Agentic RAG WhatsApp Chatbot

Agentic RAG WhatsApp Chatbot Workflow Overview This workflow creates a sophisticated, multi-modal AI-powered chatbot on WhatsApp. It functions as an intelligent agent capable of understanding and responding to user queries submitted as text, audio messages, images, and various document formats (like PDF, XLSX, and TXT). The core of the workflow is a Retrieval-Augmented Generation (RAG) system. It first processes and stores knowledge from a specified Google Doc into a MongoDB vector database. When a user sends a message, the workflow converts the input into text, uses this text to search the knowledge base for relevant information, and then feeds this context to an AI model (GPT-4o-mini) to generate an accurate, context-aware response. The chatbot also maintains a conversational memory for each user, allowing for natural, follow-up interactions. Technologies Used - Communication Channel: WhatsApp - AI Language Models: OpenAI (GPT-4o-mini for chat and vision, Whisper for audio transcription, Ada for embeddings) - Vector Database: MongoDB Atlas - Knowledge Source: Google Docs - Automation Platform: n8n (including LangChain nodes for AI orchestration) Automation & Value This workflow automates the process of providing expert-level, instant support and information retrieval directly through WhatsApp. - 24/7 Multi-modal Support: It offers round-the-clock availability and can handle diverse user inputs, from simple text questions to complex queries contained within documents or voice notes. - Grounded & Accurate Answers: By using a RAG architecture, the AI's responses are based on a specific, controlled knowledge source (the Google Doc). This drastically reduces the risk of AI "hallucinations" and ensures the information provided is accurate and relevant to the business. - Enhanced User Experience: The chatbot remembers the context of the conversation for each user, allowing for fluid, natural dialogue. Processing multiple file types and media makes interaction smooth and intuitive for the end-user. - Operational Efficiency: It offloads repetitive and information-retrieval tasks from human agents, freeing them up for more complex issues.