Oracle Database 23ai introduces AI Vector Search Indexes, a groundbreaking feature that transforms how vector data is searched and analyzed. With the ability to index and search high-dimensional vector data, organizations can now unlock valuable insights and enhance decision-making processes. AI Vector Search Indexes are designed to handle complex data types, such as word embeddings, image features, and document vectors, enabling advanced similarity searches and semantic queries. 1. Introduction to AI Vector Search: - Provide an overview of AI Vector Search Indexes and their unique capabilities for handling vector data. 2. Managing Vector Indexes: - Discuss the creation, management, and optimization of AI Vector Search Indexes, supporting various data types such as text, image, and documents. 3. Technical Insights: - Offer a technical deep dive into the inner workings of AI Vector Search Indexes, including data ingestion and vector transformation processes. 4. Performance Optimization: - Share best practices and performance tuning techniques to build efficient and scalable vector search solutions, ensuring fast and accurate query processing.