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74 lines
3.0 KiB
Plaintext
74 lines
3.0 KiB
Plaintext
---
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title: "TextFileToDocument"
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id: textfiletodocument
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slug: "/textfiletodocument"
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description: "Converts text files to documents."
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---
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# TextFileToDocument
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Converts text files to documents.
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<div className="key-value-table">
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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`: A list of paths to text files you want to convert |
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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/txt.py |
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| **Package name** | `haystack-ai` |
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</div>
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## Overview
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The `TextFileToDocument` component converts text files into documents. You can use it in an indexing pipeline to index the contents of text files into 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 you initialize the component, you can optionally set the default encoding of the text files through the `encoding` parameter. If you don't provide any value, the component uses `"utf-8"` by default. Note that if the encoding is specified in the metadata of an input ByteStream, it will override this parameter's setting.
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## Usage
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### On its own
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```python
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from pathlib import Path
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from haystack.components.converters import TextFileToDocument
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converter = TextFileToDocument()
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docs = converter.run(sources=[Path("my_file.txt")])
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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 TextFileToDocument
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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", TextFileToDocument())
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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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## Additional References
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:notebook: Tutorial: [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)
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