ducky

Developer-friendly & type-safe Typescript SDK specifically catered to leverage ducky API.

🌐 Links

🔗 Ducky Website

📘 API Documentation

Summary

Ducky API: API for managing and retrieving data from Ducky.

Table of Contents

• ducky
  • SDK Installation
  • Requirements
  • SDK Example Usage
  • Authentication
  • Available Resources and Operations
  • Standalone functions
  • File uploads
  • Retries
  • Error Handling
  • Server Selection
  • Custom HTTP Client
  • Debugging
• Development
  • Maturity
  • Contributions

SDK Installation

The SDK can be installed with either npm, pnpm, bun or yarn package managers.

NPM

npm add duckyai-ts

PNPM

pnpm add duckyai-ts

Bun

bun add duckyai-ts

Yarn

yarn add duckyai-ts

[!NOTE] This package is published with CommonJS and ES Modules (ESM) support.

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 from npm.

Add the following server definition to your claude_desktop_config.json file:

{
  "mcpServers": {
    "Ducky": {
      "command": "npx",
      "args": [
        "-y", "--package", "duckyai-ts",
        "--",
        "mcp", "start",
        "--api-key", "..."
      ]
    }
  }
}

Create a .cursor/mcp.json file in your project root with the following content:

{
  "mcpServers": {
    "Ducky": {
      "command": "npx",
      "args": [
        "-y", "--package", "duckyai-ts",
        "--",
        "mcp", "start",
        "--api-key", "..."
      ]
    }
  }
}

You can also run MCP servers as a standalone binary with no additional dependencies. You must pull these binaries from available Github releases:

curl -L -o mcp-server \
    https://github.com/{org}/{repo}/releases/download/{tag}/mcp-server-bun-darwin-arm64 && \
chmod +x mcp-server

If the repo is a private repo you must add your Github PAT to download a release -H "Authorization: Bearer {GITHUB_PAT}".

{
  "mcpServers": {
    "Todos": {
      "command": "./DOWNLOAD/PATH/mcp-server",
      "args": [
        "start"
      ]
    }
  }
}

For a full list of server arguments, run:

npx -y --package duckyai-ts -- mcp start --help

Requirements

For supported JavaScript runtimes, please consult RUNTIMES.md.

SDK Example Usage

Example 1

import { Ducky } from "duckyai-ts";
import { openAsBlob } from "node:fs";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.indexFile({
    indexName: "<value>",
    file: await openAsBlob("example.file"),
  });

  console.log(result);
}

run();

Example 2

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.indexMultimodal({
    indexName: "index_name",
    docId: "doc_id",
    image: {
      url: "https://openapi-generator.tech",
      base64: "base64",
      mimeType: "mime_type",
    },
    content: "content",
    title: "title",
    url: "url",
    metadata: {
      "key": "",
    },
  });

  console.log(result);
}

run();

Example 3

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.index({
    indexName: "index_name",
    docId: "doc_id",
    content: "content",
    title: "title",
    url: "url",
    fileId: "file_id",
    metadata: {
      "key": "",
    },
  });

  console.log(result);
}

run();

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

| Name | Type | Scheme | Environment Variable | | -------- | ------ | ------- | -------------------- | | apiKey | apiKey | API key | DUCKY_API_KEY |

To authenticate with the API the apiKey parameter must be set when initializing the SDK client instance. For example:

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.list({
    indexName: "<value>",
  });

  console.log(result);
}

run();

Available Resources and Operations

documents

• list - List documents within an index
• batchIndex - Batch index text documents
• indexFile - Index a document by uploading a file
• indexMultimodal - Index a document from an image and text content
• index - Index a document from text content
• retrieve - Retrieve documents from an index
• retrieveSimilar - Find documents similar to a given document
• delete - Delete a document
• get - Get a document by ID with pagination

indexes

• list - List indexes within a project
• create - Create an index
• delete - Delete an index
• get - Get an index
• ask - Ask a question to an index

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.

