Semantic Caching

https://youtu.be/iF-npWXuKCQ?si=-PWpvF41fsTXowTy Perplexity AI explains Semantic Caching Semantic caching is an advanced data retrieval mechanism that focuses on the meaning and intent of queries rather than exact matches. It breaks queries into reusable, context-driven fragments, allowing systems to identify semantically similar requests and reuse cached responses. This reduces latency, improves system scalability, and lowers computational costs compared to traditional caching methods[1][4][5].

How Semantic Caching Works

  1. Query Analysis: Incoming queries are analyzed for their semantic meaning using techniques like natural language processing (NLP) or embedding models.
  2. Vector Embeddings: Queries are converted into vector embeddings that capture their meaning and context.
  3. Cache Storage: The cache stores both the query results and their semantic metadata (e.g., intent, constraints).
  4. Similarity Search: When a new query arrives, the system performs a similarity search in the cache to find semantically related entries.
  5. Response Delivery: If a match is found, the cached response is served; otherwise, the system processes the query and updates the cache dynamically[2][5][12].

Technologies Using Semantic Caching

  • Generative AI Applications: Large language models (LLMs) like chatbots and Natural Language Processing systems use semantic caching to optimize responses[2][11].
  • Search Engines: Semantic search engines leverage it to improve query relevance[1][12].
  • Real-Time APIs: Applications requiring fast and scalable data retrieval[4].
  • Customer Support Systems: For handling varied but semantically similar queries[8].

Technologies Providing Semantic Caching

  • Amazon MemoryDB: Offers persistent semantic caching for generative AI workloads[10].
  • Azure Cosmos DB: Implements vector-based semantic caching for LLMs[2].
  • Fastly AI Accelerator: Provides semantic caching for LLM APIs to enhance performance and reduce costs[3].
  • Redis: Integrates semantic caching with AI embedding models for faster retrieval[13].
  • Kong AI Semantic Cache Plugin: Adds semantic caching capabilities to APIs[7].
Semantic caching is increasingly vital for modern AI systems, enabling faster, smarter, and more cost-efficient data handling in dynamic environments.
Sources [1] What Is Semantic Caching? A Guide to Smarter Data Retrieval https://www.sandgarden.com/learn/semantic-caching [2] Semantic cache for large language models - Azure Cosmos DB https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/semantic-cache [3] What does it all mean? An introduction to semantic caching ... - Fastly https://www.fastly.com/blog/what-does-it-all-mean-an-introduction-to-semantic-caching-and-fastlys-ai [4] Semantic Caching: Enhancing Data Retrieval Efficiency - Ithy https://ithy.com/article/semantic-caching-explained-tuy1hov7 [5] Semantic Cache: Accelerating AI with Lightning-Fast Data Retrieval https://qdrant.tech/articles/semantic-cache-ai-data-retrieval/ [6] Optimize LLM Applications: Semantic Caching for Speed and Savings https://upstash.com/blog/semantic-caching-for-speed-and-savings [7] AI Semantic Cache - Plugin - Kong Docs https://docs.konghq.com/hub/kong-inc/ai-semantic-cache/ [8] Semantic Caching in Generative AI and Vector Databases - PingCAP https://www.pingcap.com/article/semantic-caching-in-gen-ai-and-vector-databases/ [9] What Is Semantic Cache? - Zilliz https://zilliz.com/glossary/semantic-cache [10] Improve speed and reduce cost for generative AI workloads with a ... https://aws.amazon.com/blogs/database/improve-speed-and-reduce-cost-for-generative-ai-workloads-with-a-persistent-semantic-cache-in-amazon-memorydb/ [11] What is Semantic Caching For LLMs? | GigaSpaces AI https://www.gigaspaces.com/data-terms/semanticaching-for-llms [12] How to cache semantic search: a complete guide - Meilisearch https://www.meilisearch.com/blog/how-to-cache-semantic-search [13] Semantic caching for faster, smarter LLM apps - Redis https://redis.io/blog/what-is-semantic-caching/