> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-docs-243-plugins.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Partitioning

*Partitioning* extracts content from raw unstructured files and outputs that content as structured [document elements](/ui/document-elements).

For specific file types, such as image files and PDF files, Unstructured offers special strategies to partition them. Each of these
strategies has trade-offs for output speed, cost to output, and quality of output.

PDF files, for example, vary in quality and complexity. In simple cases, traditional natural language processing (NLP) extraction techniques might
be enough to extract all the text out of a document. In other cases, advanced image-to-text models are required
to process a PDF file. Some of these strategies implement rule-based workflows, which can be faster and cheaper, because they always
extract in the same way, but you might sometimes get lower-quality resolution. Other strategies implement
model-based workflows, which can be slower and costlier because they require a model that performs inference, but you can get higher-quality resolution.
When you choose a partitioning strategy for your files, you should be mindful of these speed, cost, and quality trade-offs.
For example, the **Fast** strategy can be about 100 times faster than leading image-to-text models.

To choose one of these strategies, select one of the following four **Partition Strategy** options in the **Partitioner** node of a workflow.

<Note>You can change a workflow's preconfigured strategy only through [Custom](/ui/workflows#create-a-custom-workflow) workflow settings.</Note>

Unstructured recommends that you choose the **Auto** partitioning strategy in most cases. With **Auto**, Unstructured does all
the heavy lifting, optimizing at runtime for the highest quality at the lowest cost page-by-page.

You should consider the following additional strategies only if you are absolutely sure that your documents are of the same
type. Each of the following strategies are best suited for specific situations. Choosing one of these
strategies other than **Auto** for sets of documents of different types could produce undesirable results,
including reduction in transformation quality.

* **VLM**: For the highest-quality transformation of these file types: `.bmp`, `.gif`, `.heic`, `.jpeg`, `.jpg`, `.pdf`, `.png`, `.tiff`, and `.webp`.
* **High Res**: For all other [supported file types](/ui/supported-file-types), and for the generation of bounding box coordinates.
* **Fast**: For text-only documents.

## Supported languages

**Fast** partitioning accepts any text inputs, though automatic language detection of those inputs is restricted to [langdetect](https://pypi.org/project/langdetect/).

**High Res** partitioning leverages Tesseract OCR. For the list of languages that Tesseract supports, see:
[Languages/Scripts supported in different versions of Tesseract](https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html).

Language support for **VLM** depends on the model used. The list of supported languages for a particular model is maintained by
that model's provider. For the list of languages that each model supports, see the following, where provided:

* Anthropic

  * Claude 3.5 Sonnet: Arabic, Bengali, Chinese (Simplified), English, French, German, Hindi, Indonesian, Italian, Japanese, Korean,
    Portuguese (Brazil), Spanish, Swahili, and Yoruba are mentioned. ([Source](https://docs.anthropic.com/en/docs/build-with-claude/multilingual-support))

* OpenAI

  * GPT-4o: Arabic, Chinese, English, French, German, Gujarati, Hindi, Italian, Japanese, Korean, Marathi, Persian,
    Portuguese, Russian, Spanish, Tamil, Telugu, Turkish, Urdu, and Vietnamese are mentioned. ([Source](https://openai.com/index/hello-gpt-4o/))

* Amazon Bedrock

  * Claude 3.5 Sonnet: "English, Spanish, Japanese, and multiple other languages" ([Source](https://aws.amazon.com/bedrock/claude/))
  * Claude 3 Opus: "English, Spanish, Japanese, and multiple other languages" ([Source](https://aws.amazon.com/bedrock/claude/))
  * Claude 3 Haiku: "English, Spanish, Japanese, and multiple other languages" ([Source](https://aws.amazon.com/bedrock/claude/))
  * Claude 3 Sonnet: "English, Spanish, Japanese, and multiple other languages" ([Source](https://aws.amazon.com/bedrock/claude/))
  * Amazon Nova Pro: "200+ languages" ([Source](https://aws.amazon.com/ai/generative-ai/nova/))
  * Amazon Nova Lite: "200+ languages" ([Source](https://aws.amazon.com/ai/generative-ai/nova/))
  * Meta Llama 3.2 90B Instruct: "English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai" ([Source](https://aws.amazon.com/bedrock/llama/))
  * Meta Llama 3.2 11B Instruct: "English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai" ([Source](https://aws.amazon.com/bedrock/llama/))