mirror of
https://github.com/R0m1k3/CollectFlow.git
synced 2026-10-12 01:36:43 +02:00
feat: Implement bulk AI product categorization through a new UI component and API route.
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@@ -11,10 +11,20 @@ const AnalyzeSchema = z.object({
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sales12m: z.record(z.string(), z.number()),
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codeGamme: z.string().nullable(),
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score: z.number().nullable().optional(),
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// Champs enrichis (optionnels, envoyés par le batch)
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weightInNomenclature2: z.number().optional(),
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adjustedCaWeight: z.number().optional(),
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nomenclature2Weight: z.number().optional(),
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scorePercentile: z.number().optional(),
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moisActifs: z.number().optional(),
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nomenclature: z.string().optional(),
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// Mode batch : retourne du JSON structuré au lieu de texte libre
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batchMode: z.boolean().optional(),
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});
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const OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions";
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const SYSTEM_PROMPT = `Tu es un expert en stratégie d'achat retail. Ton rôle est de catégoriser ce produit (A=Permanent, C=Saisonnier, Z=Sortie) via une analyse de VALEUR CONTEXTUELLE.
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const SYSTEM_PROMPT_INDIVIDUAL = `Tu es un expert en stratégie d'achat retail. Ton rôle est de catégoriser ce produit (A=Permanent, C=Saisonnier, Z=Sortie) via une analyse de VALEUR CONTEXTUELLE.
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L'IMPORTANCE ÉCONOMIQUE PRIME SUR LE SCORE DE PERFORMANCE RELATIVE.
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@@ -31,39 +41,78 @@ DÉFINITION :
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Réponse courte, mentionnant la contribution économique et le score. Ne mentionne aucun seuil en euros.
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Format: "[Recommandation]: [Justification basée sur la valeur et le score]"`;
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const SYSTEM_PROMPT_BATCH = `Tu es un expert en assortiment retail. Categorise ce produit : A (garder en permanence), C (saisonnier), Z (sortir de la gamme).
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L'IMPORTANCE ECONOMIQUE PRIME SUR LE SCORE.
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DONNEES CLES :
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- weightInNomenclature2 : % du CA du produit dans sa categorie. LE CRITERE PRINCIPAL. >= 5% = pilier de categorie → A.
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- adjustedCaWeight : % du CA produit dans le total fournisseur (extrapole reseau).
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- scorePercentile : rang du produit parmi le fournisseur (50 = median). Utilise CA POUR COMPARER, pas le score brut.
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- moisActifs : mois avec ventes > 0 sur 12 mois. >= 8 = rotation reguliere → A. <= 3 = sporadique.
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REGLES :
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1. weightInNomenclature2 >= 5% → A (pilier de categorie)
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2. moisActifs >= 8 → A (fond de rayon, rotation reguliere)
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3. scorePercentile >= 50 ET moisActifs >= 4 → A (performeur regulier)
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4. moisActifs 2-4 avec pics concentres → C (saisonnier)
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5. Z seulement si TOUT est faible : weightInNomenclature2 bas + moisActifs < 5 + scorePercentile < 30
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Reponds UNIQUEMENT en JSON : {"recommandationGamme":"A|C|Z","justificationCourte":"..."}`;
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export async function POST(req: NextRequest) {
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console.log("[AI] Analysis request received.");
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const config = await getSavedDatabaseConfig();
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const apiKey = process.env.OPENROUTER_API_KEY || config?.openRouterKey;
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const model = config?.openRouterModel || "google/gemini-flash-1.5";
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if (!apiKey) {
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console.error("[AI] OPENROUTER_API_KEY is missing (neither in env nor in config file).");
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console.error("[AI] OPENROUTER_API_KEY is missing.");
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return NextResponse.json({ error: "OPENROUTER_API_KEY not configured. Please set it in Settings." }, { status: 503 });
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}
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console.log(`[AI] Using model: ${model} with API key starting with ${apiKey.substring(0, 10)}...`);
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const body = await req.json();
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const parsed = AnalyzeSchema.safeParse(body);
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if (!parsed.success) {
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return NextResponse.json({ error: "Invalid payload", details: parsed.error.flatten() }, { status: 400 });
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}
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const { codein, libelle1, totalCa, tauxMarge, totalQuantite, sales12m, codeGamme, score } = parsed.data;
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const p = parsed.data;
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const isBatch = p.batchMode === true;
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// Format monthly sales for the prompt
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const monthlySummary = Object.entries(sales12m)
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const monthlySummary = Object.entries(p.sales12m)
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.map(([k, v]) => `${k}: ${Math.round(v)} unités`)
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.join(", ");
