Tamo
90afde435b
fix escaped quotes in filter
2022-06-09 16:03:49 +02:00
Kerollmops
69931e50d2
Add the max_values_by_facet setting to the database
2022-06-08 17:54:56 +02:00
Kerollmops
2a505503b3
Change the number of facet values returned by default to 100
2022-06-08 15:58:57 +02:00
Kerollmops
bae4007447
Remove the hard limit on the number of facet values returned
2022-06-08 15:58:57 +02:00
ManyTheFish
d212dc6b8b
Remove useless newline
2022-06-02 18:22:56 +02:00
ManyTheFish
7aabe42ae0
Refactor matching words
2022-06-02 17:59:04 +02:00
ManyTheFish
86ac8568e6
Use Charabia in milli
2022-06-02 16:59:11 +02:00
bors[bot]
74d1914a64
Merge #535
...
535: Reintroduce the max values by facet limit r=ManyTheFish a=Kerollmops
This PR reintroduces the max values by facet limit this is related to https://github.com/meilisearch/meilisearch/issues/2349 .
~I would like some help in deciding on whether I keep the default 100 max values in milli and set up the `FacetDistribution` settings in Meilisearch to use 1000 as the new value, I expose the `max_values_by_facet` for this purpose.~
I changed the default value to 1000 and the max to 10000, thank you `@ManyTheFish` for the help!
Co-authored-by: Kerollmops <clement@meilisearch.com>
2022-06-01 14:30:50 +00:00
ad hoc
25fc576696
review changes
2022-05-24 14:15:33 +02:00
ad hoc
69dc4de80f
change &Option<Set> to Option<&Set>
2022-05-24 12:14:55 +02:00
ad hoc
ac975cc747
cache context's exact words
2022-05-24 09:43:17 +02:00
ad hoc
8993fec8a3
return optional exact words
2022-05-24 09:15:49 +02:00
Kerollmops
cd7c6e19ed
Reintroduce the max values by facet limit
2022-05-18 15:57:57 +02:00
ManyTheFish
137434a1c8
Add some implementation on MatchBounds
2022-05-17 15:57:09 +02:00
bors[bot]
9db86aac51
Merge #518
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518: Return facets even when there is no value associated to it r=Kerollmops a=Kerollmops
This PR is related to https://github.com/meilisearch/meilisearch/issues/2352 and should fix the issue when Meilisearch is up-to-date with this PR.
Co-authored-by: Kerollmops <clement@meilisearch.com>
2022-04-28 09:04:36 +00:00
Kerollmops
7d1c2d97bf
Return facets even when there is no values associated to it
2022-04-26 17:59:53 +02:00
ad hoc
5c29258e8e
fix cargo warnings
2022-04-26 17:33:11 +02:00
bors[bot]
ea4bb9402f
Merge #483
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483: Enhance matching words r=Kerollmops a=ManyTheFish
# Summary
Enhance milli word-matcher making it handle match computing and cropping.
# Implementation
## Computing best matches for cropping
Before we were considering that the first match of the attribute was the best one, this was accurate when only one word was searched but was missing the target when more than one word was searched.
Now we are searching for the best matches interval to crop around, the chosen interval is the one:
1) that have the highest count of unique matches
> for example, if we have a query `split the world`, then the interval `the split the split the` has 5 matches but only 2 unique matches (1 for `split` and 1 for `the`) where the interval `split of the world` has 3 matches and 3 unique matches. So the interval `split of the world` is considered better.
2) that have the minimum distance between matches
> for example, if we have a query `split the world`, then the interval `split of the world` has a distance of 3 (2 between `split` and `the`, and 1 between `the` and `world`) where the interval `split the world` has a distance of 2. So the interval `split the world` is considered better.
3) that have the highest count of ordered matches
> for example, if we have a query `split the world`, then the interval `the world split` has 2 ordered words where the interval `split the world` has 3. So the interval `split the world` is considered better.
## Cropping around the best matches interval
Before we were cropping around the interval without checking the context.
Now we are cropping around words in the same context as matching words.
This means that we will keep words that are farther from the matching words but are in the same phrase, than words that are nearer but separated by a dot.
> For instance, for the matching word `Split` the text:
`Natalie risk her future. Split The World is a book written by Emily Henry. I never read it.`
will be cropped like:
`…. Split The World is a book written by Emily Henry. …`
and not like:
`Natalie risk her future. Split The World is a book …`
Co-authored-by: ManyTheFish <many@meilisearch.com>
2022-04-19 11:42:32 +00:00
ManyTheFish
f1115e274f
Use Copy impl of FormatOption instead of clonning
2022-04-19 10:35:50 +02:00
ad hoc
dda28d7415
exclude excluded canditates from search result candidates
2022-04-13 12:10:35 +02:00
ad hoc
bbb6728d2f
add distinct attributes to cli
2022-04-13 12:10:35 +02:00
ManyTheFish
5809d3ae0d
Add first benchmarks on formatting
2022-04-12 16:31:58 +02:00
ManyTheFish
827cedcd15
Add format option structure
