Execute. Automate. Analyze
Chat is just one of the many things LLMs can do...
They can act and execute.
In this first of a 5 part series, I show a demo, architecture and process tracing. Next 4 are all detailed hands-on implementation guides.
𝐕𝐓𝐄𝐗𝐄𝐑: 𝐕𝐨𝐢𝐜𝐞-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐋𝐋𝐌 𝐀𝐜𝐭𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭 𝐀𝐩𝐩 𝐟𝐨𝐫 𝐓𝐚𝐬𝐤 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 Automation Query and Research. I demonstrate how you can, with natural language voice instructions:
- Update Excel Sheet
- Update Google Sheet
- Update tables in remote Data Warehouses
- Generate and email print formatted report
- Generate and email a slide format
- Query MySQL database, including updating and modifying data
- And few additional things shown in the demo.
And yes, it can also chat.
𝐓𝐡𝐞 𝐚𝐩𝐩 𝐮𝐬𝐞𝐬 𝐋𝐋𝐌 𝐑𝐞𝐀𝐜𝐭 𝐀𝐠𝐞𝐧𝐭𝐬, Reasoning and Action Agents. Called as such since they can execute tasks via API calls, also called Function Calls / Tool Calling. Fairly easy to setup in Flowise. FlowiseAI (YC S23) has a marvelous functionality to access an agent / chat flow via an API endpoint. Allows you to fully customize the UI and response flow. Using that extensively here (glad i remembered it from Van Zyl's videos).
The app has a React.js frontend, with Flowise providing complete middle layer of LLM Agents and ChatFlows. Backend of automation workflows in Make.com, automation scripts in Google Apps Script and database connectivity via a custom-built FastAPI server.
𝐀𝐋𝐋 𝐜𝐨𝐝𝐢𝐧𝐠 𝐝𝐨𝐧𝐞 𝐛𝐲 𝐆𝐏𝐓, 𝐋𝐋𝐌𝐬 & 𝐀𝐈 𝐀𝐬𝐬𝐢𝐭𝐞𝐝 𝐂𝐨𝐝𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬. For Google Scripts and Python I like GPT-4o. For React in general it is Claude Sonnet. For React.js apps like this one, my current favorites are Claude Dev, a VS Code extension and Cursor AI IDE, a VS Code fork. Claude Dev works on Github Codespaces also... I have a bit of preference for cloud over local. Both are able to create new files as well as modify files across whole codebase. Claude Dev even has terminal access, so does pretty much everything.
In this video:
1. Demo
2. Architecture Overview
3. Tracing of Agent Flow - Component Level
4. Implementation Guide - process steps
The total video content for the implementation guide is more than 2 hours. I will be publishing it in 4 Parts over the coming week or so. Along with the GitHub Repo, Codes, Schemas, Blueprints, etc. Free to use.
Part 1 : Demo / Architecture / Agent Process Tracing (this one)
Part 2 : ReAct LLM Agent in Flowise AI and Make.com workflow
Part 3 : Setup Google App Scripts and connect to Make.com
Part 4 : Custom Frontend with AI-Assisted Tools. From develop to live deploy.
Part 5 : Integrate rest of ReAct Agents & Chatflows in Flowise. Deploy to Vercel. Go Live