Understanding Semantic Search with Oracle AI Database 26ai
I recently had the pleasure of presenting at the Quest Oracle Community April 2026 SIG Meeting, where we discussed the evolving landscape of database technology. One of the most transformative shifts we are seeing is the move from simple keyword lookups to Semantic Search — understanding the intent and context behind data, rather than just matching characters. e.g.
The Query: ”How can I make my queries run faster? “
Keyword Search (Traditional): The database looks for the literal characters “faster” or “queries”. It might return a document that says, “Queries are the foundation of SQL,” which is irrelevant, or it might miss the best solution entirely because the word “faster” isn’t in the text.
Semantic Search (Oracle 26ai): The database understands the intent is “performance optimization”. Even if the document doesn’t contain the word “faster”.
In this post, I want to expand on the core concepts from that session, specifically how Oracle AI Vector Search and In-Memory 26ai are redefining high-performance data architecture.
What is Vector?
At its core, a vector is a mathematical representation of the meaning of data — whether that is text, images, or audio. Instead of searching for the word “performance,” a vector allows the database to find results for “In-Memory optimization” because it understands they are conceptually related. Oracle has created a database function called vector_distance to find related items.
The Oracle Advantage: Convergence
I touched during our SIG discussion was the benefit of Oracle’s “Converged Database” approach:
- Native VECTOR Type: You store these mathematical coordinates directly in your existing tables.
- No Data Silos: There is no need to move data to a specialized vector database, your relational data and vectors live together.
- Internal Model Hosting: Using the ONNX (Open Neural Network Exchange) standard, you can import models like all_MiniLM_L12_v2 directly into the database schema using DBMS_VECTOR.LOAD_ONNX_MODEL.
Performance: The Need for Speed
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During the demo portion of my presentation, we looked at how indexing affects search latency. If you are comparing a query to millions of rows, “Exact Search” (brute force) becomes a bottleneck.
Oracle 26ai offers two primary Approximate Nearest Neighbor (ANN) indexes to solve this:
- HNSW (Hierarchical Navigable Small World): Builds a graph structure for lightning-fast, high-accuracy searches.
- IVF (Inverted File Index): Groups vectors into clusters, ideal for massive datasets with less memory overhead.
I compared performance gain in my test environment using indexes and in-memory
Based on the testing shared in the session, the performance gains are staggering:
We discussed during the Quest SIG meeting, the integration of AI directly into the data layer isn’t just a trend, it’s a requirement for modern enterprise applications. By leveraging VECTOR_DISTANCE and VECTOR_EMBEDDING within standard SQL, we can now build intelligent applications faster and more securely than ever before.
In my next blog, I’ll show you how to perform this testing in your own environment and provide all the necessary scripts.
