deepset-ai--haystack/docs-website/docs/pipeline-components/converters/imagefiletodocument.mdx

---
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                 |
| **Package name** | `haystack-ai` |

</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.

The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:

```shell
pip install sentence-transformers-haystack
```

```python
from haystack import Pipeline
from haystack.components.converters.image import ImageFileToDocument
from haystack_integrations.components.embedders.sentence_transformers 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)