Oracle APEX Vector Search Demo | Semantic Search with Oracle 26ai & ONNX

Описание: In this video, I demonstrate Vector Search in Oracle APEX using the Oracle 26ai Database.

This is a demo-focused video where we explore how semantic search works inside Oracle — without relying on traditional keyword-based queries. Instead of matching text, Oracle converts both the stored data and the search input into vectors and compares their meaning using cosine similarity.

You’ll see how Oracle APEX can return highly relevant results even when the search text does not contain exact keywords. This demo uses an ONNX embedding model (MiniLM) running directly inside Oracle Database 26ai.

Important:
This video focuses only on demonstrating the vector search capabilities.
The complete setup, configuration, and explanation of what happens behind the scenes will be covered in Part 2.

Prerequisites:
– Oracle 26ai Database
– ONNX model setup (MiniLM)

I have already covered these prerequisites in earlier videos. Links to those videos are provided below.

In Part 2, we will cover:
– Generating vector embeddings
– Understanding VECTOR_EMBEDDING and VECTOR_DISTANCE
– Building the complete Oracle APEX application step by step
– How cosine similarity works internally
– Entire APEX app setup

If this video helps you understand how semantic search works in Oracle APEX, consider liking the video, sharing your thoughts in the comments, and subscribing for more Oracle, APEX, and AI-related content.

Keywords:
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Chapters:
00:00 Channel Introduction
00:07 Video Introduction
00:21 Prerequisites for Vector search in Oracle APEX
01:14 Demo Begins
01:23 Feature 1 - Semantic Search
02:37 Feature 2 - Create vectors from description
03:30 Demo Conclusion
04:04 Channel Outro



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