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English | 中文

AwesomeChat

演示 gif

目录

特色

🚀快速回复 🤖流式回复 😎对话记忆 👏聊天中断 😃快速部署

安装

npm install awesomechat
or
yarn add awesomechat
or
pnpm install awesomechat

使用

简单对话

import AwesomeChatBot from "awesomechat";
const key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"; // replace with your key
const systemDescription = "You are a good robot";
const chatBot = new AwesomeChatBot(key,systemDescription);

chatBot.chat({
  chatContext: "hello?",
  onReplying(word, reply, id) {
    console.log("onReplying", reply);
  },
  onError(error) {
    console.log("onError", error);
  },
});

上下文对话

import AwesomeChatBot from "awesomechat";
const key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"; // replace with your key
const systemDescription =
  "You're a robot with emotions, and you need to add an emoji to the front of each sentence to express your emotions";

const chatBot = new AwesomeChatBot(key, systemDescription);

chatBot.chat({
  chatContext: [
    { role: "user", content: "100" },
    { role: "user", content: "200" },
    { role: "user", content: "300" },
    {
      role: "user",
      content: "What is the maximum number in the dialog above",
    },
  ],

  onReplying(word, reply, id) {
    console.log("onReplying", reply);
  },
  onReplyEnd(completion, id, usage) {
    if (completion.includes("300")) {
      console.log("😄You are right!");
    }
  },
  onError(error) {
    console.log("onError", error);
  },
});

聊天中断

import AwesomeChatBot from "awesomechat";
const key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"; // replace with your key
const chatBot = new AwesomeChatBot(key);

chatBot.chat({
  chatContext: "Write a 500-word essay about AI",
  onReplyStart() {
    console.log("🚀copy that!");
  },
  onReplying(word, reply) {
    console.log("onReplying", reply);
  },
  onCancel(id, usage) {
    console.log("onCancel", id, usage);
  },
});

setTimeout(() => {
  chatBot.cancel();
}, 6000);

API

bot.chat({options})

键 类型 描述
chatContext ChatContext[] / string 聊天上下文对象数组或表示当前对话上下文的字符串。
beforeReplyStart (promptTokens: number) => boolean; 在对话开始前执行，返回一个布尔值。如果该值为 true，则对话继续进行；如果该值为 false，则会中断后续内容。通常在条件验证和身份验证等场景中使用。
onReplyStart (id: string) => void; 当模型开始生成响应时调用的函数。
onReplying (word: string, reply: string, id: string) => void; 当模型正在生成响应时调用的函数。word参数是当前生成的字符，reply 参数是模型当前生成内容的拼接结果
onReplyEnd (completion: string, id: string, usage: ChatUsage) => void; 当模型生成响应完成时调用的函数。completion 参数是最终生成的响应。
onError (error: Error) => void; 当出现错误时调用的回调函数。error 参数是错误对象。
onCancel (id: string, usage: ChatUsage) => void; 当聊天被取消时执行的回调函数。返回聊天 ID 和使用信息。
chatConfig ChatConfig 额外的配置选项，用于传递给模型。

bot.cancel()

当聊天进行中时取消当前聊天

types

ChatContext

type ChatContext = {
    role: "assistant" | "user" | "system";
    content: string;
};

ChatConfig

type ChatConfig = {
    /**  What sampling temperature to use, between 0 and 2 */
    temperature?: number;
    /**  An alternative to sampling with temperature, where the model considers the results of the tokens with top_p probability mass */
    top_p?: number;
    /**  How many chat completion choices to generate for each input message */
    n?: number;
    /**  Up to 4 sequences where the API will stop generating further tokens */
    stop?: string | string[];
    /**  The maximum number of tokens to generate in the chat completion */
    max_tokens?: number;
    /**  Number between -2.0 and 2.0 */
    presence_penalty?: number;
    /**  Number between -2.0 and 2.0 */
    frequency_penalty?: number;
    /**  A json object that maps tokens to an associated bias value from -100 to 100 */
    logit_bias?: {
        [key: string]: number;
    };
    /**  A unique identifier representing your end-user */
    user?: string;
};

ChatUsage

type ChatUsage = {
    /**  The number of total tokens used*/
    totalTokens: number;
    /**  The number of requests made to the API */
    promptTokens: number;
    /**  The number of tokens generated by the model */
    completionTokens: number;
};