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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 question answering via WhatsApp. What problem is this workflow solving? 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. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response via WhatsApp Listens for incoming WhatsApp user messages, supporting various types: Text messages: Plain text queries from users. Audio messages: Voice notes transcribed into text for processing. Image messages: Photos or screenshots analyzed to provide contextual answers. Document messages: PDFs, spreadsheets, or other files parsed for relevant content. Converts incoming queries to vector embeddings and performs similarity search on the MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce concise, context-aware answers. Maintains conversation context across multiple turns using a memory buffer node. Routes different message types to appropriate processing nodes to maximize answer quality. Setup Setting up vector embeddings 1. Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. 2. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. 3. Ensure the index name matches the one configured in n8n (data_index). 4. See the example MongoDB search index template below for reference. Setting up chat 1. Authenticate the WhatsApp node with your Meta account credentials to enable message receiving and sending. 2. Connect the MongoDB collection containing embedded product documentation to the MongoDB Vector Search node used for similarity queries. 3. Set up the system prompt in the Knowledge Base Agent node to reflect your company’s tone, answering style, and any business rules, ensuring it references the connected MongoDB collection for context retrieval. Make sure 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). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }
by NovaNodeDocument-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 MohamedSetup & Instructions — fluidX: Create Session, Analyze & Notify Goal: This workflow demonstrates the full fluidX THE EYE integration — starting a live session, inviting both the customer (via SMS) and the service agent (via email), and then accessing the media (photos and videos) created during the session. Captured images are automatically analyzed with AI, uploaded to an external storage (such as Google Drive), and a media summary for the session is generated at the end. The agent receives an email with a link to join the live session. The customer receives an SMS with a link to start sharing their camera. Once both are connected, the agent can view the live feed, and the system automatically stores uploaded images and videos in Google Drive. When the session ends, the workflow collects all media and creates a complete AI-powered session summary (stored and updated in Google Drive). Below is an example screenshot from the customer’s phone: --- Prerequisites Developer account: https://live.fluidx.digital (activate the TEST plan, €0) API docs (Swagger): fluidX.digital API --- Required Credentials 1 fluidX API key (HTTP Header Auth) • Credential name in n8n: fluidx API key • Header name: x-api-key • Header value: YOUR_API_KEY 2 SMTP account (for outbound email) • Credential name in n8n: SMTP account • Configure host, port, username, and password according to your provider • Enable TLS/SSL as required 3 Google Drive account • Used to store photos, videos, and automatically update the session summary files. 4 OpenAI API (for AI analysis & summary) •Used in the Analyze Images (AI) and Generate Summary parts of the workflow. • Credential type: OpenAI • Credential name (suggested): OpenAI account • API Key: your OpenAI API key • Model: e.g. gpt-4.1, gpt-4o, or similar (choose in the OpenAI node settings) --- Configuration (in the “Set Config” node) BASE_URL: https://live.fluidx.digital company / project / billingcode / sku: adjust as needed emailAgent: set before running (empty in template) phoneNumberUser: set before running (empty in template) --- Flow Overview Form Trigger → Create Session → Set Session Vars → Send SMS (User) → Send Email (Agent) → Monitor Media → Analyze Images (AI) → Upload Files to Google Drive → Generate Summary → Update Summary File The workflow starts automatically when a Form submission is received. Users enter the customer’s phone number and agent’s email, and the system creates a new fluidX THE EYE session. As media is uploaded during the session, the workflow automatically retrieves, stores, analyzes, and summarizes it — providing a complete end-to-end automation example for remote inspection, support, or field-service use cases. --- Notes Do not store real personal data inside the template. Manage API keys and secrets via n8n Credentials or environment variables. Log out of https://live.fluidx.digital in the agent’s browser before testing, to ensure a clean invite flow and session creation.
by Olaf Titel