Better Vector Search for Long Documents: Chunking Inside Manticore Search

A lot of the article focuses on problems induced by a 512-token input limit. For example, one needs a lot more chunks with such a small input, especially with overlap. I realize that some embedding models do have input contexts that small, but 8K and 32K are fairly widely supported and reduce chunking-related problems.

For languages like English, there's also usually a lot of redundancy within a text, so 512 tokens might not give a very clear indication of the context. Lots of documents have similar introductions (like "#include <foo.h>\n") that make short contexts and truncation particularly harmful.

Also, "Nothing in the document past that point can ever be retrieved, and nothing anywhere told you." This is user-hostile behavior, even if they didn't want to admit to users that the auto-embedding support was poor.

Finally, the paragraph later on about truncation being "what you already have" reads like Claude talking to the developer, not like a vendor talking to users. But sure, maybe this is a good default for a database searching page titles, chat logs and Xeets?