Oracle Database Vector Search

Oracle Database Vector Search introduces vector search capabilities natively within Oracle's core database platform. This enables enterprises to perform scalable vector queries as part of their data management workflows, supporting both approximate and hybrid search methods.

Features

• Native Vector Data Type Support: Implement and manage vector data types within Oracle Database 23ai.
• Embedding Generation and Storage: Generate and store vector embeddings within and outside the Oracle Database.
• Similarity Search: Perform both exact and approximate similarity searches (including K-Nearest Neighbors, KNN).
• Vector Indexing: Create and optimize vector indexes such as HNSW (Hierarchical Navigable Small World) and IVF (Inverted File) for efficient AI vector search.
• Hybrid Search: Combine vector search with traditional search techniques for enterprise-scale use cases.
• Retrieval-Augmented Generation (RAG) Application Development: Build RAG applications using PL/SQL and Python.
• AI Storage and Distributed Processing: Utilize Exadata AI Storage and Oracle GoldenGate for distributed AI processing and vector search acceleration.
• Efficient Data Management: Load and manage vector data using tools like SQL Loader and Oracle Data Pump.
• Natural Language Querying: Query data using natural language prompts via Select AI and Autonomous Database.

Category

• Vector Database Engines

Tags

• enterprise
• vector-search
• hybrid-search
• knn

Pricing

No specific pricing information is provided in the available content. The certification exam fee ($245) is currently waived until May 15, 2025, for the associated learning path and certification.