2022-04-12 13:42:14 +02:00
ManyTheFish
011f8210ed
Make compute_matches more rust idiomatic
2022-04-12 10:19:02 +02:00
ManyTheFish
a16de5de84
Symplify format and remove intermediate function
2022-04-08 11:20:41 +02:00
ManyTheFish
a769e09dfa
Make token_crop_bounds more rust idiomatic
2022-04-07 20:15:14 +02:00
ManyTheFish
c8ed1675a7
Add some documentation
2022-04-07 17:32:13 +02:00
ManyTheFish
b1905dfa24
Make split_best_frequency returns references instead of owned data
2022-04-07 17:05:44 +02:00
Irevoire
4f3ce6d9cd
nested fields
2022-04-07 16:58:46 +02:00
ManyTheFish
fa7d3a37c0
Make some cleaning and add comments
2022-04-05 17:48:56 +02:00
ManyTheFish
3bb1e35ada
Fix match count
2022-04-05 17:48:45 +02:00
ManyTheFish
56e0edd621
Put crop markers direclty around words
2022-04-05 17:41:32 +02:00
ManyTheFish
a93cd8c61c
Fix prefix highlight with special chars
2022-04-05 17:41:32 +02:00
ManyTheFish
b3f0f39106
Make some cleaning
2022-04-05 17:41:32 +02:00
ManyTheFish
6dc345bc53
Test and Fix prefix highlight
2022-04-05 17:41:32 +02:00
ManyTheFish
bd30ee97b8
Keep separators at start of the croped string
2022-04-05 17:41:32 +02:00
ManyTheFish
29c5f76d7f
Use new matcher in http-ui
2022-04-05 17:41:32 +02:00
ManyTheFish
734d0899d3
Publish Matcher
2022-04-05 17:41:32 +02:00
ManyTheFish
4428cb5909
Add some tests and fix some corner cases
2022-04-05 17:41:32 +02:00
ManyTheFish
844f546a8b
Add matches algorithm V1
2022-04-05 17:41:32 +02:00
ManyTheFish
3be1790803
Add crop algorithm with naive match algorithm
2022-04-05 17:41:32 +02:00
ManyTheFish
d96e72e5dc
Create formater with some tests
2022-04-05 17:41:32 +02:00
ad hoc
6b2c2509b2
fix bug in exact search
2022-04-04 20:54:03 +02:00
ad hoc
56b4f5dce2
add exact prefix to query_docids
2022-04-04 20:54:03 +02:00
ad hoc
21ae4143b1
add exact_word_prefix to Context
2022-04-04 20:54:03 +02:00
ad hoc
c4c6e35352
query exact_word_docids in resolve_query_tree
2022-04-04 20:54:02 +02:00
ad hoc
c882d8daf0
add test for exact words
2022-04-04 20:54:01 +02:00
ad hoc
7e9d56a9e7
disable typos on exact words
2022-04-04 20:54:01 +02:00
ad hoc
0fd55db21c
fmt
2022-04-04 20:10:55 +02:00
ad hoc
559e46be5e
fix bad rebase bug
2022-04-04 20:10:55 +02:00
ad hoc
8b1e5d9c6d
add test for exact words
2022-04-04 20:10:55 +02:00
ad hoc
774fa8f065
disable typos on exact words
2022-04-04 20:10:55 +02:00
ad hoc
853b4a520f
fmt
2022-04-04 10:41:46 +02:00
ad hoc
fdaf45aab2
replace hardcoded value with constant in TestContext
2022-04-04 10:41:46 +02:00
ad hoc
950a740bd4
refactor typos for readability
2022-04-04 10:41:46 +02:00
ad hoc
66020cd923
rename min_word_len* to use plain letter numbers
2022-04-04 10:41:46 +02:00
ad hoc
286dd7b2e4
rename min_word_len_2_typo
2022-04-01 11:17:03 +02:00
ad hoc
55af85db3c
add tests for min_word_len_for_typo
2022-04-01 11:17:02 +02:00
ad hoc
a1a3a49bc9
dynamic minimum word len for typos in query tree builder
2022-04-01 11:17:02 +02:00
ad hoc
9fe40df960
add word derivations tests
2022-04-01 11:05:18 +02:00
ad hoc
d5ddc6b080
fix 2 typos word derivation bug
2022-04-01 10:51:22 +02:00
ad hoc
6ef3bb9d83
fmt
2022-03-31 14:06:23 +02:00
ad hoc
f782fe2062
add authorize_typo_test
2022-03-31 10:08:39 +02:00
ad hoc
c4653347fd
add authorize typo setting
2022-03-31 10:05:44 +02:00
bors[bot]
90276d9a2d
Merge #472
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472: Remove useless variables in proximity r=Kerollmops a=ManyTheFish
Was passing by plane sweep algorithm to find some inspiration, and I discover that we have useless variables that were not detected because of the recursive function.
Co-authored-by: ManyTheFish <many@meilisearch.com>
2022-03-16 15:33:11 +00:00
ManyTheFish
49d59d88c2
Remove useless variables in proximity
2022-03-16 16:12:52 +01:00
Bruno Casali
adc71742c8
Move string concat to the struct instead of in the calling
2022-03-16 10:26:12 -03:00
Bruno Casali
4822fe1beb
Add a better error message when the filterable attrs are empty
...
Fixes https://github.com/meilisearch/meilisearch/issues/2140
2022-03-15 18:13:59 -03:00
bors[bot]
ad4c982c68
Merge #439
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439: Optimize typo criterion r=Kerollmops a=MarinPostma
This pr implements a couple of optimization for the typo criterion:
- clamp max typo on concatenated query words to 1: By considering that a concatenated query word is a typo, we clamp the max number of typos allowed o it to 1. This is useful because we noticed that concatenated query words often introduced words with 2 typos in queries that otherwise didn't allow for 2 typo words.
- Make typos on the first letter count for 2. This change is a big performance gain: by considering the typos on the first letter to count as 2 typos, we drastically restrict the search space for 1 typo, and if we reach 2 typos, the search space is reduced as well, as we only consider: (2 typos ∩ correct first letter) ∪ (wrong first letter ∩ 1 typo) instead of 2 typos anywhere in the word.
