Chunking Strategies for Efficient Text Processing

This lesson explores breaking down large documents into smaller, manageable pieces for tasks like information retrieval and text analysis. This chunkingChunking is a cognitive process where information is broken down into smaller, manageable units or "chunks" to improve memory and processing. process is crucial for extracting meaningful information from text data.

Why chunking matters

Imagine trying to understand a giant wall of text. It’s overwhelming! Chunking helps us break down the text into digestible bites, similar to how we break down a pizza before eating it. By chunking documents, we can:

• Extract features: Each chunk becomes a unit for analysis, allowing us to identify key aspects like keywords, entities, or sentiment.

• Faster processing: Dividing text into smaller chunks allows for quicker and more efficient information processing.

• Embed semantics: We can convert chunks into numerical representations that capture their meaning, enabling tasks like similarity comparisons.

• Improve accuracy and relevance: The right chunk size ensures we capture enough context while avoiding information overload for processing.

Choosing the right chunking strategy

There’s no one-size-fits-all approach to chunking. The best strategy depends on several factors:

• Content type: The nature of the content we are dealing with significantly influences the chunking strategy. For instance, news articles may benefit from paragraph-level chunks, while scientific papers might require section-level chunks (like abstract, methods, and results). Conversely, codes often need to be chunked by logical blocks or functions to maintain context.

• Embedding model: Different embedding models have limitations on chunk size. For example, models like BERT have a maximum token limit of 512 tokens, and exceeding this limit requires splitting the text into smaller, meaningful chunks. GPT-4 on the other hand, can handle larger chunks of text (up to 4096 tokens). This capability allows for more extensive context within a single chunk but still requires careful chunking to maintain coherence and relevance.

• User queries: The expected length and complexity of user queries also play a role. If users are likely to ask detailed, specific questions, chunking the text into smaller, more precise segments can help in retrieving the most relevant information. Conversely, for more general queries, larger chunks might suffice to provide adequate context.

• Application purpose: Consider how the retrieved information will be used. If the application requires precise, pinpointed answers (such as in a QA system), smaller, contextually rich chunks are preferable. However, for applications like content summarization or topic modeling, larger chunks might be more appropriate to capture the overall theme and context.

Chunking strategies

Here’s a breakdown of common chunking strategies with code examples (using the Python library LangChain):

• Fixed-size (character) overlapping sliding window

• Recursive structure-aware splitting

• Structure aware splitting (by sentence, paragraph)

• Content-aware splitting

• Chunking through NLTKTextSplitter from LangChain

• Semantic chunking

• Agentic chunking

Fixed-size (character) overlapping sliding window

This method chops the text into equal-sized chunks based on character count. Overlapping chunks ensure sentences aren’t cut in half. The following code implements the fixed-size (character) overlapping sliding window chunking technique: ...