Build a read-through semantic cache with Amazon OpenSearch Serverless and Amazon Bedrock

The article discusses building a read-through semantic cache using Amazon OpenSearch Serverless and Amazon Bedrock to optimize large language model (LLM) performance and reduce costs.

• Addresses latency and cost challenges in generative AI applications
• Uses a serverless cache that stores and retrieves semantically similar queries
• Leverages Amazon Bedrock embedding models to transform queries into vector embeddings
• Enables quick lookups of previously generated responses, reducing LLM call times
• Demonstrates significant performance improvements, reducing response times from 2 seconds to under 0.5 seconds

The solution provides a flexible, cost-effective approach to improving LLM-based applications by implementing a semantic caching strategy that can be customized based on specific use cases.