Build a Chatbot, Voice Agent and Phone Agent with Voiceflow, Google Calendar and RAG. Uses googleCalendar, lmChatOpenAi, chainLlm, vectorStoreQdrant. Webhook trigger; 34 nodes.
Bitrix24 Open Chanel RAG Chatbot Application Workflow example with Webhook Integration. Uses httpRequest, noOp, respondToWebhook, documentDefaultDataLoader. Webhook trigger; 34 nodes.
This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering
Voiceflow is a no-code platform that allows you to design, prototype, and deploy conversational assistants across multiple channels—such as chat, voice, and phone—with advanced logic and natural langu
This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store.
This workflow implements a complete Voice AI Chatbot system for Wordress that integrates speech recognition, guardrails for safety, retrieval-augmented generation (RAG), Qdrant vector search, and audi
Transform your Bitrix24 Open Line channels with an intelligent chatbot that leverages Retrieval-Augmented Generation (RAG) technology to provide accurate, document-based responses to customer inquirie
Automate AI-Powered RAG System with Contextual Q&A, Google Sheets Integration, and Glide Frontend—Powered by n8n, OpenAI, Supabase, and Google Apps Script.
This workflow is built for individuals, teams, and businesses that receive regular inquiries via email and want to automate responses in a way that’s intelligent, brand-aligned, and always up to date.
AI Sales. Uses gmailTrigger, agent, lmChatOpenAi, httpRequest. Event-driven trigger; 34 nodes.
Build a Chatbot, Voice and Phone Agent with Voiceflow, Google Calendar and RAG. Uses googleCalendar, lmChatOpenAi, chainLlm, vectorStoreQdrant. Webhook trigger; 34 nodes.
Ai Agent To Chat With Files In Supabase Storage. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, extractFromFile. Event-driven trigger; 33 nodes.
Wait Code Export. Uses manualTrigger, httpRequest, html, embeddingsMistralCloud. Event-driven trigger; 33 nodes.
Ai Agent To Chat With Files In Supabase Storage. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, extractFromFile. Event-driven trigger; 33 nodes.
supabase. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Event-driven trigger; 33 nodes.
This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying.
Need to turn a one-line chat request into a fully-wired n8n workflow template—complete with AI agents, RAG, and web-search super-powers—without lifting a finger? That’s exactly what Agent Builder auto
I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n.
This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API.
This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant.
This n8n template demonstrates how to build an intelligent entity research system that automatically discovers, researches, and creates comprehensive profiles for business entities, concepts, and term
Build a fully functional AI chatbot for any website using Retrieval-Augmented Generation (RAG). This workflow automatically crawls and indexes your entire site into a Qdrant vector database, then powe
My workflow 2. Uses googleGemini, formTrigger, httpRequest, googleDrive. Event-driven trigger; 33 nodes.
This workflow builds a WhatsApp customer-support chatbot using OpenWA and OpenAI, enriched with Qdrant RAG knowledge and optional Gmail escalation to a human agent, plus a manual knowledge-base ingest
n8n telegram RAG. Uses lmChatGoogleGemini, embeddingsGoogleGemini, memoryManager, vectorStoreSupabase. Event-driven trigger; 32 nodes.
An AI-powered sales agent on WhatsApp that handles product inquiries using your Supabase knowledge base and n8n catalog. Customers can send text, voice notes, or images to ask about products, pricing,
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.
This workflow combines website chatbot intelligence with automated document ingestion and vectorization — enabling live Q&A from both chat input and processed Google Drive files. It uses Mistral AI fo
This workflow allows you to generate riddle-themed vertical videos (9:16), render them using Creatomate, and upload them directly to YouTube — all automatically. It's optimized for low-cost operation
Ideal for businesses that receive frequent inquiries about products or services and want to automate responses, freeing up time to focus on core operations. Polls your inbox for new incoming emails Cl
This n8n template lets you automatically build and maintain an AI-ready knowledge base from Outlook emails and Notion pages. It stores both sources in a Pinecone vector database so your AI agent can r
Click here to access this Workflow for free.
This n8n workflow automates the process of collecting, storing, and summarizing customer reviews from the Apple App Store for multiple apps. It fetches daily reviews, stores them in a Pinecone vector
Transform your customer support with this intelligent Gmail-based automation system that combines AI analysis, vector knowledge bases, and smart escalation workflows. This comprehensive solution autom
This workflow automates academic and professional plagiarism detection by processing multi-modal submissions — documents, audio recordings, and images,through specialized AI agents. It targets educato
This n8n workflow is designed for content creators, bloggers, digital marketers, and social media managers who want to fully automate their content distribution pipeline. The workflow creates an end-t
This workflow ingests published Notion knowledge base articles into an in-memory vector store using Cohere embeddings, then monitors a support Gmail inbox and uses Groq to draft grounded reply emails
Draft grounded support replies from a Notion knowledge base using Groq and Gmail. Uses notion, vectorStoreInMemory, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven
Effortless Email Management with AI. Uses emailReadImap, markdown, emailSend, vectorStoreQdrant. Event-driven trigger; 31 nodes.
Effortless Email Management with AI. Uses emailReadImap, markdown, emailSend, vectorStoreQdrant. Event-driven trigger; 31 nodes.
WooCommerce AI Chatbot Workflow for Post-Sales Support. Uses chatTrigger, memoryBufferWindow, wooCommerceTool, toolCalculator. Chat trigger; 31 nodes.
This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or sugges
Based on the workflow image, here is the complete n8n template submission:
This WooCommerce-integrated chatbot is designed to transform post-sales customer support by combining automation and artificial intelligence to deliver fast, secure, and personalized assistance.
This workflow automates enterprise ticket management by combining AI-powered classification with knowledge base retrieval. It receives support tickets via webhook, routes them through multiple AI mode
This template gives your HR or operations team an AI-powered Slack bot that answers employee questions about internal policies — directly in DM, available to everyone in the workspace, with no per-use
This workflow is a complete AI-powered customer support automation for WooCommerce e-commerce websites. It combines conversational AI, Retrieval-Augmented Generation (RAG), vector search, WooCommerce
ejemplo RAG vs CRAG. Uses googleDrive, vectorStoreQdrant, embeddingsOllama, agent. Event-driven trigger; 30 nodes.
This workflow automatically fetches reviews for one or more Google Play Store apps, summarizes the feedback using OpenAI, stores and manages review data with Pinecone, and posts the summary to a Slack
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
Overview
This workflow is designed for support teams, data engineers, and AI developers who want to centralize Jira issue data into a vector database. It collects open issues and their associated comments, con
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,
This workflow allows users to send any newspaper or article link to a Telegram bot. The workflow then: Validates the URL Scrapes the webpage (title, description, full text, images, OG metadata) Proces
Requirement-to-Task-V5. Uses lmChatOpenAi, memoryBufferWindow, textClassifier, agent. Event-driven trigger; 30 nodes.
crawl4Ai-rag. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 30 nodes.
Build A Financial Documents Assistant Using Qdrant And Mistral.Ai. Uses localFileTrigger, manualTrigger, stickyNote, readWriteFile. Event-driven trigger; 29 nodes.
Localfile. Uses localFileTrigger, manualTrigger, stickyNote, readWriteFile. Event-driven trigger; 29 nodes.
Order and Delivery Support. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 29 nodes.
Supabase RAG AI Agent PDFs & More. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 29 nodes.
