Module elasticsearch_dsl::search::highlight

Expand description

Highlighters enable you to get highlighted snippets from one or more fields in your search results so you can show users where the query matches are.

When you request highlights, the response contains an additional highlight element for each search hit that includes the highlighted fields and the highlighted fragments.

Offsets Strategy

To create meaningful search snippets from the terms being queried, the highlighter needs to know the start and end character offsets of each word in the original text. These offsets can be obtained from:

The postings list. If index_options is set to offsets in the mapping, the unified highlighter uses this information to highlight documents without re-analyzing the text. It re-runs the original query directly on the postings and extracts the matching offsets from the index, limiting the collection to the highlighted documents. This is important if you have large fields because it doesn’t require reanalyzing the text to be highlighted. It also requires less disk space than using term_vectors.
Term vectors. If term_vector information is provided by setting term_vector to with_positions_offsets in the mapping, the unified highlighter automatically uses the term_vector to highlight the field. It’s fast especially for large fields (> 1MB) and for highlighting multi-term queries like prefix or wildcard because it can access the dictionary of terms for each document. The fvh highlighter always uses term vectors.
Plain highlighting. This mode is used by the unified when there is no other alternative. It creates a tiny in-memory index and re-runs the original query criteria through Lucene’s query execution planner to get access to low-level match information on the current document. This is repeated for every field and every document that needs highlighting. The plain highlighter always uses plain highlighting.

Warning
Plain highlighting for large texts may require substantial amount of time and memory. To protect against this, the maximum number of text characters that will be analyzed has been limited to 1000000. This default limit can be changed for a particular index with the index setting index.highlight.max_analyzed_offset.

https://www.elastic.co/guide/en/elasticsearch/reference/current/highlighting.html

Structs

DefaultHighlighter

Highlighting settings can be set on a global level and overridden at the field level

FastVectorHighlighter

The fvh highlighter uses the Lucene Fast Vector highlighter. This highlighter can be used on fields with term_vector set to with_positions_offsets in the mapping. The fast vector highlighter:

Highlight

Highlight structure

MatchedFields

Matched fields logic with type conversions

PlainHighlighter

The plain highlighter uses the standard Lucene highlighter. It attempts to reflect the query matching logic in terms of understanding word importance and any word positioning criteria in phrase queries.

PrePostTags

Contains pre_tags and post_tags highlighting values

UnifiedHighlighter

The unified highlighter uses the Lucene Unified Highlighter. This highlighter breaks the text into sentences and uses the BM25 algorithm to score individual sentences as if they were documents in the corpus. It also supports accurate phrase and multi-term (fuzzy, prefix, regex) highlighting. This is the default highlighter.

Enums

Encoder

Indicates if the snippet should be HTML encoded.

Fragmenter

Specifies how text should be broken up in highlight snippets.

FvhBoundaryScanner

Specifies how to break the highlighted fragments. Defaults to sentence.

Highlighter

Highlighter settings

Order

Sorts highlighted fragments by score when set to score. By default, fragments will be output in the order they appear in the field (order: none). Setting this option to score will output the most relevant fragments first. Each highlighter applies its own logic to compute relevancy scores. See the document How highlighters work internally for more details how different highlighters find the best fragments.