Context-Enabled Semantic Caching (Python Notebook)

This notebook operationalizes the concept of “semantic caching” into “context-enabled” caching. It shows how to store responses with embeddings, retrieve cache hits by semantic similarity, and then apply user context to tailor the final response, so different users can get different answers even when the same cached entry is reused. It also covers practical setup guidance for running Redis with vector search (local Docker or managed options) and frames the approach around real enterprise constraints like cost, latency, and relevance.

• Making the “cache hit → personalize” workflow simple enough to reuse, but realistic enough to resemble production.

• Ensuring the infrastructure guidance works across local and managed Redis environments.

• Keeping the approach focused on measurable wins (latency/cost) while demonstrating relevance improvements.

• A reproducible reference implementation that teams can adapt into apps and demos for personalized semantic caching.

• Clear guidance for running the required Redis vector search environment without requiring a large platform buildout.