The Unstructured JavaScript/TypeScript SDK client allows you to send one file at a time for processing by the Unstructured Partition Endpoint.To use the JavaScript/TypeScript SDK, you’ll first need to set an environment variable named UNSTRUCTURED_API_KEY,
representing your Unstructured API key. To get your API key, do the following:
If you do not already have an Unstructured account, sign up for free.
After you sign up, you are automatically signed in to your new Unstructured Starter account, at https://platform.unstructured.io.
If you have an Unstructured Starter or Team account and are not already signed in, sign in to your account at https://platform.unstructured.io.
For an Enterprise account, see your Unstructured account administrator for instructions,
or email Unstructured Support at support@unstructured.io.
Get your Unstructured API key: a. After you sign in to your Unstructured Starter account, click API Keys on the sidebar.
For a Team or Enterprise account, before you click API Keys, make sure you have selected the organizational workspace you want to create an API key
for. Each API key works with one and only one organizational workspace. Learn more.
b. Click Generate API Key.
c. Follow the on-screen instructions to finish generating the key.
d. Click the Copy icon next to your new key to add the key to your system’s clipboard. If you lose this key, simply return and click the Copy icon again.
In order to speed up processing of large PDF files, the splitPdfPage* parameter is true by default. This
causes the PDF to be split into small batches of pages before sending requests to the API. The client
awaits all parallel requests and combines the responses into a single response object. This is specific to PDF files and other
filetypes are ignored.The number of parallel requests is controlled by splitPdfConcurrencyLevel*.
The default is 8 and the max is set to 15 to avoid high resource usage and costs.If at least one request is successful, the responses are combined into a single response object. An
error is returned only if all requests failed or there was an error during splitting.
This feature may lead to unexpected results when chunking because the server does not see the entire
document context at once. If you’d like to chunk across the whole document and still get the speedup from
parallel processing, you can:
Partition the PDF with splitPdfPage set to true, without any chunking parameters.
Store the returned elements in results.json.
Partition this JSON file with the desired chunking parameters.
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client.general.partition({ partitionParameters: { files: { content: data, fileName: filename, }, strategy: Strategy.HiRes, // Set to `false` to disable PDF splitting splitPdfPage: true, // Continue PDF splitting even if some earlier split operations fail. splitPdfAllowFailed: true, // Modify splitPdfConcurrencyLevel to set the number of parallel requests splitPdfConcurrencyLevel: 10, }})
You can also change the defaults for retries through the retryConfig*
when initializing the client. If a request to the API fails, the client will retry the
request with an exponential backoff strategy up to a maximum interval of one minute. The
function keeps retrying until the total elapsed time exceeds maxElapsedTime*,
which defaults to one hour:
The code example in the Basics section processes a single PDF file. But what if you want to process
multiple files inside a directory with a mixture of subdirectories and files with different file types?The following example takes an input directory path to read files from and an output directory path to
write the processed data to, processing one file at a time.
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import { UnstructuredClient } from "unstructured-client";import * as fs from "fs";import * as path from "path";import { Strategy } from "unstructured-client/sdk/models/shared/index.js";import { PartitionResponse } from "unstructured-client/sdk/models/operations";// Send all files in the source path to Unstructured for processing.// Send the processed data to the destination path.function processFiles( client: UnstructuredClient, sourcePath: string, destinationPath: string): void { // If an output directory does not exist for the corresponding input // directory, then create it. if (!fs.existsSync(destinationPath)) { fs.mkdirSync(destinationPath, { recursive: true }); } // Get all folders and files at the current level of the input directory. const items = fs.readdirSync(sourcePath); // For each folder and file in the input directory... for (const item of items) { const inputPath = path.join(sourcePath, item); const outputPath = path.join(destinationPath, item) // If it's a folder, call this function recursively. if (fs.statSync(inputPath).isDirectory()) { processFiles(client, inputPath, outputPath); } else { // If it's a file, send it to Unstructured for processing. const data = fs.readFileSync(inputPath); client.general.partition({ partitionParameters: { files: { content: data, fileName: inputPath }, strategy: Strategy.HiRes, splitPdfPage: true, splitPdfConcurrencyLevel: 15, splitPdfAllowFailed: true } }).then((res: PartitionResponse) => { // If successfully processed, write the processed data to // the destination directory. if (res.statusCode == 200) { const jsonElements = JSON.stringify(res, null, 2) fs.writeFileSync(outputPath + ".json", jsonElements) } }).catch((e) => { if (e.statusCode) { console.log(e.statusCode); console.log(e.body); } else { console.log(e); } }); } }}const client = new UnstructuredClient({ security: { apiKeyAuth: process.env.UNSTRUCTURED_API_KEY }, serverURL: process.env.UNSTRUCTURED_API_URL});processFiles( client, process.env.LOCAL_FILE_INPUT_DIR, process.env.LOCAL_FILE_OUTPUT_DIR);
The parameter names used in this document are for the JavaScript/TypeScript SDK, which follows the camelCase
convention. The Python SDK follows the snake_case convention. Other than this difference in naming convention,
the names used in the SDKs are the same across all methods.
Refer to the API parameters page for the full list of available parameters.
Refer to the Examples page for some inspiration on using the parameters.