diff --git a/.env b/.env index f67ff42f..e12f7cf7 100644 --- a/.env +++ b/.env @@ -36,4 +36,8 @@ COOKIE_DOMAIN=localhost INFRA_DIRECTUS_URL=http://localhost:8059 INFRA_DIRECTUS_TOKEN=59fb8f4c1a51b18fe28ad947f713914e GATEKEEPER_ORIGIN=http://klz.localhost -OPENROUTER_API_KEY=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 \ No newline at end of file +OPENROUTER_API_KEY=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 +# Payload CMS Local Development +POSTGRES_URI=postgres://klz_db_user:klz_db_pass@127.0.0.1:54322/directus +PAYLOAD_SECRET=D7F36ED8D3B2F77A5E6B4A3962 + diff --git a/.gitignore b/.gitignore index b0930a3a..4d5dac69 100644 --- a/.gitignore +++ b/.gitignore @@ -1,18 +1,17 @@ node_modules .next .DS_Store +.pnpm-store public/uploads +public/media # Lighthouse CI .lighthouseci/ lighthouserc.cjs .lighthouserc.json -# Directus +# Legacy (Directus) cleanup directus/uploads -!directus/extensions/ -!directus/schema/ -!directus/migrations/ .next-docker diff --git a/Dockerfile.dev b/Dockerfile.dev new file mode 100644 index 00000000..4b0dfa7a --- /dev/null +++ b/Dockerfile.dev @@ -0,0 +1,18 @@ +FROM node:20-alpine + +# Install essential build tools if needed (e.g., for node-gyp) +RUN apk add --no-cache libc6-compat python3 make g++ + +WORKDIR /app + +# Enable corepack for pnpm +RUN corepack enable + +# Pre-set the pnpm store directory +ENV PNPM_HOME="/pnpm" +ENV PATH="$PNPM_HOME:$PATH" + +# Set up pnpm store configuration +RUN pnpm config set store-dir /pnpm/store + +EXPOSE 3000 diff --git a/app/(payload)/admin/[[...segments]]/page.tsx b/app/(payload)/admin/[[...segments]]/page.tsx new file mode 100644 index 00000000..362452d7 --- /dev/null +++ b/app/(payload)/admin/[[...segments]]/page.tsx @@ -0,0 +1,17 @@ +import configPromise from '@payload-config'; +import { RootPage } from '@payloadcms/next/views'; +import { importMap } from '../importMap'; + +type Args = { + params: Promise<{ + segments: string[]; + }>; + searchParams: Promise<{ + [key: string]: string | string[]; + }>; +}; + +const Page = ({ params, searchParams }: Args) => + RootPage({ config: configPromise, importMap, params, searchParams }); + +export default Page; diff --git a/app/(payload)/admin/importMap.js b/app/(payload)/admin/importMap.js new file mode 100644 index 00000000..d89d83f5 --- /dev/null +++ b/app/(payload)/admin/importMap.js @@ -0,0 +1,77 @@ +import { RscEntryLexicalCell as RscEntryLexicalCell_44fe37237e0ebf4470c9990d8cb7b07e } from '@payloadcms/richtext-lexical/rsc'; +import { RscEntryLexicalField as RscEntryLexicalField_44fe37237e0ebf4470c9990d8cb7b07e } from '@payloadcms/richtext-lexical/rsc'; +import { LexicalDiffComponent as LexicalDiffComponent_44fe37237e0ebf4470c9990d8cb7b07e } from '@payloadcms/richtext-lexical/rsc'; +import { BlocksFeatureClient as BlocksFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { InlineToolbarFeatureClient as InlineToolbarFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { HorizontalRuleFeatureClient as HorizontalRuleFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { UploadFeatureClient as UploadFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { BlockquoteFeatureClient as BlockquoteFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { RelationshipFeatureClient as RelationshipFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { LinkFeatureClient as LinkFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { ChecklistFeatureClient as ChecklistFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { OrderedListFeatureClient as OrderedListFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { UnorderedListFeatureClient as UnorderedListFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { IndentFeatureClient as IndentFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { AlignFeatureClient as AlignFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { HeadingFeatureClient as HeadingFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { ParagraphFeatureClient as ParagraphFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { InlineCodeFeatureClient as InlineCodeFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { SuperscriptFeatureClient as SuperscriptFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { SubscriptFeatureClient as SubscriptFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { StrikethroughFeatureClient as StrikethroughFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { UnderlineFeatureClient as UnderlineFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { BoldFeatureClient as BoldFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { ItalicFeatureClient as ItalicFeatureClient_e70f5e05f09f93e00b997edb1ef0c864 } from '@payloadcms/richtext-lexical/client'; +import { CollectionCards as CollectionCards_f9c02e79a4aed9a3924487c0cd4cafb1 } from '@payloadcms/next/rsc'; + +export const importMap = { + '@payloadcms/richtext-lexical/rsc#RscEntryLexicalCell': + RscEntryLexicalCell_44fe37237e0ebf4470c9990d8cb7b07e, + '@payloadcms/richtext-lexical/rsc#RscEntryLexicalField': + RscEntryLexicalField_44fe37237e0ebf4470c9990d8cb7b07e, + '@payloadcms/richtext-lexical/rsc#LexicalDiffComponent': + LexicalDiffComponent_44fe37237e0ebf4470c9990d8cb7b07e, + '@payloadcms/richtext-lexical/client#BlocksFeatureClient': + BlocksFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#InlineToolbarFeatureClient': + InlineToolbarFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#HorizontalRuleFeatureClient': + HorizontalRuleFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#UploadFeatureClient': + UploadFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#BlockquoteFeatureClient': + BlockquoteFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#RelationshipFeatureClient': + RelationshipFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#LinkFeatureClient': + LinkFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#ChecklistFeatureClient': + ChecklistFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#OrderedListFeatureClient': + OrderedListFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#UnorderedListFeatureClient': + UnorderedListFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#IndentFeatureClient': + IndentFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#AlignFeatureClient': + AlignFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#HeadingFeatureClient': + HeadingFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#ParagraphFeatureClient': + ParagraphFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#InlineCodeFeatureClient': + InlineCodeFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#SuperscriptFeatureClient': + SuperscriptFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#SubscriptFeatureClient': + SubscriptFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#StrikethroughFeatureClient': + StrikethroughFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#UnderlineFeatureClient': + UnderlineFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#BoldFeatureClient': + BoldFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/richtext-lexical/client#ItalicFeatureClient': + ItalicFeatureClient_e70f5e05f09f93e00b997edb1ef0c864, + '@payloadcms/next/rsc#CollectionCards': CollectionCards_f9c02e79a4aed9a3924487c0cd4cafb1, +}; diff --git a/app/(payload)/api/[...slug]/route.ts b/app/(payload)/api/[...slug]/route.ts new file mode 100644 index 00000000..bec81863 --- /dev/null +++ b/app/(payload)/api/[...slug]/route.ts @@ -0,0 +1,14 @@ +import config from '@payload-config'; +import { + REST_GET, + REST_OPTIONS, + REST_PATCH, + REST_POST, + REST_DELETE, +} from '@payloadcms/next/routes'; + +export const GET = REST_GET(config); +export const POST = REST_POST(config); +export const DELETE = REST_DELETE(config); +export const PATCH = REST_PATCH(config); +export const OPTIONS = REST_OPTIONS(config); diff --git a/app/(payload)/api/graphql/route.ts b/app/(payload)/api/graphql/route.ts new file mode 100644 index 00000000..7270fe74 --- /dev/null +++ b/app/(payload)/api/graphql/route.ts @@ -0,0 +1,4 @@ +import config from '@payload-config'; +import { GRAPHQL_POST } from '@payloadcms/next/routes'; + +export const POST = GRAPHQL_POST(config); diff --git a/app/(payload)/custom.scss b/app/(payload)/custom.scss new file mode 100644 index 00000000..5793104f --- /dev/null +++ b/app/(payload)/custom.scss @@ -0,0 +1 @@ +/* Custom Payload CMS admin styles can go here. Do not import payloadcms/ui/scss/app.scss as it is handled by @payloadcms/next/css */ diff --git a/app/(payload)/layout.tsx b/app/(payload)/layout.tsx new file mode 100644 index 00000000..c9b7e796 --- /dev/null +++ b/app/(payload)/layout.tsx @@ -0,0 +1,31 @@ +import configPromise from '@payload-config'; +import { RootLayout } from '@payloadcms/next/layouts'; +import React from 'react'; + +import '@payloadcms/next/css'; +import './custom.scss'; +import { handleServerFunctions } from '@payloadcms/next/layouts'; +import { importMap } from './admin/importMap'; + +type Args = { + children: React.ReactNode; +}; + +const serverFunction: any = async function (args: any) { + 'use server'; + return handleServerFunctions({ + ...args, + config: configPromise, + importMap, + }); +}; + +const Layout = ({ children }: Args) => { + return ( + + {children} + + ); +}; + +export default Layout; diff --git a/app/[locale]/[slug]/page.tsx b/app/[locale]/[slug]/page.tsx index 9512dad9..5ec2bffe 100644 --- a/app/[locale]/[slug]/page.tsx +++ b/app/[locale]/[slug]/page.tsx @@ -1,10 +1,9 @@ import { notFound } from 'next/navigation'; -import { MDXRemote } from 'next-mdx-remote/rsc'; import { Container, Badge, Heading } from '@/components/ui'; import { getTranslations, setRequestLocale } from 'next-intl/server'; import { Metadata } from 'next'; import { getPageBySlug, getAllPages } from '@/lib/pages'; -import { mdxComponents } from '@/components/blog/MDXComponents'; +import PayloadRichText from '@/components/PayloadRichText'; import { SITE_URL } from '@/lib/schema'; import TrackedLink from '@/components/analytics/TrackedLink'; @@ -102,7 +101,7 @@ export default async function StandardPage({ params }: PageProps) { {/* Main content with shared blog components */}
- +
{/* Support Section */} diff --git a/app/[locale]/blog/[slug]/page.tsx b/app/[locale]/blog/[slug]/page.tsx index 8b628c9b..813140de 100644 --- a/app/[locale]/blog/[slug]/page.tsx +++ b/app/[locale]/blog/[slug]/page.tsx @@ -1,19 +1,19 @@ import { notFound } from 'next/navigation'; import JsonLd from '@/components/JsonLd'; import { SITE_URL } from '@/lib/schema'; -import { MDXRemote } from 'next-mdx-remote/rsc'; -import { getPostBySlug, getAdjacentPosts, getReadingTime, getHeadings } from '@/lib/blog'; +import { getPostBySlug, getAdjacentPosts, getReadingTime } from '@/lib/blog'; import { Metadata } from 'next'; import Link from 'next/link'; import Image from 'next/image'; import PostNavigation from '@/components/blog/PostNavigation'; import PowerCTA from '@/components/blog/PowerCTA'; -import TableOfContents from '@/components/blog/TableOfContents'; -import { mdxComponents } from '@/components/blog/MDXComponents'; import { Heading } from '@/components/ui'; import { setRequestLocale } from 'next-intl/server'; import BlogEngagementTracker from '@/components/analytics/BlogEngagementTracker'; +// Payload CMS Imports +import PayloadRichText from '@/components/PayloadRichText'; + interface BlogPostProps { params: Promise<{ locale: string; @@ -60,7 +60,8 @@ export default async function BlogPost({ params }: BlogPostProps) { notFound(); } - const headings = getHeadings(post.content); + // Convert Lexical content into a plain string to estimate reading time roughly + const rawTextContent = JSON.stringify(post.content); return (
@@ -68,7 +69,7 @@ export default async function BlogPost({ params }: BlogPostProps) { title={post.frontmatter.title} slug={slug} category={post.frontmatter.category} - readingTime={getReadingTime(post.content)} + readingTime={getReadingTime(rawTextContent)} /> {/* Featured Image Header */} @@ -76,7 +77,7 @@ export default async function BlogPost({ params }: BlogPostProps) {
{post.frontmatter.title} - {getReadingTime(post.content)} min read + {getReadingTime(rawTextContent)} min read {(new Date(post.frontmatter.date) > new Date() || post.frontmatter.public === false) && ( <> @@ -146,7 +147,7 @@ export default async function BlogPost({ params }: BlogPostProps) { })} - {getReadingTime(post.content)} min read + {getReadingTime(rawTextContent)} min read {(new Date(post.frontmatter.date) > new Date() || post.frontmatter.public === false) && ( <> @@ -175,9 +176,9 @@ export default async function BlogPost({ params }: BlogPostProps) {
)} - {/* Main content with enhanced styling */} + {/* Main content with enhanced styling rendering Payload Lexical */}
- +
{/* Power CTA */} @@ -220,10 +221,10 @@ export default async function BlogPost({ params }: BlogPostProps) {
- {/* Right Column: Sticky Sidebar */} + {/* Right Column: Sticky Sidebar - Temporarily Hidden without ToC */} @@ -262,8 +263,8 @@ export default async function BlogPost({ params }: BlogPostProps) { '@id': `${SITE_URL}/${locale}/blog/${slug}`, }, articleSection: post.frontmatter.category, - wordCount: post.content.split(/\s+/).length, - timeRequired: `PT${getReadingTime(post.content)}M`, + wordCount: rawTextContent.split(/\s+/).length, + timeRequired: `PT${getReadingTime(rawTextContent)}M`, } as any } /> diff --git a/app/[locale]/blog/page.tsx b/app/[locale]/blog/page.tsx index 1185e1e0..89186f56 100644 --- a/app/[locale]/blog/page.tsx +++ b/app/[locale]/blog/page.tsx @@ -63,7 +63,7 @@ export default async function BlogIndex({ params }: BlogIndexProps) { {featuredPost && featuredPost.frontmatter.featuredImage && ( <> {featuredPost.frontmatter.title} {post.frontmatter.title} }) { - console.log('🖼️ OG Image Handler Called'); const { locale } = await params; const t = await getTranslations({ locale, namespace: 'Index.meta' }); const fonts = await getOgFonts(); diff --git a/app/[locale]/products/[...slug]/page.tsx b/app/[locale]/products/[...slug]/page.tsx index cbf9bd06..810840be 100644 --- a/app/[locale]/products/[...slug]/page.tsx +++ b/app/[locale]/products/[...slug]/page.tsx @@ -12,11 +12,11 @@ import { mapFileSlugToTranslated, mapSlugToFileSlug } from '@/lib/slugs'; import { Metadata } from 'next'; import { getTranslations, setRequestLocale } from 'next-intl/server'; import { getProductOGImageMetadata } from '@/lib/metadata'; -import { MDXRemote } from 'next-mdx-remote/rsc'; import Image from 'next/image'; import Link from 'next/link'; import { notFound } from 'next/navigation'; import ProductEngagementTracker from '@/components/analytics/ProductEngagementTracker'; +import PayloadRichText from '@/components/PayloadRichText'; interface ProductPageProps { params: Promise<{ @@ -103,76 +103,7 @@ export async function generateMetadata({ params }: ProductPageProps): Promise ( -

- ), - h1: (props: any) => ( -

-

-
-
- ), - h2: (props: any) => ( -

- ), - h3: (props: any) => ( -

- ), - ul: (props: any) =>
    , - section: (props: any) =>
    , - li: (props: any) => ( -
  • -
    - -
  • - ), - strong: (props: any) => , - table: (props: any) => ( -
    - - - ), - th: (props: any) => ( -
    - ), - td: (props: any) => ( - - ), - hr: () =>
    , - blockquote: (props: any) => ( -
    -
    -
    -
    - ), -}; +// ... the rest of the file layout ... export default async function ProductPage({ params }: ProductPageProps) { const { locale, slug } = await params; @@ -181,7 +112,6 @@ export default async function ProductPage({ params }: ProductPageProps) { const t = await getTranslations('Products'); const productsSlug = await mapFileSlugToTranslated('products', locale); - // Check if it's a category page const categories = [ 'low-voltage-cables', 'medium-voltage-cables', @@ -191,6 +121,7 @@ export default async function ProductPage({ params }: ProductPageProps) { const fileSlug = await mapSlugToFileSlug(productSlug, locale); if (categories.includes(fileSlug)) { + // (Skipping category page block, same as before) const allProducts = await getAllProducts(locale); const categoryKey = fileSlug .replace(/-cables$/, '') @@ -199,14 +130,12 @@ export default async function ProductPage({ params }: ProductPageProps) { ? t(`categories.${categoryKey}.title`) : fileSlug; - // Filter products for this category const filteredProducts = allProducts.filter((p) => p.frontmatter.categories.some( (cat) => cat.toLowerCase().replace(/\s+/g, '-') === fileSlug || cat === categoryTitle, ), ); - // Get translated product slugs const productsWithTranslatedSlugs = await Promise.all( filteredProducts.map(async (p) => ({ ...p, @@ -257,7 +186,6 @@ export default async function ProductPage({ params }: ProductPageProps) { className="object-contain p-8 transition-transform duration-700 group-hover:scale-110 z-10" sizes="(max-width: 768px) 100vw, (max-width: 1024px) 50vw, 33vw" /> - {/* Subtle reflection/shadow effect */}
    )} @@ -314,17 +242,14 @@ export default async function ProductPage({ params }: ProductPageProps) { notFound(); } - // Extract technical data for schema - const technicalDataMatch = product.content.match( - /technicalData=\{\}/s, - ); + // Extract technical data natively from the Lexical AST for Schema.org let technicalItems = []; - if (technicalDataMatch) { - try { - const data = JSON.parse(technicalDataMatch[1]); - technicalItems = data.technicalItems || []; - } catch (e) { - console.error('Failed to parse technical data for schema', e); + if (product.content?.root?.children) { + const productTabsBlock = product.content.root.children.find( + (node: any) => node.type === 'block' && node.fields?.blockType === 'productTabs', + ); + if (productTabsBlock && productTabsBlock.fields?.technicalItems) { + technicalItems = productTabsBlock.fields.technicalItems; } } @@ -347,23 +272,6 @@ export default async function ProductPage({ params }: ProductPageProps) { /> ); - const productComponents = { - ...components, - ProductTabs: (props: any) => , - }; - - // Pre-process content to convert raw HTML tags to Markdown so they use our custom components - const processedContent = product.content - .replace(/]*>(.*?)<\/h1>/gs, '\n# $1\n') // Maps to our custom h1 (which renders h2) - .replace(/]*>(.*?)<\/h2>/gs, '\n## $1\n') // Maps to our custom h2 (which renders h3) - .replace(/]*>(.*?)<\/h3>/gs, '\n### $1\n') // Maps to our custom h3 (which renders h4) - .replace(/]*>(.*?)<\/p>/gs, '\n$1\n') - .replace(/]*>(.*?)<\/ul>/gs, '\n$1\n') - .replace(/]*>(.*?)<\/li>/gs, '\n- $1\n') - .replace(/]*>(.*?)<\/strong>/gs, '**$1**') - .replace(/]*>/gs, '') - .replace(/<\/section>/gs, ''); - return (
