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Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens...

77,293 次观看 • 1 年前 •via X (Twitter)

12 条评论

🔥 Matt Dancho (Business Science) 🔥 的头像
🔥 Matt Dancho (Business Science) 🔥1 年前

P.S. Want free AI, Machine Learning, and Data Science Tips with Python code every Sunday? Don't forget to sign up for my AI/ML Tips Newsletter Here:

🔮 metaschool 的头像
🔮 metaschool1 年前

How to build an AI agent: 1. Buy this AI Agent Template ($29) 2. Replace "your-data-here" 3. Ship it.

Adam Lawson 的头像
Adam Lawson1 年前

Could this be used for modernization efforts? Say sql SSRS/SSIS to pipelines for a modern data warehouse? Medallion architecture etc

🔥 Matt Dancho (Business Science) 🔥 的头像
🔥 Matt Dancho (Business Science) 🔥1 年前

I’m sure it could. As long as the schema and tables are provided then it should work.

StockNTricks 🥷 YAHUWAH-IS-All 🍄 的头像
StockNTricks 🥷 YAHUWAH-IS-All 🍄1 年前

this is so cool, ive been waiting for a tool like this. Thank you

🔥 Matt Dancho (Business Science) 🔥 的头像
🔥 Matt Dancho (Business Science) 🔥1 年前

Thank you !

Edwina aka ALF 的头像
Edwina aka ALF1 年前

@88choppertime Isn’t it the year of the Snake? 🐍

🔥 Matt Dancho (Business Science) 🔥 的头像
🔥 Matt Dancho (Business Science) 🔥1 年前

@88choppertime Year of the bot 🤖

jaykee 的头像
jaykee1 年前

good

Yaser Abbass 的头像
Yaser Abbass1 年前

Automating SQL with AI can save countless hours. Impressive!

Dr.Yev🔥 的头像
Dr.Yev🔥1 年前

the SQL part you think more for beginners to figure out or for advanced data scientists to leverage better with AI?

🔥 Matt Dancho (Business Science) 🔥 的头像
🔥 Matt Dancho (Business Science) 🔥1 年前

Both. With advances in LLM, sql will be nothing for an AI as long as it has good knowledge of the database (schema, tables, etc). It’s extremely powerful even for tricky SQL. And you can stack it with a data wrangler AI (pandas agent) that can do stuff SQL can’t handle. So yeah, future is bright for this kinda thing.

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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 7 个月前