n8n workflows for Qdrant Vector Store.

Api Schema Extractor. Uses manualTrigger, httpRequest, splitOut, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 88 nodes.

Wait Splitout. Uses manualTrigger, httpRequest, splitOut, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 88 nodes.

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema

This workflow implements an advanced AI automation agent (OpenClaw Agent) that interacts with users through Telegram and integrates multiple AI models, external tools, and cloud services to automate c

Alfred (funcional). Uses gmailTool, googleCalendarTool, gmail, embeddingsOpenAi. Event-driven trigger; 83 nodes.

This comprehensive workflow bundle is designed as a powerful starter kit, enabling you to build a multi-functional AI assistant on Telegram. It seamlessly integrates AI-powered voice interactions, an

Agent IA Projet Client. Uses executeWorkflowTrigger, lmChatOpenAi, toolWorkflow, vectorStoreQdrant. Event-driven trigger; 79 nodes.

⚡AI-Powered YouTube Playlist & Video Summarization and Analysis v2. Uses lmChatGoogleGemini, agent, splitOut, chainLlm. Chat trigger; 72 nodes.

This n8n workflow transforms entire YouTube playlists or single videos into interactive knowledge bases you can chat with. Ask questions and get summaries without needing to watch hours of content. 🔗

HeyDinastia. Uses executeCommand, httpRequest, youTube, postgres. Webhook trigger; 66 nodes.

main_workflow. Uses agent, n8n-nodes-upstage, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Webhook trigger; 63 nodes.

Build a powerful, customizable AI chatbot for your WordPress website that intelligently retrieves posts, answers questions, and engages in natural conversations. This complete solution handles content

I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel.

Iroko-chatbot-green-API. Uses toolCalculator, agent, n8n-nodes-whatsapp-green-api, chatTrigger. Webhook trigger; 54 nodes.

Code Extractfromfile. Uses manualTrigger, sort, httpRequest, compression. Event-driven trigger; 50 nodes.

🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant. Uses documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, splitInBatches. Event-driven trigger; 50 nodes.

2464. Uses httpRequest, compression, editImage, documentDefaultDataLoader. Event-driven trigger; 50 nodes.

Workflow 2464. Uses httpRequest, compression, editImage, documentDefaultDataLoader. Event-driven trigger; 50 nodes.

This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI.

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI

Splitout Code. Uses stickyNote, toolWorkflow, mcpTrigger, executeWorkflowTrigger. Event-driven trigger; 44 nodes.

This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdran

Workflow 3636. Uses toolWorkflow, mcpTrigger, executeWorkflowTrigger, httpRequest. Event-driven trigger; 44 nodes.

Survey Insights With Qdrant, Python And Information Extractor. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, googleSheets. Event-driven trigger; 42 node

Breakdown Documents Into Study Notes Using Templating Mistralai And Qdrant. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-

Splitout Code. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, googleSheets. Event-driven trigger; 42 nodes.

Localfile Wait. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Workflow 2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights

This n8n template demonstrates how to automate comprehensive web research using multiple AI models to find, analyze, and extract insights from authoritative sources.

2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. 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

Email AI Auto-responder. Summerize and send email. Uses emailReadImap, lmChatOpenAi, emailSend, vectorStoreQdrant. Event-driven trigger; 41 nodes.

Unlock adaptive, context-aware AI chat in your automations—no coding required!

Description This workflow automatically classifies user queries and retrieves the most relevant information based on the query type. 🌟 It uses adaptive strategies like; Factual, Analytical, Opinion, a

Adaptive RAG. Uses agent, chatTrigger, lmChatGoogleGemini, memoryBufferWindow. Chat trigger; 39 nodes.

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

This n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) framework. It recognizes that the best way to retrieve information often depends on the type of question ask

Upload a construction photo via web form → get a detailed cost estimate with work breakdown, resource costs, and professional HTML report. Powered by GPT-4 Vision and the open-source DDC CWICR databas

This workflow helps users find the most relevant n8n templates using AI.

RSSフィードから海外のテック記事を収集し、AIで選定・翻訳・要約する. Uses rssFeedRead, n8n-nodes-qdrant, vectorStoreQdrant, documentDefaultDataLoader. Webhook trigger; 39 nodes.

Wait Splitout. Uses manualTrigger, embeddingsMistralCloud, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 38 nodes.

Wait Splitout. Uses manualTrigger, embeddingsMistralCloud, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 38 nodes.

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for y

The benefits being (1) the vision model doesn't need to keep all document scans in context (expensive) and (2) ability to query on graphical content such as charts, graphs and tables. Page extracts fr

Customer Insights With Qdrant, Python And Information Extractor. Uses manualTrigger, html, splitOut, documentDefaultDataLoader. Event-driven trigger; 37 nodes.

