Personal shopper chatbot for WooCommerce with RAG using Google Drive and openAI
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 21 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects OpenAI Chat Model, Vector Store Question Answer, Qdrant Vector Store and Embeddings OpenAI and 2 more.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- HTTP Request: calls an HTTP API (POST)
- Edit Fields (Edit Fields (Set)): sets 2 fields: sessionId, chatInput
- Google Drive: work with file folder
- Information Extractor: extracts structured fields from text with AI, with model: OpenAI Chat Model
- Google Drive: download
- AI Agent: runs an AI agent, with model: OpenAI Chat Model; 3 tools (RAG, Calculator, personal_shopper); memory: Window Buffer Memory
- Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
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 (16)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Calculator
@n8n/n8n-nodes-langchain.toolCalculator - Edit Fields (Set)
n8n-nodes-base.set - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Woo Commerce Tool
n8n-nodes-base.wooCommerceTool - Information Extractor
@n8n/n8n-nodes-langchain.informationExtractor - Google Drive
n8n-nodes-base.googleDrive - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Token Splitter
@n8n/n8n-nodes-langchain.textSplitterTokenSplitter - Manual Trigger
n8n-nodes-base.manualTrigger - HTTP Request
n8n-nodes-base.httpRequest - AI Agent
@n8n/n8n-nodes-langchain.agent
From the author
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: 1. Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching…