refactor: Consolidate all AI calls to use OpenRouter provider switch

- bulk-scan.ts: Now uses OpenRouter for batch analysis
- scan-label.ts: Now uses OpenRouter with Gemini fallback
- analyze-bottle.ts: Now uses OpenRouter with Gemini fallback
- All AI calls now respect AI_PROVIDER env variable
- Uses Nebius/FP8 provider preferences consistently
- Unified logging: [FunctionName] Using provider: openrouter/gemini
This commit is contained in:
2025-12-26 21:21:56 +01:00
parent e978499b54
commit 9c5f538efb
4 changed files with 362 additions and 191 deletions

View File

@@ -6,6 +6,7 @@ import { createClient } from '@/lib/supabase/server';
import { createHash } from 'crypto';
import { trackApiUsage } from '@/services/track-api-usage';
import { checkCreditBalance, deductCredits } from '@/services/credit-service';
import { getAIProvider, getOpenRouterClient, OPENROUTER_PROVIDER_PREFERENCES } from '@/lib/openrouter';
import sharp from 'sharp';
// Native Schema Definition for Gemini API
@@ -30,10 +31,30 @@ const metadataSchema = {
required: ["name", "is_whisky", "confidence"],
};
const SCAN_PROMPT = `Extract whisky label metadata. Return JSON with:
- name: Full product name
- distillery: Distillery name
- bottler: Independent bottler if applicable
- category: e.g. "Single Malt", "Bourbon"
- abv: Alcohol percentage
- age: Age statement in years
- vintage: Vintage year
- distilled_at: Distillation date
- bottled_at: Bottling date
- batch_info: Batch or cask info
- is_whisky: boolean
- confidence: 0-1`;
export async function scanLabel(input: any): Promise<AnalysisResponse> {
if (!process.env.GEMINI_API_KEY) {
const provider = getAIProvider();
// Check API key based on provider
if (provider === 'gemini' && !process.env.GEMINI_API_KEY) {
return { success: false, error: 'GEMINI_API_KEY is not configured.' };
}
if (provider === 'openrouter' && !process.env.OPENROUTER_API_KEY) {
return { success: false, error: 'OPENROUTER_API_KEY is not configured.' };
}
let supabase;
try {
@@ -101,7 +122,7 @@ export async function scanLabel(input: any): Promise<AnalysisResponse> {
};
}
// Step 3: AI-Specific Image Optimization (Grayscale, Normalize, Resize)
// Step 3: AI-Specific Image Optimization
const startOptimization = performance.now();
const optimizedBuffer = await sharp(buffer)
.resize(1024, 1024, { fit: 'inside', withoutEnlargement: true })
@@ -117,94 +138,155 @@ export async function scanLabel(input: any): Promise<AnalysisResponse> {
const uploadSize = optimizedBuffer.length;
const endEncoding = performance.now();
// Step 5: Model Initialization & Step 6: API Call
// Step 5: AI Analysis
const startAiTotal = performance.now();
let jsonData;
let validatedData;
try {
const startModelInit = performance.now();
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({
model: 'gemini-2.5-flash',
generationConfig: {
responseMimeType: "application/json",
responseSchema: metadataSchema as any,
console.log(`[ScanLabel] Using provider: ${provider}`);
if (provider === 'openrouter') {
// OpenRouter path
const client = getOpenRouterClient();
const startApi = performance.now();
const response = await client.chat.completions.create({
model: 'google/gemma-3-27b-it',
messages: [{
role: 'user',
content: [
{ type: 'image_url', image_url: { url: `data:${mimeType};base64,${base64Data}` } },
{ type: 'text', text: SCAN_PROMPT + '\n\nRespond ONLY with valid JSON.' },
],
}],
temperature: 0.1,
},
safetySettings: [
{ category: HarmCategory.HARM_CATEGORY_HARASSMENT, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold: HarmBlockThreshold.BLOCK_NONE },
] as any,
});
const endModelInit = performance.now();
max_tokens: 1024,
// @ts-ignore
provider: OPENROUTER_PROVIDER_PREFERENCES,
});
const instruction = "Extract whisky label metadata.";
const startApi = performance.now();
const result = await model.generateContent([
{ inlineData: { data: base64Data, mimeType: mimeType } },
{ text: instruction },
]);
const endApi = performance.now();
const endApi = performance.now();
const content = response.choices[0]?.message?.content || '{}';
const startParse = performance.now();
jsonData = JSON.parse(result.response.text());
const endParse = performance.now();
const startParse = performance.now();
let jsonStr = content;
const jsonMatch = content.match(/```(?:json)?\s*([\s\S]*?)```/);
