Memory Buffer Window → Qdrant Vector Store

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 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. 🔗

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

🤖📈 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

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 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 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.

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

The benefits being (1) the vision model doesn't need to keep all document scans in context (expensive) and (2) ability to query on graphical content such as charts, graphs and tables. Page extracts fr

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

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

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