llm-caching

Reduce LLM API costs and latency by implementing multi-layered caching using exact match with Redis, semantic similarity with GPTCache and Qdrant, and provider-side prompt caching for Anthropic and OpenAI. This strategy optimizes throughput and cuts API costs by 30-70%.

Semantic LLM caching stores and retrieves responses for semantically similar queries rather than just exact matches. It works by using GPTCache to generate embeddings via sentence-transformers and Qdrant to perform vector similarity searches to serve cached responses.

Yes, you can use prompt caching for long system prompts in Claude and OpenAI. This Skill implements provider-side prompt caching to save on token costs for repeated context, significantly reducing both API expenses and response latency.

Yes, implementing this multi-layer LLM caching requires Redis for exact match retrieval and Qdrant for semantic similarity search. It also depends on GPTCache, sentence-transformers, litellm, and specific provider SDKs like openai and anthropic.

The best way to optimize an FAQ bot handling similar questions is deploying multi-layered LLM caching. It catches exact matches in Redis and semantically similar queries in Qdrant, serving cached answers to avoid repeated expensive API calls.

Semantic caching for LLM responses is not suitable when precise, deterministic answers are required for nuanced queries, as similar vector embeddings might return a cached response that lacks the exact specific details needed for unique requests.