Loading page
Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented qu…
Download this n8n workflow template and start using it instantly.
This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering via WhatsApp.
Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI via WhatsApp messaging.
Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection with:
An embedding field storing vector data,
Relevant metadata fields (e.g., document ID, source), and
The same vector index name configured (e.g., data_index).
{ "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }
Knowledge Base Agent (Agent): The AI agent that orchestrates the workflow — it reasons over the input and decides which tools to call.
OpenAI Chat Model (LLM Chat Open AI): Provides the OpenAI chat model (GPT) that powers the AI reasoning in this workflow.
Embeddings OpenAI (Embeddings Open AI): Turns text into vector embeddings using OpenAI (for semantic search / RAG).
When clicking "Execute Workflow" (Manual Trigger): Starts the workflow manually when you click 'Execute workflow' — used for testing and manual runs.
Simple Memory (Memory Buffer Window): Gives the AI short-term conversational memory of recent messages.
MongoDB Vector Search (Vector Store Mongo DBAtlas): Stores and retrieves embeddings for retrieval-augmented generation (RAG).
Document Section Loader (Document Default Data Loader): Loads and prepares documents for the AI to read (RAG ingestion).
Document Chunker (Text Splitter Recursive Character Text Splitter): Splits long documents into chunks the AI can process.
MongoDB Vector Store Inserter (Vector Store Mongo DBAtlas): Stores and retrieves embeddings for retrieval-augmented generation (RAG).
OpenAI Embeddings Generator (Embeddings Open AI): Turns text into vector embeddings using OpenAI (for semantic search / RAG).
Google Docs Importer (Google Docs): Reads from or writes to a Google Docs document.
WhatsApp Trigger (Whats App Trigger): Starts the workflow when the Whats App event occurs.
OpenAI (Open AI): Calls the OpenAI API for chat, image, or audio generation.
OpenAI1 (Open AI): Calls the OpenAI API for chat, image, or audio generation.
Extract from PDF (Extract From File): Handles the Extract From File step in this workflow.
Extract from XLS (Extract From File): Handles the Extract From File step in this workflow.
Extract from XLSX (Extract From File): Handles the Extract From File step in this workflow.
Map JSON (Set): Sets or transforms workflow data — prepares, renames, or injects fields before the next step.
Map file extensions (Code): Runs custom JavaScript to transform or manipulate the data flowing through the workflow.
Map document prompt (Set): Sets or transforms workflow data — prepares, renames, or injects fields before the next step.
Map image prompt (Set): Sets or transforms workflow data — prepares, renames, or injects fields before the next step.
Map text prompt (Set): Sets or transforms workflow data — prepares, renames, or injects fields before the next step.
Gets WhatsApp Voicemail Source URL (Whats App): Sends or receives WhatsApp messages.
Gets WhatsApp Image Source URL (Whats App): Sends or receives WhatsApp messages.
Gets WhatsApp Document Source URL (Whats App): Sends or receives WhatsApp messages.
Download Voicemail (HTTP Request): Makes an external API call (HTTP request) to send or retrieve data from a third-party service.
Download Image (HTTP Request): Makes an external API call (HTTP request) to send or retrieve data from a third-party service.
Download Document (HTTP Request): Makes an external API call (HTTP request) to send or retrieve data from a third-party service.
Route Types (Switch): Routes the workflow down one of several paths based on a value.
Route Document Types (Switch): Routes the workflow down one of several paths based on a value.
Send Unsupported Response (Whats App): Sends or receives WhatsApp messages.
Send Response (Whats App): Sends or receives WhatsApp messages.
Document-Aware WhatsApp AI Bot for Customer Support Google Docs-Powered WhatsApp Support Agent 24/7 WhatsApp AI Assistant with Live Knowledge from Google Docs Description Template Smart WhatsApp AI Assistant Using Google Docs Help customers instantly on WhatsApp using a smart AI assistant that reads your company’s internal knowledge from a Google Doc in real time. Built for clubs, restaurants, agencies, or any business where clients ask questions based on a policy, FAQ, or services document. How it works Users send free-form questions to your WhatsApp Business number (e.g. “What are the gym rules?” or “Are you open today?”) The bot automatically reads your company’s internal Google Doc (policy, schedule, etc.) It merges the document content with today’s date and the user’s question to craft a custom AI prompt The AI (Gemini or ChatGPT) then replies back on WhatsApp using natural, helpful language All conversations are logged to Google Sheets for reporting or audit > Bonus: The AI even understands dates inside the document and compares them to today’s date — e.g. if your document says “Closed May 25 for 30 days,” it will say “We're currently closed until June 24. Set up steps 1. Connect your WhatsApp Cloud API account (Meta) 2. Add your Google account and grant access to the Doc containing your company info 3. Choose your AI model (ChatGPT/OpenAI or Gemini) 4. Paste your document ID into the Google Docs node 5. Connect your WhatsApp webhook to Meta (only takes 5 minutes) 6. Done — start receiving and answering customer questions! > Works best with free-tier OpenAI/Gemini, Google Docs, and Meta's Cloud API (no phone required). Everything is modular, extensible, and low-code. Customization Tips Change the Google Doc anytime to update answers — no retraining needed Add your logo and business name in the AI agent’s “System Prompt” Add fallback routes like “Escalate to human” if the bot can't help Clone for multiple brands by duplicating the workflow and swapping in new docs Need Help Setting It Up? If you'd like help connecting your WhatsApp Business API, setting up Google Docs access, or customizing this AI assistant for your business or clients… I offer setup, branding, and customization services: WhatsApp Cloud API setup & verification Google OAuth & Doc structure guidance AI model configuration (OpenAI / Gemini) Branding & prompt tone customization Logging, reporting, and escalation logic Just send a message via: Email: tharwat.elsayed2000@gmail.com WhatsApp: +20 106 180 3236
by Tharwat MohamedThis n8n workflow template creates an intelligent data analysis chatbot that can answer questions about data stored in Google Sheets using OpenAI's GPT-5 Mini model. The system automatically analyzes your spreadsheet data and provides insights through natural language conversations. What This Workflow Does Chat Interface: Provides a conversational interface for asking questions about your data Smart Data Analysis: Uses AI to understand column structures and data relationships Google Sheets Integration: Connects directly to your Google Sheets data Memory Buffer: Maintains conversation context for follow-up questions Automated Column Detection: Automatically identifies and describes your data columns Try It Out! --- 1. Set Up OpenAI Connection Get Your API Key 1. Visit the OpenAI API Keys page. 2. Go to OpenAI Billing. 3. Add funds to your billing account. 4. Copy your API key into your OpenAI credentials in n8n (or your chosen platform). --- 2. Prepare Your Google Sheet Connect Your Data in Google Sheets Data must follow this format: Sample Marketing Data First row contains column names. Data should be in rows 2–100. Log in using OAuth, then select your workbook and sheet. --- 3. Ask Questions of Your Data You can ask natural language questions to analyze your marketing data, such as: Total spend across all campaigns. Spend for Paid Search only. Month-over-month changes in ad spend. Top-performing campaigns by conversion rate. Cost per lead for each channel. --- Need Help or Want to Customize This? rbreen@ynteractive.com LinkedIn n8n Automation Experts
by Robert BreenBuild a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation 1. Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit 2. Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it 1. First run the "Data Ingestion" part and upload as many PDF files as you want 2. Run the Chatbot and start asking questions about the documents you uploaded
by Thomas JanssenThis template quickly shows how to use RAG in n8n. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs 1. Add custom instructions to your Agent by changing the prompts in it. 2. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. 2. Exchange the Simple Vector Store nodes with your own vector store tools ready for production. 3. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.
by n8n Team