deepset-ai--haystack/docs-website/docs/concepts/data-classes/filecontent.mdx

---
title: "FileContent"
id: filecontent
slug: "/filecontent"
description: "`FileContent` represents file payloads in chat messages, including base64 data, MIME type, filename, and provider-specific metadata."
---

# FileContent

`FileContent` represents a file payload that can be attached to a [`ChatMessage`](chatmessage.mdx). Use it when a chat model accepts file inputs, such as PDFs or other documents, together with the user's text prompt.

If you need the full list of parameters and methods, see the [`FileContent` API reference](/reference/data-classes-api#filecontent).

## Attributes

```python
@dataclass
class FileContent:
    base64_data: str
    mime_type: str | None = None
    filename: str | None = None
    extra: dict[str, Any] = field(default_factory=dict)
    validation: bool = True
```

- `base64_data` stores the file content as a base64-encoded string.
- `mime_type` identifies the file type, for example `application/pdf`. Providing it explicitly is recommended because many model providers require it.
- `filename` is optional, but some providers use it when processing uploaded files.
- `extra` can store provider-specific metadata. Values should be JSON serializable.
- `validation` checks that `base64_data` is valid and tries to infer the MIME type when one is not provided.

## Create from a file path

Use `from_file_path` to read a local file, base64-encode it, infer the MIME type from the path, and populate the filename.

```python
from haystack.dataclasses import ChatMessage, FileContent

file_content = FileContent.from_file_path("data/attention-is-all-you-need.pdf")

message = ChatMessage.from_user(
    content_parts=[
        file_content,
        "Summarize the key ideas in this paper.",
    ]
)
```

Pass `filename` or `extra` when a provider expects a specific filename or provider-specific options:

```python
file_content = FileContent.from_file_path(
    "data/report.pdf",
    filename="quarterly-report.pdf",
    extra={"source": "finance"},
)
```

## Create from a URL

Use `from_url` to download a file and convert it into a `FileContent` instance.

```python
from haystack.dataclasses import FileContent

file_content = FileContent.from_url(
    "https://example.com/reports/quarterly-report.pdf",
    timeout=30,
)
```

If no filename is provided, Haystack uses the final path segment of the URL.

## Create from base64 data

If you already have file bytes, encode them and pass the MIME type explicitly.

```python
import base64
from pathlib import Path

from haystack.dataclasses import FileContent

data = Path("data/manual.pdf").read_bytes()
file_content = FileContent(
    base64_data=base64.b64encode(data).decode("utf-8"),
    mime_type="application/pdf",
    filename="manual.pdf",
)
```

Set `validation=False` only when the base64 data and MIME type are already trusted and you want to skip validation.

## Inspect files in a ChatMessage

After adding `FileContent` to a `ChatMessage`, use the `file` and `files` properties to access file payloads.

```python
from haystack.dataclasses import ChatMessage, FileContent

file_content = FileContent.from_file_path("data/invoice.pdf")
message = ChatMessage.from_user(content_parts=[file_content, "Extract the invoice total."])

print(message.file)
print(message.files)
```

`message.file` returns the first file payload, or `None` if there are no files. `message.files` returns all file payloads.

## Serialization

Use `to_dict` and `from_dict` to serialize and restore file content.

```python
payload = file_content.to_dict()
restored = FileContent.from_dict(payload)
```

For tracing, Haystack replaces the full base64 payload with a placeholder so large files are not sent to the tracing backend.