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76 lines
2.5 KiB
Plaintext
76 lines
2.5 KiB
Plaintext
---
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title: "CSVToDocument"
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id: csvtodocument
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slug: "/csvtodocument"
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description: "Converts CSV files to documents."
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---
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# CSVToDocument
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Converts CSV 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 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/csv.py |
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| **Package name** | `haystack-ai` |
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</div>
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## Overview
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`CSVToDocument` converts one or more CSV files into a text document.
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The component uses UTF-8 encoding by default, but you may specify a different encoding if needed during initialization.
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You can optionally attach metadata to each document with a `meta` parameter when running the component.
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## Usage
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### On its own
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```python
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from haystack.components.converters.csv import CSVToDocument
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converter = CSVToDocument()
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results = converter.run(
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sources=["sample.csv"],
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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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# 'col1,col2\now1,row1\nrow2row2\n'
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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 CSVToDocument
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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", CSVToDocument())
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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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