This workflow powers dynamic character interactions for investigative games. It ingests lore from Google Drive into a vector database, using Google Gemini agents to generate daily briefings, spawn NPC
Apple RAG Chatbot V2. Uses googleDrive, documentDefaultDataLoader, agent, toolVectorStore. Chat 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.
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 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 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
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.
Geminis. Uses toolSerpApi, googleGemini, chatTrigger, agent. Event-driven 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
AI Funamentalist. Uses httpRequest, formTrigger, vectorStorePinecone, embeddingsOpenAi. Event-driven trigger; 22 nodes.
RAG. Uses lmChatOpenAi, documentDefaultDataLoader, toolVectorStore, googleDrive. Event-driven trigger; 22 nodes.
This workflow rebuilds a Qdrant-based knowledge base from Google Drive documents and then answers ElevenLabs voice-assistant questions using an OpenAI-powered RAG agent with conversation memory, retur
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.
Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone. Uses manualTrigger, agent, vectorStorePinecone, embeddingsGoogleGemini. Event-driven 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
This workflow enables automated, scalable collection of high-quality, AI-ready data from websites using Bright Data’s Web Unlocker, with a focus on preparing that data for LLM training. Leveraging LLM
RestaurantBot Pro is a complete AI-powered restaurant ordering system that transforms your WhatsApp into a smart ordering platform. This intelligent automation handles customer interactions in any lan
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
Build a custom, intelligent knowledge base in minutes. This n8n workflow provides a complete Retrieval-Augmented Generation (RAG) system using Google Gemini and Supabase. It features a seamless dual-f
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
Runner QA system. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 21 nodes.
ai-fitness-2. Uses googleDrive, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 21 nodes.
Workflow-Rag. Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 21 nodes.
ai agent flow. Uses httpRequest, agent, lmChatOllama, executeCommand. Webhook trigger; 21 nodes.
This workflow indexes text files from Google Drive into a Supabase vector store using OpenAI embeddings, then answers Placetel webhook questions with a Groq-hosted Llama model using retrieval-augmente
DSAProjeto8. Uses googleSheets, googleDrive, vectorStorePinecone, embeddingsGoogleGemini. Event-driven trigger; 21 nodes.
This workflow ingests FAQs from Google Sheets into a Supabase vector table using Google Gemini embeddings, then serves a webhook-based support chatbot that answers only from that FAQ knowledge base an
41. The secret no one told you to maximize RAG AI Agent accuracy. Uses googleDrive, vectorStorePinecone, documentDefaultDataLoader, agent. Chat trigger; 21 nodes.
This workflow syncs an internal Notion knowledge base to Pinecone on a daily schedule, then runs a Slack bot that answers questions in a specific channel using Pinecone retrieval and an OpenAI chat mo
This workflow pulls candidates from Google Sheets, uses an Anthropic-powered AI agent with an in-memory vector-store rubric (RAG) to score and shortlist applicants, writes results back to the sheet, t
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.
Gmail to Vector Embeddings with PGVector and Ollama. Uses embeddingsOllama, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, gmailTrigger. Event-driven trigger; 20 nodes.
RAG AI Agent. Uses lmChatOpenAi, memoryBufferWindow, googleDrive, documentDefaultDataLoader. Webhook trigger; 20 nodes.
Google Drive Knowledge Sync. Uses googleDriveTrigger, googleDrive, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Event-driven 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.
⚠️ 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
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
The system, named LENOHA (Low Energy, No Hallucination, Leave No One Behind Architecture), uses a high-precision classifier to differentiate between high-stakes queries and casual conversation. Querie
Carga Documentos. Uses googleDrive, openAi, googleDriveTrigger, embeddingsOpenAi. Event-driven trigger; 20 nodes.
n8n_ollama_pgvector. Uses chatTrigger, vectorStorePGVector, embeddingsGoogleGemini, documentDefaultDataLoader. Chat 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
Generate Company Stories from LinkedIn with Bright Data & Google Gemini. Uses manualTrigger, lmChatGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven tri
Extract & Summarize Bing Copilot Search Results with Gemini AI and Bright Data. Uses manualTrigger, lmChatGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-dri
voice-assistant. Uses googleDriveTrigger, supabase, googleDrive, vectorStoreSupabase. Event-driven trigger; 19 nodes.
