Consistency Levels

Optimization technique that allows HNSW vector searches to exit early when the candidate queue remains saturated, reducing latency and resource usage with minimal recall impact.

Indexing strategy where vector indexes are stored in the same partitions as corresponding table rows, ensuring data locality and operational advantages in distributed databases.

Modal Labs' FUSE-based filesystem implementation that loads container images and dependencies on-demand, enabling sub-second container startup times for GPU workloads.

Multi-Version Concurrency Control for vector indexes enabling transactional guarantees and consistent reads in distributed vector databases like YugabyteDB.

The time required to generate vector embeddings from text, images, or other data via API calls or local inference. Embedding latency significantly impacts RAG system performance, with typical ranges from 10ms (local, batch) to 500ms+ (API, single) depending on model size and deployment.

A caching technique that uses vector embeddings to identify and reuse responses for semantically similar queries, reducing LLM costs and latency. Unlike traditional caches based on exact matches, semantic caching achieves cache hit ratios of up to 92% by matching queries based on semantic similarity.