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105 lines
3.9 KiB
Plaintext
105 lines
3.9 KiB
Plaintext
---
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title: "ImageFileToDocument"
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id: imagefiletodocument
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slug: "/imagefiletodocument"
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description: "Converts image file references into empty `Document` objects with associated metadata."
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---
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# ImageFileToDocument
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Converts image file references into empty `Document` objects with associated metadata.
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<div className="key-value-table">
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| --- | --- |
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| **Most common position in a pipeline** | Before a component that processes images, like `SentenceTransformersImageDocumentEmbedder` or `LLMDocumentContentExtractor` |
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| **Mandatory run variables** | `sources`: A list of image file paths or ByteStreams |
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| **Output variables** | `documents`: A list of empty Document objects with associated metadata |
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| **API reference** | [Image Converters](/reference/image-converters-api) |
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| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/file_to_document.py |
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</div>
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## Overview
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`ImageFileToDocument` converts image file sources into empty `Document` objects with associated metadata.
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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`.
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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.
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Each source can be:
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- A file path (string or `Path`), or
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- A `ByteStream` object.
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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`.
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## Usage
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### On its own
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This component is primarily meant to be used in pipelines.
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```python
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from haystack.components.converters.image import ImageFileToDocument
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converter = ImageFileToDocument()
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sources = ["image.jpg", "another_image.png"]
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result = converter.run(sources=sources)
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documents = result["documents"]
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print(documents)
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## [Document(id=..., content=None, meta={'file_path': 'image.jpg'}),
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## Document(id=..., content=None, meta={'file_path': 'another_image.png'})]
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```
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### In a pipeline
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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.
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```python
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from haystack import Pipeline
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from haystack.components.converters.image import ImageFileToDocument
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from haystack.components.embedders.image import (
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SentenceTransformersDocumentImageEmbedder,
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)
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from haystack.components.writers.document_writer import DocumentWriter
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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## Create our document store
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doc_store = InMemoryDocumentStore()
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## Define pipeline with components
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indexing_pipe = Pipeline()
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indexing_pipe.add_component(
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"image_converter",
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ImageFileToDocument(store_full_path=True),
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)
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indexing_pipe.add_component(
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"image_doc_embedder",
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SentenceTransformersDocumentImageEmbedder(),
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)
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indexing_pipe.add_component("document_writer", DocumentWriter(doc_store))
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indexing_pipe.connect("image_converter.documents", "image_doc_embedder.documents")
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indexing_pipe.connect("image_doc_embedder.documents", "document_writer.documents")
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indexing_result = indexing_pipe.run(
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data={"image_converter": {"sources": ["apple.jpg", "kiwi.png"]}},
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)
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indexed_documents = doc_store.filter_documents()
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print(f"Indexed {len(indexed_documents)} documents")
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## Indexed 2 documents
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```
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## Additional References
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🧑🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
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