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argot-open-ai

Developer-friendly & type-safe Typescript SDK specifically catered to leverage argot-open-ai API.



Important

This SDK is not yet ready for production use. To complete setup please follow the steps outlined in your workspace. Delete this section before > publishing to a package manager.

Summary

OpenAI API: The OpenAI REST API. Please see https://platform.openai.com/docs/api-reference for more details.

Table of Contents

SDK Installation

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

NPM

npm add argot-open-ai

PNPM

pnpm add argot-open-ai

Bun

bun add argot-open-ai

Yarn

yarn add argot-open-ai zod

# Note that Yarn does not install peer dependencies automatically. You will need
# to install zod as shown above.

Requirements

For supported JavaScript runtimes, please consult RUNTIMES.md.

SDK Example Usage

Example

import { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  });

  // Handle the result
  console.log(result);
}

run();

Available Resources and Operations

Available methods

assistants

audio

auditLogs

  • listAuditLogs - List user actions and configuration changes within this organization.

batch

  • createBatch - Creates and executes a batch from an uploaded file of requests
  • listBatches - List your organization's batches.
  • retrieveBatch - Retrieves a batch.
  • cancelBatch - Cancels an in-progress batch. The batch will be in status cancelling for up to 10 minutes, before changing to cancelled, where it will have partial results (if any) available in the output file.

chat

completions

  • createCompletion - Creates a completion for the provided prompt and parameters.

embeddings

  • createEmbedding - Creates an embedding vector representing the input text.

files

  • listFiles - Returns a list of files that belong to the user's organization.
  • createFile - Upload a file that can be used across various endpoints. Individual files can be up to 512 MB, and the size of all files uploaded by one organization can be up to 100 GB.

The Assistants API supports files up to 2 million tokens and of specific file types. See the Assistants Tools guide for details.

The Fine-tuning API only supports .jsonl files. The input also has certain required formats for fine-tuning chat or completions models.

The Batch API only supports .jsonl files up to 100 MB in size. The input also has a specific required format.

Please contact us if you need to increase these storage limits.

fineTuning

  • createFineTuningJob - Creates a fine-tuning job which begins the process of creating a new model from a given dataset.

Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

Learn more about fine-tuning

Learn more about fine-tuning

images

invites

  • listInvites - Returns a list of invites in the organization.
  • inviteUser - Create an invite for a user to the organization. The invite must be accepted by the user before they have access to the organization.
  • retrieveInvite - Retrieves an invite.
  • deleteInvite - Delete an invite. If the invite has already been accepted, it cannot be deleted.

models

  • listModels - Lists the currently available models, and provides basic information about each one such as the owner and availability.
  • retrieveModel - Retrieves a model instance, providing basic information about the model such as the owner and permissioning.
  • deleteModel - Delete a fine-tuned model. You must have the Owner role in your organization to delete a model.

moderations

projects

uploads

  • createUpload - Creates an intermediate Upload object that you can add Parts to. Currently, an Upload can accept at most 8 GB in total and expires after an hour after you create it.

Once you complete the Upload, we will create a File object that contains all the parts you uploaded. This File is usable in the rest of our platform as a regular File object.

For certain purposes, the correct mime_type must be specified. Please refer to documentation for the supported MIME types for your use case:

For guidance on the proper filename extensions for each purpose, please follow the documentation on creating a File.

  • addUploadPart - Adds a Part to an Upload object. A Part represents a chunk of bytes from the file you are trying to upload.

Each Part can be at most 64 MB, and you can add Parts until you hit the Upload maximum of 8 GB.

It is possible to add multiple Parts in parallel. You can decide the intended order of the Parts when you complete the Upload.

Within the returned Upload object, there is a nested File object that is ready to use in the rest of the platform.

You can specify the order of the Parts by passing in an ordered list of the Part IDs.

The number of bytes uploaded upon completion must match the number of bytes initially specified when creating the Upload object. No Parts may be added after an Upload is completed.

  • cancelUpload - Cancels the Upload. No Parts may be added after an Upload is cancelled.

users

  • listUsers - Lists all of the users in the organization.
  • retrieveUser - Retrieves a user by their identifier.
  • modifyUser - Modifies a user's role in the organization.
  • deleteUser - Deletes a user from the organization.

vectorStores

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.

Available standalone functions

The Assistants API supports files up to 2 million tokens and of specific file types. See the Assistants Tools guide for details.

The Fine-tuning API only supports .jsonl files. The input also has certain required formats for fine-tuning chat or completions models.

The Batch API only supports .jsonl files up to 100 MB in size. The input also has a specific required format.

Please contact us if you need to increase these storage limits.

Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

Learn more about fine-tuning

Learn more about fine-tuning

Each Part can be at most 64 MB, and you can add Parts until you hit the Upload maximum of 8 GB.

It is possible to add multiple Parts in parallel. You can decide the intended order of the Parts when you complete the Upload.

Within the returned Upload object, there is a nested File object that is ready to use in the rest of the platform.

You can specify the order of the Parts by passing in an ordered list of the Part IDs.

The number of bytes uploaded upon completion must match the number of bytes initially specified when creating the Upload object. No Parts may be added after an Upload is completed.

