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.

src/index.ts

@@ -0,0 +1,155 @@
+import { readProducts } from "./reader.ts";
+import { fetchKeepaDataBatch } from "./keepa.ts";
+import { fetchSpApiData } from "./sp-api.ts";
+import { connectCache, getCache, setCache, disconnectCache } from "./cache.ts";
+import { analyzeProducts } from "./llm.ts";
+import { printResults, writeResultsCsv } from "./writer.ts";
+import type { EnrichedProduct, AnalysisResult, KeepaData, ProductRecord } from "./types.ts";
+
+const LLM_BATCH_SIZE = 5;
+
+function parseArgs(): { inputFile: string; outputFile?: string } {
+  const args = process.argv.slice(2);
+  const inputFile = args.find((a) => !a.startsWith("--"));
+  const outIdx = args.indexOf("--out");
+  const outputFile = outIdx !== -1 ? args[outIdx + 1] : undefined;
+
+  if (!inputFile) {
+    console.error("Usage: bun run src/index.ts <input.csv|xlsx> [--out results.csv]");
+    process.exit(1);
+  }
+  return { inputFile, outputFile };
+}
+
+async function main() {
+  const { inputFile, outputFile } = parseArgs();
+
+  console.log("Connecting to Redis...");
+  await connectCache();
+
+  console.log(`\nReading ${inputFile}...`);
+  const products = readProducts(inputFile);
+
+  if (products.length === 0) {
+    console.error("No valid products found in input file.");
+    process.exit(1);
+  }
+
+  // Phase 1: Check cache for all ASINs
+  console.log(`\nChecking cache for ${products.length} products...`);
+  const cached = new Map<string, EnrichedProduct>();
+  const uncachedProducts: ProductRecord[] = [];
+
+  for (const p of products) {
+    const hit = await getCache(p.asin);
+    if (hit) {
+      console.log(`  [cache hit] ${p.asin}`);
+      cached.set(p.asin, hit);
+    } else {
+      uncachedProducts.push(p);
+    }
+  }
+  console.log(`${cached.size} cached, ${uncachedProducts.length} to fetch`);
+
+  // Phase 2: Batch fetch from Keepa (all uncached ASINs in one request if ≤100)
+  let keepaResults = new Map<string, KeepaData>();
+  if (uncachedProducts.length > 0) {
+    console.log(`\nFetching ${uncachedProducts.length} ASINs from Keepa...`);
+    try {
+      keepaResults = await fetchKeepaDataBatch(uncachedProducts.map((p) => p.asin));
+    } catch (err) {
+      console.warn(`Keepa batch fetch failed: ${err}`);
+    }
+  }
+
+  // Phase 3: Build enriched products
+  console.log(`\nEnriching products...`);
+  const enriched: EnrichedProduct[] = [];
+
+  for (const p of products) {
+    const cachedProduct = cached.get(p.asin);
+    if (cachedProduct) {
+      enriched.push(cachedProduct);
+      continue;
+    }
+
+    const keepa = keepaResults.get(p.asin) ?? null;
+    const spApi = await fetchSpApiData(p.asin);
+
+    if (keepa?.currentPrice && spApi.estimatedSalePrice === 0) {
+      spApi.estimatedSalePrice = keepa.currentPrice;
+    }
+
+    const product: EnrichedProduct = {
+      record: p,
+      keepa,
+      spApi,
+      fetchedAt: new Date().toISOString(),
+    };
+
+    await setCache(p.asin, product);
+    enriched.push(product);
+
+    if (keepa) {
+      console.log(`  [enriched] ${p.asin} — price: $${keepa.currentPrice ?? "N/A"}, rank: ${keepa.salesRank ?? "N/A"}`);
+    } else {
+      console.log(`  [no keepa] ${p.asin} — using spreadsheet data only`);
+    }
+  }
+
+  // Phase 4: LLM analysis in batches
+  console.log(`\nAnalyzing products via LLM (batch size: ${LLM_BATCH_SIZE})...\n`);
+
+  const results: AnalysisResult[] = [];
+  for (let i = 0; i < enriched.length; i += LLM_BATCH_SIZE) {
+    const batch = enriched.slice(i, i + LLM_BATCH_SIZE);
+    const batchNum = Math.floor(i / LLM_BATCH_SIZE) + 1;
+    const totalBatches = Math.ceil(enriched.length / LLM_BATCH_SIZE);
+    console.log(`  LLM batch ${batchNum}/${totalBatches}...`);
+
+    // Wait between batches to avoid overwhelming LM Studio
+    if (i > 0) {
+      console.log(`  Waiting 5s before next batch...`);
+      await new Promise((r) => setTimeout(r, 5000));
+    }
+
+    let verdicts;
+    try {
+      verdicts = await analyzeProducts(batch);
+    } catch {
+      console.warn(`  LLM batch error, retrying after 10s...`);
+      await new Promise((r) => setTimeout(r, 10_000));
+      try {
+        verdicts = await analyzeProducts(batch);
+      } catch (retryErr) {
+        console.error(`  LLM analysis failed: ${retryErr}`);
+        verdicts = null;
+      }
+    }
+
+    for (let j = 0; j < batch.length; j++) {
+      results.push({
+        product: batch[j]!,
+        verdict: verdicts?.[j] ?? {
+          asin: batch[j]!.record.asin,
+          verdict: "SKIP",
+          confidence: 0,
+          reasoning: "LLM analysis failed",
+        },
+      });
+    }
+  }
+
+  printResults(results);
+
+  if (outputFile) {
+    writeResultsCsv(results, outputFile);
+  }
+
+  await disconnectCache();
+}
+
+main().catch((err) => {
+  console.error("Fatal error:", err);
+  process.exit(1);
+});