Essential Design Skills for Oracle 23ai Vector Search

Embedding Semantic Intelligence in Enterprise Applications

As Oracle 23ai brings native vector search into the relational database, the promise of semantic search is no longer a futuristic concept—it’s here, and it’s powerful. But moving from impressive demos to scalable, production-grade systems requires more than just enthusiasm. It demands design maturity.

In this post, we’ll explore the essential design skills developers and architects need to confidently embed vector search into enterprise-scale Oracle applications—without accumulating technical debt that could limit future innovation.

Why Vector Search in Oracle 23ai Matters

Vector search enables semantic understanding—the ability to retrieve information based on meaning, not just keywords. With 23ai, this capability is now embedded directly into Oracle’s relational engine, allowing you to:

• Search across structured and unstructured data

• Power intelligent assistants and RAG (Retrieval-Augmented Generation) systems

• Build smarter, more intuitive enterprise applications

But to do this well, you need to make smart design choices early.

Key Design Decisions for Scalable Vector Search

Here are the core areas where thoughtful design makes all the difference:

1. What to Vectorize

• Structured rows: Useful for enhancing traditional queries with semantic context.

• Metadata: Helps enrich search with contextual filters.

• Unstructured content: Documents, emails, support tickets—these are goldmines for semantic search.

2. Granularity of Vectors

• Should you embed entire documents, paragraphs, or sentences?

• The right level of granularity depends on your retrieval goals and query patterns.

3. Embedding Lifecycle Management

• Data changes—so should your vectors.

• Design strategies for re-embedding and versioning to keep your vector store fresh and relevant.

4. Performance and Storage Design

• Indexing strategies (e.g., HNSW, IVF)

• Partitioning and sharding for scale

• Balancing query speed with storage efficiency

Designing for RAG and Intelligent Agents

When integrating with RAG architectures, your vector store becomes the backbone of intelligent retrieval. A centralized, query-friendly vector layer simplifies:

• Cross-domain search

• Agent orchestration

• Prompt engineering and grounding

This is where Oracle 23ai’s tight integration with relational data shines—enabling context-rich, enterprise-aware AI.

From Prototype to Production

Many teams start with proof-of-concepts. But scaling requires:

• Governance: Who owns the embeddings? How are they updated?

• Monitoring: How do you track vector drift or query performance?

• Extensibility: Can your design evolve as new use cases emerge?

Approaches for Storing and Exposing Vector Embeddings in Oracle 23ai

As you design for Vector Search and Retrieval-Augmented Generation (RAG), one critical consideration is how and where to store your vector embeddings within the relational model. Below are three common approaches, along with insights into their benefits, challenges, and ideal use cases.

1. Store Vectors in Each Source Table (Embedded Design)

Description:
Add a vector column (e.g., EMBEDDING_VECTOR) directly to each table that holds data you want to semantically search.

Pros:

Keeps embeddings tightly coupled with source data.

Simple to implement for isolated use cases.

Updates can be managed transactionally with the row.

Cons:

Doesn’t scale well across many tables.

Requires custom vector indexes per table.

Difficult for RAG agents to perform unified search across sources.

Best For:
Standalone search within a single table or tightly scoped feature. Not ideal for cross-domain RAG as there would be many tables to query.

2. Centralized Vector Table with Source References (Decoupled Design)

Description:
Store all vector embeddings in a central table, with metadata columns referencing the source table, row ID, document type, etc.

Pros:

Supports a unified vector index and search experience.

Cleaner integration for RAG agents—one table to query.

Easier to add source-specific logic via metadata filters (e.g., content type, domain, permissions).

Cons:

Adds complexity to sync updates between source data and vector store.

Requires careful orchestration for lifecycle and data integrity.

Best For:
Large-scale RAG pipelines, heterogeneous data sources, or when embeddings span multiple domains.

3. Maintain Vectors in Each Table, but Expose via a Union View (Hybrid Design)

Description:
Vectors are stored in their respective source tables, but a materialized or logical view consolidates them into a unified interface for querying.

Pros:

Offers centralized access while preserving source-local storage.

Enables partial centralization without changing the physical model.

Easier for RAG agents to use if the view is well-structured.

Cons:

View performance can suffer if not properly indexed or if too many sources are involved.

Still requires consistent schema (e.g., vector column names/types) across tables.