    {/* Product Hero */} @@ -474,8 +382,8 @@ export default async function ProductPage({ params }: ProductPageProps) {
    {/* Main Content Area */} -
    - +
    +
    {/* Datasheet Download Section - Only for Medium Voltage for now */} diff --git a/app/[locale]/products/page.tsx b/app/[locale]/products/page.tsx index d654f3bf..268a5276 100644 --- a/app/[locale]/products/page.tsx +++ b/app/[locale]/products/page.tsx @@ -236,7 +236,7 @@ export default async function ProductsPage({ params }: ProductsPageProps) { className="group whitespace-nowrap w-full md:w-auto md:h-16 md:px-10 md:text-xl" > {t('cta.button')} - + diff --git a/app/actions/contact.ts b/app/actions/contact.ts index bbd180de..2b582f61 100644 --- a/app/actions/contact.ts +++ b/app/actions/contact.ts @@ -1,7 +1,5 @@ 'use server'; -import client, { ensureAuthenticated } from '@/lib/directus'; -import { createItem } from '@directus/sdk'; import { sendEmail } from '@/lib/mail/mailer'; import { render, ContactFormNotification, ConfirmationMessage } from '@mintel/mail'; import React from 'react'; @@ -41,31 +39,30 @@ export async function sendContactFormAction(formData: FormData) { return { success: false, error: 'Missing required fields' }; } - // 1. Save to Directus + // 1. Save to CMS try { - await ensureAuthenticated(); - if (productName) { - await client.request( - createItem('product_requests', { - product_name: productName, - email, - message, - }), - ); - logger.info('Product request stored in Directus'); - } else { - await client.request( - createItem('contact_submissions', { - name, - email, - message, - }), - ); - logger.info('Contact submission stored in Directus'); - } + const { getPayload } = await import('payload'); + const configPromise = (await import('@payload-config')).default; + const payload = await getPayload({ config: configPromise }); + + await payload.create({ + collection: 'form-submissions', + data: { + name, + email, + message, + type: productName ? 'product_quote' : 'contact', + productName: productName || undefined, + }, + }); + + logger.info('Successfully saved form submission to Payload CMS', { + type: productName ? 'product_quote' : 'contact', + email, + }); } catch (error) { - logger.error('Failed to store submission in Directus', { error }); - services.errors.captureException(error, { action: 'directus_store_submission' }); + logger.error('Failed to store submission in Payload CMS', { error }); + services.errors.captureException(error, { action: 'payload_store_submission' }); } // 2. Send Emails diff --git a/app/api/health/cms/route.ts b/app/api/health/cms/route.ts index 1871f2b7..af30e2e0 100644 --- a/app/api/health/cms/route.ts +++ b/app/api/health/cms/route.ts @@ -1,9 +1,9 @@ -import { checkHealth } from '@/lib/directus'; import { NextResponse } from 'next/server'; export const dynamic = 'force-dynamic'; export async function GET() { - const health = await checkHealth(); - return NextResponse.json(health, { status: health.status === 'ok' ? 200 : 503 }); + // Payload is embedded within the Next.js app, so if this route responds, the CMS is up. + // Further DB health checks can be implemented via Payload Local API later. + return NextResponse.json({ status: 'ok', message: 'Payload CMS is embedded.' }, { status: 200 }); } diff --git a/app/errors/api/relay/route.ts b/app/errors/api/relay/route.ts index 59b5df3f..edb44f25 100644 --- a/app/errors/api/relay/route.ts +++ b/app/errors/api/relay/route.ts @@ -17,6 +17,11 @@ export async function POST(request: NextRequest) { const logger = services.logger.child({ component: 'sentry-relay' }); try { + // Prevent 403 Forbidden console noise in local dev + if (process.env.NODE_ENV === 'development') { + return NextResponse.json({ status: 'ignored_in_dev' }, { status: 200 }); + } + const envelope = await request.text(); // Sentry envelopes can contain multiple parts separated by newlines diff --git a/app/stats/api/send/route.ts b/app/stats/api/send/route.ts index 10824628..3acd30f4 100644 --- a/app/stats/api/send/route.ts +++ b/app/stats/api/send/route.ts @@ -19,6 +19,11 @@ export async function POST(request: NextRequest) { const logger = services.logger.child({ component: 'umami-smart-proxy' }); try { + // Prevent 400 Bad Request console noise in local dev + if (process.env.NODE_ENV === 'development') { + return NextResponse.json({ status: 'ignored_in_dev' }, { status: 200 }); + } + const body = await request.json(); const { type, payload } = body; diff --git a/check-data.ts b/check-data.ts new file mode 100644 index 00000000..7f34c10e --- /dev/null +++ b/check-data.ts @@ -0,0 +1,39 @@ +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; + +async function checkData() { + try { + const payload = await getPayload({ config: configPromise }); + + const { docs: posts } = await payload.find({ collection: 'posts', limit: 3 }); + const { docs: products } = await payload.find({ collection: 'products', limit: 3 }); + const { docs: pages } = await payload.find({ collection: 'pages', limit: 3 }); + + const checkDocs = (name: string, docs: any[]) => { + console.log(`\n----- ${name.toUpperCase()} -----`); + docs.forEach((p) => { + console.log(`ID: ${p.id}, Slug: ${p.slug}`); + if (Array.isArray(p.content)) { + console.log( + 'Content is ARRAY (Slate format!)', + JSON.stringify(p.content).substring(0, 100), + ); + } else if (p.content && p.content.root) { + console.log('Content is Lexical format.'); + } else { + console.log('Content is UNKNOWN format.'); + console.log(JSON.stringify(p.content).substring(0, 100)); + } + }); + }; + + checkDocs('posts', posts); + checkDocs('products', products); + checkDocs('pages', pages); + } catch (err) { + console.error(err); + } + process.exit(0); +} + +checkData(); diff --git a/components/PayloadRichText.tsx b/components/PayloadRichText.tsx new file mode 100644 index 00000000..1ceac3b9 --- /dev/null +++ b/components/PayloadRichText.tsx @@ -0,0 +1,213 @@ +import { RichText } from '@payloadcms/richtext-lexical/react'; +import type { JSXConverters } from '@payloadcms/richtext-lexical/react'; +import Image from 'next/image'; + +// Import all custom React components that were previously mapped via MDX +import StickyNarrative from '@/components/blog/StickyNarrative'; +import ComparisonGrid from '@/components/blog/ComparisonGrid'; +import VisualLinkPreview from '@/components/blog/VisualLinkPreview'; +import TechnicalGrid from '@/components/blog/TechnicalGrid'; +import HighlightBox from '@/components/blog/HighlightBox'; +import AnimatedImage from '@/components/blog/AnimatedImage'; +import ChatBubble from '@/components/blog/ChatBubble'; +import PowerCTA from '@/components/blog/PowerCTA'; +import { Callout } from '@/components/ui/Callout'; +import Stats from '@/components/blog/Stats'; +import SplitHeading from '@/components/blog/SplitHeading'; +import ProductTabs from '@/components/ProductTabs'; +import ProductTechnicalData from '@/components/ProductTechnicalData'; +const jsxConverters: JSXConverters = { + blocks: { + // Map the custom Payload Blocks created in src/payload/blocks to their React components + // Payload Lexical exposes blocks using the 'block-[slug]' pattern + stickyNarrative: ({ node }: any) => ( + + ), + 'block-stickyNarrative': ({ node }: any) => ( + + ), + comparisonGrid: ({ node }: any) => ( + + ), + 'block-comparisonGrid': ({ node }: any) => ( + + ), + visualLinkPreview: ({ node }: any) => ( + + ), + 'block-visualLinkPreview': ({ node }: any) => ( + + ), + technicalGrid: ({ node }: any) => ( + + ), + 'block-technicalGrid': ({ node }: any) => { + console.log('[PayloadRichText] Rendering block-technicalGrid:', node.fields.title); + return ; + }, + highlightBox: ({ node }: any) => ( + + + + ), + 'block-highlightBox': ({ node }: any) => ( + + + + ), + animatedImage: ({ node }: any) => ( + + ), + 'block-animatedImage': ({ node }: any) => ( + + ), + chatBubble: ({ node }: any) => ( + + + + ), + 'block-chatBubble': ({ node }: any) => ( + + + + ), + powerCTA: ({ node }: any) => , + 'block-powerCTA': ({ node }: any) => , + callout: ({ node }: any) => ( + + + + ), + 'block-callout': ({ node }: any) => ( + + + + ), + stats: ({ node }: any) => , + 'block-stats': ({ node }: any) => , + splitHeading: ({ node }: any) => ( + + {node.fields.title} + + ), + 'block-splitHeading': ({ node }: any) => ( + + {node.fields.title} + + ), + productTabs: ({ node }: any) => ( + + } + > + <> + + ), + 'block-productTabs': ({ node }: any) => ( + + } + > + <> + + ), + }, + // Custom converter for the Payload "upload" Lexical node (Media collection) + // This natively reconstructs Next.js tags pointing to the focal-point cropped sizes + upload: ({ node }: any) => { + // Attempt to extract the highly optimized 'card' generated size from Payload, fallback to raw url + let src = node?.value?.sizes?.card?.url || node?.value?.url; + const alt = node?.value?.alt || 'Blog Post Media'; + + if (!src) return null; + + // Strip legacy imgproxy query parameters (e.g. ?ar=16:9) that crash Next.js 14+ localPatterns + if (src.includes('?')) { + src = src.split('?')[0]; + } + + // Fallback dimensions if unmapped or loading from raw + const width = node?.value?.sizes?.card?.width || 800; + const height = node?.value?.sizes?.card?.height || 600; + + return ( +
    + {alt} + {node?.value?.caption && ( +
    + {node.value.caption} +
    + )} +
    + ); + }, +}; + +export default function PayloadRichText({ data }: { data: any }) { + if (!data) return null; + + return ( +
    + +
    + ); +} diff --git a/components/ProductSidebar.tsx b/components/ProductSidebar.tsx index 87f33bb9..14c8a0af 100644 --- a/components/ProductSidebar.tsx +++ b/components/ProductSidebar.tsx @@ -35,7 +35,7 @@ export default function ProductSidebar({
    {productName} {product.frontmatter.title} { - trackEvent(eventName, { - href, - ...eventProperties, - }); + try { + trackEvent(eventName, { + href, + ...eventProperties, + }); + } catch (_e) { + // Analytics tracking should not block navigation, so we catch and ignore errors. + } if (onClick) onClick(); }; diff --git a/components/blog/PostNavigation.tsx b/components/blog/PostNavigation.tsx index 5c592609..61f82fac 100644 --- a/components/blog/PostNavigation.tsx +++ b/components/blog/PostNavigation.tsx @@ -31,7 +31,7 @@ export default function PostNavigation({ {prev.frontmatter.featuredImage ? (
    ) : (
    @@ -82,7 +82,7 @@ export default function PostNavigation({ {next.frontmatter.featuredImage ? (
    ) : (
    diff --git a/components/blog/VisualLinkPreview.tsx b/components/blog/VisualLinkPreview.tsx index c089f613..87b5724b 100644 --- a/components/blog/VisualLinkPreview.tsx +++ b/components/blog/VisualLinkPreview.tsx @@ -29,7 +29,7 @@ export default function VisualLinkPreview({ url, title, summary, image }: Visual
    {image ? ( {title} {post.frontmatter.title} + sh -c "pnpm install && pnpm next dev --turbo --hostname 0.0.0.0" + labels: + - "traefik.enable=true" + - "traefik.http.services.${PROJECT_NAME:-klz}-app-svc.loadbalancer.server.port=3000" + - "traefik.docker.network=infra" + - "caddy=http://${TRAEFIK_HOST:-klz.localhost}" + - "caddy.reverse_proxy={{upstreams 3000}}" + + klz-db: + image: postgres:15-alpine + restart: unless-stopped + env_file: + - ${ENV_FILE:-.env} + environment: + POSTGRES_DB: ${DIRECTUS_DB_NAME:-payload} + POSTGRES_USER: ${DIRECTUS_DB_USER:-payload} + POSTGRES_PASSWORD: ${DIRECTUS_DB_PASSWORD:-120in09oenaoinsd9iaidon} + volumes: + - klz_db_data:/var/lib/postgresql/data + networks: + - default + ports: + - "54322:5432" + +networks: + default: + name: ${PROJECT_NAME:-klz-cables}-internal + infra: + external: true + +volumes: + klz_db_data: + external: false + klz_node_modules: + klz_next_cache: + klz_pnpm_store: diff --git a/docker-compose.override.yml b/docker-compose.override.yml deleted file mode 100644 index 8ffe9613..00000000 --- a/docker-compose.override.yml +++ /dev/null @@ -1,45 +0,0 @@ -services: - klz-app: - build: - context: . - dockerfile: Dockerfile - target: development - volumes: - - .:/app - - /app/node_modules - - /app/.next - environment: - - WATCHPACK_POLLING=true # Useful for Docker volume mounting issues on some systems - restart: "no" - container_name: klz-app-dev - labels: - - "traefik.enable=true" - # Clear any production middlewares/headers redirect - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-web.middlewares=" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-web.rule=Host(`${TRAEFIK_HOST:-klz.localhost}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-web.entrypoints=web" - # Configure main router for local HTTP without auth - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}.rule=Host(`${TRAEFIK_HOST:-klz.localhost}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}.entrypoints=web" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}.middlewares=" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}.tls=false" - - "traefik.docker.network=infra" - - klz-cms: - container_name: klz-cms-dev - restart: "no" - ports: - - "8055:8055" - labels: - - "traefik.enable=true" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-cms.rule=Host(`${DIRECTUS_HOST:-cms.klz.localhost}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-cms.entrypoints=${TRAEFIK_ENTRYPOINT:-web}" - - "traefik.http.routers.${PROJECT_NAME:-klz-cables}-cms.service=${PROJECT_NAME:-klz-cables}-cms" - - "traefik.http.services.${PROJECT_NAME:-klz-cables}-cms.loadbalancer.server.port=8055" - - "traefik.docker.network=infra" - - klz-db: - restart: "no" - - klz-gatekeeper: - restart: "no" diff --git a/docker-compose.yml b/docker-compose.yml index 58f83408..00153287 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,12 +1,6 @@ services: klz-app: - build: - context: . - dockerfile: Dockerfile - args: - NEXT_PUBLIC_BASE_URL: ${NEXT_PUBLIC_BASE_URL} - DIRECTUS_URL: "${DIRECTUS_URL}" - image: registry.infra.mintel.me/mintel/klz-cables.com:${IMAGE_TAG:-latest} + image: registry.infra.mintel.me/mintel/klz-2026:${IMAGE_TAG:-latest} restart: unless-stopped networks: default: @@ -15,8 +9,6 @@ services: - klz.localhost env_file: - ${ENV_FILE:-.env} - environment: - IMGPROXY_URL: ${IMGPROXY_URL:-http://klz-imgproxy:8080} labels: - "traefik.enable=true" # HTTP ⇒ HTTPS redirect @@ -31,43 +23,23 @@ services: - "traefik.http.routers.${PROJECT_NAME:-klz}.service=${PROJECT_NAME:-klz}-app-svc" - "traefik.http.routers.${PROJECT_NAME:-klz}.middlewares=${AUTH_MIDDLEWARE:-klz-ratelimit,klz-forward,klz-compress}" - # Public Router (Whitelist for OG Images, Sitemaps, Health) - - "traefik.http.routers.${PROJECT_NAME:-klz}-public.rule=(${TRAEFIK_HOST_RULE:-Host(`${TRAEFIK_HOST:-klz-cables.com}`)}) && (PathPrefix(`/health`) || PathPrefix(`/sitemap.xml`) || PathPrefix(`/robots.txt`) || PathPrefix(`/manifest.webmanifest`) || PathPrefix(`/api/og`) || PathPrefix(`/de/api/og`) || PathPrefix(`/en/api/og`) || PathPrefix(`/logo-white.svg`) || PathPrefix(`/icon-white.svg`) || PathPrefix(`/opengraph-image`) || PathPrefix(`/de/opengraph-image`) || PathPrefix(`/en/opengraph-image`) || PathPrefix(`/blog/opengraph-image`) || PathPrefix(`/de/blog/opengraph-image`) || PathPrefix(`/en/blog/opengraph-image`) || PathRegexp(`^/sitemap(-[0-9]+)?\\.xml$`) || PathRegexp(`.*\\.(svg|png|jpg|jpeg|gif|webp|ico|webm|mp4|map)$`))" + # Public Router (Whitelist) + - "traefik.http.routers.${PROJECT_NAME:-klz}-public.rule=(${TRAEFIK_HOST_RULE:-Host(`${TRAEFIK_HOST:-klz-cables.com}`)}) && (PathPrefix(`/health`) || PathPrefix(`/sitemap.xml`) || PathPrefix(`/robots.txt`) || PathPrefix(`/manifest.webmanifest`) || PathPrefix(`/api/og`) || PathRegexp(`.*opengraph-image.*`) || PathRegexp(`^/sitemap(-[0-9]+)?\\.xml$`))" - "traefik.http.routers.${PROJECT_NAME:-klz}-public.entrypoints=${TRAEFIK_ENTRYPOINT:-web}" - "traefik.http.routers.${PROJECT_NAME:-klz}-public.tls.certresolver=${TRAEFIK_CERT_RESOLVER:-}" - "traefik.http.routers.${PROJECT_NAME:-klz}-public.tls=${TRAEFIK_TLS:-false}" - "traefik.http.routers.${PROJECT_NAME:-klz}-public.service=${PROJECT_NAME:-klz}-app-svc" - - "traefik.http.routers.${PROJECT_NAME:-klz}-public.middlewares=${AUTH_MIDDLEWARE_UNPROTECTED:-klz-ratelimit,klz-forward,klz-compress}" - "traefik.http.routers.${PROJECT_NAME:-klz}-public.priority=2000" - - "traefik.http.services.${PROJECT_NAME:-klz}-app-svc.loadbalancer.server.scheme=http" - "traefik.http.services.${PROJECT_NAME:-klz}-app-svc.loadbalancer.server.port=3000" - "traefik.docker.network=infra" - - "caddy=http://${TRAEFIK_HOST:-klz.localhost}" - - "caddy.reverse_proxy={{upstreams 3000}}" - # Middleware Definitions + # Middlewares - "traefik.http.middlewares.${PROJECT_NAME:-klz}-compress.compress=true" - - # Forwarded Headers - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-forward.headers.customrequestheaders.X-Forwarded-Proto=https" - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-forward.headers.customrequestheaders.X-Forwarded-Ssl=on" - - # Authentication Middleware (ForwardAuth) - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-auth.forwardauth.address=http://${PROJECT_NAME:-klz}-gatekeeper:3000/gatekeeper/api/verify" - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-auth.forwardauth.trustForwardHeader=true" - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-auth.forwardauth.authRequestHeaders=X-Forwarded-Host,X-Forwarded-Proto,X-Forwarded-For,Cookie" - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-auth.forwardauth.authResponseHeaders=X-Auth-User" - - # Rate Limit Middleware - "traefik.http.middlewares.${PROJECT_NAME:-klz}-ratelimit.ratelimit.average=100" - "traefik.http.middlewares.${PROJECT_NAME:-klz}-ratelimit.ratelimit.burst=50" - healthcheck: - test: [ "CMD", "curl", "-f", "http://127.0.0.1:3000/health" ] - interval: 15s - timeout: 10s - retries: 3 - start_period: 45s + - "traefik.http.middlewares.${PROJECT_NAME:-klz}-forward.headers.customrequestheaders.X-Forwarded-Proto=https" + - "traefik.http.middlewares.${PROJECT_NAME:-klz}-forward.headers.customrequestheaders.X-Forwarded-Ssl=on" klz-gatekeeper: profiles: [ "gatekeeper" ] @@ -81,125 +53,26 @@ services: - ${ENV_FILE:-.env} environment: PORT: 3000 - PROJECT_NAME: ${PROJECT_NAME:-KLZ Cables} - PROJECT_COLOR: "#82ed20" - COOKIE_DOMAIN: ${COOKIE_DOMAIN} - AUTH_COOKIE_NAME: ${AUTH_COOKIE_NAME:-klz_gatekeeper_session} - GATEKEEPER_PASSWORD: ${GATEKEEPER_PASSWORD} - NEXT_PUBLIC_BASE_URL: ${GATEKEEPER_ORIGIN} labels: - "traefik.enable=true" - - "traefik.docker.network=infra" - - "traefik.http.routers.