This workflow implements a complete Retrieval-Augmented Generation (RAG) knowledge assistant with built-in document ingestion, conversational AI, and automated analytics using n8n, OpenAI, and Pinecon
This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremen
This workflow is an AI-powered multi-agent system built for startup founders and small business owners who want to automate decision-making, accountability, research, and communication, all through Wh
This n8n workflow demonstrates how to manage your Qdrant vector store when there is a need to keep it in sync with local files. It covers creating, updating and deleting vector store records ensuring
This workflow implements a Retrieval-Augmented Generation (RAG) system that:
Answers should given only within provided text. Chat interface powered by LLM (Ollama) Retrieval-Augmented Generation (RAG) using Supabase Vector DB Multi-format file support (PDF, Excel, Google Docs,
This powerful AI automation add-on upgrades your Telegram Bot Starter Template by integrating a fully functional AI chatbot and a context-aware AI agent that answers user questions using your internal
Empower your workflows with an intelligent AI chat assistant that retrieves real-time context from Google Sheets and a Pinecone knowledge base using Retrieval-Augmented Generation (RAG). 🤖📂 This workf
<h2>📧 Analyze, classify, and summarize emails using RAG (automatic taxonomy learning)</h2>
Deploy a personal AI assistant that answers recruiter questions about your skills and projects, then automatically emails your CV as a PDF attachment when requested. Upload your portfolio documents (r
Security incidents. Uses googleDriveTrigger, googleDrive, vectorStorePinecone, documentDefaultDataLoader. Event-driven trigger; 29 nodes.
Business WhatsApp AI RAG Chatbot. Uses respondToWebhook, agent, stickyNote, lmChatOpenAi. Webhook trigger; 28 nodes.
Building Your First Whatsapp Chatbot. Uses whatsAppTrigger, lmChatOpenAi, memoryBufferWindow, toolVectorStore. Event-driven trigger; 28 nodes.
AI Document Assistant via Telegram + Supabase. Uses lmChatGoogleGemini, openWeatherMapTool, agent, telegramTrigger. Event-driven trigger; 28 nodes.
This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions.
The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. Webhook Setup: The workflow begins by setting up webhooks for verification and r
This template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-processing for rich form
A complete AI-powered study assistant system that lets you chat naturally with your documents stored in Google Drive:
The IngestionDocs workflow is a fully automated **document ingestion and knowledge management system built with n8n**. Its purpose is to continuously ingest organizational documents from Google Drive,
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI and vector search.
AI-powered n8n workflow that creates viral LinkedIn posts by learning from successful content. Features two modules: (1) Telegram-based scraper that builds a vector database of viral LinkedIn posts, a
Business WhatsApp AI RAG Chatbot. Uses agent, lmChatOpenAi, vectorStoreQdrant, httpRequest. Webhook trigger; 28 nodes.
RAG_AI_Agent_PDFs_Excel. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Webhook trigger; 28 nodes.
Business WhatsApp AI RAG Chatbot. Uses agent, lmChatOpenAi, vectorStoreQdrant, httpRequest. Webhook trigger; 28 nodes.
Workflow 2465. Uses whatsAppTrigger, lmChatOpenAi, memoryBufferWindow, toolVectorStore. Event-driven trigger; 28 nodes.
Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI. Uses manualTrigger, github, extractFromFile, embeddingsOpenAi. Event-driven trigger; 27 nodes.
HR & IT Helpdesk Chatbot with Audio Transcription. Uses stickyNote, manualTrigger, httpRequest, extractFromFile. Event-driven trigger; 27 nodes.
Parents smart bot. Uses telegramTrigger, agent, toolWorkflow, toolHttpRequest. Event-driven trigger; 27 nodes.
HR & IT Helpdesk Chatbot with Audio Transcription. Uses stickyNote, manualTrigger, httpRequest, extractFromFile. Event-driven trigger; 27 nodes.
Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI. Uses manualTrigger, github, extractFromFile, embeddingsOpenAi. Event-driven trigger; 27 nodes.
Supabase RAG AI Agent Custom Auth. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 27 nodes.
