Foundation for large language model vertical applications with Unified Database. #DatabaseFoundation

Unified Database: Laying the foundation for large language model vertical applications

Large language models (LLMs) have revolutionized content creation, language comprehension, and intelligent dialogue, but they can produce erroneous or fictitious information due to fixed training data. Retrieval augmented generation (RAG) combines fresh external information with LLMs to enhance performance. A unified database is crucial for managing diverse knowledge bases efficiently, supporting LLMs. The VBase query system addresses the challenge of managing vector and scalar databases by providing a unified foundation for efficient scanning.

SPFresh introduces the first vector index supporting real-time, incremental updates, improving the accuracy of LLM generation results. Existing methods for updating vector databases are slow and resource-intensive, but SPFresh uses a lightweight protocol to dynamically split and reallocate vectors for efficient updates. OneSparse unifies sparse and dense vector indexes, enabling multi-index queries and optimal merge plans for rapid operations.

Unified databases like MSVBASE accelerate LLM development and hardware innovation by providing powerful tools for RAG mechanisms. By leveraging relaxed monotonicity and lightweight update methods, MSVBASE supports semantic analysis of multimodal data, driving innovation in AI. Unified databases facilitate knowledge transfer between data types, supporting large models and advancing data-enhanced AI.

Overall, these advancements in unified databases are crucial for optimizing LLM performance, improving query accuracy, and driving innovation in AI and hardware development.

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