Pradip
30 Jul, 2025
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How Oracle Database 23.5’s AI Vector Search is Changing the Way We Search

A few days back, I came across a short post on LinkedIn where someone mentioned Oracle’s new “AI Vector Search” in version 23.5. That name caught my eye.

Being a database enthusiast and someone always curious about what’s next in tech, I started digging deeper. And what I found made me realize: this is something worth talking about. So, a day before yesterday, I decided to compile everything into a detailed article, for you, for me, for anyone who’s working with data, databases, or even app design.

Let’s dive into what AI Vector Search is, how it works, and why it might be the most exciting search feature Oracle has rolled out in years.

What is Oracle AI Vector Search?

First, let’s break it down.

Traditionally, databases rely on keyword-based searches. That means if you search “beach vacation,” the engine looks for records where “beach” and “vacation” are stored literally. Simple. But also limited.

Now imagine you’re using a travel app and you type:

“chill beach vacation with sunset views.”

Traditional search would pick up “beach,” maybe “vacation,” and match those keywords.

But AI Vector Search? It doesn’t just match words, it understands the intent and context. It knows you want something calm, relaxing, maybe in Bali, Goa, or the Maldives. That’s the magic of semantic understanding.

This is possible because Oracle 23.5 allows you to store vector embeddings, mathematical representations of text, images, or even audio, and perform similarity searches on them using AI/ML models.

What are Vector Embeddings?

Okay, a bit technical now, but stick with me.

A vector embedding is a numerical representation of data, like a sentence, a product description, or a review, converted into a vector of floating-point numbers (think arrays of 128 or 768 numbers).

For example, if you pass the sentence “beach with sunset view” into an embedding model, you’ll get a vector like:

[0.23, -0.45, 0.12, ..., 0.67]

Now, any similar text will generate vectors that are close in this multidimensional space. This closeness can be measured using cosine similarity or Euclidean distance.

This is exactly what Oracle 23.5’s vector search does. It allows you to store these vectors and efficiently search “semantically similar” items.

Why It Matters: Real World Scenarios

Let me give you some real use cases to help you visualize:

1. Travel & Hospitality Apps

As discussed, imagine a travel agency using Oracle. When a user types a vague phrase like “romantic weekend getaway with spa,” traditional keyword matching will fail. But AI Vector Search can understand the meaning and return results from Goa, Kerala, or even hidden resorts in Coorg or Ooty that match the vibe.

2. E-commerce Product Search

Amazon or Flipkart use similar models. When you search “stylish blue shoes for party,” AI Vector Search matches intent, color, use case, and design, not just keywords. Oracle now enables even medium-sized businesses to implement this kind of intelligent search.

3. Fraud Detection and Security

Security logs and messages can be vectorized. If a new suspicious log resembles past fraudulent events (even if exact text is different), Oracle can flag them. Pattern matching becomes smarter.

4. Customer Support

Instead of manually tagging FAQs and responses, you store all past chat data as vectors. When a new customer query comes in, Oracle can match it to the closest resolved case, even if the words are different.

How Oracle Has Integrated Vector Search in 23.5

Here’s the exciting part for DBAs, Devs, and Architects:

Oracle has now made vector search native to its SQL engine!

No need for separate vector databases like Pinecone, Weaviate, or FAISS. You can now:

Store vector embeddings in Oracle tables using the new VECTOR datatype
Use ANN (Approximate Nearest Neighbor) algorithms like HNSW directly in Oracle
Perform semantic search using SQL, e.g.:

SELECT product_id
FROM products
ORDER BY COSINE_DISTANCE(product_embedding, :search_embedding)
FETCH FIRST 5 ROWS ONLY;

This means:

Lower latency
Fewer moving parts
Easier integration with existing Oracle workflows

And Oracle supports integrations with major embedding providers like OpenAI, Cohere, Hugging Face, and in some cases, even allows local LLMs.

Performance & Efficiency

You might be wondering: “Is this even scalable?”

Absolutely. Oracle has worked hard to optimize the performance. Here are some key features:

HNSW Indexing: Oracle uses Hierarchical Navigable Small World graphs for high-performance ANN searches.
Parallelism Support: Multi-threaded execution enables you to scale across CPUs.
Storage Efficiency: You can compress and index vectors efficiently without bloating your tables.
In-Memory Optimization: Oracle’s in-memory columnar format works beautifully with vectorized data.

Why Should You Care?

If you’re a:

DBA: You now have more advanced use cases to offer your business teams.
Data Engineer: You can integrate AI searches into your pipelines natively.
Developer: You can build smarter apps without relying on multiple tools.
Business Leader: You can offer next-gen app experiences with AI-powered personalization.
Tech Curious Mind: This is a good gateway to understanding real-life AI applications without jumping headfirst into ML.

Future of Search: AI + Databases

This move by Oracle is a huge signal.

