n8n workflows for Google Gemini Embeddings.

Camila IA. Uses postgres, crypto, redis, agent. Webhook trigger; 92 nodes.

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 acts as a 24/7 sales agent, engaging leads across WhatsApp, Instagram, Facebook, Telegram, and your website. It intelligently transcribes audio messages, answers questions using a knowle

⚡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. 🔗

This simple philosophy changes the way we think about automated sales agents. Context changes everything. In this 4-part workflow, we start by creating a knowledge base that will act as context across

This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, t

Tech Radar. Uses googleDrive, documentDefaultDataLoader, stickyNote, mySql. Scheduled trigger; 53 nodes.

This project is built on top of the famous open source ThoughtWorks Tech Radar.

This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, t

This template creates a comprehensive, production-ready Retrieval-Augmented Generation (RAG) system. It builds a sophisticated AI agent that can answer questions based on documents stored in a specifi

use cases: research stock market in Indonesia. analyze the performance of companies belonging to certain subsectors or company comparing financial metrics between BBCA and BBRI providing technical ana

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 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

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

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

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

This workflow ingests educational PDF URLs from Google Sheets, extracts and chunks their text, generates embeddings with Google Gemini, and stores them in a Supabase pgvector table for retrieval, whil

Personal Portfolio Resume CV Chatbot. Uses embeddingsGoogleGemini, stickyNote, scheduleTrigger, lmChatGoogleGemini. Scheduled trigger; 35 nodes.

This template is perfect for:

ReviewFlow. Uses executeWorkflowTrigger, httpRequest, mongoDb, agent. Event-driven trigger; 33 nodes.

My workflow 2. Uses googleGemini, formTrigger, httpRequest, googleDrive. Event-driven trigger; 33 nodes.

n8n telegram RAG. Uses lmChatGoogleGemini, embeddingsGoogleGemini, memoryManager, vectorStoreSupabase. Event-driven trigger; 32 nodes.

An extendable RAG template to build powerful, explainable AI assistants — with query understanding, semantic metadata, and support for free-tier tools like Gemini, Gemma and Supabase.

AI Document Assistant via Telegram + Supabase. Uses lmChatGoogleGemini, openWeatherMapTool, agent, telegramTrigger. Event-driven trigger; 28 nodes.

This template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-processing for rich form

A complete AI-powered study assistant system that lets you chat naturally with your documents stored in Google Drive:

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

Categories: Business Automation, Customer Support, AI, Knowledge Management

Turn your website chat into a lead-generating machine. Visitors chat with an AI that answers questions from your knowledge base, captures their contact info, syncs everything to Google Sheets, and aut

AI chatbots are only as good as the data they learn from. Most large language models (LLM) rely only on their training datasets.

Turn WhatsApp chats into instant answers and real-time bookings—all in one n8n workflow. Your AI Agent leverages Gemini embeddings + Pinecone for on-the-fly knowledge retrieval, then logs reservations

It uses Retrieval-Augmented Generation (RAG) to allow users to upload documents, which are then indexed into a vector database, enabling the bot to answer questions based only on the provided content.

RAG + CHAT IA. Uses vectorStorePinecone, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, agent. Event-driven trigger; 25 nodes.

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

**Type of data is binary

This workflow automates customer chat with Chatwoot & n8n AI agent that handles incoming chats, qualifies leads, answers FAQs from Pinecone knowledge base, and escalates to a live human agent when one

This template is designed for podcasters, researchers, educators, product teams, and support teams who work with audio content and want to turn it into searchable knowledge. It is especially useful fo

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

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-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

Geminis. Uses toolSerpApi, googleGemini, chatTrigger, agent. Event-driven trigger; 22 nodes.

Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone. Uses manualTrigger, agent, vectorStorePinecone, embeddingsGoogleGemini. Event-driven trigger; 21 nodes.

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

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 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-fitness-2. Uses googleDrive, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 21 nodes.

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

[](https://www.youtube.com/watch?v=AqVSLj7qb2Q)

n8n_ollama_pgvector. Uses chatTrigger, vectorStorePGVector, embeddingsGoogleGemini, documentDefaultDataLoader. Chat 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 —

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

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 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

RAG Workflow For Stock Earnings Report Analysis. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes

google-drive-rag. Uses vectorStorePinecone, embeddingsGoogleGemini, 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 project is an AI-powered WhatsApp virtual agent built using n8n, designed to handle both text and voice-based customer messages automatically. The workflow integrates Google Gemini, Pinecone, and

This automation operates in three distinct phases: Ingestion, Storage, and Generation.

RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini. Uses manualTrigger, splitInBatches, lmChatOpenRouter, vectorStorePinecone. Event-driven trigger; 17 nodes.

Turn documents into an AI-powered knowledge base.

Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval

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

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

Karakeep. Uses httpRequest, vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader. Webhook trigger; 17 nodes.

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!

📌 Description

RAG. Uses httpRequest, agent, lmChatGoogleGemini, memoryPostgresChat. Webhook trigger; 16 nodes.

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

This workflow builds a Retrieval-Augmented Generation (RAG) document chat assistant inside n8n using Supabase Vector Store and AI models.

Rag. Uses documentDefaultDataLoader, agent, rerankerCohere, memoryBufferWindow. Event-driven trigger; 15 nodes.

Google Drive Automation. Uses agent, googleDriveTrigger, googleDrive, extractFromFile. Event-driven 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

contoh-rag-agent. Uses vectorStoreSupabase, postgresTool, agent, chatTrigger. Webhook trigger; 14 nodes.

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

d27-content-automation. Uses googleSheets, chainRetrievalQa, retrieverVectorStore, vectorStoreSupabase. 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.

17 · Company RAG Chatbot qua Telegram: AI Agent + Pinecone + DeepSeek. Uses telegramTrigger, agent, lmChatDeepSeek, memoryBufferWindow. Event-driven trigger; 11 nodes.

17 · Company RAG Chatbot qua Telegram: AI Agent + Pinecone + Gemini. Uses telegramTrigger, agent, lmChatGoogleGemini, memoryBufferWindow. Event-driven trigger; 11 nodes.

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

d23-RAG. Uses chatTrigger, chainRetrievalQa, lmChatGoogleGemini, retrieverVectorStore. Chat trigger; 7 nodes.

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

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

CHAT_works. Uses chatTrigger, embeddingsGoogleGemini, agent, lmChatGoogleGemini. Chat trigger; 11 nodes.

This workflow automates the creation of a Retrieval-Augmented Generation (RAG) pipeline using content from the GLPI Knowledge Base. It retrieves and processes FAQ articles directly via the GLPI API, c

rag_query. Uses vectorStorePinecone, telegram, agent, lmChatGoogleGemini. Event-driven trigger; 9 nodes.

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

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

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