Lazy Loading Filesystem

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.

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.

Representation learning approach encoding information at multiple granularities, allowing embeddings to be truncated while maintaining performance. Enables 14x smaller sizes and 5x faster search.

Advanced algorithm designed to make hybrid searches combining metadata filters and vector similarity more efficient, implemented in Apache Solr and other vector search systems.

Compression technique that converts full-precision vectors to binary representations, achieving 32x storage reduction while maintaining 90-95% recall for efficient large-scale vector search.

Distance metric for comparing binary vectors using XOR operations, enabling efficient similarity search with dramatically reduced storage requirements compared to full-precision vectors.