OpenSearchCon India 2026

Organizations generate huge amounts of internal documentation across wikis, runbooks, architecture guides, and operational documents. Finding the right information quickly can be difficult when traditional keyword search cannot capture the intent behind a question.

In this talk, I will share how a Retrieval Augmented Generation assistant can be built using OpenSearch vector search to retrieve relevant knowledge from enterprise documentation. The system converts user queries into embeddings, retrieves the most relevant documents from OpenSearch, and provides them to a language model to generate grounded answers.

The session will walk through the architecture of the system, including document ingestion, embedding generation, vector indexing, and semantic retrieval. We will also discuss strategies for improving retrieval quality, managing latency in the query pipeline, and ensuring that enterprise data remains secure.

Attendees will learn how OpenSearch can power scalable semantic search systems and how vector search can be combined with language models to build reliable knowledge assistants for modern engineering organizations.