Memorystore for Redis Cluster supports storing and querying vector data. This page provides information about vector search on Memorystore for Redis Cluster.
Vector search on Memorystore for Redis Cluster is compatible with the open-source LLM framework LangChain. Using vector search with LangChain lets you build solutions for the following use cases:
- Retrieval Augmented Generation (RAG)
- LLM cache
- Recommendation engine
- Semantic search
- Image similarity search
The advantage of using Memorystore to store your generative AI data is Memorystore's speed. Vector search on Memorystore for Redis Cluster leverages multi-threaded queries, resulting in high query throughput (QPS) at low latency.
Memorystore also provides two distinct search approaches to help you find the right balance between speed and accuracy. The HNSW (Hierarchical Navigable Small World) option delivers fast, approximate results - ideal for large datasets where a close match is sufficient. If you require absolute precision, the 'FLAT' approach produces exact answers, though it may take slightly longer to process.
If you want to optimize your application for the fastest vector data read and write speeds, Memorystore for Redis Cluster is likely the best option for you.