Chainretrievalqa → Retrievervectorstore

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

crawl4 ai. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Breakdown Documents Into Study Notes Using Templating Mistralai And Qdrant. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-

Localfile Wait. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Workflow 2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.

Advanced Ai Demo Presented At Ai Developers 14 Meetup. Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.

Advanced Ai Demo (Presented At Ai Developers #14 Meetup). Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.

Workflow 2358. Uses slack, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 39 nodes.

2358. Uses slack, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 39 nodes.

This workflow was presented at the AI Developers meet up in San Fransico on 24 July, 2024. Categorize incoming Gmail emails and assign custom Gmail labels. This example uses the Text Classifier node,

Generate Exam Questions. Uses manualTrigger, vectorStoreQdrant, httpRequest, embeddingsOpenAi. Event-driven trigger; 37 nodes.

This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval

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

Upsert Huge Documents In A Vector Store With Supabase And Notion. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

Webhook Schedule. Uses manualTrigger, stickyNote, agent, lmChatOpenAi. Event-driven trigger; 34 nodes.

RAG on living data. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

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

This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store.

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

https://n8n-tools.streamlit.app/

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

Build A Financial Documents Assistant Using Qdrant And Mistral.Ai. Uses localFileTrigger, manualTrigger, stickyNote, readWriteFile. Event-driven trigger; 29 nodes.