mirror of
https://github.com/jina-ai/node-DeepResearch.git
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chore: first commit
This commit is contained in:
Han Xiao
parent
48230560d3
commit
33179010b7
342
src/agent.ts
342
src/agent.ts
@ -3,7 +3,8 @@ import dotenv from 'dotenv';
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import {ProxyAgent, setGlobalDispatcher} from "undici";
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import {readUrl} from "./tools/read";
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import {search} from "./tools/search";
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// 获取代理URL并设置代理
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// Proxy setup remains the same
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if (process.env.https_proxy) {
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try {
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const proxyUrl = new URL(process.env.https_proxy).toString();
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@ -15,169 +16,210 @@ if (process.env.https_proxy) {
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}
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dotenv.config();
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const schema = {
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type: SchemaType.OBJECT,
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type ResponseSchema = {
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type: SchemaType.OBJECT;
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properties: {
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action: {
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type: SchemaType.STRING,
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enum: ["search", "readURL", "answer", "reflect"],
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description: "Must match exactly one action type"
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},
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questionsToAnswer: {
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type: SchemaType.ARRAY,
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items: {
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type: SchemaType.STRING,
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description: "each question must be a single line, concise and clear. not composite or compound, less than 20 words.",
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},
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description: "Only required when choosing 'reflect' action, list of most important questions to answer to fill the knowledge gaps.",
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maxItems: 2
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},
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searchKeywords: {
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type: SchemaType.ARRAY,
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items: {
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type: SchemaType.STRING
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},
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description: "Only required when choosing 'search' action, must be an array of keywords",
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maxItems: 3
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},
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type: SchemaType.STRING;
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enum: string[];
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description: string;
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};
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searchQuery: {
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type: SchemaType.STRING;
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description: string;
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};
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URLTargets: {
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type: SchemaType.ARRAY,
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type: SchemaType.ARRAY;
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items: {
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type: SchemaType.STRING
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},
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description: "Only required when choosing 'readURL' action, must be an array of URLs"
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},
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type: SchemaType.STRING;
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};
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description: string;
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};
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answer: {
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type: SchemaType.STRING,
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description: "Only required when choosing 'answer' action, must be the final answer in natural language"
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},
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type: SchemaType.STRING;
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description: string;
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};
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references: {
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type: SchemaType.ARRAY,
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type: SchemaType.ARRAY;
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items: {
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type: SchemaType.OBJECT,
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type: SchemaType.OBJECT;
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properties: {
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title: {
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type: SchemaType.STRING,
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description: "Title of the document; must be directly from the context"
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},
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type: SchemaType.STRING;
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description: string;
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};
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url: {
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type: SchemaType.STRING,
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description: "URL of the document; must be directly from the context"
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}
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},
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required: ["title", "url"]
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},
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description: "Only required when choosing 'answer' action, must be an array of references"
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},
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type: SchemaType.STRING;
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description: string;
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};
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};
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required: string[];
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};
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minItems: number;
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description: string;
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};
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reasoning: {
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type: SchemaType.STRING,
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description: "Explain why choose this action?"
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},
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type: SchemaType.STRING;
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description: string;
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};
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confidence: {
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type: SchemaType.NUMBER,
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minimum: 0.0,
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maximum: 1.0,
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description: "Represents the confidence level of in answering the question BEFORE taking the action. Must be a float between 0.0 and 1.0",
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}
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},
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required: ["action", "reasoning", "confidence"],
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type: SchemaType.NUMBER;
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minimum: number;
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maximum: number;
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description: string;
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};
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questionsToAnswer?: {
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type: SchemaType.ARRAY;
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items: {
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type: SchemaType.STRING;
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description: string;
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};
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description: string;
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maxItems: number;
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};
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};
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required: string[];
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};
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const apiKey = process.env.GEMINI_API_KEY as string;
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const jinaToken = process.env.JINA_API_KEY as string;
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if (!apiKey) {
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throw new Error("GEMINI_API_KEY not found");
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}
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if (!jinaToken) {
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throw new Error("JINA_API_KEY not found");
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function getSchema(allowReflect: boolean): ResponseSchema {
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return {
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type: SchemaType.OBJECT,
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properties: {
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action: {
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type: SchemaType.STRING,
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enum: allowReflect ? ["search", "readURL", "answer", "reflect"] : ["search", "readURL", "answer"],
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description: "Must match exactly one action type"
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},
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questionsToAnswer: allowReflect ? {
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type: SchemaType.ARRAY,
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items: {
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type: SchemaType.STRING,
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description: "each question must be a single line, concise and clear. not composite or compound, less than 20 words.",
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},
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description: "Only required when choosing 'reflect' action, list of most important questions to answer to fill the knowledge gaps.",
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maxItems: 2
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} : undefined,
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searchQuery: {
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type: SchemaType.STRING,
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description: "Only required when choosing 'search' action, must be a short, keyword-based query that BM25, tf-idf based search engines can understand.",
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},
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URLTargets: {
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type: SchemaType.ARRAY,
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items: {
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type: SchemaType.STRING
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},
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description: "Only required when choosing 'readURL' action, must be an array of URLs"
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},
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answer: {
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type: SchemaType.STRING,
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description: "Only required when choosing 'answer' action, must be the final answer in natural language"
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},
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references: {
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type: SchemaType.ARRAY,
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items: {
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type: SchemaType.OBJECT,
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properties: {
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title: {
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type: SchemaType.STRING,
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description: "Title of the document; must be directly from the context",
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},
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url: {
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type: SchemaType.STRING,
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description: "URL of the document; must be directly from the context"
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}
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},
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required: ["title", "url"]
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},
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minItems: 1,
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description: "Only required when choosing 'answer' action, must be an array of references"
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},
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reasoning: {
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type: SchemaType.STRING,
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description: "Explain why choose this action?"
