typeai下載 - typeai源代碼下載

Typeai：一個AI工程框架

Typeai示例

Typeai是使用Typescript構建啟用AI的應用程序的工具包，使事情看起來如此簡單，看起來像魔術。更重要的是，它使LLMS“感覺”的建築物像普通代碼一樣，阻抗不匹配。

一個例子：

 import { toAIFunction } from '@typeai/core'

/** @description Given `text`, returns a number between 1 (positive) and -1 (negative) indicating its sentiment score. */
function sentimentSpec ( text : string ) : number | void { }
const sentiment = toAIFunction ( sentimentSpec )

const score = await sentiment ( 'That was surprisingly easy!' )

只需像自然而然地指定您的類型和功能簽名，Typeai將生成尊重您類型聲明的適當實現。沒有加載單獨的模式文件，沒有提示工程，也沒有手動編寫功能的JSON模式表示。

內容

安裝
用法
- 使用Typeai生成功能
  - AI模型
  - AI功能
  - AI分類器
- 使用Typeai將功能暴露於LLM
  - AI“工具功能”
陷入困境
它如何工作？
未來的方向和招待
致謝
執照

支持

在Twitter上關注我：

安裝

為了提供有關您的功能和類型的運行時類型信息，需要DeepKit。

npm install @typeai/core @deepkit/core

注意：目前，JSDOC的自動提取@Description標籤需要這些分叉的NPM軟件包構建 @deepkit/type和 @deepkit/type-compiler

npm install @deepkit/type@npm:@jefflaporte/[email protected]
npm install --save-dev @deepkit/type-compiler@npm:@jefflaporte/[email protected]
# Bash
./node_modules/.bin/deepkit-type-install
# PowerShell
pwsh ./node_modules/.bin/deepkit-type-install.ps1

tsconfig.json

 // tsconfig.json
{
  "compilerOptions" : {
    // ...

    // Note: DeepKit says that experimentalDecorators is not necessary when using @deepkit/type,
    // but I have found that deepkit's typeOf() does not always work with TypeScript > 4.9
    // without experimentalDecorators set.
    "experimentalDecorators" : true
  } ,
  "reflection" : true
}

注意：某些運行時間，例如tsx ，無法與DeepKit一起使用。有關更多信息，請參見Gotchas。

在執行時間

 export OPENAI_API_KEY= ' ... '    # currently required for core functionality
export BING_API_KEY= ' ... '      # if using predefined SearchWeb Tool function

Typeai通過使用運行時類型的反射在打字條代碼上使用運行時類型的反射來生成OpenAI函數調用功能所需的JSON模式，並通過處理函數調度和結果傳遞到LLM，將您的功能和類型連接到AI API，例如OpenAI聊天完成端點輕量級。

用法

Typeai目前提供了兩個主要功能領域：

產生“魔術” AI支持的功能
- AI模型
- AI功能
- AI分類器
LLM工具功能膠的生成和交接
- AI“工具功能”

AI功能

要創建AI支持的函數，請編寫一個存根函數並將其傳遞給toAIFunction() ，該功能將生成具有所需行為的AI支持的函數。

 /** @description Given `text`, returns a number between 1 (positive) and -1 (negative) indicating its sentiment score. */
function sentimentSpec ( text : string ) : number | void { }
const sentiment = toAIFunction ( sentimentSpec )

const score = await sentiment ( 'That was surprisingly easy!' )

具有復雜輸入和輸出輸入類型的功能也可以正常工作。這是一個更有趣的例子：

 type Patient = {
  name : string
  age : number
  isSmoker : boolean
}
type Diagnosis = {
  condition : string
  diagnosisDate : Date
  stage ?: string
  type ?: string
  histology ?: string
  complications ?: string
}
type Treatment = {
  name : string
  startDate : Date
  endDate ?: Date
}
type Medication = Treatment & {
  dose ?: string
}
type BloodTest = {
  name : string
  result : string
  testDate : Date
}
type PatientData = {
  patient : Patient
  diagnoses : Diagnosis [ ]
  treatments : Treatment | Medication [ ]
  bloodTests : BloodTest [ ]
}

/** @description Returns a PatientData record generate from the content of doctorsNotes notes. */
function generateElectronicHealthRecordSpec ( input : string ) : PatientData | void { }
const generateElectronicHealthRecord = toAIFunction ( generateElectronicHealthRecordSpec , {
  model : 'gpt-4' ,
} )

打字稿枚舉AI支持的分類器

 enum AppRouteEnum {
  USER_PROFILE = '/user-profile' ,
  SEARCH = '/search' ,
  NOTIFICATIONS = '/notifications' ,
  SETTINGS = '/settings' ,
  HELP = '/help' ,
  SUPPORT_CHAT = '/support-chat' ,
  DOCS = '/docs' ,
  PROJECTS = '/projects' ,
  WORKSPACES = '/workspaces' ,
}
const AppRoute = toAIClassifier ( AppRouteEnum )

const appRouteRes = await AppRoute ( 'I need to talk to somebody about billing' )

