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Oracle Database 23ai in Practice
Oracle Database 23ai folds vector search, graph, JSON and in-database machine learning into the engine you already run — so AI features work on your data without a second system to license, secure and sync. This is that capability set, from the delivery side.
Overview
Oracle calls 23ai a converged database: one engine that handles relational, JSON, graph and vector data together. In practice that means an application can run a semantic vector search, traverse a graph and join both to ordinary relational rows in a single SQL statement — without moving data between specialised systems.
For AI work, the headline feature is AI Vector Search: a native VECTOR data type, vector indexes and distance functions that let you build retrieval-augmented generation (RAG) on private enterprise data while it stays inside the database, under existing Oracle security. Select AI and DBMS_CLOUD_AI add natural-language-to-SQL, and Oracle Machine Learning trains and scores models where the data lives.
The articles below are the technical detail — vector search and JSON Duality, Select AI, property graphs and SQL/PGQ, production RAG, a head-to-head with PostgreSQL, and why real-world performance tuning is an ecosystem problem, not just a SQL one.
Articles in this cluster
Each links to the full guide. No gated PDFs — the architecture, the trade-offs and the compliance detail in full.
Oracle Database 23ai: Vector Search, JSON Duality Views & AI Vector Index Deep Dive
The features that turn your existing Oracle DB into an AI-native data platform
Oracle Database 23ai: Select AI, DBMS_CLOUD_AI & LLM Integration — Complete Technical Guide
How to wire GPT-4o, Cohere, and OCI Generative AI directly into Oracle SQL using Select AI and DBMS_CLOUD_AI
Building Production RAG on Oracle Database 23ai: Vector Search + LangChain + Python End-to-End
Complete walkthrough — embed documents, store vectors natively in Oracle, build a LangChain retriever, and serve a RAG c...
Oracle Database 23ai Property Graph, SQL/PGQ & In-Database Machine Learning — Technical Deep Dive
Graph analytics with SQL/PGQ queries, Oracle Machine Learning AutoML UI, and combining ML predictions with vector search...
Oracle Database 23ai vs PostgreSQL 16 for Enterprise AI Workloads: A Technical Comparison
Vector indexing, native ML, graph queries, JSON support, and operational security compared head-to-head — which database...
Oracle Performance Tuning Is Not About SQL, It Is About the Entire Ecosystem
Why two databases with the same CPU and RAM perform completely differently, and the five changes that deliver the larges...
Oracle Analytics Cloud + AI: Machine Learning Pipelines Without Writing Code
AutoML, Augmented Analytics, and Embedded ML Models in OAC explained
Frequently asked questions
What is Oracle Database 23ai?
What does a converged database actually give you?
When should we use AI Vector Search instead of a dedicated vector database?
Can Oracle 23ai run RAG on private enterprise data?
Where can Oracle Database 23ai run?
More Oracle topics
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Putting Oracle Database 23ai to work?
From AI Vector Search and RAG on your own data to graph, in-database ML and honest performance tuning — we help enterprises adopt 23ai on the estate they already run. Talk to the team that wrote these guides.
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