Answer WhatsApp Questions from PDF Documents using RAG, Google Drive and Pinecone

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.)