Caching mechanism for storing and reusing previously computed embeddings to reduce API costs and latency. Essential optimization for production RAG systems processing repeated or similar content.
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Semantic Caching
AI caching pattern that stores vector embeddings of LLM queries and responses, serving cached results when new queries are semantically similar. Cuts LLM costs by 50%+ with millisecond response times versus seconds for fresh calls.
Redis LangCache
Redis as vector database via RediSearch module supports HNSW/Flat indexes for real-time vector search in key-value store. Features: sub-ms latency, JSON payloads, modules ecosystem; use cases: caching + search hybrids. Vs dedicated VDBs, Redis excels in low-latency but limited scale for pure vectors.
Matryoshka Embeddings
Representation learning approach encoding information at multiple granularities, allowing embeddings to be truncated while maintaining performance. Enables 14x smaller sizes and 5x faster search.
ACORN Algorithm for Filtered Vector 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.
Binary Quantization for Vector Search
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.
Early Termination Strategy for HNSW
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.
