--- title: "CSVToDocument" id: csvtodocument slug: "/csvtodocument" description: "Converts CSV files to documents." --- # CSVToDocument Converts CSV files to documents.
| | | | --- | --- | | **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline | | **Mandatory run variables** | `sources`: A list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects | | **Output variables** | `documents`: A list of documents | | **API reference** | [Converters](/reference/converters-api) | | **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/csv.py | | **Package name** | `haystack-ai` |
## Overview `CSVToDocument` converts one or more CSV files into a text document. The component uses UTF-8 encoding by default, but you may specify a different encoding if needed during initialization. You can optionally attach metadata to each document with a `meta` parameter when running the component. ## Usage ### On its own ```python from haystack.components.converters.csv import CSVToDocument converter = CSVToDocument() results = converter.run( sources=["sample.csv"], meta={"date_added": datetime.now().isoformat()}, ) documents = results["documents"] print(documents[0].content) # 'col1,col2\now1,row1\nrow2row2\n' ``` ### In a pipeline ```python from haystack import Pipeline from haystack.document_stores.in_memory import InMemoryDocumentStore from haystack.components.converters import CSVToDocument from haystack.components.preprocessors import DocumentCleaner from haystack.components.preprocessors import DocumentSplitter from haystack.components.writers import DocumentWriter document_store = InMemoryDocumentStore() pipeline = Pipeline() pipeline.add_component("converter", CSVToDocument()) pipeline.add_component("cleaner", DocumentCleaner()) pipeline.add_component( "splitter", DocumentSplitter(split_by="sentence", split_length=5), ) pipeline.add_component("writer", DocumentWriter(document_store=document_store)) pipeline.connect("converter", "cleaner") pipeline.connect("cleaner", "splitter") pipeline.connect("splitter", "writer") pipeline.run({"converter": {"sources": file_names}}) ```