• documentsBatchIndex - Batch index text documents
• documentsDelete - Delete a document
• documentsGet - Get a document by ID with pagination
• documentsIndex - Index a document from text content
• documentsIndexFile - Index a document by uploading a file
• documentsIndexMultimodal - Index a document from an image and text content
• documentsList - List documents within an index
• documentsRetrieve - Retrieve documents from an index
• documentsRetrieveSimilar - Find documents similar to a given document
• indexesAsk - Ask a question to an index
• indexesCreate - Create an index
• indexesDelete - Delete an index
• indexesGet - Get an index
• indexesList - List indexes within a project

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.
• Bun: The native 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 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.

import { Ducky } from "duckyai-ts";
import { openAsBlob } from "node:fs";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.indexFile({
    indexName: "<value>",
    file: await openAsBlob("example.file"),
  });

  console.log(result);
}

run();

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:

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.list({
    indexName: "<value>",
  }, {
    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:

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  retryConfig: {
    strategy: "backoff",
    backoff: {
      initialInterval: 1,
      maxInterval: 50,
      exponent: 1.1,
      maxElapsedTime: 100,
    },
    retryConnectionErrors: false,
  },
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.list({
    indexName: "<value>",
  });

  console.log(result);
}

run();

Error Handling

DuckyError is the base class for all HTTP error responses. It has the following properties:

| Property | Type | Description | | ------------------- | ---------- | --------------------------------------------------------------------------------------- | | error.message | string | Error message | | error.statusCode | number | HTTP response status code eg 404 | | error.headers | Headers | HTTP response headers | | error.body | string | HTTP body. Can be empty string if no body is returned. | | error.rawResponse | Response | Raw HTTP response | | error.data$ | | Optional. Some errors may contain structured data. See Error Classes. |

Example

import { Ducky } from "duckyai-ts";
import * as errors from "duckyai-ts/models/errors";

const ducky = new Ducky({
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  try {
    const result = await ducky.documents.list({
      indexName: "<value>",
    });

    console.log(result);
  } catch (error) {
    // The base class for HTTP error responses
    if (error instanceof errors.DuckyError) {
      console.log(error.message);
      console.log(error.statusCode);
      console.log(error.body);
      console.log(error.headers);

      // Depending on the method different errors may be thrown
      if (error instanceof errors.ErrorResponse) {
        console.log(error.data$.error); // string
      }
    }
  }
}

run();

Error Classes

Primary errors:

• DuckyError: The base class for HTTP error responses.
  • ErrorResponse: Generic error.

Network errors:

• ConnectionError: HTTP client was unable to make a request to a server.
• RequestTimeoutError: HTTP request timed out due to an AbortSignal signal.
• RequestAbortedError: HTTP request was aborted by the client.
• InvalidRequestError: Any input used to create a request is invalid.
• UnexpectedClientError: Unrecognised or unexpected error.

Inherit from DuckyError:

• ResponseValidationError: Type mismatch between the data returned from the server and the structure expected by the SDK. See error.rawValue for the raw value and error.pretty() for a nicely formatted multi-line string.

Server Selection

Override Server URL Per-Client

The default server can be overridden globally by passing a URL to the serverURL: string optional parameter when initializing the SDK client instance. For example:

import { Ducky } from "duckyai-ts";

const ducky = new Ducky({
  serverURL: "https://api.ducky.ai",
  apiKey: process.env["DUCKY_API_KEY"] ?? "",
});

async function run() {
  const result = await ducky.documents.list({
    indexName: "<value>",
  });

  console.log(result);
}

run();

Custom HTTP Client

The TypeScript SDK makes API calls using an HTTPClient that wraps the native 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:

import { Ducky } from "duckyai-ts";
import { HTTPClient } from "duckyai-ts/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 Ducky({ httpClient: httpClient });

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.

import { Ducky } from "duckyai-ts";

const sdk = new Ducky({ debugLogger: console });

You can also enable a default debug logger by setting an environment variable DUCKY_DEBUG to true.

Development

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. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

SDK Created by Speakeasy