Search
Lucenia provides many features for customizing your search use cases and improving search relevance.
Search methods
Lucenia supports the following search methods:
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Traditional lexical search
- Keyword (BM25) search: Searches the document corpus for words that appear in the query.
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Machine learning (ML)-powered search
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Vector search
- k-NN search: Searches for k-nearest neighbors to a search term across an index of vectors.
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Query languages
In Lucenia, you can use the following query languages to search your data:
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Query domain-specific language (DSL): The primary Lucenia query language that supports creating complex, fully customizable queries.
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Query string query language: A scaled-down query language that you can use in a query parameter of a search request or in OpenSearch Dashboards.
Search performance
Lucenia offers several ways to improve search performance:
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Asynchronous search: Runs resource-intensive queries asynchronously.
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Concurrent segment search: Searches segments concurrently.
Search relevance
Lucenia provides the following search relevance features:
- Compare Search Results: A search comparison tool in OpenSearch Dashboards that you can use to compare results from two queries side by side.
Search results
Lucenia supports the following commonly used operations on search results:
Search pipelines
You can process search queries and search results with search pipelines.