Engine Overview
The Lindorm vector engine stores, indexes, and retrieves vector data at scale. It supports multiple indexing algorithms, distance functions, and a variety of integrated data retrieval methods, including integrated full-text and vector retrieval—essential for Retrieval-Augmented Generation (RAG) systems to improve the accuracy of large models. Use cases include personalized recommendation, NLP, and intelligent Q&A.
Key features
Low cost and high performance
Disk-based indexing and shared storage architecture enable cost-effective storage. Supports tens of billions of vectors per index with query latency in tens of milliseconds.
Ease of use
Supports real-time data updates and access over protocols such as OpenSearch, SQL, and REST.
Multimodal capabilities
Combines scalar, full-text, and vector retrieval for flexible multimodal queries and various data query capabilities.
One-stop solution
Integrates with the built-in AI engine for embedding inference, providing foundational capabilities for large-model RAG systems.