Best approach to build AI agent from fixed-schema external CSV (3000+ rows)

[Fjmtrigo]

Hi @Alex_MindStudio

I’m building an AI agent to analyse a CSV file (≈ 3000 rows, 40 fixed fields), hosted at a publicly accessible URL. I need to build an AI-driven data-analysis bot that:

• reads the CSV directly from the URL;

• treats this CSV as its sole knowledge base;

• supports queries: filters, aggregations, comment-level retrieval, comparisons between fields;

• returns structured results;

• presents outputs via clean UI (Custom Interface / Dynamic HTML / Generate Asset), not raw workflow blocks.

My questions:

1. Is the packaged “Analyze CSV / Analyze Data” action the recommended solution for this use case?

2. Does it support external CSV loading from URL (not needing manual upload)?

3. Can it handle datasets of this size and return filtered subsets (e.g. all comments with sentiment = “Negative” and Department = “X”)?

4. What is the best way to build a clean user-facing UI (chatBot) so that end users don’t see the internal workflow execution steps?

5. Do you have a sample/remix project demonstrating this setup?

Thanks in advance for any guidance.

[Alex_MindStudio]

Hi @Fjmtrigo,

Here’s how your setup could look like:

1. Extract Text from File / HTTP Request / Custom Function: to fetch the CSV
2. Generate Asset with a SPA Bundle if you want an app-style interface where users can stay in one view to send queries and see results
3. Analyze CSV to process queries and return filtered results

You can also replace the Analyze CSV block with your own Custom Function if your setup requires something more specific.

@Fjmtrigo

Is the packaged “Analyze CSV / Analyze Data” action the recommended solution for this use case?

The Analyze CSV block is a good starting point for your use case

@Fjmtrigo

Does it support external CSV loading from URL (not needing manual upload)?

The block requires a string input, so you’ll need to fetch the CSV first using Extract Text from File, HTTP Request, or a Custom Function, then pass the string into the block

@Fjmtrigo

Can it handle datasets of this size and return filtered subsets (e.g. all comments with sentiment = “Negative” and Department = “X”)?

Yes, it should be able to handle datasets of that size and return subsets like the ones you described

@Fjmtrigo

What is the best way to build a clean user-facing UI (chatBot) so that end users don’t see the internal workflow execution steps?

The Generate Asset block with a SPA Bundle is likely the best option because it lets you build your own React interface that would look and work like an app

Alternatively, you could use a Custom Interface with the new minimalistic animation while the agent runs:

MindStudio: New Minimal Runtime Notifications

@Fjmtrigo

Do you have a sample/remix project demonstrating this setup?

We don’t have a template for this yet, but I’d love to see what you build!