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Amazon OpenSearch Service vector database capabilities revisited
- Amazon OpenSearch Service has evolved since 2023, with improved performance, cost-effectiveness, and new features for hybrid search methods using dense and sparse vectors.
- In 2024, there was a shift towards production use of Retrieval Augmented Generation (RAG) applications and semantic search workloads to enhance relevance.
- 2025 brings support for OpenSearch 2.17, featuring enhancements focused on lowering costs, reducing latency, and improving search accuracy.
- OpenSearch Service offers a vector database supporting FAISS, NMSLIB, and Lucene engines for exact and approximate nearest-neighbor matching with various distance metrics.
- Builders are adopting a hybrid search approach combining lexical and semantic retrieval methods to cater to diverse user queries effectively.
- OpenSearch improved hybrid search capabilities in 2024 through conditional scoring logic, optimized structures, and parallel query processing, reducing latency and post-filtering for refined results.
- Sparse vector search simplifies the integration of lexical and semantic information, enhancing query processing latency in 2024.
- OpenSearch introduced strategies in 2024 to reduce costs for production workloads, including scalar and binary quantization, in-memory handling optimizations, and support for JDK21 and SIMD instruction sets.
- Innovations like k-NN query updates, chunking strategies, and reduced RAM consumption methods contribute to improved accuracy and efficiency in 2024.
- OpenSearch's focus on dense vector handling, cost reduction through quantization, and support for AI-native pipelines highlights a commitment to advancing search AI use cases and integrations.
- Overall, OpenSearch continues to enhance its capabilities for semantic search and vector databases, offering builders powerful, scalable solutions for AI-driven applications.
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