n8n workflows for OpenAI Embeddings.

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

Overview Automated LinkedIn content generator that:

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

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.

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

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,

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.

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.

MCP_SUPABASE_AGENT. Uses mcpTrigger, vectorStoreSupabase, embeddingsOpenAi, stickyNote. Event-driven trigger; 27 nodes.

WhatsApp Agent. Uses whatsAppTrigger, agent, memoryBufferWindow, whatsApp. 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

Version: 1.0.0 n8n Version: 1.88.0+ Author: Koresolucoes License: MIT

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

This template is for businesses, customer support teams, and professionals who want to deliver AI-powered WhatsApp assistance. It helps automate conversations, schedule meetings, answer FAQs, and send

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

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. Uses lmChatOpenAi, toolWorkflow, telegram, telegramTrigger. Event-driven trigger; 27 nodes.

Whatsapp. Uses whatsAppTrigger, agent, memoryBufferWindow, whatsApp. Event-driven trigger; 27 nodes.

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.

claude - Agent Yedid AI. Uses httpRequest, agent, lmChatOpenAi, memoryBufferWindow. Webhook trigger; 27 nodes.

20-building-your-first-whatsapp-chatbot. Uses whatsAppTrigger, lmChatOpenAi, memoryBufferWindow, toolVectorStore. Event-driven trigger; 27 nodes.

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.

Create Publish Instagram Carousel Posts With Gpt-4.1-Mini Imgur Graph Api. Uses agent, lmChatOpenAi, vectorStoreInMemory, embeddingsOpenAi. Scheduled 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.

Version: 2.0.0 n8n Version: 1.88.0+ Author: Unknown License: MIT

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

&gt; 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.

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.

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 integrates a Retrieval-Augmented Generation (RAG) system with a post-sales AI agent for WooCommerce. It combines vector-based search (Qdrant + OpenAI embeddings) with LLMs (Google Gemini

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

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.

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

Email_sequencing_SDR. Uses gmail, lmChatOpenAi, googleSheets, agent. Scheduled trigger; 25 nodes.

Fadhil. Uses agent, chatTrigger, mySql, vectorStoreSupabase. Chat trigger; 25 nodes.

Corvus — Ingestão via Formulário. Uses agent, lmChatOpenAi, vectorStoreSupabase, embeddingsOpenAi. Webhook trigger; 25 nodes.

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.

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.

This n8n template automates the collection, storage, and summarization of technology news from top sites, turning it into a concise, personalized weekly newsletter.

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

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) &gt; 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

Embeddingfile. Uses googleDriveTrigger, googleDocs, vectorStorePinecone, embeddingsOpenAi. 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.

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.

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.

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

&gt; Summary: &gt; 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 workflow is built for e-commerce businesses, retail store owners, and entrepreneurs who want to provide intelligent customer support and seamless order taking through Telegram. If you're tired of

Automatically draft email replies using AI. This workflow monitors your Gmail inbox, filters out automated emails (newsletters, receipts, notifications), and uses AI to create draft responses only for

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.

ai-voice-chatbot-with-elevenlabs-&-openai-for-customer-service-and-restaurants. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.

31-ai-voice-chatbot-with-elevenlabs-&-openai-for-customer-service-and-restaurants. Uses agent, toolVectorStore, vectorStoreQdrant, embeddingsOpenAi. Event-driven trigger; 23 nodes.

Generating Image Embeddings Via Textual Summarisation. Uses manualTrigger, googleDrive, editImage, documentDefaultDataLoader. Event-driven trigger; 22 nodes.

Manual Googledrive. Uses manualTrigger, embeddingsOpenAi, stickyNote, documentDefaultDataLoader. Event-driven trigger; 22 nodes.

Manual Stickynote. Uses manualTrigger, googleDrive, editImage, documentDefaultDataLoader. Event-driven trigger; 22 nodes.

Splitout Limit. Uses lmChatOpenAi, manualTrigger, httpRequest, html. Event-driven trigger; 22 nodes.

This n8n template demonstrates an approach to image embeddings for purpose of building a quick image contextual search. Use-cases could for a personal photo library, product recommendations or searchi

Who is This For? This is for normal people or people just starting off and wanting to have a AI chatbot that can process data to use when talking to the user.

