pgvector vs. OpenSearch
pgvectorとOpenSearchの以下の能力セットで比較します。私たちでなくても、あなたに最適なデータベースを選んでほしいです。
As AI technologies evolve, vector similarity search has become essential for powering modern AI applications like retrieval-augmented generation (RAG), semantic search, and recommendation engines. There are various vector search solutions available, including purpose-built vector databases, vector search libraries, and traditional databases with vector search as an add-on. Selecting the right solution is crucial for the success of your AI applications.
pgvector and OpenSearch both bring unique strengths to vector search workloads, each with its own capabilities and limitations. The best choice depends on your specific use case and requirements. In the following sections, we’ll compare both databases regarding functionality, scalability, and availability, helping you determine the most suitable option for your needs—even if it’s not us.
pgvector vs. OpenSearch at a Glance
pgvector は Postgres のベクトル検索アドオンです。
ベクター検索をアドオンとした検索・分析エンジンだ。
PostgreSQLライセンス (MITに類似)
アパッチ2.0
15,276
N/A
オンプレム
オンプレム、クラウド（AWS OpenSearch）
pgvector の概要
pgvectorはPostgreSQLの拡張であり、データベース内で直接ベクトルの類似検索のサポートを追加します。pgvectorは、PostgreSQLの成熟したエコシステムを活用し、従来のリレーショナルクエリとベクトルベースの検索を組み合わせたハイブリッドアプリケーションに最適です。
オープンサーチの概要
OpenSearch は、全文検索、ログ分析、観測可能性など、幅広い機能を提供する分散型のオープンソース検索・分析プラットフォームです。k-NNプラグインを通じてベクトル検索をサポートし、AI駆動型アプリケーションの近似最近傍クエリーを可能にする。OpenSearchの拡張性とスケーラビリティは、従来の検索とベクトルベースの検索を組み合わせたハイブリッドなユースケースに適している。
Benchmarking pgvector and OpenSearch on your own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.