Vector Cache

Vector cache is a memory-resident store of frequently used embedding vectors, query results, or intermediate computations, designed to reduce latency and cost in production RAG pipelines. Caching can apply at multiple levels: embedding model output cache (repeat embeddings of the same text), query result cache (repeat vector searches), and KV cache (transformer attention state for autoregressive generation). Embedding caches are particularly valuable in RAG systems where the same documents are re-embedded during reingestion or testing, since embedding model inference is expensive even on dedicated infrastructure. Redis, Memcached, and in-memory dictionaries are common caching layers, often keyed by content hash of the input. AI governance teams document cache eviction policy, hit rates, and TTL because cached embeddings can become stale relative to model updates — if the embedding model is upgraded, all cached embeddings must be invalidated. Modern vector databases often include internal caches for hot vectors and recent queries, reducing infrastructure load without application-level caching.

Embedding cache governance in Centralpoint: Centralpoint coordinates embedding caching across whatever infrastructure you operate, ensuring cache invalidation aligns with model upgrades. The model-agnostic platform meters cache-aware token costs, keeps prompts local, and deploys cache-optimized chatbots through one line of JavaScript on any portal.


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