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- Guidance for Semantic Caching for GenerativeAI applications using Amazon ElastiCache for Valkey
Guidance for Semantic Caching for GenerativeAI applications using Amazon ElastiCache for Valkey
Overview
This Guidance demonstrates how to implement semantic caching for generative AI applications to reduce response latency and costs by storing and retrieving similar query results using Amazon ElastiCache for Valkey's vector search capabilities. The implementation uses vector embeddings generated from popular AI providers like Amazon Bedrock, Amazon SageMaker, Anthropic, or OpenAI to create searchable representations of queries and responses. ElastiCache for Valkey enables microsecond-latency searches across billions of high-dimensional vectors with up to 99% recall accuracy, making it ideal for caching semantically similar requests in real-time applications. You can significantly reduce your generative AI application costs and improve user experience by avoiding redundant API calls for semantically similar queries while maintaining high-quality responses.
Benefits
Serve cached responses for semantically similar prompts with microsecond latency, eliminating redundant foundation model calls. This approach can significantly lower operational expenses by reusing previously generated responses for similar user queries.
Deliver AI responses with microsecond latency for semantically similar queries using ElastiCache for Valkey's vector search capabilities. Users receive immediate responses to common questions without waiting for foundation model processing, creating a more responsive application experience.
Combine semantic caching with personalized memory storage to maintain context across user interactions. The solution stores user preferences and conversation history in ElastiCache, enabling more contextually appropriate responses tailored to individual users.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
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