A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market…

For a RAG application with a small dataset requiring high accuracy and performance, Amazon MemoryDB with the Hierarchical Navigable Small World (HNSW) algorithm is the optimal choice. MemoryDB is an in-memory database, providing the ultra-low latency needed for high-performance similarity searches. The HNSW algorithm offers an excellent balance between search speed and accuracy (recall), making it superior to exhaustive search methods like 'Flat' for performance-sensitive applications. Vertical scaling is a straightforward way to manage performance for a small but growing index.

A. The 'Flat' algorithm performs an exhaustive search, which provides perfect accuracy but has very poor performance (high latency) and does not scale well, violating the high-performance requirement.
C. While Amazon Aurora with pgvector is a valid option, an in-memory database like MemoryDB is purpose-built for lower latency, which is critical for maintaining high performance in a RAG application's retrieval step.
D. Amazon DocumentDB is a document-oriented database. For a pure vector similarity search workload as described, a specialized in-memory vector store like MemoryDB is a more direct and performant solution.

- AWS Database Blog

"Build AI-powered search in Amazon MemoryDB for Redis with vector search"

Nov 2023. This article highlights that HNSW indexes are "fast and memory-efficient" and provide a "good balance between search speed and search recall."

- Amazon MemoryDB for Redis Developer Guide

"Vector search in MemoryDB". The documentation describes the supported index types

noting that HNSW is an approximate nearest neighbor (ANN) algorithm suitable for low-latency

high-throughput use cases.