## benches
```
group main typo
----- ---- ----
smol-songs.csv: asc + default/Notstandskomitee 2.51 5.8±0.01ms ? ?/sec 1.00 2.3±0.01ms ? ?/sec
smol-songs.csv: asc + default/charles 2.48 3.0±0.01ms ? ?/sec 1.00 1190.9±1.29µs ? ?/sec
smol-songs.csv: asc + default/charles mingus 5.56 10.8±0.01ms ? ?/sec 1.00 1935.3±1.00µs ? ?/sec
smol-songs.csv: asc + default/david 1.65 3.9±0.00ms ? ?/sec 1.00 2.4±0.01ms ? ?/sec
smol-songs.csv: asc + default/david bowie 3.34 12.5±0.02ms ? ?/sec 1.00 3.7±0.00ms ? ?/sec
smol-songs.csv: asc + default/john 1.00 1849.7±3.74µs ? ?/sec 1.01 1875.1±4.65µs ? ?/sec
smol-songs.csv: asc + default/marcus miller 4.32 15.7±0.01ms ? ?/sec 1.00 3.6±0.01ms ? ?/sec
smol-songs.csv: asc + default/michael jackson 3.31 12.5±0.01ms ? ?/sec 1.00 3.8±0.00ms ? ?/sec
smol-songs.csv: asc + default/tamo 1.05 565.4±0.86µs ? ?/sec 1.00 539.3±1.22µs ? ?/sec
smol-songs.csv: asc + default/thelonious monk 3.49 11.5±0.01ms ? ?/sec 1.00 3.3±0.00ms ? ?/sec
smol-songs.csv: asc/Notstandskomitee 2.59 5.6±0.02ms ? ?/sec 1.00 2.2±0.01ms ? ?/sec
smol-songs.csv: asc/charles 6.05 2.1±0.00ms ? ?/sec 1.00 347.8±0.60µs ? ?/sec
smol-songs.csv: asc/charles mingus 14.46 9.4±0.01ms ? ?/sec 1.00 649.2±0.97µs ? ?/sec
smol-songs.csv: asc/david 3.87 2.4±0.00ms ? ?/sec 1.00 618.2±0.69µs ? ?/sec
smol-songs.csv: asc/david bowie 10.14 9.8±0.01ms ? ?/sec 1.00 970.8±1.55µs ? ?/sec
smol-songs.csv: asc/john 1.00 546.5±1.10µs ? ?/sec 1.00 547.1±2.11µs ? ?/sec
smol-songs.csv: asc/marcus miller 11.45 10.4±0.06ms ? ?/sec 1.00 907.9±1.37µs ? ?/sec
smol-songs.csv: asc/michael jackson 10.56 9.7±0.01ms ? ?/sec 1.00 919.6±1.03µs ? ?/sec
smol-songs.csv: asc/tamo 1.03 43.3±0.18µs ? ?/sec 1.00 42.2±0.23µs ? ?/sec
smol-songs.csv: asc/thelonious monk 4.16 10.7±0.02ms ? ?/sec 1.00 2.6±0.00ms ? ?/sec
smol-songs.csv: basic filter: <=/Notstandskomitee 1.00 95.7±0.20µs ? ?/sec 1.15 109.6±10.40µs ? ?/sec
smol-songs.csv: basic filter: <=/charles 1.00 27.8±0.15µs ? ?/sec 1.01 27.9±0.18µs ? ?/sec
smol-songs.csv: basic filter: <=/charles mingus 1.72 119.2±0.67µs ? ?/sec 1.00 69.1±0.13µs ? ?/sec
smol-songs.csv: basic filter: <=/david 1.00 22.3±0.33µs ? ?/sec 1.05 23.4±0.19µs ? ?/sec
smol-songs.csv: basic filter: <=/david bowie 1.59 86.9±0.79µs ? ?/sec 1.00 54.5±0.31µs ? ?/sec
smol-songs.csv: basic filter: <=/john 1.00 17.9±0.06µs ? ?/sec 1.06 18.9±0.15µs ? ?/sec
smol-songs.csv: basic filter: <=/marcus miller 1.65 102.7±1.63µs ? ?/sec 1.00 62.3±0.18µs ? ?/sec
smol-songs.csv: basic filter: <=/michael jackson 1.76 128.2±1.85µs ? ?/sec 1.00 72.9±0.19µs ? ?/sec
smol-songs.csv: basic filter: <=/tamo 1.00 17.9±0.13µs ? ?/sec 1.05 18.7±0.20µs ? ?/sec
smol-songs.csv: basic filter: <=/thelonious monk 1.53 157.5±2.38µs ? ?/sec 1.00 102.8±0.88µs ? ?/sec
smol-songs.csv: basic filter: TO/Notstandskomitee 1.00 100.9±4.36µs ? ?/sec 1.04 105.0±8.25µs ? ?/sec
smol-songs.csv: basic filter: TO/charles 1.00 28.4±0.36µs ? ?/sec 1.03 29.4±0.33µs ? ?/sec
smol-songs.csv: basic filter: TO/charles mingus 1.71 118.1±1.08µs ? ?/sec 1.00 68.9±0.26µs ? ?/sec
smol-songs.csv: basic filter: TO/david 1.00 24.0±0.26µs ? ?/sec 1.03 24.6±0.43µs ? ?/sec
smol-songs.csv: basic filter: TO/david bowie 1.72 95.2±0.30µs ? ?/sec 1.00 55.2±0.14µs ? ?/sec
smol-songs.csv: basic filter: TO/john 1.00 18.8±0.09µs ? ?/sec 1.06 19.8±0.17µs ? ?/sec
smol-songs.csv: basic filter: TO/marcus miller 1.61 102.4±1.65µs ? ?/sec 1.00 63.4±0.24µs ? ?/sec
smol-songs.csv: basic filter: TO/michael jackson 1.77 132.1±1.41µs ? ?/sec 1.00 74.5±0.59µs ? ?/sec
smol-songs.csv: basic filter: TO/tamo 1.00 18.2±0.14µs ? ?/sec 1.05 19.2±0.46µs ? ?/sec
smol-songs.csv: basic filter: TO/thelonious monk 1.49 150.8±1.92µs ? ?/sec 1.00 101.3±0.44µs ? ?/sec
smol-songs.csv: basic placeholder/ 1.00 27.3±0.07µs ? ?/sec 1.03 28.0±0.05µs ? ?/sec
smol-songs.csv: basic with quote/"Notstandskomitee" 1.00 122.4±0.17µs ? ?/sec 1.03 125.6±0.16µs ? ?/sec
smol-songs.csv: basic with quote/"charles" 1.00 88.8±0.30µs ? ?/sec 1.00 88.4±0.15µs ? ?/sec
smol-songs.csv: basic with quote/"charles" "mingus" 1.00 685.2±0.74µs ? ?/sec 1.01 689.4±6.07µs ? ?/sec
