Personal Shopper with RAG and WooCommerce
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An n8n workflow that acts as a personal shopper, integrating OpenAI for conversational AI, RAG for information retrieval, and WooCommerce for product catalog access.
About This Workflow
This n8n workflow simulates a personal shopper for an online store. It leverages OpenAI's language models to understand user queries, a Retrieval-Augmented Generation (RAG) system powered by Qdrant and Google Drive documents for store information, and the WooCommerce API to search and retrieve products. The workflow can identify product search intents, extract relevant product attributes (like SKU, price range, keywords), and respond with product information or general store details.
Key Features
- Conversational AI: Uses OpenAI Chat Models for natural language understanding and response generation.
- Intent Recognition: Employs an
Information Extractorto determine if a user is looking for a product or asking general questions. - Product Search: Integrates with WooCommerce to search for products based on extracted criteria (SKU, price, keyword).
- RAG System: Utilizes Qdrant as a vector store and Google Drive documents for retrieving contextual information about the store.
- Memory Management: Includes
Window Buffer Memoryto maintain conversation history. - Tool Integration: Demonstrates the use of various Langchain tools within n8n, including
toolCalculator,toolVectorStore, and custom tools.
How To Use
- Setup Credentials: Configure OpenAI API credentials, Qdrant API credentials, and WooCommerce API credentials.
- Configure RAG: Ensure your Qdrant instance is running and has a collection named 'scarperia'. Populate this collection with embeddings of your store's documentation, potentially sourced from Google Drive.
- Google Drive Setup: Ensure Google Drive credentials are set up and the specified folder (
1lmnqpLFKS-gXmXT92C5VG0P1XlcoeFOb) contains the documents for the RAG system. - WooCommerce Setup: Configure WooCommerce credentials to access your product catalog.
- Webhook Trigger: Activate the
When chat message receivednode, which acts as the entry point for user interactions via a webhook. - Information Extraction: The
Information Extractornode processes the incoming chat message to identify product search intent and extract relevant parameters. - AI Agent Orchestration: The
AI Agentnode orchestrates the workflow based on the extracted intent, deciding whether to use thepersonal_shoppertool (for product searches) or the RAG system (for general queries). - Product Search: If a product search is initiated, the
personal_shoppernode queries WooCommerce. - RAG Retrieval: For general queries, the RAG system, utilizing
OpenAI Chat Model1,RAG, andQdrant Vector Store, provides context-aware answers. - Memory:
Window Buffer Memorymaintains conversation context for a more coherent interaction.
Apps Used
Workflow JSON
{
"id": "f2ec35f2-8d4d-46eb-b3cc-5352d04dbaa2",
"name": "Personal Shopper with RAG and WooCommerce",
"nodes": 27,
"category": "E-commerce",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: f2ec35f2-8d4d...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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