EmilySun621 opened a new pull request, #5119:
URL: https://github.com/apache/texera/pull/5119
> Paste a URL. Drop a file. Open a SQLite database. Ask the AI agent. **Four
new import paths, zero manual download.**
---
## What's New
**🔗 URL Import** — Paste any CSV/JSON URL on the Datasets page, click
Import. Server-side fetch, auto-format detection.
**📁 Local File Drop** — Drag & drop CSV, JSON, XLSX, TSV, SQLite directly
onto the Datasets page.
**🗄️ SQLite Import** — Drop a .sqlite file → pick tables from a list → each
table becomes a dataset. Uses Bun's built-in `bun:sqlite`, no external
dependencies.
**⚡ REST API Agent Tool** — `fetch_api_data` tool lets the AI agent fetch
from any API endpoint. Auto-flattens nested JSON to tabular format.
---
## How It Works
```
Frontend (Datasets page) Agent Service (port 3001) Texera
┌─────────────────────┐ ┌─────────────────────────┐
┌──────────┐
│ URL input ──────────┼────→ │ POST /fetch-url │─────→│ Dataset
│
│ File drop zone ─────┼────→ │ POST /sqlite-tables │ │ Creation
│
│ Agent chat ─────────┼────→ │ POST /sqlite-export │ │ API
│
└─────────────────────┘ │ Tool: fetch_api_data │
└──────────┘
└─────────────────────────┘
```
---
## Verified
```bash
$ curl -X POST localhost:3001/api/data-source/fetch-url \
-H "Content-Type: application/json" \
-d
'{"url":"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"}'
→ {"rows":150, "columns":["5.1","3.5","1.4","0.2","Iris-setosa"],
"format":"csv"} ✅
```
---
## Files Changed
**New:** `agent-service/src/api/data-source-api.ts` (3 endpoints),
`data-source-tools.ts` (agent tool)
**Modified:** `user-dataset.component.*` (URL input + drop zone),
`dataset.service.ts` (fetch methods), `proxy.config.json`,
`DatasetSearchQueryBuilder.scala` (fix: new datasets now appear in list
immediately)
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