Answer WhatsApp Questions from PDF Documents using RAG, Google Drive and Pinecone
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Neeraj Chouhan
Description
Good to know:
This workflow creates a WhatsApp chatbot that answers questions using your own PDFs through RAG (Retrieval-Augmented Generation). Every time you upload a document to Google Drive, it is processed into embeddings and stored in Pinecone—allowing the bot to respond with accurate, context-aware answers directly on WhatsApp.
Who is this for?
Anyone building a custom WhatsApp chatbot.
Businesses wanting a private knowledge based assistant
Teams that want their documents to be searchable via chat
Creators/coaches who want automated Q&A from their PDFs
Developers who want a no-code RAG pipeline using n8n
What problem is this workflow solving?
What this workflow does:
✅ Monitors a Google Drive folder for new PDFs
✅ Extracts and splits text into chunks
✅ Generates embeddings using OpenAI/Gemini
✅ Stores embeddings in a Pinecone vector index
✅ Receives user questions via WhatsApp
✅ Retrieves the most relevant info using vector search
✅ Generates a natural response using an AI Agent
✅ Sends the answer back to the user on WhatsApp
How it works:
1️⃣ Google Drive Trigger detects a new or updated PDF
2️⃣ File is downloaded and its text is split into chunks
3️⃣ Embeddings are generated and stored in Pinecone
4️⃣ WhatsApp Trigger receives a user’s question
5️⃣ The question is embedded and matched with Pinecone
6️⃣ AI Agent uses retrieved context to generate a response
7️⃣ The message is delivered back to the user on WhatsApp
How to use:
Connect your Google Drive account
Add your Pinecone API key and index name
Add your OpenAI/Gemini API key
Connect your WhatsApp trigger + sender nodes
Upload a sample PDF to your Drive folder
Send a test WhatsApp message to see the bot reply
Requirements:
✅ n8n cloud or self-hosted
✅ Google Drive account
✅ Pinecone vector database
✅ OpenAI or Gemini API key
✅ WhatsApp integration (Cloud API or provider)
Customizing this workflow:
🟢 Change the Drive folder or add file-type filters
🟢 Adjust chunk size or embedding model
🟢 Modify the AI prompt for tone, style, or restrictions
🟢 Add memory, logging, or analytics
🟢 Add multiple documents or delete old vector entries
🟢 Swap the AI model (OpenAI ↔ Gemini ↔ Groq, etc.)