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Ryan Giggs
Ryan Giggs

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Oracle AI Vector Search in Oracle Database 23ai

#oracledatabase23ai #machinelearning #vectordatabase

Oracle Database 23ai (formerly 23c) represents a significant leap forward in integrating artificial intelligence capabilities directly into the database. One of its most groundbreaking features is AI Vector Search, which enables the database to understand conceptual similarity between pieces of data. This powerful feature eliminates the need for separate vector databases and allows you to perform semantic searches alongside traditional business queries within a unified system.

What is Oracle AI Vector Search?

Oracle AI Vector Search enables searching both structured and unstructured data by semantics or meaning, rather than just by values, fundamentally changing how we approach information retrieval. Instead of traditional keyword-based searches that match exact terms, vector search understands the context and meaning behind your queries.

Key Benefits of Oracle AI Vector Search

1. Semantic Search on Unstructured Data

Vector search is considered better than keyword search because it's based on the meaning and context behind words, not the actual words themselves. This allows you to find relevant information even when the exact keywords don't match.

2. Unified Relational and Semantic Search

One of the biggest advantages is the ability to combine semantic search on unstructured data with relational search on business data in a single system. This allows search results based on meaning, not just text matching – a fundamental shift for information retrieval.

3. Eliminates Data Fragmentation

No need to maintain separate vector databases or move data between systems. Your vectors live alongside your business data, benefiting from Oracle's robust security, availability, and performance features.

4. Enhanced Security and Governance

Oracle Database's security features (encryption, RBAC, auditing, Data Vault policies) extend automatically to vector data types. Vectors and graph data can be secured using the same roles and privileges as your tables.

Understanding the Core Components

The VECTOR Data Type

The VECTOR data type was introduced with Oracle Database 23ai, providing the foundation to store vector embeddings alongside business data. To use the VECTOR data type, the COMPATIBLE initialization parameter must be set to 23.4.0 or higher.

Here's a basic example:

CREATE TABLE documents (
    doc_id INT,
    doc_text CLOB,
    doc_vector VECTOR
);
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The VECTOR data type can store vectors in different formats:

  • INT8 (8-bit integers)
  • FLOAT32 (32-bit floating-point numbers)
  • FLOAT64 (64-bit floating-point numbers)

You can specify the number of dimensions and storage format when declaring a column:

CREATE TABLE products (
    product_id NUMBER,
    description VARCHAR2(4000),
    embedding VECTOR(1536, FLOAT32)
);
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Vector Embeddings: The Foundation

Vector embeddings are mathematical vector representations of data points that describe the semantic meaning behind content such as words, documents, audio tracks, or images. They translate semantic similarity, as perceived by humans, into proximity in a mathematical vector space.

How Vector Embeddings Work:

  1. Unstructured data (text, images, audio) is converted into numerical vectors
  2. Semantically similar content has vectors that are close together in vector space
  3. Searching semantic similarity in a dataset is equivalent to searching nearest neighbors in a vector space

Creating Vector Embeddings:

You have several options for generating embeddings:

  1. ONNX Embedding Models: Import pre-trained models into Oracle Database using the ONNX (Open Neural Network Exchange) format
  2. Third-Party REST APIs: Access external embedding services
  3. Local Models: Use models like all-MiniLM-L12-v2 for text embeddings or ResNet for image embeddings

Example of loading an ONNX model:

BEGIN
  DBMS_VECTOR.LOAD_ONNX_MODEL(
    directory => 'DM_DUMP',
    file_name => 'all-MiniLM-L6-v2.onnx',
    model_name => 'doc_model'
  );
END;
/
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Vector Indexes: Accelerating Similarity Search

While exact similarity searches are accurate, they can be slow for large datasets. Oracle AI Vector Search supports three types of vector indexes that use Approximate Nearest Neighbor algorithms to reduce distance calculations:

1. HNSW (Hierarchical Navigable Small World) Index

HNSW is an In-Memory Neighbor Graph vector index that's very efficient for approximate similarity search. It creates a layered graph structure that enables fast navigation through high-dimensional space.

Characteristics:

  • In-memory only (stored in Vector Memory Pool in SGA)
  • Extremely fast query performance
  • Memory can be estimated as 1.3 times the product of vector format size, number of dimensions, and number of rows
  • Best for datasets that fit in available memory

Example:

CREATE VECTOR INDEX product_hnsw_idx ON products (embedding)
ORGANIZATION INMEMORY NEIGHBOR GRAPH
DISTANCE COSINE
WITH TARGET ACCURACY 95;
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2. IVF (Inverted File Flat) Index

The IVF index is a storage-based Neighbor Partition vector index that enhances search efficiency by narrowing the search area through neighbor partitions or clusters.