smol-songs.csv: basic with quote/"david" 1.00 161.6±0.42µs ? ?/sec 1.01 162.6±0.17µs ? ?/sec
smol-songs.csv: basic with quote/"david" "bowie" 1.00 731.7±0.73µs ? ?/sec 1.02 743.1±0.77µs ? ?/sec
smol-songs.csv: basic with quote/"john" 1.00 267.1±0.33µs ? ?/sec 1.01 270.9±0.33µs ? ?/sec
smol-songs.csv: basic with quote/"marcus" "miller" 1.00 138.7±0.31µs ? ?/sec 1.02 140.9±0.13µs ? ?/sec
smol-songs.csv: basic with quote/"michael" "jackson" 1.01 841.4±0.72µs ? ?/sec 1.00 833.8±0.92µs ? ?/sec
smol-songs.csv: basic with quote/"tamo" 1.01 189.2±0.26µs ? ?/sec 1.00 188.2±0.71µs ? ?/sec
smol-songs.csv: basic with quote/"thelonious" "monk" 1.00 1100.5±1.36µs ? ?/sec 1.01 1111.7±2.17µs ? ?/sec
smol-songs.csv: basic without quote/Notstandskomitee 3.40 7.9±0.02ms ? ?/sec 1.00 2.3±0.02ms ? ?/sec
smol-songs.csv: basic without quote/charles 2.57 494.4±0.89µs ? ?/sec 1.00 192.5±0.18µs ? ?/sec
smol-songs.csv: basic without quote/charles mingus 1.29 2.8±0.02ms ? ?/sec 1.00 2.1±0.01ms ? ?/sec
smol-songs.csv: basic without quote/david 1.95 623.8±0.90µs ? ?/sec 1.00 319.2±1.22µs ? ?/sec
smol-songs.csv: basic without quote/david bowie 1.12 5.9±0.00ms ? ?/sec 1.00 5.2±0.00ms ? ?/sec
smol-songs.csv: basic without quote/john 1.24 1340.9±2.25µs ? ?/sec 1.00 1084.7±7.76µs ? ?/sec
smol-songs.csv: basic without quote/marcus miller 7.97 14.6±0.01ms ? ?/sec 1.00 1826.0±6.84µs ? ?/sec
smol-songs.csv: basic without quote/michael jackson 1.19 3.9±0.00ms ? ?/sec 1.00 3.3±0.00ms ? ?/sec
smol-songs.csv: basic without quote/tamo 1.65 737.7±3.58µs ? ?/sec 1.00 446.7±0.51µs ? ?/sec
smol-songs.csv: basic without quote/thelonious monk 1.16 4.5±0.02ms ? ?/sec 1.00 3.9±0.04ms ? ?/sec
smol-songs.csv: big filter/Notstandskomitee 3.27 7.6±0.02ms ? ?/sec 1.00 2.3±0.01ms ? ?/sec
smol-songs.csv: big filter/charles 8.26 1957.5±1.37µs ? ?/sec 1.00 236.8±0.34µs ? ?/sec
smol-songs.csv: big filter/charles mingus 18.49 11.2±0.06ms ? ?/sec 1.00 607.7±3.03µs ? ?/sec
smol-songs.csv: big filter/david 3.78 2.4±0.00ms ? ?/sec 1.00 622.8±0.80µs ? ?/sec
smol-songs.csv: big filter/david bowie 9.00 12.0±0.01ms ? ?/sec 1.00 1336.0±3.17µs ? ?/sec
smol-songs.csv: big filter/john 1.00 554.2±0.95µs ? ?/sec 1.01 560.4±0.79µs ? ?/sec
smol-songs.csv: big filter/marcus miller 18.09 12.0±0.01ms ? ?/sec 1.00 664.7±0.60µs ? ?/sec
smol-songs.csv: big filter/michael jackson 8.43 12.0±0.01ms ? ?/sec 1.00 1421.6±1.37µs ? ?/sec
smol-songs.csv: big filter/tamo 1.00 86.3±0.14µs ? ?/sec 1.01 87.3±0.21µs ? ?/sec
smol-songs.csv: big filter/thelonious monk 5.55 14.3±0.02ms ? ?/sec 1.00 2.6±0.01ms ? ?/sec
smol-songs.csv: desc + default/Notstandskomitee 2.52 5.8±0.01ms ? ?/sec 1.00 2.3±0.01ms ? ?/sec
smol-songs.csv: desc + default/charles 3.04 2.7±0.01ms ? ?/sec 1.00 893.4±1.08µs ? ?/sec
smol-songs.csv: desc + default/charles mingus 6.77 10.3±0.01ms ? ?/sec 1.00 1520.8±1.90µs ? ?/sec
smol-songs.csv: desc + default/david 1.39 5.7±0.00ms ? ?/sec 1.00 4.1±0.00ms ? ?/sec
smol-songs.csv: desc + default/david bowie 2.34 15.8±0.02ms ? ?/sec 1.00 6.7±0.01ms ? ?/sec
smol-songs.csv: desc + default/john 1.00 2.5±0.00ms ? ?/sec 1.02 2.6±0.01ms ? ?/sec
smol-songs.csv: desc + default/marcus miller 5.06 14.5±0.02ms ? ?/sec 1.00 2.9±0.01ms ? ?/sec
smol-songs.csv: desc + default/michael jackson 2.64 14.1±0.05ms ? ?/sec 1.00 5.4±0.00ms ? ?/sec
smol-songs.csv: desc + default/tamo 1.00 567.0±0.65µs ? ?/sec 1.00 565.7±0.97µs ? ?/sec
smol-songs.csv: desc + default/thelonious monk 3.55 11.6±0.02ms ? ?/sec 1.00 3.3±0.00ms ? ?/sec
smol-songs.csv: desc/Notstandskomitee 2.58 5.6±0.02ms ? ?/sec 1.00 2.2±0.02ms ? ?/sec
smol-songs.csv: desc/charles 6.04 2.1±0.00ms ? ?/sec 1.00 348.1±0.57µs ? ?/sec
smol-songs.csv: desc/charles mingus 14.51 9.4±0.01ms ? ?/sec 1.00 646.7±0.99µs ? ?/sec
smol-songs.csv: desc/david 3.86 2.4±0.00ms ? ?/sec 1.00 620.7±2.46µs ? ?/sec
smol-songs.csv: desc/david bowie 10.10 9.8±0.01ms ? ?/sec 1.00 973.9±3.31µs ? ?/sec
smol-songs.csv: desc/john 1.00 545.5±0.78µs ? ?/sec 1.00 547.2±0.48µs ? ?/sec
