- Rename directory keep-notes -> memento-note with all code references - Prisma: SQLite -> PostgreSQL (both app and MCP server schemas) - Sync MCP schema with main app (add missing fields, relations, indexes) - Delete 17 SQLite migrations (clean slate for PostgreSQL) - Remove SQLite dependencies (@libsql/client, better-sqlite3, etc.) - Fix MCP server: hardcoded Windows DB paths -> DATABASE_URL env var - Fix MCP server: .dockerignore excluded index-sse.js (SSE mode broken) - MCP Dockerfile: node:20 -> node:22 - Docker Compose: add postgres service, remove SQLite volume - Generate favicon.ico, icon-192.png, icon-512.png, apple-icon.png - Update layout.tsx icons and manifest.json for PNG icons - Update all .env files for PostgreSQL - Rewrite README.md with updated sections - Remove mcp-server/node_modules and prisma/client-generated from git tracking Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
97 lines
2.7 KiB
TypeScript
97 lines
2.7 KiB
TypeScript
import { NextRequest, NextResponse } from 'next/server'
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import { getSystemConfig } from '@/lib/config'
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// Modèles populaires pour chaque provider (2025)
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const PROVIDER_MODELS = {
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ollama: {
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tags: [
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'llama3:latest',
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'llama3.2:latest',
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'granite4:latest',
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'mistral:latest',
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'mixtral:latest',
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'phi3:latest',
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'gemma2:latest',
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'qwen2:latest'
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],
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embeddings: [
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'embeddinggemma:latest',
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'mxbai-embed-large:latest',
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'nomic-embed-text:latest'
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]
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},
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openai: {
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tags: [
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'gpt-4o',
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'gpt-4o-mini',
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'gpt-4-turbo',
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'gpt-4',
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'gpt-3.5-turbo'
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],
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embeddings: [
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'text-embedding-3-small',
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'text-embedding-3-large',
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'text-embedding-ada-002'
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]
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},
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custom: {
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tags: [], // Will be loaded dynamically
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embeddings: [] // Will be loaded dynamically
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}
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}
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export async function GET(request: NextRequest) {
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try {
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const config = await getSystemConfig()
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const provider = (config.AI_PROVIDER || 'ollama').toLowerCase()
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let models = PROVIDER_MODELS[provider as keyof typeof PROVIDER_MODELS] || { tags: [], embeddings: [] }
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// Pour Ollama, essayer de récupérer la liste réelle depuis l'API locale
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if (provider === 'ollama') {
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try {
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const ollamaBaseUrl = config.OLLAMA_BASE_URL || process.env.OLLAMA_BASE_URL || 'http://localhost:11434'
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const response = await fetch(`${ollamaBaseUrl}/api/tags`, {
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method: 'GET',
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headers: { 'Content-Type': 'application/json' }
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})
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if (response.ok) {
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const data = await response.json()
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const allModels = data.models || []
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// Séparer les modèles de tags et d'embeddings
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const tagModels = allModels
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.filter((m: any) => !m.name.includes('embed') && !m.name.includes('Embedding'))
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.map((m: any) => m.name)
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.slice(0, 20) // Limiter à 20 modèles
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const embeddingModels = allModels
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.filter((m: any) => m.name.includes('embed') || m.name.includes('Embedding'))
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.map((m: any) => m.name)
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models = {
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tags: tagModels.length > 0 ? tagModels : models.tags,
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embeddings: embeddingModels.length > 0 ? embeddingModels : models.embeddings
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}
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}
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} catch (error) {
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// Garder les modèles par défaut
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}
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}
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return NextResponse.json({
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provider,
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models: models || { tags: [], embeddings: [] }
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})
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} catch (error: any) {
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return NextResponse.json(
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{
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error: error.message || 'Failed to fetch models',
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models: { tags: [], embeddings: [] }
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},
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{ status: 500 }
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)
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}
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}
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