Nous allons créer un agent IA capable de rechercher sur Wikipédia et de répondre aux questions en fonction des informations qu'il trouve. Cet agent ReAct (Reason and Act) utilise l'API Google Generative AI pour traiter les requêtes et générer des réponses. Notre agent pourra :
Un Agent ReAct est un type spécifique d'agent qui suit un cycle Réflexion-Action. Il réfléchit à la tâche en cours, sur la base des informations disponibles et des actions qu'il peut effectuer, puis décide quelle action entreprendre ou s'il faut conclure la tâche.
Notre agent ReAct aura trois états principaux :
Construisons l'agent ReAct étape par étape, en mettant en évidence chaque état.
Tout d'abord, configurez le projet et installez les dépendances :
mkdir react-agent-project cd react-agent-project npm init -y npm install axios dotenv @google/generative-ai
Créez un fichier .env à la racine du projet :
GOOGLE_AI_API_KEY=your_api_key_here
Créez Tools.js avec le contenu suivant :
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;
Créez ReactAgent.js avec le contenu suivant :
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;
Créez index.js avec le contenu suivant :
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);
L'interaction avec Wikipédia se fait en deux étapes principales :
Recherche initiale (fonction wikipedia) :
Recherche détaillée (fonction wikipedia_with_pageId) :
Ce processus permet à l'agent d'abord d'obtenir un aperçu des sujets liés à la requête, puis d'approfondir des sections spécifiques si nécessaire.
Ce qui précède est le contenu détaillé de. pour plus d'informations, suivez d'autres articles connexes sur le site Web de PHP en chinois!
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