See how Oracle AI Database 26ai is redefining enterprise AI search. Discover the latest on AI Vector Search—including hybrid and sparse vectors, next-generation indexing, and secure private-AI—plus a look ahead at Oracle’s roadmap for Agentic RAG and powerful new AI features. Try it hands-on and find out what’s possible: https://livelabs.oracle.com/ords/r/dbpm/livelabs/home
Oracle’s AI Vector Search transforms enterprise data with robust, scalable similarity search that unites structured and unstructured information for breakthrough insights. Recent enhancements take integration further than ever, enabling developers to run context-aware queries on text, images, and business data—all with the trusted security and high availability you expect from Oracle AI Database. Learn about new capabilities like hybrid vector indexes (combining keyword and vector semantic search), database-resident embedding, private AI options, and frictionless SQL integration. See practical examples for apps, analytics, and generative AI, and preview what’s next for AI agent workflows and trusted AI infrastructure.
00:00:00 Welcome And Session Overview
00:00:23 Why Vector Search Matters
00:03:41 National Parks App Demo
00:07:47 Enterprise Deployment Choices
00:10:31 House Hunting Vector Search
00:13:25 Creating Embeddings In Database
00:16:06 Scaling With Vector Indexes
00:19:11 Disk-Based IVF Index Explained
00:21:16 Hybrid Index: Text And Vectors
00:24:09 IVF Include Columns Feature
00:25:10 RAG With Database Content
00:29:21 Agentic Workflows Vs One-Shot RAG
00:34:58 SQLcl MCP Server And LLM Chat
00:36:43 Annotations To Explain Schemas
00:39:04 Roadmap: Agents And Private AI
00:41:36 Autonomous Vector Database Service
00:43:34 Trusted Answer Search Concept
00:44:51 Recap And Try-It Resources
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