ImageFileToDocument

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

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
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/file_to_document.py

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