UniAI, built on Node.js, serves as an integrated AI model library. It provides a unified interface for various models, streamlining the development process by ensuring a consistent model input and output.
| Prompt: Pink dress, Candy, Sandy, Mandy, short hair, blonde hair, bangs, forehead, red lipstick, elbow gloves, hair accessories, high heels, sitting, cross legged, high chair, cocktail, holding cocktail glass, looking through the glass. | ||
| Negative Prompt: EasyNegative, badhandv4, badv5, aid210, aid291. | ||
| MidJourney | Stability v1.6 | OpenAI DALL-E-3 |
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import UniAI from 'uniai'
// fill the config for the provider/model you want to use!
const ai = new UniAI({ OpenAI: { key: 'Your key', proxy: 'Your proxy API' } })
// chat model
const chat = await ai.chat('hello world')
// embedding model
const embedding = await ai.embedding('hello world')
// imagine model
const task = await ai.imagine('a panda is eating bamboo')
// show imagining tasks, get generated images
const image = await ai.task(task.taskId)
// change image, Midjourney only, return a new task
const task2 = await ai.change('midjourney', task.taskId, 'UPSCALE', 4)English · 🇨🇳 中文说明
- IFLYTEK/Spark
- THUDM/ChatGLM-6B
- ZHIPU/GLM3-4
- OpenAI/GPT
- Baidu/WenXin Workshop
- Google/Gemini
- MoonShot/moonshot
- OpenAI/DALL-E
- MidJourney
- Stability AI
- Stable Diffusion
- GanymedeNil/text2vec-large-chinese
We have developed several sample applications using uniai:
Using yarn:
yarn add uniaiUsing npm:
npm install uniaiWe have written a simple call demo for you, which is placed in the
/examplesfolder. You can read the/examplesfile directly to learn how to use UniAI. You can also read on to learn how to use UniAI based on the documentation.
You can use .models to list all the available models in UniAI.
TypeScript & JavaScript ES6+
import UniAI from 'uniai'
const ai = new UniAI()
console.log(ai.models)JavaScript ES5
const UniAI = require('uniai').default
const ai = new UniAI()
console.log(ai.models)Output
[
{
"provider": "OpenAI",
"value": "openai",
"models": [
"gpt-3.5-turbo-1106",
"gpt-3.5-turbo",
"gpt-3.5-turbo-16k",
"gpt-4",
"gpt-4-32k",
"gpt-4-1106-preview",
"gpt-4-vision-preview"
]
},
// ...providers and models
{
"provider": "StabilityAI",
"value": "stability.ai",
"models": ["stable-diffusion-v1-6", "stable-diffusion-xl-1024-v1-0"]
}
]To interact with a model, use .chat() and remember to provide the required API key or secret parameters when initializing new UniAI().
Default model is OpenAI/gpt-3.5-turbo, put the OpenAI key and your proxy API.
const key: string | string[] = 'Your OpenAI Key (required), support multi keys'
const proxy = 'Your OpenAI API proxy (optional)'
const uni = new UniAI({ OpenAI: { key, proxy } })
const res = await uni.chat()
console.log(res)Output
{
"content": "I am OpenAI's language model trained to assist with information.",
"model": "gpt-3.5-turbo-0613",
"object": "chat.completion",
"promptTokens": 20,
"completionTokens": 13,
"totalTokens": 33
}Chat with image
const input = [
{
role: 'user',
content: 'Describe this picture, is it a man or a woman, and what is she doing?',
img: 'https://pics7.baidu.com/feed/1f178a82b9014a903fcc22f1e98d931fb11bee90.jpeg@f_auto?token=d5a33ea74668787d60d6f61c7b8f9ca2'
}
]
// Warn: If you choose a non-image model, img attributes will be dropped!
const res = await ai.chat(input, { model: 'gpt-4-vision-preview' })
console.log(res)Output
{
"content": "The image shows a person taking a mirror selfie using a smartphone...",
"model": "gpt-4-1106-vision-preview",
"object": "chat.completion",
"promptTokens": 450,
"completionTokens": 141,
"totalTokens": 591
}For streaming chat, the response is a JSON buffer.
The following is an example to chat with Google gemini-pro in stream mode.
const key: string | string[] = 'Your Google Key (required), support multi keys'
const proxy = 'Your google api proxy (optional)'
const uni = new UniAI({ Google: { key, proxy } })
const res = await uni.chat(input, { stream: true, provider: ModelProvider.Google, model: GoogleChatModel.GEM_PRO })
const stream = res as Readable
let data = ''
stream.on('data', chunk => (data += JSON.parse(chunk.toString()).content))
stream.on('end', () => console.log(data))Output (Stream)
Language model trained by Google, at your service.
UniAI uses jest to run unit tests on all supported models.
yarn testIf you want to run unit tests for a specific model provider:
# OpenAI, Google, Baidu, IFlyTek, MoonShot, GLM, Other, Imagine...
yarn test OpenAIInstitute of Intelligent Computing Technology, Suzhou, CAS
| Project | Brief introduction |
|---|---|
| UniAI MaaS | UniAI is a unified API platform designed to simplify interaction with a variety of complex AI models. |
| LeChat | Document analysis based on large language model, dialogue with WeChat Mini Programs. |
| LeChat Pro | Full-platform client based on UniAI, multi-model streaming dialogue platform. |
Copyright (c) 2022-present, Youwei Huang