Reduce LLM Costs with Semantic Caching using Redis Vector Store and HuggingFace

Stop Paying for the Same Answer Twice Your LLM is answering the same questions over and over. "What's the weather?" "How's the weather today?" "Tell me about the weather." Same answer, three API calls, triple the cost. This workflow fixes that. What Does It Do? Semantic caching with superpowers. When someone asks a question, it checks if you've answered something similar before. Not exact matches—semantic similarity. If it finds a match, boom, instant cached response. No LLM call, no cost, no waiting. First time: "What's your refund policy?" → Calls LLM, caches answer Next time: "How do refunds work?" → Instant cached response (it knows these are the same!) Result: Faster responses + way low

Help me set up the n8n workflow "Reduce LLM Costs with Semantic Caching using Redis Vector Store and HuggingFace" (https://n8n.io/workflows/10887). It uses: Code, AI Agent, Embeddings Hugging Face Inference, OpenAI Chat Model, Redis Chat Memory, Recursive Character Text Splitter, Default Data Loader, Redis Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.