Add Context-Enabled Semantic Caching recipe to semantic cache folder #99
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
@redis-phil Thanks for the PR and 👋. Can you take a look at the failing build and make any changes to the notebook needed to get it passing? https://github.com/redis-developer/redis-ai-resources/actions/runs/16683036159/job/47230595099?pr=99 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Add Context-Enabled Semantic Caching Recipe
Overview
This PR adds a comprehensive Jupyter notebook demonstrating Context-Enabled Semantic Caching (CESC) - a three-tier caching architecture that combines Redis vector search with user personalization to deliver tailored LLM responses at 60-80% lower cost and sub-100ms latency.
What's Added
Complete production-ready implementation using Redis, OpenAI/Azure OpenAI, and comprehensive telemetry tracking
Real-world IT support scenario showcasing semantic similarity matching with user memory integration for personalized responses
Enterprise Impact
Provides quantifiable ROI with 200-400% first-year returns for organizations processing >10K daily LLM queries through intelligent model selection (GPT-4o for new content, GPT-4o-mini for personalization) and Redis-powered vector caching.