Characteristics:

  • Storage-based (not constrained by memory like HNSW)
  • Can be used for very large datasets and still provide excellent performance compared to exhaustive similarity search
  • Supports both global and local partitioning on partitioned tables
  • Ideal for large datasets where HNSW won't fit in memory

Example:

CREATE VECTOR INDEX product_ivf_idx ON products (embedding)
ORGANIZATION NEIGHBOR PARTITIONS
DISTANCE COSINE
WITH TARGET ACCURACY 95;
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3. Hybrid Vector Index

Hybrid Vector Index combines full text and semantic search in one index, allowing users to run textual queries, vector similarity queries, or hybrid queries. This is particularly useful when you need both keyword matching and semantic understanding.

Practical Implementation Example

Here's a complete example of using vector search:

-- Step 1: Create table with vector column
CREATE TABLE knowledge_base (
    id NUMBER PRIMARY KEY,
    content CLOB,
    content_vector VECTOR(384, FLOAT32)
);

-- Step 2: Insert data and generate embeddings
INSERT INTO knowledge_base VALUES (
    1,
    'Oracle Database 23ai introduces AI Vector Search',
    NULL
);

-- Step 3: Update vectors using embedding model
UPDATE knowledge_base
SET content_vector = VECTOR_EMBEDDING(doc_model USING content AS DATA);

-- Step 4: Create vector index for performance
CREATE VECTOR INDEX kb_vector_idx ON knowledge_base (content_vector)
ORGANIZATION INMEMORY NEIGHBOR GRAPH
DISTANCE COSINE
WITH TARGET ACCURACY 95;

-- Step 5: Perform similarity search
SELECT id, content
FROM knowledge_base
ORDER BY VECTOR_DISTANCE(
    content_vector,
    VECTOR_EMBEDDING(doc_model USING 'AI features in databases' AS DATA),
    COSINE
)
FETCH FIRST 5 ROWS ONLY;
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Distance Metrics

Oracle supports multiple distance metrics for vector comparisons:

  • COSINE: Measures angular distance (best for text embeddings)
  • EUCLIDEAN: Measures straight-line distance
  • DOT_PRODUCT: Useful for normalized vectors
  • MANHATTAN: Sum of absolute differences

Use Cases

Oracle AI Vector Search enables powerful applications:

  1. Semantic Document Search: Find documents by meaning, not just keywords
  2. Recommendation Systems: Suggest products based on similarity
  3. Image Similarity Search: Find visually similar images
  4. Chatbots and Q&A Systems: Build context-aware conversational AI
  5. Anomaly Detection: Identify outliers in high-dimensional data
  6. Natural Language Processing: Power advanced text analysis applications

Performance Considerations

Vector Pool Sizing

For HNSW indexes, you need to configure the Vector Memory Pool:

ALTER SYSTEM SET VECTOR_MEMORY_SIZE = 2G SCOPE=BOTH;
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Choosing the Right Index

  • Use HNSW when: Dataset fits in memory and you need maximum speed
  • Use IVF when: Dataset is very large or memory is limited
  • Use Hybrid when: You need both keyword and semantic search

Target Accuracy

The TARGET ACCURACY parameter balances speed vs. precision. Higher accuracy (e.g., 95-99) returns more precise results but requires more computation.

Integration with Existing Features

One of the most powerful aspects of Oracle AI Vector Search is its deep integration with existing Oracle Database features:

  • Oracle RAC: Full support for clustered environments
  • Data Guard: Vector data replicates alongside business data
  • Partitioning: IVF indexes support local partitioning
  • Security: All Oracle security features apply to vectors
  • ORDS: Expose vector search through REST APIs
  • Oracle APEX: Build AI-powered applications with minimal code

Getting Started

Oracle AI Vector Search is available in:

  • Oracle Autonomous Database (Always Free tier available)
  • Oracle Database 23ai Free (Developer edition)
  • Oracle Exadata Database Service
  • Oracle Base Database Service

To start experimenting:

  1. Download Oracle Database 23ai Free from Oracle Container Registry
  2. Set COMPATIBLE parameter to 23.4.0 or higher
  3. Configure Vector Memory Pool for HNSW indexes
  4. Load an ONNX embedding model
  5. Create tables with VECTOR columns
  6. Start building AI-powered applications!

Oracle Database 23ai brings AI algorithms to where the data lives, instead of having to move the data to where the AI algorithm lives. This architectural approach provides significant advantages in terms of security, performance, and simplicity.

By combining semantic search capabilities with traditional relational data operations in a single, converged database, Oracle AI Vector Search represents a fundamental shift in how we build AI-powered applications. Whether you're implementing RAG-based chatbots, recommendation engines, or advanced search systems, Oracle Database 23ai provides the foundation for secure, scalable, and high-performance AI applications.

The elimination of data fragmentation, coupled with enterprise-grade security and proven Oracle reliability, makes Oracle AI Vector Search an compelling choice for organizations looking to integrate AI capabilities into their data infrastructure.

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