Dual-Path Customer Support System with Google Sheets, Vectors & Gemini

This n8n workflow template implements a dual-path architecture for AI customer support, based on the principles outlined in the research paper "A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluation" (Sato et al.).

The system, named LENOHA (Low Energy, No Hallucination, Leave No One Behind Architecture), uses a high-precision classifier to differentiate between high-stakes queries and casual conversation. Queries matching a known FAQ are answered with a pre-approved, verbatim response, structurally eliminating hallucination risk. All other queries are routed to a standard generative LLM for conversational flexibility.

This template provides a practical ++blueprint++ for building safer, more reliable, and cost-efficient AI agents, particularly in regulated or high-stakes domains where factual accuracy is critical.

What This Template Does (Step-by-Step) Loads an expert-curated FAQ from Google Sheets and creates a searchable vector store from the questions during a one-time setup flow. Receives incoming user queries in real-time via a chat trigger. Classifies user intent by converting the query to an embedding and searching the vector store for the most semantically similar FAQ question. Routes the query down one of two paths based on a configurable similarity score threshold. Responds with a verbatim, pre-approved answer if a match is found (safe path), or generates a conversational reply via an LLM if no match is found (casual path).

Important Note for Production Use This template uses an in-memory Simple Vector Store for demonstration purposes. For a production application, this should be replaced with a persistent vector database (e.g., Pinecone, Chroma, Weaviate, Supabase) to store your embeddings permanently.

Required Integrations: Google Sheets (for the FAQ knowledge base) Hugging Face API (for creating embeddings) An LLM provider (e.g., OpenAI, Anthropic, Mistral) (Recommended) A persistent Vector Store integration.

Best For: 🏦 Organizations in regulated industries (finance, healthcare) requiring high accuracy. 💰 Applications where reducing LLM operational costs is a priority. ⚙️ Technical support agents that must provide precise, unchanging information. 🔒 Systems where auditability and deterministic responses for known issues are required.

Key Benefits: ✅ Structurally eliminates hallucination risk for known topics. ✅ Reduces reliance on expensive generative models for common queries. ✅ Ensures deterministic, accurate, and consistent answers for your FAQ. ✅ Provides high-speed classification via vector search. ✅ Implements a research-backed architecture for building safer AI systems.