An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via
Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishe
This workflow turns your WhatsApp number into an intelligent AI-powered Sales Agent that answers product queries using real data extracted from a PDF brochure. It loads a product brochure via HTTP Req
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
This workflow transforms your n8n instance into a fully automated AI sales assistant for WooCommerce stores. It detects customer intent from chat, searches products, answers FAQs, generates Stripe pay
What it is: An n8n workflow that enables AI-first WhatsApp support with seamless human handoff. Why it’s unique: The AI agent answers queries using RAG (Supabase vector store + Gemini). If a human int
This workflow is for Product Managers, Indie Hackers, and Customer Success teams who collect feature requests but struggle to notify specific users when those features actually ship. It helps you turn
Categories: Business Automation, Customer Support, AI, Knowledge Management
This workflow is the official backend for the StopSlopIn Chrome extension – it classifies LinkedIn posts as quality or slop using a strict LLM quality gate and learns from user votes over time via a Q
How it works A manual trigger sets the research topic and generates three parallel Tavily web search queries A Summarizer Agent (Groq Llama 3.3 70B) and an Analyst Agent (OpenRouter GPT) process the s
AI-Business-Agent. Uses vectorStoreSupabase, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 27 nodes.
n8n-4-1: Qdrant. Uses vectorStoreQdrant, embeddingsOpenAi, textClassifier, chainSummarization. Event-driven trigger; 27 nodes.
20-building-your-first-whatsapp-chatbot. Uses whatsAppTrigger, lmChatOpenAi, memoryBufferWindow, toolVectorStore. Event-driven trigger; 27 nodes.
This workflow syncs files from Supabase Storage into a Supabase vector store using OpenAI embeddings, then exposes an n8n chat trigger where an AI agent answers questions by retrieving relevant docume
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, markdown, lmChatOpenAi, emailSend. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, markdown, lmChatOpenAi, emailSend. Event-driven trigger; 26 nodes.
Automate Siem Alert Enrichment With Mitre Att&Ck, Qdrant & Zendesk In N8N. Uses chatTrigger, agent, lmChatOpenAi, splitOut. Chat trigger; 26 nodes.
Splitout Zendesk. Uses chatTrigger, agent, lmChatOpenAi, splitOut. Chat trigger; 26 nodes.
Airbnb Guest Assistant. Uses lmChatOpenAi, googleDrive, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Chat trigger; 26 nodes.
RAG Reranking. Uses googleDrive, documentDefaultDataLoader, extractFromFile, chatTrigger. Chat trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
AI-Powered Email Automation with RAG. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional commun
This workflow is ideal for: Cybersecurity teams & SOC analysts who want to automate SIEM alert enrichment. IT security professionals looking to integrate MITRE ATT&CK intelligence into their ticketing
This template is designed for internal support teams, product specialists, and knowledge managers who want to build an AI-powered knowledge assistant with retrieval-augmented generation (RAG) and rein
This comprehensive n8n workflow template creates an intelligent AI chatbot that automatically transforms your Google Drive documents into a searchable knowledge base. The chatbot uses OpenAI's GPT mod
This workflow implements an AI-powered incident investigation and root cause analysis system that automatically analyzes operational signals when a system incident occurs.
This is a template for n8n's evaluation feature.
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
Unlock unparalleled efficiency and elevate customer satisfaction with our AI-Powered Customer Support: Email, Knowledge Base & Human Escalation Automation template. This sophisticated n8n workflow is
This workflow automatically indexes your n8n workflows every 24 hours, converts them into vector embeddings using OpenAI and stores them in Supabase. It exposes a webhook that lets you query your work
> Zoom + n8n + GPT-4o + Supabase RAG
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
AI-Powered Email Automation for Business: Summarize & Respond with RAG. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
AI-powered Email Auto-responder with Qdrant Knowledge Base. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
Reranker. Uses googleDrive, documentDefaultDataLoader, vectorStoreSupabase, rerankerCohere. Event-driven trigger; 26 nodes.
Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 26 nodes.
AI-Powered Customer Support: Email, Knowledge Base & Human Escalation Automation. Uses lmChatOpenAi, agent, gmail, googleSheets. Event-driven trigger; 26 nodes.
