{"id":31573,"date":"2026-01-06T08:41:25","date_gmt":"2026-01-06T08:41:25","guid":{"rendered":"http:\/\/kovaion-new1.local\/ai-vector-search-in-oracle-database-23ai\/"},"modified":"2026-01-06T10:10:26","modified_gmt":"2026-01-06T10:10:26","slug":"ai-vector-search-in-oracle-database-23ai","status":"publish","type":"post","link":"https:\/\/stage.kovaionplay.com\/stage-kovaion\/ai-vector-search-in-oracle-database-23ai\/","title":{"rendered":"AI Vector Search in Oracle Database 23ai"},"content":{"rendered":"

Oracle’s 23ai release introduces a groundbreaking feature: Vector Search. This innovation empowers Oracle Database to seamlessly integrate diverse data types, enabling you to perform powerful, semantic searches alongside traditional business data queries within a single system.<\/p>\n

Unlike keyword-based searches, Oracle AI Vector Search leverages AI to understand the meaning behind your queries. This eliminates the need for separate vector databases, streamlining your data infrastructure and reducing complexity.<\/p>\n

Furthermore, Vector Search seamlessly integrates with other Oracle Database features, ensuring robust security, optimal performance, and high availability. You can directly access and manipulate data using standard SQL, without requiring any data format conversions. The ability to effortlessly combine disparate data types through simple joins further enhances the power and flexibility of Vector Search.<\/p>\n

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Introduction<\/strong><\/span><\/h2>\n

Vector search is an advanced technology that enables searches by analyzing the similarity or distance between data points represented as multidimensional vectors. Unlike traditional keyword-based search, vector search dives deeper by focusing on the underlying meaning and context of words rather than relying solely on exact word matches. This approach makes vector search a highly effective solution for delivering more accurate and relevant results, enhancing search experiences by aligning with the intent behind the query.<\/p>\n

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Fig 1: Overview of Oracle AI Vector Search<\/strong><\/figcaption><\/figure>\n

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The main process of the vector search<\/strong><\/span><\/h2>\n

These three are the main processes of vector search:<\/p>\n

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1. Generating Vector Embeddings:<\/strong><\/h3>\n

Use a vector embedding model to create a vector representation model and load it into the database.<\/p>\n

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2. Storing Vector Embeddings:<\/strong><\/h3>\n

Create a column in the table with datatype as a vector where we going to do the vector search and update the added column with the loaded model.<\/p>\n

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3. Similarity Search:<\/strong><\/h3>\n

Performing similarity searches using SQL.<\/p>\n

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Defining and Utilizing Vectors for Enhanced Data Representation in Oracle Database<\/strong><\/span><\/h2>\n

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VECTOR Data Type:<\/strong><\/h3>\n