PrismLib – semantic LLM cache and cluster mesh that cuts token spend (github.com)

PrismLib has unveiled a powerful tool designed to significantly optimize the performance and costs associated with Large Language Models (LLMs). This innovative package features three core components: PrismCache, which provides a semantic cache that catches repeated queries with an impressive 91–96% hit rate; PrismDriver, a database driver that streamlines latency by replacing network calls with local access, achieving a remarkable 98.6% reduction in response time; and PrismLib Micro, which shares cached answers across containers to lower token usage by 76% for clustered applications. The significance of PrismLib lies in its ability to drastically cut costs and latency for organizations leveraging LLMs. By enabling the caching of semantically identical queries and reducing database round-trips, it allows for efficient API usage, ultimately saving up to 80% in token fees. Moreover, the system operates entirely in-process and eliminates the need for external services like Redis or Kubernetes, simplifying setup and maintenance. Built on robust open-source libraries—PrismResonance for vector indexing and CHORUS Fabric for secure streaming—PrismLib not only enhances performance and cost-efficiency but also ensures data isolation between tenants through advanced mathematical projections. This positions PrismLib as an essential development for teams working in AI/ML, promising heightened efficiency in deploying sophisticated LLM applications.