> ## Documentation Index
> Fetch the complete documentation index at: https://docs.retab.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Documents

### Introduction

Retab offers a consolidated, production-grade pipeline for processing any types of documents with AI.

**Our model read documents the way humans do.** It accepts native digital files (Images, PDFs, DOCX, XLSX, E-mail) and parses text, detects visual structure across pages, tables, forms, and figures.

Please check the [API Reference](https://docs.retab.com/api-reference) for more details.

```mermaid theme={null}
flowchart LR
    A[Document] --> B[Processing resources]

    subgraph Resources
    B --> E[client.extractions.create] --> F[Structured Data]
    B --> P[client.parses.create] --> G[Raw Text]
    B --> D[client.edits.create] --> H[Filled PDF]
    B --> S[client.splits.create] --> I[Page Ranges]
    B --> C[client.classifications.create] --> J[Category]
    end
```

The SDK exposes document processing through dedicated resources:

| Resource                            | Purpose                                                                                                                                                                                                         |
| ----------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **`client.extractions.create`**     | Executes the extraction and returns the parsed object (optionally with consensus voting). One-step OCR + LLM parsing when only the structured output is required.                                               |
| **`client.parses.create`**          | Converts any document into structured text content with page-by-page extraction. Perfect for RAG, text extraction, and preparing documents for further processing or indexing.                                  |
| **`client.edits.create`**           | Automatically detects form fields in PDFs using OCR and LLM, then fills them based on natural language instructions. Ideal for automated form completion workflows.                                             |
| **`client.splits.create`**          | Analyzes multi-page documents and classifies pages into user-defined subdocuments, returning the assigned pages for each section. Perfect for separating mixed document batches and organizing content by type. |
| **`client.classifications.create`** | Classifies a document against user-defined categories.                                                                                                                                                          |

The complexities of OCR, layout reconstruction are handled internally, allowing to focus solely on downstream prompt and context-engineering logic.

## The document data structure

Documents in Retab are represented as `MIMEData` objects, which encapsulate the file content and metadata. This structure allows you to work with documents in a consistent way regardless of their original format. **The `url` field directly matches [OpenAI's expected format for image inputs](https://platform.openai.com/docs/guides/images-vision?api-mode=chat\&format=base64-encoded).**

<ResponseField name="MIMEData Object Structure" type="object">
  <Expandable title="document">
    <ResponseField name="filename" type="string">
      The original filename of the document.
    </ResponseField>

    <ResponseField name="url" type="string">
      A data URL containing the base64-encoded content of the document, prefixed
      with the appropriate MIME type.
    </ResponseField>
  </Expandable>
</ResponseField>

```json MIMEData Object Structure theme={null}
{
  "document": {
    "filename": "Alphabet-10Q-Q1-25.pdf",
    "url": "data:application/pdf;base64,JVBERi0xLjQKJfbk/N8KMSAwIG9iago8PAovVHlwZS…"
  }
}
```

The python SDK is flexible and allows you to use the `document` parameter as a file path, bytes, or a `PIL.Image.Image` object, and we will automatically convert it to a `MIMEData` object for you.