# Caching

> Browse 8 items tagged with Caching.

## Items

- [Amazon ElastiCache Vector Search](https://vectordb.ever.works/items/aws-elasticache-vector-search) — Vector search extension for Amazon ElastiCache for Redis, featuring HNSW indexing for k-NN similarity, hybrid lexical+vector search with BM25 fusion capabilities. Used for enterprise semantic caching, real-time recommendations, and RAG applications. Integrated Redis module offers sub-microsecond latency vs standalone like Weaviate, optimized for hot data workloads.
- [Redis LangCache](https://vectordb.ever.works/items/redis-langcache) — Redis as vector database via RediSearch module supports HNSW/Flat indexes for real-time vector search in key-value store. Features: sub-ms latency, JSON payloads, modules ecosystem; use cases: caching + search hybrids. Vs dedicated VDBs, Redis excels in low-latency but limited scale for pure vectors.
- [Vector Search Caching](https://vectordb.ever.works/items/vector-search-caching) — Strategies for caching vector search results, embeddings, and frequently accessed data to reduce latency and costs in RAG systems. Effective caching can eliminate redundant embedding API calls and vector searches for common queries, significantly improving performance and reducing infrastructure costs.
- [Semantic Caching](https://vectordb.ever.works/items/semantic-caching-embeddings) — A caching technique that uses vector embeddings to identify and reuse responses for semantically similar queries, reducing LLM costs and latency. Unlike traditional caches based on exact matches, semantic caching achieves cache hit ratios of up to 92% by matching queries based on semantic similarity.
- [LLM Caching for Vector Search](https://vectordb.ever.works/items/llm-caching-for-vector-search) — Caching strategies for LLM and vector search systems including semantic caching, embedding caching, and response caching to reduce costs and improve latency in RAG applications.
- [Embedding Cache](https://vectordb.ever.works/items/embedding-cache) — Caching mechanism for storing and reusing previously computed embeddings to reduce API costs and latency. Essential optimization for production RAG systems processing repeated or similar content.
- [GPTCache (Semantic Cache)](https://vectordb.ever.works/items/gptcache-semantic-cache) — Open-source semantic caching library for LLMs that uses embedding similarity to identify and retrieve responses for similar queries, reducing API costs by up to 70% and improving response times for ChatGPT and other language models.
- [Semantic Caching](https://vectordb.ever.works/items/semantic-caching) — AI caching pattern that stores vector embeddings of LLM queries and responses, serving cached results when new queries are semantically similar. Cuts LLM costs by 50%+ with millisecond response times versus seconds for fresh calls.

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