Die Picturepark Suche bietet 3 Suchmodi, sowie Suchvorschläge aus den Listeneinstellungen.
Wann welcher Suchmodus zu verwenden ist, wird unten erklärt.
AND Suche
Die AND Suche sucht nach Inhalten, die alle eingegebenen Suchbegriffe enthalten. Wenn Sie zum Beispiel nach “Stock shot” suchen, übersetzt Picturepark dies in Stock AND shot und sucht nach Bildern, die diese beiden Werte enthalten.
OR Suche
Bei Verwendung der OR Suche übersetzt die Picturepark Suche den Suchbegriff “Stock Shot” in “Stock OR Shot” und sucht nach Inhalten, die einen oder mehrere eingegebene Suchbegriffe enthalten.
Erweiterte Suche
Der Picturepark erlaubt eine Vielzahl von exakten, Fuzzy oder ersetzenden Suchen. Sie können auf das Cheat Sheet der erweiterten Suche mit den Beispielen unten zugreifen. Diese Suchanfragen funktionieren nur im “Advanced Mode”. Diese Suchabfragen erlauben es nach spezifischen Werten in spezifischen Feldern zu suchen auf spezifischen layern. Überprüfen Sie die individuelle syntax pro Feld.
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Simple Search Analyzer
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Simple Search Analyzeraccess in search queries: simple The simple search analyzer is a custom Picturepark implementation not using Elastic search defaults. The custom analyzer uses a regex:
If you want to test the simple search analyzer, you can check your terms in a regex tester to see the outcome.
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No Diacritics Analyzeraccess in search queries: no-diacritics The no diacritics analyzer:
An example can be found in Elastic Search Documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-asciifolding-tokenfilter.html |
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Path Hierarchy Analyzeraccess in search queries: pathHierarchy The path hierarchy analyzer will:
You should only configure this analyzer if being used via API. The simple search in Picturepark escapes Special Characters, and therefore you won't find assets when searching for some of the tokens generated by this analyzer. |
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Language Analyzeraccess in search queries: language There are several language analyzers available for elastic search. Language analyzers prevent stemming from language-specific values and language-specific stopwords. |
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Ngram Analyzeraccess in search queries: ngram Starting point for exact substring matches was ngram tokenizing, which indexes all the substrings up to length n. The drawback of ngram tokenizing is a large amount of disk space used.
Settings allow to define min and max grams created on indexing and token_chars, which are characters classes to keep in the tokens, Elasticsearch splits on characters that don't belong to any of these classes.
Example: Search "Pegasus"
Examples are in Elastic Search Documentation: |
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Edge NGram Analyzeraccess in search queries: edgeNGram This tokenizer is very similar to nGram but only keeps n-grams that start at the beginning of a token. Settings allow to define min and max grams created on indexing and token_chars, which are characters classes to keep in the tokens, Elasticsearch splits on characters that don't belong to any of these classes. Examples are in Elastic Search Documentation: |
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Useful Links in ElasticSearch DocumentationSimple Analyzer: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-simple-analyzer.html No Diacritics: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-asciifolding-tokenfilter.html Path Hierarchy: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-pathhierarchy-tokenizer.html Language: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html NGram: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-ngram-tokenizer.html EdgeNgram: https://www.elastic.co/guide/en/elasticsearch/reference/7.6/analysis-edgengram-tokenizer.html |
For advanced search queries on analyzed fields, the query can be adjusted to consider the analyzer.
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