feat: initialize asin-check project with Bun
- Add README.md with installation and usage instructions. - Create bun.lock for dependency management. - Add package.json to define project metadata and dependencies. - Implement caching with Redis in cache.ts for ASIN data. - Configure environment variables in config.ts for API keys and Redis URL. - Develop main application logic in index.ts to read products, fetch data, and analyze results. - Integrate Keepa API for product data retrieval in keepa.ts. - Create LLM analysis functionality in llm.ts for product viability assessment. - Implement product reading from Excel files in reader.ts. - Stub SP-API integration in sp-api.ts for future implementation. - Define TypeScript types in types.ts for product and analysis data structures. - Write results to console and CSV in writer.ts. - Configure TypeScript settings in tsconfig.json for project compilation.
This commit is contained in:
Victor Noguera
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src/llm.ts
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160
src/llm.ts
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import { config } from "./config.ts";
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import type { EnrichedProduct, LlmVerdict } from "./types.ts";
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const SYSTEM_PROMPT = `You are an expert Amazon product analyst specializing in FBA and FBM fulfillment strategy.
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Given product data, evaluate each product's viability for selling on Amazon. Consider:
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1. **Sales Velocity**: monthlySold and salesRankDrops30 are the most important signals. A product that doesn't sell is worthless regardless of margin. salesRankDrops30 = approximate units sold in 30 days. monthlySold is Keepa's estimate.
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2. **Margin Analysis**: Sale price minus unit cost minus fees (FBA or FBM). Aim for >30% ROI minimum. The spreadsheet may include FBA NET and gross profit estimates — cross-check against Keepa pricing data.
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3. **Sales Rank (BSR)**: Lower rank = higher demand. Rank <50,000 is good, <1,000 is excellent.
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4. **Sales Rank Trend**: Compare current rank vs 90d average. Lower current = improving demand.
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5. **Competition**: Number of sellers and Buy Box dynamics. Fewer sellers = easier entry.
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6. **Price Stability**: Large price swings (high max vs low min over 90d) = volatile/risky.
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7. **FBA vs FBM**: FBA preferred for fast-selling, small/light items. FBM for oversized, slow-moving, or high-margin items where fee savings matter.
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8. **MOQ & Capital**: High MOQ with thin margins is risky.
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9. **Supply Availability**: Total quantity available from supplier — low stock means limited runway.
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Return ONLY a raw JSON array (no markdown, no code fences, no explanation before or after). One verdict per product:
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[{ "asin": "B...", "verdict": "FBA" | "FBM" | "SKIP", "confidence": 0-100, "reasoning": "..." }]
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Keep each reasoning under 100 characters to stay within output limits.`;
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export async function analyzeProducts(
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products: EnrichedProduct[],
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): Promise<LlmVerdict[]> {
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const productSummaries = products.map(summarizeForLlm);
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const res = await fetch(`${config.llmUrl}/chat/completions`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: "Bearer lm-studio",
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},
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body: JSON.stringify({
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model: config.llmModel,
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "user", content: JSON.stringify(productSummaries, null, 2) },
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],
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temperature: 0.3,
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max_tokens: 2048,
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}),
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});
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if (!res.ok) {
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throw new Error(`LLM API error ${res.status}: ${await res.text()}`);
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}
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const data = (await res.json()) as {
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choices?: { message?: { content?: string } }[];
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};
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const content = data.choices?.[0]?.message?.content ?? "";
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return parseVerdicts(content, products);
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}
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function summarizeForLlm(p: EnrichedProduct) {
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const salePrice = p.keepa?.currentPrice ?? p.spApi.estimatedSalePrice;
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const referralFee = salePrice * (p.spApi.referralFeePercent / 100);
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const fbaProfit =
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salePrice - p.record.unitCost - p.spApi.fbaFee - referralFee;
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const fbmProfit =
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salePrice - p.record.unitCost - p.spApi.fbmFee - referralFee;
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return {
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asin: p.record.asin,
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name: p.record.name,
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brand: p.record.brand,
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category: p.record.category ?? p.keepa?.categoryTree?.join(" > "),
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unitCost: p.record.unitCost,
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currentPrice: salePrice,
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priceRange90d: p.keepa
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? {
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min: p.keepa.minPrice90,
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max: p.keepa.maxPrice90,
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avg: p.keepa.avgPrice90,
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}
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: null,
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salesRank: p.keepa?.salesRank ?? p.record.amazonRank,
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salesRankAvg90d: p.keepa?.salesRankAvg90,
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sellerCount: p.keepa?.sellerCount,
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salesVelocity: {
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monthlySold: p.keepa?.monthlySold,
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salesRankDrops30: p.keepa?.salesRankDrops30,
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salesRankDrops90: p.keepa?.salesRankDrops90,
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},
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spreadsheetEstimates: {
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fbaNet: p.record.fbaNet,
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grossProfit: p.record.grossProfit,
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grossProfitPct: p.record.grossProfitPct,
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},
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moq: p.record.moq,
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moqCost: p.record.moqCost,
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totalQtyAvail: p.record.totalQtyAvail,
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fees: {
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fbaFee: p.spApi.fbaFee,
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fbmFee: p.spApi.fbmFee,
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referralFeePercent: p.spApi.referralFeePercent,
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referralFee: Math.round(referralFee * 100) / 100,
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},
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estimatedProfit: {
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fba: Math.round(fbaProfit * 100) / 100,
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fbm: Math.round(fbmProfit * 100) / 100,
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},
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estimatedROI: {
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fba:
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p.record.unitCost > 0
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? Math.round((fbaProfit / p.record.unitCost) * 100)
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: null,
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fbm:
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p.record.unitCost > 0
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? Math.round((fbmProfit / p.record.unitCost) * 100)
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: null,
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},
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};
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}
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function cleanLlmJson(text: string): string {
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// Remove ```json ... ``` or ``` ... ``` wrapping
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const fenceMatch = text.match(/```(?:json)?\s*\n?([\s\S]*?)```/);
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let cleaned = fenceMatch ? fenceMatch[1]!.trim() : text.trim();
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// Fix trailing comma-quote before closing brace: ,"} → "}
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cleaned = cleaned.replace(/,"\s*}/g, '"}');
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// Fix trailing commas before ] or }
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cleaned = cleaned.replace(/,\s*([}\]])/g, "$1");
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return cleaned;
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}
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function parseVerdicts(
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content: string,
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products: EnrichedProduct[],
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): LlmVerdict[] {
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try {
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const cleaned = cleanLlmJson(content);
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const parsed = JSON.parse(cleaned);
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const arr = Array.isArray(parsed)
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? parsed
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: (parsed.verdicts ?? parsed.results ?? [parsed]);
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return arr.map((v: Record<string, unknown>) => ({
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asin: String(v.asin ?? ""),
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verdict: (["FBA", "FBM", "SKIP"].includes(String(v.verdict))
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? v.verdict
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: "SKIP") as LlmVerdict["verdict"],
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confidence: typeof v.confidence === "number" ? v.confidence : 0,
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reasoning: String(v.reasoning ?? "No reasoning provided"),
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}));
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} catch (err) {
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console.warn("Failed to parse LLM response, marking all as ANALYSIS_FAILED");
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console.warn("Raw LLM content:", content.slice(0, 500));
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return products.map((p) => ({
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asin: p.record.asin,
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verdict: "SKIP" as const,
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confidence: 0,
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reasoning: `Analysis failed: could not parse LLM output`,
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}));
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}
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}
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