Issues with Data Chunk Overlapping During Chunking – Retrieval Accuracy Problems

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Hi everyone,

I’m currently working on a data chunking process where I split a large dataset into multiple smaller chunks. To avoid losing context, I add some overlap between consecutive chunks. However, I’ve been facing an issue: despite the overlap, the retrieval results sometimes miss important pieces of information, and the accuracy seems inconsistent.

It appears that the overlap is not always preserving enough context, or perhaps there is something else I’m missing in the chunking or retrieval approach.

Has anyone else encountered similar issues with overlapping chunks during chunking? What strategies can improve retrieval accuracy in this context? Any advice on optimal overlap size or potential pitfalls I should be aware of?

Thanks in advance!

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Hi!

I’ll dump my 2 cents on the topic :slight_smile:

naive chunking with overlap is basically the same as performing a low resolution discrete box convolution over your text.

It’s basically a box blur:

The image is blurry, showing a cemetery with headstones and trees in the foreground and an out-of-focus street and sky in the background. (Captioned by AI)
The image is blurry, showing a cemetery with headstones and trees in the foreground and an out-of-focus street and sky in the background. (Captioned by AI)750×1000 38.6 KB

(Box blur - Wikipedia)

Here are some issues I see:

  1. your cutoffs likely introduce artifacts. Half finished sentences or paragraphs really don’t help with comprehension.

  2. it’s quite likely that the chunks themselves don’t always contain enough contextual information to properly inform the topic

  3. it’s quite likely that the chunks often contain multiple concepts that get blurred into one embedding vector

  4. it’s possible that the embedding model you chose can’t actually understand deep contextual information, (i.e. grok the entire chunk)

Solutions:

  1. use a semantic chunking approach
    1.1. consider using a chunk rewriting approach (i.e. inverse document generation)
  2. use a better embedding model

Good luck!

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