> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentset.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Tabular Data

> Ingest spreadsheets, CSVs, and tables embedded in documents

Agentset processes tabular data so that rows stay intact and headers remain associated with their values. This makes spreadsheets, CSVs, and tables embedded in documents searchable without losing structure.

## Formats

Agentset detects tabular data in two ways:

| Method                  | Description                                                                                     |
| :---------------------- | :---------------------------------------------------------------------------------------------- |
| **Spreadsheet files**   | Upload CSV, Excel (`.xls`, `.xlsx`), or OpenDocument (`.ods`) files directly.                   |
| **Tables in documents** | Tables embedded in PDFs, Word docs, and presentations are automatically detected and extracted. |

Both methods preserve table structure automatically—no configuration required.

## Why table-aware processing matters

Standard text chunking breaks tables at arbitrary points, separating headers from their data and splitting rows mid-content. This destroys the relationships that make tabular data meaningful.

Consider a product inventory table:

| Product        | SKU    | Price   | Stock |
| :------------- | :----- | :------ | :---- |
| Wireless Mouse | WM-001 | \$29.99 | 150   |
| USB Keyboard   | KB-002 | \$49.99 | 89    |
| Monitor Stand  | MS-003 | \$79.99 | 34    |
| Webcam HD      | WC-004 | \$89.99 | 67    |
| USB Hub        | UH-005 | \$24.99 | 203   |
| Laptop Stand   | LS-006 | \$59.99 | 56    |

**Without table-aware processing**, a chunk boundary might fall between rows 3 and 4. Headers are lost from the second chunk.

```text theme={null}
Chunk 1:
"Product SKU Price Stock Wireless Mouse WM-001 $29.99 150 USB Keyboard
KB-002 $49.99 89 Monitor Stand MS-003 $79.99"

Chunk 2:
"34 Webcam HD WC-004 $89.99 67 USB Hub UH-005 $24.99 203 Laptop Stand
LS-006 $59.99 56"
```

**With Agentset**, chunks are generated as a markdown table, always containing the full header and are not split mid-row.

```text theme={null}
Chunk 1:
| Product        | SKU    | Price  | Stock |
|----------------|--------|--------|-------|
| Wireless Mouse | WM-001 | $29.99 | 150   |
| USB Keyboard   | KB-002 | $49.99 | 89    |
| Monitor Stand  | MS-003 | $79.99 | 34    |

Chunk 2:
| Product       | SKU    | Price  | Stock |
|---------------|--------|--------|-------|
| Webcam HD     | WC-004 | $89.99 | 67    |
| USB Hub       | UH-005 | $24.99 | 203   |
| Laptop Stand  | LS-006 | $59.99 | 56    |
```

A search for "laptop accessories under \$50" can now match relevant rows because each row contains the full context needed for retrieval.

## Upload a spreadsheet

<CodeGroup>
  ```typescript TypeScript theme={null}
  import { Agentset } from "agentset";

  const agentset = new Agentset({
    apiKey: process.env.AGENTSET_API_KEY,
  });

  const ns = agentset.namespace("YOUR_NAMESPACE_ID");

  const job = await ns.ingestion.create({
    payload: {
      type: "FILE",
      fileUrl: "https://example.com/inventory.csv",
    },
  });

  console.log(`Upload started: ${job.id}`);
  ```

  ```python Python theme={null}
  import os
  from agentset import Agentset

  client = Agentset(
      namespace_id="YOUR_NAMESPACE_ID",
      token=os.environ["AGENTSET_API_KEY"],
  )

  job = client.ingest_jobs.create(
      payload={
          "type": "FILE",
          "fileUrl": "https://example.com/inventory.csv",
      }
  )

  print(f"Upload started: {job.data.id}")
  ```
</CodeGroup>

Tables embedded in PDFs, Word documents, and presentations are processed the same way—upload the file and Agentset handles the rest. See [File Uploads](/data-ingestion/file-uploads) for uploading local files and other options.

<Info>
  Tables are automatically detected and processed. No additional configuration is required.
</Info>

## Next steps

* [API Reference](/api-reference/endpoint/ingest-jobs/create) — Tabular data ingestion parameters
* [File Uploads](/data-ingestion/file-uploads) — Learn about supported file types and upload methods
* [Document Metadata](/data-ingestion/document-metadata) — Attach metadata for filtering
* [Search](/search-and-retrieval/search) — Query your tabular content