Personal Shopper Chatbot for WooCommerce with RAG using Google Drive and openAI

This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios:
Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce.
General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive.

How It Works

1. Chat Interaction & Intent Detection
   Chat Trigger**:
   Starts when a user sends a message ("When chat message received").
   Information Extractor**:
   Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question.
   Extracts:
   search (true/false).
   keyword, priceRange, SKU, category (if product-related).
   Example:
   {
   "search": true,
   "keyword": "red handbags",
   "priceRange": { "min": 50, "max": 100 },
   "SKU": "BAG123",
   "category": "women's accessories"
   }

2. Product Search (WooCommerce Integration)
   AI Agent**:
   If search: true, routes the request to the personal_shopper tool.
   WooCommerce Node:
   Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU).
   Filters products in stock (stockStatus: "instock").
   Returns matching products (e.g., "red handbags under €100").

3. General Inquiries (RAG System)
   RAG Tool**:
   If search: false, uses the Qdrant Vector Store to retrieve store information from documents.
   Google Drive Integration:
   Documents (e.g., store policies, FAQs) are stored in Google Drive.
   Downloaded, split into chunks, and embedded into Qdrant for semantic search.
   OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM").

Set Up Steps

1. Configure the RAG System
   Google Drive Setup**:
   Upload store documents .
   Update the Google Drive2 node with your folder ID.
   Qdrant Vector Database**:
   Clean the collection (update Qdrant Vector Store node with your URL).
   Use Embeddings OpenAI to convert documents into vectors.

2. Configure OpenAI & WooCommerce
   OpenAI Credentials**:
   Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.).
   WooCommerce Integration**:
   Connect your WooCommerce store (credentials in the personal_shopper node).
   Ensure product data is synced and accessible.

3. Customize the AI Agent
   Intent Detection**:
   Modify the Information Extractor’s system prompt to align with your store’s terminology.
   RAG Responses**:
   Update the tool description to reflect your store’s documents.

Notes
This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support.

Need help customizing?
Contact me for consulting and support or add me on Linkedin.