AI is no longer an “add-on” to databases. It’s becoming a core feature.

With the increasing shift toward multi-modal data, you’ll soon be able to vectorize and search not just text, but:

Product images
Voice queries
Videos
Documents
Medical records

In fact, industries like legal tech, healthcare, insurance, and customer support are already experimenting with hybrid search systems where both keyword and semantic searches are combined.

Oracle is simply bringing this capability to everyone on its platform.

Real-World Case Studies of Oracle AI Vector Search

Case Study 1: Travel Booking Platform (Like Expedia or MakeMyTrip)

A major travel aggregator integrated Oracle Database 23.5 with Vector Search to personalize vacation suggestions. Earlier, when users searched for “romantic beach honeymoon,” the database matched the term “beach” literally, showing Goa, Bali, etc.

After AI Vector Search:

The engine began recognizing deeper semantics like “romantic” and “honeymoon.”
It started recommending lesser-known but highly rated resorts with candlelight dinners, sunset views, and couple-friendly packages, like Gili Islands or Santorini.
Bounce rates dropped by 22%, and booking conversion increased by 31% within 6 months.

Case Study 2: Academic Research Platform

An ed-tech firm used Oracle’s AI Vector Search to help students and researchers find relevant papers based on abstract queries like “impact of climate change on rural economies.”

Earlier, only exact keyword matches worked. Now:

The system interprets intent and context.
It surfaces documents discussing agricultural disruption, migration trends, and economic policies, even if those exact terms weren’t in the query.
Result: User satisfaction scores rose by 40%, and time spent on the platform doubled.

Case Study 3: Healthcare Insights

A hospital network used Vector Search to sift through vast patient records for predictive diagnosis.

Use case:

A doctor types “persistent cough with low-grade fever, travel to South Asia” into the system.
The AI Vector Search doesn’t just match keywords but evaluates semantic patterns across past patient histories.
It alerts doctors to consider tuberculosis or viral pneumonia, factoring in geolocation and historical context.
Diagnostic accuracy increased, and early detection rates improved by 28%.

Future Scope of AI Vector Search in Oracle Database

Now, let’s talk about where this is going.

1. Natural Language Dashboards

Imagine querying dashboards in plain English:

“Show me revenue trends where customer sentiment was positive but churn increased.”

Oracle is already moving in this direction, Vector Search can make querying feel like a conversation, not a code sprint.

2. Enterprise Chatbots That Actually Understand You

Forget scripted chatbot responses. When integrated with AI Vector Search, enterprise chatbots can:

Truly understand complex queries.
Pull data from ERP, CRM, and even unstructured documents.
Give contextual answers with drill-down capabilities. This is a massive boost for internal helpdesks, IT support, and HR bots.

3. Multimodal Search (Text + Image + Voice)

As Oracle integrates further with AI APIs and LLMs:

You might soon upload a chart or speak a voice command, and the system would return semantically relevant results.
Imagine saying: “Find similar dashboards to this graph,” and Oracle returning matching financial trendlines.

4. Data Privacy + Responsible AI

Oracle is doubling down on AI with security. Expect:

Built-in bias detection in vector models.
Audit trails for AI-based decisions.
Role-based query responses.

This makes it usable even in regulated sectors like banking, pharma, and defense.

5. Smarter eCommerce

Picture a customer typing:

“Gifts for mom who loves gardening and meditation” The AI finds personalized gift boxes, yoga kits, bonsai plants, even if those keywords weren’t typed. Soon, this tech will enable real-time product bundling and smarter inventory mapping.

My Final Thoughts

When I first read about Oracle Vector Search, I was curious.

After digging into how it works, its performance, and how it integrates into the existing Oracle ecosystem, I’m truly impressed. It’s not just another buzzword.

If you’re building apps, working in analytics, or managing data infrastructure, understanding this feature gives you a clear edge.

Like I said, this article came from a genuine curiosity. When I realized how powerful this could be, I wanted to break it down for you, all in one place.

If this helped you learn something new, feel free to share your thoughts below. Also let me know, which app do you think nails recommendations using AI the best?

Bonus: How to Try It

If you’re on Oracle Cloud or working with Oracle Database 23c (now available on OCI), you can try vector search using:

Oracle AI Vector Search documentation
SQL Developer / APEX with built-in ML features
REST APIs for search and embeddings

Stay Curious, Stay Ahead

We’re moving toward a world where “search” isn’t about matching words anymore, it’s about understanding people. And technologies like this are going to be at the heart of it.

Let’s keep exploring, experimenting, and building smarter.

At Learnomate Technologies, we make sure you not only understand such cutting-edge features but also know how to implement them in real-world projects. Whether you’re a beginner looking to break into the database world or an experienced professional upgrading your skillset—we’ve got your back with the most practical, hands-on training in Oracle technologies.

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