Splitout Code Export. Uses manualTrigger, html, splitOut, documentDefaultDataLoader. Event-driven trigger; 37 nodes.

Generate Exam Questions. Uses manualTrigger, vectorStoreQdrant, httpRequest, embeddingsOpenAi. Event-driven trigger; 37 nodes.

Tetra_Blind_Eval_RAG_TEST+Ejentum_Harness. Uses embeddingsGoogleGemini, vectorStoreQdrant, httpRequestTool, agent. Event-driven trigger; 37 nodes.

This Workflow simulates an AI-powered phone agent with RetellAI with two main functions: 📅 Appointment Booking – It can schedule appointments directly into Google Calendar. 🧠 RAG-based Information Ret

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights

This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval

AI Phone Agent with RetellAI. Uses lmChatOpenAi, outputParserStructured, vectorStoreQdrant, embeddingsOpenAi. Webhook trigger; 36 nodes.

Splitout Code. Uses manualTrigger, hackerNews, splitOut, vectorStoreQdrant. Event-driven trigger; 36 nodes.

Indexation. Uses formTrigger, embeddingsOllama, textSplitterRecursiveCharacterTextSplitter, modelSelector. Event-driven trigger; 36 nodes.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights

How it works Automates systematic literature review by downloading papers from Google Drive, extracting text, and evaluating them against strict inclusion/exclusion criteria using LLM agents Routes in

This workflow is a complete AI-powered customer support automation for e-commerce businesses.

2374. Uses hackerNews, vectorStoreQdrant, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 36 nodes.

This n8n workflow automates the process of ingesting documents from multiple sources (Google Drive and web forms) into a Qdrant vector database for semantic search capabilities. It handles batch proce

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 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.

Wait Code Export. Uses manualTrigger, httpRequest, html, embeddingsMistralCloud. Event-driven trigger; 33 nodes.

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

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

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

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

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 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.

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.

Build a Multi-Agent system with n8n, Qdrant, Gmail & OpenAI. Uses vectorStoreQdrant, toolWorkflow, executeWorkflowTrigger, googleDrive. Event-driven trigger; 29 nodes.

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 template presents a multi-agent system in which a coordinating agent manages specialized sub-agents: an AI agent for RAG and document summarization, and an email agent. Each agent effectively ope

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:

Overview Automated LinkedIn content generator that:

Business WhatsApp AI RAG Chatbot. Uses respondToWebhook, agent, stickyNote, lmChatOpenAi. Webhook trigger; 28 nodes.

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

Business WhatsApp AI RAG Chatbot. Uses agent, lmChatOpenAi, vectorStoreQdrant, httpRequest. 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.

Parents smart bot. Uses telegramTrigger, agent, toolWorkflow, toolHttpRequest. 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.

RAG Agent Integration Hub mit Knowledge Management. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 27 nodes.

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 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

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

n8n-4-1: Qdrant. Uses vectorStoreQdrant, embeddingsOpenAi, textClassifier, chainSummarization. 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.

Telegram bot. Uses telegramTrigger, embeddingsOllama, lmChatOllama, toolVectorStore. 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 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

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.

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.

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

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

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

Indexation. Uses formTrigger, embeddingsOllama, textSplitterRecursiveCharacterTextSplitter, modelSelector. Event-driven trigger; 25 nodes.

Business WhatsApp AI RAG Chatbot. Uses respondToWebhook, agent, stickyNote, lmChatOpenAi. Webhook trigger; 24 nodes.

Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

V1 ocal RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

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

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.

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.

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 workflow helps automatically analyze alerts occurring in the infrastructure and suggest solutions even before the on-duty engineer sees the alert. Workflow receives alert from Alertmanager via We

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

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

AI-powered sub-workflow that answers questions about a your infrastructure configuration directly in a Mattermost channel or thread OpenRouter/OpenAI/Anthropic API key Google Gemini API key — for embe

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.

Local RAG AI Agent with Knowledge Management. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 22 nodes.

This workflow automates compliance validation between a policy/procedure and a corresponding uploaded document. It leverages an AI agent to determine whether the content of the document aligns with th

This automation is a game-changer for content creators, marketers, and authors. It transforms any book or long document into a treasure trove of over 100 ready-to-use, short-form content ideas for pla

AI-powered SRE sub-workflow that investigates user-reported incidents coming from a Mattermost channel and posts a structured diagnostic report back into the same thread. The result is a four-section

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

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

This is a sub-workflow that converts a free-form DevOps request posted in Mattermost into a properly formatted Jira task OpenRouter/OpenAI/Anthropic API key Google Gemini API key — for embeddings Jira

This comprehensive Retrieval-Augmented Generation (RAG) system enables businesses to effectively manage and query their knowledge base. Users can seamlessly upload documents via a web form, automatica

AI Mail Assistant. Uses emailReadImap, lmChatOllama, n8n-nodes-imap, agent. Manual trigger; 21 nodes.

