Google Gemini Chat → Textsplitterrecursivecharactertextsplitter

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

My Workflow. Uses outputParserStructured, httpRequest, lmChatGoogleGemini, chainLlm. Scheduled trigger; 82 nodes.

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

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

This n8n template automatically classifies incoming emails (Sales, Support, Internal, Finance, Promotions) and routes them to a dedicated OpenAI LLM Agent for processing. Depending on the category, th

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

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

My workflow 3. Uses formTrigger, splitInBatches, lmChatGoogleGemini, httpRequest. Event-driven trigger; 36 nodes.

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:

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

Bitrix24 Open Chanel RAG Chatbot Application Workflow example with Webhook Integration. Uses httpRequest, noOp, respondToWebhook, documentDefaultDataLoader. Webhook trigger; 34 nodes.

Transform your Bitrix24 Open Line channels with an intelligent chatbot that leverages Retrieval-Augmented Generation (RAG) technology to provide accurate, document-based responses to customer inquirie

This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant.

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.

This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremen