# Oracle Database 26ai Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/oracle-database-26ai
> Markdown URL: https://aitinkerers.org/technologies/oracle-database-26ai.md
> Technology record last updated: 2026-03-10T06:14:59Z
> Generated: 2026-09-20T16:37:05Z

Oracle Database 26ai embeds AI Vector Search and Generative AI directly into the converged database: bring AI to your data, not data to AI.

This is the next Long-Term Support (LTS) release, delivering over 300 new features and thousands of enhancements. The core capability is AI Vector Search, simplifying Retrieval Augmented Generation (RAG) pipelines by executing the entire process—vector generation, search, and LLM prompting—directly in SQL, eliminating complex external systems. Autonomous management features (self-optimizing queries, indexing) reduce database downtime by up to 60% (a critical metric). Security is hardened with the in-database SQL Firewall. The platform unifies relational, JSON, graph, and vector data in one secure, high-performance system for mission-critical workloads.

- Official technology site: https://www.oracle.com/database/26ai/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Oracle & NVIDIA AI‑Q: A Blueprint for High‑Performance Research Automation](https://singapore.aitinkerers.org/talks/rsvp_M4Vy0ySvk_Y)

Oracle & Nvidia will be presenting a technical deep-dive demo of an AI Research Assistant built using the NVIDIA AI‑Q blueprint approach, implemented with an enterprise-grade Oracle + NVIDIA stack—using Oracle Database 26ai as the system of record for vectors and retrieval, and NVIDIA’s accelerated AI software for embedding, retrieval optimization, and inference. At a high level, this presentation shows how to build and run a production-ready Retrieval-Augmented Generation (RAG) + agentic workflow where: Enterprise documents are ingested and embedded, using NVIDIA’s AI software stack (including NIM microservices and retrieval components). Embeddings are stored directly inside Oracle Database 26ai using its native VECTOR data type—so we don’t need a separate vector database. Semantic retrieval happens in Oracle Database 26ai using SQL (AI Vector Search), enabling vector similarity search combined with enterprise relational filters and governance. The retrieved context is then fed into a reasoning model to generate grounded answers and structured insights. What the Audience Will Learn / Take Away 1) How Oracle + NVIDIA changes enterprise AI architecture NVIDIA AI Enterprise is available natively through the OCI Console, reducing friction in provisioning AI software and accelerating adoption. Oracle and NVIDIA are co‑engineering deeper integrations, including NVIDIA NIM microservices support and NeMo Retriever integration with Oracle Database 26ai, enabling smoother RAG pipelines. 2) Why Oracle Database 26ai is a key differentiator for RAG Instead of deploying a separate vector DB, I’ll show how Oracle Database 26ai provides AI Vector Search directly inside the database, using the VECTOR data type—letting teams store embeddings next to business data and query semantically in SQL. 3) How to build a faster, simpler, more governable RAG workflow The demo will highlight architectural improvements that enterprise teams care about: - fewer moving parts, - less data duplication, - simpler security and governance patterns, - and a clean operational model that aligns with existing Oracle enterprise data platforms. What we’ll Show in the Demo (Step-by-Step) - Document ingestion (technical PDFs and enterprise materials) - Embedding generation using NVIDIA-optimized AI tooling (NIM microservices / retrieval stack). - Semantic retrieval using Oracle AI Vector Search in SQL (top‑K similarity search + filters). - Answer generation using reasoning over retrieved context

- Event context: AI Tinkerers - The Age of AI & Infrastructure (Singapore) — 2026-02-11 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_M4Vy0ySvk_Y

## Related Technologies

- [Embedding](https://aitinkerers.org/technologies/embedding) ([Markdown](https://aitinkerers.org/technologies/embedding.md)) — 2 public demos
- [Embeddings](https://aitinkerers.org/technologies/embeddings) ([Markdown](https://aitinkerers.org/technologies/embeddings.md)) — 22 public demos
- [Inference](https://aitinkerers.org/technologies/inference) ([Markdown](https://aitinkerers.org/technologies/inference.md)) — 9 public demos
- [NeMo Retriever](https://aitinkerers.org/technologies/nemo-retriever) ([Markdown](https://aitinkerers.org/technologies/nemo-retriever.md)) — 1 public demo
- [NVIDIA AI Enterprise](https://aitinkerers.org/technologies/nvidia-ai-enterprise) ([Markdown](https://aitinkerers.org/technologies/nvidia-ai-enterprise.md)) — 1 public demo
- [Oracle Vector Search](https://aitinkerers.org/technologies/oracle-vector-search) ([Markdown](https://aitinkerers.org/technologies/oracle-vector-search.md)) — 1 public demo
- [Retrieval](https://aitinkerers.org/technologies/retrieval) ([Markdown](https://aitinkerers.org/technologies/retrieval.md)) — 4 public demos
- [SQL](https://aitinkerers.org/technologies/sql) ([Markdown](https://aitinkerers.org/technologies/sql.md)) — 17 public demos
- [Vector Search](https://aitinkerers.org/technologies/vector-search) ([Markdown](https://aitinkerers.org/technologies/vector-search.md)) — 12 public demos