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84 lines
3.1 KiB
Plaintext
84 lines
3.1 KiB
Plaintext
---
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title: "PDFMinerToDocument"
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id: pdfminertodocument
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slug: "/pdfminertodocument"
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description: "A component that converts complex PDF files to documents using pdfminer arguments."
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---
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# PDFMinerToDocument
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A component that converts complex PDF files to documents using pdfminer arguments.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
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| **Mandatory run variables** | `sources`: PDF file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
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| **Output variables** | `documents`: A list of documents |
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| **API reference** | [Converters](/reference/converters-api) |
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| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/pdfminer.py |
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| **Package name** | `haystack-ai` |
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</div>
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## Overview
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The `PDFMinerToDocument` component converts PDF files into documents using [PDFMiner](https://pdfminersix.readthedocs.io/en/latest/) extraction tool arguments.
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You can use it in an indexing pipeline to index the contents of a PDF file in a Document Store. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream)objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
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When initializing the component, you can adjust several parameters to fit your PDF. See the full parameter list and descriptions in our [API reference](/reference/converters-api#pdfminertodocument).
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## Usage
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First, install `pdfminer` package to start using this converter:
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```shell
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pip install pdfminer.six
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```
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### On its own
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```python
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from haystack.components.converters import PDFMinerToDocument
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converter = PDFMinerToDocument()
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results = converter.run(
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sources=["sample.pdf"],
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meta={"date_added": datetime.now().isoformat()},
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)
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documents = results["documents"]
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print(documents[0].content)
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# 'This is a text from the PDF file.'
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```
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### In a pipeline
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```python
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from haystack import Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.converters import PDFMinerToDocument
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from haystack.components.preprocessors import DocumentCleaner
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from haystack.components.preprocessors import DocumentSplitter
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from haystack.components.writers import DocumentWriter
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document_store = InMemoryDocumentStore()
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pipeline = Pipeline()
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pipeline.add_component("converter", PDFMinerToDocument())
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pipeline.add_component("cleaner", DocumentCleaner())
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pipeline.add_component(
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"splitter",
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DocumentSplitter(split_by="sentence", split_length=5),
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)
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pipeline.add_component("writer", DocumentWriter(document_store=document_store))
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pipeline.connect("converter", "cleaner")
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pipeline.connect("cleaner", "splitter")
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pipeline.connect("splitter", "writer")
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pipeline.run({"converter": {"sources": file_names}})
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```
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