typeai
1.0.0
Typeai是使用Typescript构建启用AI的应用程序的工具包，使事情看起来如此简单，看起来像魔术。更重要的是，它使LLMS“感觉”的建筑物像普通代码一样，阻抗不匹配。
一个例子：
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类型和功能签名，Typeai将生成尊重您类型声明的适当实现。没有加载单独的模式文件，没有提示工程，也没有手动编写功能的JSON模式表示。
在Twitter上关注我：
为了提供有关您的功能和类型的运行时类型信息，需要DeepKit。
npm install @typeai/core @deepkit/core注意：目前，JSDOC的自动提取@Description标签需要这些分叉的NPM软件包构建 @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.ps1tsconfig.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：某些运行时间，例如tsx ，无法与DeepKit一起使用。有关更多信息，请参见Gotchas。
在执行时间
export OPENAI_API_KEY= ' ... ' # currently required for core functionality
export BING_API_KEY= ' ... ' # if using predefined SearchWeb Tool functionTypeai通过使用运行时类型的反射在打字条代码上使用运行时类型的反射来生成OpenAI函数调用功能所需的JSON模式，并通过处理函数调度和结果传递到LLM，将您的功能和类型连接到AI API，例如OpenAI聊天完成端点轻量级。
Typeai目前提供了两个主要功能领域：
要创建AI支持的函数，请编写一个存根函数并将其传递给toAIFunction() ，该功能将生成具有所需行为的AI支持的函数。
/** @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!' )具有复杂输入和输出输入类型的功能也可以正常工作。这是一个更有趣的例子：
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' ,
} ) 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工具函数是为LLM提供的函数，以便其自身生成答案。
假设您有一个功能，并希望为OpenAI的LLM提供其功能，以供其功能调用功能。
看：
Typeai提供了三个功能，使您的功能和模型将您的功能和模型暴露于GPT-3.5/4，并处理来自GPT-3/4的结果函数呼叫请求，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 >它们可以像这样使用：
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!
*/ 由于DeepKit注入了TSC的类型编译器变换的方式，因此某些运行时间可能行不通。这些知道不工作：
tsx typeai使用@deepkit/type提供的打字稿运行时类型信息：
这导致了感觉“本地”的编码体验。
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 ) } ` )注意：OpenAI完成API不喜欢void函数响应。
请参阅LICENDE.TXT
