212: Introduce integration test on criteria r=Kerollmops a=ManyTheFish
- add pre-ranked dataset
- test each criterion 1 by 1
- test all criteria in several order
222: Move the `UpdateStore` into the http-ui crate r=Kerollmops a=Kerollmops
We no more need to have the `UpdateStore` inside of the mill crate as this is the job of the caller to stack the updates and sequentially give them to milli.
223: Update dataset links r=Kerollmops a=curquiza
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
220: Make hard separators split phrase query r=Kerollmops a=ManyTheFish
hard separators will now split a phrase query as two sequential phrases (double-quoted strings):
the query `"Radioactive (Imagine Dragons)"` would be considered equivalent to `"Radioactive" "Imagine Dragons"` which as the little disadvantage of not keeping the order of the two (or more) separate phrases.
Fix#208
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
193: Fix primary key behavior r=Kerollmops a=MarinPostma
this pr:
- Adds early returns on empty document additions, avoiding error messages to be returned when adding no documents and no primary key was set.
- Changes the primary key inference logic to match that of legacy meilisearch.
close#194
Co-authored-by: Marin Postma <postma.marin@protonmail.com>
Co-authored-by: marin postma <postma.marin@protonmail.com>
202: Add field id word count docids database r=Kerollmops a=LegendreM
This PR introduces a new database, `field_id_word_count_docids`, that maps the number of words in an attribute with a list of document ids. This relation is limited to attributes that contain less than 11 words.
This database is used by the exactness criterion to know if a document has an attribute that contains exactly the query without any additional word.
Fix#165Fix#196
Related to [specifications:#36](https://github.com/meilisearch/specifications/pull/36)
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
203: Make the MatchingWords return the number of matching bytes r=Kerollmops a=LegendreM
Make the MatchingWords return the number of matching bytes using a custom Levenshtein algorithm.
Fix#138
Co-authored-by: many <maxime@meilisearch.com>
184: Transfer numbers and strings facets into the appropriate facet databases r=Kerollmops a=Kerollmops
This pull request is related to https://github.com/meilisearch/milli/issues/152 and changes the layout of the facets values, numbers and strings are now in dedicated databases and the user no more needs to define the type of the fields. No more conversion between the two types is done, numbers (floats and integers converted to f64) go to the facet float database and strings go to the strings facet database.
There is one related issue that I found regarding CSVs, the values in a CSV are always considered to be strings, [meilisearch/specifications#28](d916b57d74/text/0028-indexing-csv.md) fixes this issue by allowing the user to define the fields types using `:` in the "CSV Formatting Rules" section.
All previous tests on facets have been modified to pass again and I have also done hand-driven tests with the 115m songs dataset. Everything seems to be good!
Fixes#192.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
- pass excluded document to criteria to remove them in higher levels of the bucket-sort
- merge already returned document with excluded documents to avoid duplicas
Related to #125 and #112Fix#170
This reverts commit 12fb509d84.
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
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
