An OpenSearch plugin that expands its capabilities with the custom `knn_vector` data type, enabling storage of embeddings and providing methods for k-NN similarity searches, including Approximate k-NN, Script Score k-NN, and Painless extensions.
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Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.
Neural and hybrid search capability in OpenSearch that combines lexical queries with vector-based neural search using a pipeline of normalization and score combination techniques. It enables semantic (vector) search and hybrid search over indices such as `neural_search_pqa`, suitable for AI and vector database-style retrieval use cases.
Open-source AI-native database layer that adds vector search, model integration, and AI workflows on top of existing databases like MongoDB and Postgres.
Lantern is a PostgreSQL extension that enables efficient vector search capabilities, allowing users to perform similarity searches directly within their PostgreSQL databases.
MariaDB Vector is an extension or feature of MariaDB, providing capabilities for handling and querying vector data within the MariaDB ecosystem.
An enhancement to the Neo4j graph database providing vector search capabilities through dedicated indexes.
The k-NN plugin enhances OpenSearch by introducing a custom knn_vector data type. This enables the storage of vector embeddings and provides robust methods for performing k-nearest neighbors (k-NN) similarity searches.
knn_vector data type into OpenSearch.Pricing information is not available.
License information is available, but specific details are not provided in the content. As an OpenSearch project plugin, it is typically open-source.