> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/app/setting-up-your-database/file-connector/file-connector-file-content-types.md).

# Supported file and content types

A file group can contain files that contain CSV, XML, JSON, Parquet, or Avro content.

By default, the file connector assumes that files use UTF-8 encoding. In your file groups, you can [specify the type of encoding that the files use](/app/setting-up-your-database/file-connector/file-connector-file-groups.md#file-group-csv-encoding).

## Allowed file types <a href="#supported-file-extensions" id="supported-file-extensions"></a>

Files can include the following types:&#x20;

* .csv, .tsv, .xml, .json, .parquet, and .avro files.
* .txt files that contain CSV, XML, or JSON content.
* .gzip files that contain compressed CSV, XML, or JSON content.\
  \
  .gzip files are only supported in workspaces that use files from cloud storage. They are not supported in workspaces that use local files.

## Parquet file limitations <a href="#supported-files-parquet-limitations" id="supported-files-parquet-limitations"></a>

For Parquet files, you cannot select files with the Variant Parquet data type.

## Avro file limitations <a href="#supported-files-avro-limitations" id="supported-files-avro-limitations"></a>

For Avro files, you cannot select files with the following Avro data types:

* Map
* Record

Structural only supports Snappy and Zstandard compression. To request support for additional compression types, contact support.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.tonic.ai/app/setting-up-your-database/file-connector/file-connector-file-content-types.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
