Implementing Durable Semantic Caching with Amazon MemoryDB
Generative AI and the Need for Scaling
- Generative AI has captured the imagination of millions and companies are in the early stages of leveraging large language models (LLMs) in production.
- LLMs can be expensive as usage scales, leading to a linear increase in costs.
- Implementing durable semantic caching can help avoid the linear increase in costs while maintaining performance and relevancy.
Understanding Semantic Caching
- Semantic caching relies on vector embeddings, which can capture the semantic relationships between different elements.
- By storing the vector representations of queries in a durable cache, subsequent semantically similar queries can be served from the cache, avoiding the expensive call to the foundation model.
Durable Semantic Caching in Action
- Cache Miss: When a new query is received, its vector embedding is created and searched in the durable semantic cache. If a similar vector is not found, the query is processed by the foundation model, and the response is returned to the user. The vector and response are then stored in the cache.
- Cache Hit: When a subsequent, semantically similar query is received, its vector is matched against the cache, and the cached response is returned, avoiding the expensive call to the foundation model.
Benefits of Durable Semantic Caching
- Cost Savings: Significant cost savings can be achieved by reducing the number of calls to the foundation model, with up to 75% cost savings for a 75% cache hit ratio.
- Scalability: Durable semantic caching can handle more users without a proportional increase in costs.
- Improved Performance: Responses can be served within single-digit millisecond latencies, significantly improving the user experience.
Choosing the Right Database for Durable Semantic Caching
- The database should support semantic search and vector similarity search, with low read and write latencies, and durability to handle cache misses and rebuilds.
- Amazon MemoryDB is a purpose-built database that meets these requirements, providing the fastest durable database with microSecond read latencies and low single-digit millisecond write latencies, along with advanced vector similarity search capabilities.
Implementing Durable Semantic Caching with Amazon MemoryDB
- MemoryDB uses the open-source Vaulty database, providing compatibility with the Redis ecosystem and the rich data structures it offers.
- MemoryDB's multi-AZ transactional log ensures high availability and durability, with data replicated across at least three copies in two availability zones.
- MemoryDB provides advanced vector similarity search capabilities, leveraging the HNSW (Hierarchical Navigable Small World) indexing algorithm to balance performance and recall.
- Techniques like memory deduplication help optimize memory usage for the vector data.
Best Practices and Considerations
- Manage data staleness by configuring time-to-live (TTL) on cache entries, balancing staleness and cache hit rate.
- Optimize the similarity threshold and leverage filters (e.g., metadata tags) to balance cache hit rate and relevancy.
- Monitor memory and space consumption using MemoryDB's built-in commands to tune the cache configuration.
Conclusion
Durable semantic caching with Amazon MemoryDB can help scale generative AI applications, providing significant cost savings, improved performance, and enhanced user experience, without sacrificing relevancy.
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