Google Drive → Qdrant Vector Store

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 comprehensive workflow bundle is designed as a powerful starter kit, enabling you to build a multi-functional AI assistant on Telegram. It seamlessly integrates AI-powered voice interactions, an

🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant. Uses documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, splitInBatches. Event-driven trigger; 50 nodes.

This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI.

🤖📈 This workflow is my personal solution for the Agentic Arena Community Contest, where the goal is to build a Retrieval-Augmented Generation (RAG) AI agent capable of answering questions based on a p

This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to proce

This Workflow simulates an AI-powered phone agent with RetellAI with two main functions: 📅 Appointment Booking – It can schedule appointments directly into Google Calendar. 🧠 RAG-based Information Ret

AI Phone Agent with RetellAI. Uses lmChatOpenAi, outputParserStructured, vectorStoreQdrant, embeddingsOpenAi. Webhook trigger; 36 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 is a complete AI-powered customer support automation for e-commerce businesses.

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

Build a Chatbot, Voice Agent and Phone Agent with Voiceflow, Google Calendar and RAG. Uses googleCalendar, lmChatOpenAi, chainLlm, vectorStoreQdrant. Webhook trigger; 34 nodes.

This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering

Voiceflow is a no-code platform that allows you to design, prototype, and deploy conversational assistants across multiple channels—such as chat, voice, and phone—with advanced logic and natural langu

This workflow implements a complete Voice AI Chatbot system for Wordress that integrates speech recognition, guardrails for safety, retrieval-augmented generation (RAG), Qdrant vector search, and audi

Automate AI-Powered RAG System with Contextual Q&A, Google Sheets Integration, and Glide Frontend—Powered by n8n, OpenAI, Supabase, and Google Apps Script.

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

This workflow combines website chatbot intelligence with automated document ingestion and vectorization — enabling live Q&A from both chat input and processed Google Drive files. It uses Mistral AI fo

Effortless Email Management with AI. Uses emailReadImap, markdown, emailSend, vectorStoreQdrant. Event-driven trigger; 31 nodes.

Effortless Email Management with AI. Uses emailReadImap, markdown, emailSend, vectorStoreQdrant. Event-driven trigger; 31 nodes.

WooCommerce AI Chatbot Workflow for Post-Sales Support. Uses chatTrigger, memoryBufferWindow, wooCommerceTool, toolCalculator. Chat trigger; 31 nodes.

This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or sugges