RAG+URL. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmOpenAi. Chat trigger; 19 nodes.
Affine Content Sync to Vector Store. Uses httpRequest, postgres, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Scheduled trigger; 19 nodes.
The LinkedIn Company Story Generator is an automated workflow that extracts company profile data from LinkedIn using Bright Data's web scraping infrastructure, then transforms that data into a profess
AI Agent to learn directly from your GitHub repository. It automatically syncs source files, converts them into vectorized knowledge
This workflow automates the process of querying Bing's Copilot Search, extracting structured data from the results, summarizing the information, and sending a notification via webhook. It leverages th
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.
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)
Convert any website into a searchable vector database for AI chatbots. Submit a URL, choose scraping scope, and this workflow handles everything: scraping, cleaning, chunking, embedding, and storing i
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
This AI-powered workflow transforms n8n workflow JSON files into publication-ready, SEO-optimized markdown posts for the n8n community. Simply upload your workflow's JSON, and let Google Gemini 2.5 Pr
POC Sistema GenAI Híbrido para uso interno iFood -> Agente RAG + LLM Chain Routing. Uses chatTrigger, chainLlm, googleDrive, documentDefaultDataLoader. Chat trigger; 19 nodes.
RAG. Uses formTrigger, vectorStoreQdrant, embeddingsOllama, documentDefaultDataLoader. Event-driven trigger; 19 nodes.
40. Upload unlimited files to your vector database from Google Drive in seconds!. Uses googleDrive, vectorStorePinecone, agent, toolVectorStore. Chat trigger; 19 nodes.
This workflow ingests quarterly earnings PDFs listed in Google Sheets from Google Drive into a Pinecone vector index using Google Gemini embeddings, then uses an OpenAI-powered agent with Pinecone ret
RAG Workflow For Stock Earnings Report Analysis. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes
RAG Workflow For Company Documents stored in Google Drive. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger
Splitout Code. Uses manualTrigger, stickyNote, documentDefaultDataLoader, lmChatOpenAi. Event-driven trigger; 18 nodes.
RAG Workflow For Stock Earnings Report Analysis. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes
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.
google-drive-rag. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes.
EJ 7 - RAG (archivo pdf en la web). Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes.
This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated docum
This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector sear
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
The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email.
Many websites lack a smart, searchable interface. Visitors often leave due to unanswered questions. This workflow transforms any website into a Retrieval-Augmented Generation (RAG) chatbot—automatical
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 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 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.
This automation operates in three distinct phases: Ingestion, Storage, and Generation.
📊 Description
Opo45V5U31Hszckj. Uses documentDefaultDataLoader, embeddingsOpenAi, textSplitterCharacterTextSplitter, vectorStoreSupabase. Event-driven trigger; 18 nodes.
Main Workflow. Uses documentDefaultDataLoader, vectorStorePinecone, lmChatXAiGrok, embeddingsOpenAi. Webhook trigger; 18 nodes.
This workflow powers a support chat experience that uses an OpenAI RAG agent with in-memory vector search to answer questions from a knowledge base, and includes a manual ingestion flow that fetches a
Dynamic-Rag-Vector. Uses documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, httpRequest, embeddingsGoogleGemini. Event-driven trigger; 18 nodes.
RAG Workflow For Company Documents stored in Google Drive. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger
Webhook Respondtowebhook. Uses stickyNote, manualTrigger, googleDrive, documentDefaultDataLoader. Event-driven trigger; 17 nodes.
Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI). Uses manualTrigger, httpRequest, vectorStorePinecone, documentDefaultDataLoader. Event-driven trigger; 17 nodes.
RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini. Uses manualTrigger, splitInBatches, lmChatOpenRouter, vectorStorePinecone. Event-driven trigger; 17 nodes.
Search & Summarize Web Data with Perplexity, Gemini AI & Bright Data to Webhooks. Uses manualTrigger, lmChatGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-d
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
Turn documents into an AI-powered knowledge base.
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
Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval
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.
Retrieval-Augmented Generation (RAG) allows Large Language Models (LLMs) to provide context-aware answers by retrieving information from an external vector database. In this post, we’ll walk through a
This workflow is designed for professionals and teams who need real-time, structured insights from Perplexity Search results without manual effort.
