Multimedia Management in Oracle 23ai

In Oracle 12c, multimedia management focused on storing and processing specific content types—images, audio, and video—through Oracle Multimedia (interMedia). By contrast, Oracle 23ai represents a fundamental shift: instead of treating multimedia as specialized file data, it treats it as semantic data. This change reflects Oracle’s broader goal of integrating AI-driven understanding and search capabilities directly into the database.

1. AI Vector Search for Multimedia

Oracle 23ai introduces AI Vector Search, which allows semantic and similarity-based searches on multimedia content. Rather than searching by filename or metadata, users can now query based on meaning—for example, finding “images similar to this photo of a golden retriever.”

• Vector Embeddings: Multimedia files—images, audio spectrograms, or even video frames—can be converted into numerical representations called vector embeddings.
• VECTOR Data Type: These embeddings are stored using Oracle’s new VECTOR data type, optimized for AI workloads.
• Similarity Search: Queries can compare vector similarity directly in SQL, combining semantic relevance with business criteria.
• Unified Queries: Vector search integrates seamlessly with relational, JSON, and spatial data, allowing unified analytics across multiple domains.

-- Example: Find products visually similar to an input image
SELECT product_id, name
FROM products
WHERE VECTOR_DISTANCE(image_embedding, :input_vector) < 0.25;

Oracle 23ai’s architecture enables integrations with third-party APIs such as TwelveLabs, which generate rich embeddings that capture visual, audio, and contextual meaning from video content.

2. Traditional Large Object (LOB) Storage

While AI Vector Search provides the processing intelligence, traditional Large Object (LOB) types remain the backbone for storing multimedia data. These LOB types ensure efficient binary and text-based storage inside or outside the database.

• BLOB (Binary Large Object): Stores binary data such as photos, videos, or audio clips.
• CLOB (Character Large Object): Used for large text-based content like transcripts, captions, or metadata in JSON/XML formats.
• BFILE (Binary File): Provides read-only access to multimedia files stored on the server’s file system rather than in the database itself.

These data types ensure flexibility—developers can choose whether to keep multimedia files inside the database for transactional consistency or externally for performance optimization.

3. LOB Enhancements in Oracle 23ai

Oracle 23ai introduces several new features to make LOB management more efficient and self-optimizing:

• Increased Inline LOB Size: LOBs can now be stored inline up to 8,000 bytes, improving performance for small images and icons.
• Automatic SecureFiles Shrink: Reclaims unused space in SecureFiles LOB segments automatically, reducing administrative effort and optimizing storage costs.
• Enhanced Encryption: All LOBs can be encrypted using Transparent Data Encryption (TDE), securing multimedia data both at rest and in transit.

4. Combining Storage and AI Search

Oracle 23ai combines two powerful approaches:

1. Storage: Use LOBs (BLOB, CLOB) to store the original multimedia content efficiently and securely.
2. Search and Understanding: Use the new VECTOR data type and AI Vector Search to store embeddings that capture semantic meaning—enabling similarity, classification, and contextual retrieval.

This dual-layer model enables developers to build intelligent applications that understand multimedia rather than merely indexing it. For example:

• Search for “images containing blue skies” even if the filename or metadata does not mention color.
• Locate video clips similar in mood or tone based on AI-generated embeddings.
• Integrate image similarity results directly with business logic in SQL or RESTful APIs.

5. Transition from Oracle Multimedia to AI Vector Search

Older Oracle releases (like 12c) used Oracle Multimedia (interMedia) with specialized data types such as ORDImage and ORDVideo. These have now been deprecated. Oracle 23ai’s modern approach eliminates rigid type definitions in favor of flexible, AI-native models that can integrate with any multimedia format.

This modernization reflects a shift from managing multimedia files to understanding and reasoning about multimedia content. AI Vector Search positions Oracle 23ai as not just a data store, but a semantic engine for multimodal data.

Summary

Oracle 23ai provides a two-part framework for multimedia:

• Storage: Traditional LOBs (BLOB, CLOB, BFILE) manage raw multimedia content efficiently.
• Processing and Search: The new VECTOR data type and AI Vector Search enable intelligent, similarity-based retrieval of multimedia based on content meaning rather than metadata.

This architecture bridges structured business data with unstructured multimedia, giving organizations a powerful platform for AI-driven search, analytics, and decision-making directly inside Oracle Database 23ai.