Wikipedia를 검색하고 찾은 정보를 바탕으로 질문에 답할 수 있는 AI 에이전트를 만들어 보겠습니다. 이 ReAct(Reason and Act) 에이전트는 Google Generative AI API를 사용하여 쿼리를 처리하고 응답을 생성합니다. 우리 대리인은 다음을 수행할 수 있습니다.
ReAct Agent는 Reflection-Action 주기를 따르는 특정 유형의 에이전트입니다. 사용 가능한 정보와 수행할 수 있는 작업을 기반으로 현재 작업을 반영한 다음 수행할 작업 또는 작업 종료 여부를 결정합니다.
ReAct 에이전트에는 세 가지 주요 상태가 있습니다.
각 상태를 강조하면서 ReAct Agent를 단계별로 구축해 보겠습니다.
먼저 프로젝트를 설정하고 종속 항목을 설치합니다.
mkdir react-agent-project cd react-agent-project npm init -y npm install axios dotenv @google/generative-ai
프로젝트 루트에 .env 파일을 만듭니다.
GOOGLE_AI_API_KEY=your_api_key_here
다음 콘텐츠로 Tools.js를 만듭니다.
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;
다음 콘텐츠로 ReactAgent.js를 만듭니다.
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;
다음 콘텐츠로 index.js를 만듭니다.
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);
Wikipedia와의 상호작용은 두 가지 주요 단계로 이루어집니다.
초기 검색(위키피디아 기능):
상세 검색(wikipedia_with_pageId 기능):
이 프로세스를 통해 상담원은 먼저 검색어와 관련된 주제의 개요를 확인한 다음 필요에 따라 특정 섹션을 더 자세히 살펴볼 수 있습니다.
위 내용은 nodeJS를 사용하여 처음부터 ReAct 에이전트 만들기(wikipedia 검색)의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!
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