The Picturepark search offers 3 search modes. When to use which search mode is explained below.
AND search
The AND search finds content that contains all search terms entered. For example, if you search for “Stock shot” the Picturepark translates it to Stock AND shot and searches for images that contain these two values.
OR search
When using the OR search the Picturepark search translates the search term “Stock shot” into “Stock OR Shot and finds content that contains one or more search terms entered
Advanced search
The advanced search allows a variety of exact, fuzzy or replacement searches. You can access the advanced search cheat sheet with search examples below. These queries only work in "Advanced Mode". Using these queries allows searching/accessing specific values in specific fields on specific layers. Check the individual syntax per field.
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Search Analyzers
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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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