Agent skill · redis
redis-semantic-cache
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.
What it needs
About 2k tokens when loaded.
What this skill does
Redis Semantic Cache Semantic caching for LLM responses with Redis Cloud's LangCache service. Stores prompts as embeddings; subsequent semantically-similar prompts return the cached response without re-calling the model. LangCache is currently in preview on Redis Cloud. Features and behavior may change. When to apply Wrapping an LLM call (OpenAI, Anthropic, etc.) with a cache layer to cut cost and latency. Caching RAG answers, classification outputs, or any deterministic LLM workload. Tuning the precision/hit-rate trade-off for a semantic cache. Splitting one application's LLM workloads across multiple cache instances. 1. The cache-aside flow LangCache fits in front of any LLM call as a standard cache-aside pattern: 1. Send the user's prompt to LangCache's search. 2. Cache hit — return the stored response directly. 3. Cache miss — call the LLM, then set the response so future similar prompts hit. The same operations are available via REST (POST /v1/caches/{cacheId}/entries/search and POST /v1/caches/{cacheId}/entries) when an SDK isn't an option. See references/langcache-usage.md for full SDK + REST samples and attribute-based storage. 2. Tune the similarity threshold The threshold controls how close (in embedding cosine distance) a new prompt must be to a cached one to count as a hit. Higher = stricter match, fewer false positives. Lower = more hits, more risk of returning an off-topic answer. Threshold Behavior Use when --- --- --- 0.95+ Near-exact match required Customer-facing answers where wrong responses are costly 0.9 Balanced default Most workloads — start here 0.8 Loose semantic match Internal tools, exploratory queries, FAQ deduplication Adjust by watching the actual cache-hit rate and spot-checking that returned answers are still relevant. See references/best-practices.md. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills redis/redis-semantic-cache--004fb9