Help:CirrusSearch/fr

CirrusSearch est le nouveau moteur de recherche de MediaWiki. Il dispose d'améliorations majeures par rapport au précédent moteur de recherche, LuceneSearch. Cette page décrit les changements liés à cette transition.

Questions fréquentes
Si votre question ne trouve pas de réponse ici, n’hésitez pas à demander sur la page de discussion et quelqu’un vous y répondra.

Quelles sont les améliorations ?
Le nouveau moteur de recherche apporte trois améliorations principales par rapport à l'ancien, à savoir :


 * Meilleur support pour la recherche dans différentes langues.
 * Des mises à jour plus rapides pour l'indexation, ce qui veut dire que les changements seront visibles dans les résultats de recherche plus vite.
 * Développement des modèles, ce qui signifie que tout le contenu dans un article qui est à l’intérieur d’un modèle se trouve maintenant dans les résultats de recherche.

Mises à jour
L'indexation est réalisée presque en temps réel. Vous devriez voir vos changements aussitôt que vous les avez faits. Les modifications apportées aux modèles devraient prendre effet dans les articles qui les incluent au bout de quelques minutes. Les modifications de modèles utilisent la job queue, donc les performances peuvent varier. Un null edit à l'article forcera la modification, mais cela ne devrait être fait qu'en cas de nécessité.

Suggestions de recherche
The search suggestions you get when you type into the search box that drops down candidate pages is substantively the same with articles sorted by the number of incoming links. Worth noting is that if you start your search with ~ we won't find any articles as you type and you can safely hit enter at any time to jump to the search results page.

ASCII/accents/diacritics folding is turned on for English text, but there are some formatting problems with the result. See 52656.

Recherche de texte brut
La recherche de texte brut (qui vous amène sur la page de recherche) s'effectue dans les titres, les redirections, les titres de paragraphe ou le texte des pages donc ne devrait présenter aucune surprise. Le changement le plus important ici est que c'est le contenu des modèles dans les pages en question qui est pris en compte, plutôt que le wikicode du modèle lui-même.

Il y a encore quelques bogues :


 * Strict phrase matches (those that turn off stemming) aren't always properly highlighted

Filtres (intitle:, et incategory:)


We've tightened up the syntax around these quite a bit.


 * intitle:foo
 * Trouve les articles dont le titre contient « foo ». Trouve aussi ses dérivés simples (foos, par exemple).
 * intitle:"foo bar"
 * Trouve les articles dont le titre contient « foo » et « bar ». Trouve aussi leurs dérivés.
 * intitle:foo bar
 * Trouve les articles dont le titre contient « foo » ou dont le texte contient « bar ».
 * -intitle:foo bar
 * Trouve les articles dont le titre ne contient pas « foo » ou dont le texte contient « bar »
 * intitle: foo bar
 * Erreur de syntaxe, revient à chercher les article dont le titre ou le texte contient « intitle: », « foo » et « bar ».
 * incategory:Musique
 * Trouve les articles de la Catégorie:Musique
 * incategory:"histoire musicale"
 * Trouve les articles qui sont dans la Catégorie:Histoire_musicale
 * incategory:"musicals" incategory:"1920"
 * Find articles that are in both Category:Musicals and Category:1920
 * -incategory:"musicals" incategory:"1920"
 * Find articles that are not in Category:Musicals but are in Category:1920
 * cow*
 * Find articles whose title or text contains words that start with cow

prefix:
The prefix: syntax in its current form is relied upon for a bunch of functionality so it's been recreated as exactly as possible.


 * prefix:cow
 * Find articles in the content namespaces whose title starts with the word "cow".
 * domestic prefix:cow
 * Find articles in the content namespaces whose title starts with the word "cow" and that contain the word "domestic".
 * domestic prefix:Cow/
 * Find all sub-pages of the article "Cow" in the content namespaces that contain the word "domestic". This is a very common search and is frequently built using a special URL parameter called.
 * domestic prefix:Talk:Cow/
 * Find all sub-pages of the talk page "Talk:Cow" in the talk namespace that contain the word "domestic".
 * cow prefix:Pink Floyd/
 * Find all sub-pages of the article "Pink Floyd" in the content namespaces that contain the word "cow". The space is now insignificant.

Note that the old rule of having to put prefix: at the end of the query still applies.

