Search
OpenSearch provides many features for customizing your search use cases and improving search relevance.
Search methods
OpenSearch 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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Neural search: Neural search facilitates generating vector embeddings at ingestion time and searching them at search time. Neural search lets you integrate ML models into your search and serves as a framework for implementing other search methods. The following search methods are built on top of neural search:
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Semantic search: Considers the meaning of the words in the search context. Uses dense retrieval based on text embedding models to search text data.
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Multimodal search: Uses multimodal embedding models to search text and image data.
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Neural sparse search: Uses sparse retrieval based on sparse embedding models to search text data.
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Hybrid search: Combines traditional search and vector search to improve search relevance.
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Conversational search: Implements a retrieval-augmented generative search.
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Query languages
In OpenSearch, you can use the following query languages to search your data:
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Query domain-specific language (DSL): The primary OpenSearch 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.
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SQL: A traditional query language that bridges the gap between traditional relational database concepts and the flexibility of OpenSearch’s document-oriented data storage.
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Piped Processing Language (PPL): The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.
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Dashboards Query Language (DQL): A simple text-based query language for filtering data in OpenSearch Dashboards.
Search performance
OpenSearch 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
OpenSearch provides the following search relevance features:
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Compare Search Results: A search comparison tool in OpenSearch Dashboards that you can use to compare results from two queries side by side.
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Querqy: Offers query rewriting capability.
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User Behavior Insights: Links user behavior to user queries to improve search quality.
Search results
OpenSearch supports the following commonly used operations on search results:
Search pipelines
You can process search queries and search results with search pipelines.