redis-semantic-cache

Semantic caching stores LLM responses to reuse for semantically similar prompts, avoiding repeated model calls. It reduces latency and costs by retrieving cached answers when a new prompt matches a previous one based on vector similarity.

You can reduce LLM API costs by implementing semantic caching to store and reuse responses for similar prompts. This avoids redundant model invocations for repetitive chatbot, QA, or RAG workloads.

Yes, semantic caching works with RAG pipelines by storing and retrieving semantically similar answers. You can configure separate caches per task type and use attribute-based filtering to isolate data.

You balance hit rate and precision by configuring tunable similarity thresholds for your semantic cache. Adjusting the threshold determines how closely a new prompt must match a cached one to trigger a hit.

Yes, you can isolate cached LLM responses by creating separate caches per task type. You can also apply optional attribute-based filtering to segment data within your semantic cache.

To set up a semantic cache on Redis Cloud, configure LangCache with a dedicated cache and test a prompt to verify a cache hit. This implements search and set operations for your LLM workloads.