AutomationFlows › Vectorstoremongodbatlas

n8n workflows for Vectorstoremongodbatlas.

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

Most-used Vectorstoremongodbatlas workflows

  1. Build a Whatsapp AI Shopping Bot with Virtual Try-on Using Gemini and Gpt (83 nodes)
  2. Build a Whatsapp Assistant with Memory, Google Suite & Multi-ai Research and Imaging — n8n Vectorstoremongodbatlas workflow (71 nodes)
  3. Build a Wordpress RAG Chatbot with Openai, Qdrant or Mongodb (63 nodes)
  4. 4827 — n8n Vectorstoremongodbatlas workflow (35 nodes)
  5. Ai-powered Whatsapp Chatbot for Text, Voice, Images, and PDF with RAG (35 nodes)
  6. Whatsapp (agent) — n8n Vectorstoremongodbatlas workflow (35 nodes)
  7. Reviewflow (33 nodes)
  8. Build a Chatbot with Reinforced Learning Human Feedback (rlhf) and RAG — n8n Vectorstoremongodbatlas workflow (26 nodes)
  9. Generate Linkedin Posts Using Google Gemini, Mongodb Atlas, Google Drive and Sheets (18 nodes)
  10. Create a Factual Learning Assistant with Rag, Gemini, Telegram & Mongodb — n8n Vectorstoremongodbatlas workflow (17 nodes)
AI & RAG
Build a Whatsapp AI Shopping Bot with Virtual Try-on Using Gemini and Gpt

📌 Overview

Redis, WhatsApp, OpenAI Chat +12
AI & RAG
Build a Whatsapp Assistant with Memory, Google Suite & Multi-ai Research and Imaging

The "WhatsApp Productivity Assistant with Memory and AI Imaging" is a comprehensive n8n workflow that transforms your WhatsApp into a powerful, multi-talented AI assistant. It's designed to handle a w

WhatsApp Trigger, Agent, HTTP Request +20
AI & RAG
Build a Wordpress RAG Chatbot with Openai, Qdrant or Mongodb

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

Qdrant Vector Store, OpenAI Embeddings, Document Default Data Loader +10
AI & RAG
4827

4827. Uses agent, lmChatOpenAi, embeddingsOpenAi, memoryBufferWindow. Event-driven trigger; 35 nodes.

Agent, OpenAI Chat, OpenAI Embeddings +9
AI & RAG
Ai-powered Whatsapp Chatbot for Text, Voice, Images, and PDF with RAG

This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven,

Agent, OpenAI Chat, OpenAI Embeddings +9
AI & RAG
Whatsapp (agent)

Whatsapp. Uses agent, lmChatOpenAi, embeddingsOpenAi, memoryBufferWindow. Event-driven trigger; 35 nodes.

Agent, OpenAI Chat, OpenAI Embeddings +9
AI & RAG
Reviewflow

ReviewFlow. Uses executeWorkflowTrigger, httpRequest, mongoDb, agent. Event-driven trigger; 33 nodes.

Execute Workflow Trigger, HTTP Request, MongoDB +4
AI & RAG
Build a Chatbot with Reinforced Learning Human Feedback (rlhf) and RAG

This template is designed for internal support teams, product specialists, and knowledge managers who want to build an AI-powered knowledge assistant with retrieval-augmented generation (RAG) and rein

Agent, OpenAI Chat, OpenAI Embeddings +7
AI & RAG
Generate Linkedin Posts Using Google Gemini, Mongodb Atlas, Google Drive and Sheets

This automation operates in three distinct phases: Ingestion, Storage, and Generation.

Vector Store Mongo Dbatlas, Agent, Google Gemini Embeddings +7
AI & RAG
Create a Factual Learning Assistant with Rag, Gemini, Telegram & Mongodb

This template is perfect for educational institutions, coaching centers (like UPSC, GMAT, or specialized technical training), internal corporate knowledge bases, and SaaS companies that need to provid

Document Default Data Loader, Chat Trigger, Google Drive Trigger +9
AI & RAG
4526

4526. Uses agent, lmChatOpenAi, embeddingsOpenAi, memoryBufferWindow. Event-driven trigger; 15 nodes.

Agent, OpenAI Chat, OpenAI Embeddings +6
AI & RAG
Build a Knowledge Base Chatbot with Openai, RAG and Mongodb Vector Embeddings

This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven,

Agent, OpenAI Chat, OpenAI Embeddings +6
AI & RAG
Travel Assistantagent

Travel AssistantAgent. Uses chatTrigger, memoryMongoDbChat, lmChatGoogleGemini, vectorStoreMongoDBAtlas. Chat trigger; 14 nodes.

Chat Trigger, Memory Mongo Db Chat, Google Gemini Chat +5
AI & RAG
Travel Planning Assistant with Mongodb Atlas, Gemini LLM and Vector Search

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration.

Chat Trigger, Memory Mongo Db Chat, Google Gemini Chat +5

14 of 14 workflows in this view · Browse all →

FAQ

How many n8n Vectorstoremongodbatlas workflows are in the catalog?

14 n8n workflows in AutomationFlows currently use the Vectorstoremongodbatlas integration — triggers, actions, or both.

How do I connect Vectorstoremongodbatlas in n8n?

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