Automated RAG-Powered WhatsApp Chatbots with GPT-4o-Mini and MongoDB

Overview

This workflow automates the creation of Retrieval-Augmented Generation (RAG) chatbots for WhatsApp, leveraging GPT-4o-Mini and MongoDB Atlas. It enables users to interact with document-based knowledge bases via WhatsApp, providing intelligent, context-aware responses.

Key Features

• Integrates OpenAI's GPT-4o-Mini for advanced conversational AI.
• Utilizes MongoDB Atlas as a vector store for efficient document retrieval.
• Processes and chunks documents using text splitters and loaders.
• Embeds document data with OpenAI embeddings for semantic search.
• Maintains conversation context with memory buffer nodes.
• Manual trigger for workflow execution and testing.

Benefits

• Delivers instant, accurate answers from internal documents via WhatsApp.
• Reduces manual support workload and response times.
• Scalable knowledge management with automated document ingestion.
• Enhances customer and employee self-service experiences.

Use Cases

• Customer support chatbots for product documentation.
• Internal knowledge assistants for HR or IT teams.
• Automated FAQ bots for sales or onboarding processes.

Integrations & Automation

The workflow connects OpenAI's language and embedding models with MongoDB Atlas for vector search, orchestrated by n8n's automation platform. This seamless integration ensures fast, relevant responses and minimizes manual intervention, saving significant operational time.