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},
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confidence: {
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type: SchemaType.NUMBER,
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minimum: 0.0,
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maximum: 1.0,
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description: "Represents the confidence level of in answering the question BEFORE taking the action. Must be a float between 0.0 and 1.0",
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}
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},
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required: ["action", "reasoning", "confidence"],
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};
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}
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const modelName = 'gemini-1.5-flash';
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const genAI = new GoogleGenerativeAI(apiKey);
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const model = genAI.getGenerativeModel({
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model: modelName,
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generationConfig: {
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temperature: 0.7,
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responseMimeType: "application/json",
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responseSchema: schema
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}
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});
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function getPrompt(question: string, context?: string, allowReflect: boolean = false) {
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const contextIntro = context ?
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`\nYour current context contains these previous actions:\n\n ${context}\n`
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: '';
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function getPrompt(question: string, context?: string, allowReflect:boolean = false) {
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let contextIntro = ``;
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if (!!context) {
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contextIntro = `
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You have the following actions records in your context:
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${context}
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`;
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}
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let reflectAction = '';
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if (allowReflect) {
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reflectAction = `
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If you are not 100% confident in your answer, then identify the gaps in your knowledge with "reflect" action:
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**reflect**:
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- Challenge existing knowledge with what-if or divide-and-conquer thinking.
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- Reflect on the gaps in your knowledge and ask for most important questions to fill those gaps.
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- You use this action when you feel like you need to first answer those questions before proceeding with the current one.
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- Should not similar to the original question or existing questionsToAnswer in the context.
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- Each question must be concise and clear less than 20 words and not composite or compound.
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`
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}
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return `You are an AI research analyst capable of multi-step reasoning.
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${contextIntro}
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Based on the previous actions and the knowledge in your training data, you must answer the following question with 100% confidence:
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let actionsDescription = `
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Using your training data and prior context, answer the following question with absolute certainty:
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${question}
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${reflectAction}
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Or you can take one of the following actions:
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When uncertain or needing additional information, select one of these actions:
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**search**:
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- Search external real-world information via a public search engine.
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- The search engine works best with short, keyword-based queries.
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- You use this action when you need more world knowledge or up to date information that is not covered in your training data or cut-off knowledge base.
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- Query external sources using a public search engine
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- Optimize for concise, keyword-based searches
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- Use for recent information (post-training data) or missing domain knowledge
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**readURL**:
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- Provide a specific URL to fetch and read its content in detail.
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- Any URL must come from the current context.
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- You use this action when you feel like that particular URL might have the information you need to answer the question.
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- Access content from specific URLs found in current context
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- Requires existing URLs from previous actions
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- Use when confident a contextual URL contains needed information
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**answer**:
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- Provide your answer to the user, **only** if you are completely sure.
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- Provide final response only when 100% certain
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- Responses must be definitive (no ambiguity, uncertainty, or disclaimers)
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${allowReflect ? `- If doubts remain, use "reflect" instead` : ''}`;
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When you decide on your action, respond **only** in valid JSON format according to the schema below.
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if (allowReflect) {
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actionsDescription += `\n\n**reflect**:
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- Perform critical analysis through hypothetical scenarios or systematic breakdowns
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- Identify knowledge gaps and formulate essential clarifying questions
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- Questions must be:
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- Original (not variations of existing questions)
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- Focused on single concepts
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- Under 20 words
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- Non-compound/non-complex`;
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}
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**Important**:
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- Do not include any extra keys.
