Fast and Precise Business and Semantic Data Search with AI Vector Search
Introduction
As more businesses develop and deploy AI-driven applications, there’s a strategic decision to make: What vector database do we use? Vectors, which are unique strings of numbers calculated to represent unstructured data, let companies add context to generic large language models (LLMs). Vectors enable rapid semantic search of the unstructured data they represent, a critical capability for use cases such as making product recommendations or showing correlations among data or objects.
Oracle recently added vector data to the growing list of data types incorporated into Oracle Database. This support comes in the form of a new capability in Oracle Database 23ai called “AI Vector Search.” It includes vectors as a native data type as well as vector indexes and vector search SQL operators, which together make it possible to store the semantic content of unstructured data as vectors. You can then run fast similarity queries on documents, images, and any other unstructured data represented as vectors.
Oracle’s AI Vector Search supports retrieval-augmented generation (RAG), an advanced generative AI technique that combines LLMs and private business data to deliver responses to natural language questions. RAG provides higher accuracy and avoids having to expose private data by including it in the LLM training data.
Demo
