unstructured-client

<h3 align="center"> <img src="https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/img/unstructured_logo.png" height="200" > </h3> <div align="center"> <a href="https://speakeasyapi.dev/"><img src="https://custom-icon-badges.demolab.com/badge/-Built%20By%20Speakeasy-212015?style=for-the-badge&logoColor=FBE331&logo=speakeasy&labelColor=545454" /></a> </div> <h2 align="center"> <p>TypeScript SDK for the Unstructured API</p> </h2> This is a HTTP client for the [Unstructured Platform API](https://docs.unstructured.io/platform-api/overview). You can sign up [here](https://unstructured.io/developers) and process 1000 free pages per day for 14 days. Please refer to the our documentation for a full guide on integrating the [Partition Endpoint](https://docs.unstructured.io/platform-api/partition-api/sdk-jsts) into your JavaScript/TypeScript code. Support for the [Workflow Endpoint](https://docs.unstructured.io/platform-api/api/overview) is coming soon. ## SDK Installation ### NPM ```bash npm install unstructured-client --include=dev ``` ### Yarn ```bash yarn add unstructured-client --dev ``` ### Model Context Protocol (MCP) Server This SDK is also an installable MCP server where the various SDK methods are exposed as tools that can be invoked by AI applications. > Node.js v20 or greater is required to run the MCP server. <details> <summary>Claude installation steps</summary> Add the following server definition to your `claude_desktop_config.json` file: ```json { "mcpServers": { "Unstructured": { "command": "npx", "args": [ "-y", "--package", "unstructured-client", "--", "mcp", "start", ] } } } ``` </details> <details> <summary>Cursor installation steps</summary> Go to `Cursor Settings > Features > MCP Servers > Add new MCP server` and use the following settings: - Name: Unstructured - Type: `command` - Command: ```sh npx -y --package unstructured-client -- mcp start ``` </details> For a full list of server arguments, run: ```sh npx -y --package unstructured-client -- mcp start --help ```  ## SDK Example Usage ### Example ```typescript import { UnstructuredClient } from "unstructured-client"; import { PartitionResponse } from "unstructured-client/sdk/models/operations"; import { Strategy } from "unstructured-client/sdk/models/shared"; import * as fs from "fs"; const unstructuredClient = new UnstructuredClient({ security: { apiKeyAuth: "YOUR_API_KEY", }, }); const filename = "./sample-file"; const data = fs.readFileSync(filename); unstructuredClient.general.partition({ partitionParameters: { files: { content: data, fileName: filename, }, strategy: Strategy.Auto, } }).then((res: PartitionResponse) => { if (res.statusCode == 200) { console.log(res.elements); } }).catch((e) => { console.log(e.statusCode); console.log(e.body); }); ```  Refer to the [API parameters page](https://docs.unstructured.io/api-reference/api-services/api-parameters) for all available parameters. ## Change the base URL If you are self hosting the API, or developing locally, you can change the server URL when setting up the client. ```typescript const client = new UnstructuredClient({ serverURL: "http://localhost:8000", security: { apiKeyAuth: key, }, }); // OR const client = new UnstructuredClient({ serverURL: "https://my-server-url", security: { apiKeyAuth: key, }, }); ```  ## Custom HTTP Client The TypeScript SDK makes API calls using an `HTTPClient` that wraps the native [Fetch API](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API). This client is a thin wrapper around `fetch` and provides the ability to attach hooks around the request lifecycle that can be used to modify the request or handle errors and response. The `HTTPClient` constructor takes an optional `fetcher` argument that can be used to integrate a third-party HTTP client or when writing tests to mock out the HTTP client and feed in fixtures. The following example shows how to use the `"beforeRequest"` hook to to add a custom header and a timeout to requests and how to use the `"requestError"` hook to log errors: ```typescript import { UnstructuredClient } from "unstructured-client"; import { HTTPClient } from "unstructured-client/lib/http"; const httpClient = new HTTPClient({ // fetcher takes a function that has the same signature as native `fetch`. fetcher: (request) => { return fetch(request); } }); httpClient.addHook("beforeRequest", (request) => { const nextRequest = new Request(request, { signal: request.signal || AbortSignal.timeout(5000) }); nextRequest.headers.set("x-custom-header", "custom value"); return nextRequest; }); httpClient.addHook("requestError", (error, request) => { console.group("Request Error"); console.log("Reason:", `${error}`); console.log("Endpoint:", `${request.method} ${request.url}`); console.groupEnd(); }); const sdk = new UnstructuredClient({ httpClient }); ```   ## Retries Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK. To change the default retry strategy for a single API call, simply provide a retryConfig object to the call: ```typescript import { openAsBlob } from "node:fs"; import { UnstructuredClient } from "unstructured-client"; import { Strategy, VLMModel, VLMModelProvider, } from "unstructured-client/sdk/models/shared"; const unstructuredClient = new UnstructuredClient(); async function run() { const result = await unstructuredClient.general.partition({ partitionParameters: { chunkingStrategy: "by_title", files: await openAsBlob("example.file"), splitPdfPageRange: [ 1, 10, ], strategy: Strategy.Auto, vlmModel: VLMModel.Gpt4o, vlmModelProvider: VLMModelProvider.Openai, }, }, { retries: { strategy: "backoff", backoff: { initialInterval: 1, maxInterval: 50, exponent: 1.1, maxElapsedTime: 100, }, retryConnectionErrors: