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Generative AI and Vector Search

Published by Oracle

The document explores how generative AI (GenAI) and vector search are transforming data accessibility and analysis by enabling faster, more context-rich search results across unstructured data. It explains how vector embedding models convert complex data types into vectors, allowing for rapid semantic searches that surpass traditional keyword searches. The paper highlights the benefits of combining vector search with Retrieval Augmented Generation (RAG) to improve large language model (LLM) outputs by tapping into domain-specific data, reducing errors like hallucinations. Oracle's integration of AI Vector Search into its database architecture enhances scalability and performance, making it a powerful tool for enterprises​.

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Related Categories Artificial Intelligence, Generative AI, Deep Learning, Cognitive Computing, Machine Learning Models, AI in Business, AI-powered Analytics, AI in Healthcare, Business & Enterprise Solutions, Business Analytics, Corporate Strategy, Market Analysis

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