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OpenSearch provides many features for customizing your search use cases and improving search relevance.

Search methods

OpenSearch supports the following search methods:

  • Traditional lexical search

  • Machine learning (ML)-powered search

    • Vector search

      • k-NN search: Searches for k-nearest neighbors to a search term across an index of vectors.
    • 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:

      • Semantic search: Considers the meaning of the words in the search context. Uses dense retrieval based on text embedding models to search text data.

      • Multimodal search: Uses multimodal embedding models to search text and image data.

      • Neural sparse search: Uses sparse retrieval based on sparse embedding models to search text data.

      • Hybrid search: Combines traditional search and vector search to improve search relevance.

      • Conversational search: Implements a retrieval-augmented generative search.

Query languages

In OpenSearch, you can use the following query languages to search your data:

  • Query domain-specific language (DSL): The primary OpenSearch query language that supports creating complex, fully customizable queries.

  • 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.

  • SQL: A traditional query language that bridges the gap between traditional relational database concepts and the flexibility of OpenSearch’s document-oriented data storage.

  • Piped Processing Language (PPL): The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.

  • 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:

Search relevance

OpenSearch 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.

  • Querqy: Offers query rewriting capability.

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.

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