AI WhatsApp Chatbot with RAG for Text, Voice, Images & PDFs
Automates document ingestion from Google Docs into MongoDB vector store and powers multi-modal WhatsApp chatbot using RAG for accurate, context-aware responses.
This n8n workflow template consists of two interconnected automations for tech support teams. The first workflow manually ingests product documentation from Google Docs, chunks it for efficiency, generates OpenAI embeddings, and indexes them in MongoDB Atlas for semantic search. This enables fast, accurate retrieval from large knowledge bases.
The second workflow listens for WhatsApp messages, handling text, voice notes (transcribed to text), images (analyzed for context), and PDFs (parsed for content). It embeds queries, performs similarity searches on the vector store, and uses GPT-4o-mini for retrieval-augmented generation (RAG) to deliver precise answers while maintaining conversation context across turns.
Benefits include reduced support response times, consistent answers from docs, and scalability for internal teams. Use cases: product support chatbots, knowledge management, customer service automation in tech firms, or educational Q&A bots for learning materials.
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How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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