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Optimize Your LLM Application with Upstash Semantic Cache

In this video, I'll show you how to set up a semantic cache to improve the performance of your LLM application, reducing response times from seconds to milliseconds. I'll explain the benefits of semantic caching, like lowering inference and API costs, and achieving faster, more deterministic results. I'll be using Upstash Redis's new AI offerings to implement this caching strategy. From creating a vector database and setting up environment variables to coding in VS Code and integrating with an answer engine, this step-by-step guide will walk you through the entire process. By the end, you'll have an advanced understanding of how to leverage semantic caching to make your applications more efficient and cost-effective.

Links:
https://upstash.com/
https://github.com/upstash/semantic-c...
https://github.com/developersdigest/l...

00:00 Introduction to Semantic Caching
00:09 Understanding the Benefits and Costs of LLM Applications
00:48 Setting Up with Upstash
01:08 Creating a Vector Database in Upstash
01:57 Project Setup in VS Code
02:40 Implementing Semantic Cache in Your Application
03:12 Exploring Semantic Similarity and Cache Mechanics
04:14 Practical Example: Setting Up Semantic Cache
05:29 Integrating Semantic Cache with the Answer Engine
08:17 Frontend Integration and Cache Management
12:47 Conclusion and Thanks