false, }, }); console.log(result); } run(); ``` If you'd like to override the default retry strategy for all operations that support retries, you can provide a retryConfig at SDK initialization: ```typescript import { openAsBlob } from "node:fs"; import { UnstructuredClient } from "unstructured-client"; import { Strategy, VLMModel, VLMModelProvider, } from "unstructured-client/sdk/models/shared"; const unstructuredClient = new UnstructuredClient({ retryConfig: { strategy: "backoff", backoff: { initialInterval: 1, maxInterval: 50, exponent: 1.1, maxElapsedTime: 100, }, retryConnectionErrors: false, }, }); async function run() { const result = await unstructuredClient.general.partition({ partitionParameters: { chunkingStrategy: "by_title", files: await openAsBlob("example.file"), splitPdfPageRange: [ 1, 10, ], strategy: Strategy.Auto, vlmModel: VLMModel.Gpt4o, vlmModelProvider: VLMModelProvider.Openai, }, }); console.log(result); } run(); ```  ### Splitting PDF by pages See [page splitting](https://docs.unstructured.io/api-reference/api-services/sdk#page-splitting) for more details. In order to speed up processing of large PDF files, the client splits up PDFs into smaller files, sends these to the API concurrently, and recombines the results. `splitPdfPage` can be set to `false` to disable this. The amount of parallel requests is controlled by `splitPdfConcurrencyLevel` parameter. By default it equals to 5. It can't be more than 15, to avoid too high resource usage and costs. The size of each batch is determined internally and it can vary between 2 and 20 pages per split. ```typescript client.general.partition({ partitionParameters: { files: { content: data, fileName: filename, }, // Set splitPdfPage parameter to false in order to disable splitting PDF splitPdfPage: true, // Modify splitPdfConcurrencyLevel to change the limit of parallel requests splitPdfConcurrencyLevel: 10, }, }}; ```  ## Summary   ## Table of Contents  * [SDK Installation](#sdk-installation) * [SDK Example Usage](#sdk-example-usage) * [Change the base URL](#change-the-base-url) * [Custom HTTP Client](#custom-http-client) * [Retries](#retries) * [Requirements](#requirements) * [Standalone functions](#standalone-functions) * [File uploads](#file-uploads) * [Debugging](#debugging)   ## Requirements For supported JavaScript runtimes, please consult [RUNTIMES.md](RUNTIMES.md).   ## Standalone functions All the methods listed above are available as standalone functions. These functions are ideal for use in applications running in the browser, serverless runtimes or other environments where application bundle size is a primary concern. When using a bundler to build your application, all unused functionality will be either excluded from the final bundle or tree-shaken away. To read more about standalone functions, check [FUNCTIONS.md](./FUNCTIONS.md). <details> <summary>Available standalone functions</summary> - [`generalPartition`](docs/sdks/general/README.md#partition) - Summary </details>   ## File uploads Certain SDK methods accept files as part of a multi-part request. It is possible and typically recommended to upload files as a stream rather than reading the entire contents into memory. This avoids excessive memory consumption and potentially crashing with out-of-memory errors when working with very large files. The following example demonstrates how to attach a file stream to a request. > [!TIP] > > Depending on your JavaScript runtime, there are convenient utilities that return a handle to a file without reading the entire contents into memory: > > - **Node.js v20+:** Since v20, Node.js comes with a native `openAsBlob` function in [`node:fs`](https://nodejs.org/docs/latest-v20.x/api/fs.html#fsopenasblobpath-options). > - **Bun:** The native [`Bun.file`](https://bun.sh/docs/api/file-io#reading-files-bun-file) function produces a file handle that can be used for streaming file uploads. > - **Browsers:** All supported browsers return an instance to a [`File`](https://developer.mozilla.org/en-US/docs/Web/API/File) when reading the value from an `<input type="file">` element. > - **Node.js v18:** A file stream can be created using the `fileFrom` helper from [`fetch-blob/from.js`](https://www.npmjs.com/package/fetch-blob). ```typescript import { openAsBlob } from "node:fs"; import { UnstructuredClient } from "unstructured-client"; import { Strategy, VLMModel, VLMModelProvider, } from "unstructured-client/sdk/models/shared"; const unstructuredClient = new UnstructuredClient(); async function run() { const result = await unstructuredClient.general.partition({ partitionParameters: { chunkingStrategy: "by_title", files: await openAsBlob("example.file"), splitPdfPageRange: [ 1, 10, ], strategy: Strategy.Auto, vlmModel: VLMModel.Gpt4o, vlmModelProvider: VLMModelProvider.Openai, }, }); console.log(result); } run(); ```        ## Debugging You can setup your SDK to emit debug logs for SDK requests and responses. You can pass a logger that matches `console`'s interface as an SDK option. > [!WARNING] > Beware that debug logging will reveal secrets, like API tokens in headers, in log messages printed to a console or files. It's recommended to use this feature only during local development and not in production. ```typescript import { UnstructuredClient } from "unstructured-client"; const sdk = new UnstructuredClient({ debugLogger: console }); ```   ### Maturity This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version. ### Contributions While we value open-source contributions to this SDK, this library is generated programmatically. Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release! ### SDK Created by [Speakeasy](https://docs.speakeasyapi.dev/docs/using-speakeasy/client-sdks)