The Goldilocks Zone of NLP Search: Why Chunk Size Matters in Your AI Vector | Blog

In the exciting world of AI, especially within the realm of Natural Language Processing (NLP) and vector-hybrid search, we're constantly striving for more efficient and accurate ways to retrieve information. My ongoing AI vector hybrid project aims to blend the best of both worlds – the semantic understanding of vector embeddings with the precision of traditional keyword search. A critical, yet often overlooked, component in this delicate dance is the chunk size of your text embeddings. Getting this "just right" is like finding the Goldilocks zone for your NLP search, directly impacting the efficiency and efficacy of your returns.

When we talk about vector embeddings in NLP, we're essentially transforming human language into numerical representations (vectors) that capture the semantic meaning of words, sentences, or larger blocks of text. These vectors are then stored in a vector database, enabling lightning-fast similarity searches. But before we can embed, we need to decide how to break down our raw text data – this is where chunking comes in.

The Chunking Conundrum: Too Small, Too Big, or Just Right?

Imagine you have a vast repository of documents – research papers, customer support logs, legal contracts, or even just a collection of blog posts. You want your AI search to be able to find the most relevant snippets quickly and accurately when a user poses a query. This is where the chunk size becomes paramount.

If your chunks are too small:

Loss of Context: Breaking text into tiny fragments can sever crucial contextual links. A single sentence might not carry enough meaning on its own to be accurately represented by an embedding. This leads to a fragmented understanding, where the nuanced relationships between ideas are lost.
Increased Storage and Computational Overhead: Smaller chunks mean more chunks overall. More chunks translate to more vectors in your database, increasing storage requirements and the computational cost of generating and querying these embeddings. Every search needs to compare the query vector against a larger number of document vectors, which can slow down retrieval.
Reduced Recall: While small chunks might offer high precision for very specific keyword matches, they can significantly reduce recall. If the answer to a query spans across multiple tiny chunks, your search might miss it entirely because no single chunk contains the complete relevant context.

If your chunks are too large:

Diluted Semantic Focus: Large chunks can contain a multitude of ideas and topics. When you embed such a chunk, the resulting vector becomes a blend of all these meanings, potentially diluting the semantic focus. This can lead to less precise search results, as the embedding might be vaguely similar to many queries but precisely relevant to none.
Increased Noise and Hallucinations: When an LLM (Large Language Model) is presented with an overly large chunk as context, it has to sift through a lot of irrelevant information to find what's important. This "noise" can make it harder for the model to identify the core relevant information and, in some cases, can even lead to "hallucinations" where the model generates plausible but incorrect information.
LLM Context Window Limitations: While LLMs are growing in their context window capacity, they still have limits. Feeding an LLM excessively large chunks means you can only provide a limited number of chunks as context, potentially missing other relevant information that couldn't fit.
Higher Computational Cost for LLMs: Processing larger chunks within an LLM's context window can increase the computational resources and time required for generation, leading to higher latency and increased API costs.

The "Just Right" Zone: Finding the Optimal Chunk Size

So, what's the sweet spot? The ideal chunk size is not a one-size-fits-all answer. It largely depends on the nature of your data, the type of queries you anticipate, the specific embedding model you're using, and the downstream application (e.g., semantic search, question answering, summarisation).

Here are some key considerations and strategies for finding your optimal chunk size:

Nature of the Content:

Concise, Fact-Based Data: For datasets with short, direct answers (e.g., FAQs, glossaries), smaller chunks (e.g., 100-200 tokens) might yield higher precision.
Detailed, Context-Rich Documents: For lengthy documents like research papers, legal documents, or technical manuals, larger chunks (e.g., 300-500 tokens, or even up to 1000 tokens) might be necessary to capture sufficient context.

Embedding Model Limitations:

Different embedding models have varying input token limits. Always ensure your chunk size adheres to these limits to avoid truncation or incomplete embeddings.

Expected Query Complexity:

Specific Queries: If users are likely to ask highly specific, pinpoint questions, smaller chunks that are focused on single ideas will be more effective.
Broad/Exploratory Queries: For queries requiring a broader understanding or summarisation, larger chunks can provide richer, more nuanced answers.

Overlap Strategy:

Employing overlapping chunks (e.g., 10-20% overlap between consecutive chunks) can help maintain context continuity, especially when splitting sentences or paragraphs. This ensures that important information at chunk boundaries is not lost.

Semantic Boundaries vs. Fixed Size:

Fixed-Size Chunking: Simple to implement, breaking text into uniform lengths (by characters, words, or tokens). However, it can cut off sentences or paragraphs abruptly.
Semantic Chunking: A more advanced approach that aims to split text based on natural language boundaries (sentences, paragraphs, sections) or even topical shifts. This preserves semantic coherence within chunks, leading to more meaningful embeddings.

Experimentation and Evaluation:

There's no substitute for testing. Start with a sensible baseline (e.g., 256 or 512 tokens) and systematically experiment with different chunk sizes. Evaluate your search performance using metrics like:

Precision: How many of the retrieved chunks are actually relevant?
Recall: How many of the truly relevant chunks were retrieved?
F1-score: A balance between precision and recall.
Answer Quality: For RAG systems, assessing the quality and coherence of the generated answers based on the retrieved chunks.
Latency: The time it takes to retrieve results.

In my AI vector hybrid project, fine-tuning the chunk size is an iterative process. It's a continuous balance between capturing enough context for semantic understanding and maintaining the granularity needed for precise retrieval, all while keeping computational efficiency in mind. By strategically setting the right chunk size, we can unlock the full potential of our NLP search, ensuring that our AI not only understands the nuances of language but also delivers the most efficient and accurate returns possible. This commitment to detail is what transforms a good AI system into a truly exceptional one.