---
title: "ImageFileToDocument"
id: imagefiletodocument
slug: "/imagefiletodocument"
description: "Converts image file references into empty `Document` objects with associated metadata."
---

# ImageFileToDocument

Converts image file references into empty `Document` objects with associated metadata.

<div className="key-value-table">

|                                        |                                                                                                                             |
| -------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | Before a component that processes images, like `SentenceTransformersImageDocumentEmbedder` or `LLMDocumentContentExtractor` |
| **Mandatory run variables**            | `sources`: A list of image file paths or ByteStreams                                                                        |
| **Output variables**                   | `documents`: A list of empty Document objects with associated metadata                                                      |
| **API reference**                      | [Image Converters](/reference/image-converters-api)                                                                                |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/file_to_document.py                 |

</div>

## Overview

`ImageFileToDocument` converts image file sources into empty `Document` objects with associated metadata.

This component is useful in pipelines where image file paths need to be wrapped in `Document` objects to be processed by downstream components such as  `SentenceTransformersImageDocumentEmbedder` or `LLMDocumentContentExtractor`.

It _does not_ extract any content from the image files, but instead creates `Document` objects with `None` as their content and attaches metadata such as file path and any user-provided values.

Each source can be:

- A file path (string or `Path`), or
- A `ByteStream` object.

Optionally, you can provide metadata using the `meta` parameter. This can be a single dictionary (applied to all documents) or a list matching the length of `sources`.

## Usage

### On its own

This component is primarily meant to be used in pipelines.

```python

from haystack.components.converters.image import ImageFileToDocument

converter = ImageFileToDocument()

sources = ["image.jpg", "another_image.png"]

result = converter.run(sources=sources)
documents = result["documents"]

print(documents)

## [Document(id=..., content=None, meta={'file_path': 'image.jpg'}),
## Document(id=..., content=None, meta={'file_path': 'another_image.png'})]
```

### In a pipeline

In the following Pipeline, image documents are created using the `ImageFileToDocument` component, then they are enriched with image embeddings and saved in the Document Store.

```python
from haystack import Pipeline
from haystack.components.converters.image import ImageFileToDocument
from haystack.components.embedders.image import SentenceTransformersDocumentImageEmbedder
from haystack.components.writers.document_writer import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore

## Create our document store
doc_store = InMemoryDocumentStore()

## Define pipeline with components
indexing_pipe = Pipeline()
indexing_pipe.add_component("image_converter", ImageFileToDocument(store_full_path=True))
indexing_pipe.add_component("image_doc_embedder", SentenceTransformersDocumentImageEmbedder())
indexing_pipe.add_component("document_writer", DocumentWriter(doc_store))

indexing_pipe.connect("image_converter.documents", "image_doc_embedder.documents")
indexing_pipe.connect("image_doc_embedder.documents", "document_writer.documents")

indexing_result = indexing_pipe.run(
    data={"image_converter": {"sources": [
        "apple.jpg",
        "kiwi.png"
    ]}},
)

indexed_documents = doc_store.filter_documents()
print(f"Indexed {len(indexed_documents)} documents")
## Indexed 2 documents

```

## Additional References

🧑‍🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
