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Live Demonstration

Oracle AI Vector Search

This page demonstrates Oracle Database 26ai's native AI Vector Search operating against the technical resources published on lacampanelli.com.

Instead of searching for exact keywords, Oracle converts your question into a semantic embedding and compares it against embeddings generated from every technical article stored in the database. The result is a search based on meaning, allowing you to ask questions naturally and discover relevant content even when your wording differs from the article itself.

Every result is generated in real time using Oracle Database 26ai AI Vector Search. The retrieved content can then serve as trusted context for Retrieval-Augmented Generation (RAG), AI assistants, enterprise knowledge search, and intelligent Oracle APEX applications.

Ask a Question

Ask a question using natural language. Oracle Database 26ai will perform a semantic search across the published technical content.

Try asking questions like:

  • ✓ What is Oracle AI Vector Search?
  • ✓ Explain JavaScript MLE in Oracle Database 26ai.
  • ✓ How do I install an embedding model in Autonomous Database?
  • ✓ How do I call a REST service using JavaScript MLE?
  • ✓ What is AI-Native Architecture?
  • ✓ Show examples of Oracle Cloud Infrastructure integration.

Try an unrelated question

"Who won the Super Bowl in 1997?"

This information does not exist in the website's knowledge base, so Oracle Vector Search should return either low-confidence matches or indicate that no relevant content was found. This demonstrates an important characteristic of a well-designed Retrieval-Augmented Generation (RAG) system—it retrieves information that exists rather than inventing an answer.

Powered entirely by Oracle Database 26ai, Oracle AI Vector Search, Oracle APEX, and Oracle Cloud Infrastructure.