#3786 Semantic Search Best Chunk Size

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#3786 Semantic Search Best Chunk Size

#3786 When applying semantic search of an embedded document, what should the chunk size be for the embedding when the search is based on one sentence?

For semantic search based on a single-sentence query, the most effective chunk size for the embedded document is typically small and often aligned with sentence-level chunking.

Here's why:

  • Higher Precision: When the query is a single, focused sentence, you want the retrieved document chunks to be similarly focused. Smaller chunks (e.g., 100-200 tokens or a couple of sentences) generally offer higher precision, as they are more likely to contain a single, coherent idea that closely matches the query's semantic vector.

  • Reduced Noise: Larger chunks (e.g., 500+ tokens) contain more context, but this can also introduce irrelevant ideas or "noise" that dilutes the semantic focus. The embedding vector for a large, multi-topic chunk may not accurately reflect the precise information needed to answer a single-sentence query, potentially leading to a lower similarity score for the most relevant part of the document.

  • Optimal Starting Point: While there is no universal "best" chunk size—it depends on your specific data, embedding model, and use case—a common and sensible starting point for experimentation, especially for high-precision, specific queries, is around 250 tokens or a strategy of breaking the document into individual sentences or small groups of sentences (sentence-based chunking).

For optimal performance, many systems use a "small-to-big" or parent-child chunking approach:

  1. Small Chunks (Child): Used for the initial retrieval (semantic search) to get high-precision matches.

  2. Larger Chunks (Parent/Neighbor): The relevant small chunk's larger context (e.g., the full paragraph or surrounding sentences) is then sent to the final language model (for RAG) to provide richer context for generating the final answer.

The key takeaway is to make the chunks as small as possible while still containing a complete thought or idea (like a sentence or a small, coherent paragraph) to maximize the chance of a precise semantic match to your short query.

This video explains different approaches to splitting documents, which is essential for determining the right chunk size in a semantic search application. Chunking Strategies Explained


From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro

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