Summarize:
Please provide the content you would like summarized.
The Complete n8n AI Agent Workflow for Financial Data – FREE (Avoid Using RAG for Sheets & CSV!)
#Ultimate #n8n #Agent #Workflow #Financial #Data #FREE #Dont #RAG #Sheets #CSV
🔥 Join The AI Forge Community 👇
https://www.skool.com/the-ai-forge
n8n: https://aibuilders.short.gy/n8n
Workflow: https://leonardo467.gumroad.com/l/qnako
In this video, I’m walking you through a workflow that takes data from a Google Sheet and lets an AI chatbot answer questions using SQL. A lot of solutions out there rely on vector databases, which work great for some things but aren’t ideal for numerical queries. Instead, I’m using PostgreSQL to store the data and generate SQL queries dynamically.
Now, just to be clear—this isn’t about saying other approaches are bad. A lot of tutorials show methods that work in different contexts, but for structured data like this, I wanted something more efficient and scalable. That said, this workflow is still a work in progress, and there’s definitely room for improvement, especially when it comes to updating the database properly instead of just recreating it every time.
I’ll go over the full setup, test out the SQL agent, and talk about system prompting, which plays a huge role in making AI generate better queries. If you’re interested in trying this out or improving it, all the details are in the video.
Drop any questions in the comments, or check out the community if you need help setting it up!
Article: https://pear-catboat-4f2.notion.site/Building-a-Data-Visualization-Agent-with-LangGraph-Cloud-3cd220670e40419d9f5cf961eb160514
🔥 Scrape with Firecrawl : https://firecrawl.link/leo
timestamps:
00:00 – Introduction
01:30 – Don’t do this
04:08 – Solution
06:38 – Testing the sql Agent
07:07 – The tools
07:36 – How the database is updated (improve this!)
08:51 – System prompting resource
09:12 – Workflow Support
Click here to learn more about this YouTuber
