Who is this workflow for? This workflow leverages n8n and OpenAI to create an AI-driven Retrieval-Augmented Generation (RAG) chatbot for WhatsApp. It integrates seamlessly with various tools to handle customer inquiries, providing accurate and contextually relevant responses by accessing a robust knowledge base..

What does this workflow do?

  • Webhook Setup
  • Verification Webhook: Configures a webhook to handle GET requests, responding with a verification code to establish the connection with Meta’s WhatsApp API.
  • Response Webhook: Sets up a webhook to manage incoming POST requests from WhatsApp, ensuring messages are correctly received and processed.
  • Message Handling
  • Incoming Message Check: Evaluates the incoming JSON data to determine if it contains a user message.
  • Processing Logic: If a user message is present, it proceeds to process the message; otherwise, it sends a predefined generic response.
  • AI Agent Interaction
  • Conversational Agent: Routes the user’s message to the AI Agent node, which operates with a system prompt tailored for an electronics store to ensure relevant and professional responses.
  • Knowledge Base Utilization
  • Qdrant Integration: References a knowledge base stored in Qdrant, a vector database.
  • Document Management: Downloads documents from Google Drive, generates embeddings using OpenAI, and stores them in Qdrant for efficient retrieval during interactions.
  • Response Generation
  • AI Processing: The AI Agent uses OpenAI’s GPT-4 model to generate a contextual response based on the retrieved information.
  • WhatsApp Delivery: Sends the AI-generated response back to the user through WhatsApp.
  • Setup Steps
  • Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables and initialize the collection using the Create Collection HTTP request node.
  • Vectorize Documents: Configure nodes to fetch documents from a designated Google Drive folder, generate embeddings with OpenAI, and store them in Qdrant.
  • Configure Webhooks: Ensure both Verify and Respond webhooks share the same URL, with appropriate HTTP methods (GET for verification and POST for responses).
  • Set Up AI Agent: Define a system prompt for the AI Agent, outlining guidelines for handling product information, technical support, and customer service. Link the AI Agent to OpenAI and configure additional tools as necessary.
  • Test Workflow: Manually trigger the workflow using the Test Workflow node to verify functionality, monitor data flow, and ensure accurate response generation.

🤖 Why Use This Automation Workflow?

  • Enhanced Customer Support: Automate responses to common queries, reducing response time and improving customer satisfaction.
  • Scalable Solution: Easily manage increasing volumes of messages without additional human resources.
  • Intelligent Responses: Utilize AI to deliver precise and context-aware answers, enhancing the user experience.

👨‍💻 Who is This Workflow For?

This workflow is ideal for businesses of all sizes seeking to automate their customer service on WhatsApp. It is particularly beneficial for companies in the electronics sector but can be adapted to various industries requiring reliable and intelligent chatbot interactions.

🎯 Use Cases

  1. Customer Service Automation: Handle inquiries about product specifications, availability, and troubleshooting without human intervention.
  2. Sales Support: Assist customers in selecting products, placing orders, and providing personalized recommendations based on their preferences.
  3. Technical Support: Offer detailed technical assistance by accessing and referencing a comprehensive knowledge base stored in a vector database.

TL;DR

This n8n workflow provides a robust solution for deploying an AI-powered WhatsApp chatbot. By integrating OpenAI’s advanced language models and a structured knowledge base, it ensures efficient and intelligent handling of customer inquiries, enhancing service quality and operational efficiency.

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