OpenSearch Features

Ability to search across different data sources, such as databases, intranets, and applications.

Offers search for a variety of file types.

Ability to search in multiple languages without any additional work required.

Ability of search to handle typos.

Allows the end user to filter and refine search results.

Ability to define synonyms for search terms.

Allows the user to see highlighted results to see which words or phrases match the search query.

Allows the user to search in a natural, intuitive manner.

Gives the user targeted, personalized results based on their activity or preferences.

Allows the user to understand how other users are using the search functionality through dashboards, KPIs, etc.

Ability to plug the search capabilities into other applications or tools.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Embed generative (RAG) capabilities for enhanced answer generation using retrieved content

Allow tuning relevance and ranking through machine-learning or Learning-to-Rank models

Enable the system to understand and interpret natural-language queries

Offers search for a variety of file types.

Ability to search across different data sources, such as databases, intranets, and applications.

Ability to search in multiple languages without any additional work required.

Gives the user targeted, personalized results based on their activity or preferences.

Allows the user to understand how other users are using the search functionality through dashboards, KPIs, etc.

Ability to plug the search capabilities into other applications or tools.

Allows the user to see highlighted results to see which words or phrases match the search query.

Allows the end user to filter and refine search results.

Ability of search to handle typos.

Ability to define synonyms for search terms.

Orchestrates retrieval, reranking, and enrichment steps within RAG workflows

Optimizes chunk selection, context assembly, and grounding specifically for LLM consumption

Enables advanced tuning of lexical, semantic, and reranked retrieval strategies

Provides insights into query behavior, retrieval quality, and relevance performance

Ensures resilience through redundancy, failover, and recovery mechanisms

Manages embedding updates, re-indexing, and model version changes over time

Enables semantic search across text, images, audio, or video using embeddings

Allows teams to bring their own embedding models or LLM providers and change them without re-architecting

Supports near-real-time index updates as source data changes

Enriches content using chunking, metadata inference, entity extraction, or OCR during indexing

Enforces document-, field-, or metadata-level permissions during retrieval

Supports regional data controls, retention rules, and regulatory compliance

Tracks queries, retrieved content, and access decisions for compliance and debugging

Enables fine-grained control over ranking using rules, weights, feedback loops, or learning-to-rank models

Improves retrieval quality through query rewriting, semantic expansion, or intent detection

Supports multi-step retrieval pipelines where initial candidates are reranked using ML or LLM-based approaches

Adjusts retrieval and ranking based on user context, behavior, or application-level signals