Node.js Vercel LLM API

This is a reverse engineered API wrapper for the Vercel AI Playground, which allows for free access to many LLMs, including OpenAI's ChatGPT, Cohere's Command Nightly, as well as some open source models.

Also a JavaScript implementation of the ading2210's vercel-llm-api Python library.

Table of Contents

• Features
• Limitations
• Installation
• Documentation
  • Using the Client
    • Downloading the Available Models
    • Generating Text
    • Generating Chat Messages
  • Using StreamHandler
  • Miscellaneous
    • Listening to Debug Messages

Table of contents generated with markdown-toc

Features

• Download the available models
• Generate text
• Generate chat messages
• Set custom parameters
• Stream the responses

Limitations

• No auth support
• Can't use "pro" or "hobby" models

Installation

You can install this library by running the following command:

npm install vercel-llm-api

Documentation

Note that the entire library requires the use of async/await.

Using the Client

To use this library, simply require('vercel-llm-api') and create a Client instance. You can specify custom Axios request configurations as an argument.

See here for the Axios request config.

const { Client } = require('vercel-llm-api'),
  client = new Client();

client.on('ready', async () => {
  // the client is ready to do whatever
});

Note that the following examples assume client is the name of your Client instance and that it is inside an async function.

Downloading the Available Models

The client downloads the available models upon initialization, and stores them in client.models.

>>> console.log(client.models)

{
  "anthropic:claude-instant-v1": { 
    "id": "anthropic:claude-instant-v1", // the model's id
    "provider": "anthropic",             // the model's provider
    "providerHumanName": "Anthropic",    // the provider's display name
    "makerHumanName": "Anthropic",       // the maker of the model
    "minBillingTier": "hobby",           // the minimum billing tier needed to use the model
    "parameters": {                      // an object of optional parameters that can be passed to the generate function
      "temperature": {                   // the name of the parameter
        "value": 1,                      // the default value for the parameter
        "range": [0, 1]                  // a range of possible values for the parameter
      },
      ...
    }
    ...
  }
}

Note that, since there is no auth yet, if a model has the "minBillingTier" property present, it can't be used.

A list of model IDs is also available in client.model_ids.

>>> console.log(client.model_ids)
[
  "anthropic:claude-instant-v1", // locked to hobby tier; unusable
  "anthropic:claude-v1",         // locked to hobby tier; unusable
  "replicate:replicate/alpaca-7b",
  "replicate:stability-ai/stablelm-tuned-alpha-7b",
  "huggingface:bigscience/bloom",
  "huggingface:bigscience/bloomz",
  "huggingface:google/flan-t5-xxl",
  "huggingface:google/flan-ul2",
  "huggingface:EleutherAI/gpt-neox-20b",
  "huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
  "huggingface:bigcode/santacoder",
  "cohere:command-medium-nightly",
  "cohere:command-xlarge-nightly",
  "openai:gpt-4",                // locked to pro tier; unusable
  "openai:code-cushman-001",
  "openai:code-davinci-002",
  "openai:gpt-3.5-turbo",
  "openai:text-ada-001",
  "openai:text-babbage-001",
  "openai:text-curie-001",
  "openai:text-davinci-002",
  "openai:text-davinci-003"
]

An Object of default parameters for each model can be found at client.model_params.

>>> console.log(client.model_defaults)
{
  "anthropic:claude-instant-v1": {
    "temperature": 1,
    "maximumLength": 200,
    "topP": 1,
    "topK": 1,
    "presencePenalty": 1,
    "frequencyPenalty": 1,
    "stopSequences": [
      "\n\nHuman:"
    ]
  },
  ...
}

Generating Text

To generate some text, use the client.generate function, which accepts the following arguments:

• model - The ID of the model you want to use.
• prompt - Your prompt.
• params - An Object of optional parameters. See the previous section for how to find these.

The function returns the newly generated text as a ReadableStream.

await client.generate('openai:gpt-3.5-turbo', 'Summarize the GNU GPL v3');

Generating Chat Messages

To generate chat messages, use the client.chat function, which accepts the following arguments:

• model - The ID of the model you want to use.
• messages - A list of messages. The format for this is identical to how you would use the official OpenAI API.
• params - An Object of optional parameters. See the "Downloading the Available Models" section for how to find these.

The function returns the newly generated text as a ReadableStream.

const messages = [
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "Who won the world series in 2020?"},
  {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
  {"role": "user", "content": "Where was it played?"}
];

await client.chat('openai:gpt-3.5-turbo', messages);

Using StreamHandler

StreamHandler is a utility function to handle the returned ReadableStream of the instantiated Client's chat and generate functions.

StreamHandler accepts the following arguments:

• stream - The ReadableStream.
• callback - An optional callback to process each chunk of the stream.

...and returns an Array of Strings.

const { Client, StreamHandler } = require('vercel-llm-api'),
  client = new Client();

client.on('ready', async () => {
  const messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
    {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
    {"role": "user", "content": "Where was it played?"}
  ];
  
  const stream = await client.chat('openai:gpt-3.5-turbo', messages),
    response = await StreamHandler(stream);

  console.log(response); // returns [ "The", " 2020", " World", " Series", " was", " played", ... ]
});

Miscellaneous

Listening to Debug Messages

If you want to show the debug messages, simply listen to the debug event of the Client instance.

const { Client } = require('vercel-llm-api'),
  client = new Client();

client.on('debug', console.log);