Awesome
llm-ts
A Typescript library to use LLM providers APIs in a unified way.
Features include:
- Models list
- Chat completion
- Chat streaming
- Text Attachments
- Vision model (image attachments)
- Function calling
- Usage reporting (tokens count)
Providers supported
Not all providers support a "get models" end point. Those who do are listed as dynamic
in the table below. For those who are listed as static
, the list of models is hardcoded.
Provider | id | Models | Completion | Streaming | Vision | Function calling | Usage reporting |
---|---|---|---|---|---|---|---|
Anthropic | anthropic | static | yes | yes | yes | yes | yes |
Cerebras | cerebras | static | yes | yes | no | no | yes |
google | static | yes | yes | yes | yes | yes | |
Groq | groq | static | yes | yes | yes | yes | yes |
MistralAI | mistralai | dynamic | yes | yes | yes | yes | yes |
Ollama | ollama | dynamic | yes | yes | yes | yes | yes |
OpenAI | openai | dynamic | yes | yes | yes<sup>1</sup> | yes<sup>1</sup> | yes |
TogetherAI<sup>2</sup> | openai | dynamic | yes | yes | yes<sup>1</sup> | yes<sup>1</sup> | yes |
xAI | xai | static | yes | yes | no | yes | yes |
See it in action
npm i
API_KEY=your-openai-api-key npm run example
You can run it for another provider:
npm i
API_KEY=your-anthropic_api_key ENGINE=anthropic MODEL=claude-3-haiku-20240307 npm run example
Usage
Installation
npm i multi-llm-ts
Loading models
You can download the list of available models for any provider.
const config = { apiKey: 'YOUR_API_KEY' }
const models = await loadModels('PROVIDER_ID', config)
console.log(models.chat)
Chat completion
const llm = igniteEngine('PROVIDER_ID', { apiKey: 'YOUR_API_KEY' })
const messages = [
new Message('system', 'You are a helpful assistant'),
new Message('user', 'What is the capital of France?'),
]
await llm.complete('MODEL_ID', messages)
Chat streaming
const llm = igniteEngine('PROVIDER_ID', { apiKey: 'YOUR_API_KEY' })
const messages = [
new Message('system', 'You are a helpful assistant'),
new Message('user', 'What is the capital of France?'),
]
const stream = llm.generate('MODEL_ID', messages)
for await (const chunk of stream) {
console.log(chunk)
}
Function calling
const llm = igniteEngine('PROVIDER_ID', { apiKey: 'YOUR_API_KEY' })
llm.addPlugin(new MyPlugin())
const messages = [
new Message('system', 'You are a helpful assistant'),
new Message('user', 'What is the capital of France?'),
]
const stream = llm.generate('MODEL_ID', messages)
for await (const chunk of stream) {
// use chunk.type to decide what to do
// type == 'tool' => tool usage status information
// type == 'content' => generated text
console.log(chunk)
}
You can easily implement Image generation using DALL-E with a Plugin class such as:
export default class extends Plugin {
constructor(config: PluginConfig) {
super(config)
}
isEnabled(): boolean {
return config?.apiKey != null
}
getName(): string {
return 'dalle_image_generation'
}
getDescription(): string {
return 'Generate an image based on a prompt. Returns the path of the image saved on disk and a description of the image.'
}
getPreparationDescription(): string {
return this.getRunningDescription()
}
getRunningDescription(): string {
return 'Painting pixels…'
}
getParameters(): PluginParameter[] {
const parameters: PluginParameter[] = [
{
name: 'prompt',
type: 'string',
description: 'The description of the image',
required: true
}
]
// rest depends on model
if (store.config.engines.openai.model.image === 'dall-e-2') {
parameters.push({
name: 'size',
type: 'string',
enum: [ '256x256', '512x512', '1024x1024' ],
description: 'The size of the image',
required: false
})
} else if (store.config.engines.openai.model.image === 'dall-e-3') {
parameters.push({
name: 'quality',
type: 'string',
enum: [ 'standard', 'hd' ],
description: 'The quality of the image',
required: false
})
parameters.push({
name: 'size',
type: 'string',
enum: [ '1024x1024', '1792x1024', '1024x1792' ],
description: 'The size of the image',
required: false
})
parameters.push({
name: 'style',
type: 'string',
enum: ['vivid', 'natural'],
description: 'The style of the image',
required: false
})
}
// done
return parameters
}
async execute(parameters: any): Promise<any> {
// init
const client = new OpenAI({
apiKey: config.apiKey,
dangerouslyAllowBrowser: true
})
// call
console.log(`[openai] prompting model ${model}`)
const response = await client.images.generate({
model: 'dall-e-2',
prompt: parameters?.prompt,
response_format: 'b64_json',
size: parameters?.size,
style: parameters?.style,
quality: parameters?.quality,
n: parameters?.n || 1,
})
// return an object
return {
path: fileUrl,
description: parameters?.prompt
}
}
}
Tests
npm run test