This workflow automates the end-to-end process of capturing company information from Google Drive, storing it semantically in Pinecone, and interacting with users via an intelligent AI chatbot. It eli
This template is perfect for educational institutions, coaching centers (like UPSC, GMAT, or specialized technical training), internal corporate knowledge bases, and SaaS companies that need to provid
Transform your email workflow with this intelligent automation that drafts professional emails through Telegram commands using AI and contact retrieval. Key Features
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)
Обработка обратной связи. Uses [[[providers, vectorStorePGVector, documentDefaultDataLoader, agent. Event-driven trigger; 17 nodes.
Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI). Uses httpRequest, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigg
Обработка обратной связи. Uses lmChatGoogleGemini, embeddingsOpenAi, vectorStorePGVector, documentDefaultDataLoader. Event-driven trigger; 17 nodes.
Karakeep. Uses httpRequest, vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader. Webhook trigger; 17 nodes.
Rag-Strapi. Uses lmChatOllama, embeddingsOllama, chatTrigger, httpRequest. Chat trigger; 17 nodes.
Larry Llama. Uses agent, lmChatOllama, memoryPostgresChat, embeddingsOllama. Webhook trigger; 17 nodes.
lec 8 - upload KB. Uses vectorStoreSupabase, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 17 nodes.
Rag Workflow. Uses googleDrive, vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader. Webhook trigger; 17 nodes.
InsightsLM - Upsert to Vector Store. Uses vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Webhook trigger; 17 nodes.
Manual Stickynote. Uses googleDrive, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, stickyNote. Chat trigger; 16 nodes.
Splitout Limit. Uses manualTrigger, stickyNote, httpRequest, lmChatOpenAi. Event-driven trigger; 16 nodes.
2Chat Chatbot. Uses agent, memoryBufferWindow, formTrigger, vectorStoreInMemory. Webhook trigger; 16 nodes.
This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excell
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
This tutorial explains how to build the backend workflow in n8n that indexes YouTube video transcripts into a Pinecone vector database. Note: This workflow handles the processing and indexing of trans
This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI-powered applications:
Use cases are many: Populate a custom chatbot's knowledge base, create a powerful search index for your website, or build a comprehensive repository of information for internal tools!
This guide is designed for developers, data scientists, and AI enthusiasts who want to create intelligent chatbots capable of understanding and using custom data. Whether you are building a research a
📌 Description
Provide your S3 bucket containing documents such as PDFs and MS Word in the "Get Files from S3" node. You will need to provide AWS credentials that will allow the node to access the bucket and downloa
beyscolleciton. Uses chatTrigger, agent, lmChatOpenAi, memoryBufferWindow. Chat trigger; 16 nodes.
et. Uses httpRequest, chainSummarization, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 16 nodes.
Cognito FAQ. Uses googleDrive, vectorStorePinecone, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 16 nodes.
scrape-and-summarize-webpages-with-ai. Uses httpRequest, lmChatOpenAi, chainSummarization, documentDefaultDataLoader. Event-driven trigger; 16 nodes.
Sitemap To Supabase. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 16 nodes.
21-scrape-and-summarize-webpages-with-ai. Uses httpRequest, lmChatOpenAi, chainSummarization, documentDefaultDataLoader. Event-driven trigger; 16 nodes.
vectordb_many_pdfs. Uses vectorStoreQdrant, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Chat trigger; 16 nodes.
This template ingests internal docs into an in-memory vector knowledge base using OpenAI embeddings, then runs a public support chat powered by Anthropic Claude that answers only from that knowledge a
Vector DB Loader from Google Drive. Uses documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, vectorStorePGVector. Event-driven trigger; 15 nodes.
Scrape And Summarize Webpages With Ai. Uses manualTrigger, httpRequest, html, stickyNote. Event-driven trigger; 15 nodes.
Manual Stickynote. Uses stickyNote, manualTrigger, vectorStorePinecone, chatTrigger. Event-driven trigger; 15 nodes.
4526. Uses agent, lmChatOpenAi, embeddingsOpenAi, memoryBufferWindow. Event-driven trigger; 15 nodes.
My workflow 6. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmChatOpenAi. Event-driven trigger; 15 nodes.
The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to
This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven,
Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n.
This template helps you to create an intelligent document assistant that can answer questions from uploaded files.
