# Use Unstructured to Process Documents

Convert files to documents using the Unstructured API.

***

Unstructured provides tools to extract content from files and transform it into clean documents ready to be chunked and embedded. For a list of supported formats, see [Unstructured documentation](https://docs.unstructured.io/api-reference/api-services/overview#supported-file-types). You can use free Unstructured API or paid Unstructured Serverless API.

## Prerequisites

You need an API key to your Unstructured account.

## Use Unstructured

First, connect <ProductName /> to Unstructured through the Integrations page. You can set up a connection for a single workspace or for the whole organization:

<AddIntegration />

Then, add the `UnstructuredFileConverter` component to your index. 

## Usage Examples

This is an example of an index that uses Unstructured API to process files:

```yaml Index
components:
  ...
  unstructured_converter:
    type: haystack_integrations.components.converters.unstructured.converter.UnstructuredFileConverter
    init_parameters: {}

  splitter:
    type: deepset_cloud_custom_nodes.preprocessors.document_splitter.DeepsetDocumentSplitter
    init_parameters:
      split_by: word
      split_length: 250
      split_overlap: 30
      respect_sentence_boundary: True
      language: en

  document_embedder:
    type: haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder
    init_parameters:
      model: "intfloat/e5-base-v2"

  writer:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          embedding_dim: 768
          similarity: cosine
      policy: OVERWRITE

connections:  # Defines how the components are connected
- sender: unstructured_converter.documents
  receiver: splitter.documents
- sender: splitter.documents
  receiver: document_embedder.documents
- sender: document_embedder.documents
  receiver: writer.documents

max_loops_allowed: 100

inputs:  # Define the inputs for your index
  files: "file_classifier.sources"  # This component will receive the files to index as input

```