Step-by-step guides for building production-ready RAG systems with Valkey. From setup to scaling.
Set up Valkey, create your first vector index, store embeddings, and run your first semantic search query.
Cache LLM responses by semantic similarity. Tune similarity thresholds, implement TTL strategies, measure hit rates.
HNSW vs FLAT indexes, hybrid search combining vectors with keywords, metadata filtering, performance tuning.
TTL-based expiration, event-driven invalidation, version tagging, and patterns for keeping cache fresh.
Cluster mode, replication, memory optimization, handling millions of vectors, load balancing strategies.
Track cache hit rates, search latencies, memory usage. Set up alerts, dashboards, and performance baselines.
See these concepts in action with our browser-based RAG simulator.
Launch Demo →