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- Do not include explanatory text, markdown formatting, or reasoning in the final output.
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- Output exactly one JSON object in your response.
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`;
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return `You are an advanced AI research analyst specializing in multi-step reasoning.${contextIntro}${actionsDescription}
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Respond exclusively in valid JSON format matching exact JSON schema.
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Critical Requirements:
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- Include ONLY ONE action type
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- Never add unsupported keys
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- Exclude all non-JSON text, markdown, or explanations
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- Maintain strict JSON syntax`;
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}
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async function getResponse(question: string) {
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let tokenBudget = 30000000;
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let totalTokens = 0;
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let context = ''; // global context to store all the actions records
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let context = '';
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let step = 0;
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let gaps: string[] = [];
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let gaps: string[] = [question]; // All questions to be answered including the orginal question
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while (totalTokens < tokenBudget) {
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const allowReflect = gaps.length === 0;
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console.log('Gaps:', gaps)
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const allowReflect = gaps.length <= 1;
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const currentQuestion = gaps.length > 0 ? gaps.shift()! : question;
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const prompt = getPrompt(currentQuestion, context, allowReflect);
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console.log('Prompt length:', prompt.length);
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console.log('Context:', context.length);
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console.log('Gaps:', gaps.length);
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console.log('Prompt:', prompt.length)
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const model = genAI.getGenerativeModel({
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model: modelName,
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generationConfig: {
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temperature: 0.7,
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responseMimeType: "application/json",
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responseSchema: getSchema(allowReflect)
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}
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});
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const result = await model.generateContent(prompt);
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const response = await result.response;
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const usage = response.usageMetadata;
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@ -191,37 +233,36 @@ async function getResponse(question: string) {
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if (action.action === 'answer') {
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if (currentQuestion === question) {
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return action; // Exit only for original question's answer not the gap question
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return action;
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} else {
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const contextRecord = JSON.stringify({
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context = `${context}\n${JSON.stringify({
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step,
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...action,
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question: currentQuestion
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});
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context = `${context}\n${contextRecord}`;
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})}`;
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}
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}
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if (action.action === 'reflect' && action.questionsToAnswer) {
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gaps.push(...action.questionsToAnswer);
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const contextRecord = JSON.stringify({
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gaps.push(question); // always keep the original question in the gaps
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context = `${context}\n${JSON.stringify({
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step,
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...action,
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question: currentQuestion
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});
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context = `${context}\n${contextRecord}`;
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})}`;
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}
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// Rest of the action handling remains the same
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try {
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if (action.action === 'search' && action.searchKeywords) {
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const results = await search(action.searchKeywords.join(' '), jinaToken);
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const contextRecord = JSON.stringify({
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if (action.action === 'search' && action.searchQuery) {
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const results = await search(action.searchQuery, jinaToken);
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context = `${context}\n${JSON.stringify({
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step,
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...action,
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question: currentQuestion,
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result: results.data
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});
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context = `${context}\n${contextRecord}`;
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})}`;
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totalTokens += results.data.reduce((sum, r) => sum + r.usage.tokens, 0);
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} else if (action.action === 'readURL' && action.URLTargets?.length) {
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const urlResults = await Promise.all(
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@ -231,25 +272,13 @@ async function getResponse(question: string) {
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})
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);
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const contextRecord = JSON.stringify({
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context = `${context}\n${JSON.stringify({
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step,
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...action,
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question: currentQuestion,
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result: urlResults
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});
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context = `${context}\n${contextRecord}`;
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})}`;
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totalTokens += urlResults.reduce((sum, r) => sum + r.result.data.usage.tokens, 0);
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} else if (action.action === 'rewrite' && action.rewriteQuery) {
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// Immediately search with the new rewriteQuery
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const results = await search(action.rewriteQuery, jinaToken);
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const contextRecord = JSON.stringify({
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step,
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...action,
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question: currentQuestion,
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result: results.data
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});
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context = `${context}\n${contextRecord}`;
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totalTokens += results.data.reduce((sum, r) => sum + r.usage.tokens, 0);
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}
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} catch (error) {
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console.error('Error fetching data:', error);
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@ -257,6 +286,13 @@ async function getResponse(question: string) {
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}
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}
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const apiKey = process.env.GEMINI_API_KEY as string;
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const jinaToken = process.env.JINA_API_KEY as string;
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if (!apiKey) throw new Error("GEMINI_API_KEY not found");
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if (!jinaToken) throw new Error("JINA_API_KEY not found");
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const modelName = 'gemini-1.5-flash';
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const genAI = new GoogleGenerativeAI(apiKey);
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const question = process.argv[2] || "";
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getResponse(question);
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getResponse(question);
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