An end-to-end Retrieval-Augmented Generation (RAG) customer support workflow for n8n, using a cache-first strategy (LangCache) combined with a Redis vector store powered by OpenAI embeddings.
An end-to-end Retrieval-Augmented Generation (RAG) customer support workflow for n8n, using a cache-first strategy (LangCache) combined with a Redis vector store powered by OpenAI embeddings. This template is designed for fast, accurate, and cost-efficient customer support chatbots, internal help desks, and knowledge-base assistants. Overview This workflow implements a production-ready RAG architecture optimized for customer support use cases. Incoming chat messages are processed through a s
Marketplace
Independent
Category
operations
More like this
Browse operations agents →
Asana Intelligence
AI built into Asana to accelerate team execution
$10.99/mo
operationsLayer
Build visual tree structures of your projects and goals in just a few clicks
Free · Paid plans available
operationsEraser
Generate AI diagrams and docs from simple text prompts
Free · Paid plans available
operationsDocumind
Open-source platform for extracting structured data from documents
Free · Paid plans available