This intelligent customer support chatbot leverages Retrieval-Augmented Generation (RAG) to provide accurate, contextual responses by combining your knowledge base with AI capabilities. The system aut
This template creates an intelligent AI assistant for WhatsApp that can: Respond naturally to messages using Google Gemini AI Remember previous conversations for each user Access a knowledge base for
Webhook trigger receives voice note data including title, transcript, and timestamp from external services (example here: voicenotes.com) Field extraction isolates the key data fields (title, transcri
Automate expense reviews with AI-powered CFO-level analysis. This workflow monitors Airtable expense submissions, uses GPT-4 to analyze expenses like an experienced CFO, flags suspicious expenses with
Ever wanted to just ask your repository what's going on instead of scrolling through endless issue lists? This workflow lets you do exactly that.
This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone vector database. It retrieves relevant context from your files in re
This workflow is designed for companies looking to onboard new employees and interns efficiently. It's perfect for HR teams, team leaders, and organizations that want to provide instant access to comp
This workflow builds a Retrieval-Augmented Generation (RAG) document chat assistant inside n8n using Supabase Vector Store and AI models.
V3_RAG_Chatbot_Copy. Uses googleDrive, vectorStorePinecone, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 15 nodes.
N8N-Rag-Ingestion-Workflow. Uses googleDriveTrigger, googleDrive, vectorStoreSupabase, documentDefaultDataLoader. Event-driven trigger; 15 nodes.
Rag. Uses documentDefaultDataLoader, agent, rerankerCohere, memoryBufferWindow. Event-driven trigger; 15 nodes.
26-ask-questions-about-a-pdf-using-ai. Uses vectorStorePinecone, chatTrigger, agent, googleDrive. Event-driven trigger; 15 nodes.
Telebot Knowledgehub. Uses memoryBufferWindow, vectorStoreInMemory, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Event-driven trigger; 15 nodes.
Google Drive Automation. Uses agent, googleDriveTrigger, googleDrive, extractFromFile. Event-driven trigger; 14 nodes.
Summarize Glassdoor Company Info with Google Gemini and Bright Data Web Scraper. Uses manualTrigger, lmChatGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-dr
RAG AI Agent with Milvus and Cohere. Uses documentDefaultDataLoader, embeddingsCohere, chatTrigger, googleDriveTrigger. Chat trigger; 14 nodes.
Travel AssistantAgent. Uses chatTrigger, memoryMongoDbChat, lmChatGoogleGemini, vectorStoreMongoDBAtlas. Chat trigger; 14 nodes.
RAG AI Agent with Milvus and Cohere. Uses documentDefaultDataLoader, embeddingsCohere, chatTrigger, googleDriveTrigger. Chat trigger; 14 nodes.
RAG AI Agent with Milvus and Cohere. Uses documentDefaultDataLoader, embeddingsCohere, chatTrigger, googleDriveTrigger. Chat trigger; 14 nodes.
This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using
This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections:
🧠 Google Drive Upload Trigger → Pinecone Vector Upsert for Document Indexing Category: AI & LLM / Document Indexing Level: Intermediate Tags: Google Drive, Pinecone, OpenAI, Embeddings, Vector Store,
Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration.
This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector
This workflow is designed for HR professionals, employer branding teams, talent acquisition strategists, market researchers, and business intelligence analysts who want to monitor, understand, and act
This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in
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
This workflow automates a full RAG ingestion pipeline. When a new OCR JSON file is added to a Google Drive folder, the workflow extracts lesson metadata, parses and cleans the Arabic text, generates s
My workflow 10. Uses vectorStoreSupabase, googleDrive, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 14 nodes.
validator. Uses memoryBufferWindow, embeddingsOpenAi, vectorStorePGVector, formTrigger. Event-driven trigger; 14 nodes.
Restaurant digital transformation. Uses googleDriveTrigger, googleDrive, vectorStoreSupabase, embeddingsOpenAi. Event-driven trigger; 14 nodes.
Qdrant Vector Database Embedding Pipeline. Uses vectorStoreQdrant, manualTrigger, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 13 nodes.
Firecrawl RAG. Uses embeddingsOpenAi, vectorStoreSupabase, lmChatOpenAi, agent. Event-driven trigger; 13 nodes.
This n8n template shows you how to automate document summarization while keeping full digital sovereignty. By combining Nextcloud for file storage and the IONOS AI Model Hub, your sensitive documents
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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.