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const userMessage = `Produit: "${libelle1}" (Code: ${codein})
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Gamme actuelle: ${codeGamme ?? "Non définie"}
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Score de performance: ${score ?? "N/A"}/100
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CA total 12m: ${totalCa.toFixed(0)}€ | Taux de marge: ${tauxMarge.toFixed(1)}% | Volume: ${Math.round(totalQuantite)} unités
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let userMessage: string;
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let systemPrompt: string;
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if (isBatch) {
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// Mode batch : prompt simplifié avec métriques enrichies, PAS de codeGamme
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systemPrompt = SYSTEM_PROMPT_BATCH;
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const enrichedLines = [
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`Produit: "${p.libelle1}" (${p.codein})`,
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p.nomenclature ? `Nomenclature: ${p.nomenclature}` : null,
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`CA: ${p.totalCa.toFixed(0)} EUR | Marge: ${p.tauxMarge.toFixed(1)}% | Volume: ${Math.round(p.totalQuantite)} unites`,
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p.weightInNomenclature2 != null ? `Poids dans sa categorie N2: ${p.weightInNomenclature2}%` : null,
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p.adjustedCaWeight != null ? `Poids global fournisseur (ajuste): ${p.adjustedCaWeight}%` : null,
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p.nomenclature2Weight != null ? `Poids de la categorie N2 dans le fournisseur: ${p.nomenclature2Weight}%` : null,
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p.scorePercentile != null ? `Rang percentile: ${p.scorePercentile}/100` : null,
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p.moisActifs != null ? `Mois actifs (ventes > 0): ${p.moisActifs}/12` : null,
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`Historique: ${monthlySummary}`,
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].filter(Boolean).join("\n");
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userMessage = enrichedLines;
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} else {
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// Mode individuel : prompt original
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systemPrompt = SYSTEM_PROMPT_INDIVIDUAL;
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userMessage = `Produit: "${p.libelle1}" (Code: ${p.codein})
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Gamme actuelle: ${p.codeGamme ?? "Non définie"}
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Score de performance: ${p.score ?? "N/A"}/100
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CA total 12m: ${p.totalCa.toFixed(0)}€ | Taux de marge: ${p.tauxMarge.toFixed(1)}% | Volume: ${Math.round(p.totalQuantite)} unités
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Historique mensuel: ${monthlySummary}
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Quelle gamme recommandes-tu et pourquoi ?`;
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}
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try {
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const response = await fetch(OPENROUTER_URL, {
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@@ -77,23 +126,47 @@ Quelle gamme recommandes-tu et pourquoi ?`;
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body: JSON.stringify({
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model,
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "system", content: systemPrompt },
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{ role: "user", content: userMessage },
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],
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...(isBatch ? { response_format: { type: "json_object" } } : {}),
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max_tokens: 150,
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temperature: 0.3,
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temperature: 0.1,
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}),
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});
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if (response.status === 429) {
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const retryAfter = response.headers.get("Retry-After") || response.headers.get("x-ratelimit-reset-requests");
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const waitSeconds = retryAfter ? parseInt(retryAfter, 10) : 60;
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return NextResponse.json({ error: "rate_limited", retryAfter: waitSeconds }, { status: 429 });
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}
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if (!response.ok) {
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const err = await response.text();
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return NextResponse.json({ error: `OpenRouter error: ${err}` }, { status: response.status });
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}
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const data = await response.json();
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const insight = data.choices?.[0]?.message?.content ?? "Analyse indisponible.";
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const content: string = data.choices?.[0]?.message?.content ?? "";
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return NextResponse.json({ insight, codein });
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if (isBatch) {
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// Parse JSON response for batch mode
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let jsonStr = content;
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const start = content.indexOf("{");
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const end = content.lastIndexOf("}");
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if (start !== -1 && end !== -1) {
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jsonStr = content.slice(start, end + 1);
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}
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const result = JSON.parse(jsonStr);
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return NextResponse.json({
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codein: p.codein,
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recommandationGamme: result.recommandationGamme || "A",
