[heikkilevanto]

Good explanation on tokenizing English text for regular search. But it is far from universal, and will not work well in Finnish, for example.

Folding diacritics makes "vähä" (little) into "vaha" (wax).

Dropping stop words like "The" misses the word for "tea" (in rather old-fashioned finnish, but also in current Danish).

Stemming Finnish words is also much more complex, as we tend to append suffixes to the words instead of small words in front to the word. "talo" is "house", "talosta" is "from the house", "talostani" is "from my house", and "talostaniko" makes it a question "from my house?"

If that sounds too easy, consider Japanese. From what little I know they don't use whitespace to separate words, mix two phonetic alphabets with Chinese ideograms, etc.

[philippemnoel]

That's true. For this reason, most modern search engines support language-aware stemming and tokenization. Popular tokenizers for CJK languages include Lindera and Jieba.

We (ParadeDB) use a search library called Tantivy under the hood, which supports stemming in Finnish, Danish and many other languages: https://docs.paradedb.com/documentation/token-filters/stemmi...

[ashirviskas]

Yep and I find that this really worsens LLM performance. For example `Ben,Alice` would be tokenized as `Ben|,A|lice`. And having to connect `lice` to the name `Alice` does not make it any easier for LLMs. However, formatting it as `Ben, Alice` tokenizes it as `Ben|,| Alice`. I found it kind of useful to improve performance by just formatting the data a bit differently.

I actually just started working on a data formatter that applies principles like these to drastically reduce the amount of tokens without decreasing the performance, like other formats do (looking at you, tson).