+ "source": "# LLM Caching\n\nThis notebook demonstrates how to use RedisVL's `SemanticCache` to cache LLM responses based on semantic similarity. Semantic caching can significantly reduce API costs and latency by retrieving cached responses for semantically similar prompts instead of making redundant API calls.\n\nKey features covered:\n- Basic cache operations (store, check, clear)\n- Customizing semantic similarity thresholds\n- TTL policies for cache expiration\n- Performance benchmarking\n- Access controls with tags and filters for multi-user scenarios\n\nPrerequisites:\n- Ensure `redisvl` is installed in your Python environment\n- Have a running instance of [Redis Stack](https://redis.io/docs/install/install-stack/) or [Redis Cloud](https://redis.io/cloud)\n- OpenAI API key for the examples",
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