Semantic Cache & Request Deduplication

The Semantic Cache & Request Deduplication Gateway (GPTCache, Redis Semantic Caching) is an intelligent acceleration layer positioned in front of LLM inference and RAG retrieval pipelines; unlike traditional exact-string hash caches, semantic caches embed incoming queries into dense vectors and execute sub-3ms cosine similarity lookups in memory: if similarity with a cached query exceeds a calibrated threshold $\tau$ (e.g. $\cos(q_1, q_2) \ge 0.95$), the cached retrieval context and LLM response are returned instantly; paired with Singleflight distributed mutex locks, it prevents cache stampedes on viral prompts.

High-concurrency AI customer service desks, trending enterprise queries, and LLM API cost/latency optimization.

LLMs are expensive and slow (seconds per response); semantic caching intercepts 30%-50% of recurring semantic intents, compressing response times from 2000ms to 5ms while slashing Token expenditure by over 40%.

Compare Exact String Match vs Semantic Vector Match across cache hit rates, lookup latencies, and semantic hallucination risks?

How to rigorously calibrate the similarity threshold $\tau$ to prevent false cache collisions on semantically opposite prompts ('open account' vs 'close account')?

Design a Singleflight concurrency barrier preventing Cache Stampedes when thousands of users ask identical breaking queries simultaneously?

How to design cascading cache invalidation via metadata reverse indexes when underlying knowledge base documents are updated?

Explain cryptographic and metadata-scoped partitioning ensuring Tenant A's private responses never leak to Tenant B's semantic cache hits?