OpenAI Personal Shopper with RAG and WooCommerce

Workflow Name

OpenAI Personal Shopper with RAG and WooCommerce

Key Features and Highlights

This workflow integrates OpenAI’s language models and Retrieval-Augmented Generation (RAG) technology with WooCommerce’s product database to deliver an intelligent personal shopping assistant. It understands user intent during chat interactions, automatically extracting product search keywords, price ranges, SKUs, and other relevant information to accurately match products within WooCommerce. For non-product-related inquiries (such as store address, business hours, etc.), the system leverages RAG based on store documents stored in the Qdrant vector database to provide intelligent responses, enhancing the user experience. The entire process supports contextual memory management to ensure conversational coherence.

Core Problems Addressed

• Automatically identifying whether the user’s chat contains shopping-related needs, preventing irrelevant queries from disrupting the shopping flow.
• Precisely extracting key information from user requests to improve product search accuracy.
• Combining a knowledge base to answer store-related questions, solving challenges in customer service automation.
• Unified management of conversational context to provide personalized and continuous shopping recommendations.

Application Scenarios

• Intelligent customer service and shopping assistants for e-commerce platforms.
• Upgrading online services for physical stores by helping customers quickly find products via chatbots.
• Unified response to multi-channel customer inquiries to improve service efficiency.
• Complex Q&A scenarios requiring integration of product databases and knowledge bases.

Main Workflow Steps

1. Chat Trigger: Receive user messages through Langchain’s chat trigger node.
2. Field Extraction: Extract conversation ID and user input as the basis for subsequent processing.
3. Information Extraction: Use OpenAI models to determine if the request is a product search and extract keywords, price ranges, SKUs, etc.
4. Intelligent Agent Decision: Decide whether to invoke the personal shopping tool or the RAG knowledge base based on extracted information.
5. Personal Shopping Query: Query WooCommerce API for products matching the criteria and check inventory.
6. Knowledge Base Retrieval: Answer non-shopping-related questions using Qdrant vector database and store documents from Google Drive.
7. Context Management: Maintain conversational continuity and context using a window buffer memory node.
8. Response Generation: Generate the final reply using OpenAI chat models and send it to the user.

Involved Systems and Services

• OpenAI API: Natural language understanding and generation, information extraction, chat modeling.
• WooCommerce: Product inventory querying and filtering.
• Qdrant Vector Database: Vector storage and retrieval for the store knowledge base.
• Google Drive: Storage of store-related documents for knowledge base construction.
• n8n Nodes: Including Langchain tools, HTTP requests, memory management, etc.

Target Users and Value

• E-commerce businesses and store operators seeking to enhance customer shopping experience and service efficiency through intelligent customer service.
• Technical teams building intelligent Q&A systems that integrate knowledge bases with product databases.
• Merchants aiming to automate handling of diverse customer inquiries to reduce manual customer service workload.
• Enterprises pursuing digital transformation by leveraging AI for personalized recommendations and intelligent search.

This workflow enables businesses to automate intelligent shopping assistance, accurately fulfilling user shopping needs while smartly answering store-related questions, significantly improving customer satisfaction and operational efficiency.