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GPTCACHE
Semantic cache for LLMs — cut costs, speed up responses
MIT
ABOUT
Every LLM API call costs money and takes hundreds of milliseconds — yet many queries are semantically identical to previous ones (e.g. "What's the capital of France?" and "Tell me the capital of France"). Traditional exact-match caches miss these because small wording changes produce different text. GPTCache solves this with semantic caching: it embeds incoming queries and retrieves cached responses for semantically similar requests. This reduces latency from seconds to milliseconds, cuts API costs by reusing responses, and handles eviction, embedding, and similarity search out of the box. Integrated with LangChain, LlamaIndex, and OpenAI.
INSTALL
pip install gptcacheINTEGRATION GUIDE
1. Cache LLM responses in a FAQ chatbot so identical or similar questions return instantly without API calls
2. Reduce API costs in a high-volume application by caching semantically equivalent user queries
3. Speed up batch data processing pipelines that repeatedly query LLMs with similar prompts
4. Build a hybrid cache for agentic workflows where tool calls produce repeatable results
5. Deploy as a sidecar cache layer alongside any OpenAI-compatible API endpoint
TAGS
llmcachingsemantic-cachecost-optimizationperformanceopen-source