AI-Powered Personal Shopper with RAG and WooCommerce Integration
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Leverage OpenAI and RAG to create an intelligent personal shopper that integrates with WooCommerce. This workflow processes chat messages, extracts product information, searches your store, and provides personalized recommendations.
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About This Workflow
Overview
This n8n workflow automates a personalized shopping assistant experience powered by OpenAI and Retrieval Augmented Generation (RAG). It begins by receiving chat messages via a trigger node. The Information Extractor node then analyzes the user's input to identify product search intent, keywords, price ranges, SKUs, and categories. If a product is being sought, the workflow proceeds to query your WooCommerce store using the personal_shopper (WooCommerce Tool) node with the extracted details. For more contextual and accurate responses, the workflow also utilizes RAG. It retrieves relevant store information from a Qdrant vector store, processes it through OpenAI embeddings, and splits it into manageable chunks using a Token Splitter. This enriched data is then fed into OpenAI Chat Models to generate more informed responses and recommendations, effectively acting as a sophisticated personal shopper.
Key Features
- Intelligent Chat Interaction: Utilizes OpenAI for natural language understanding and response generation.
- Product Information Extraction: Accurately identifies keywords, pricing, SKUs, and categories from user queries.
- WooCommerce Integration: Seamlessly searches and retrieves product data from your WooCommerce store.
- Retrieval Augmented Generation (RAG): Enhances AI responses with relevant, up-to-date store information from a Qdrant vector store.
- Contextual Memory: Maintains conversation history for more personalized interactions.
How To Use
- Configure Triggers: Set up the
When chat message receivednode to connect to your chat interface. - Set Up Credentials: Ensure your OpenAI API, Qdrant API, and WooCommerce API credentials are correctly configured.
- Initialize Data: The workflow automatically loads and embeds store information from Google Drive (if configured) into the Qdrant vector store for RAG.
- Define Information Extraction: Review and adjust the
Information Extractornode's system prompt and schema to match your product data and desired extraction fields. - Customize WooCommerce Tool: Ensure the
personal_shoppernode is correctly mapped to your WooCommerce API and its parameters align with your product attributes. - Configure RAG Components: Verify the
Qdrant Vector Store,Embeddings OpenAI, andToken Splitternodes are correctly pointed to your data and configured for optimal embedding and retrieval. - Set Up Memory: Configure the
Window Buffer Memorynode to manage conversation context. - Test Workflow: Use the
When clicking ‘Test workflow’or a live chat to send test messages and observe the workflow's execution and generated responses.
Apps Used
Workflow JSON
{
"id": "ea7d007c-7b66-4eeb-b858-07b95b761fae",
"name": "AI-Powered Personal Shopper with RAG and WooCommerce Integration",
"nodes": 0,
"category": "AI & LLMs",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: ea7d007c-7b66...
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