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You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it... show more

Santiago

452,863 subscribers

209,454 просмотров • 2 лет назад •via X (Twitter)

Anya Rossi• Live Now

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Комментарии: 10

Фото профиля Raul Junco

Raul Junco2 лет назад

A common business requirement is solved in a smart way.

Фото профиля Kris Lukanov

Kris Lukanov2 лет назад

That's super cool 😎

Фото профиля John

John2 лет назад

Looks very interesting, but I would like to know more about the actual connection to your DB. ie: does MindsDB have access to your actual login details for that db?

Фото профиля Juan Andrés Arriaga

Juan Andrés Arriaga2 лет назад

Kind of worried about security with this implementation. This is a great start but may not be useful with sensitive data

Фото профиля Rajasekar Nonburaj (𝑅𝒥)

Rajasekar Nonburaj (𝑅𝒥)2 лет назад

Super cool and remind me of sparksql.

Фото профиля Munsif

Munsif2 лет назад

Looks great

Фото профиля Yippee Ki Yay

Yippee Ki Yay2 лет назад

what about SQLite

Фото профиля Marcus Gill Greenwood

Marcus Gill Greenwood2 лет назад

please tell me it caches! 😬

Фото профиля Luke Skyward

Luke Skyward2 лет назад

Smart!

Фото профиля Yasar Arafath

Yasar Arafath2 лет назад

@jaleeledathol

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$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

39,331 просмотров • 5 месяцев назад

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Santiago

20,785 просмотров • 2 месяцев назад

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Santiago

110,133 просмотров • 1 год назад

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Santiago

142,696 просмотров • 1 год назад

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Santiago

44,842 просмотров • 1 год назад

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Santiago

108,835 просмотров • 2 лет назад

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Santiago

72,306 просмотров • 2 лет назад

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Santiago

78,133 просмотров • 1 год назад

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Tyler Shukert

26,500 просмотров • 3 месяцев назад

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Santiago

44,783 просмотров • 1 год назад

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Santiago

230,503 просмотров • 1 год назад

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Mark Gadala-Maria

79,177 просмотров • 2 лет назад

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70,812 просмотров • 10 месяцев назад

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UI x AI by Christabel

23,058 просмотров • 1 месяц назад

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Mr Farman Ai

87,363 просмотров • 26 дней назад

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The Food You Eat Is Fake, Here’s Some Proof “Fake Fruits In Walmart GMO Is Killing Us” 🥭 ☣️🚫 “This is a mango and it looks fake, it feels fake like I'm beyond pissed and this is like look at this. This is GMO. I'm gonna give it a big no. Like I’m beyond pissed… like look at this this is GMO” “Exactly and this is why I say none of you are vegetarians none of you are vegans none of you are eating real food I'm gonna show it to you here now look this is an organic pair but on the pair you see the label right you see these labels they put on it says trust well can we really trust it now if you look at the number there if you can see it on the live can you see it all right if you look at the number there is designated by a 9, 8 or 4 if it's a 4 it's pesticides right so it's conventionally raised which means it's on a regular farm there's no real regulation they can do whatever they need to do to grow that fruit” This is a long video with a lot of great information, I can’t transcribe it all due to X’s text limits but it’s worth a watch

Wall Street Apes

404,666 просмотров • 2 лет назад