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    2. Vector Database Engines
    3. Oracle Database Vector Search

    Oracle Database Vector Search

    Oracle's core database now includes vector search capabilities, enabling enterprises to perform scalable vector queries natively as part of their data management workflows. Oracle includes vector search capabilities in its database platform, supporting approximate KNN and hybrid search for enterprise-scale use cases.

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    Websitelearn.oracle.com
    PublishedMay 13, 2025

    Categories

    1 Item
    Vector Database Engines

    Tags

    4 Items
    #Enterprise#Vector Search#Hybrid Search#KNN

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    6 result(s)
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    Solr is a mature open-source search engine that has incorporated vector search capabilities, making it relevant for enterprises looking to implement vector-based search alongside traditional keyword search.

    Vespa
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    Open-source AI search platform combining vector search, keyword retrieval, structured filtering, and ML ranking. Powers applications at Spotify, Yahoo, and Wix with sub-100ms response times. This is an OSS platform under Apache 2.0 with managed cloud option.

    Infinity

    Infinity is an AI-native database built for LLM applications, offering fast hybrid search of dense vectors, sparse vectors, tensors, and full-text data.

    Amazon Web Services Vector Search

    AWS has introduced vector search in several of its managed database services, including OpenSearch, Bedrock, MemoryDB, Neptune, and Amazon Q, making it a comprehensive platform for vector search solutions.

    Elasticsearch

    Elasticsearch is a distributed search engine supporting various data types, including vectors, and provides scalable vector search capabilities, making it a popular choice for modern AI-powered applications. It can be extended with the k-NN plugin to provide scalable vector search using HNSW and Lucene, enabling hybrid semantic and keyword search capabilities.

    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.