n8n workflows for Documentdefaultdataloader.

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

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.

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.

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.

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.

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.

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

🧠 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

This Workflow auto-ingests Google Drive documents, parses them with LlamaIndex, and stores Azure OpenAI embeddings in an in-memory vector store—cutting manual update time from ~30 minutes to under 2 m

handoff. Uses chatTrigger, agent, lmChatOpenAi, memoryBufferWindow. Chat trigger; 13 nodes.

This template is a workflow that registers Jira tickets to Pinecone.

HelloAgent_n8nCase. Uses gmailTrigger, lmChatGoogleGemini, memoryBufferWindow, toolSerpApi. Event-driven trigger; 12 nodes.

dssat-rag. Uses chatTrigger, embeddingsOpenAi, agent, documentDefaultDataLoader. Chat trigger; 11 nodes.

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

This workflow vectorizes the TUSS (Terminologia Unificada da Saúde Suplementar) table by transforming medical procedures into vector embeddings ready for semantic search.

Post de discourser. Uses httpRequest, vectorStoreSupabase, embeddingsOpenAi, documentDefaultDataLoader. Scheduled trigger; 11 nodes.

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

meeting_notes. Uses googleDocs, slack, chainLlm, vectorStorePinecone. Webhook trigger; 10 nodes.

Process Tour PDF from Google Drive to Pinecone Vector DB with OpenAI Embeddings

Bazz-Doc Master (AI Document OCR & Extraction). Uses httpRequest, agent, lmChatOpenAi, toolDocumentLoader. Webhook trigger; 10 nodes.

Store Notion's Pages as Vector Documents into Supabase with OpenAI. Uses stickyNote, embeddingsOpenAi, textSplitterTokenSplitter, notionTrigger. Event-driven trigger; 9 nodes.

Store Notion's Pages as Vector Documents into Supabase with OpenAI. Uses stickyNote, embeddingsOpenAi, textSplitterTokenSplitter, notionTrigger. Event-driven trigger; 9 nodes.

crtnvecdb. Uses googleDriveTrigger, googleDrive, vectorStorePinecone, embeddingsOpenAi. Event-driven trigger; 9 nodes.

IMS - Backend. Uses postgres, vectorStoreMilvus, embeddingsCohere, documentDefaultDataLoader. Scheduled trigger; 9 nodes.

Prod: Notion to Vector Store - Dimension 768. Uses textSplitterTokenSplitter, notionTrigger, notion, summarize. Event-driven trigger; 8 nodes.

This n8n automation is designed to extract, process, and store content from Notion pages into a Pinecone vector store. Here's a breakdown of the workflow:

NGO.tools Knowledge Base Ingestion. Uses postgres, vectorStorePGVector, documentDefaultDataLoader, embeddingsOpenAi. Webhook trigger; 8 nodes.

rag_faq_indexation. Uses vectorStorePinecone, googleDrive, documentDefaultDataLoader, embeddingsOpenAi. Event-driven trigger; 8 nodes.

RAG — Ingestion. Uses httpRequest, vectorStorePinecone, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 7 nodes.

17 · RAG Ingest: Nạp Company Knowledge vào Pinecone (Gemini Embedding). Uses formTrigger, vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigg

FPCVectorStoreIngestion. Uses vectorStorePGVector, embeddingsOpenAi, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 6 nodes.

Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI. Uses chatTrigger, lmChatGoogleGemini, stickyNote, documentDefaultDataLoader. Chat trigger; 13 nodes.

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

Click here to view the YouTube Tutorial

This template shows how to use the Question and Answer tool to save costs in RAG use cases.

This workflow is ideal for: Professionals Project managers Sales and support teams Anyone managing high volumes of Gmail messages

A comprehensive RAG (Retrieval-Augmented Generation) workflow that transforms PDF documents into searchable vector embeddings using advanced AI technologies. PDF Document Processing: Upload and extrac

> Disclaimer: this workflow template uses the community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes.

This workflow integrates Google Sheets with Supabase Vector Store for storing personal data as vectors. It utilizes OpenAI and Google Gemini AI models for enhanced data processing and querying.

Description 📌 Overview

Demo: RAG in n8n. Uses formTrigger, documentDefaultDataLoader, vectorStoreInMemory, agent. Event-driven trigger; 13 nodes.

RAG Pipeline & Chatbot. Uses stickyNote, googleDriveTrigger, googleDrive, vectorStorePinecone. Event-driven trigger; 12 nodes.

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini. Uses chatTrigger, agent, memoryBufferWindow, lmChatGoogleGemini. Chat trigger; 12 nodes.

RAG_pipeline_to_chatbot_using_google_drive_and_pinecone. Uses googleDriveTrigger, googleDrive, vectorStorePinecone, documentDefaultDataLoader. Event-driven trigger; 12 nodes.

prototype. Uses vectorStoreInMemory, documentDefaultDataLoader, embeddingsHuggingFaceInference, readWriteFile. Event-driven trigger; 12 nodes.

Poc-Rag-Llm. Uses lmChatOllama, embeddingsOllama, chatTrigger, agent. Chat trigger; 12 nodes.

Rag Ejemplo. Uses formTrigger, embeddingsOpenAi, documentDefaultDataLoader, vectorStoreInMemory. Event-driven trigger; 12 nodes.

[Lab] n8n RAG in memory vector. Uses formTrigger, embeddingsOpenAi, documentDefaultDataLoader, vectorStoreInMemory. Event-driven trigger; 12 nodes.

Knowledge store agent (with Google Drive). Uses documentDefaultDataLoader, googleDrive, embeddingsOpenAi, agent. Chat trigger; 12 nodes.

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

This template quickly shows how to use RAG in n8n.

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

🔍 What This Workflow Does

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

File upload. Uses localFileTrigger, vectorStorePGVector, embeddingsMistralCloud, readWriteFile. Event-driven trigger; 11 nodes.

RAG Agent. Uses vectorStoreInMemory, documentDefaultDataLoader, agent, lmChatOllama. Webhook trigger; 11 nodes.

Chat. Uses readWriteFile, vectorStoreSupabase, documentDefaultDataLoader, embeddingsOpenAi. Event-driven trigger; 10 nodes.

ingest_RAG. Uses googleDrive, vectorStoreSupabase, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 9 nodes.

Click here to watch the full tutorial on YouTube

d22-knowledge-base. Uses rssFeedRead, vectorStoreSupabase, embeddingsGoogleGemini, documentDefaultDataLoader. Event-driven trigger; 7 nodes.

AI Workflow Knowledge Ingestion Pipeline. Uses vectorStorePinecone, embeddingsOpenAi, documentDefaultDataLoader, googleDriveTrigger. Event-driven trigger; 7 nodes.

Manual Googledrive. Uses manualTrigger, lmChatOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 6 nodes.

SupaBase. Uses manualTrigger, googleDrive, vectorStoreSupabase, documentDefaultDataLoader. Event-driven trigger; 6 nodes.

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

d27-slack-RAG. Uses googleDrive, vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 6 nodes.

Company Knowledgebase. Uses googleDrive, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Event-driven trigger; 6 nodes.

This workflow includes advanced features like text summarization and tokenization, it's ideal for automating document processing tasks that require parsing and summarizing text data from Google Drive.

pd_automacao_n8n_fluxo_rag. Uses googleDrive, documentDefaultDataLoader, embeddingsOpenAi, vectorStoreSupabase. Event-driven trigger; 6 nodes.

KB. Uses formTrigger, vectorStoreSupabase, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 5 nodes.

vectorstore_insert. Uses vectorStorePinecone, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 5 nodes.