Complexity increases with schema evolution.

Best For:
Mid-sized systems where source teams control their own embeddings but want a shared semantic query layer.

Sample SQL for Union View (Design #3)

Assume you have two tables: CUSTOMERS and PRODUCTS, each with a vector column (EMBEDDING_VECTOR) and primary key (CUSTOMER_ID, PRODUCT_ID).

You want a unified view: ALL_EMBEDDINGS_VIEW with the following columns:

• SOURCE_TABLE_NAME

• SOURCE_ROW_ID

• EMBEDDING_VECTOR

Example SQL for the Union View Approach:

CREATE OR REPLACE VIEW ALL_EMBEDDINGS_VIEW AS

SELECT 

  'CUSTOMERS' AS SOURCE_TABLE_NAME,

  CUSTOMER_ID AS SOURCE_ROW_ID,

  EMBEDDING_VECTOR

FROM CUSTOMERS

WHERE EMBEDDING_VECTOR IS NOT NULL

UNION ALL

SELECT 

  'PRODUCTS' AS SOURCE_TABLE_NAME,

  PRODUCT_ID AS SOURCE_ROW_ID,

  EMBEDDING_VECTOR

FROM PRODUCTS

WHERE EMBEDDING_VECTOR IS NOT NULL

;

N.B. You can add more fields (e.g., EMBEDDING_TYPE, UPDATED_AT, DOMAIN) to help filter during RAG retrieval.

4. External Vector Store Synced with Oracle (Externalized Design)

Description:
Use a separate vector store (e.g. OCI OpenSearch) that is periodically synchronized with Oracle. Embeddings are stored externally but contain keys/metadata to reference back into Oracle.

Pros:

• Good for ultra-high-performance similarity search workloads or advanced RAG scenarios.

• Allows decoupled scaling, including GPU-heavy workloads for vector generation.

• Useful if integrating with multiple data sources beyond Oracle.

Cons:

• Loss of native Oracle integration and transactional consistency.

• More complex infrastructure and sync logic.

Best For:
High-scale or cross-system RAG applications where Oracle is just one data source among many.

5. On-the-Fly Embedding (Virtual/Runtime Embedding Design)

Description:
Instead of storing vector embeddings, compute them at query time using an the 23ai built in embedding capability.

Pros:

• No need for storage or update logic.

• Embeddings always reflect current data.

Cons:

• High latency and cost.

• Not suitable for large-scale similarity searches.

• Cannot build vector indexes.

Best For:
Ad-hoc similarity search or scenarios where data is highly volatile or not worth persisting embeddings (e.g. there are too few candidate vectors in the application.

Additional Consideration:

Regardless of the approach, make sure to:

Track embedding versioning to ensure consistency after data updates.

Normalize vector dimensions and types to avoid index or runtime issues.

Consider security and access control at the view or central table level, especially in multi-tenant or cross-functional environments.

Comparing the Approaches

Here's a comparison table showing five different approaches for storing and exposing vectors in Oracle (23ai) and how they align with common design factors like simplicity, query flexibility, RAG integration, performance, and maintainability.

Vector Storage & Exposure Design Matrix

The following table summarizes how one might analyze how each approach stacks up against several vector storage design factors.  Of course, opinions may vary; well it’s not really opinions, but rather the importance or priority of the vector storage design factor to your use case.

Summary Definitions of Each Approach:

1. In Each Table
   Store a VECTOR(…) column directly in each source table.

2. Common Vector Table
   One table with: vector, source_table_name, source_pk, metadata.

3. Union View
   Vector in each table + define a UNION ALL view pulling vector + metadata from each source table.

1. (alternate) Materialized View
   Like the Union View approach, but materialized as a Materialized View with vector and metadata (refreshed regularly).

2. Externalized Hybrid Table
   ETL/ELT process extracts and vectorizes content into a shared table (not linked live to relational sources).

3. On-the-Fly Embedding
   Instead of storing vector embeddings, compute them at query time using an the 23ai built in embedding capability.

Final Thoughts

Oracle 23ai opens the door to a new era of semantic intelligence in enterprise applications. But to walk through that door confidently, you need the right design skills.

By mastering the principles of vector selection, embedding strategy, performance tuning, and RAG integration, you can build intelligent search features that are not only powerful today—but also future-proof.