mirror of
https://github.com/deepset-ai/haystack.git
synced 2026-02-09 08:53:27 +00:00
101 lines
3.9 KiB
Plaintext
101 lines
3.9 KiB
Plaintext
---
|
|
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)
|