smol-songs.csv: desc/marcus miller 11.39 10.3±0.01ms ? ?/sec 1.00 903.7±0.95µs ? ?/sec
smol-songs.csv: desc/michael jackson 10.51 9.7±0.01ms ? ?/sec 1.00 924.7±2.02µs ? ?/sec
smol-songs.csv: desc/tamo 1.01 43.2±0.33µs ? ?/sec 1.00 42.6±0.35µs ? ?/sec
smol-songs.csv: desc/thelonious monk 4.19 10.8±0.03ms ? ?/sec 1.00 2.6±0.00ms ? ?/sec
smol-songs.csv: prefix search/a 1.00 1008.7±1.00µs ? ?/sec 1.00 1005.5±0.91µs ? ?/sec
smol-songs.csv: prefix search/b 1.00 885.0±0.70µs ? ?/sec 1.01 890.6±1.11µs ? ?/sec
smol-songs.csv: prefix search/i 1.00 1051.8±1.25µs ? ?/sec 1.00 1056.6±4.12µs ? ?/sec
smol-songs.csv: prefix search/s 1.00 724.7±1.77µs ? ?/sec 1.00 721.6±0.59µs ? ?/sec
smol-songs.csv: prefix search/x 1.01 212.4±0.21µs ? ?/sec 1.00 210.9±0.38µs ? ?/sec
smol-songs.csv: proximity/7000 Danses Un Jour Dans Notre Vie 18.55 48.5±0.09ms ? ?/sec 1.00 2.6±0.03ms ? ?/sec
smol-songs.csv: proximity/The Disneyland Sing-Along Chorus 8.41 56.7±0.45ms ? ?/sec 1.00 6.7±0.05ms ? ?/sec
smol-songs.csv: proximity/Under Great Northern Lights 15.74 38.9±0.14ms ? ?/sec 1.00 2.5±0.00ms ? ?/sec
smol-songs.csv: proximity/black saint sinner lady 11.82 40.1±0.13ms ? ?/sec 1.00 3.4±0.02ms ? ?/sec
smol-songs.csv: proximity/les dangeureuses 1960 6.90 26.1±0.13ms ? ?/sec 1.00 3.8±0.04ms ? ?/sec
smol-songs.csv: typo/Arethla Franklin 14.93 5.8±0.01ms ? ?/sec 1.00 390.1±1.89µs ? ?/sec
smol-songs.csv: typo/Disnaylande 3.18 7.3±0.01ms ? ?/sec 1.00 2.3±0.00ms ? ?/sec
smol-songs.csv: typo/dire straights 5.55 15.2±0.02ms ? ?/sec 1.00 2.7±0.00ms ? ?/sec
smol-songs.csv: typo/fear of the duck 28.03 20.0±0.03ms ? ?/sec 1.00 713.3±1.54µs ? ?/sec
smol-songs.csv: typo/indochie 19.25 1851.4±2.38µs ? ?/sec 1.00 96.2±0.13µs ? ?/sec
smol-songs.csv: typo/indochien 14.66 1887.7±3.18µs ? ?/sec 1.00 128.8±0.18µs ? ?/sec
smol-songs.csv: typo/klub des loopers 37.73 18.0±0.02ms ? ?/sec 1.00 476.7±0.73µs ? ?/sec
smol-songs.csv: typo/michel depech 10.17 5.8±0.01ms ? ?/sec 1.00 565.8±1.16µs ? ?/sec
smol-songs.csv: typo/mongus 15.33 1897.4±3.44µs ? ?/sec 1.00 123.8±0.13µs ? ?/sec
smol-songs.csv: typo/stromal 14.63 1859.3±2.40µs ? ?/sec 1.00 127.1±0.29µs ? ?/sec
smol-songs.csv: typo/the white striper 10.83 9.4±0.01ms ? ?/sec 1.00 866.0±0.98µs ? ?/sec
smol-songs.csv: typo/thelonius monk 14.40 3.8±0.00ms ? ?/sec 1.00 261.5±1.30µs ? ?/sec
smol-songs.csv: words/7000 Danses / Le Baiser / je me trompe de mots 5.54 70.8±0.09ms ? ?/sec 1.00 12.8±0.03ms ? ?/sec
smol-songs.csv: words/Bring Your Daughter To The Slaughter but now this is not part of the title 3.48 119.8±0.14ms ? ?/sec 1.00 34.4±0.04ms ? ?/sec
smol-songs.csv: words/The Disneyland Children's Sing-Alone song 8.98 71.9±0.12ms ? ?/sec 1.00 8.0±0.01ms ? ?/sec
smol-songs.csv: words/les liaisons dangeureuses 1793 11.88 37.4±0.07ms ? ?/sec 1.00 3.1±0.01ms ? ?/sec
smol-songs.csv: words/seven nation mummy 22.86 23.4±0.04ms ? ?/sec 1.00 1024.8±1.57µs ? ?/sec
smol-songs.csv: words/the black saint and the sinner lady and the good doggo 2.76 124.4±0.15ms ? ?/sec 1.00 45.1±0.09ms ? ?/sec
smol-songs.csv: words/whathavenotnsuchforth and a good amount of words to pop to match the first one 2.52 107.0±0.23ms ? ?/sec 1.00 42.4±0.66ms ? ?/sec
group main-wiki typo-wiki
----- --------- ---------
smol-wiki-articles.csv: basic placeholder/ 1.02 13.7±0.02µs ? ?/sec 1.00 13.4±0.03µs ? ?/sec
smol-wiki-articles.csv: basic with quote/"film" 1.02 409.8±0.67µs ? ?/sec 1.00 402.6±0.48µs ? ?/sec
smol-wiki-articles.csv: basic with quote/"france" 1.00 325.9±0.91µs ? ?/sec 1.00 326.4±0.49µs ? ?/sec
smol-wiki-articles.csv: basic with quote/"japan" 1.00 218.4±0.26µs ? ?/sec 1.01 220.5±0.20µs ? ?/sec
smol-wiki-articles.csv: basic with quote/"machine" 1.00 143.0±0.12µs ? ?/sec 1.04 148.8±0.21µs ? ?/sec
smol-wiki-articles.csv: basic with quote/"miles" "davis" 1.00 11.7±0.06ms ? ?/sec 1.00 11.8±0.01ms ? ?/sec
smol-wiki-articles.csv: basic with quote/"mingus" 1.00 4.4±0.03ms ? ?/sec 1.00 4.4±0.00ms ? ?/sec
smol-wiki-articles.csv: basic with quote/"rock" "and" "roll" 1.00 43.5±0.08ms ? ?/sec 1.01 43.8±0.06ms ? ?/sec
