Introduction to Oracle Vector Search – Concepts, Requirements & Use Cases

 

Title:

Unlocking the Power of Oracle Vector Search: Concepts, Requirements, and Real-World Use Cases

Introduction:

In today’s AI-driven world, traditional keyword-based search techniques are no longer sufficient for applications like image recognition, recommendation engines, or natural language search. This is where vector search steps in — enabling machines to find semantically similar data even when exact keywords don't match.

Oracle has embraced this next-gen capability with native vector search support in Oracle Database 23ai, making it possible to run efficient similarity searches — even on BLOB-based image embeddings.

This blog kicks off our 5-part series where we explore Oracle Vector Search, especially focusing on handling images as BLOBs and converting them into meaningful vector embeddings for AI-powered search.

What is Vector Search?

Vector search, also known as nearest neighbor search, enables finding items based on similarity in a high-dimensional space.

Each data item — an image, sentence, product, or user profile — is represented as a dense vector. When you search with a query vector, the engine finds vectors in the database that are "closest" using distance metrics like:

• Cosine Similarity

• Euclidean Distance

• Dot Product

 Example:

Let’s say we have a BLOB column storing product images. We convert each image to a 512-dimensional vector using EfficientNet. When a user uploads a new image, we:

1. Convert it to a vector.

2. Run a similarity search on the database using that vector.

3. Retrieve the top 5 most similar items — visually alike, even if metadata or filenames differ.

Why Oracle Vector Search?

Oracle’s 23ai release brings native support for vector data types and similarity search, making it a powerful platform for:

• AI and ML-driven applications

• Enterprise-scale vector storage

• Seamless integration with SQL and APEX

• Supporting large unstructured datasets like images stored as BLOBs

Core Requirements for Vector Search in Oracle

To build a vector search solution in Oracle, you’ll need:

1. Oracle Database 23ai with Vector capabilities enabled.

2. Vectors or embeddings representing your data. For images, this means converting BLOBs into numerical vectors using ONNX models or other inference engines.

3. Vector data type support: Oracle introduces a VECTOR data type and functions like VECTOR_DOT_PRODUCT, VECTOR_COSINE_DISTANCE, etc.

4. Optional: OML4Py or OCI AI services for generating embeddings.

5. Indexing using Approximate Nearest Neighbor (ANN) indexes for performance optimization.

Component	Purpose
Oracle Database 23ai	Enables native VECTOR data type and search functions
OML4Py or Python + ONNX Runtime	To generate image embeddings from BLOBs
ONNX Model	Like EfficientNetB0 or ArcFace, pre-trained for visual similarity
VECTOR Index	For fast retrieval of nearest neighbors
BLOB storage	To store the raw image before processing

How Oracle Vector Search Works (Simplified Flow)

1. Data Preparation: Store raw data (text, image, etc.) — images as BLOBs in your Oracle table.

2. Embedding Generation: Use an ONNX model (like EfficientNet for images) to convert the BLOB into a vector.

3. Storage: Save the vector using Oracle's VECTOR data type.

4. Indexing: Create a VECTOR INDEX using ANN methods.

5. Querying: Run similarity search queries using ORDER BY VECTOR_DISTANCE(...).

Popular Use Cases

Use Case	Description
Image Search	Store product photos and allow users to find similar-looking items using an uploaded image.
Face Recognition	Identify or group people based on facial similarity using ArcFace or FaceNet embeddings.
Recommendation Engines	Suggest similar content or products using customer behavior vectors.
Semantic Text Search	Enhance keyword search with meaning-based matching using language model embeddings.
Anomaly Detection	Compare current system logs or sensor data with historical embeddings to detect anomalies.

⚙️ How Vector Search Works in Oracle

Here’s a simplified pipeline you can visualize in your blog:

🧩 Key Features of Oracle’s Vector Search Support

• New VECTOR data type to store high-dimensional vectors

• Functions like VECTOR_DISTANCE, VECTOR_COSINE_DISTANCE, etc.

• Integration with SQL and PL/SQL

• Compatible with Oracle Text and JSON for hybrid search

• Easy to plug into APEX and REST-enabled services

Series Roadmap

Here's what to expect in upcoming blogs:

🔹 Blog 2: Generating Image Embeddings from BLOBs using ONNX in Oracle
— Learn how to use EfficientNet or ArcFace ONNX models with OML4Py to extract vector embeddings.

🔹 Blog 3: Storing and Indexing Vectors in Oracle
— Create VECTOR columns, optimize with indexing, and enable fast ANN (Approximate Nearest Neighbor) search.

🔹 Blog 4: Building a RESTful Vector Search API with Oracle APEX
— Use Oracle APEX and REST modules to enable real-time vector search via UI and API.

🔹 Blog 5: Visual Product Search App in APEX: Real-World Case Study
— A complete mini project using BLOBs, ONNX, VECTOR search, and Oracle APEX.

📝 Final Thoughts

Vector search transforms how we interact with data. Oracle’s native support allows you to build scalable, AI-native applications within your existing database ecosystem.

In the next blog, we’ll start by converting image BLOBs into embeddings using ONNX in Oracle.

💡 Tip: If you're using Oracle APEX or ATP, this opens a massive opportunity to integrate AI directly into your low-code applications.