller, manageable chunks that fit within the llm's context window. this tutorial explores various chunking strategies, their trade-offs, and provides python code examples using the `transformers` library.

*i. understanding the problem: context window limitations*

llms process input text sequentially, storing a representation of the text within their context window. once the window is full, older information is typically discarded. this poses challenges when dealing with:

*long documents:* summarizing a lengthy research paper or legal document requires breaking it into smaller parts.
*extended conversations:* maintaining context across a long dialogue demands careful management of past utterances.
*complex tasks:* tasks like question answering over large datasets necessitate chunking to ensure relevant information remains accessible.

*ii. chunking strategies:*

the optimal chunking strategy depends on the specific application and the nature of the input text. here are some common approaches:

*a. fixed-size chunking:*

this is the simplest approach, where the text is divided into chunks of a predefined size (e.g., number of tokens or characters).



*limitations:* this method might split sentences or paragraphs awkwardly, potentially losing context across chunk boundaries.

*b. sliding window chunking:*

this method uses a sliding window to create overlapping chunks. this helps maintain some context across chunks.



*limitations:* increased computational cost due to overlapping chunks.


**c. sentence-base ...

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Chunking strategies
LLM applications
natural language processing
text segmentation
information retrieval
data processing
machine learning techniques
text analysis
context-aware chunking
efficient data handling
semantic understanding
model optimization
training data preparation
cognitive load reduction
language model efficiency