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justificationCourte: result.justificationCourte || "",
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isDuplicate: false,
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});
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} else {
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return NextResponse.json({ insight: content, codein: p.codein });
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}
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} catch (err) {
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const msg = err instanceof Error ? err.message : "Unknown error";
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return NextResponse.json({ error: msg }, { status: 500 });
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@@ -4,7 +4,6 @@ import React, { useState } from "react";
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import { useGridStore } from "@/features/grid/store/use-grid-store";
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import { useAiCopilotStore } from "@/features/ai-copilot/store/use-ai-copilot-store";
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import { Sparkles, Loader2, CheckCircle2 } from "lucide-react";
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import { ProductRow } from "@/types/grid";
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export function BulkAiAnalyzer() {
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const [isAnalyzing, setIsAnalyzing] = useState(false);
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@@ -15,29 +14,16 @@ export function BulkAiAnalyzer() {
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const handleAnalyze = async () => {
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const { rows } = useGridStore.getState();
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const supplierTotalCa = rows.reduce((acc, row) => acc + (row.totalCa || 0), 0);
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const supplierTotalMarge = rows.reduce((acc, row) => acc + (row.totalMarge || 0), 0);
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console.log("[BulkAiAnalyzer] Starting analysis on", rows.length, "rows", { supplierTotalCa, supplierTotalMarge });
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console.log("[BulkAiAnalyzer] Starting analysis on", rows.length, "rows");
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// --- Statistiques globales pour contextualiser le score ---
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const allScores = rows.map(r => r.score || 0).sort((a, b) => a - b);
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const totalProducts = rows.length;
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const medianScore = totalProducts > 0
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? (totalProducts % 2 === 0
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? (allScores[totalProducts / 2 - 1] + allScores[totalProducts / 2]) / 2
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: allScores[Math.floor(totalProducts / 2)])
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: 0;
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const scoreDistribution = {
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above70: allScores.filter(s => s > 70).length,
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between30and70: allScores.filter(s => s >= 30 && s <= 70).length,
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below30: allScores.filter(s => s < 30).length,
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};
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const maxStoreCount = Math.max(...rows.map(r => r.workingStores?.length || 1), 1);
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// Percentile par produit (rang centile 0-100)
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// Percentile par produit
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const scorePercentileMap = new Map<string, number>();
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rows.forEach(r => {
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const productScore = r.score || 0;
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@@ -45,7 +31,7 @@ export function BulkAiAnalyzer() {
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scorePercentileMap.set(r.codein, Math.round((rank / totalProducts) * 100));
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});
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// --- Double poids par nomenclature niveau 2 ---
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// Double poids par nomenclature niveau 2
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const nomenclature2CaMap = new Map<string, number>();
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rows.forEach(r => {
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const n2 = r.libelleNiveau2 || "Non classe";
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@@ -59,99 +45,69 @@ export function BulkAiAnalyzer() {
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: 0);
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});
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const nomenclature2Count = nomenclature2CaMap.size;
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// Préparer les payloads enrichis pour chaque produit
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const productPayloads = rows.map(r => {
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const rawCaWeight = supplierTotalCa > 0
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? parseFloat(((r.totalCa / supplierTotalCa) * 100).toFixed(3))
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: 0;
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const storeCount = r.workingStores?.length || 1;
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const storeRatio = storeCount < maxStoreCount ? maxStoreCount / storeCount : 1;
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const adjustedCaWeight = parseFloat((rawCaWeight * storeRatio).toFixed(3));
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const n2 = r.libelleNiveau2 || "Non classe";
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const n2Ca = nomenclature2CaMap.get(n2) || 1;
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const weightInNomenclature2 = parseFloat((((r.totalCa || 0) / n2Ca) * 100).toFixed(2));
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const moisActifs = Object.values(r.sales12m || {}).filter(v => v > 0).length;
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console.log("[BulkAiAnalyzer] Stats:", { totalProducts, medianScore, scoreDistribution, nomenclature2Count, maxStoreCount });
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return {
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codein: r.codein,
