ホームページ > 記事 > ウェブフロントエンド > NodeJS を使用して ReAct Agent を最初から作成する (wikipedia 検索)
Wikipedia を検索し、見つかった情報に基づいて質問に答えることができる AI エージェントを作成します。この ReAct (Reason and Act) エージェントは、Google Generative AI API を使用してクエリを処理し、応答を生成します。私たちのエージェントは次のことができるようになります:
ReAct Agent は、Reflection-Action サイクルに従う特定のタイプのエージェントです。利用可能な情報と実行可能なアクションに基づいて現在のタスクを反映し、どのアクションを実行するか、またはタスクを完了するかどうかを決定します。
ReAct Agent には 3 つの主な状態があります:
各状態を強調表示しながら、ReAct Agent を段階的に構築してみましょう。
まず、プロジェクトをセットアップし、依存関係をインストールします。
mkdir react-agent-project cd react-agent-project npm init -y npm install axios dotenv @google/generative-ai
プロジェクトのルートに .env ファイルを作成します:
GOOGLE_AI_API_KEY=your_api_key_here
次の内容を含む Tools.js を作成します:
const axios = require("axios"); class Tools { static async wikipedia(q) { try { const response = await axios.get("https://en.wikipedia.org/w/api.php", { params: { action: "query", list: "search", srsearch: q, srwhat: "text", format: "json", srlimit: 4, }, }); const results = await Promise.all( response.data.query.search.map(async (searchResult) => { const sectionResponse = await axios.get( "https://en.wikipedia.org/w/api.php", { params: { action: "parse", pageid: searchResult.pageid, prop: "sections", format: "json", }, }, ); const sections = Object.values( sectionResponse.data.parse.sections, ).map((section) => `${section.index}, ${section.line}`); return { pageTitle: searchResult.title, snippet: searchResult.snippet, pageId: searchResult.pageid, sections: sections, }; }), ); return results .map( (result) => `Snippet: ${result.snippet}\nPageId: ${result.pageId}\nSections: ${JSON.stringify(result.sections)}`, ) .join("\n\n"); } catch (error) { console.error("Error fetching from Wikipedia:", error); return "Error fetching data from Wikipedia"; } } static async wikipedia_with_pageId(pageId, sectionId) { if (sectionId) { const response = await axios.get("https://en.wikipedia.org/w/api.php", { params: { action: "parse", format: "json", pageid: parseInt(pageId), prop: "wikitext", section: parseInt(sectionId), disabletoc: 1, }, }); return Object.values(response.data.parse?.wikitext ?? {})[0]?.substring( 0, 25000, ); } else { const response = await axios.get("https://en.wikipedia.org/w/api.php", { params: { action: "query", pageids: parseInt(pageId), prop: "extracts", exintro: true, explaintext: true, format: "json", }, }); return Object.values(response.data?.query.pages)[0]?.extract; } } } module.exports = Tools;
次の内容で ReactAgent.js を作成します:
require("dotenv").config(); const { GoogleGenerativeAI } = require("@google/generative-ai"); const Tools = require("./Tools"); const genAI = new GoogleGenerativeAI(process.env.GOOGLE_AI_API_KEY); class ReActAgent { constructor(query, functions) { this.query = query; this.functions = new Set(functions); this.state = "THOUGHT"; this._history = []; this.model = genAI.getGenerativeModel({ model: "gemini-1.5-flash", temperature: 2, }); } get history() { return this._history; } pushHistory(value) { this._history.push(`\n ${value}`); } async run() { this.pushHistory(`**Task: ${this.query} **`); try { return await this.step(); } catch (e) { if (e.message.includes("exhausted")) { return "Sorry, I'm exhausted, I can't process your request anymore. ><"; } return "Unable to process your request, please try again? ><"; } } async step() { const colors = { reset: "\x1b[0m", yellow: "\x1b[33m", red: "\x1b[31m", cyan: "\x1b[36m", }; console.log("===================================="); console.log( `Next Movement: ${ this.state === "THOUGHT" ? colors.yellow : this.state === "ACTION" ? colors.red : this.state === "ANSWER" ? colors.cyan : colors.reset }${this.state}${colors.reset}`, ); console.log(`Last Movement: ${this.history[this.history.length - 1]}`); console.log("===================================="); switch (this.state) { case "THOUGHT": await