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

# CSV and QVD

> Turn CSV and Qlik QVD files on the backend host into SQL tables the agent can query

The **CSV** and **QVD** connectors expose data files as **SQL tables**. You point each connection at file paths or glob patterns, and every matching file becomes one table the agent can query with SQL, join, and aggregate.

<Tip>
  Use these when the file *is* a dataset. If you want the agent to search and read documents — PDFs, Word files, mixed folders — use [Files and Directories](/data-sources/connectors/files-and-directories) instead, which exposes files as a searchable catalog rather than as tables.
</Tip>

## How it works

Both connectors run on **DuckDB** inside Bag of words. Paths are resolved on the **backend host**, exactly like the Files and Directories connector, so a local folder or an already-mounted SMB/NFS share both work. There are no credential fields — access is whatever the operating system grants the backend process.

* **CSV** is read natively by DuckDB. There is no conversion or cache: each resolved file is exposed directly as a table at connect time. One file, one table.
* **QVD** is a proprietary binary format, so each file is converted to a columnar cache on the backend the first time it is indexed, then queried with SQL from there.

Glob patterns are expanded at connect time, so adding a file that matches an existing pattern makes it available on the next index without editing the connection.

## Before you start

* The files exist on the **Bag of words backend host** and are readable by the process that runs it.
* For a network share, mount it with the OS first. If Bag of words runs in a container, the mount must be visible inside the container.
* Give each file a stable, meaningful filename — the filename becomes the table name the agent sees.

***

## CSV

Go to **Data Sources → Add data source → CSV**.

| Field        | Required | Default   | Notes                                                                                               |
| :----------- | :------- | :-------- | :-------------------------------------------------------------------------------------------------- |
| `file_paths` | Yes      | —         | CSV file paths or glob patterns, one per line, e.g. `/data/*.csv` or `/mnt/exports/sales_2025.csv`. |
| `delimiter`  | No       | *(blank)* | Column delimiter. Leave blank to auto-detect. Examples: `,` `;` `&#124;`, or `\t` for tab.          |
| `has_header` | No       | `true`    | Whether the first row contains column names. Turn off for headerless files.                         |
| `encoding`   | No       | `utf-8`   | File encoding, e.g. `utf-8`, `latin-1`.                                                             |

The settings apply to **every** file in the connection. If some of your CSVs are semicolon-delimited and others comma-delimited, or they use different encodings, create a separate connection per group.

### Authentication

A single mode, **No Authentication** (`none`), at system scope. Nothing to enter.

***

## QVD

<Note>
  The QVD connector is part of the **Enterprise Edition**.
</Note>

Go to **Data Sources → Add data source → Qlik (QVD)**.

| Field        | Required | Default | Notes                                                              |
| :----------- | :------- | :------ | :----------------------------------------------------------------- |
| `file_paths` | Yes      | —       | QVD file paths or glob patterns, one per line, e.g. `/data/*.qvd`. |

QVD files carry their own field names and types, so there is nothing to configure beyond the paths.

<Note>
  Indexing a large QVD set takes longer than CSV, because each file is converted before it can be queried. The conversion is cached, so subsequent indexing only touches files that changed.
</Note>

### Authentication

A single mode, **No Authentication** (`none`), at system scope. Nothing to enter.

***

## Troubleshooting

<AccordionGroup>
  <Accordion title="No tables appear after saving">
    The glob matched nothing. Check that the path is absolute and correct **from the backend host's point of view** — a path that works on your laptop is not necessarily the path inside the container.
  </Accordion>

  <Accordion title="A CSV loads with one giant column, or the columns are shifted">
    Auto-detection picked the wrong delimiter. Set `delimiter` explicitly on the connection.
  </Accordion>

  <Accordion title="Column names look like data (col0, col1, …)">
    The file has no header row but `has_header` is on, or vice versa. Toggle `has_header`.
  </Accordion>

  <Accordion title="Accented or non-Latin characters are mangled">
    Set `encoding` to match the file — `latin-1` is the usual fix for exports from older Windows tools.
  </Accordion>
</AccordionGroup>

## Related

* [Files and Directories](/data-sources/connectors/files-and-directories) — the same paths, exposed as a searchable document catalog instead of tables.
* [Amazon S3](/data-sources/connectors/s3) — data files in object storage.