Google Drive Knowledge Sync. Uses googleDriveTrigger, googleDrive, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Event-driven trigger; 20 nodes.

⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to

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

Receives messages to technical support and classifies them. OpenRouter/OpenAI/Anthropic API key Google Gemini API key — for embeddings (models/gemini-embedding-2-preview) used with Qdrant Slack bot —

Affine Content Sync to Vector Store. Uses httpRequest, postgres, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Scheduled trigger; 19 nodes.

This workflow helps automatically analyze alerts occurring in the infrastructure and suggest solutions even before the on-duty engineer sees the alert.

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

QdrantVectorStore:*. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven 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.

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 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 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)

Automatically classify and route DevOps requests from your team chat using LLM + on-call calendar lookup.

Larry Llama. Uses agent, lmChatOllama, memoryPostgresChat, embeddingsOllama. Webhook trigger; 17 nodes.

PaperReady — Validate. Uses httpRequest, agent, lmChatGoogleGemini, vectorStoreQdrant. Webhook trigger; 16 nodes.

Who's it for

This template helps you to create an intelligent document assistant that can answer questions from uploaded files.

Turn form submissions into AI-curated quotes with SKU selection straight from Qdrant, branded PDF generation, and automatic email—now streamlined for quoting only (FAQ removed). (Improved from the pre

This part collects data from the ServiceNow Knowledge Article table, processes it into embeddings, and stores it in Qdrant. Trigger: When clicking ‘Execute workflow’

Ingest PDF files from S3, extract text, chunk, embed with OpenAI embeddings, and index into a Qdrant collection with metadata. Provide a chat entry point that uses an Agent with OpenAI to retrieve fro

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

🧠 This workflow is designed for one purpose only, to bulk-upload structured JSON articles from an FTP server into a Qdrant vector database for use in LLM-powered semantic search, RAG systems, or AI as

AppFlowy Content Sync to Vector Store. Uses n8n-nodes-appflowy, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader, embeddingsOllama. Event-driven trigger; 11 nodes.

RAG Search Agent. Uses executeWorkflowTrigger, agent, lmChatOpenAi, toolVectorStore. Event-driven trigger; 11 nodes.

Agente_Ecommerce_v3_subflujo. Uses embeddingsGoogleGemini, vectorStoreQdrant, executeWorkflowTrigger, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 10 nodes.

Fluidflow Slack Triage Bot. Uses slack, slackTrigger, lmChatOpenAi, vectorStoreQdrant. Event-driven trigger; 8 nodes.

ingest_documents. Uses readBinaryFile, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, vectorStoreQdrant. Event-driven trigger; 5 nodes.

tool_search_products. Uses executeWorkflowTrigger, embeddingsOpenAi, vectorStoreQdrant. Event-driven trigger; 4 nodes.

RAG Pipeline. Uses formTrigger, vectorStoreQdrant, embeddingsOllama, documentDefaultDataLoader. Event-driven trigger; 13 nodes.

Click here to view the YouTube Tutorial

RAG Agent. Uses vectorStoreQdrant, documentDefaultDataLoader, agent, chatTrigger. Event-driven trigger; 12 nodes.

Provides one workflow to maintain the knowledge base and another one to query the knowledge base. Uploaded documents are saved into the Qdrant vector store. When a query is made, the most relevant doc

Overview This template allows users to set up an AI-powered chatbot that retrieves and processes knowledge from Google Drive documents using Retrieval-Augmented Generation (RAG). By leveraging Llama 3

noc_zabbix_ai_triage_agent. Uses agent, memoryRedisChat, lmChatOllama, outputParserStructured. Webhook trigger; 11 nodes.

Chatbot. Uses memoryMongoDbChat, httpRequestTool, lmChatMistralCloud, agent. Webhook trigger; 9 nodes.

Chatbot. Uses agent, lmChatGoogleGemini, vectorStoreQdrant, embeddingsGoogleGemini. Webhook trigger; 9 nodes.

Click here to watch the full tutorial on YouTube

small dick. Uses executeWorkflowTrigger, vectorStoreQdrant, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 6 nodes.

RAG_qdrant. Uses formTrigger, embeddingsOllama, vectorStoreQdrant, documentDefaultDataLoader. Event-driven trigger; 5 nodes.

Knowledge Base Upload. Uses vectorStoreQdrant, documentDefaultDataLoader, textSplitterTokenSplitter, embeddingsOpenAi. Event-driven trigger; 5 nodes.