This reverts commit 12fb509d8470e6d0c3a424756c9838a1efe306d2.
We revert this commit because it's causing the bug #150.
The initial algorithm we implemented for the stop_words was:
1. remove the stop_words from the dataset
2. keep the stop_words in the query to see if we can generate new words by
integrating typos or if the word was a prefix
=> This was causing the bug since, in the case of “The hobbit”, we were
**always** looking for something starting with “t he” or “th e”
instead of ignoring the word completely.
For now we are going to fix the bug by completely ignoring the
stop_words in the query.
This could cause another problem were someone mistyped a normal word and
ended up typing a stop_word.
For example imagine someone searching for the music “Won't he do it”.
If that person misplace one space and write “Won' the do it” then we
will loose a part of the request.
One fix would be to update our query tree to something like that:
---------------------
OR
OR
TOLERANT hobbit # the first option is to ignore the stop_word
AND
CONSECUTIVE # the second option is to do as we are doing
EXACT t # currently
EXACT he
TOLERANT hobbit
---------------------
This would increase drastically the size of our query tree on request
with a lot of stop_words. For example think of “The Lord Of The Rings”.
For now whatsoever we decided we were going to ignore this problem and consider
that it doesn't reduce too much the relevancy of the search to do that
while it improves the performances.
fixes after review
bump the version of the tokenizer
implement a first version of the stop_words
The front must provide a BTreeSet containing the stop words
The stop_words are set at None if an empty Set is provided
add the stop-words in the http-ui interface
Use maplit in the test
and remove all the useless drop(rtxn) at the end of all tests
Integrate the stop_words in the querytree
remove the stop_words from the querytree except if it was a prefix or a typo
more fixes after review