This workflow builds a searchable HR policy knowledge base from a PDF and runs a Telegram helpdesk bot that answers employee questions from that content, handling both text messages and voice notes vi
OpenAI Personal Shopper with RAG and WooCommerce. Uses chatTrigger, memoryBufferWindow, toolCalculator, lmChatOpenAi. Chat trigger; 25 nodes.
Insert and retrieve documents. Uses manualTrigger, httpRequest, html, splitOut. Event-driven trigger; 25 nodes.
OpenAI Personal Shopper with RAG and WooCommerce. Uses chatTrigger, memoryBufferWindow, toolCalculator, lmChatOpenAi. Chat trigger; 25 nodes.
Agente de Procesamiento de Documentos. Uses httpRequest, vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 25 nodes.
AI chatbots are only as good as the data they learn from. Most large language models (LLM) rely only on their training datasets.
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user inten
Document Ingestion & Processing
This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: Data collection/processing Retrieval/r
Transform your AI assistants into intelligent agents with persistent memory capabilities. This production-ready workflow implements a sophisticated long-term memory system using vector databases, enab
The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_groundedness This evaluation works best for an agent that requires documen
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
Turn WhatsApp chats into instant answers and real-time bookings—all in one n8n workflow. Your AI Agent leverages Gemini embeddings + Pinecone for on-the-fly knowledge retrieval, then logs reservations
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.
It uses Retrieval-Augmented Generation (RAG) to allow users to upload documents, which are then indexed into a vector database, enabling the bot to answer questions based only on the provided content.
This n8n template turns any website or documentation portal into a fully functional AI-powered support chatbot — no manual copy-pasting, no static FAQs. It uses MrScraper to crawl and extract your sit
RAG + CHAT IA. Uses vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, agent. Event-driven trigger; 25 nodes.
Indexation. Uses formTrigger, embeddingsOllama, textSplitterRecursiveCharacterTextSplitter, modelSelector. Event-driven trigger; 25 nodes.
Fadhil. Uses agent, chatTrigger, mySql, vectorStoreSupabase. Chat trigger; 25 nodes.
This workflow runs weekly and crawls your website sitemap, scrapes each page, generates page-specific FAQs with OpenAI GPT-4o, embeds the Q&A content using OpenAI text-embedding-3-small, and upserts t
Business WhatsApp AI RAG Chatbot. Uses respondToWebhook, agent, stickyNote, lmChatOpenAi. Webhook trigger; 24 nodes.
Personalized AI Tech Newsletter Using RSS, OpenAI and Gmail. Uses splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 24 nodes.
Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.
Supabase RAG AI Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 24 nodes.
Supabase RAG AI Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 24 nodes.
Contextual Retrieval. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 24 nodes.
V1 ocal RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.
This n8n template automates the collection, storage, and summarization of technology news from top sites, turning it into a concise, personalized weekly newsletter.
This template is ideal for IT support teams, internal helpdesk automation engineers, and developers building intelligent ticketing systems. It helps streamline ITSM workflows by automatically classify
An upgraded Retrieval-Augmented Generation (RAG) chatbot built in n8n that lets users ask questions via Telegram and receive accurate answers from uploaded PDFs. It embeds documents using OpenAI and b
This workflow provides comprehensive AI-driven stock analysis, generating detailed deep reports by leveraging advanced vector-based data retrieval and API integrations for precise financial analytics
**Type of data is binary
This workflow deploys a fully customizable AI chatbot that can be embedded on any website, from custom-coded sites to platforms like WordPress. The chatbot is powered by n8n, uses Supabase for memory
[](https://www.youtube.com/watch?v=2EWgC5UKiBQ) > Empower employees to instantly access and understand the company’s Code of Conduct via a Slack chatbot, powered by Retrieval-Augmented Generation (
Answer HR and company policy questions via Slack, powered by a Knowledge Base of internal documents stored in S3. The assistant uses vector search and an OpenAI Chat Model to retrieve accurate answers
V1 Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.