if (jsonMatch) jsonStr = jsonMatch[1].trim();
jsonData = JSON.parse(jsonStr);
const endParse = performance.now();
const startValidation = performance.now();
validatedData = BottleMetadataSchema.parse(jsonData);
const endValidation = performance.now();
const startValidation = performance.now();
validatedData = BottleMetadataSchema.parse(jsonData);
const endValidation = performance.now();
// Cache record
await supabase.from('vision_cache').insert({ hash: imageHash, result: validatedData });
await supabase.from('vision_cache').insert({ hash: imageHash, result: validatedData });
await trackApiUsage({
userId: userId,
apiType: 'gemini_ai',
endpoint: 'scanLabel',
success: true
});
await deductCredits(userId, 'gemini_ai', 'Bottle OCR scan');
await trackApiUsage({
userId: userId,
apiType: 'gemini_ai',
endpoint: 'scanLabel_openrouter',
success: true
});
await deductCredits(userId, 'gemini_ai', 'Bottle OCR scan (OpenRouter)');
const totalTime = performance.now() - perfTotal;
return {
success: true,
data: validatedData,
perf: {
imagePrep: endImagePrep - startImagePrep,
optimization: endOptimization - startOptimization,
cacheCheck: endCacheCheck - startCacheCheck,
encoding: endEncoding - startEncoding,
modelInit: endModelInit - startModelInit,
apiCall: endApi - startApi,
parsing: endParse - startParse,
validation: endValidation - startValidation,
total: totalTime,
cacheHit: false
},
raw: jsonData
} as any;
return {
success: true,
data: validatedData,
perf: {
imagePrep: endImagePrep - startImagePrep,
optimization: endOptimization - startOptimization,
cacheCheck: endCacheCheck - startCacheCheck,
encoding: endEncoding - startEncoding,
apiCall: endApi - startApi,
parsing: endParse - startParse,
validation: endValidation - startValidation,
total: performance.now() - perfTotal,
cacheHit: false
},
raw: jsonData
} as any;
} else {
// Gemini path
const startModelInit = performance.now();
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);
const model = genAI.getGenerativeModel({
model: 'gemini-2.5-flash',
generationConfig: {
responseMimeType: "application/json",
responseSchema: metadataSchema as any,
temperature: 0.1,
},
safetySettings: [
{ category: HarmCategory.HARM_CATEGORY_HARASSMENT, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold: HarmBlockThreshold.BLOCK_NONE },
{ category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold: HarmBlockThreshold.BLOCK_NONE },
] as any,
});
const endModelInit = performance.now();
const startApi = performance.now();
const result = await model.generateContent([
{ inlineData: { data: base64Data, mimeType: mimeType } },
{ text: 'Extract whisky label metadata.' },
]);
const endApi = performance.now();
const startParse = performance.now();
jsonData = JSON.parse(result.response.text());
const endParse = performance.now();
const startValidation = performance.now();
validatedData = BottleMetadataSchema.parse(jsonData);
const endValidation = performance.now();
await supabase.from('vision_cache').insert({ hash: imageHash, result: validatedData });
await trackApiUsage({
userId: userId,
apiType: 'gemini_ai',
endpoint: 'scanLabel_gemini',
success: true
});
await deductCredits(userId, 'gemini_ai', 'Bottle OCR scan (Gemini)');
return {
success: true,
data: validatedData,
perf: {
imagePrep: endImagePrep - startImagePrep,
optimization: endOptimization - startOptimization,
cacheCheck: endCacheCheck - startCacheCheck,
encoding: endEncoding - startEncoding,
modelInit: endModelInit - startModelInit,
apiCall: endApi - startApi,
parsing: endParse - startParse,
validation: endValidation - startValidation,
total: performance.now() - perfTotal,
cacheHit: false
},
raw: jsonData
} as any;
}
} catch (aiError: any) {
console.warn('[ScanLabel] AI Analysis failed, providing fallback path:', aiError.message);
console.warn(`[ScanLabel] ${provider} failed:`, aiError.message);
// Track failure
await trackApiUsage({
userId: userId,
apiType: 'gemini_ai',
endpoint: 'scanLabel',
endpoint: `scanLabel_${provider}`,
success: false,
errorMessage: aiError.message
});
// Return a specific structure that ScanAndTasteFlow can use to fallback to placeholder
return {
success: false,
isAiError: true,
error: aiError.message,
imageHash: imageHash // Useful for local tracking
imageHash: imageHash
} as any;
}