Oracle AI Vector Search Features

General

Vector data type

With Oracle Database 23ai, you can use the new native vector data type to store vectors directly within tables. Support for vectors with different dimension counts and formats mean that you can use the vector embedding model of your choice to simplify application development and deployment.

Flexible vector generation

Use the ONNX framework to import the embedding models of your choice and use them to generate vectors for your data or use database APIs to generate vectors from your preferred embedding service. You also have the option of importing vectors directly into the database.

Simple standard SQL for querying vectors

Use simple, intuitive SQL to perform similarity search on vectors and freely combine vectors with relational, text, JSON, and other data types within the same query.

Seamless combination of AI vector data with business data

Combine sophisticated business data search with AI vector similarity search using simple, intuitive SQL and the full power of a converged database—JSON, graph, text, relational, spatial, and more—all within a single query.

Vector indexes

Accelerate similarity searches using highly accurate approximate search indexes (vector indexes), such as the in-memory neighbor graph index for maximum performance and neighbor partition indexes for massive data sets.

Hybrid vector indexes

Index and query documents using a combination of full-text search and semantic vector search to improve the overall search experience and provide users with more-accurate information.

Simple search accuracy specification

Specify target search accuracy as a simple percentage instead of being required to specify advanced algorithmic parameters. Define default accuracy during index creation and override in search queries if needed.

Powering retrieval-augmented generation

Enhance large language model (LLM) interactions by providing context-specific private data to improve the accuracy of responses through a combination of similarity search and business data search. Further enrich retrieval-augmented generation (RAG) using built-in business criteria, such as security filters, business metrics, and business rules.

Industry-leading security

Oracle AI Vector Search integrates seamlessly with Oracle's industry-leading database security features to reduce risk and simplify compliance. By leveraging robust tools, such as encryption, data masking, privileged user access controls, activity monitoring, and auditing, organizations can secure their data while taking full advantage of advanced AI search capabilities.

Support for a full generative AI pipeline

Perform all aspects of the generative AI pipeline using native database APIs from end to end, making it easier for developers to build next-gen AI applications using their business data—all from directly within the database.

AI Vector Search with a full machine learning suite

Handle a wide range of AI use cases involving machine learning actions (decisions, predictions, classification, forecasts, and so on) combined with the power of AI-based vector search. For instance, it is easy to combine inference and classification with Oracle AI Vector Search within the same SQL query.

Exadata optimizations

Accelerate vector index creation and search with Exadata System Software 24ai optimizations. Gain the high performance, scale, and availability Exadata provides to enterprise databases.