Special prefixes

 * morelike:Endothermic
 * Find articles whose text is similar to Endothermic.
 * Talk:Foo
 * Find articles in the talk namespace whose title or text contains the word foo

Did you mean
"Did you mean" suggestions are designed to notice if you misspell an uncommon phrase that happens to be an article title. If so, they'll let you know. They also seem to suggest more things then they ought to sometimes.

Prefer phrase matches
If you don't have too much special syntax in your query we'll give perfect phrase matches a boost. I'm being intentionally vague because I'm not sure exactly what "too much special syntax" should be. Right now if you add any explicit phrases to your search we'll turn off this feature.

Fuzzy search
Putting a ~ after a search term (but not double quotes) activates fuzzy search. You can also put a number from 0 to 1 to control the "fuzziness" fraction, e.g. nigtmare~.9 or lighnin~.1 or lighnin~0.1. Closer to one is less fuzzy.

Phrase search and proximity
Surrounding some words with quotes declares that you are searching for those words close together. You can add a ~ and then a number after the second quote to control just how close you mean. The proper name for this "closeness" is "phrase slop". The default "phrase slop" is 1.

Quotes and exact matches
Quotes turn on exact term matches. You can add a ~ to the quote to go back to the more aggressive matcher you know and love.

prefer-recent:
You can give recently edited articles a boost in the search results by adding "prefer-recent:" to the beginning of your search. By default this will scale 60% of the score exponentially with the time since the last edit, with a half life of 160 days. This can be modified like this: "prefer-recent:,". proportion_of_score_to_scale must be a number between 0 and 1 inclusive. half_life_in_days must be greater than 0 but allows decimal points. This number works pretty well if very small. I've tested it around .0001, which is 8.64 seconds.

This will eventually be on by default for Wikinews, but there is no reason why you can't activate it in any of your searches.

hastemplate:
You can filter pages to just those that use a template by adding  to the search. We try to emulate the template inclusion syntax so  finds pages with   and   would find transclusions of the article   from the main namespace. You can omit the quotes if the template's title you are looking for does not contain a space. will filter pages that contain that template.

You can combine all sorts of fun search syntax to get only middle quality images of china.

boost-templates:
You can boost pages scores based on what templates they contain. This can be done directly in the search via  or you can set the default for all searches via the new   message. replaces the contents of  if the former is specified. The syntax is a bit funky but was chosen for simplicity. Some examples:
 * Find files in the China category sorting quality images first.
 * Find files in the China category sorting quality images first.


 * Find files in the China category sorting quality images first and low quality images last.
 * Find files in the China category sorting quality images first and low quality images last.


 * Find files about popcorn sorting quality images first and low quality images last. Remember that through the use of the  message this can be reduced to just.
 * Find files about popcorn sorting quality images first and low quality images last. Remember that through the use of the  message this can be reduced to just.

Don't try to add decimal points to the percentages. They don't work and search scoring is such that they are unlikely to matter much.

A word of warning about : if you add really really big or small percentages they can poison the full text scoring. Think, for example, if enwiki boosted featured articles by a million percent. Then searches for terms mentioned in featured articles would find the featured articles before exact title matches of the terms. Phrase matching would be similarly blown away so a search like  would find a featured article with those words scattered throughout it instead of the article for Brave New World.

Sorry for the inconsistent  in the name. Sorry again but the quotes are required on this one. Sorry also for the funky syntax. Sorry we don't try to emulate the template transclusion syntax like we do with.

insource:

 * Coming with 1.24wmf10.

will search text just in the wikitext. This will pick up template parameter names, URLs in link tags, etc. It has two flavors:
 * and
 * These work pretty similarly to  in that they search the pre-tokenized version of the source.  This should be just as quick as a regular search.  The quoted version searches for phrases in the source.


 * and
 * These run Regular expressions against the page source. They aren't efficient and we only allow a few of them to run at a time on the search cluster, but they are very powerful.  This link contains an explanation of the syntax and this link contains an actual grammar for the regex language. The version with the extra   runs the expression case insensitive and is even more inefficient.

Auxiliary Text
Cirrus considers some text in the page to be "auxiliary" to what the page is actually about. Examples include table contents, image captions, and "This article is about the XYZ. For ZYX see ZYX" style links. You can also mark article text as auxiliary by adding the  class to the html element containing the text.

Auxiliary text is worth less then the rest of the article text and it is in the snippet only if there are no main article snippets matching the search.

Lead Text
Cirrus assumes that non-auxiliary text that is between the top of the page and the first heading is the "lead in" paragraph. Matches from the lead in paragraph are worth more in article ranking.

Voir aussi

 * Full specifications in the browser tests