n8n workflows for Textsplitterrecursivecharactertextsplitter.

This workflow transforms any webpage into an AI-narrated audio summary delivered via WhatsApp: Receive URL - WhatsApp Trigger captures incoming messages and passes them to URL extraction Extract & val

Agent: Local AI RAG: Ollama & Supabase Vector. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat 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

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 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

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.

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

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

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

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.

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.

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

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

n8n_ollama_pgvector. Uses chatTrigger, vectorStorePGVector, embeddingsGoogleGemini, documentDefaultDataLoader. Chat trigger; 20 nodes.

Ai Summarize Podcast Episode And Enhance Using Wikipedia. Uses manualTrigger, documentJsonInputLoader, textSplitterRecursiveCharacterTextSplitter, stickyNote. Event-driven trigger; 19 nodes.

Podcast Digest. Uses manualTrigger, documentJsonInputLoader, textSplitterRecursiveCharacterTextSplitter, stickyNote. Event-driven trigger; 19 nodes.

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

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

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.

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

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

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

📊 Description

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.

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

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

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 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

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.

Rag-Strapi. Uses lmChatOllama, embeddingsOllama, chatTrigger, httpRequest. Chat 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 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

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

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.

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.

Schedule Telegram. Uses lmChatOpenAi, scheduleTrigger, textSplitterRecursiveCharacterTextSplitter, chainSummarization. Scheduled trigger; 15 nodes.

Schedule Telegram. Uses lmChatOpenAi, scheduleTrigger, textSplitterRecursiveCharacterTextSplitter, chainSummarization. Scheduled 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.

The n8n template for creating kids' stories in Arabic offers a versatile platform for storytellers to captivate young audiences with educational and interactive tales. It allows for customization to s

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

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

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.

N8N-Rag-Ingestion-Workflow. Uses googleDriveTrigger, googleDrive, vectorStoreSupabase, documentDefaultDataLoader. Event-driven trigger; 15 nodes.

Workflow 2234. Uses lmChatOpenAi, textSplitterRecursiveCharacterTextSplitter, chainSummarization, openAi. Scheduled trigger; 15 nodes.

26-ask-questions-about-a-pdf-using-ai. Uses vectorStorePinecone, chatTrigger, agent, googleDrive. Event-driven trigger; 15 nodes.

Schedule Telegram. Uses lmChatOpenAi, scheduleTrigger, stickyNote, chainSummarization. Scheduled trigger; 14 nodes.

Schedule Telegram. Uses lmChatOpenAi, scheduleTrigger, stickyNote, chainSummarization. Scheduled trigger; 14 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:

Check this example: https://t.me/st0ries95

🧠 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 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.

Firecrawl RAG. Uses embeddingsOpenAi, vectorStoreSupabase, lmChatOpenAi, agent. Event-driven trigger; 13 nodes.

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

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.

SearchApi Youtube Video Summary. Uses manualTrigger, chainSummarization, textSplitterRecursiveCharacterTextSplitter, splitOut. Event-driven trigger; 9 nodes.

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

NGO.tools Knowledge Base Ingestion. Uses postgres, vectorStorePGVector, documentDefaultDataLoader, embeddingsOpenAi. Webhook 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

RAG Ingestion Pipeline. Uses textSplitterRecursiveCharacterTextSplitter, httpRequest, postgres. Webhook trigger; 5 nodes.

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

RAG Pipeline & Chatbot. Uses stickyNote, googleDriveTrigger, googleDrive, vectorStorePinecone. Event-driven 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.

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

🔍 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.

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

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

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

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