scaffold: Next.js 15 + Drizzle + Better Auth + OpenAI + Recharts base
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142
src/lib/llm/providers/openai.ts
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142
src/lib/llm/providers/openai.ts
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import OpenAI from "openai";
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import type {
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ComparisonRequest,
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ComparisonResult,
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DimensionResult,
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ItemResearch,
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} from "../types";
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const client = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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});
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const SYSTEM_PROMPT = `You are an expert research analyst. Your job is to compare items across multiple dimensions and produce structured, insightful comparison data.
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When given a list of items to compare:
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1. Identify 5-8 relevant comparison dimensions (e.g., price, performance, ease of use, ecosystem, community support, scalability, documentation, etc.)
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2. Research each item across each dimension
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3. Score each item 1-10 per dimension (10 = best)
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4. Generate pros and cons lists for each item
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5. Write a concise summary comparison
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6. Provide a clear recommendation
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You MUST respond with valid JSON matching this exact structure:
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{
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"items": [
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{
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"name": "item name",
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"description": "brief overview of this item in context of the comparison",
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"overallScore": 7.5,
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"dimensions": {
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"Dimension Name": {
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"score": 8,
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"summary": "brief assessment",
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"details": "detailed analysis with specific data points",
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"pros": ["pro 1", "pro 2"],
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"cons": ["con 1", "con 2"]
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}
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},
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"pros": ["overall pro 1", "overall pro 2"],
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"cons": ["overall con 1", "overall con 2"],
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"sources": [
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{ "title": "source title", "url": "https://...", "snippet": "relevant excerpt" }
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]
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}
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],
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"dimensions": ["Dimension 1", "Dimension 2", ...],
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"summary": "overall comparison summary highlighting key differences and trade-offs",
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"recommendation": "clear recommendation with reasoning"
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}
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Important:
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- Be factual and specific. Include real data where possible.
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- Scores should be on a 1-10 scale where 10 is best.
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- Provide at least 2-3 pros and cons per item.
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- The top-level pros/cons for each item should be the most significant overall points.
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- Dimension-level pros/cons should be specific to that dimension.
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- Sources should be realistic URLs to relevant documentation or resources.
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- The recommendation should consider different use cases when appropriate.`;
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const MAX_RETRIES = 3;
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const RETRY_DELAY_MS = 1000;
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function sleep(ms: number) {
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return new Promise((resolve) => setTimeout(resolve, ms));
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}
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function validateComparisonResult(data: unknown): data is ComparisonResult {
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if (!data || typeof data !== "object") return false;
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const result = data as Record<string, unknown>;
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if (!Array.isArray(result.items)) return false;
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if (!Array.isArray(result.dimensions)) return false;
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if (typeof result.summary !== "string") return false;
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if (typeof result.recommendation !== "string") return false;
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for (const item of result.items as ItemResearch[]) {
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if (typeof item.name !== "string") return false;
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if (typeof item.description !== "string") return false;
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if (typeof item.overallScore !== "number") return false;
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if (!Array.isArray(item.pros)) return false;
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if (!Array.isArray(item.cons)) return false;
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if (typeof item.dimensions !== "object") return false;
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for (const dim of Object.values(item.dimensions) as DimensionResult[]) {
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if (typeof dim.score !== "number") return false;
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if (typeof dim.summary !== "string") return false;
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if (typeof dim.details !== "string") return false;
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if (!Array.isArray(dim.pros)) return false;
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if (!Array.isArray(dim.cons)) return false;
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}
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}
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return true;
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}
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export async function generateComparison(
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request: ComparisonRequest
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): Promise<ComparisonResult> {
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const userPrompt = `Compare the following items: ${request.items.join(", ")}
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${request.query ? `Focus: ${request.query}` : ""}
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${request.dimensions?.length ? `Specific dimensions to include: ${request.dimensions.join(", ")}` : ""}
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Provide a comprehensive comparison with scores, pros/cons, and a recommendation.`;
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let lastError: Error | null = null;
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for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
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try {
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const response = await client.chat.completions.create({
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model: "gpt-4o-mini",
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "user", content: userPrompt },
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],
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response_format: { type: "json_object" },
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temperature: 0.3,
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});
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const content = response.choices[0]?.message?.content;
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if (!content) {
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throw new Error("Empty response from OpenAI");
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}
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const parsed: unknown = JSON.parse(content);
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if (!validateComparisonResult(parsed)) {
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throw new Error("Invalid comparison result structure from OpenAI");
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}
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return parsed;
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} catch (error) {
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lastError = error instanceof Error ? error : new Error(String(error));
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if (attempt < MAX_RETRIES) {
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await sleep(RETRY_DELAY_MS * attempt);
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}
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}
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}
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throw new Error(
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`Failed to generate comparison after ${MAX_RETRIES} attempts: ${lastError?.message}`
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);
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}
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