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libelle1: r.libelle1 || "",
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totalCa: r.totalCa || 0,
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tauxMarge: r.tauxMarge || 0,
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totalQuantite: r.totalQuantite || 0,
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sales12m: r.sales12m || {},
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codeGamme: r.codeGamme || null,
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score: r.score || 0,
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// Champs enrichis
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weightInNomenclature2,
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adjustedCaWeight,
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nomenclature2Weight: nomenclature2WeightMap.get(n2) ?? 0,
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scorePercentile: scorePercentileMap.get(r.codein) ?? 0,
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moisActifs,
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nomenclature: `${r.libelleNiveau1 || ""} > ${r.libelleNiveau2 || ""} > ${r.libelle3 || ""}`,
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batchMode: true,
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};
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});
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setIsAnalyzing(true);
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let completed = 0;
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let errors = 0;
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setProgress({ current: 0, total: productPayloads.length, message: "Initialisation...", errors: 0 });
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try {
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// Group by Nomenclature (libelle3 or a fallback)
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const groups: Record<string, ProductRow[]> = {};
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rows.forEach(r => {
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const rayon = r.libelle3 || "Non classifié";
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if (!groups[rayon]) groups[rayon] = [];
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groups[rayon].push(r);
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});
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const CHUNK_SIZE = 50;
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const chunks: { rayon: string; items: ProductRow[] }[] = [];
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for (const [rayon, items] of Object.entries(groups)) {
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for (let i = 0; i < items.length; i += CHUNK_SIZE) {
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chunks.push({ rayon, items: items.slice(i, i + CHUNK_SIZE) });
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}
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}
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setProgress({ current: 0, total: chunks.length, message: "Initialisation...", errors: 0 });
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// Process with a continuous concurrency limit
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const CONCURRENCY = 3;
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const remainingChunks = [...chunks];
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const CONCURRENCY = 5;
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const remaining = [...productPayloads];
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const processNext = async () => {
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const chunk = remainingChunks.shift();
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if (!chunk) return;
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const payloadProducts = chunk.items.map(r => {
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const rawCaWeight = supplierTotalCa > 0
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? parseFloat(((r.totalCa / supplierTotalCa) * 100).toFixed(3))
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: 0;
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const storeCount = r.workingStores?.length || 1;
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const storeRatio = storeCount < maxStoreCount ? maxStoreCount / storeCount : 1;
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const adjustedCaWeight = parseFloat((rawCaWeight * storeRatio).toFixed(3));
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const n2 = r.libelleNiveau2 || "Non classe";
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const n2Ca = nomenclature2CaMap.get(n2) || 1;
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const weightInNomenclature2 = parseFloat((((r.totalCa || 0) / n2Ca) * 100).toFixed(2));
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// Compter les mois actifs (ventes > 0) pré-calculé
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const moisActifs = Object.values(r.sales12m || {}).filter(v => v > 0).length;
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return {
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codein: r.codein,
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nom: r.libelle1,
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ca: r.totalCa || 0,
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adjustedCaWeight,
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weightInNomenclature2,
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nomenclature2Weight: nomenclature2WeightMap.get(n2) ?? 0,
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ventes: r.totalQuantite || 0,
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marge: r.totalMarge ? parseFloat(((r.totalMarge / (r.totalCa || 1)) * 100).toFixed(1)) : 0,
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scorePercentile: scorePercentileMap.get(r.codein) ?? 0,
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moisActifs,
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storeCount,
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nomenclature: `${r.libelleNiveau1 || ""} > ${r.libelleNiveau2 || ""} > ${r.libelle3 || ""}`,
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};
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});
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const payload = remaining.shift();
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if (!payload) return;
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try {
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let res: Response | null = null;
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const MAX_CHUNK_RETRIES = 3;
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const MAX_RETRIES = 3;