smol-wiki-articles.csv: basic with quote/"spain" 1.00 137.3±0.35µs ? ?/sec 1.05 144.4±0.23µs ? ?/sec
smol-wiki-articles.csv: basic without quote/film 1.00 125.3±0.30µs ? ?/sec 1.06 133.1±0.37µs ? ?/sec
smol-wiki-articles.csv: basic without quote/france 1.21 1782.6±1.65µs ? ?/sec 1.00 1477.0±1.39µs ? ?/sec
smol-wiki-articles.csv: basic without quote/japan 1.28 1363.9±0.80µs ? ?/sec 1.00 1064.3±1.79µs ? ?/sec
smol-wiki-articles.csv: basic without quote/machine 1.73 760.3±0.81µs ? ?/sec 1.00 439.6±0.75µs ? ?/sec
smol-wiki-articles.csv: basic without quote/miles davis 1.03 17.0±0.03ms ? ?/sec 1.00 16.5±0.02ms ? ?/sec
smol-wiki-articles.csv: basic without quote/mingus 1.07 5.3±0.01ms ? ?/sec 1.00 5.0±0.00ms ? ?/sec
smol-wiki-articles.csv: basic without quote/rock and roll 1.01 63.9±0.18ms ? ?/sec 1.00 63.0±0.07ms ? ?/sec
smol-wiki-articles.csv: basic without quote/spain 2.07 667.4±0.93µs ? ?/sec 1.00 322.8±0.29µs ? ?/sec
smol-wiki-articles.csv: prefix search/c 1.00 343.1±0.47µs ? ?/sec 1.00 344.0±0.34µs ? ?/sec
smol-wiki-articles.csv: prefix search/g 1.00 374.4±3.42µs ? ?/sec 1.00 374.1±0.44µs ? ?/sec
smol-wiki-articles.csv: prefix search/j 1.00 359.9±0.31µs ? ?/sec 1.00 361.2±0.79µs ? ?/sec
smol-wiki-articles.csv: prefix search/q 1.01 102.0±0.12µs ? ?/sec 1.00 101.4±0.32µs ? ?/sec
smol-wiki-articles.csv: prefix search/t 1.00 536.7±1.39µs ? ?/sec 1.00 534.3±0.84µs ? ?/sec
smol-wiki-articles.csv: prefix search/x 1.00 400.9±1.00µs ? ?/sec 1.00 399.5±0.45µs ? ?/sec
smol-wiki-articles.csv: proximity/april paris 3.86 14.4±0.01ms ? ?/sec 1.00 3.7±0.01ms ? ?/sec
smol-wiki-articles.csv: proximity/diesel engine 12.98 10.4±0.01ms ? ?/sec 1.00 803.5±1.13µs ? ?/sec
smol-wiki-articles.csv: proximity/herald sings 1.00 12.7±0.06ms ? ?/sec 5.29 67.1±0.09ms ? ?/sec
smol-wiki-articles.csv: proximity/tea two 6.48 1452.1±2.78µs ? ?/sec 1.00 224.1±0.38µs ? ?/sec
smol-wiki-articles.csv: typo/Disnaylande 3.89 8.5±0.01ms ? ?/sec 1.00 2.2±0.01ms ? ?/sec
smol-wiki-articles.csv: typo/aritmetric 3.78 10.3±0.01ms ? ?/sec 1.00 2.7±0.00ms ? ?/sec
smol-wiki-articles.csv: typo/linax 8.91 1426.7±0.97µs ? ?/sec 1.00 160.1±0.18µs ? ?/sec
smol-wiki-articles.csv: typo/migrosoft 7.48 1417.3±5.84µs ? ?/sec 1.00 189.5±0.88µs ? ?/sec
smol-wiki-articles.csv: typo/nympalidea 3.96 7.2±0.01ms ? ?/sec 1.00 1810.1±2.03µs ? ?/sec
smol-wiki-articles.csv: typo/phytogropher 3.71 7.2±0.01ms ? ?/sec 1.00 1934.3±6.51µs ? ?/sec
smol-wiki-articles.csv: typo/sisan 6.44 1497.2±1.38µs ? ?/sec 1.00 232.7±0.94µs ? ?/sec
smol-wiki-articles.csv: typo/the fronce 6.92 2.9±0.00ms ? ?/sec 1.00 418.0±1.76µs ? ?/sec
smol-wiki-articles.csv: words/Abraham machin 16.63 10.8±0.01ms ? ?/sec 1.00 649.7±1.08µs ? ?/sec
smol-wiki-articles.csv: words/Idaho Bellevue pizza 27.15 25.6±0.03ms ? ?/sec 1.00 944.2±5.07µs ? ?/sec
smol-wiki-articles.csv: words/Kameya Tokujirō mingus monk 26.87 40.7±0.05ms ? ?/sec 1.00 1515.3±2.73µs ? ?/sec
smol-wiki-articles.csv: words/Ulrich Hensel meilisearch milli 11.99 48.8±0.10ms ? ?/sec 1.00 4.1±0.02ms ? ?/sec
smol-wiki-articles.csv: words/the black saint and the sinner lady and the good doggo 4.90 110.0±0.15ms ? ?/sec 1.00 22.4±0.03ms ? ?/sec
```
Co-authored-by: mpostma <postma.marin@protonmail.com>
Co-authored-by: ad hoc <postma.marin@protonmail.com>
2022-03-15 16:43:36 +00:00
ad hoc
3f24555c3d
custom fst automatons
2022-03-15 17:38:35 +01:00
ad hoc
628c835a22
fix tests
2022-03-15 17:38:34 +01:00
Kerollmops
21ec334dcc
Fix the compilation error of the dependency versions
2022-03-15 11:17:45 +01:00
ad hoc
13de251047
rewrite word pair distance gathering
2022-02-03 15:57:20 +01:00
mpostma
7541ab99cd
review changes
2022-02-02 12:59:01 +01:00
mpostma
d0aabde502
optimize 2 typos case
2022-02-02 12:56:09 +01:00
mpostma
55e6cb9c7b
typos on first letter counts as 2
2022-02-02 12:56:09 +01:00
mpostma
642c01d0dc
set max typos on ngram to 1
2022-02-02 12:56:08 +01:00
ad hoc
d852dc0d2b
fix phrase search
2022-02-01 20:21:33 +01:00
Marin Postma
0c84a40298
document batch support
...