AI“工具功能”幫助者

AI工具函數是為LLM提供的函數，以便其自身生成答案。

假設您有一個功能，並希望為OpenAI的LLM提供其功能，以供其功能調用功能。

看：

OpenAI博客：函數調用和其他API更新
OpenAI API：創建聊天完成

Typeai提供了三個功能，使您的功能和模型將您的功能和模型暴露於GPT-3.5/4，並處理來自GPT-3/4的結果函數呼叫請求，透明：

 static ToolFunction . from < R > (
  fn : ( ... args : any [ ] ) => R ,
  options ?: ToolFunctionFromOptions
) : ToolFunction

static ToolFunction . modelSubmissionToolFor < T > (
  cb : ( arg : T ) => Promise < void >
) : ToolFunction

function handleToolUse (
  openAIClient : OpenAIApi ,
  originalRequest : CreateChatCompletionRequest ,
  responseData : CreateChatCompletionResponse ,
  options ?: {
    model ?: string ,
    registry ?: SchemaRegistry ,
    handle ?: 'single' | 'multiple'
  } ,
) : Promise < CreateChatCompletionResponse | undefined >

它們可以像這樣使用：

 import {
  OpenAIApi ,
  Configuration ,
  CreateChatCompletionRequest ,
  ChatCompletionRequestMessage ,
  ChatCompletionRequestMessageRoleEnum ,
} from 'openai'
import { ToolFunction , handleToolUse } from '@typeai/core'
import { getCurrentWeather } from 'yourModule'

// Init OpenAI client
const configuration = new Configuration ( { apiKey : process . env . OPENAI_API_KEY } )
const openai = new OpenAIApi ( configuration )

// Generate JSON Schema for function and dependent types
const getCurrentWeatherTool = ToolFunction . from ( getCurrentWeather )

// Run a chat completion sequence
const messages : ChatCompletionRequestMessage [ ] = [
  {
    role : ChatCompletionRequestMessageRoleEnum . User ,
    content : "What's the weather like in Boston? Say it like a weather reporter." ,
  } ,
]
const request : CreateChatCompletionRequest = {
  model : 'gpt-3.5-turbo' ,
  messages ,
  functions : [ getCurrentWeatherTool . schema ] ,
  stream : false ,
  max_tokens : 1000 ,
}
const { data : response } = await openai . createChatCompletion ( request )

// Transparently handle any LLM calls to your function.
// handleToolUse() returns OpenAI's final response after
// any/all function calls have been completed
const responseData = await handleToolUse ( openai , request , response )
const result = responseData ?. choices [ 0 ] . message

/*
Good afternoon, Boston! This is your weather reporter bringing you the latest
updates. Currently, we're experiencing a pleasant temperature of 82 degrees Celsius. The sky is a mix of sunshine and clouds, making for a beautiful day. However, there is a 25% chance of precipitation, so you might want to keep an umbrella handy. Additionally, the atmospheric pressure is at 25 mmHg. Overall, it's a great day to get outside and enjoy the city. Stay safe and have a wonderful time!
*/

陷入困境

由於DeepKit注入了TSC的類型編譯器變換的方式，因此某些運行時間可能行不通。這些知道不工作：

tsx

它如何工作？

typeai使用@deepkit/type提供的打字稿運行時類型信息：

與您的功能存根相同的簽名生成替換功能
生成json架構描述您的功能和相關類型，這些類型已提供給OpenAI API，以便它可以對您所需的類型結構進行評估

這導致了感覺“本地”的編碼體驗。

例子

 import { ToolFunction , handleToolUse } from '@typeai/core'

// Your type definitions
// ...
// Your function definitions dependent on your types
// ...
// eg:
const getCurrentWeather = function getCurrentWeather (
  location : string ,
  unit : TemperatureUnit = 'fahrenheit' ,
  options ?: WeatherOptions ,
) : WeatherInfo {
  const weatherInfo : WeatherInfo = {
    location : location ,
    temperature : 82 ,
    unit : unit ,
    precipitationPct : options ?. flags ?. includePrecipitation ? 25 : undefined ,
    pressureMmHg : options ?. flags ?. includePressure ? 25 : undefined ,
    forecast : [ 'sunny' , 'cloudy' ] ,
  }
  return weatherInfo
}

// Register your function and type info
const getCurrentWeatherTool = ToolFunction . from ( getCurrentWeather )

// Run a completion series
const messages : ChatCompletionRequestMessage [ ] = [
  {
    role : ChatCompletionRequestMessageRoleEnum . User ,
    content : "What's the weather like in Boston? Say it like a weather reporter." ,
  } ,
]
const request : CreateChatCompletionRequest = {
  model : 'gpt-3.5-turbo-0613' ,
  messages ,
  functions : [ getCurrentWeatherTool . schema ] ,
  stream : false ,
  max_tokens : 1000 ,
}
const { data : response } = await openai . createChatCompletion ( request )
const responseData = await handleToolUse ( openai , request , response )
const result = responseData ?. choices [ 0 ] . message
console . log ( `LLM final result: ${ JSON . stringify ( result , null , 2 ) } ` )