Effective semantic search relies heavily on the quality and format of the data fed into the embedding model. Raw data, whether scraped from websites, extracted from documents, or sourced from databases, is rarely in an optimal state for direct vectorization. Just as raw ingredients need preparation before cooking, your data needs cleaning and structuring before it can be meaningfully embedded and indexed. This section focuses on the essential steps of data preparation and, significantly, the strategies for splitting large pieces of information into manageable, semantically relevant chunks. Getting this stage right is fundamental to building a search system that retrieves truly relevant results based on meaning.
Before we even consider splitting data, we must ensure it's clean. Embedding models are powerful, but they are sensitive to noise. Irrelevant content within your text can dilute the semantic meaning captured in the resulting vector, leading to less accurate search results. Common cleaning steps include:
<p>
, <div>
), CSS styles, JavaScript code, website navigation bars, headers, footers, advertisements, and boilerplate text that doesn't contribute to the core meaning of the content. Regular expressions or dedicated HTML parsing libraries (like BeautifulSoup in Python) are often used here.Once the data is clean, the next challenge is its size. Most current embedding models, particularly transformer-based ones, have a maximum input sequence length, often measured in tokens (roughly corresponding to words or sub-words). For instance, many BERT variants have a limit of 512 tokens. Attempting to embed a document significantly longer than this limit will result in either an error or, more commonly, truncation – the model will simply ignore the text beyond its limit, losing valuable information.
This technical limitation, there are strong semantic reasons to chunk:
Therefore, breaking down large documents into smaller, coherent chunks is a standard and necessary practice in building semantic search systems.
The goal of chunking is to create pieces of text that are small enough for the embedding model while preserving as much semantic context as possible. There's no single "best" strategy; the optimal approach depends on the nature of your data and your application's requirements. Here are common techniques:
This is the simplest approach: divide the text into segments of a fixed length, measured either in characters or tokens.
To mitigate the context loss issue of fixed-size chunking, a common refinement is to introduce overlap between consecutive chunks.
Visualization comparing chunking with and without overlap. Overlapping chunks (bottom) share some content (darker blue) to maintain context across boundaries.
Instead of arbitrary fixed sizes, this approach attempts to split text based on its inherent structure or semantic boundaries.
\n\n
), sentences (using NLP sentence tokenizers like those in NLTK or spaCy), or logical sections indicated by headings or other markers.This is often a practical and effective compromise, aiming to respect semantic boundaries while keeping chunks within size limits.
["\n\n", "\n", ". ", " ", ""]
). Try splitting the text using the first separator. If any resulting chunks are still too large, recursively apply the next separator in the list to those oversized chunks. Continue until all chunks are below the desired size limit. Often combined with overlap.The best chunking strategy depends on several factors:
It's common to experiment with different chunking strategies and parameters (chunk size, overlap) and evaluate their impact on downstream search performance using metrics discussed later in this chapter.
{
"data": [
{
"type": "bar",
"x": ["Fixed (No Overlap)", "Fixed (Overlap)", "Paragraph Split", "Recursive"],
"y": [65, 72, 78, 80],
"marker": {
"color": ["#ffc9c9", "#a5d8ff", "#b2f2bb", "#ffec99"],
"line": {"color": "#495057", "width": 1}
},
"name": "Relevance Score"
}
],
"layout": {
"title": {
"text": "Retrieval Relevance by Chunking Strategy",
"font": {"family": "sans-serif", "size": 16, "color": "#495057"}
},
"xaxis": {
"title": {"text": "Chunking Strategy", "font": {"family": "sans-serif", "size": 12, "color": "#495057"}},
"tickfont": {"family": "sans-serif", "size": 11, "color": "#495057"}
},
"yaxis": {
{"title": {"text": "Relevance Score (e.g., NDCG@10)", "font": {"family": "sans-serif", "size": 12, "color": "#495057"}},
"range": [0, 100],
"tickfont": {"family": "sans-serif", "size": 11, "color": "#495057"}
},
"margin": {"l": 60, "r": 20, "t": 40, "b": 50},
"paper_bgcolor": "#ffffff",
"plot_bgcolor": "#e9ecef"
}
}
Comparison showing how more context-aware chunking strategies might lead to better search relevance scores compared to simple fixed-size methods. Actual results depend heavily on the data and task.
TextSplitter
implementations (RecursiveCharacterTextSplitter, MarkdownTextSplitter, etc.). NLP libraries like NLTK and spaCy provide sentence tokenization.In summary, preparing and chunking your data is not just a preliminary chore; it's a critical design step in building a semantic search system. Thoughtful cleaning removes noise, while effective chunking ensures your data fits model constraints and produces focused, semantically meaningful vectors. The choices made here directly influence the granularity and relevance of your final search results.
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