reusable transform
rework update api
add indexer config
fix tests
review changes
Co-authored-by: Clément Renault <clement@meilisearch.com>
fmt
2022-01-19 12:40:20 +01:00
Tamo
01968d7ca7
ensure we get no documents and no error when filtering on an empty db
2022-01-18 11:40:30 +01:00
bors[bot]
8f4499090b
Merge #433
...
433: fix(filter): Fix two bugs. r=Kerollmops a=irevoire
- Stop lowercasing the field when looking in the field id map
- When a field id does not exist it means there is currently zero
documents containing this field thus we return an empty RoaringBitmap
instead of throwing an internal error
Will fix https://github.com/meilisearch/MeiliSearch/issues/2082 once meilisearch is released
Co-authored-by: Tamo <tamo@meilisearch.com>
2022-01-17 14:06:53 +00:00
Tamo
d1ac40ea14
fix(filter): Fix two bugs.
...
- Stop lowercasing the field when looking in the field id map
- When a field id does not exist it means there is currently zero
documents containing this field thus we returns an empty RoaringBitmap
instead of throwing an internal error
2022-01-17 13:51:46 +01:00
Samyak S Sarnayak
2d7607734e
Run cargo fmt on matching_words.rs
2022-01-17 13:04:33 +05:30
Samyak S Sarnayak
5ab505be33
Fix highlight by replacing num_graphemes_from_bytes
...
num_graphemes_from_bytes has been renamed in the tokenizer to
num_chars_from_bytes.
Highlight now works correctly!
2022-01-17 13:02:55 +05:30
Samyak S Sarnayak
e752bd06f7
Fix matching_words tests to compile successfully
...
The tests still fail due to a bug in https://github.com/meilisearch/tokenizer/pull/59
2022-01-17 11:37:45 +05:30
Samyak S Sarnayak
30247d70cd
Fix search highlight for non-unicode chars
...
The `matching_bytes` function takes a `&Token` now and:
- gets the number of bytes to highlight (unchanged).
- uses `Token.num_graphemes_from_bytes` to get the number of grapheme
clusters to highlight.
In essence, the `matching_bytes` function returns the number of matching
grapheme clusters instead of bytes. Should this function be renamed
then?
Added proper highlighting in the HTTP UI:
- requires dependency on `unicode-segmentation` to extract grapheme
clusters from tokens
- `<mark>` tag is put around only the matched part
- before this change, the entire word was highlighted even if only a
part of it matched
2022-01-17 11:37:44 +05:30
Tamo
98a365aaae
store the geopoint in three dimensions
2021-12-14 12:21:24 +01:00
Clément Renault
25faef67d0
Remove the database setup in the filter_depth test
2021-12-09 11:57:53 +01:00
Clément Renault
65519bc04b
Test that empty filters return a None
2021-12-09 11:57:53 +01:00
Clément Renault
ef59762d8e
Prefer returning None instead of the Empty Filter state
2021-12-09 11:57:52 +01:00
Clément Renault
ee856a7a46
Limit the max filter depth to 2000
2021-12-07 17:36:45 +01:00
Clément Renault
32bd9f091f
Detect the filters that are too deep and return an error
2021-12-07 17:20:11 +01:00
Clément Renault
90f49eab6d
Check the filter max depth limit and reject the invalid ones
2021-12-07 16:32:48 +01:00
Marin Postma
6eb47ab792
remove update_id in UpdateBuilder
2021-11-16 13:07:04 +01:00
Irevoire
0ea0146e04
implement deref &str on the tokens
2021-11-09 11:34:10 +01:00
Tamo
7483c7513a
fix the filterable fields
2021-11-07 01:52:19 +01:00
Tamo
e5af3ac65c
rename the filter_condition.rs to filter.rs
2021-11-06 16:37:55 +01:00
Tamo
6831c23449
merge with main
2021-11-06 16:34:30 +01:00
Tamo
b249989bef
fix most of the tests
2021-11-06 01:32:12 +01:00
Tamo
27a6a26b4b
makes the parse function part of the filter_parser
2021-11-05 10:46:54 +01:00
Tamo
76d961cc77
implements the last errors
2021-11-04 17:42:06 +01:00
Tamo
8234f9fdf3
recreate most filter error except for the geosearch
2021-11-04 17:24:55 +01:00
Tamo
07a5ffb04c
update http-ui
2021-11-04 15:52:22 +01:00
Tamo
a58bc5bebb
update milli with the new parser_filter
2021-11-04 15:02:36 +01:00
Tamo
76a2adb7c3
re-enable the tests in the parser and start the creation of an error type
2021-11-02 17:35:17 +01:00
many
ed6db19681
Fix PR comments
2021-10-28 11:18:32 +02:00
many
2be755ce75
Lower error check, already check in meilisearch
2021-10-27 19:50:41 +02:00
many
3599df77f0
Change some error messages
2021-10-27 19:33:01 +02:00
bors[bot]
d7943fe225
Merge #402
...
