Hi everyone!

It’s been a while since Oracle 23c (or 23c AI) was released, and there’s already a ton of great content out there on one of its coolest new features: Vector Indexes and Searches. This feature has opened up exciting possibilities for AI-driven applications, from advanced recommendations to similarity searches within databases. Now, it’s finally my turn to dive in and put it to the test!

Vector search in Oracle 23c AI is a feature designed for searching, comparing, and finding similarities between data represented as vectors (numerical arrays). This capability is particularly useful for AI and machine learning applications, where data like images, text, and products are often encoded as vector embeddings. Oracle’s vector search enables the database to efficiently process these embeddings, allowing users to perform similarity-based queries within the database. Oracle’s vector search in 23c AI makes it possible to bring advanced similarity-based search and recommendation capabilities directly into the database, facilitating powerful new applications that leverage the latest in AI and machine learning.

The base or the foundation of this feature is VECTOR data type, a powerful tool for storing and managing vector embeddings directly within the database. This capability allows Oracle to support advanced AI and machine learning applications, such as similarity searches and recommendation engines, by efficiently handling multi-dimensional arrays that represent data features, semantics, or other complex characteristics.

Key Advantages of the VECTOR Data Type

1. Compact Multi-Dimensional Array Storage:

• VECTOR allows fixed-length arrays of numbers (often floating-point) to be stored compactly in a single column. A 100-dimensional vector, for instance, might represent an embedding for text, an image, or a user profile, capturing nuanced data in a manageable form.

1. Efficient Similarity Calculations:

• With Oracle 23c AI, VECTOR data can be processed for similarity searches directly in the database. Functions like VECTOR_DISTANCE make it easy to calculate similarity scores or distances between vectors, enabling Oracle to quickly identify similar items without needing external tools or complex data transformations.

1. Tailored for AI and Machine Learning:

• VECTOR is ideal for applications that require semantic searches, such as finding similar products, media, or documents, and for AI-driven recommendations. This makes it well-suited for Natural Language Processing (NLP), recommendation engines, and image recognition tasks.

1. Support for Various Distance Metrics:

• Oracle’s VECTOR data type is compatible with common distance metrics, such as Euclidean and cosine similarity, which measure how close or far two vectors are from each other. This enables efficient and flexible similarity calculations, essential for applications relying on vector comparisons.

1. Scalability with Vector Indexing:

• Oracle 23c AI allows for VECTOR data indexing through VECTOR_INDEX, optimizing performance for vector-based queries. This makes real-time similarity searches on large datasets feasible, supporting applications that need quick response times, such as real-time AI insights and personalized recommendations.

The VECTOR data type in Oracle 23c AI is a robust addition for data scientists and developers, enabling high-performance similarity searches, efficient data storage, and a foundation for complex AI and machine learning workloads directly within the Oracle database.

Alright, so now when the stage is set, lets stay tuned as I explore how Vector Indexes and Searches can bring a whole new dimension to Oracle 23c. Here’s a detailed, step-by-step demo to help you showcase vector search and indexes in Oracle Database 23c AI, which will be ideal for your blog post. This example will simulate a product recommendation system using vector embeddings to represent product features.

1. Set Up the Environment

Ensure you’re using Oracle 23c AI, as vector indexes and the VECTOR data type are new features available in this release.

2. Create a Table with Vector Embeddings

In this example, each product is represented by a 10-dimensional vector embedding. This vector could represent a combination of product attributes, like color, category, brand, and other numerical encodings.

CREATE TABLE products (
    product_id NUMBER PRIMARY KEY,
    product_name VARCHAR2(100),
    product_description VARCHAR2(255),
    product_embedding VECTOR(10) -- Vector with 10 dimensions
);

3. Insert Sample Data

Populate the table with sample products and their vector embeddings. Here’s an example where each vector represents the product’s characteristics:

