AutomationFlows › Retrievervectorstore

n8n workflows for Retrievervectorstore.

All n8n workflows that use the Retrievervectorstore integration. Each is integration-tagged, privacy-stripped, and importable into your n8n instance in one click.

Most-used Retrievervectorstore workflows

My Workflow (output Parser Structured) (82 nodes)
Build a Multi-functional Telegram Bot with Gemini, RAG PDF Search & Google Suite — n8n Retrievervectorstore workflow (79 nodes)
Search Worflow Docker Complete (71 nodes)
Heydinastia — n8n Retrievervectorstore workflow (66 nodes)
Crawl4 AI (65 nodes)
Search Worflow Docker — n8n Retrievervectorstore workflow (65 nodes)
Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant (42 nodes)
Localfile Wait — n8n Retrievervectorstore workflow (42 nodes)
Workflow 2339 (42 nodes)
Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant (local File Trigger) — n8n Retrievervectorstore workflow (42 nodes)

AI & RAG

My Workflow (output Parser Structured)

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

Output Parser Structured, HTTP Request, Google Gemini Chat +15

AI & RAG

Build a Multi-functional Telegram Bot with Gemini, RAG PDF Search & Google Suite

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

Telegram Trigger, Telegram, OpenAI +19

AI & RAG

Search Worflow Docker Complete

Search Worflow Docker Complete. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 71 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +14

AI & RAG

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

Execute Command, HTTP Request, YouTube +15

AI & RAG

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

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12

AI & RAG

Search Worflow Docker

Search Worflow Docker. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12

AI & RAG

Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant

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

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9

AI & RAG

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

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9

AI & RAG

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

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9

AI & RAG

Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant (local File Trigger)

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.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9

AI & RAG

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

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9

AI & RAG

Create an AI Knowledge Base Assistant Using Ollama, Pgvector and Telegram

This workflow automatically converts uploaded documents and text into an AI-powered searchable knowledge base using semantic vector embeddings and Retrieval-Augmented Generation (RAG). Users can uploa

Form Trigger, Vector Store Pgvector, Ollama Embeddings +7

AI & RAG

Advanced AI Demo Presented at AI Developers 14 Meetup

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

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14

AI & RAG

Advanced AI Demo (presented at AI Developers #14 Meetup)

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

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14

AI & RAG

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

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14

AI & RAG

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

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14

AI & RAG

Advanced AI Demo (presented at AI Developers #14 Meetup) (slack)

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,

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14

AI & RAG

Generate Exam Questions

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

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12

AI & RAG

Ai-powered Auto-generate Exam Questions and Answers From Google Docs with Gemini

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

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12

AI & RAG

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

Form Trigger, Google Gemini Chat, HTTP Request +10

AI & RAG

Upsert Huge Documents in a Vector Store with Supabase and Notion

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

OpenAI Embeddings, Text Splitter Token Splitter, Chain Retrieval Qa +8

AI & RAG

Webhook Schedule

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

Agent, OpenAI Chat, Memory Buffer Window +7

AI & RAG

RAG on Living Data

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

OpenAI Embeddings, Text Splitter Token Splitter, Chain Retrieval Qa +8

AI & RAG

Bitrix24 Open Chanel RAG Chatbot Application Workflow Example with Webhook Integration

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

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6

AI & RAG

Build a PDF Document RAG System with Mistral Ocr, Qdrant and Gemini AI

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

HTTP Request, OpenAI Embeddings, Document Default Data Loader +9

AI & RAG

Upsert Huge Documents in a Vector Store with Supabase and Notion (embeddings Open Ai)

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.

OpenAI Embeddings, Text Splitter Token Splitter, Chain Retrieval Qa +8

AI & RAG

Bitrix24 Ai-powered RAG Chatbot for Open Line Channels

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

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6

AI & RAG

🛠️ Auto Workflow Positioning

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

Agent, OpenAI Chat, Memory Buffer Window +7

AI & RAG

Build a Self-updating RAG System with Openai, Qdrant and Google Drive

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

OpenAI Embeddings, Document Default Data Loader, HTTP Request +8

AI & RAG

Detect Multi-modal Plagiarism with Openai Gpt-4, Whisper, and Vector Search

This workflow automates academic and professional plagiarism detection by processing multi-modal submissions — documents, audio recordings, and images,through specialized AI agents. It targets educato

OpenAI, In-Memory Vector Store, OpenAI Embeddings +5

AI & RAG

Build a Financial Documents Assistant Using Qdrant and Mistral.ai

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

Local File Trigger, Read Write File, Embeddings Mistral Cloud +8

AI & RAG

Localfile. Uses localFileTrigger, manualTrigger, stickyNote, readWriteFile. Event-driven trigger; 29 nodes.

Local File Trigger, Read Write File, Embeddings Mistral Cloud +8

AI & RAG

Build and Update RAG System with Google Drive, Qdrant, and Gemini Chat

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

OpenAI Embeddings, Document Default Data Loader, Qdrant Vector Store +7

AI & RAG

Build a Financial Documents Assistant Using Qdrant and Mistral.ai (local File Trigger)

This n8n workflow demonstrates how to manage your Qdrant vector store when there is a need to keep it in sync with local files. It covers creating, updating and deleting vector store records ensuring

Local File Trigger, Read Write File, Embeddings Mistral Cloud +8

AI & RAG

Build a RAG System with Automatic Citations Using Qdrant, Gemini & Openai

This workflow implements a Retrieval-Augmented Generation (RAG) system that:

OpenAI Embeddings, Document Default Data Loader, Qdrant Vector Store +7

AI & RAG

Voice AI Customer Support for Woocommerce Using Vapi, Gpt-4o & Gemini with RAG

This workflow integrates a Retrieval-Augmented Generation (RAG) system with a post-sales AI agent for WooCommerce. It combines vector-based search (Qdrant + OpenAI embeddings) with LLMs (Google Gemini

Chain Retrieval Qa, Google Gemini Chat, Retriever Vector Store +9

AI & RAG

Automate a 'chat with Your Pdf' Bot on Telegram with Google Gemini & Pinecone

It uses Retrieval-Augmented Generation (RAG) to allow users to upload documents, which are then indexed into a vector database, enabling the bot to answer questions based only on the provided content.

Document Default Data Loader, Text Splitter Recursive Character Text Splitter, Stop And Error +7

AI & RAG

Automated Book Summarization with Deepseek Ai, Qdrant Vector Db & Google Drive

This n8n workflow automates the process of summarizing uploaded books from Google Drive using vector databases and LLMs. It uses Cohere for embeddings, Qdrant for storage and retrieval, and DeepSeek o

Qdrant Vector Store, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +10

AI & RAG

Splitout Limit (lm Chat Open Ai) #2

Splitout Limit. Uses lmChatOpenAi, manualTrigger, httpRequest, html. Event-driven trigger; 22 nodes.

OpenAI Chat, HTTP Request, Chat Trigger +6

AI & RAG

Transform Books Into 100+ Social Media Posts with Deepseek AI and Google Drive

This automation is a game-changer for content creators, marketers, and authors. It transforms any book or long document into a treasure trove of over 100 ready-to-use, short-form content ideas for pla

Qdrant Vector Store, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +11

AI & RAG

Supabase Insertion Upsertion Retrieval

Supabase Insertion Upsertion Retrieval. Uses googleDrive, documentDefaultDataLoader, stickyNote, chainRetrievalQa. Chat trigger; 21 nodes.

Google Drive, Document Default Data Loader, Chain Retrieval Qa +7

AI & RAG

Supabase Insertion & Upsertion & Retrieval

Supabase Insertion & Upsertion & Retrieval. Uses googleDrive, documentDefaultDataLoader, stickyNote, chainRetrievalQa. Chat trigger; 21 nodes.

Google Drive, Document Default Data Loader, Chain Retrieval Qa +7

AI & RAG

Supabase Insertion & Upsertion & Retrieval (google Drive)

This is a demo workflow to showcase how to use Supabase to embed a document, retrieve information from the vector store via chat and update the database. set your credentials for Supabase set your cre

Google Drive, Document Default Data Loader, Chain Retrieval Qa +7

AI & RAG

Telegram RAG PDF

Telegram RAG pdf. Uses telegramTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 20 nodes.

Telegram Trigger, OpenAI Embeddings, Document Default Data Loader +7

AI & RAG

Telegram RAG PDF (telegram Trigger)

Telegram RAG pdf. Uses telegramTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 20 nodes.

Telegram Trigger, OpenAI Embeddings, Document Default Data Loader +7

AI & RAG

Ai-powered Upwork Cover Letter Generator – Pinecone, Groq, Google Gemin, Serpapi

[](https://www.youtube.com/watch?v=AqVSLj7qb2Q)

Agent, Pinecone Vector Store, Google Gemini Embeddings +7

AI & RAG

N8n Ollama Pgvector

n8n_ollama_pgvector. Uses chatTrigger, vectorStorePGVector, embeddingsGoogleGemini, documentDefaultDataLoader. Chat trigger; 20 nodes.

Chat Trigger, Vector Store Pgvector, Google Gemini Embeddings +8

AI & RAG

RAG+URL. Uses embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmOpenAi. Chat trigger; 19 nodes.

OpenAI Embeddings, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6

AI & RAG

aiNodes. Uses openAi, gmailTool, chainLlm, lmChatOpenAi. Event-driven trigger; 19 nodes.

OpenAI, Gmail Tool, Chain Llm +11

AI & RAG

Qdrantvectorstore:*

QdrantVectorStore:*. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 18 nodes.

OpenAI Embeddings, Document Default Data Loader, Text Splitter Token Splitter +5

AI & RAG

Create a Travel Support RAG Chatbot with Google Drive, Pinecone and Openai

📊 Description

Google Drive, Pinecone Vector Store, OpenAI Embeddings +5

AI & RAG

Stock Q&a Workflow

Stock Q&A Workflow. Uses embeddingsOpenAi, manualChatTrigger, stickyNote, chainRetrievalQa. Chat trigger; 17 nodes.

OpenAI Embeddings, Manual Chat Trigger, Chain Retrieval Qa +6

AI & RAG

Webhook Respondtowebhook (google Drive)

Webhook Respondtowebhook. Uses stickyNote, manualTrigger, googleDrive, documentDefaultDataLoader. Event-driven trigger; 17 nodes.

Google Drive, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6

AI & RAG

AI Crew to Automate Fundamental Stock Analysis - Q&a Workflow

Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis.

Google Drive, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6

AI & RAG

Document Q&a with Rag: Query PDF Content Using Weaviate and Openai

This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes.

Weaviate Vector Store, Document Default Data Loader, OpenAI Embeddings +6

AI & RAG

bug_reporter. Uses telegramTrigger, chainRetrievalQa, lmChatOpenAi, retrieverVectorStore. Event-driven trigger; 17 nodes.

Telegram Trigger, Chain Retrieval Qa, OpenAI Chat +6

AI & RAG

Manual Stickynote (google Drive) #2

Manual Stickynote. Uses googleDrive, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, stickyNote. Chat trigger; 16 nodes.

Google Drive, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +6

AI & RAG

This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections:

Text Splitter Recursive Character Text Splitter, Document Default Data Loader, Chain Retrieval Qa +6

AI & RAG

My Workflow 10 (vector Store Supabase)

My workflow 10. Uses vectorStoreSupabase, googleDrive, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 14 nodes.

Supabase Vector Store, Google Drive, OpenAI Embeddings +6

AI & RAG

D27 Content Automation

d27-content-automation. Uses googleSheets, chainRetrievalQa, retrieverVectorStore, vectorStoreSupabase. Event-driven trigger; 11 nodes.

Google Sheets, Chain Retrieval Qa, Retriever Vector Store +4

AI & RAG

d23-RAG. Uses chatTrigger, chainRetrievalQa, lmChatGoogleGemini, retrieverVectorStore. Chat trigger; 7 nodes.

Chat Trigger, Chain Retrieval Qa, Google Gemini Chat +3

AI & RAG

rag_chat_bot. Uses lmChatOpenAi, chainLlm, retrieverVectorStore, vectorStorePinecone. Chat trigger; 8 nodes.

OpenAI Chat, Chain Llm, Retriever Vector Store +4

AI & RAG

PDF agent. Uses chainRetrievalQa, lmChatMistralCloud, retrieverVectorStore, vectorStorePGVector. Event-driven trigger; 6 nodes.

Chain Retrieval Qa, Lm Chat Mistral Cloud, Retriever Vector Store +3

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FAQ

How many n8n Retrievervectorstore workflows are in the catalog?

63 n8n workflows in AutomationFlows currently use the Retrievervectorstore integration — triggers, actions, or both.

How do I connect Retrievervectorstore in n8n?

After importing the workflow JSON, n8n will prompt for Retrievervectorstore credentials on the relevant nodes. AutomationFlows strips credential IDs before publishing — you'll add your own.

Can I combine these with other integrations?

Yes — most Retrievervectorstore workflows pair with adjacent tools (Slack alerts, Google Sheets logging, OpenAI summarisation). Browse the integration tags on each workflow page to discover pairings.