AI Agent

import { AIAgent } from "@aigne/core";
import { OpenAI } from "@aigne/openai";

// 使用するモデルを設定
const model = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  model: "gpt-4o",
});

// AI Agent を作成
const chatAgent = new AIAgent({
  instructions: "You are a helpful assistant.",
  inputKey: "question",
  outputKey: "answer",
});

// Agent を実行するには、AIGNE の invoke メソッドを使用します
// const aigne = new AIGNE({ model });
// const response = await aigne.invoke(chatAgent, { question: "What is AIGNE?" });
// console.log(response.answer);

import { AIAgent, FunctionAgent } from "@aigne/core";
import { OpenAI } from "@aigne/openai";

// 天気を取得する単純な関数
function getCurrentWeather(location) {
  if (location.toLowerCase().includes("tokyo")) {
    return JSON.stringify({ location: "Tokyo", temperature: "15", unit: "celsius" });
  }
  return JSON.stringify({ location, temperature: "unknown" });
}

// 関数を FunctionAgent でラップしてツールにする
const weatherTool = new FunctionAgent({
  name: "get_current_weather",
  description: "Get the current weather in a given location",
  inputSchema: {
    type: "object",
    properties: { location: { type: "string", description: "The city and state" } },
    required: ["location"],
  },
  process: ({ location }) => getCurrentWeather(location),
});

// モデルを設定
const model = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  model: "gpt-4o",
});

// 天気ツールを使用できる AI Agent を作成
const weatherAssistant = new AIAgent({
  instructions: "You are a helpful assistant that can provide weather forecasts.",
  inputKey: "query",
  outputKey: "response",
});

// 呼び出す際に、ツールをスキルとして提供する
// const aigne = new AIGNE({ model, skills: [weatherTool] });
// const result = await aigne.invoke(weatherAssistant, { query: "What's the weather like in Tokyo?" });
// console.log(result.response); // LLM はツールの出力を使用して応答します

import { AIAgent } from "@aigne/core";
import { OpenAI } from "@aigne/openai";

const sentimentAnalyzer = new AIAgent({
  instructions: `
    Analyze the sentiment of the user's message.
    Respond with a single word summary, followed by a structured analysis.
    Place the structured analysis in YAML format inside <metadata> tags.
    The structure should contain 'sentiment' (positive, negative, or neutral) and a 'score' from -1.0 to 1.0.
  `,
  inputKey: "message",
  outputKey: "summary",
  structuredStreamMode: true,
});

// 呼び出されると、出力にはテキストの要約と
// 解析された JSON オブジェクトの両方が含まれます。
// const aigne = new AIGNE({ model: new OpenAI(...) });
// const result = await aigne.invoke(sentimentAnalyzer, { message: "AIGNE is an amazing framework!" });
/*
  期待される結果:
  {
    summary: "Positive.",
    sentiment: "positive",
    score: 0.9
  }
*/