Advanced vector search in air-gapped environments

In air-gapped environments where external network connections are absent, implementing advanced search and AI technologies poses significant challenges, particularly for organizations in sensitive sectors like national security and defense. Elastic has been a trusted solution in these settings for over a decade, providing agile technology for essential use cases such as multimodal vector search. As AI becomes integral to daily operations, the ability to efficiently retrieve and analyze both structured and unstructured data is crucial. Challenges include a lack of technical expertise, high data volume, and data formats that hinder AI analysis. To address these, Elastic employs Retrieval Augmented Generation (RAG), which first queries a vector database to provide relevant context before engaging a large language model (LLM), thus optimizing the use of proprietary data. The model evolves with agentic AI, allowing queries to interact with AI agents for more refined results. Multimodal search, combining text, image, audio, and video inputs, enhances information retrieval, especially valuable in the public sector where data is often stored in complex formats like lengthy PDFs and videos. Elastic's hybrid search, integrating keyword and vector search, further refines results, offering a tailored dataset by reranking and personalizing search outputs.