> ## Documentation Index > Fetch the complete documentation index at: https://unstructured-53-kapa-ai.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # API Parameters > The Unstructured API provides parameters to customize the processing of documents. Below are the details for these parameters. ## Parameters The only required parameter is `files` - the file you wish to process. [//]: # "TODO: In the future we should automatically generate this table to make sure it's up to date" | Python & direct call | JavaScript | Description | | ------------------------------------------ | ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `files` (*shared.Files*) | `files` (*File*, *Blob*, *shared.Files*) | The file to process. | | `coordinates` (*bool*) | `coordinates` (*boolean*) | If true, return bounding box coordinates for each element extracted via OCR. Default: false | | `encoding` (*str*) | `encoding` (*string*) | The encoding method used to decode the text input. Default: `utf-8` | | `extract_image_block_types` (*List\[str]*) | `extractImageBlockTypes` (*string\[]*) | The types of elements to extract, for use in extracting image blocks as base64 encoded data stored in metadata fields | | `gz_uncompressed_content_type` (*str*) | `gzUncompressedContentType` (*string*) | If file is gzipped, use this content type after unzipping. Example: `application/pdf` | | `hi_res_model_name` (*str*) | `hiResModelName` (*string*) | The name of the inference model used when strategy is `hi_res`. Example: `chipper` | | `include_page_breaks` (*bool*) | `includePageBreaks` (*boolean*) | If True, the output will include page breaks if the filetype that supports it. Default: `false` | | `languages` (*List\[str]*) | `languages` (*string\[]*) | The languages present in the document, for use in partitioning and/or OCR. See the [Tesseract documentation](https://github.com/tesseract-ocr/tessdata) for a full list of languages. | | `output_format` (*str*) | `outputFormat` (*string*) | The format of the response. Supported formats are `application/json` and `text/csv`. Default: `application/json`. | | `pdf_infer_table_structure` (*bool*) | `pdfInferTableStructure` (*boolean*) | **Deprecated!** If True and strategy=hi\_res, any Table Elements extracted from a PDF will include an additional metadata field, 'text\_as\_html', where the value (string) is a just a transformation of the data into an HTML table. | | `skip_infer_table_types` (*List\[str]*) | `skipInferTableTypes` (*string\[]*) | The document types that you want to skip table extraction with. Default: \['pdf', 'jpg', 'png', 'heic'] | | `split_pdf_page` (*bool*) | `splitPdfPage` (*boolean*) | Should the pdf file be split at client. Ignored on backend. | | `strategy` (*str*) | `strategy` (*string*) | The strategy to use for partitioning PDF/image. Options are `fast`, `hi_res`, `auto`. Default: `auto` | | `unique_element_ids` (*bool*) | `uniqueElementIds` (*boolean*) | When True, assign UUIDs to element IDs, which guarantees their uniqueness (useful when using them as primary keys in database). Otherwise a SHA-256 of element text is used. Default: False | | `xml_keep_tags` (*bool*) | `xmlKeepTags` (*boolean*) | If True, will retain the XML tags in the output. Otherwise it will simply extract the text from within the tags. Only applies to XML documents. | | `chunking_strategy` (*str*) | `chunkingStrategy` (*string*) | Use one of the supported strategies to chunk the returned elements after partitioning. When `chunking_strategy` is not specified, no chunking is performed and any other chunking parameters provided are ignored. Supported strategies: `"basic"`, `"by_title"` | The following parameters only apply when a `chunking_strategy` is specified. Otherwise, they are ignored. | Python & direct call | JavaScript | Description | | -------------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `combine_under_n_chars` (*int*) | `combineUnderNChars` (*number*) | Applies only when chunking strategy is set to `"by_title"`. Use this parameter to combines small chunks until the combined chunk reaches a length of n chars. This can mitigate the appearance of small chunks created by short paragraphs, not intended as section headings, being identified as `Title` elements in certain documents. Default: the same value as `max_characters` | | `include_orig_elements` (*bool*) | `includeOrigElements` (*boolean*) | When True (the default), the elements used to form a chunk appear in `.metadata.orig_elements` for that chunk. | | `max_characters` (*int*) | `maxCharacters` (*number*) | Cut off new sections after reaching a length of n chars (hard max). Default: 500 | | `multipage_sections` (*bool*) | `multipageSections` (*boolean*) | Applies only when `chunking_strategy` is set to `by_title`. Determines if a chunk can include elements from more than one page. Default: true | | `new_after_n_chars` (*int*) | `newAfterNChars` (*number*) | Applies only when `chunking_strategy` is specified. Cut off new sections after reaching a length of n chars (soft max). Default: 1500 | | `overlap` (*int*) | `overlap` (*number*) | A prefix of this many trailing characters from the prior text-split chunk is applied to second and later chunks formed from oversized elements by text-splitting. Default: None | | `overlap_all` (*bool*) | `overlapAll` (*boolean*) | When True, overlap is also applied to 'normal' chunks formed by combining whole elements. Use with caution as this can introduce noise into otherwise clean semantic units. Default: None | Need help getting started? Check out the [Examples page](/api-reference/api-services/examples) for some inspiration.