this.thought(); break; case "ACTION": await this.action(); break; case "ANSWER": await this.answer(); break; } } async promptModel(prompt) { const result = await this.model.generateContent(prompt); const response = await result.response; return response.text(); } async thought() { const availableFunctions = JSON.stringify(Array.from(this.functions)); const historyContext = this.history.join("\n"); const prompt = `Your task to FullFill ${this.query}. Context contains all the reflection you made so far and the ActionResult you collected. AvailableActions are functions you can call whenever you need more data. Context: "${historyContext}" << AvailableActions: "${availableFunctions}" << Task: "${this.query}" << Reflect uppon Your Task using Context, ActionResult and AvailableActions to find your next_step. print your next_step with a Thought or FullFill Your Task `; const thought = await this.promptModel(prompt); this.pushHistory(`\n **${thought.trim()}**`); if ( thought.toLowerCase().includes("fullfill") || thought.toLowerCase().includes("fulfill") ) { this.state = "ANSWER"; return await this.step(); } this.state = "ACTION"; return await this.step(); } async action() { const action = await this.decideAction(); this.pushHistory(`** Action: ${action} **`); const result = await this.executeFunctionCall(action); this.pushHistory(`** ActionResult: ${result} **`); this.state = "THOUGHT"; return await this.step(); } async decideAction() { const availableFunctions = JSON.stringify(Array.from(this.functions)); const historyContext = this.history; const prompt = `Reflect uppon the Thought, Query and AvailableActions ${historyContext[historyContext.length - 2]} Thought <<< ${historyContext[historyContext.length - 1]} Query: "${this.query}" AvailableActions: ${availableFunctions} output only the function,parametervalues separated by a comma. For example: "wikipedia,ronaldinho gaucho, 1450"`; const decision = await this.promptModel(prompt); return `${decision.replace(/`/g, "").trim()}`; } async executeFunctionCall(functionCall) { const [functionName, ...args] = functionCall.split(","); const func = Tools[functionName.trim()]; if (func) { return await func.call(null, ...args); } throw new Error(`Function ${functionName} not found`); } async answer() { const historyContext = this.history; const prompt = `Based on the following context, provide a complete, detailed and descriptive formated answer for the Following Task: ${this.query} . Context: ${historyContext} Task: "${this.query}"`; const finalAnswer = await this.promptModel(prompt); this.history.push(`Answer: ${this.finalAnswer}`); console.log("WE WILL ANSWER >>>>>>>", finalAnswer); return finalAnswer; } } module.exports = ReActAgent;
次の内容でindex.jsを作成します:
const ReActAgent = require("./ReactAgent.js"); async function main() { const query = "What does England border with?"; const functions = [ [ "wikipedia", "params: query", "Semantic Search Wikipedia API for snippets, pageIds and sectionIds >> \n ex: Date brazil has been colonized? \n Brazil was colonized at 1500, pageId, sections : []", ], [ "wikipedia_with_pageId", "params : pageId, sectionId", "Search Wikipedia API for data using a pageId and a sectionIndex as params. \n ex: 1500, 1234 \n Section information about blablalbal", ], ]; const agent = new ReActAgent(query, functions); try { const result = await agent.run(); console.log("THE AGENT RETURN THE FOLLOWING >>>", result); } catch (e) { console.log("FAILED TO RUN T.T", e); } } main().catch(console.error);
Wikipedia との対話は 2 つの主な手順で行われます:
初期検索 (wikipedia 機能):
詳細検索 (wikipedia_with_pageId 関数):
このプロセスにより、エージェントはまずクエリに関連するトピックの概要を把握し、次に必要に応じて特定のセクションをさらに深く掘り下げることができます。
以上がNodeJS を使用して ReAct Agent を最初から作成する (wikipedia 検索)の詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。