INSERT INTO products (product_id, product_name, product_description, product_embedding) VALUES 
    (1, 'Laptop A', 'High-performance laptop for professionals', VECTOR(0.5, 0.2, 0.7, 0.1, 0.4, 0.3, 0.8, 0.6, 0.9, 0.3)),
    (2, 'Smartphone B', 'Latest smartphone with advanced features', VECTOR(0.6, 0.1, 0.8, 0.5, 0.2, 0.7, 0.4, 0.3, 0.6, 0.1)),
    (3, 'Tablet C', 'Compact and powerful tablet for multimedia', VECTOR(0.4, 0.9, 0.5, 0.7, 0.3, 0.6, 0.2, 0.8, 0.1, 0.9)),
    (4, 'Laptop D', 'Affordable laptop with reliable performance', VECTOR(0.7, 0.6, 0.4, 0.9, 0.8, 0.2, 0.5, 0.3, 0.1, 0.4)),
    (5, 'Headphones E', 'Noise-canceling headphones with high fidelity', VECTOR(0.3, 0.8, 0.1, 0.9, 0.6, 0.4, 0.7, 0.2, 0.5, 0.6));

4. Create a Vector Index for Efficient Searches

Creating a vector index allows Oracle to search these embeddings efficiently based on similarity, which is ideal for recommendation systems, image retrieval, and text similarity searches.

CREATE INDEX product_vector_idx ON products (product_embedding)
    INDEXTYPE IS VECTOR_INDEX;

5. Perform a Vector Similarity Search

With the vector index in place, you can now perform searches to find products that are similar to a given vector. For example, suppose a user has shown interest in products that correspond to a vector representation of [0.6, 0.2, 0.5, 0.4, 0.3, 0.5, 0.7, 0.8, 0.2, 0.6]. You can use this vector to find products with similar embeddings.

SELECT product_name, product_description, product_embedding
FROM products
ORDER BY VECTOR_DISTANCE(product_embedding, VECTOR(0.6, 0.2, 0.5, 0.4, 0.3, 0.5, 0.7, 0.8, 0.2, 0.6)) ASC
FETCH FIRST 5 ROWS ONLY;

In this query:

• The VECTOR_DISTANCE function calculates the similarity (distance) between the target vector and each product’s embedding. Sorting by VECTOR_DISTANCE allows you to retrieve the closest matches.
• This example fetches the top 5 most similar products.

6. Use Case: Personalized Product Recommendation

This setup is ideal for applications such as a product recommendation engine. Suppose you have a user profile represented as a vector, you could use a vector search to find products similar to that profile, offering highly relevant recommendations.

7. Handling Updates to Vector Embeddings

If a product’s features change, you may need to update its embedding. Here’s how you could update the vector for a specific product:

UPDATE products
SET product_embedding = VECTOR(0.7, 0.3, 0.9, 0.5, 0.6, 0.4, 0.8, 0.2, 0.3, 0.5)
WHERE product_id = 2;

8. Advanced Use Case: Nearest Neighbor Search with Filtering

Let’s extend the search by filtering for specific criteria. For example, if you only want to recommend products that contain “Laptop” in the name, you can add a WHERE clause to the query.

SELECT product_name, product_description, product_embedding
FROM products
WHERE product_name LIKE '%Laptop%'
ORDER BY VECTOR_DISTANCE(product_embedding, VECTOR(0.6, 0.2, 0.5, 0.4, 0.3, 0.5, 0.7, 0.8, 0.2, 0.6)) ASC
FETCH FIRST 5 ROWS ONLY;

9. Explain the Vector Distance Metric

For your blog, it’s useful to explain the concept of vector similarity:

• Vector Distance (Similarity): This metric measures how similar or dissimilar two vectors are. A smaller distance implies a higher similarity. Oracle 23c AI’s VECTOR_DISTANCE function calculates this distance to identify closely related items, making it ideal for applications in recommendations, content search, and clustering.

10. Additional Notes for Optimization and Use Cases

• Index Maintenance: Like other indexes, a vector index should be maintained for performance. Ensure to monitor the index usage and rebuild if necessary for large datasets.
• Scalability: Oracle 23c AI’s vector search is optimized for large-scale deployments, making it suitable for extensive recommendation systems, content-based searches, and personalized user experiences.

This detailed walkthrough should provide a comprehensive demo for your blog post, showcasing Oracle 23c AI’s new vector search and indexing features. Let me know if you need any additional details or further customization!

Hope It Helped!
Prashant Dixit