Go beyond basic Retrieval-Augmented Generation (RAG) with this advanced template. While a simple RAG setup can answer straightforward questions, it often fails when faced with complex queries and can

This n8n workflow ensures data freshness in the RAG system by handling modifications to existing files. It complements the "Document Ingestion" workflow by triggering whenever a file in the monitored

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

This workflow synchronizes MySQL database table schemas with a vector database in a controlled, idempotent manner. Each database table is indexed as a single vector to preserve complete schema context

N8N Workflow Fixed. Uses chatTrigger, agent, lmChatOpenAi, memoryBufferWindow. Chat trigger; 22 nodes.

Chat With Pdf. Uses embeddingsOpenAi, documentDefaultDataLoader, googleDrive, chatTrigger. Event-driven trigger; 22 nodes.

This workflow ingests PDF cost-engineering manuals from Google Drive into a Pinecone vector index using OpenAI embeddings, then answers user questions via an n8n chat webhook using a retrieval-augment

Supabase Insertion Upsertion Retrieval. Uses googleDrive, documentDefaultDataLoader, stickyNote, chainRetrievalQa. Chat trigger; 21 nodes.

Supabase Insertion & Upsertion & Retrieval. Uses googleDrive, documentDefaultDataLoader, stickyNote, chainRetrievalQa. Chat trigger; 21 nodes.

Slack AI Chatbot with RAG for company staff. Uses agent, memoryBufferWindow, embeddingsOpenAi, vectorStoreQdrant. Event-driven trigger; 21 nodes.

Agent Milvus tool. Uses manualTrigger, httpRequest, html, splitOut. Event-driven trigger; 21 nodes.

Supabase RAG AI Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 21 nodes.

This is a demo workflow to showcase how to use Supabase to embed a document, retrieve information from the vector store via chat and update the database. set your credentials for Supabase set your cre

Imagine having an AI chatbot on Slack that seamlessly integrates with your company’s workflow, automating repetitive requests. No more digging through emails or documents to find answers about IT requ

What Problem Does This Solve? 🛠️ This workflow automates the process of extracting information from a Google Doc, storing it in a Pinecone vector database, and using it to personalize and send emails

This workflow creates a RAG (Retrieval-Augmented Generation) system using Milvus vector database to search Paul Graham essays: Scrape & Load: Fetches Paul Graham essays, extracts text, and stores them

This n8n template demonstrates how to build an AI-powered customer support workflow that automatically handles incoming Gmail messages, classifies them, finds answers from your knowledge base, and sen

This workflow implements a two-stage news automation system designed for reusable and topic-driven email delivery. News articles are continuously collected from multiple platforms using RSS feeds and

Runner QA system. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 21 nodes.

Workflow-Rag. Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 21 nodes.

OPO. Uses openAi, telegram, telegramTrigger, agent. Event-driven trigger; 21 nodes.

Telegram RAG pdf. Uses telegramTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 20 nodes.

Telegram RAG pdf. Uses telegramTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 20 nodes.

Manual Code. Uses manualTrigger, stickyNote, vectorStorePinecone, chatTrigger. Event-driven trigger; 20 nodes.

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

What this does

This workflow allows you to ask questions about a PDF document. The answers are provided by an AI model of your choice, and the answer includes a citation pointing to the information it used.

This workflow automates the process of reading EDI files generated by Sabre, parsing them using an AI Agent, and producing structured accounting reports like:

Automatically sync files from Google Drive into a searchable AI knowledge base with Pinecone, and answer user queries using GPT-4o with conversational memory.

Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research. n8n instance (cloud or self-hosted) Google Drive account (with PD

Enhance content quality, SEO performance, and editorial consistency using an AI-powered optimization engine that blends OpenAI, Google Sheets history, Pinecone knowledge, and real-time SERP intelligen

Carga Documentos. Uses googleDrive, openAi, googleDriveTrigger, embeddingsOpenAi. Event-driven trigger; 20 nodes.

This workflow indexes Google Drive documents into a Supabase vector store using OpenAI embeddings, then exposes a webhook that uses a GPT-4o-mini RAG agent to answer incoming questions with short, voi

LLM Testing. Uses chatTrigger, toolVectorStore, vectorStorePinecone, embeddingsOpenAi. Chat trigger; 19 nodes.

voice-assistant. Uses googleDriveTrigger, supabase, googleDrive, vectorStoreSupabase. Event-driven trigger; 19 nodes.

RAG+URL. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmOpenAi. Chat trigger; 19 nodes.

aiNodes. Uses openAi, gmailTool, chainLlm, lmChatOpenAi. Event-driven trigger; 19 nodes.