${PROJECT_NAME:-klz}-gatekeeper.rule=(Host(`${TRAEFIK_HOST:-testing.klz-cables.com}`) && PathPrefix(`/gatekeeper`))" - - "traefik.http.routers.${PROJECT_NAME:-klz}-gatekeeper.entrypoints=${TRAEFIK_ENTRYPOINT:-web}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-gatekeeper.tls.certresolver=${TRAEFIK_CERT_RESOLVER:-}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-gatekeeper.tls=${TRAEFIK_TLS:-false}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-gatekeeper.service=${PROJECT_NAME:-klz}-gatekeeper-svc" - "traefik.http.services.${PROJECT_NAME:-klz}-gatekeeper-svc.loadbalancer.server.port=3000" - "traefik.docker.network=infra" - klz-cms: - image: registry.infra.mintel.me/mintel/directus:latest - restart: unless-stopped - command: [ "node", "cli.js", "start" ] - env_file: - - ${ENV_FILE:-.env} - environment: - KEY: ${DIRECTUS_KEY} - SECRET: ${DIRECTUS_SECRET} - ADMIN_EMAIL: ${DIRECTUS_ADMIN_EMAIL} - ADMIN_PASSWORD: ${DIRECTUS_ADMIN_PASSWORD} - DB_CLIENT: 'pg' - DB_HOST: 'klz-db' - DB_PORT: '5432' - DB_DATABASE: ${DIRECTUS_DB_NAME:-directus} - DB_USER: ${DIRECTUS_DB_USER:-directus} - DB_PASSWORD: ${DIRECTUS_DB_PASSWORD:-120in09oenaoinsd9iaidon} - WEBSOCKETS_ENABLED: 'true' - PUBLIC_URL: ${DIRECTUS_URL:-https://cms.klz-cables.com} - HOST: '0.0.0.0' - networks: - - default - - infra - volumes: - - ./directus/uploads:/directus/uploads - - ./directus/extensions:/directus/extensions - - ./directus/schema:/directus/schema - - ./directus/migrations:/directus/migrations - healthcheck: - disable: true - labels: - - "traefik.enable=true" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.rule=Host(`${DIRECTUS_HOST:-cms.klz-cables.com}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.entrypoints=websecure" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.priority=5000" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.tls=true" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.tls.certresolver=le" - - "traefik.http.routers.${PROJECT_NAME:-klz}-cms.service=${PROJECT_NAME:-klz}-cms-svc" - - "traefik.http.services.${PROJECT_NAME:-klz}-cms-svc.loadbalancer.server.port=8055" - - "traefik.docker.network=infra" - - "caddy=http://${DIRECTUS_HOST:-cms.klz-cables.com}" - - "caddy.reverse_proxy={{upstreams 8055}}" klz-db: image: postgres:15-alpine restart: unless-stopped env_file: - ${ENV_FILE:-.env} environment: - POSTGRES_DB: ${DIRECTUS_DB_NAME:-directus} - POSTGRES_USER: ${DIRECTUS_DB_USER:-directus} + POSTGRES_DB: ${DIRECTUS_DB_NAME:-payload} + POSTGRES_USER: ${DIRECTUS_DB_USER:-payload} POSTGRES_PASSWORD: ${DIRECTUS_DB_PASSWORD:-120in09oenaoinsd9iaidon} volumes: - - directus-db-data:/var/lib/postgresql/data + - klz_db_data:/var/lib/postgresql/data networks: - default - - klz-imgproxy: - build: - context: ../at-mintel - dockerfile: apps/image-service/Dockerfile - restart: unless-stopped - networks: - - default - - infra - extra_hosts: - - "klz.localhost:host-gateway" - - "cms.klz.localhost:host-gateway" - - "host.docker.internal:host-gateway" - environment: - OPENROUTER_API_KEY: ${OPENROUTER_API_KEY} - # explicitly map localhost, production and staging to bypass gatekeeper - IMGPROXY_URL_MAPPING: "https://staging.klz-cables.com:http://klz-app:3000,https://klz-cables.com:http://klz-app:3000,https://${TRAEFIK_HOST:-klz.localhost}:http://klz-app:3000,${DIRECTUS_URL}:http://klz-cms:8055,https://cms.klz-cables.com:http://klz-cms:8055" - IMGPROXY_LOG_LEVEL: debug - - labels: - - "traefik.enable=true" - # Existing Local HTTP Router - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy.rule=Host(`img.${TRAEFIK_HOST:-klz.localhost}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy.entrypoints=web" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy.service=${PROJECT_NAME:-klz}-imgproxy-svc" - - # NEW: Direct Public Staging Router for /_img (Bypasses Next.js rewrites) - # This fixes the Next.js URL-decoding bug on dynamic image proxy paths - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.rule=(Host(`${TRAEFIK_HOST:-klz.localhost}`) || Host(`staging.klz-cables.com`) || Host(`testing.klz-cables.com`)) && PathPrefix(`/_img`)" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.priority=99999" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.entrypoints=${TRAEFIK_ENTRYPOINT:-web}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.tls=${TRAEFIK_TLS:-false}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.tls.certresolver=${TRAEFIK_CERT_RESOLVER:-}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.service=${PROJECT_NAME:-klz}-imgproxy-svc" - - "traefik.http.services.${PROJECT_NAME:-klz}-imgproxy-svc.loadbalancer.server.port=8080" - - "traefik.http.routers.${PROJECT_NAME:-klz}-img.middlewares=${PROJECT_NAME:-klz}-img-strip" - - "traefik.http.middlewares.${PROJECT_NAME:-klz}-img-strip.stripprefix.prefixes=/_img" - # HTTPS router (staging/prod) - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy-secure.rule=Host(`img.${TRAEFIK_HOST:-klz.localhost}`)" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy-secure.entrypoints=${TRAEFIK_ENTRYPOINT:-web}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy-secure.tls=${TRAEFIK_TLS:-false}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy-secure.tls.certresolver=${TRAEFIK_CERT_RESOLVER:-}" - - "traefik.http.routers.${PROJECT_NAME:-klz}-imgproxy-secure.service=${PROJECT_NAME:-klz}-imgproxy-svc" - - "traefik.http.services.${PROJECT_NAME:-klz}-imgproxy-svc.loadbalancer.server.port=8080" - - "traefik.docker.network=infra" - - "caddy=http://img.${TRAEFIK_HOST:-klz.localhost}" - - "caddy.reverse_proxy={{upstreams 8080}}" + ports: + - "54322:5432" networks: default: @@ -208,6 +81,5 @@ networks: external: true volumes: - directus-db-data: - external: true - name: klz-cablescom_directus-db-data + klz_db_data: + external: false diff --git a/lib/blog.ts b/lib/blog.ts index 7f882a9f..9adc5aa5 100644 --- a/lib/blog.ts +++ b/lib/blog.ts @@ -1,7 +1,5 @@ -import fs from 'fs'; -import path from 'path'; -import matter from 'gray-matter'; -import { mapSlugToFileSlug } from './slugs'; +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; import { config } from '@/lib/config'; export function extractExcerpt(content: string): string { @@ -42,7 +40,7 @@ export interface PostFrontmatter { export interface PostMdx { slug: string; frontmatter: PostFrontmatter; - content: string; + content: any; // Mapped to Lexical SerializedEditorState } export function isPostVisible(post: { frontmatter: { date: string; public?: boolean } }) { @@ -57,87 +55,81 @@ export function isPostVisible(post: { frontmatter: { date: string; public?: bool } export async function getPostBySlug(slug: string, locale: string): Promise { - // Map translated slug to file slug - const fileSlug = await mapSlugToFileSlug(slug, locale); - const postsDir = path.join(process.cwd(), 'data', 'blog', locale); - const filePath = path.join(postsDir, `${fileSlug}.mdx`); + const payload = await getPayload({ config: configPromise }); - if (!fs.existsSync(filePath)) { - return null; - } + const { docs } = await payload.find({ + collection: 'posts', + where: { + slug: { equals: slug }, + locale: { equals: locale }, + }, + draft: process.env.NODE_ENV === 'development', + limit: 1, + }); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data, content } = matter(fileContent); + if (!docs || docs.length === 0) return null; - const postInfo = { - slug: fileSlug, + const doc = docs[0]; + + return { + slug: doc.slug, frontmatter: { - ...data, - excerpt: data.excerpt || extractExcerpt(content), + title: doc.title, + date: doc.date, + excerpt: doc.excerpt || '', + category: doc.category || '', + locale: doc.locale, + featuredImage: + typeof doc.featuredImage === 'object' && doc.featuredImage !== null + ? doc.featuredImage.sizes?.card?.url || doc.featuredImage.url + : null, + public: doc._status === 'published', } as PostFrontmatter, - content, + content: doc.content as any, // Native Lexical Editor State }; - - if (!isPostVisible(postInfo)) { - return null; - } - - return postInfo; } export async function getAllPosts(locale: string): Promise { - const postsDir = path.join(process.cwd(), 'data', 'blog', locale); - if (!fs.existsSync(postsDir)) return []; + const payload = await getPayload({ config: configPromise }); + // Query only published posts (access checks applied automatically by Payload!) + const { docs } = await payload.find({ + collection: 'posts', + where: { + locale: { + equals: locale, + }, + }, + sort: '-date', + draft: process.env.NODE_ENV === 'development', // Includes Drafts if running locally + limit: 100, + }); - const files = fs.readdirSync(postsDir); - const posts = files - .filter((file) => file.endsWith('.mdx')) - .map((file) => { - const filePath = path.join(postsDir, file); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data, content } = matter(fileContent); - return { - slug: file.replace(/\.mdx$/, ''), - frontmatter: { - ...data, - excerpt: data.excerpt || extractExcerpt(content), - } as PostFrontmatter, - content, - }; - }) - .filter(isPostVisible) - .sort( - (a, b) => new Date(b.frontmatter.date).getTime() - new Date(a.frontmatter.date).getTime(), - ); - - return posts; + return docs.map((doc) => { + return { + slug: doc.slug, + frontmatter: { + title: doc.title, + date: doc.date, + excerpt: doc.excerpt || '', + category: doc.category || '', + locale: doc.locale, + featuredImage: + typeof doc.featuredImage === 'object' && doc.featuredImage !== null + ? doc.featuredImage.sizes?.card?.url || doc.featuredImage.url + : null, + } as PostFrontmatter, + // Pass the Lexical content object rather than raw markdown string + content: doc.content as any, + }; + }); } export async function getAllPostsMetadata(locale: string): Promise[]> { - const postsDir = path.join(process.cwd(), 'data', 'blog', locale); - if (!fs.existsSync(postsDir)) return []; - - const files = fs.readdirSync(postsDir); - return files - .filter((file) => file.endsWith('.mdx')) - .map((file) => { - const filePath = path.join(postsDir, file); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data } = matter(fileContent); - return { - slug: file.replace(/\.mdx$/, ''), - frontmatter: { - ...data, - excerpt: data.excerpt || extractExcerpt(fileContent.replace(/^---[\s\S]*?---/, '')), - } as PostFrontmatter, - }; - }) - .filter(isPostVisible) - .sort( - (a, b) => - new Date(b.frontmatter.date as string).getTime() - - new Date(a.frontmatter.date as string).getTime(), - ); + const posts = await getAllPosts(locale); + return posts.map((p) => ({ + slug: p.slug, + frontmatter: p.frontmatter, + })); } export async function getAdjacentPosts( diff --git a/lib/config.ts b/lib/config.ts index 3ccc492e..f9d18f63 100644 --- a/lib/config.ts +++ b/lib/config.ts @@ -13,7 +13,10 @@ let memoizedConfig: ReturnType | undefined; function createConfig() { const env = getRawEnv(); - const target = env.NEXT_PUBLIC_TARGET || env.TARGET; + const target = + env.NEXT_PUBLIC_TARGET || + env.TARGET || + (env.NODE_ENV === 'development' ? 'development' : 'production'); console.log('[Config] Initializing Toggles:', { feedbackEnabled: env.NEXT_PUBLIC_FEEDBACK_ENABLED, @@ -65,17 +68,9 @@ function createConfig() { from: env.MAIL_FROM, recipients: env.MAIL_RECIPIENTS, }, - directus: { - url: env.DIRECTUS_URL, - adminEmail: env.DIRECTUS_ADMIN_EMAIL, - password: env.DIRECTUS_ADMIN_PASSWORD, - token: env.DIRECTUS_API_TOKEN, - internalUrl: env.INTERNAL_DIRECTUS_URL, - proxyPath: '/cms', - }, infraCMS: { - url: env.INFRA_DIRECTUS_URL || env.DIRECTUS_URL, - token: env.INFRA_DIRECTUS_TOKEN || env.DIRECTUS_API_TOKEN, + url: env.INFRA_DIRECTUS_URL, + token: env.INFRA_DIRECTUS_TOKEN, }, notifications: { gotify: { @@ -139,9 +134,6 @@ export const config = { get mail() { return getConfig().mail; }, - get directus() { - return getConfig().directus; - }, get notifications() { return getConfig().notifications; }, @@ -192,12 +184,6 @@ export function getMaskedConfig() { from: c.mail.from, recipients: c.mail.recipients, }, - directus: { - url: c.directus.url, - adminEmail: mask(c.directus.adminEmail), - password: mask(c.directus.password), - token: mask(c.directus.token), - }, notifications: { gotify: { url: c.notifications.gotify.url, diff --git a/lib/directus.ts b/lib/directus.ts deleted file mode 100644 index 36c71ef6..00000000 --- a/lib/directus.ts +++ /dev/null @@ -1,192 +0,0 @@ -import { readItems, readCollections } from '@directus/sdk'; -import { createMintelDirectusClient, ensureDirectusAuthenticated } from '@mintel/next-utils'; -import { config } from './config'; -import { getServerAppServices } from './services/create-services.server'; - -/** - * Directus Schema Definitions - */ -export interface Schema { - products: any[]; - categories: any[]; - contact_submissions: any[]; - product_requests: any[]; - translations: any[]; - categories_link: any[]; -} - -// Initialize client using Mintel standards (environment-aware) -const client = createMintelDirectusClient(); - -/** - * Helper to determine if we should show detailed errors - */ -const shouldShowDevErrors = config.isTesting || config.isDevelopment; - -/** - * Genericizes error messages for production/staging - */ -function formatError(error: any) { - if (shouldShowDevErrors) { - return error.errors?.[0]?.message || error.message || 'An unexpected error occurred.'; - } - return 'A system error occurred. Our team has been notified.'; -} - -export async function ensureAuthenticated() { - try { - await ensureDirectusAuthenticated(client); - } catch (e: any) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(e, { part: 'directus_auth' }); - } - console.error(`Failed to authenticate with Directus:`, e.message); - throw e; - } -} - -/** - * Maps the new translation-based schema back to the application's Product interface - */ -function mapDirectusProduct(item: any, locale: string): any { - const langCode = locale === 'en' ? 'en-US' : 'de-DE'; - const translation = - item.translations?.find((t: any) => t.languages_code === langCode) || - item.translations?.[0] || - {}; - - return { - id: item.id, - sku: item.sku, - title: translation.name || '', - description: translation.description || '', - content: translation.content || '', - technicalData: { - technicalItems: translation.technical_items || [], - voltageTables: translation.voltage_tables || [], - }, - locale: locale, - // Use standardized proxy path for assets to avoid CORS - data_sheet_url: item.data_sheet ? `/api/directus/assets/${item.data_sheet}` : null, - categories: (item.categories_link || []) - .map((c: any) => c.categories_id?.translations?.[0]?.name) - .filter(Boolean), - }; -} - -export async function getProducts(locale: string = 'de') { - await ensureAuthenticated(); - try { - const items = await client.request( - readItems('products', { - fields: ['*', 'translations.*', 'categories_link.categories_id.translations.name'], - }), - ); - return items.map((item) => mapDirectusProduct(item, locale)); - } catch (error) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(error, { part: 'directus_get_products' }); - } - console.error('Error fetching products:', error); - return []; - } -} - -export async function getProductBySlug(slug: string, locale: string = 'de') { - await ensureAuthenticated(); - const langCode = locale === 'en' ? 'en-US' : 'de-DE'; - try { - const items = await client.request( - readItems('products', { - filter: { - translations: { - slug: { _eq: slug }, - languages_code: { _eq: langCode }, - }, - }, - fields: ['*', 'translations.*', 'categories_link.categories_id.translations.name'], - limit: 1, - }), - ); - - if (!items || items.length === 0) return null; - return mapDirectusProduct(items[0], locale); - } catch (error) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(error, { - part: 'directus_get_product_by_slug', - slug, - }); - } - console.error(`Error fetching product ${slug}:`, error); - return null; - } -} - -export async function checkHealth() { - try { - // 1. Connectivity & Auth Check - try { - await ensureAuthenticated(); - await client.request(readCollections()); - } catch (e: any) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(e, { part: 'directus_health_auth' }); - } - console.error('Directus authentication or collection-read failed during health check:', e); - return { - status: 'error', - message: shouldShowDevErrors - ? `Directus Health Error: ${e.message || 'Unknown'}` - : 'CMS is currently unavailable due to an internal authentication or connection error.', - code: e.code || 'HEALTH_AUTH_FAILED', - details: shouldShowDevErrors - ? { message: e.message, code: e.code, errors: e.errors } - : undefined, - }; - } - - // 2. Schema check (does the contact_submissions table exist?) - try { - await client.request(readItems('contact_submissions', { limit: 1 })); - } catch (e: any) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(e, { part: 'directus_health_schema' }); - } - if ( - e.message?.includes('does not exist') || - e.code === 'INVALID_PAYLOAD' || - e.status === 404 - ) { - return { - status: 'error', - message: shouldShowDevErrors - ? `The "contact_submissions" collection is missing or inaccessible. Error: ${e.message || 'Unknown'}` - : 'Required data structures are currently unavailable.', - code: 'SCHEMA_MISSING', - }; - } - return { - status: 'error', - message: shouldShowDevErrors - ? `Schema error: ${e.errors?.[0]?.message || e.message || 'Unknown error'}` - : 'The data schema is currently misconfigured.', - code: 'SCHEMA_ERROR', - }; - } - - return { status: 'ok', message: 'Directus is reachable and responding.' }; - } catch (error: any) { - if (typeof window === 'undefined') { - getServerAppServices().errors.captureException(error, { part: 'directus_health_critical' }); - } - console.error('Directus health check failed with unexpected error:', error); - return { - status: 'error', - message: formatError(error), - code: error.code || 'UNKNOWN', - }; - } -} - -export default client; diff --git a/lib/env.ts b/lib/env.ts index 832f51d5..f6a4ed1d 100644 --- a/lib/env.ts +++ b/lib/env.ts @@ -43,12 +43,6 @@ const envExtension = { MAIL_PASSWORD: z.string().optional(), MAIL_FROM: z.string().optional(), MAIL_RECIPIENTS: z.string().optional(), - - // Directus Authentication - DIRECTUS_URL: z.string().url().optional(), - DIRECTUS_ADMIN_EMAIL: z.string().email().optional(), - DIRECTUS_ADMIN_PASSWORD: z.string().optional(), - DIRECTUS_API_TOKEN: z.string().optional(), }; /** diff --git a/lib/imgproxy-loader.ts b/lib/imgproxy-loader.ts deleted file mode 100644 index cce62b8a..00000000 --- a/lib/imgproxy-loader.ts +++ /dev/null @@ -1,71 +0,0 @@ -import { getImgproxyUrl } from './imgproxy'; - -/** - * Next.js Image Loader for imgproxy - * - * @param {Object} props - properties from Next.js Image component - * @param {string} props.src - The source image URL - * @param {number} props.width - The desired image width - * @param {number} props.quality - The desired image quality (ignored for now as imgproxy handles it) - */ -export default function imgproxyLoader({ - src, - width, - _quality, -}: { - src: string; - width: number; - _quality?: number; -}) { - // Skip imgproxy for SVGs as they are vectors and don't benefit from resizing, - // and often cause 404s if the source is not correctly resolvable by imgproxy. - if (src.toLowerCase().endsWith('.svg')) { - return src; - } - - // Check if src contains custom gravity or aspect ratio query parameters - let gravity = 'sm'; // Use smart gravity (content-aware) by default - let cleanSrc = src; - let calculatedHeight = 0; - let resizingType: 'fit' | 'fill' = 'fit'; - - try { - // Dummy base needed for relative URLs - const url = new URL(src, 'http://localhost'); - const customGravity = url.searchParams.get('gravity'); - const aspectRatio = url.searchParams.get('ar'); // e.g. "16:9" - - if (customGravity) { - gravity = customGravity; - url.searchParams.delete('gravity'); - } - - if (aspectRatio) { - const parts = aspectRatio.split(':'); - if (parts.length === 2) { - const arW = parseFloat(parts[0]); - const arH = parseFloat(parts[1]); - if (!isNaN(arW) && !isNaN(arH) && arW > 0) { - calculatedHeight = Math.round(width * (arH / arW)); - resizingType = 'fill'; // Must use fill to allow imgproxy to crop - } - } - url.searchParams.delete('ar'); - } - - if (customGravity || aspectRatio) { - cleanSrc = src.startsWith('http') ? url.href : url.pathname + url.search; - } - } catch (e) { - // Fallback if parsing fails - } - - // We use the width provided by Next.js for responsive images - // Height is calculated from aspect ratio if provided, otherwise 0 to maintain aspect ratio - return getImgproxyUrl(cleanSrc, { - width, - height: calculatedHeight, - resizing_type: resizingType, - gravity, - }); -} diff --git a/lib/imgproxy.ts b/lib/imgproxy.ts deleted file mode 100644 index fdb58a57..00000000 --- a/lib/imgproxy.ts +++ /dev/null @@ -1,59 +0,0 @@ -/** - * Generates an imgproxy URL for a given source image and options. - * - * Documentation: https://docs.imgproxy.net/usage/processing - */ - -interface ImgproxyOptions { - width?: number; - height?: number; - resizing_type?: 'fit' | 'fill' | 'fill-down' | 'force' | 'auto'; - gravity?: string; - enlarge?: boolean; - extension?: string; -} - -export function getImgproxyUrl(src: string, options: ImgproxyOptions = {}): string { - // Use local proxy path which is rewritten in next.config.mjs - const baseUrl = '/_img'; - - // Handle local paths or relative URLs - let absoluteSrc = src; - if (src.startsWith('/')) { - const baseUrlForSrc = - process.env.NEXT_PUBLIC_BASE_URL || - (typeof window !== 'undefined' ? window.location.origin : 'https://klz-cables.com'); - if (baseUrlForSrc) { - absoluteSrc = `${baseUrlForSrc.replace(/\/$/, '')}${src}`; - } - } - - // Development mapping: Map local domains to internal Docker hostnames - // so imgproxy can fetch images without SSL issues or external routing - if (process.env.NODE_ENV === 'development') { - if (absoluteSrc.includes('klz.localhost')) { - absoluteSrc = absoluteSrc.replace(/^https?:\/\/klz\.localhost/, 'http://klz-app:3000'); - } else if (absoluteSrc.includes('cms.klz.localhost')) { - absoluteSrc = absoluteSrc.replace(/^https?:\/\/cms\.klz\.localhost/, 'http://klz-cms:8055'); - } - // Also handle direct container names if needed - } - - const { width = 0, height = 0, enlarge = false, extension = '' } = options; - - let quality = 80; - if (extension) quality = 90; - - // Re-map imgproxy URL to our new parameter structure - // e.g. /process?url=...&w=...&h=...&q=...&format=... - const queryParams = new URLSearchParams({ - url: absoluteSrc, - }); - - if (width > 0) queryParams.set('w', width.toString()); - if (height > 0) queryParams.set('h', height.toString()); - if (extension) queryParams.set('format', extension.replace('.', '')); - if (quality) queryParams.set('q', quality.toString()); - - return `${baseUrl}/process?${queryParams.toString()}`; -} diff --git a/lib/mdx.ts b/lib/mdx.ts index 06912185..81e65b90 100644 --- a/lib/mdx.ts +++ b/lib/mdx.ts @@ -1,6 +1,5 @@ -import fs from 'fs'; -import path from 'path'; -import matter from 'gray-matter'; +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; import { mapSlugToFileSlug } from './slugs'; export interface ProductFrontmatter { @@ -10,65 +9,69 @@ export interface ProductFrontmatter { categories: string[]; images: string[]; locale: string; + isFallback?: boolean; } export interface ProductMdx { slug: string; frontmatter: ProductFrontmatter; - content: string; + content: any; // Lexical AST from Payload } export async function getProductMetadata( slug: string, locale: string, ): Promise | null> { - // Map translated slug to file slug + const payload = await getPayload({ config: configPromise }); const fileSlug = await mapSlugToFileSlug(slug, locale); - const productsDir = path.join(process.cwd(), 'data', 'products', locale); - if (!fs.existsSync(productsDir)) return null; + let result = await payload.find({ + collection: 'products', + where: { + and: [{ slug: { equals: fileSlug } }, { locale: { equals: locale } }], + }, + depth: 1, // To auto-resolve Media relation (images array) + limit: 1, + }); - // Recursive search for the file - const findFile = (dir: string): string | null => { - const files = fs.readdirSync(dir); - for (const file of files) { - const fullPath = path.join(dir, file); - const stat = fs.statSync(fullPath); - if (stat.isDirectory()) { - const found = findFile(fullPath); - if (found) return found; - } else if (file === `${fileSlug}.mdx` || file === `${fileSlug}-2.mdx`) { - return fullPath; - } - } - return null; - }; + let isFallback = false; - let filePath = findFile(productsDir); - - if (!filePath && locale !== 'en') { + if (result.docs.length === 0 && locale !== 'en') { // Fallback to English - const enProductsDir = path.join(process.cwd(), 'data', 'products', 'en'); - if (fs.existsSync(enProductsDir)) { - filePath = findFile(enProductsDir); + result = await payload.find({ + collection: 'products', + where: { + and: [{ slug: { equals: fileSlug } }, { locale: { equals: 'en' } }], + }, + depth: 1, + limit: 1, + }); + if (result.docs.length > 0) { + isFallback = true; } } - if (filePath && fs.existsSync(filePath)) { - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data } = matter(fileContent); + if (result.docs.length > 0) { + const doc = result.docs[0]; - // Filter out products without images to match getProductBySlug behavior - if (!data.images || data.images.length === 0 || !data.images[0]) { - return null; - } + // Process Images + const resolvedImages = ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean); + + if (resolvedImages.length === 0) return null; // Original logic skipped docs without images return { - slug: fileSlug, + slug: doc.slug, frontmatter: { - ...data, - isFallback: filePath.includes('/en/'), - } as any, + title: doc.title, + sku: doc.sku, + description: doc.description, + categories: Array.isArray(doc.categories) ? doc.categories.map((c: any) => c.category) : [], + images: resolvedImages, + locale: doc.locale, + isFallback, + }, }; } @@ -76,111 +79,159 @@ export async function getProductMetadata( } export async function getProductBySlug(slug: string, locale: string): Promise { - // Map translated slug to file slug + const payload = await getPayload({ config: configPromise }); const fileSlug = await mapSlugToFileSlug(slug, locale); - const productsDir = path.join(process.cwd(), 'data', 'products', locale); - if (!fs.existsSync(productsDir)) return null; + let result = await payload.find({ + collection: 'products', + where: { + and: [{ slug: { equals: fileSlug } }, { locale: { equals: locale } }], + }, + depth: 1, // Auto-resolve Media logic + limit: 1, + }); - // Recursive search for the file - const findFile = (dir: string): string | null => { - const files = fs.readdirSync(dir); - for (const file of files) { - const fullPath = path.join(dir, file); - const stat = fs.statSync(fullPath); - if (stat.isDirectory()) { - const found = findFile(fullPath); - if (found) return found; - } else if (file === `${fileSlug}.mdx` || file === `${fileSlug}-2.mdx`) { - return fullPath; - } - } - return null; - }; - - let filePath = findFile(productsDir); let isFallback = false; - if (!filePath && locale !== 'en') { + if (result.docs.length === 0 && locale !== 'en') { // Fallback to English - const enProductsDir = path.join(process.cwd(), 'data', 'products', 'en'); - if (fs.existsSync(enProductsDir)) { - filePath = findFile(enProductsDir); - if (filePath) isFallback = true; + result = await payload.find({ + collection: 'products', + where: { + and: [{ slug: { equals: fileSlug } }, { locale: { equals: 'en' } }], + }, + depth: 1, + limit: 1, + }); + if (result.docs.length > 0) { + isFallback = true; } } - if (filePath && fs.existsSync(filePath)) { - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data, content } = matter(fileContent); - const product = { - slug: fileSlug, + if (result.docs.length > 0) { + const doc = result.docs[0]; + + // Map Images correctly from resolved Media docs + const resolvedImages = ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean); + + if (resolvedImages.length === 0) return null; + + return { + slug: doc.slug, frontmatter: { - ...data, + title: doc.title, + sku: doc.sku, + description: doc.description, + categories: Array.isArray(doc.categories) ? doc.categories.map((c: any) => c.category) : [], + images: resolvedImages, + locale: doc.locale, isFallback, - } as any, - content, + }, + content: doc.content, // Lexical payload instead of raw MDX String }; - - // Filter out products without images - if ( - !product.frontmatter.images || - product.frontmatter.images.length === 0 || - !product.frontmatter.images[0] - ) { - return null; - } - - return product; } return null; } export async function getAllProductSlugs(locale: string): Promise { - const productsDir = path.join(process.cwd(), 'data', 'products', locale); - if (!fs.existsSync(productsDir)) return []; + const payload = await getPayload({ config: configPromise }); + const result = await payload.find({ + collection: 'products', + where: { + locale: { + equals: locale, + }, + }, + pagination: false, // get all docs + }); - const slugs: string[] = []; - const walk = (dir: string) => { - const files = fs.readdirSync(dir); - for (const file of files) { - const fullPath = path.join(dir, file); - const stat = fs.statSync(fullPath); - if (stat.isDirectory()) { - walk(fullPath); - } else if (file.endsWith('.mdx')) { - slugs.push(file.replace(/\.mdx$/, '')); - } - } - }; - - walk(productsDir); - return slugs; + return result.docs.map((doc) => doc.slug); } export async function getAllProducts(locale: string): Promise { - const slugs = await getAllProductSlugs(locale); - let allSlugs = slugs; + // Fetch ALL products in a single query to avoid N+1 getPayload() calls + const payload = await getPayload({ config: configPromise }); + // Get products for this locale + const result = await payload.find({ + collection: 'products', + where: { locale: { equals: locale } }, + depth: 1, + pagination: false, + }); + + let products: ProductMdx[] = result.docs + .filter((doc) => { + const resolvedImages = ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean); + return resolvedImages.length > 0; + }) + .map((doc) => ({ + slug: doc.slug, + frontmatter: { + title: doc.title, + sku: doc.sku, + description: doc.description, + categories: Array.isArray(doc.categories) ? doc.categories.map((c: any) => c.category) : [], + images: ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean), + locale: doc.locale, + }, + content: doc.content, + })); + + // Also include English fallbacks for slugs not in this locale if (locale !== 'en') { - const enSlugs = await getAllProductSlugs('en'); - allSlugs = Array.from(new Set([...slugs, ...enSlugs])); + const localeSlugs = new Set(products.map((p) => p.slug)); + const enResult = await payload.find({ + collection: 'products', + where: { locale: { equals: 'en' } }, + depth: 1, + pagination: false, + }); + + const fallbacks = enResult.docs + .filter((doc) => !localeSlugs.has(doc.slug)) + .filter((doc) => { + const resolvedImages = ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean); + return resolvedImages.length > 0; + }) + .map((doc) => ({ + slug: doc.slug, + frontmatter: { + title: doc.title, + sku: doc.sku, + description: doc.description, + categories: Array.isArray(doc.categories) + ? doc.categories.map((c: any) => c.category) + : [], + images: ((doc.images as any[]) || []) + .map((img) => (typeof img === 'string' ? img : img.url)) + .filter(Boolean), + locale: doc.locale, + isFallback: true, + }, + content: doc.content, + })); + + products = [...products, ...fallbacks]; } - const products = await Promise.all(allSlugs.map((slug) => getProductBySlug(slug, locale))); - return products.filter((p): p is ProductMdx => p !== null); + return products; } export async function getAllProductsMetadata(locale: string): Promise[]> { - const slugs = await getAllProductSlugs(locale); - let allSlugs = slugs; - - if (locale !== 'en') { - const enSlugs = await getAllProductSlugs('en'); - allSlugs = Array.from(new Set([...slugs, ...enSlugs])); - } - - const metadata = await Promise.all(allSlugs.map((slug) => getProductMetadata(slug, locale))); - return metadata.filter((m): m is Partial => m !== null); + // Reuse getAllProducts to avoid separate N+1 queries + const products = await getAllProducts(locale); + return products.map((p) => ({ + slug: p.slug, + frontmatter: p.frontmatter, + })); } diff --git a/lib/pages.ts b/lib/pages.ts index d927bbcf..ebe9487e 100644 --- a/lib/pages.ts +++ b/lib/pages.ts @@ -1,80 +1,112 @@ -import fs from 'fs'; -import path from 'path'; -import matter from 'gray-matter'; -import { mapSlugToFileSlug } from './slugs'; +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; export interface PageFrontmatter { title: string; excerpt: string; featuredImage: string | null; locale: string; + public?: boolean; } export interface PageMdx { slug: string; frontmatter: PageFrontmatter; - content: string; + content: any; // Lexical AST Document } export async function getPageBySlug(slug: string, locale: string): Promise { - // Map translated slug to file slug - const fileSlug = await mapSlugToFileSlug(slug, locale); - const pagesDir = path.join(process.cwd(), 'data', 'pages', locale); - const filePath = path.join(pagesDir, `${fileSlug}.mdx`); + const payload = await getPayload({ config: configPromise }); - if (!fs.existsSync(filePath)) { - return null; - } + const result = await payload.find({ + collection: 'pages' as any, + where: { + slug: { equals: slug }, + locale: { equals: locale }, + }, + limit: 1, + }); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data, content } = matter(fileContent); + const docs = result.docs as any[]; + + if (!docs || docs.length === 0) return null; + + const doc = docs[0]; return { - slug: fileSlug, - frontmatter: data as PageFrontmatter, - content, + slug: doc.slug, + frontmatter: { + title: doc.title, + excerpt: doc.excerpt || '', + locale: doc.locale, + featuredImage: + typeof doc.featuredImage === 'object' && doc.featuredImage !== null + ? doc.featuredImage.sizes?.card?.url || doc.featuredImage.url + : null, + } as PageFrontmatter, + content: doc.content as any, // Native Lexical Editor State }; } export async function getAllPages(locale: string): Promise { - const pagesDir = path.join(process.cwd(), 'data', 'pages', locale); - if (!fs.existsSync(pagesDir)) return []; + const payload = await getPayload({ config: configPromise }); - const files = fs.readdirSync(pagesDir); - const pages = await Promise.all( - files - .filter((file) => file.endsWith('.mdx')) - .map((file) => { - const fileSlug = file.replace(/\.mdx$/, ''); - const filePath = path.join(pagesDir, file); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data, content } = matter(fileContent); - return { - slug: fileSlug, - frontmatter: data as PageFrontmatter, - content, - }; - }), - ); + const result = await payload.find({ + collection: 'pages' as any, + where: { + locale: { + equals: locale, + }, + }, + limit: 100, + }); - return pages.filter((p): p is PageMdx => p !== null); + const docs = result.docs as any[]; + + return docs.map((doc: any) => { + return { + slug: doc.slug, + frontmatter: { + title: doc.title, + excerpt: doc.excerpt || '', + locale: doc.locale, + featuredImage: + typeof doc.featuredImage === 'object' && doc.featuredImage !== null + ? doc.featuredImage.sizes?.card?.url || doc.featuredImage.url + : null, + } as PageFrontmatter, + content: doc.content as any, + }; + }); } export async function getAllPagesMetadata(locale: string): Promise[]> { - const pagesDir = path.join(process.cwd(), 'data', 'pages', locale); - if (!fs.existsSync(pagesDir)) return []; + const payload = await getPayload({ config: configPromise }); - const files = fs.readdirSync(pagesDir); - return files - .filter((file) => file.endsWith('.mdx')) - .map((file) => { - const fileSlug = file.replace(/\.mdx$/, ''); - const filePath = path.join(pagesDir, file); - const fileContent = fs.readFileSync(filePath, 'utf8'); - const { data } = matter(fileContent); - return { - slug: fileSlug, - frontmatter: data as PageFrontmatter, - }; - }); + const result = await payload.find({ + collection: 'pages' as any, + where: { + locale: { + equals: locale, + }, + }, + limit: 100, + }); + + const docs = result.docs as any[]; + + return docs.map((doc: any) => { + return { + slug: doc.slug, + frontmatter: { + title: doc.title, + excerpt: doc.excerpt || '', + locale: doc.locale, + featuredImage: + typeof doc.featuredImage === 'object' && doc.featuredImage !== null + ? doc.featuredImage.sizes?.card?.url || doc.featuredImage.url + : null, + } as PageFrontmatter, + }; + }); } diff --git a/lib/services/create-services.server.ts b/lib/services/create-services.server.ts index 2cbee0a3..a45457e5 100644 --- a/lib/services/create-services.server.ts +++ b/lib/services/create-services.server.ts @@ -11,9 +11,19 @@ import { import { PinoLoggerService } from './logging/pino-logger-service'; import { config, getMaskedConfig } from '../config'; -let singleton: AppServices | undefined; +declare global { + var __appServices: AppServices | undefined; +} + +// Add a local cache to prevent re-looking up globalThis frequently +let serverSingleton: AppServices | undefined; + export function getServerAppServices(): AppServices { - if (singleton) return singleton; + if (serverSingleton) return serverSingleton; + if (globalThis.__appServices) { + serverSingleton = globalThis.__appServices; + return serverSingleton; + } // Create logger first to log initialization const logger = new PinoLoggerService('server'); @@ -74,9 +84,9 @@ export function getServerAppServices(): AppServices { level: config.logging.level, }); - singleton = new AppServices(analytics, errors, cache, logger, notifications); + globalThis.__appServices = new AppServices(analytics, errors, cache, logger, notifications); logger.info('All application services initialized successfully'); - return singleton; + return globalThis.__appServices; } diff --git a/lib/services/create-services.ts b/lib/services/create-services.ts index d0f2b5bc..57c1bcee 100644 --- a/lib/services/create-services.ts +++ b/lib/services/create-services.ts @@ -9,65 +9,18 @@ import { PinoLoggerService } from './logging/pino-logger-service'; import { NoopNotificationService } from './notifications/gotify-notification-service'; import { config, getMaskedConfig } from '../config'; -/** - * Singleton instance of AppServices. - * - * In Next.js, module singletons are per-process (server) and per-tab (client). - * This is sufficient for a small service layer and provides better performance - * than creating new instances on every request. - * - * @private - */ +declare global { + var __appServices: AppServices | undefined; +} + let singleton: AppServices | undefined; /** * Get the application services singleton. - * - * This function creates and caches the application services, including: - * - Analytics service (Umami or no-op) - * - Error reporting service (GlitchTip/Sentry or no-op) - * - Cache service (in-memory) - * - * The services are configured based on environment variables: - * - `UMAMI_WEBSITE_ID` - Enables Umami analytics - * - `SENTRY_DSN` - Enables error reporting (server-side direct, client-side via relay) - * - * @returns {AppServices} The application services singleton - * - * @example - * ```typescript - * // Get services in a client component - * import { getAppServices } from '@/lib/services/create-services'; - * - * const services = getAppServices(); - * services.analytics.track('button_click', { button_id: 'cta' }); - * ``` - * - * @example - * ```typescript - * // Get services in a server component or API route - * import { getAppServices } from '@/lib/services/create-services'; - * - * const services = getAppServices(); - * await services.cache.set('key', 'value'); - * ``` - * - * @example - * ```typescript - * // Automatic service selection based on environment - * // If UMAMI_WEBSITE_ID is set: - * // services.analytics = UmamiAnalyticsService - * // If not set: - * // services.analytics = NoopAnalyticsService (safe no-op) - * ``` - * - * @see {@link UmamiAnalyticsService} for analytics implementation - * @see {@link NoopAnalyticsService} for no-op fallback - * @see {@link GlitchtipErrorReportingService} for error reporting - * @see {@link MemoryCacheService} for caching */ export function getAppServices(): AppServices { // Return cached instance if available + if (typeof window === 'undefined' && globalThis.__appServices) return globalThis.__appServices; if (singleton) return singleton; // Create logger first to log initialization @@ -127,9 +80,6 @@ export function getAppServices(): AppServices { logger.info('Noop error reporting service initialized (error reporting disabled)'); } - // IMPORTANT: This module is imported by client components. - // Do not import Node-only modules (like the `redis` client) here. - // Use [`getServerAppServices()`](lib/services/create-services.server.ts:1) on the server. const cache = new MemoryCacheService(); logger.info('Memory cache service initialized'); @@ -139,6 +89,11 @@ export function getAppServices(): AppServices { }); // Create and cache the singleton + if (typeof window === 'undefined') { + globalThis.__appServices = new AppServices(analytics, errors, cache, logger, notifications); + return globalThis.__appServices; + } + singleton = new AppServices(analytics, errors, cache, logger, notifications); logger.info('All application services initialized successfully'); diff --git a/lib/services/errors/glitchtip-error-reporting-service.ts b/lib/services/errors/glitchtip-error-reporting-service.ts index 79f32697..e9f3a668 100644 --- a/lib/services/errors/glitchtip-error-reporting-service.ts +++ b/lib/services/errors/glitchtip-error-reporting-service.ts @@ -97,7 +97,7 @@ export class GlitchtipErrorReportingService implements ErrorReportingService { this.getSentry().then((Sentry) => Sentry.setTag(key, value)); } - withScope(fn: () => T, context?: Record): T { + withScope(fn: () => T, _context?: Record): T { if (!this.options.enabled) return fn(); // Since withScope mandates executing fn() synchronously to return T, diff --git a/middleware.ts b/middleware.ts index 9af37efa..b7733d72 100644 --- a/middleware.ts +++ b/middleware.ts @@ -10,40 +10,14 @@ const intlMiddleware = createMiddleware({ defaultLocale: 'en', }); -const imgproxyStatus = { isDown: false, lastCheck: 0 }; - -async function isImgproxyDown() { - const now = Date.now(); - if (now - imgproxyStatus.lastCheck > 60000) { - try { - const imgproxyUrl = process.env.IMGPROXY_URL || 'http://klz-imgproxy:8080'; - const checkUrl = imgproxyUrl.startsWith('http') ? imgproxyUrl : `https://${imgproxyUrl}`; - const res = await fetch(checkUrl, { signal: AbortSignal.timeout(2000) }); - imgproxyStatus.isDown = res.status >= 500; - } catch (e) { - imgproxyStatus.isDown = true; - } - imgproxyStatus.lastCheck = now; - } - return imgproxyStatus.isDown; -} - export default async function middleware(request: NextRequest) { const { method, url, headers } = request; const { pathname } = request.nextUrl; - if (pathname.startsWith('/_img/')) { - if (await isImgproxyDown()) { - const originalUrl = request.nextUrl.searchParams.get('url'); - if (originalUrl) { - return NextResponse.redirect(originalUrl); - } - } - return NextResponse.next(); - } - - // Explicit bypass for infrastructure routes to avoid locale redirects/interception + // Explicit bypass for infrastructure and Payload CMS routes to avoid locale redirects/interception if ( + pathname.startsWith('/admin') || + pathname.startsWith('/api') || pathname.startsWith('/stats') || pathname.startsWith('/errors') || pathname.startsWith('/health') || @@ -125,8 +99,7 @@ export default async function middleware(request: NextRequest) { export const config = { matcher: [ - '/((?!api|_next/static|_next/image|favicon.ico|manifest.webmanifest|.*\\.(?:svg|png|jpg|jpeg|gif|webp|pdf|txt|vcf|xml|webm|mp4|map)$).*)', - '/(de|en)/:path*', + '/((?!api|_next/static|_next/image|favicon.ico|admin|manifest.webmanifest|.*\\.(?:svg|png|jpg|jpeg|gif|webp|pdf|txt|vcf|xml|webm|mp4|map)$).*)', '/(de|en)/:path*', ], }; diff --git a/next-env.d.ts b/next-env.d.ts index c4b7818f..9edff1c7 100644 --- a/next-env.d.ts +++ b/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/dev/types/routes.d.ts"; +import "./.next/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/next.config.mjs b/next.config.mjs index da92b332..1a930534 100644 --- a/next.config.mjs +++ b/next.config.mjs @@ -2,8 +2,9 @@ import withMintelConfig from '@mintel/next-config'; import withBundleAnalyzer from '@next/bundle-analyzer'; import { withSentryConfig } from '@sentry/nextjs'; -console.log('🚀 NEXT CONFIG LOADED FROM:', process.cwd()); +import { withPayload } from '@payloadcms/next/withPayload'; +console.log('🚀 NEXT CONFIG LOADED FROM:', process.cwd()); /** @type {import('next').NextConfig} */ const nextConfig = { onDemandEntries: { @@ -11,9 +12,10 @@ const nextConfig = { maxInactiveAge: 60 * 1000, }, experimental: { - optimizeCss: true, optimizePackageImports: ['lucide-react', 'framer-motion', '@/components/ui'], }, + swcMinify: false, + reactStrictMode: false, productionBrowserSourceMaps: false, logging: { fetches: { @@ -22,55 +24,68 @@ const nextConfig = { }, output: 'standalone', async headers() { + const isProd = process.env.NODE_ENV === 'production'; const umamiDomain = new URL(process.env.UMAMI_API_ENDPOINT || 'https://analytics.infra.mintel.me').origin; - const directusDomain = new URL(process.env.DIRECTUS_URL || 'https://cms.klz-cables.com').origin; - const imgproxyDomain = new URL(process.env.IMGPROXY_URL || 'http://klz-imgproxy:8080').origin; const glitchtipDomain = new URL(process.env.SENTRY_DSN ? new URL(process.env.SENTRY_DSN).origin : 'https://errors.infra.mintel.me').origin; + // Additional domains that need to be whitelisted for images + const extraImgDomains = [ + 'https://klz-cables.com', + 'https://staging.klz-cables.com', + 'https://testing.klz-cables.com', + 'http://klz.localhost', + 'https://www.gravatar.com', + 'https://gravatar.com', + ].join(' '); const cspHeader = ` default-src 'self'; script-src 'self' 'unsafe-inline' 'unsafe-eval' ${umamiDomain}; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com; font-src 'self' https://fonts.gstatic.com; - img-src 'self' data: blob: ${imgproxyDomain} ${directusDomain}; - connect-src 'self' ${umamiDomain} ${glitchtipDomain} ${directusDomain}; + img-src 'self' data: blob: ${extraImgDomains}; + connect-src 'self' ${umamiDomain} ${glitchtipDomain}; frame-src 'self'; object-src 'none'; base-uri 'self'; form-action 'self'; frame-ancestors 'none'; - upgrade-insecure-requests; + ${isProd ? 'upgrade-insecure-requests;' : ''} `.replace(/\s{2,}/g, ' ').trim(); + const secureHeaders = [ + { + key: 'Content-Security-Policy', + value: cspHeader, + }, + { + key: 'X-Frame-Options', + value: 'DENY', + }, + { + key: 'X-Content-Type-Options', + value: 'nosniff', + }, + { + key: 'Referrer-Policy', + value: 'strict-origin-when-cross-origin', + }, + { + key: 'Permissions-Policy', + value: 'camera=(), microphone=(), geolocation=(), interest-cohort=()', + }, + ]; + + if (isProd) { + secureHeaders.push({ + key: 'Strict-Transport-Security', + value: 'max-age=63072000; includeSubDomains; preload', + }); + } + return [ { source: '/:path*', - headers: [ - { - key: 'Content-Security-Policy', - value: cspHeader, - }, - { - key: 'Strict-Transport-Security', - value: 'max-age=63072000; includeSubDomains; preload', - }, - { - key: 'X-Frame-Options', - value: 'DENY', - }, - { - key: 'X-Content-Type-Options', - value: 'nosniff', - }, - { - key: 'Referrer-Policy', - value: 'strict-origin-when-cross-origin', - }, - { - key: 'Permissions-Policy', - value: 'camera=(), microphone=(), geolocation=(), interest-cohort=()', - }, - ], + headers: secureHeaders, }, ]; }, @@ -373,33 +388,31 @@ const nextConfig = { source: '/vcf/michael-bodemer', destination: '/michael-bodemer.vcf', permanent: true, - }, + } ]; }, images: { - loader: 'custom', - loaderFile: './lib/imgproxy-loader.ts', - dangerouslyAllowSVG: true, - contentDispositionType: "attachment", - contentSecurityPolicy: "default-src 'self'; script-src 'none'; sandbox;", + formats: ['image/webp'], + remotePatterns: [ + { + protocol: 'https', + hostname: 'klz-cables.com', + }, + { + protocol: 'https', + hostname: '*.klz-cables.com', + }, + { + protocol: 'http', + hostname: 'klz.localhost', + }, + { + protocol: 'http', + hostname: 'localhost', + }, + ], }, async rewrites() { - const umamiUrl = - process.env.UMAMI_API_ENDPOINT || - process.env.UMAMI_SCRIPT_URL || - process.env.NEXT_PUBLIC_UMAMI_SCRIPT_URL || - 'https://analytics.infra.mintel.me'; - const glitchtipUrl = process.env.SENTRY_DSN - ? new URL(process.env.SENTRY_DSN).origin - : 'https://errors.infra.mintel.me'; - - const directusUrl = process.env.INTERNAL_DIRECTUS_URL || process.env.DIRECTUS_URL || 'https://cms.klz-cables.com'; - - let imgproxyUrl = process.env.IMGPROXY_URL || 'http://klz-imgproxy:8080'; - if (!imgproxyUrl.startsWith('http')) { - imgproxyUrl = `https://${imgproxyUrl}`; - } - return [ { source: '/de/produkte', @@ -409,14 +422,6 @@ const nextConfig = { source: '/de/produkte/:path*', destination: '/de/products/:path*', }, - { - source: '/cms/:path*', - destination: `${directusUrl}/:path*`, - }, - { - source: '/_img/:path*', - destination: `${imgproxyUrl}/:path*`, - }, ]; }, }; @@ -425,6 +430,6 @@ const withAnalyzer = withBundleAnalyzer({ enabled: process.env.ANALYZE === 'true', }); -export default withAnalyzer(withMintelConfig(nextConfig, { +export default withPayload(withAnalyzer(withMintelConfig(nextConfig, { hideSourceMaps: true, -})); +}))); diff --git a/openrouter_models.json b/openrouter_models.json new file mode 100644 index 00000000..c8da303c --- /dev/null +++ b/openrouter_models.json @@ -0,0 +1,13328 @@ +{ + "data": [ + { + "id": "google/gemini-3.1-pro-preview", + "canonical_slug": "google/gemini-3.1-pro-preview-20260219", + "hugging_face_id": "", + "name": "Google: Gemini 3.1 Pro Preview", + "created": 1771509627, + "description": "Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation of the Gemini 3 series, it combines high-precision reasoning across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning. The 3.1 update introduces measurable gains in SWE benchmarks and real-world coding environments, along with stronger autonomous task execution in structured domains such as finance and spreadsheet-based workflows.\n\nDesigned for advanced development and agentic systems, Gemini 3.1 Pro Preview improves long-horizon stability and tool orchestration while increasing token efficiency. It introduces a new medium thinking level to better balance cost, speed, and performance. The model excels in agentic coding, structured planning, multimodal analysis, and workflow automation, making it well-suited for autonomous agents, financial modeling, spreadsheet automation, and high-context enterprise tasks.", + "context_length": 1048576, + "architecture": { + "modality": "text+image+file+audio+video->text", + "input_modalities": ["audio", "file", "image", "text", "video"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000002", + "completion": "0.000012", + "image": "0.000002", + "audio": "0.000002", + "internal_reasoning": "0.000012", + "input_cache_read": "0.0000002", + "input_cache_write": "0.000000375" + }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "anthropic/claude-sonnet-4.6", + "canonical_slug": "anthropic/claude-4.6-sonnet-20260217", + "hugging_face_id": "", + "name": "Anthropic: Claude Sonnet 4.6", + "created": 1771342990, + "description": "Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.", + "context_length": 1000000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "web_search": "0.01", + "input_cache_read": "0.0000003", + "input_cache_write": "0.00000375" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p", + "verbosity" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3.5-plus-02-15", + "canonical_slug": "qwen/qwen3.5-plus-20260216", + "hugging_face_id": "", + "name": "Qwen: Qwen3.5 Plus 2026-02-15", + "created": 1771229416, + "description": "The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of task evaluations, the 3.5 series consistently demonstrates performance on par with state-of-the-art leading models. 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Developed by Ai2 under the Apache 2.0 license, Olmo 3.1 32B Think continues the Olmo initiative’s commitment to openness, providing full transparency across model weights, code, and training methodology.", + "context_length": 65536, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000015", "completion": "0.0000005" }, + "top_provider": { + "context_length": 65536, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.6, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "xiaomi/mimo-v2-flash", + "canonical_slug": "xiaomi/mimo-v2-flash-20251210", + "hugging_face_id": "XiaomiMiMo/MiMo-V2-Flash", + "name": "Xiaomi: MiMo-V2-Flash", + "created": 1765731308, + "description": "MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. 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[Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config).", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000009", + "completion": "0.00000029", + "input_cache_read": "0.000000045" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/nemotron-3-nano-30b-a3b:free", + "canonical_slug": "nvidia/nemotron-3-nano-30b-a3b", + "hugging_face_id": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", + "name": "NVIDIA: Nemotron 3 Nano 30B A3B (free)", + "created": 1765731275, + "description": "NVIDIA Nemotron 3 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems.\n\nThe model is fully open with open-weights, datasets and recipes so developers can easily\ncustomize, optimize, and deploy the model on their infrastructure for maximum privacy and\nsecurity.\n\nNote: For the free endpoint, all prompts and output are logged to improve the provider's model and its product and services. Please do not upload any personal, confidential, or otherwise sensitive information. This is a trial use only. 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Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks. It delivers state-of-the-art benchmark results in general reasoning, STEM problem solving, factual QA, and multimodal understanding, including leading scores on LMArena, GPQA Diamond, MathArena Apex, MMMU-Pro, and Video-MMMU. Interactions emphasize depth and interpretability: the model is designed to infer intent with minimal prompting and produce direct, insight-focused responses.\n\nBuilt for advanced development and agentic workflows, Gemini 3 Pro provides robust tool-calling, long-horizon planning stability, and strong zero-shot generation for complex UI, visualization, and coding tasks. It excels at agentic coding (SWE-Bench Verified, Terminal-Bench 2.0), multimodal analysis, and structured long-form tasks such as research synthesis, planning, and interactive learning experiences. Suitable applications include autonomous agents, coding assistants, multimodal analytics, scientific reasoning, and high-context information processing.", + "context_length": 1048576, + "architecture": { + "modality": "text+image+file+audio+video->text", + "input_modalities": ["text", "image", "file", "audio", "video"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000002", + "completion": "0.000012", + "image": "0.000002", + "audio": "0.000002", + "internal_reasoning": "0.000012", + "input_cache_read": "0.0000002", + "input_cache_write": "0.000000375" + }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "deepcogito/cogito-v2.1-671b", + "canonical_slug": "deepcogito/cogito-v2.1-671b-20251118", + "hugging_face_id": "", + "name": "Deep Cogito: Cogito v2.1 671B", + "created": 1763071233, + "description": "Cogito v2.1 671B MoE represents one of the strongest open models globally, matching performance of frontier closed and open models. This model is trained using self play with reinforcement learning to reach state-of-the-art performance on multiple categories (instruction following, coding, longer queries and creative writing). This advanced system demonstrates significant progress toward scalable superintelligence through policy improvement.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000125", "completion": "0.00000125" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5.1", + "canonical_slug": "openai/gpt-5.1-20251113", + "hugging_face_id": "", + "name": "OpenAI: GPT-5.1", + "created": 1763060305, + "description": "GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. 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GPT-5.1 serves as the primary full-capability successor to GPT-5", + "context_length": 400000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "web_search": "0.01", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5.1-chat", + "canonical_slug": "openai/gpt-5.1-chat-20251113", + "hugging_face_id": "", + "name": "OpenAI: GPT-5.1 Chat", + "created": 1763060302, + "description": "GPT-5.1 Chat (AKA Instant is the fast, lightweight member of the 5.1 family, optimized for low-latency chat while retaining strong general intelligence. 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GPT-5.1 Chat is designed for high-throughput, interactive workloads where responsiveness and consistency matter more than deep deliberation.\n", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["file", "image", "text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "web_search": "0.01", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5.1-codex", + "canonical_slug": "openai/gpt-5.1-codex-20251113", + "hugging_face_id": "", + "name": "OpenAI: GPT-5.1-Codex", + "created": 1763060298, + "description": "GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. 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It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.", + "context_length": 400000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5.1-codex-mini", + "canonical_slug": "openai/gpt-5.1-codex-mini-20251113", + "hugging_face_id": "", + "name": "OpenAI: GPT-5.1-Codex-Mini", + "created": 1763057820, + "description": "GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex", + "context_length": 400000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000025", + "completion": "0.000002", + "input_cache_read": "0.000000025" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 100000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "kwaipilot/kat-coder-pro", + "canonical_slug": "kwaipilot/kat-coder-pro-v1", + "hugging_face_id": "", + "name": "Kwaipilot: KAT-Coder-Pro V1", + "created": 1762745912, + "description": "KAT-Coder-Pro V1 is KwaiKAT's most advanced agentic coding model in the KAT-Coder series. Designed specifically for agentic coding tasks, it excels in real-world software engineering scenarios, achieving 73.4% solve rate on the SWE-Bench Verified benchmark. \n\nThe model has been optimized for tool-use capability, multi-turn interaction, instruction following, generalization, and comprehensive capabilities through a multi-stage training process, including mid-training, supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and scalable agentic RL.", + "context_length": 256000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000207", + "completion": "0.000000828", + "input_cache_read": "0.0000000414" + }, + "top_provider": { + "context_length": 256000, + "max_completion_tokens": 128000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "moonshotai/kimi-k2-thinking", + "canonical_slug": "moonshotai/kimi-k2-thinking-20251106", + "hugging_face_id": "moonshotai/Kimi-K2-Thinking", + "name": "MoonshotAI: Kimi K2 Thinking", + "created": 1762440622, + "description": "Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. 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Built on a large-scale MoE architecture with MuonClip optimization, it combines strong reasoning depth with high inference efficiency for demanding agentic and analytical tasks.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000047", + "completion": "0.000002", + "input_cache_read": "0.000000141" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "amazon/nova-premier-v1", + "canonical_slug": "amazon/nova-premier-v1", + "hugging_face_id": "", + "name": "Amazon: Nova Premier 1.0", + "created": 1761950332, + "description": "Amazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.", + "context_length": 1000000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Nova", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000025", + "completion": "0.0000125", + "input_cache_read": "0.000000625" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 32000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "stop", "temperature", "tools", "top_k", "top_p"], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "perplexity/sonar-pro-search", + "canonical_slug": "perplexity/sonar-pro-search", + "hugging_face_id": "", + "name": "Perplexity: Sonar Pro Search", + "created": 1761854366, + "description": "Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based on tokens plus $18 per thousand requests. This model powers the Pro Search mode on the Perplexity platform.\n\nSonar Pro Search adds autonomous, multi-step reasoning to Sonar Pro. So, instead of just one query + synthesis, it plans and executes entire research workflows using tools.", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.000003", "completion": "0.000015", "web_search": "0.018" }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "structured_outputs", + "temperature", + "top_k", + "top_p", + "web_search_options" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "mistralai/voxtral-small-24b-2507", + "canonical_slug": "mistralai/voxtral-small-24b-2507", + "hugging_face_id": "mistralai/Voxtral-Small-24B-2507", + "name": "Mistral: Voxtral Small 24B 2507", + "created": 1761835144, + "description": "Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio is priced at $100 per million seconds.", + "context_length": 32000, + "architecture": { + "modality": "text+audio->text", + "input_modalities": ["text", "audio"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000001", "completion": "0.0000003", "audio": "0.0001" }, + "top_provider": { + "context_length": 32000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.2, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-oss-safeguard-20b", + "canonical_slug": "openai/gpt-oss-safeguard-20b", + "hugging_face_id": "openai/gpt-oss-safeguard-20b", + "name": "OpenAI: gpt-oss-safeguard-20b", + "created": 1761752836, + "description": "gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust & safety labeling.\n\nLearn more about this model in OpenAI's gpt-oss-safeguard [user guide](https://cookbook.openai.com/articles/gpt-oss-safeguard-guide).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000075", + "completion": "0.0000003", + "input_cache_read": "0.000000037" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/nemotron-nano-12b-v2-vl:free", + "canonical_slug": "nvidia/nemotron-nano-12b-v2-vl", + "hugging_face_id": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16", + "name": "NVIDIA: Nemotron Nano 12B 2 VL (free)", + "created": 1761675565, + "description": "NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency.\n\nThe model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension.\n\nNemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost.\n\nOpen-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.", + "context_length": 128000, + "architecture": { + "modality": "text+image+video->text", + "input_modalities": ["image", "text", "video"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 128000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "seed", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/nemotron-nano-12b-v2-vl", + "canonical_slug": "nvidia/nemotron-nano-12b-v2-vl", + "hugging_face_id": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16", + "name": "NVIDIA: Nemotron Nano 12B 2 VL", + "created": 1761675565, + "description": "NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency.\n\nThe model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension.\n\nNemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost.\n\nOpen-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.", + "context_length": 131072, + "architecture": { + "modality": "text+image+video->text", + "input_modalities": ["image", "text", "video"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000007", "completion": "0.0000002" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "minimax/minimax-m2", + "canonical_slug": "minimax/minimax-m2", + "hugging_face_id": "MiniMaxAI/MiniMax-M2", + "name": "MiniMax: MiniMax M2", + "created": 1761252093, + "description": "MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.\n\nThe model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.\n\nBenchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.\n\nTo avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).", + "context_length": 196608, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000255", + "completion": "0.000001", + "input_cache_read": "0.00000003" + }, + "top_provider": { + "context_length": 196608, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 1, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-32b-instruct", + "canonical_slug": "qwen/qwen3-vl-32b-instruct", + "hugging_face_id": "Qwen/Qwen3-VL-32B-Instruct", + "name": "Qwen: Qwen3 VL 32B Instruct", + "created": 1761231332, + "description": "Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. 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Optimized for agentic interaction and visual tool use, Qwen3-VL-32B delivers state-of-the-art performance for complex real-world multimodal tasks.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": null + }, + "pricing": { "prompt": "0.000000104", "completion": "0.000000416" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": "2026-02-25" + }, + { + "id": "liquid/lfm2-8b-a1b", + "canonical_slug": "liquid/lfm2-8b-a1b", + "hugging_face_id": "LiquidAI/LFM2-8B-A1B", + "name": "LiquidAI: LFM2-8B-A1B", + "created": 1760970984, + "description": "LFM2-8B-A1B is an efficient on-device Mixture-of-Experts (MoE) model from Liquid AI’s LFM2 family, built for fast, high-quality inference on edge hardware. 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It excels at high-quality visual creation while maintaining strong text understanding, making it ideal for applications that require both efficient image generation and text processing at scale.", + "context_length": 400000, + "architecture": { + "modality": "text+image+file->text+image", + "input_modalities": ["file", "image", "text"], + "output_modalities": ["image", "text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000025", + "completion": "0.000002", + "web_search": "0.01", + "input_cache_read": "0.00000025" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "anthropic/claude-haiku-4.5", + "canonical_slug": "anthropic/claude-4.5-haiku-20251001", + "hugging_face_id": "", + "name": "Anthropic: Claude Haiku 4.5", + "created": 1760547638, + "description": "Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. 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Scoring >73% on SWE-bench Verified, Haiku 4.5 ranks among the world’s best coding models while maintaining exceptional responsiveness for sub-agents, parallelized execution, and scaled deployment.", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000001", + "completion": "0.000005", + "web_search": "0.01", + "input_cache_read": "0.0000001", + "input_cache_write": "0.00000125" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 64000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-8b-thinking", + "canonical_slug": "qwen/qwen3-vl-8b-thinking", + "hugging_face_id": "Qwen/Qwen3-VL-8B-Thinking", + "name": "Qwen: Qwen3 VL 8B Thinking", + "created": 1760463746, + "description": "Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. 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It achieves stronger temporal grounding via Interleaved-MRoPE and timestamp-aware embeddings, while maintaining robust OCR, multilingual comprehension, and text generation on par with large text-only LLMs.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.000000117", "completion": "0.000001365" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 1, "top_p": 0.95 }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-8b-instruct", + "canonical_slug": "qwen/qwen3-vl-8b-instruct", + "hugging_face_id": "Qwen/Qwen3-VL-8B-Instruct", + "name": "Qwen: Qwen3 VL 8B Instruct", + "created": 1760463308, + "description": "Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.\n\nThe model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000008", "completion": "0.0000005" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.7, "top_p": 0.8, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5-image", + "canonical_slug": "openai/gpt-5-image", + "hugging_face_id": "", + "name": "OpenAI: GPT-5 Image", + "created": 1760447986, + "description": "[GPT-5](https://openrouter.ai/openai/gpt-5) Image combines OpenAI's GPT-5 model with state-of-the-art image generation capabilities. It offers major improvements in reasoning, code quality, and user experience while incorporating GPT Image 1's superior instruction following, text rendering, and detailed image editing.", + "context_length": 400000, + "architecture": { + "modality": "text+image+file->text+image", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["image", "text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00001", + "completion": "0.00001", + "web_search": "0.01", + "input_cache_read": "0.00000125" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/o3-deep-research", + "canonical_slug": "openai/o3-deep-research-2025-06-26", + "hugging_face_id": "", + "name": "OpenAI: o3 Deep Research", + "created": 1760129661, + "description": "o3-deep-research is OpenAI's advanced model for deep research, designed to tackle complex, multi-step research tasks.\n\nNote: This model always uses the 'web_search' tool which adds additional cost.", + "context_length": 200000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00001", + "completion": "0.00004", + "web_search": "0.01", + "input_cache_read": "0.0000025" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 100000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/o4-mini-deep-research", + "canonical_slug": "openai/o4-mini-deep-research-2025-06-26", + "hugging_face_id": "", + "name": "OpenAI: o4 Mini Deep Research", + "created": 1760129642, + "description": "o4-mini-deep-research is OpenAI's faster, more affordable deep research model—ideal for tackling complex, multi-step research tasks.\n\nNote: This model always uses the 'web_search' tool which adds additional cost.", + "context_length": 200000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["file", "image", "text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000002", + "completion": "0.000008", + "web_search": "0.01", + "input_cache_read": "0.0000005" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 100000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/llama-3.3-nemotron-super-49b-v1.5", + "canonical_slug": "nvidia/llama-3.3-nemotron-super-49b-v1.5", + "hugging_face_id": "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5", + "name": "NVIDIA: Llama 3.3 Nemotron Super 49B V1.5", + "created": 1760101395, + "description": "Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. 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The model targets practical inference efficiency (high tokens/s, reduced VRAM) with Transformers/vLLM support and explicit “reasoning on/off” modes (chat-first defaults, greedy recommended when disabled). Suitable for building agents, assistants, and long-context retrieval systems where balanced accuracy-to-cost and reliable tool use matter.\n", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000001", "completion": "0.0000004" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": null, + "expiration_date": null + }, + { + "id": "baidu/ernie-4.5-21b-a3b-thinking", + "canonical_slug": "baidu/ernie-4.5-21b-a3b-thinking", + "hugging_face_id": "baidu/ERNIE-4.5-21B-A3B-Thinking", + "name": "Baidu: ERNIE 4.5 21B A3B Thinking", + "created": 1760048887, + "description": "ERNIE-4.5-21B-A3B-Thinking is Baidu's upgraded lightweight MoE model, refined to boost reasoning depth and quality for top-tier performance in logical puzzles, math, science, coding, text generation, and expert-level academic benchmarks.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000007", "completion": "0.00000028" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.6, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "google/gemini-2.5-flash-image", + "canonical_slug": "google/gemini-2.5-flash-image", + "hugging_face_id": "", + "name": "Google: Gemini 2.5 Flash Image (Nano Banana)", + "created": 1759870431, + "description": "Gemini 2.5 Flash Image, a.k.a. \"Nano Banana,\" is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations. Aspect ratios can be controlled with the [image_config API Parameter](https://openrouter.ai/docs/features/multimodal/image-generation#image-aspect-ratio-configuration)", + "context_length": 32768, + "architecture": { + "modality": "text+image->text+image", + "input_modalities": ["image", "text"], + "output_modalities": ["image", "text"], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000003", + "completion": "0.0000025", + "image": "0.0000003", + "audio": "0.000001", + "internal_reasoning": "0.0000025", + "input_cache_read": "0.00000003", + "input_cache_write": "0.00000008333333333333334" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "temperature", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-30b-a3b-thinking", + "canonical_slug": "qwen/qwen3-vl-30b-a3b-thinking", + "hugging_face_id": "Qwen/Qwen3-VL-30B-A3B-Thinking", + "name": "Qwen: Qwen3 VL 30B A3B Thinking", + "created": 1759794479, + "description": "Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0", + "completion": "0", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.8, "top_p": 0.95 }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-30b-a3b-instruct", + "canonical_slug": "qwen/qwen3-vl-30b-a3b-instruct", + "hugging_face_id": "Qwen/Qwen3-VL-30B-A3B-Instruct", + "name": "Qwen: Qwen3 VL 30B A3B Instruct", + "created": 1759794476, + "description": "Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000013", "completion": "0.00000052" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.7, "top_p": 0.8, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5-pro", + "canonical_slug": "openai/gpt-5-pro-2025-10-06", + "hugging_face_id": "", + "name": "OpenAI: GPT-5 Pro", + "created": 1759776663, + "description": "GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like \"think hard about this.\" Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.", + "context_length": 400000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000015", "completion": "0.00012", "web_search": "0.01" }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "z-ai/glm-4.6", + "canonical_slug": "z-ai/glm-4.6", + "hugging_face_id": "", + "name": "Z.ai: GLM 4.6", + "created": 1759235576, + "description": "Compared with GLM-4.5, this generation brings several key improvements:\n\nLonger context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.\nSuperior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.\nAdvanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.\nMore capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.\nRefined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.", + "context_length": 202752, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000035", "completion": "0.00000171" }, + "top_provider": { + "context_length": 202752, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.6, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "z-ai/glm-4.6:exacto", + 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style and readability, and performs more naturally in role-playing scenarios.", + "context_length": 204800, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000044", + "completion": "0.00000176", + "input_cache_read": "0.00000011" + }, + "top_provider": { + "context_length": 204800, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.6, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "anthropic/claude-sonnet-4.5", + "canonical_slug": "anthropic/claude-4.5-sonnet-20250929", + "hugging_face_id": "", + "name": "Anthropic: Claude Sonnet 4.5", + "created": 1759161676, + "description": "Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with improvements across system design, code security, and specification adherence. The model is designed for extended autonomous operation, maintaining task continuity across sessions and providing fact-based progress tracking.\n\nSonnet 4.5 also introduces stronger agentic capabilities, including improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With enhanced context tracking and awareness of token usage across tool calls, it is particularly well-suited for multi-context and long-running workflows. Use cases span software engineering, cybersecurity, financial analysis, research agents, and other domains requiring sustained reasoning and tool use.", + "context_length": 1000000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "web_search": "0.01", + "input_cache_read": "0.0000003", + "input_cache_write": "0.00000375" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 64000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 1, "top_p": 1, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "deepseek/deepseek-v3.2-exp", + "canonical_slug": "deepseek/deepseek-v3.2-exp", + "hugging_face_id": "deepseek-ai/DeepSeek-V3.2-Exp", + "name": "DeepSeek: DeepSeek V3.2 Exp", + "created": 1759150481, + "description": "DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs.", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "DeepSeek", + "instruct_type": "deepseek-v3.1" + }, + "pricing": { "prompt": "0.00000027", "completion": "0.00000041" }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.6, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "thedrummer/cydonia-24b-v4.1", + "canonical_slug": "thedrummer/cydonia-24b-v4.1", + "hugging_face_id": "thedrummer/cydonia-24b-v4.1", + "name": "TheDrummer: Cydonia 24B V4.1", + "created": 1758931878, + "description": "Uncensored and creative writing model based on Mistral Small 3.2 24B with good recall, prompt adherence, and intelligence.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000003", "completion": "0.0000005" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "relace/relace-apply-3", + "canonical_slug": "relace/relace-apply-3", + "hugging_face_id": "", + "name": "Relace: Relace Apply 3", + "created": 1758891572, + "description": "Relace Apply 3 is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at 10,000 tokens/sec on average.\n\nThe model requires the prompt to be in the following format: \n{instruction}\n{initial_code}\n{edit_snippet}\n\nZero Data Retention is enabled for Relace. Learn more about this model in their [documentation](https://docs.relace.ai/api-reference/instant-apply/apply)", + "context_length": 256000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000085", "completion": "0.00000125" }, + "top_provider": { + "context_length": 256000, + "max_completion_tokens": 128000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "seed", "stop"], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "google/gemini-2.5-flash-lite-preview-09-2025", + "canonical_slug": "google/gemini-2.5-flash-lite-preview-09-2025", + "hugging_face_id": "", + "name": "Google: Gemini 2.5 Flash Lite Preview 09-2025", + "created": 1758819686, + "description": "Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, \"thinking\" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. ", + "context_length": 1048576, + "architecture": { + "modality": "text+image+file+audio+video->text", + "input_modalities": ["text", "image", "file", "audio", "video"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000004", + "image": "0.0000001", + "audio": "0.0000003", + "internal_reasoning": "0.0000004", + "input_cache_read": "0.00000001", + "input_cache_write": "0.00000008333333333333334" + }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 65535, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-235b-a22b-thinking", + "canonical_slug": "qwen/qwen3-vl-235b-a22b-thinking", + "hugging_face_id": "Qwen/Qwen3-VL-235B-A22B-Thinking", + "name": "Qwen: Qwen3 VL 235B A22B Thinking", + "created": 1758668690, + "description": "Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math. The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.\n\nBeyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows, turning sketches or mockups into code and assisting with UI debugging, while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0", + "completion": "0", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.8, "top_p": 0.95, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-vl-235b-a22b-instruct", + "canonical_slug": "qwen/qwen3-vl-235b-a22b-instruct", + "hugging_face_id": "Qwen/Qwen3-VL-235B-A22B-Instruct", + "name": "Qwen: Qwen3 VL 235B A22B Instruct", + "created": 1758668687, + "description": "Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.\n\nBeyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.", + "context_length": 262144, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000002", + "completion": "0.00000088", + "input_cache_read": "0.00000011" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.7, "top_p": 0.8, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-max", + "canonical_slug": "qwen/qwen3-max", + "hugging_face_id": "", + "name": "Qwen: Qwen3 Max", + "created": 1758662808, + "description": "Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. The model supports over 100 languages with stronger translation and commonsense reasoning, and is optimized for retrieval-augmented generation (RAG) and tool calling, though it does not include a dedicated “thinking” mode.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000012", + "completion": "0.000006", + "input_cache_read": "0.00000024" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 1, "top_p": 1, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-coder-plus", + "canonical_slug": "qwen/qwen3-coder-plus", + "hugging_face_id": "", + "name": "Qwen: Qwen3 Coder Plus", + "created": 1758662707, + "description": "Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.", + "context_length": 1000000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000001", + "completion": "0.000005", + "input_cache_read": "0.0000002" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5-codex", + "canonical_slug": "openai/gpt-5-codex", + "hugging_face_id": "", + "name": "OpenAI: GPT-5 Codex", + "created": 1758643403, + "description": "GPT-5-Codex is a specialized version of GPT-5 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5, Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs. Reasoning effort can be adjusted with the `reasoning.effort` parameter. Read the [docs here](https://openrouter.ai/docs/use-cases/reasoning-tokens#reasoning-effort-level)\n\nCodex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.", + "context_length": 400000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "deepseek/deepseek-v3.1-terminus:exacto", + "canonical_slug": "deepseek/deepseek-v3.1-terminus", + "hugging_face_id": "deepseek-ai/DeepSeek-V3.1-Terminus", + "name": "DeepSeek: DeepSeek V3.1 Terminus (exacto)", + "created": 1758548275, + "description": "DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. ", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "DeepSeek", + "instruct_type": "deepseek-v3.1" + }, + "pricing": { + "prompt": "0.00000021", + "completion": "0.00000079", + "input_cache_read": "0.000000168" + }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "deepseek/deepseek-v3.1-terminus", + "canonical_slug": "deepseek/deepseek-v3.1-terminus", + "hugging_face_id": "deepseek-ai/DeepSeek-V3.1-Terminus", + "name": "DeepSeek: DeepSeek V3.1 Terminus", + "created": 1758548275, + "description": "DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. ", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "DeepSeek", + "instruct_type": "deepseek-v3.1" + }, + "pricing": { + "prompt": "0.00000021", + "completion": "0.00000079", + "input_cache_read": "0.0000001300000002" + }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "x-ai/grok-4-fast", + "canonical_slug": "x-ai/grok-4-fast", + "hugging_face_id": "", + "name": "xAI: Grok 4 Fast", + "created": 1758240090, + "description": "Grok 4 Fast is xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window. It comes in two flavors: non-reasoning and reasoning. Read more about the model on xAI's [news post](http://x.ai/news/grok-4-fast).\n\nReasoning can be enabled/disabled using the `reasoning` `enabled` parameter in the API. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#controlling-reasoning-tokens)", + "context_length": 2000000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Grok", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000002", + "completion": "0.0000005", + "web_search": "0.005", + "input_cache_read": "0.00000005" + }, + "top_provider": { + "context_length": 2000000, + "max_completion_tokens": 30000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "logprobs", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "alibaba/tongyi-deepresearch-30b-a3b", + "canonical_slug": "alibaba/tongyi-deepresearch-30b-a3b", + "hugging_face_id": "Alibaba-NLP/Tongyi-DeepResearch-30B-A3B", + "name": "Tongyi DeepResearch 30B A3B", + "created": 1758210804, + "description": "Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.\n\nThe model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000009", + "completion": "0.00000045", + "input_cache_read": "0.00000009" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-coder-flash", + "canonical_slug": "qwen/qwen3-coder-flash", + "hugging_face_id": "", + "name": "Qwen: Qwen3 Coder Flash", + "created": 1758115536, + "description": "Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.", + "context_length": 1000000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000003", + "completion": "0.0000015", + "input_cache_read": "0.00000006" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "opengvlab/internvl3-78b", + "canonical_slug": "opengvlab/internvl3-78b", + "hugging_face_id": "OpenGVLab/InternVL3-78B", + "name": "OpenGVLab: InternVL3 78B", + "created": 1757962555, + "description": "The InternVL3 series is an advanced multimodal large language model (MLLM). Compared to InternVL 2.5, InternVL3 demonstrates stronger multimodal perception and reasoning capabilities. \n\nIn addition, InternVL3 is benchmarked against the Qwen2.5 Chat models, whose pre-trained base models serve as the initialization for its language component. Benefiting from Native Multimodal Pre-Training, the InternVL3 series surpasses the Qwen2.5 series in overall text performance.", + "context_length": 32768, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000015", + "completion": "0.0000006", + "input_cache_read": "0.000000075" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-next-80b-a3b-thinking", + "canonical_slug": "qwen/qwen3-next-80b-a3b-thinking-2509", + "hugging_face_id": "Qwen/Qwen3-Next-80B-A3B-Thinking", + "name": "Qwen: Qwen3 Next 80B A3B Thinking", + "created": 1757612284, + "description": "Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic planning, and reports strong results across knowledge, reasoning, coding, alignment, and multilingual evaluations. Compared with prior Qwen3 variants, it emphasizes stability under long chains of thought and efficient scaling during inference, and it is tuned to follow complex instructions while reducing repetitive or off-task behavior.\n\nThe model is suitable for agent frameworks and tool use (function calling), retrieval-heavy workflows, and standardized benchmarking where step-by-step solutions are required. It supports long, detailed completions and leverages throughput-oriented techniques (e.g., multi-token prediction) for faster generation. Note that it operates in thinking-only mode.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000015", "completion": "0.0000012" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-next-80b-a3b-instruct:free", + "canonical_slug": "qwen/qwen3-next-80b-a3b-instruct-2509", + "hugging_face_id": "Qwen/Qwen3-Next-80B-A3B-Instruct", + "name": "Qwen: Qwen3 Next 80B A3B Instruct (free)", + "created": 1757612213, + "description": "Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.\n\nThe model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-next-80b-a3b-instruct", + "canonical_slug": "qwen/qwen3-next-80b-a3b-instruct-2509", + "hugging_face_id": "Qwen/Qwen3-Next-80B-A3B-Instruct", + "name": "Qwen: Qwen3 Next 80B A3B Instruct", + "created": 1757612213, + "description": "Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.\n\nThe model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000009", "completion": "0.0000011" }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meituan/longcat-flash-chat", + "canonical_slug": "meituan/longcat-flash-chat", + "hugging_face_id": "meituan-longcat/LongCat-Flash-Chat", + "name": "Meituan: LongCat Flash Chat", + "created": 1757427658, + "description": "LongCat-Flash-Chat is a large-scale Mixture-of-Experts (MoE) model with 560B total parameters, of which 18.6B–31.3B (≈27B on average) are dynamically activated per input. It introduces a shortcut-connected MoE design to reduce communication overhead and achieve high throughput while maintaining training stability through advanced scaling strategies such as hyperparameter transfer, deterministic computation, and multi-stage optimization.\n\nThis release, LongCat-Flash-Chat, is a non-thinking foundation model optimized for conversational and agentic tasks. It supports long context windows up to 128K tokens and shows competitive performance across reasoning, coding, instruction following, and domain benchmarks, with particular strengths in tool use and complex multi-step interactions.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000002", + "completion": "0.0000008", + "input_cache_read": "0.0000002" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "temperature", "top_p"], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen-plus-2025-07-28", + "canonical_slug": "qwen/qwen-plus-2025-07-28", + "hugging_face_id": "", + "name": "Qwen: Qwen Plus 0728", + "created": 1757347599, + "description": "Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.", + "context_length": 1000000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000004", "completion": "0.0000012" }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen-plus-2025-07-28:thinking", + "canonical_slug": "qwen/qwen-plus-2025-07-28", + "hugging_face_id": "", + "name": "Qwen: Qwen Plus 0728 (thinking)", + "created": 1757347599, + "description": "Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.", + "context_length": 1000000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000004", "completion": "0.0000012" }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/nemotron-nano-9b-v2:free", + "canonical_slug": "nvidia/nemotron-nano-9b-v2", + "hugging_face_id": "nvidia/NVIDIA-Nemotron-Nano-9B-v2", + "name": "NVIDIA: Nemotron Nano 9B V2 (free)", + "created": 1757106807, + "description": "NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. \n\nThe model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "nvidia/nemotron-nano-9b-v2", + "canonical_slug": "nvidia/nemotron-nano-9b-v2", + "hugging_face_id": "nvidia/NVIDIA-Nemotron-Nano-9B-v2", + "name": "NVIDIA: Nemotron Nano 9B V2", + "created": 1757106807, + "description": "NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. \n\nThe model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000004", "completion": "0.00000016" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "moonshotai/kimi-k2-0905", + "canonical_slug": "moonshotai/kimi-k2-0905", + "hugging_face_id": "moonshotai/Kimi-K2-Instruct-0905", + "name": "MoonshotAI: Kimi K2 0905", + "created": 1757021147, + "description": "Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.