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Ever wondered how to integrate AI into your business for seamless automation?
In this video, I walk you through building the backend automation for an AI chatbot using RAG (Retrieval-Augmented Generation) technology. From creating automated responses to processing and retrieving data, this tutorial covers all the steps to build a functional chatbot. Learn how tools like Gemini AI, DeepSeek, and Pinecone can simplify complex data management, making chatbots smarter and faster. Don't miss out on the future of AI-driven efficiency!
Module Code Snippets:
Sanitize Data 🧹:
{{replace(replace(replace(replace(replace(replace(replace(4.result; "/\n/g"; space); "/\r/g"; space); "/\t/g"; space); "/\f/g"; space); "/\//g"; "/"); "/\\/g"; "\\"); "/""/g"; "\""")}}
Embeddings 🦾:
{
"input": "{{5.`JSON String`}}",
"model": "text-embedding-3-small",
"encoding_format": "float"
}
Here's The Challenge with Traditional AI 🪖
Ever feel like ChatGPT can’t handle big data? That’s because, OpenAI Assistants are great for small datasets (less than 25 pages) but struggle with complex searches, scalability, and staying updated.
What Are RAG & Vectors? 🔢
Let’s say you have 10 thousand documents. Instead of searching through all of them, imagine a vast index library that AI assigns a unique "address" (a series of numbers) called a vector.
When you ask a question, the AI transforms it into a vector, compares it to the index addresses, and opens only the most relevant ones. It’s like having a laser-focused assistant that always knows where to look.
Why This Is a Game-Changer 🤯
RAG (Retrieval-Augmented Generation) paired with vectors makes AI smarter and faster by:
↳ Scaling to Billions: Chat with enormous datasets seamlessly.
↳ Precision Searches: Only retrieve what’s relevant.
↳ Automatic Updates: Add new knowledge effortlessly.
While traditional systems like ChatGPT would search every file (painfully slow), RAG works smarter—not harder—delivering accurate answers faster.
Curious to learn how this works in the real world?
Check out the video and discover how RAG + Vectors can revolutionize your AI-powered data retrieval! 🚀
🎬 Chapters:
00:00 Introduction to Chatbot Backend Automation
00:37 Demo: Chatbot Interaction with Mario's Pizza
01:22 Overview of Automation Workflow
04:20 Data Import Automation Build in Make
08:21 Data Processing with Gemini AI
12:42 Scrub & Sanitize Data before Import
14:00 Overview of OpenAI Embeddings
18:41 Pinecone/RAG Upsert Vector
28:03 Chatbot Automation Build in Make triggered by Webhoo
31:19 OpenRouter & DeepSeek
41:26 Aggregate & Iterate the Responses
43:59 Summarize responses w/ DeepSeek
46:12 Send Webhook Response to Chatbot portal
46:53 Conclusion and Resources
#AIChatbots #Automation #PineconeAI #GeminiAI #RAGTechnology