---
title: "DocumentWriter"
id: documentwriter
slug: "/documentwriter"
description: "Use this component to write documents into a Document Store of your choice."
---

# DocumentWriter

Use this component to write documents into a Document Store of your choice.

|                                        |                                                                                                   |
| :------------------------------------- | :------------------------------------------------------------------------------------------------ |
| **Most common position in a pipeline** | As the last component in an indexing pipeline                                                     |
| **Mandatory init variables**           | "document_store": A Document Store instance                                                       |
| **Mandatory run variables**            | "documents": A list of documents                                                                  |
| **Output variables**                   | "documents_written": The number of documents written (integer)                                    |
| **API reference**                      | [Document Writers](/reference/document-writers-api)                                                      |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/writers/document_writer.py |

## Overview

`DocumentWriter` writes a list of documents into a Document Store of your choice. It’s typically used in an indexing pipeline as the final step after preprocessing documents and creating their embeddings.

To use this component with a specific file type, make sure you use the correct [Converter](../converters.mdx) before it. For example, to use `DocumentWriter` with Markdown files, use the `MarkdownToDocument` component before `DocumentWriter` in your indexing pipeline.

### DuplicatePolicy

The `DuplicatePolicy` is a class that defines the different options for handling documents with the same ID in a `DocumentStore`. It has four possible values:

- **NONE**: The default policy that relies on Document Store settings.
- **OVERWRITE**: Indicates that if a document with the same ID already exists in the `DocumentStore`, it should be overwritten with the new document.
- **SKIP**: If a document with the same ID already exists, the new document will be skipped and not added to the `DocumentStore`.
- **FAIL**: Raises an error if a document with the same ID already exists in the `DocumentStore`. It prevents duplicate documents from being added.

## Usage

### On its own

Below is an example of how to write two documents into an `InMemoryDocumentStore`:

```python
from haystack import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.writers import DocumentWriter

documents = [
    Document(content="This is document 1"),
    Document(content="This is document 2"),
]

document_store = InMemoryDocumentStore()
document_writer = DocumentWriter(document_store=document_store)
document_writer.run(documents=documents)
```

### In a pipeline

Below is an example of an indexing pipeline that first uses the `SentenceTransformersDocumentEmbedder` to create embeddings of documents and then use the `DocumentWriter` to write the documents to an `InMemoryDocumentStore`:

```python
from haystack.pipeline import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.writers import DocumentWriter

documents = [
    Document(content="This is document 1"),
    Document(content="This is document 2"),
]

document_store = InMemoryDocumentStore()
embedder = SentenceTransformersDocumentEmbedder()
document_writer = DocumentWriter(
    document_store=document_store,
    policy=DuplicatePolicy.NONE,
)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component(instance=embedder, name="embedder")
indexing_pipeline.add_component(instance=document_writer, name="writer")

indexing_pipeline.connect("embedder", "writer")
indexing_pipeline.run({"embedder": {"documents": documents}})
```
