OpenSearch is an open source search and analytics engine that is a fork of Elasticsearch. It provides full-text search, analytics, and visualization capabilities.
OpenSearch can leverage large language models and other AI techniques in the following ways:
- Relevance tuning – LLMs can be used to better understand search queries and match them to relevant content. OpenSearch relevance scores can be tuned based on insights from LLMs.
- Query understanding – LLMs can analyze search queries to detect intent, extract entities, and expand/rewrite queries when necessary. This improves search performance.
- Document understanding – LLMs can analyze and extract metadata from unstructured text content to enrich documents indexed in OpenSearch. This enables better search and analytics.
- Recommendations – LLMs can power recommendation engines built on top of OpenSearch to suggest related content.
- Conversational interfaces – Chatbots and virtual assistants leveraging LLMs can use OpenSearch to access relevant information and answer natural language questions.
So in summary, OpenSearch provides the core search and analytics capabilities, which can be enhanced using LLMs and other AI techniques to improve relevance, understanding, recommendations, and conversational abilities. It’s a way to productionize AI to enhance search experiences.
What is OpenSearch? – OpenSearch Explained – AWS
Find out what is OpenSearch, how it works, why companies use OpenSearch, and how to use it on Amazon Web Services.