Automatically guide students through personalized learning sessions using AI-powered intent classification, book-based knowledge retrieval (RAG), and full session logging — all without writing a singl

Sales Automation, Artificial Intelligence, CRM Operations, Coaching & Training, AI Agents (RAG)

AI Agent to learn directly from your GitHub repository. It automatically syncs source files, converts them into vectorized knowledge

This template is an end-to-end demo of a chatbot using business data from multiple sources (e.g. Notion, Chargebee, Hubspot etc.) with RAG + SQL.

Description An intelligent conversational AI system that provides contextual responses by combining chat history, vector database knowledge retrieval, and web search capabilities. How it Works (High-l

The AI Support Agent combines Gmail, Slack, and Google Drive into a seamless support workflow powered by GPT-4o and Pinecone.

RAG AI Agent for Documents in Google Drive → Pinecone → OpenAI Chat (n8n workflow)

This workflow creates an intelligent Telegram bot with a knowledge base powered by Qdrant vector database. The bot automatically processes documents uploaded to Google Drive, stores them as embeddings

Click here to access this Workflow for free.

Upload to Supabase Demo. Uses extractFromFile, vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes.

QdrantVectorStore:*. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 18 nodes.

EJ 7 - RAG (archivo pdf en la web). Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes.

N8N Expert. Uses embeddingsOpenAi, lmChatOpenAi, supabase, chainLlm. Webhook trigger; 18 nodes.

This workflow creates a WhatsApp chatbot that answers questions using your own PDFs through RAG (Retrieval-Augmented Generation). Every time you upload a document to Google Drive, it is processed into

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

Transform any device manual into an intelligent AI assistant that provides 24/7 support for your users. This template works with ANY household appliance, electronic device, or technical equipment. Man

Advanced Gmail AI Auto-Responder with Context Intelligence The next-generation email automation that knows your communication style, remembers conversations, and responds with human-like intelligence.

This workflow turns WhatsApp voice messages into an AI assistant using Twilio, VAPI, and modular MCP servers. It handles scheduling, email, and knowledge queries all by voice. WhatsApp → Twilio → VAPI

This n8n workflow is the data ingestion pipeline for the "RAG System V2" chatbot. It automatically monitors a specific Google Drive folder for new files, processes them based on their type, and insert

This workflow automates a full RAG pipeline for structured documents (like insurance policies). Watches a Google Drive folder for new PDFs Uploads to LlamaIndex Cloud for parsing → returns clean Markd

This template monitors a Google Drive folder, converts PDF documents into clean text chunks with Unstructured, generates OpenAI embeddings, and upserts vectors into Pinecone. It’s a practical, product

Description This workflow is built for e-commerce store owners, customer support teams, and retail businesses who want to provide instant, intelligent email support without hiring additional staff. If

This workflow automates the process of summarizing recent Zendesk support tickets and sharing key insights in a Slack channel. It is ideal for support teams who want daily, AI-generated overviews of c

Learn your voice. Generate posts that sound like you — not AI.

📊 Description

Opo45V5U31Hszckj. Uses documentDefaultDataLoader, embeddingsOpenAi, textSplitterCharacterTextSplitter, vectorStoreSupabase. Event-driven trigger; 18 nodes.

Main Workflow. Uses documentDefaultDataLoader, vectorStorePinecone, lmChatXAiGrok, embeddingsOpenAi. Webhook trigger; 18 nodes.

Stock Q&A Workflow. Uses embeddingsOpenAi, manualChatTrigger, stickyNote, chainRetrievalQa. Chat trigger; 17 nodes.

Webhook Respondtowebhook. Uses stickyNote, manualTrigger, googleDrive, documentDefaultDataLoader. Event-driven trigger; 17 nodes.

AI Email processing autoresponder with approval (Yes/No). Uses emailReadImap, markdown, lmChatOpenAi, emailSend. Manual trigger; 17 nodes.

AI Email processing autoresponder with approval (Yes/No). Uses emailReadImap, markdown, lmChatOpenAi, emailSend. Manual trigger; 17 nodes.

Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI). Uses manualTrigger, httpRequest, vectorStorePinecone, documentDefaultDataLoader. Event-driven trigger; 17 nodes.

weavite. Uses vectorStoreWeaviate, embeddingsOpenAi, googleSheets, chatTrigger. Event-driven trigger; 17 nodes.

EJ 7 - RAG (web). Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 17 nodes.

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

What this does

Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis.

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and

This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the

This n8n template demonstrates how to build a WhatsApp-based AI chatbot that answers user questions using document retrieval (RAG) powered by Supabase for storage, OpenAI embeddings for semantic searc

This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes.