AutomationFlows › Vectorstorepgvector

n8n workflows for Vectorstorepgvector.

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

Most-used Vectorstorepgvector workflows

  1. Camila Ia (92 nodes)
  2. Multi-platform AI Sales Agent with Rag, CRM Logging & Appointment Booking — n8n Vectorstorepgvector workflow (84 nodes)
  3. My Workflow (output Parser Structured) (82 nodes)
  4. Search Worflow Docker Complete — n8n Vectorstorepgvector workflow (71 nodes)
  5. Create Personalized Email Outreach with Ai, Telegram Bot & Website Scraping (58 nodes)
  6. RAG AI Agent Template V5 — n8n Vectorstorepgvector workflow (56 nodes)
  7. Iroko-chatbot-green-api (54 nodes)
  8. Predict Incidents and Run Autonomous Remediation with Gpt-4 and Slack — n8n Vectorstorepgvector workflow (50 nodes)
  9. AI Email Agent - Complete System (47 nodes)
  10. Agente AI RAG — n8n Vectorstorepgvector workflow (42 nodes)
AI & RAG
Camila Ia

Camila IA. Uses postgres, crypto, redis, agent. Webhook trigger; 92 nodes.

Postgres, Crypto, Redis +13
AI & RAG
Multi-platform AI Sales Agent with Rag, CRM Logging & Appointment Booking

This workflow acts as a 24/7 sales agent, engaging leads across WhatsApp, Instagram, Facebook, Telegram, and your website. It intelligently transcribes audio messages, answers questions using a knowle

Chat Trigger, Memory Postgres Chat, Tool Workflow +20
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
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
Create Personalized Email Outreach with Ai, Telegram Bot & Website Scraping

This n8n workflow is built for AI and automation agencies to promote their workflows through an interactive demo that prospects can try themselves. The featured system is a deep personalized email dem

Telegram Trigger, HTTP Request, OpenAI +10
AI & RAG
RAG AI Agent Template V5

RAG AI Agent Template V5. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 56 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +12
AI & RAG
Iroko-chatbot-green-api

Iroko-chatbot-green-API. Uses toolCalculator, agent, n8n-nodes-whatsapp-green-api, chatTrigger. Webhook trigger; 54 nodes.

Tool Calculator, Agent, N8N Nodes Whatsapp Green Api +11
AI & RAG
Predict Incidents and Run Autonomous Remediation with Gpt-4 and Slack

This workflow automates end-to-end customer journey management by intelligently routing queries through multiple AI models (OpenAI, Claude) based on complexity and context. Designed for customer succe

HTTP Request, Agent, OpenAI Chat +7
AI & RAG
AI Email Agent - Complete System

AI Email Agent - Complete System. Uses gmailTrigger, gmail, vectorStorePGVector, embeddingsOpenAi. Event-driven trigger; 47 nodes.

Gmail Trigger, Gmail, Vector Store Pgvector +8
AI & RAG
Agente AI RAG

Agente AI RAG. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 42 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +10
AI & RAG
V3 Local Agentic RAG AI Agent

V3 Local Agentic RAG AI Agent. Uses documentDefaultDataLoader, memoryPostgresChat, chatTrigger, agent. Webhook trigger; 41 nodes.

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +9
AI & RAG
Local Document Question Answering with Ollama Ai, Agentic RAG & Pgvector

Author: Jadai kongolo

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +9
AI & RAG
Homerag

Homerag. Uses documentDefaultDataLoader, memoryPostgresChat, chatTrigger, agent. Webhook trigger; 41 nodes.

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +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
PDF RAG Agent with Telegram Chat & Auto-ingestion From Google Drive

This workflow creates an intelligent document assistant called "Mookie" that can answer questions based on your uploaded documents. Here's how it operates: Document Ingestion: The system can automatic

Document Default Data Loader, Lm Chat Azure Open Ai, Agent +9
AI & RAG
Build an Openai RAG System with Document Upload, Semantic Search and Caching

This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying.

Text Splitter Recursive Character Text Splitter, Document Default Data Loader, OpenAI Embeddings +5
AI & RAG
Ai-powered Ticket Triage with Multi-model Classification & Knowledge Base

This workflow automates enterprise ticket management by combining AI-powered classification with knowledge base retrieval. It receives support tickets via webhook, routes them through multiple AI mode

Agent, OpenAI Chat, Output Parser Structured +6
AI & RAG
Build a Local RAG Chatbot with Ollama, Qwen, Bge-m3 and Postgres Pgvector

Build a fully local RAG chatbot using Ollama that works without tool calling — ideal for smaller open-source models like Qwen that don't support native function calls. This template lets you run a pri

Memory Postgres Chat, Agent, Ollama Chat +3
AI & RAG
Telegram AI Chatbot with Document-based Answers Using Openai and Pgvector RAG

This powerful AI automation add-on upgrades your Telegram Bot Starter Template by integrating a fully functional AI chatbot and a context-aware AI agent that answers user questions using your internal

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +10
AI & RAG
Hr & It Helpdesk Chatbot with Audio Transcription

HR & IT Helpdesk Chatbot with Audio Transcription. Uses stickyNote, manualTrigger, httpRequest, extractFromFile. Event-driven trigger; 27 nodes.

HTTP Request, Vector Store Pgvector, OpenAI Embeddings +9
AI & RAG
Hr & It Helpdesk Chatbot with Audio Transcription (http Request)

HR & IT Helpdesk Chatbot with Audio Transcription. Uses stickyNote, manualTrigger, httpRequest, extractFromFile. Event-driven trigger; 27 nodes.

HTTP Request, Vector Store Pgvector, OpenAI Embeddings +9
AI & RAG
Hr & It Helpdesk Chatbot with Audio Transcription (http Request) (http Request)

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via

HTTP Request, Vector Store Pgvector, OpenAI Embeddings +9
AI & RAG
Contextual Retrieval

Contextual Retrieval. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 24 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +9
AI & RAG
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, splitInBatches, chatTrigger, vectorStorePGVector. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Gmail to Vector Embeddings with Pgvector and Ollama

Gmail to Vector Embeddings with PGVector and Ollama. Uses embeddingsOllama, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, gmailTrigger. Event-driven trigger; 20 nodes.

Ollama Embeddings, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +4
AI & RAG
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector (telegram Trigger)

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, chatTrigger, vectorStorePGVector, toolWorkflow. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Chat with Your Email History Using Telegram, Mistral and Pgvector for RAG

Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?".

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
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
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector (telegram Trigger) (telegram Trigger)

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, chatTrigger, vectorStorePGVector, toolWorkflow. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Обработка Обратной Связи

Обработка обратной связи. Uses [[[providers, vectorStorePGVector, documentDefaultDataLoader, agent. Event-driven trigger; 17 nodes.

[[[Providers, Vector Store Pgvector, Document Default Data Loader +5
AI & RAG
Обработка Обратной Связи (lm Chat Google Gemini)

Обработка обратной связи. Uses lmChatGoogleGemini, embeddingsOpenAi, vectorStorePGVector, documentDefaultDataLoader. Event-driven trigger; 17 nodes.

Google Gemini Chat, OpenAI Embeddings, Vector Store Pgvector +6
AI & RAG
Rag-strapi

Rag-Strapi. Uses lmChatOllama, embeddingsOllama, chatTrigger, httpRequest. Chat trigger; 17 nodes.

Ollama Chat, Ollama Embeddings, Chat Trigger +7
AI & RAG
Vector Db Loader From Google Drive

Vector DB Loader from Google Drive. Uses documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, vectorStorePGVector. Event-driven trigger; 15 nodes.

Document Default Data Loader, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +2
AI & RAG
📥 Transform Google Drive Documents Into Vector Embeddings

Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n.

Document Default Data Loader, Text Splitter Recursive Character Text Splitter, Vector Store Pgvector +2
AI & RAG
Airline Web Check-in Data Extraction with Ollama Ai, Google Sheets & Postgres Vector Db

This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in

Google Sheets, Chat Trigger, HTTP Request +6
AI & RAG
Validator (memory Buffer Window)

validator. Uses memoryBufferWindow, embeddingsOpenAi, vectorStorePGVector, formTrigger. Event-driven trigger; 14 nodes.

Memory Buffer Window, OpenAI Embeddings, Vector Store Pgvector +7
AI & RAG
Eworld Chat Final

eworld_chat_final. Uses agent, lmChatOpenAi, memoryPostgresChat, vectorStorePGVector. Webhook trigger; 14 nodes.

Agent, OpenAI Chat, Memory Postgres Chat +3
AI & RAG
Handoff

handoff. Uses chatTrigger, agent, lmChatOpenAi, memoryBufferWindow. Chat trigger; 13 nodes.

Chat Trigger, Agent, OpenAI Chat +7
AI & RAG
Dxnweb Chat

dxnweb_chat. Uses agent, lmChatOpenAi, memoryPostgresChat, embeddingsOpenAi. Webhook trigger; 12 nodes.

Agent, OpenAI Chat, Memory Postgres Chat +3
AI & RAG
Extract Invoice Data From Pdfs with AI - Google Sheets Email Alerts

Built by Setidure Technologies This smart n8n automation extracts invoice details from PDF files uploaded to Google Drive using AI, logs them to a Google Sheet, and notifies the billing team via email

Information Extractor, Gmail, Lm Ollama +5
AI & RAG
Vectorize Medical Procedures for Semantic Search with Tuss, Gemini & Pgvector

This workflow vectorizes the TUSS (Terminologia Unificada da Saúde Suplementar) table by transforming medical procedures into vector embeddings ready for semantic search.

Vector Store Pgvector, Text Splitter Token Splitter, Google Gemini Embeddings +2
AI & RAG
Ngo.tools Knowledge Base Ingestion

NGO.tools Knowledge Base Ingestion. Uses postgres, vectorStorePGVector, documentDefaultDataLoader, embeddingsOpenAi. Webhook trigger; 8 nodes.

Postgres, Vector Store Pgvector, Document Default Data Loader +2
AI & RAG
Fpcvectorstoreingestion

FPCVectorStoreIngestion. Uses vectorStorePGVector, embeddingsOpenAi, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 6 nodes.

Vector Store Pgvector, OpenAI Embeddings, Document Default Data Loader +1
AI & RAG
Real-time Email RAG Assistant with Gmail, Openai Gpt, and Pgvector

This workflow is ideal for: Professionals Project managers Sales and support teams Anyone managing high volumes of Gmail messages

Gmail Trigger, Gmail, Text Splitter Recursive Character Text Splitter +6
AI & RAG
Demo: RAG in N8n

Demo: RAG in n8n. Uses formTrigger, documentDefaultDataLoader, vectorStoreInMemory, agent. Event-driven trigger; 13 nodes.

Form Trigger, Document Default Data Loader, In-Memory Vector Store +5
AI & RAG
File Upload

File upload. Uses localFileTrigger, vectorStorePGVector, embeddingsMistralCloud, readWriteFile. Event-driven trigger; 11 nodes.

Local File Trigger, Vector Store Pgvector, Embeddings Mistral Cloud +4
AI & RAG
Chat_works

CHAT_works. Uses chatTrigger, embeddingsGoogleGemini, agent, lmChatGoogleGemini. Chat trigger; 11 nodes.

Chat Trigger, Google Gemini Embeddings, Agent +5
AI & RAG
Rag_answ_sub

RAG_answ_sub. Uses vectorStorePGVector, embeddingsOpenAi, agent, toolVectorStore. Event-driven trigger; 10 nodes.

Vector Store Pgvector, OpenAI Embeddings, Agent +4
AI & RAG
Build a Glpi Knowledge Base RAG Pipeline with Google Gemini and Postgresql

This workflow automates the creation of a Retrieval-Augmented Generation (RAG) pipeline using content from the GLPI Knowledge Base. It retrieves and processes FAQ articles directly via the GLPI API, c

Agent, Google Gemini Chat, Chat Trigger +3
AI & RAG
Bella Vista Customer Bookings Support

Bella Vista Customer Bookings Support. Uses chatTrigger, agent, lmChatOpenAi, embeddingsOpenAi. Chat trigger; 8 nodes.

Chat Trigger, Agent, OpenAI Chat +4
AI & RAG
PDF Agent

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

Chain Retrieval Qa, Lm Chat Mistral Cloud, Retriever Vector Store +3
AI & RAG
Fpcrequirementanalyzer

FPCRequirementAnalyzer. Uses executeWorkflowTrigger, agent, lmChatOpenAi, vectorStorePGVector. Event-driven trigger; 6 nodes.

Execute Workflow Trigger, Agent, OpenAI Chat +2
AI & RAG
Update Bella Vista Kb

Update Bella Vista KB. Uses formTrigger, embeddingsOpenAi, documentDefaultDataLoader, vectorStorePGVector. Event-driven trigger; 4 nodes.

Form Trigger, OpenAI Embeddings, Document Default Data Loader +1

53 of 53 workflows in this view · Browse all →

FAQ

How many n8n Vectorstorepgvector workflows are in the catalog?

53 n8n workflows in AutomationFlows currently use the Vectorstorepgvector integration — triggers, actions, or both.

How do I connect Vectorstorepgvector in n8n?

After importing the workflow JSON, n8n will prompt for Vectorstorepgvector 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 Vectorstorepgvector workflows pair with adjacent tools (Slack alerts, Google Sheets logging, OpenAI summarisation). Browse the integration tags on each workflow page to discover pairings.