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for (let attempt = 1; attempt <= MAX_CHUNK_RETRIES; attempt++) {
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res = await fetch("/api/ai/batch-analyze", {
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for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
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res = await fetch("/api/ai/analyze", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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rayon: chunk.rayon,
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products: payloadProducts,
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supplierTotalCa,
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supplierTotalMarge,
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supplierStats: {
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totalProducts,
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medianScore: Math.round(medianScore * 10) / 10,
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scoreDistribution,
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maxStoreCount,
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nomenclature2Count,
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}
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})
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body: JSON.stringify(payload)
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});
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if (res.status === 429 && attempt < MAX_CHUNK_RETRIES) {
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if (res.status === 429 && attempt < MAX_RETRIES) {
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const errData = await res.json().catch(() => ({}));
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const waitSecs = errData.retryAfter ?? (30 * attempt);
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const waitSecs = errData.retryAfter ?? (15 * attempt);
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for (let t = waitSecs; t > 0; t--) {
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setProgress(prev => ({ ...prev, message: `⏳ Lot ${completed + 1}/${chunks.length} — Limite API, reprise dans ${t}s...` }));
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setProgress(prev => ({ ...prev, message: `⏳ ${completed}/${productPayloads.length} — Limite API, reprise dans ${t}s...` }));
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await new Promise(r => setTimeout(r, 1000));
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}
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continue;
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@@ -160,41 +116,43 @@ export function BulkAiAnalyzer() {
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}
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if (!res || !res.ok) {
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const errText = await res?.text().catch(() => "");
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console.error(`[BulkAiAnalyzer] Erreur HTTP sur le lot ${chunk.rayon}:`, errText);
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setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
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errors++;
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} else {
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const data = await res.json();
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if (data && Array.isArray(data.results)) {
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data.results.forEach((reco: any) => {
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if (reco.codein && reco.recommandationGamme) {
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setDraftGamme(reco.codein, reco.recommandationGamme);
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const justification = `Gamme ${reco.recommandationGamme} — ${reco.justificationCourte || "Analyse effectuée."}`;
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setInsight(reco.codein, justification, reco.isDuplicate ?? false);
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}
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});
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if (data.recommandationGamme) {
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setDraftGamme(data.codein, data.recommandationGamme);
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const justification = `Gamme ${data.recommandationGamme} — ${data.justificationCourte || "Analyse effectuée."}`;
|
||||
setInsight(data.codein, justification, data.isDuplicate ?? false);
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
console.error(`[BulkAiAnalyzer] Failed fetch for chunk ${chunk.rayon}:`, e);
|
||||
setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
|
||||
} catch {
|
||||
errors++;
|
||||
} finally {
|
||||
completed++;
|
||||
setProgress(prev => ({
|
||||
...prev,
|
||||
setProgress({
|
||||
current: completed,
|
||||
message: completed === chunks.length ? "Analyse terminée" : `Analyse: ${completed}/${chunks.length} lots`
|
||||
}));
|
||||
// Process next chunk in the same worker
|
||||
total: productPayloads.length,
|
||||
message: completed === productPayloads.length
|
||||
? "Analyse terminée"
|
||||
: `Analyse: ${completed}/${productPayloads.length} produits`,
|
||||
errors,
|
||||
});
|
||||
await processNext();
|
||||
}
|
||||
};
|
||||
|
||||
// Start workers
|
||||
const workers = Array.from({ length: Math.min(CONCURRENCY, chunks.length) }, () => processNext());
|
||||
const workers = Array.from(
|
||||
{ length: Math.min(CONCURRENCY, productPayloads.length) },
|
||||
() => processNext()
|
||||
);
|
||||
await Promise.all(workers);
|
||||
|
||||
setProgress(prev => ({ ...prev, current: completed, total: chunks.length, message: `Analyse terminée ! (${prev.errors > 0 ? prev.errors + " erreurs" : "succès"})` }));
|
||||
setProgress(prev => ({
|
||||
...prev,
|
||||
current: completed,
|
||||
total: productPayloads.length,
|
||||
message: `Analyse terminée ! (${errors > 0 ? errors + " erreurs" : "succès"})`,
|
||||
}));
|
||||
setTimeout(() => setIsAnalyzing(false), 3000);
|
||||
|
||||
} catch (error) {
|
||||
@@ -227,7 +185,7 @@ export function BulkAiAnalyzer() {
|
||||
<div className="flex items-center gap-2 px-4 py-2 rounded-xl bg-emerald-50 dark:bg-emerald-900/30 border border-emerald-200 dark:border-emerald-800 cursor-default">
|
||||
<CheckCircle2 className="w-4 h-4 text-emerald-600 dark:text-emerald-500" />
|
||||
<span className="text-xs font-bold text-emerald-700 dark:text-emerald-400">
|
||||
Analyse terminée ({progress.total} lots analysés)
|
||||
Analyse terminée ({progress.total} produits analysés)
|
||||
</span>
|
||||
</div>
|
||||
);
|
||||
|
||||
Reference in new issue
Block a user