402: Optimize document transform r=MarinPostma a=MarinPostma
This pr optimizes the transform of documents additions in the obkv format. Instead on accepting any serializable objects, we instead treat json and CSV specifically:
- For json, we build a serde `Visitor`, that transform the json straight into obkv without intermediate representation.
- For csv, we directly write the lines in the obkv, applying other optimization as well.
Co-authored-by: marin postma <postma.marin@protonmail.com>
2021-10-26 09:55:28 +00:00
Clémentine Urquizar
208903ddde
Revert "Replacing pest with nom "
2021-10-25 11:58:00 +02:00
marin postma
2e62925a6e
fix tests
2021-10-25 10:26:42 +02:00
marin postma
8d70b01714
optimize document deserialization
2021-10-25 10:26:42 +02:00
Tamo
1327807caa
add some error messages
2021-10-22 19:00:33 +02:00
Tamo
c8d03046bf
add a check on the fid in the geosearch
2021-10-22 18:08:18 +02:00
Tamo
3942b3732f
re-implement the geosearch
2021-10-22 18:03:39 +02:00
Tamo
7cd9109e2f
lowercase value extracted from Token
2021-10-22 17:50:15 +02:00
Tamo
e25ca9776f
start updating the exposed function to makes other modules happy
2021-10-22 17:23:22 +02:00
Tamo
6c9165b6a8
provide a helper to parse the token but to not handle the errors
2021-10-22 16:52:13 +02:00
Tamo
efb2f8b325
convert the errors
2021-10-22 16:38:35 +02:00
Tamo
c27870e765
integrate a first version without any error handling
2021-10-22 14:33:18 +02:00
Tamo
01dedde1c9
update some names and move some parser out of the lib.rs
2021-10-22 01:59:38 +02:00
Tamo
c634d43ac5
add a simple test on the filters with an integer
2021-10-21 17:10:27 +02:00
Tamo
6c15f50899
rewrite the parser logic
2021-10-21 16:45:42 +02:00
Tamo
e1d81342cf
add test on the or and and operator
2021-10-21 13:01:25 +02:00
Tamo
423baac08b
fix the tests
2021-10-21 12:45:40 +02:00
Tamo
36281a653f
write all the simple tests
2021-10-21 12:40:11 +02:00
Tamo
661bc21af5
Fix the filter parser
...
And add a bunch of tests on the filter::from_array
2021-10-21 11:45:03 +02:00
bors[bot]
59cc59e93e
Merge #358
...
358: Replacing pest with nom r=Kerollmops a=CNLHC
Co-authored-by: 刘瀚骋 <cn_lhc@qq.com>
2021-10-16 20:44:38 +00:00
刘瀚骋
7666e4f34a
follow the suggestions
2021-10-14 21:37:59 +08:00
刘瀚骋
2ea2f7570c
use nightly cargo to format the code
2021-10-14 16:46:13 +08:00
刘瀚骋
e750465e15
check logic for geolocation.
2021-10-14 16:12:00 +08:00
刘瀚骋
cd359cd96e
WIP: extract the error trait bound to new trait.
2021-10-13 18:04:15 +08:00
刘瀚骋
5de5dd80a3
WIP: remove '_nom' suffix/redundant error enum/...
2021-10-13 11:06:15 +08:00
刘瀚骋
2c65781d91
format
2021-10-12 22:20:22 +08:00
many
360c5ff3df
Remove limit of 1000 position per attribute
...
Instead of using an arbitrary limit we encode the absolute position in a u32
using one strong u16 for the field id and a weak u16 for the relative position in the attribute.
2021-10-12 10:10:50 +02:00
刘瀚骋
d323e35001
add a test case
2021-10-12 13:30:40 +08:00
刘瀚骋
70f576d5d3
error handling
2021-10-12 13:30:40 +08:00
刘瀚骋
28f9be8d7c
support syntax
2021-10-12 13:30:40 +08:00
刘瀚骋
469d92c569
tweak error handling
2021-10-12 13:30:40 +08:00
刘瀚骋
7a90a101ee
reorganize parser logic
2021-10-12 13:30:40 +08:00
刘瀚骋
f7796edc7e
remove everything about pest
2021-10-12 13:30:40 +08:00
刘瀚骋
ac1df9d9d7
fix typo and remove pest
2021-10-12 13:30:40 +08:00
刘瀚骋
50ad750ec1
enhance error handling
2021-10-12 13:30:40 +08:00
刘瀚骋
8748df2ca4
draft without error handling
2021-10-12 13:30:40 +08:00
Tamo
11dfe38761
Update the check on the latitude and longitude
...
Latitude are not supposed to go beyound 90 degrees or below -90.
The same goes for longitude with 180 or -180.
This was badly implemented in the filters, and was not implemented for the AscDesc rules.
2021-10-07 16:10:43 +02:00
many
085bc6440c
Apply PR comments
2021-10-06 11:12:26 +02:00
many
1bd15d849b
Reduce candidates threshold
2021-10-05 18:52:14 +02:00
many
ea4bd29d14
Apply PR comments
2021-10-05 17:35:07 +02:00
many
3296bb243c
Simplify word level position DB into a word position DB
2021-10-05 12:15:02 +02:00
many
75d341d928
Re-implement set based algorithm for attribute criterion
2021-10-05 12:14:50 +02:00
