Search

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
- Keyword (BM25) search: Searches the document corpus for words that appear in the query.
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.

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.

OpenSearch offers several ways to improve search performance:

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.
User Behavior Insights: Links user behavior to user queries to improve search quality.

OpenSearch supports the following commonly used operations on search results:

You can process search queries and search results with search pipelines.