AI-Powered WhatsApp Chatbot for Instant Product Support
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Revolutionize your customer support with an AI-powered WhatsApp chatbot. This workflow leverages advanced AI models and vector databases to provide instant, context-aware answers to customer inquiries using your product documentation.
About This Workflow
This n8n workflow automates customer support by integrating with WhatsApp. It begins by importing and indexing your Google Docs product documentation into a MongoDB vector database, creating a powerful knowledge base for your AI. When a customer sends a message via WhatsApp, the workflow processes the input, including text, audio, or documents, and uses OpenAI's GPT-4o-mini model to generate contextually relevant responses. Conversation memory is maintained to ensure a seamless and personalized user experience. This solution offers an efficient and intelligent way to handle customer queries, reduce response times, and improve overall customer satisfaction.
Key Features
- Intelligent Knowledge Retrieval: Utilizes vector embeddings and MongoDB Atlas for fast, semantic search of your product documentation.
- Multi-Modal Input Processing: Handles text, audio (transcription), and document inputs from WhatsApp.
- Context-Aware AI Responses: Employs GPT-4o-mini for generating accurate and relevant answers.
- Conversation Memory: Maintains chat history for a personalized customer experience.
- Easy Documentation Integration: Imports and indexes content directly from Google Docs.
How To Use
- Import and Index Documentation: Manually trigger the 'Google Docs Importer' node to fetch content from your specified Google Doc.
- Chunk and Embed: The 'Document Chunker' and 'OpenAI Embeddings Generator' nodes process the imported document into manageable chunks and create vector embeddings.
- Store Embeddings: The 'MongoDB Vector Store Inserter' node stores these embeddings in your MongoDB Atlas collection with a defined index.
- WhatsApp Integration: Configure the 'WhatsApp Trigger' node with your WhatsApp Business API credentials.
- AI Agent Setup: The 'Knowledge Base Agent' node is configured to use OpenAI's GPT-4o-mini model and a memory buffer ('Simple Memory') to manage conversations.
- Vector Search for Context: The 'MongoDB Vector Search' node retrieves relevant documentation chunks based on the incoming WhatsApp message.
- Response Generation: The AI agent uses the retrieved information and conversation history to formulate a context-aware response sent back to the customer via WhatsApp.
Apps Used
Workflow JSON
{
"id": "a747cb70-f5c9-45e7-a0fb-b872235c2471",
"name": "AI-Powered WhatsApp Chatbot for Instant Product Support",
"nodes": 29,
"category": "Marketing",
"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: a747cb70-f5c9...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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