Build a Facebook Messenger Chatbot with n8n, OpenAI, and Supabase (RAG Memory) - answer consistency скачать в хорошем качестве

Build a Facebook Messenger Chatbot with n8n, OpenAI, and Supabase (RAG Memory) - answer consistency

Stop Messenger Bot Spam, Remember Customers, and Answer Consistently! 🚀 Stops flood messages (no more annoying double replies or bot spam!) Detects if messages are from the user or the bot – so the bot never talks to itself Remembers chat history and delivers consistent, context-aware answers every time What You’ll Learn: How to connect Facebook Messenger to n8n with webhooks How to prevent flood messages using the debounce node How to recognise and filter user vs. bot messages How to store and retrieve chat history using Supabase How to use OpenAI + RAG (Retrieval-Augmented Generation) for accurate, professional replies How to make your bot remember user details (like name, phone, email, booking info) and never ask for the same thing twice Timestamp: 00:00 – Intro: Why Build a Smart Messenger Chatbot? What problems does this workflow solve? (bot flood, consistency, professional replies) 01:00 – How Facebook Messages Enter n8n (Webhook + Token Check) Overview of the webhook node and basic security verification 02:00 – Splitting & Structuring Incoming Messages Breaking up message batches, extracting sender/recipient/message details for processing 03:00 – Preventing Duplicate & Flood Messages (Debounce Node) How the workflow stops multiple replies, and what “debounce” means for Messenger bots 04:00 – Saving Every Message to Supabase for Memory & Context Why storing chat history matters; overview of the Supabase nodes for storage 05:00 – Recognising User vs. Bot Messages How the workflow distinguishes between real customer messages and bot replies, and why this matters 06:00 – Only Reply When Needed (No Bot-to-Bot Loops) Demo of logic that prevents endless bot loops and ensures clean user experience 07:00 – Retrieving and Merging Chat History for Consistency How the workflow checks what’s already been said before crafting a new reply 08:00 – Date & Time Detection in Conversations Parsing user messages like “tomorrow,” “next Monday,” and booking intent 08:45 – AI Agent: Combining Chat History, FAQ, and RAG for Answers How your OpenAI Agent works with RAG, checks the FAQ first, and uses memory for personalisation 10:00 – Business Logic: Deposit, Booking, and Escalation Policies How your workflow never asks for deposits twice, always applies booking rules, and escalates to a human if needed 11:30 – Sending Replies Back to Facebook Messenger Demo of the final reply node; showing seamless real-time replies 12:00 – Live Test: End-to-End Conversation Example Walk through a real conversation showing the workflow’s logic in action 13:00 – Recap, Tips, and How to Customise for Your Business What makes this chatbot “next level,” and how to tweak it for your own needs Why This Workflow Rocks: ✅ No More Double Replies: Even if Facebook sends duplicate messages or a user types fast, the bot only responds once. ✅ Smart Message Recognition: The workflow knows if the message is from the user or the bot, so it only replies when it should. ✅ Consistent & Professional Answers: By reading the chat history before every reply, the bot always knows the latest state—giving accurate, up-to-date answers your customers will love. ✅ Easy to Scale & Customise: All logic is visual in n8n, so you can add features or tweak business rules anytime. Subscribe for more AI and automation tutorials! with Australian Beauty Industrial #chatwoot #n8n #openai #chatbot #automation #customersupport #nocode #tutorial #marketingwithMrS