Complete business WhatsApp AI-powered RAG chatbot using OpenAI
Needs changesWhat this workflow does
This AI workflow has 22 nodes and runs manually, when you click “Execute workflow” (plus 2 other triggers). Along the way it connects OpenAI Chat Model, Qdrant Vector Store, Google Drive and Embeddings OpenAI and 2 more. It also calls qdranturl over HTTP.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 2 other triggers).
- Create collection (HTTP Request): calls qdranturl (POST)
- Refresh collection (HTTP Request): calls an HTTP API (POST)
- Respond to Webhook: sends the response back to the webhook caller
- is Message? (If): checks a condition and branches into true / false
- Get folder (Google Drive): work with file folder
- AI Agent: runs an AI agent, with model: OpenAI Chat Model; 1 tool (RAG); memory: Window Buffer Memory
- Only message (WhatsApp Business Cloud): send
- Download Files (Google Drive): download
- Send (WhatsApp Business Cloud): send
- Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
HTTP calls to: qdranturl
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in AI Agent (v1.7): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (15)
- Respond to Webhook
n8n-nodes-base.respondToWebhook - AI Agent
@n8n/n8n-nodes-langchain.agent - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Manual Trigger
n8n-nodes-base.manualTrigger - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - HTTP Request
n8n-nodes-base.httpRequest - Google Drive
n8n-nodes-base.googleDrive - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Token Splitter
@n8n/n8n-nodes-langchain.textSplitterTokenSplitter - Webhook
n8n-nodes-base.webhook - If
n8n-nodes-base.if - WhatsApp Business Cloud
n8n-nodes-base.whatsApp - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore
From the author
The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. --- How it works: 1. Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives…