Amazon MemoryDB has now introduced Vector Search, a new feature that allows users to store, index, retrieve, and search vectors for real-time machine learning and generative artificial intelligence applications. This new capability offers the fastest vector search performance with high recall rates among popular vector databases on AWS. Users can store application data and millions of vectors with single-digit response times in milliseconds, simplifying generative AI architecture while delivering maximum performance.
With Vector Search for MemoryDB, users can implement various generative AI use cases such as Recovery Augmented Generation (RAG), anomaly detection, document retrieval, and real-time recommendation engines. By storing vector embeddings in MemoryDB generated through AI/ML services like Amazon SageMaker, users can enhance their applications with semantic search and improve performance.
Specific use cases that benefit from Vector Search for MemoryDB include real-time semantic search for RAG, durable and low-latency semantic caching, and real-time anomaly (fraud) detection. By leveraging vector search, users can implement advanced AI applications that respond faster with lower costs and reduce the risk of fraud by identifying similarities between incoming transactions and fraudulent activity.
To implement a semantic search application using Vector Search for MemoryDB, users can create a cluster, generate vector embeddings using embedding models like Amazon Titan, and create a vector index to query the vector data effectively. By using FT.CREATE and FT.SEARCH commands, users can find relevant results filtered by the distance between vectors and improve the efficiency of their generative AI applications.
The latest features in Vector Search for MemoryDB include improvements in semantic caching, better similarity filtering with SCORE, shared memory to avoid duplicating vectors, and enhanced performance at high filter rates. This feature is now available in all regions where MemoryDB is offered, giving users the opportunity to explore and implement vector search for their AI applications.
Overall, Vector Search for Amazon MemoryDB offers a powerful solution for developers looking to enhance their generative AI capabilities by enabling efficient storage, indexing, and searching of vectors with high performance and durability across multiple use cases.
Article Source
https://aws.amazon.com/blogs/aws/vector-search-for-amazon-memorydb-is-now-generally-available/