V1 Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.
Agent: Local AI RAG: Ollama & Qdrant. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.
personalized-ai-tech-newsletter-using-rss,-openai-and-gmail. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmChatOpenAi. Scheduled trigger; 24 nodes.
16-personalized-ai-tech-newsletter-using-rss,-openai-and-gmail. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmChatOpenAi. Scheduled trigger; 24 nodes.
agente de whatsapp. Uses agent, lmChatOpenAi, vectorStoreQdrant, httpRequest. Webhook trigger; 24 nodes.
Voice RAG Chatbot with ElevenLabs and OpenAI. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.
Voice RAG Chatbot with ElevenLabs and OpenAI. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.
Chatbot. Uses googleDrive, vectorStoreSupabase, googleDriveTrigger, documentDefaultDataLoader. Event-driven trigger; 23 nodes.
RAG Agent supabase. Uses chatTrigger, lmChatOpenAi, embeddingsOpenAi, formTrigger. Chat trigger; 23 nodes.
Crawl4AI Agent. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 23 nodes.
V2 Supabase RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 23 nodes.
The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information.
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
This template is designed for podcasters, researchers, educators, product teams, and support teams who work with audio content and want to turn it into searchable knowledge. It is especially useful fo
This template provides a full end-to-end Retrieval-Augmented Generation (RAG) system using n8n. It includes two connected workflows: A data ingestion pipeline that crawls a website and stores its cont
This n8n workflow automates email support using AI and vector database technology to provide smart, context-aware responses. It seamlessly integrates email automation and document management, ensuring
An on-premises, domain-specific AI assistant for Kaggle (tested on binary disaster-tweet classification), combining LLM, an n8n workflow engine, and Qdrant-backed Retrieval-Augmented Generation (RAG).
> 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
Enable smart, real-time answers in your WhatsApp groups using a custom webhook, Pinecone vector database, and no Facebook Business setup.
This n8n workflow automates the process of summarizing uploaded books from Google Drive using vector databases and LLMs. It uses Cohere for embeddings, Qdrant for storage and retrieval, and DeepSeek o
This workflow transforms any webpage into an AI-narrated audio summary delivered via WhatsApp: Receive URL - WhatsApp Trigger captures incoming messages and passes them to URL extraction Extract & val
n8n-3-2: c4ai — Local Supabase. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 23 nodes.
n8n-4-2: c4ai — Local Supabase RAG. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 23 nodes.
Agent: Local AI RAG: Ollama & Supabase Vector. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 23 nodes.
ai-voice-chatbot-with-elevenlabs-&-openai-for-customer-service-and-restaurants. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.
This workflow ingests a local PDF into Qdrant with Ollama embeddings, then supports hybrid retrieval by querying Qdrant with both dense vectors and BM25 sparse vectors from an n8n chat trigger. Starts
31-ai-voice-chatbot-with-elevenlabs-&-openai-for-customer-service-and-restaurants. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.
contoh-rag-embedding. Uses documentDefaultDataLoader, googleDrive, googleDriveTrigger, supabase. Event-driven trigger; 23 nodes.
200 of 696 workflows on page 2 of 4 · Browse all →
FAQ
How many n8n Documentdefaultdataloader workflows are in the catalog?
696 n8n workflows in AutomationFlows currently use the Documentdefaultdataloader integration — triggers, actions, or both.
How do I connect Documentdefaultdataloader in n8n?
After importing the workflow JSON, n8n will prompt for Documentdefaultdataloader credentials on the relevant nodes. AutomationFlows strips credential IDs before publishing — you'll add your own.
Can I combine these with other integrations?
Yes — most Documentdefaultdataloader workflows pair with adjacent tools (Slack alerts, Google Sheets logging, OpenAI summarisation). Browse the integration tags on each workflow page to discover pairings.