Multimedia Storage and Search in Oracle AI Database 26ai

Multimedia includes images, audio, video, and combinations of these formats with text. Product photographs, recorded customer calls, training videos, and scanned documents are common examples. Multimedia remains relevant to Oracle applications because this content often needs to be associated with customers, products, transactions, and access permissions.

Oracle AI Database 26ai can store media content, manage its metadata, and search vector embeddings derived from it. These are separate responsibilities. Storing a photograph in a database does not automatically identify its contents, and storing a vector does not replace the original photograph.

1. What Changed Since Oracle Multimedia?

Older releases provided Oracle Multimedia, formerly known as interMedia, with specialized types and APIs such as ORDImage. Oracle Multimedia was desupported beginning with Oracle Database 19c. It is not the foundation for new multimedia applications in Oracle AI Database 26ai.

The current approach combines database storage with application libraries, supported machine learning models, or external services. For example, an application can store a product image in a SecureFiles LOB, use an image library to create a thumbnail, and generate an embedding for similarity search. Each component performs a defined task.

AI Vector Search adds retrieval capabilities. It does not directly replace media decoding, image conversion, video transcoding, or every API formerly supplied by Oracle Multimedia.

2. Store the Original Content and Its Metadata

Choose storage according to transaction, backup, delivery, and access requirements. The main choices are:

A BFILE is not a general-purpose URL for a cloud object. Object storage access uses the relevant service APIs or supported integration tools. External storage is not automatically faster; evaluate the application's access patterns and delivery requirements.

Useful metadata includes a media identifier, owning product or customer, MIME type, creation time, description, and access classification. Keeping these attributes in Oracle allows applications to retrieve the correct content and enforce business rules even when the media itself is stored elsewhere.

3. Manage Large Objects with SecureFiles

SecureFiles is Oracle's recommended storage architecture for persistent LOBs. It supports options such as compression, deduplication, and encryption, subject to configuration and applicable licensing. A SecureFiles LOB is not automatically encrypted merely because it uses SecureFiles storage.

• Inline or out-of-line storage: Small LOB values can be stored within the table row; larger values are stored in a separate LOB segment. The behavior depends on the storage definition and value size. Avoid treating 8,000 bytes as a universal maximum or assuming that more inline storage always improves performance.
• Space reclamation: Automatic SecureFiles Shrink can select eligible out-of-line SecureFiles segments and reclaim unused space in the background. It is disabled by default and does not apply to BasicFiles or inline SecureFiles LOBs.
• Encryption: Transparent Data Encryption protects configured database data at rest. It does not encrypt network traffic. Configure transport encryption separately, such as TLS for database connections and HTTPS for web APIs.

For a multimedia application, a DBA must also plan storage growth, backup duration, recovery requirements, and the effect of large content transfers on the workload. These remain important whether or not the application uses AI.

4. Generate Embeddings for Multimedia Search

An embedding is a numerical representation produced by a trained model. Oracle's VECTOR data type stores these representations, and AI Vector Search compares them using a distance metric. The model determines which features are represented and which comparisons are meaningful.

• Image similarity: An image embedding model can represent photographs so that an application can retrieve visually related examples.
• Text-to-image search: This requires compatible text and image encoders that map both inputs into a shared embedding space. An arbitrary text model cannot search arbitrary image embeddings meaningfully.
• Audio and video search: A pipeline might embed transcripts, selected frames, or segments using an appropriate model. Transcript search captures spoken content, while frame or multimodal embeddings can capture different information.

Embeddings can be generated in application code, through supported external services, or inside Oracle using a supported imported ONNX model. Oracle 26ai documents image embedding support, but model compatibility and accepted input formats still matter. The documented in-database image input path has restrictions; perform required conversion or preprocessing before generating embeddings.

Store the model identifier or version with each embedding. Query embeddings and stored embeddings must use compatible encoders, dimensions, and preprocessing. Equal vector lengths alone do not guarantee compatibility. When content or the embedding model changes, regenerate affected embeddings.

5. Example: Find Similar Product Images

Assume that PRODUCT_IMAGES contains PRODUCT_ID, IMAGE_ID, and an IMAGE_EMBEDDING column of type VECTOR. The application has already populated nonzero image embeddings from one compatible model and binds :query_embedding as a compatible vector. This example uses cosine distance, which must be appropriate for that model.

SELECT product_id,
       image_id,
       VECTOR_DISTANCE(image_embedding, :query_embedding, COSINE)
           AS distance
FROM product_images
WHERE image_embedding IS NOT NULL
ORDER BY VECTOR_DISTANCE(image_embedding, :query_embedding, COSINE)
FETCH EXACT FIRST 10 ROWS ONLY;

The query returns up to ten image records ordered from smallest to largest cosine distance. Lower distance indicates greater similarity under this metric. It compares existing vectors; it neither reads the original image to generate an embedding nor proves that two images depict the same subject.

This is an exact nearest-neighbor search. Larger collections may benefit from approximate search with an appropriate vector index, trading some retrieval accuracy for speed. Evaluate results using representative application data rather than assuming a distance threshold such as 0.25 is meaningful for every model.

Applications can add relational filters, such as product category or publication status, to constrain the candidates. Access-control predicates must also apply when required. A high similarity score is never a substitute for authorization.

6. Combine Storage, Processing, and Retrieval

For a product catalog, a practical workflow is:

1. Store the original image in a BLOB or object storage and record its metadata.
2. Use application libraries or a media service for validation, resizing, and format conversion.
3. Generate an embedding using the selected image model and store it with the image identifier and model version.
4. Generate a compatible embedding for the search input and run a similarity query.
5. Return authorized matching images together with product names, prices, and other relational data.

The same division of responsibilities applies to recordings, videos, and documents, although their processing pipelines differ. Vector search is useful when similarity or semantic retrieval serves the application. Ordinary metadata queries remain sufficient for tasks such as retrieving a known product's approved photograph.

What to Remember

Multimedia remains an important application workload in Oracle AI Database 26ai. Oracle manages media storage and business metadata, while suitable libraries, models, and services perform media processing. AI Vector Search provides an additional way to retrieve related content through embeddings. Designing these parts explicitly produces a more accurate and maintainable application than treating the database as an automatic multimedia-understanding system.

Oracle Documentation

• Oracle Multimedia desupport beginning in 19c
• Introduction to large objects and SecureFiles and BFILEs
• LOB storage configuration and Automatic SecureFiles Shrink
• VECTOR_EMBEDDING and image input restrictions
• Exact similarity search and Approximate search using vector indexes