Customer Support with N8n Nodes Agent + N8n Nodes Lmchatopenai Pack

An extendable RAG template to build powerful, explainable AI assistants - with query understanding, semantic metadata, and support for free-tier tools like Gemini, Gemma, and Supabase. ## Description This workflow helps you build smart, production-ready RAG agents that go far beyond basic document

# Simple RAG Customer Support Chatbot ## Overview This intelligent customer support chatbot leverages Retrieval-Augmented Generation (RAG) to provide accurate, contextual responses by combining your knowledge base with AI capabilities. The system automatically retrieves relevant documents from you

RAG-Based Customer Support Agent (GP-5 + Telegram) Description: This workflow builds a powerful Retrieval-Augmented Generation (RAG) Customer Support Agent that interacts with users directly through Telegram using the GP-5 model. It combines real-time conversational capabilities with context-aware

This workflow automates the creation of an AI-powered knowledge assistant using retrieval-augmented generation (RAG) and reinforcement learning from human feedback (RLHF) through Telegram. It streamlines knowledge management by importing and indexing documentation into MongoDB for efficient semantic

This workflow sets up a Retrieval-Augmented Generation (RAG) pipeline using n8n, enabling dynamic interaction with a vector store to provide context-aware responses.

This n8n workflow extracts website content, indexes it in Pinecone, and uses Airtable to power a chat agent for customer Q&A. Ideal for creating a knowledge base and chatbots that utilize your site's content.

Build a specialized chatbot that leverages n8n documentation to provide accurate answers using a Retrieval-Augmented Generation (RAG) pipeline with Gemini models.

This n8n workflow automates IT support by transforming static documents into an interactive knowledge base. It integrates Google Drive, Pinecone, and AI chat agents to provide instant, context-aware responses to support queries.

Build an AI chatbot that answers questions using content from PDF documents with a Retrieval-Augmented Generation (RAG) system. Integrate OpenAI, Pinecone, and Cohere for enhanced performance.

Create an AI chatbot that specializes in the official n8n documentation using a Retrieval-Augmented Generation (RAG) pipeline. This workflow scrapes, processes, and stores documentation in a Supabase vector store, allowing the AI to provide accurate, context-based answers.

Develop a sophisticated chatbot using RLHF and RAG techniques to assist users effectively.

Engage with GitHub API documentation through an AI-powered chatbot using RAG and OpenAI.

Transform your customer interactions with the RAG Agent Workflow, designed for seamless chat automation.

n8n workflow building a RAG chatbot for company policies: ingests docs from Google Drive, embeds via OpenAI, stores in Pinecone, and retrieves context-aware answers.

Retrieval Augmented Generation (RAG) chatbot for querying GitHub API documentation naturally. Uses OpenAI embeddings/LLM and Pinecone vector DB for accurate, context-aware responses.

Automates document ingestion from Google Docs, vector embedding with OpenAI, and MongoDB storage for AI-powered RAG chatbots that deliver accurate, context-aware answers to user queries.