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AssigneeOracle International Corporation
InventorsAleksandar Seovic, Jonathan Knight, Liyaaqatali Mukadam, Philip Chung, Sherwood Zern, Julian Ortiz, Timothy Middleton
Estimating the term…
In accordance with an embodiment, described herein are systems and methods for use of an in-memory data grid as a vector database, with linearly-scalable data ingestion, for use in generative artificial intelligence (AI), data visualization, or other applications that include the use of a large language model (LLM) or a retrieval-augmented generation (RAG) process. In accordance with an embodiment, where AI-related tasks or processes, such as content ingestion and vectorization, or vector similarity searches, can be performed in parallel, the in-memory data grid provides efficient scaling and execution of such processes. When tasked with large amounts of content to be vectorized—for example in a cloud environment or as part of an on-premise solution—the system can scale its processing of the content, in parallel where indicated, to perform an optimal utilization of available computing hardware resources, and expeditiously perform required tasks or processes.
A system for use of an in-memory data grid as a vector database, with linearly-scalable data ingestion, comprising:
A method for use of an in-memory data grid as a vector database, with linearly-scalable data ingestion, comprising:
A non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform a method comprising:
Dependencies are inferred from the claim text.