  • uploadsCreateUpload - Creates an intermediate Upload object that you can add Parts to. Currently, an Upload can accept at most 8 GB in total and expires after an hour after you create it.

Once you complete the Upload, we will create a File object that contains all the parts you uploaded. This File is usable in the rest of our platform as a regular File object.

For certain purposes, the correct mime_type must be specified. Please refer to documentation for the supported MIME types for your use case:

For guidance on the proper filename extensions for each purpose, please follow the documentation on creating a File.

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 { ArgotOpenAi } from "argot-open-ai";
import { openAsBlob } from "node:fs";

const argotOpenAi = new ArgotOpenAi({
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.images.createImageEdit({
    image: await openAsBlob("example.file"),
    prompt: "A cute baby sea otter wearing a beret",
    model: "dall-e-2",
    n: 1,
    responseFormat: "url",
    size: "1024x1024",
    user: "user-1234",
  });

  // Handle the result
  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 { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  }, {
    retries: {
      strategy: "backoff",
      backoff: {
        initialInterval: 1,
        maxInterval: 50,
        exponent: 1.1,
        maxElapsedTime: 100,
      },
      retryConnectionErrors: false,
    },
  });

  // Handle the result
  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 { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  retryConfig: {
    strategy: "backoff",
    backoff: {
      initialInterval: 1,
      maxInterval: 50,
      exponent: 1.1,
      maxElapsedTime: 100,
    },
    retryConnectionErrors: false,
  },
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  });

  // Handle the result
  console.log(result);
}

run();

Error Handling

All SDK methods return a response object or throw an error. By default, an API error will throw a errors.SDKError.

If a HTTP request fails, an operation my also throw an error from the models/errors/httpclienterrors.ts module:

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

In addition, when custom error responses are specified for an operation, the SDK may throw their associated Error type. You can refer to respective Errors tables in SDK docs for more details on possible error types for each operation. For example, the modifyProject method may throw the following errors:

Error Type Status Code Content Type
errors.ErrorResponse 400 application/json
errors.SDKError 4XX, 5XX */* 
import { ArgotOpenAi } from "argot-open-ai";
import { ErrorResponse, SDKValidationError } from "argot-open-ai/models/errors";

const argotOpenAi = new ArgotOpenAi({
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  let result;
  try {
    result = await argotOpenAi.projects.modifyProject({
      projectId: "<id>",
      projectUpdateRequest: {
        name: "<value>",
      },
    });

    // Handle the result
    console.log(result);
  } catch (err) {
    switch (true) {
      case (err instanceof SDKValidationError): {
        // Validation errors can be pretty-printed
        console.error(err.pretty());
        // Raw value may also be inspected
        console.error(err.rawValue);
        return;
      }
      case (err instanceof ErrorResponse): {
        // Handle err.data$: ErrorResponseData
        console.error(err);
        return;
      }
      default: {
        throw err;
      }
    }
  }
}

run();

Validation errors can also occur when either method arguments or data returned from the server do not match the expected format. The SDKValidationError that is thrown as a result will capture the raw value that failed validation in an attribute called rawValue. Additionally, a pretty() method is available on this error that can be used to log a nicely formatted string since validation errors can list many issues and the plain error string may be difficult read when debugging.

Server Selection

Select Server by Index

You can override the default server globally by passing a server index to the serverIdx optional parameter when initializing the SDK client instance. The selected server will then be used as the default on the operations that use it. This table lists the indexes associated with the available servers:

# Server Variables
0 https://api.openai.com/v1 None 
import { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  serverIdx: 0,
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  });

  // Handle the result
  console.log(result);
}

run();

Override Server URL Per-Client

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

import { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  serverURL: "https://api.openai.com/v1",
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  });

  // Handle the result
  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 { ArgotOpenAi } from "argot-open-ai";
import { HTTPClient } from "argot-open-ai/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 ArgotOpenAi({ httpClient });

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name Type Scheme Environment Variable
apiKeyAuth http HTTP Bearer ARGOTOPENAI_API_KEY_AUTH

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

import { ArgotOpenAi } from "argot-open-ai";

const argotOpenAi = new ArgotOpenAi({
  apiKeyAuth: process.env["ARGOTOPENAI_API_KEY_AUTH"] ?? "",
});

async function run() {
  const result = await argotOpenAi.chat.createChatCompletion({
    messages: [
      {
        content: "<value>",
        role: "user",
      },
      {
        content: [
          {
            type: "text",
            text: "<value>",
          },
          {
            type: "image_url",
            imageUrl: {
              url: "https://fixed-circumference.com",
            },
          },
        ],
        role: "user",
      },
      {
        role: "tool",
        content: "<value>",
        toolCallId: "<id>",
      },
    ],
    model: "gpt-4o",
    n: 1,
    temperature: 1,
    topP: 1,
    user: "user-1234",
  });

  // Handle the result
  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.

import { ArgotOpenAi } from "argot-open-ai";

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

You can also enable a default debug logger by setting an environment variable ARGOTOPENAI_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

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typescript - v0.0.5 - 2024-10-24 14:02:22 Latest
Oct 24, 2024
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