OpenAI Embeddings → Textsplittercharactertextsplitter

OIL Rag. Uses lmChatOpenAi, embeddingsOpenAi, agent, telegramTrigger. Event-driven trigger; 53 nodes.

This n8n workflow automates the process of ingesting files from Google Drive into a Supabase database, preparing them for a knowledge base system. It supports text-based files (PDF, DOCX, TXT, etc.) a

Ultimate Agentic RAG AI Agent Template. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, stickyNote. Event-driven trigger; 42 nodes.

🤖📈 This workflow is my personal solution for the Agentic Arena Community Contest, where the goal is to build a Retrieval-Augmented Generation (RAG) AI agent capable of answering questions based on a p

This template crawls a website from its sitemap, deduplicates URLs in Supabase, scrapes pages with Crawl4AI, cleans and validates the text, then stores content + metadata in a Supabase vector store us

This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to proce

📺 Full walkthrough video: https://youtu.be/r5kN_la0O7I

Indoor Farming Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Webhook trigger; 36 nodes.

This workflow is perfect for: Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. Researchers who want to quickly analyze and

This workflow creates a conversational assistant that can answer questions based on your Google Drive documents. It automatically processes various file types and uses Retrieval-Augmented Generation (

This workflow is your all-in-one pipeline to turn any document into a powerful searchable knowledge base using RAG (Retrieval-Augmented Generation). From the moment a file lands in your Google Drive,

Supabase RAG AI Agent PDFs & More. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 29 nodes.

Use cases include: Building a knowledge base* from your website. Creating a chatbot* that answers customer queries using your own site content. Powering RAG workflows* for FAQs, support docs, or produ

Airbnb Guest Assistant. Uses lmChatOpenAi, googleDrive, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Chat trigger; 26 nodes.

Document Ingestion & Processing

One-line summary : Answers WhatsApp in under 100 words, understands voice notes, and retrieves trusted answers from your Google Drive docs (RAG) kept fresh weekly.

Crawl4AI Agent. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 23 nodes.

> Summary: > This workflow listens for new Gmail messages, extracts and cleans email content, generates embeddings via OpenAI, stores them in a Qdrant vector database, and then enables a Retriev

This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

RAG AI Agent. Uses lmChatOpenAi, memoryBufferWindow, googleDrive, documentDefaultDataLoader. Webhook trigger; 20 nodes.

Use Vectors in RAG. Uses googleDrive, documentDefaultDataLoader, textSplitterCharacterTextSplitter, embeddingsOpenAi. Event-driven trigger; 17 nodes.

This workflow is perfect for: Healthcare ecommerce businesses that want to automate product recommendations. Founders or developers building an AI assistant using retrieval-augmented generation (RAG)

This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone vector database. It retrieves relevant context from your files in re

Qdrant Vector Database Embedding Pipeline. Uses vectorStoreQdrant, manualTrigger, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 13 nodes.