OpenAI Chat → Qdrant Vector Store

Build a powerful, customizable AI chatbot for your WordPress website that intelligently retrieves posts, answers questions, and engages in natural conversations. This complete solution handles content

I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel.

Code Extractfromfile. Uses manualTrigger, sort, httpRequest, compression. Event-driven trigger; 50 nodes.

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

2464. Uses httpRequest, compression, editImage, documentDefaultDataLoader. Event-driven trigger; 50 nodes.

Workflow 2464. Uses httpRequest, compression, editImage, documentDefaultDataLoader. 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.

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI

Survey Insights With Qdrant, Python And Information Extractor. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, googleSheets. Event-driven trigger; 42 node

Splitout Code. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, googleSheets. Event-driven trigger; 42 nodes.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights

This n8n template demonstrates how to automate comprehensive web research using multiple AI models to find, analyze, and extract insights from authoritative sources.

🤖📈 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

Upload a construction photo via web form → get a detailed cost estimate with work breakdown, resource costs, and professional HTML report. Powered by GPT-4 Vision and the open-source DDC CWICR databas

Wait Splitout. Uses manualTrigger, embeddingsMistralCloud, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 38 nodes.

Wait Splitout. Uses manualTrigger, embeddingsMistralCloud, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 38 nodes.

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for y

Customer Insights With Qdrant, Python And Information Extractor. Uses manualTrigger, html, splitOut, documentDefaultDataLoader. Event-driven trigger; 37 nodes.

Splitout Code Export. Uses manualTrigger, html, splitOut, documentDefaultDataLoader. Event-driven trigger; 37 nodes.

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

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights

AI Phone Agent with RetellAI. Uses lmChatOpenAi, outputParserStructured, vectorStoreQdrant, embeddingsOpenAi. Webhook trigger; 36 nodes.

Splitout Code. Uses manualTrigger, hackerNews, splitOut, vectorStoreQdrant. Event-driven trigger; 36 nodes.