AI Solution

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 23c 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

Demo: Fast and Precise Business and Semantic Data Search with AI Vector Search (56:48)

注:为免疑义,本网页所用以下术语专指以下含义:

  1. Oracle专指Oracle境外公司而非甲骨文中国。
  2. 相关Cloud或云术语均指代Oracle境外公司提供的云技术或其解决方案。