\n\nThis update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000004", + "completion": "0.000002", + "input_cache_read": "0.00000015" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "moonshotai/kimi-k2-0905:exacto", + "canonical_slug": "moonshotai/kimi-k2-0905", + "hugging_face_id": "moonshotai/Kimi-K2-Instruct-0905", + "name": "MoonshotAI: Kimi K2 0905 (exacto)", + "created": 1757021147, + "description": "Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). 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The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000006", "completion": "0.0000025" }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-30b-a3b-thinking-2507", + "canonical_slug": "qwen/qwen3-30b-a3b-thinking-2507", + "hugging_face_id": "Qwen/Qwen3-30B-A3B-Thinking-2507", + "name": "Qwen: Qwen3 30B A3B Thinking 2507", + "created": 1756399192, + "description": "Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. 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It supports JSON mode, schema adherence, function calling, and tool use, and is designed for greater steerability with reduced refusal rates.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000013", "completion": "0.0000004" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "nousresearch/hermes-4-405b", + "canonical_slug": "nousresearch/hermes-4-405b", + "hugging_face_id": "NousResearch/Hermes-4-405B", + "name": "Nous: Hermes 4 405B", + "created": 1756235463, + "description": "Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. 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Hermes 4 is trained for steerability, lower refusal rates, and alignment toward neutral, user-directed behavior.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.000001", "completion": "0.000003" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "deepseek/deepseek-chat-v3.1", + "canonical_slug": "deepseek/deepseek-chat-v3.1", + "hugging_face_id": "deepseek-ai/DeepSeek-V3.1", + "name": "DeepSeek: DeepSeek V3.1", + "created": 1755779628, + "description": "DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. 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This enhancement allows the model to detect nuances within audio recordings and add depth to generated user experiences. Audio outputs are currently not supported. Audio tokens are priced at $40 per million input and $80 per million output audio tokens.", + "context_length": 128000, + "architecture": { + "modality": "text+audio->text+audio", + "input_modalities": ["audio", "text"], + "output_modalities": ["text", "audio"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000025", "completion": "0.00001", "audio": "0.00004" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "mistralai/mistral-medium-3.1", + "canonical_slug": "mistralai/mistral-medium-3.1", + "hugging_face_id": "", + "name": "Mistral: Mistral Medium 3.1", + "created": 1755095639, + "description": "Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. 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Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000004", "completion": "0.000002" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "baidu/ernie-4.5-21b-a3b", + "canonical_slug": "baidu/ernie-4.5-21b-a3b", + "hugging_face_id": "baidu/ERNIE-4.5-21B-A3B-PT", + "name": "Baidu: ERNIE 4.5 21B A3B", + "created": 1755034167, + "description": "A sophisticated text-based Mixture-of-Experts (MoE) model featuring 21B total parameters with 3B activated per token, delivering exceptional multimodal understanding and generation through heterogeneous MoE structures and modality-isolated routing. Supporting an extensive 131K token context length, the model achieves efficient inference via multi-expert parallel collaboration and quantization, while advanced post-training techniques including SFT, DPO, and UPO ensure optimized performance across diverse applications with specialized routing and balancing losses for superior task handling.", + "context_length": 120000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000007", "completion": "0.00000028" }, + "top_provider": { + "context_length": 120000, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.8, "top_p": 0.8, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "baidu/ernie-4.5-vl-28b-a3b", + "canonical_slug": "baidu/ernie-4.5-vl-28b-a3b", + "hugging_face_id": "baidu/ERNIE-4.5-VL-28B-A3B-PT", + "name": "Baidu: ERNIE 4.5 VL 28B A3B", + "created": 1755032836, + "description": "A powerful multimodal Mixture-of-Experts chat model featuring 28B total parameters with 3B activated per token, delivering exceptional text and vision understanding through its innovative heterogeneous MoE structure with modality-isolated routing. Built with scaling-efficient infrastructure for high-throughput training and inference, the model leverages advanced post-training techniques including SFT, DPO, and UPO for optimized performance, while supporting an impressive 131K context length and RLVR alignment for superior cross-modal reasoning and generation capabilities.", + "context_length": 30000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000014", "completion": "0.00000056" }, + "top_provider": { + "context_length": 30000, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "z-ai/glm-4.5v", + "canonical_slug": "z-ai/glm-4.5v", + "hugging_face_id": "zai-org/GLM-4.5V", + "name": "Z.ai: GLM 4.5V", + "created": 1754922288, + "description": "GLM-4.5V is a vision-language foundation model for multimodal agent applications. 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[Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)", + "context_length": 65536, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000006", + "completion": "0.0000018", + "input_cache_read": "0.00000011" + }, + "top_provider": { + "context_length": 65536, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.75, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "ai21/jamba-large-1.7", + "canonical_slug": "ai21/jamba-large-1.7", + "hugging_face_id": "ai21labs/AI21-Jamba-Large-1.7", + "name": "AI21: Jamba Large 1.7", + "created": 1754669020, + "description": "Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context window, it delivers more accurate, contextually grounded responses and better steerability than previous versions.", + "context_length": 256000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.000002", "completion": "0.000008" }, + "top_provider": { + "context_length": 256000, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "stop", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-5-chat", + "canonical_slug": "openai/gpt-5-chat-2025-08-07", + "hugging_face_id": "", + "name": "OpenAI: GPT-5 Chat", + "created": 1754587837, + "description": "GPT-5 Chat is designed for advanced, natural, multimodal, and context-aware conversations for enterprise applications.", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["file", "image", "text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "web_search": "0.01", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "response_format", "seed", "structured_outputs"], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-5", + "canonical_slug": "openai/gpt-5-2025-08-07", + "hugging_face_id": "", + "name": "OpenAI: GPT-5", + "created": 1754587413, + "description": "GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like \"think hard about this.\" Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.", + "context_length": 400000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.00001", + "web_search": "0.01", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 400000, + "max_completion_tokens": 128000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-5-mini", + "canonical_slug": "openai/gpt-5-mini-2025-08-07", + "hugging_face_id": "", + "name": "OpenAI: GPT-5 Mini", + "created": 1754587407, + "description": "GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost. 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Once a request is greater than 128k input tokens, the higher pricing is used.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000022", + "completion": "0.000001", + "input_cache_read": "0.000000022" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-coder:exacto", + "canonical_slug": "qwen/qwen3-coder-480b-a35b-07-25", + "hugging_face_id": "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "name": "Qwen: Qwen3 Coder 480B A35B (exacto)", + "created": 1753230546, + "description": "Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. 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Once a request is greater than 128k input tokens, the higher pricing is used.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000022", + "completion": "0.0000018", + "input_cache_read": "0.000000022" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "bytedance/ui-tars-1.5-7b", + "canonical_slug": "bytedance/ui-tars-1.5-7b", + "hugging_face_id": "ByteDance-Seed/UI-TARS-1.5-7B", + "name": "ByteDance: UI-TARS 7B ", + "created": 1753205056, + "description": "UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. 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UI-TARS-1.5 supports thought decomposition during inference and shows strong scaling across variants, with the 1.5 version notably exceeding the performance of earlier 72B and 7B checkpoints.", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000001", "completion": "0.0000002" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "google/gemini-2.5-flash-lite", + "canonical_slug": "google/gemini-2.5-flash-lite", + "hugging_face_id": "", + "name": "Google: Gemini 2.5 Flash Lite", + "created": 1753200276, + "description": "Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, \"thinking\" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. ", + "context_length": 1048576, + "architecture": { + "modality": "text+image+file+audio+video->text", + "input_modalities": ["text", "image", "file", "audio", "video"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000004", + "image": "0.0000001", + "audio": "0.0000003", + "internal_reasoning": "0.0000004", + "input_cache_read": "0.00000001", + "input_cache_write": "0.00000008333333333333334" + }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 65535, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-235b-a22b-2507", + "canonical_slug": "qwen/qwen3-235b-a22b-07-25", + "hugging_face_id": "Qwen/Qwen3-235B-A22B-Instruct-2507", + "name": "Qwen: Qwen3 235B A22B Instruct 2507", + "created": 1753119555, + "description": "Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. 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It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { "prompt": "0.000000071", "completion": "0.0000001" }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "reasoning_effort", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "switchpoint/router", + "canonical_slug": "switchpoint/router", + "hugging_face_id": "", + "name": "Switchpoint Router", + "created": 1752272899, + "description": "Switchpoint AI's router instantly analyzes your request and directs it to the optimal AI from an ever-evolving library. \n\nAs the world of LLMs advances, our router gets smarter, ensuring you always benefit from the industry's newest models without changing your workflow.\n\nThis model is configured for a simple, flat rate per response here on OpenRouter. It's powered by the full routing engine from [Switchpoint AI](https://www.switchpoint.dev).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000085", "completion": "0.0000034" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "moonshotai/kimi-k2", + "canonical_slug": "moonshotai/kimi-k2", + "hugging_face_id": "moonshotai/Kimi-K2-Instruct", + "name": "MoonshotAI: Kimi K2 0711", + "created": 1752263252, + "description": "Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000005", "completion": "0.0000024" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/devstral-medium", + "canonical_slug": "mistralai/devstral-medium-2507", + "hugging_face_id": "", + "name": "Mistral: Devstral Medium", + "created": 1752161321, + "description": "Devstral Medium is a high-performance code generation and agentic reasoning model developed jointly by Mistral AI and All Hands AI. Positioned as a step up from Devstral Small, it achieves 61.6% on SWE-Bench Verified, placing it ahead of Gemini 2.5 Pro and GPT-4.1 in code-related tasks, at a fraction of the cost. It is designed for generalization across prompt styles and tool use in code agents and frameworks.\n\nDevstral Medium is available via API only (not open-weight), and supports enterprise deployment on private infrastructure, with optional fine-tuning capabilities.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000004", "completion": "0.000002" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "mistralai/devstral-small", + "canonical_slug": "mistralai/devstral-small-2507", + "hugging_face_id": "mistralai/Devstral-Small-2507", + "name": "Mistral: Devstral Small 1.1", + "created": 1752160751, + "description": "Devstral Small 1.1 is a 24B parameter open-weight language model for software engineering agents, developed by Mistral AI in collaboration with All Hands AI. Finetuned from Mistral Small 3.1 and released under the Apache 2.0 license, it features a 128k token context window and supports both Mistral-style function calling and XML output formats.\n\nDesigned for agentic coding workflows, Devstral Small 1.1 is optimized for tasks such as codebase exploration, multi-file edits, and integration into autonomous development agents like OpenHands and Cline. It achieves 53.6% on SWE-Bench Verified, surpassing all other open models on this benchmark, while remaining lightweight enough to run on a single 4090 GPU or Apple silicon machine. The model uses a Tekken tokenizer with a 131k vocabulary and is deployable via vLLM, Transformers, Ollama, LM Studio, and other OpenAI-compatible runtimes.\n", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000001", "completion": "0.0000003" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "cognitivecomputations/dolphin-mistral-24b-venice-edition:free", + "canonical_slug": "venice/uncensored", + "hugging_face_id": "cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", + "name": "Venice: Uncensored (free)", + "created": 1752094966, + "description": "Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving user control over alignment, system prompts, and behavior. Intended for advanced and unrestricted use cases, Venice Uncensored emphasizes steerability and transparent behavior, removing default safety and alignment layers typically found in mainstream assistant models.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "x-ai/grok-4", + "canonical_slug": "x-ai/grok-4-07-09", + "hugging_face_id": "", + "name": "xAI: Grok 4", + "created": 1752087689, + "description": "Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not exposed, reasoning cannot be disabled, and the reasoning effort cannot be specified. Pricing increases once the total tokens in a given request is greater than 128k tokens. See more details on the [xAI docs](https://docs.x.ai/docs/models/grok-4-0709)", + "context_length": 256000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Grok", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "web_search": "0.005", + "input_cache_read": "0.00000075" + }, + "top_provider": { + "context_length": 256000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "logprobs", + "max_tokens", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "google/gemma-3n-e2b-it:free", + "canonical_slug": "google/gemma-3n-e2b-it", + "hugging_face_id": "google/gemma-3n-E2B-it", + "name": "Google: Gemma 3n 2B (free)", + "created": 1752074904, + "description": "Gemma 3n E2B IT is a multimodal, instruction-tuned model developed by Google DeepMind, designed to operate efficiently at an effective parameter size of 2B while leveraging a 6B architecture. Based on the MatFormer architecture, it supports nested submodels and modular composition via the Mix-and-Match framework. Gemma 3n models are optimized for low-resource deployment, offering 32K context length and strong multilingual and reasoning performance across common benchmarks. This variant is trained on a diverse corpus including code, math, web, and multimodal data.", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "tencent/hunyuan-a13b-instruct", + "canonical_slug": "tencent/hunyuan-a13b-instruct", + "hugging_face_id": "tencent/Hunyuan-A13B-Instruct", + "name": "Tencent: Hunyuan A13B Instruct", + "created": 1751987664, + "description": "Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000014", "completion": "0.00000057" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "reasoning", + "response_format", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "tngtech/deepseek-r1t2-chimera", + "canonical_slug": "tngtech/deepseek-r1t2-chimera", + "hugging_face_id": "tngtech/DeepSeek-TNG-R1T2-Chimera", + "name": "TNG: DeepSeek R1T2 Chimera", + "created": 1751986985, + "description": "DeepSeek-TNG-R1T2-Chimera is the second-generation Chimera model from TNG Tech. It is a 671 B-parameter mixture-of-experts text-generation model assembled from DeepSeek-AI’s R1-0528, R1, and V3-0324 checkpoints with an Assembly-of-Experts merge. The tri-parent design yields strong reasoning performance while running roughly 20 % faster than the original R1 and more than 2× faster than R1-0528 under vLLM, giving a favorable cost-to-intelligence trade-off. The checkpoint supports contexts up to 60 k tokens in standard use (tested to ~130 k) and maintains consistent token behaviour, making it suitable for long-context analysis, dialogue and other open-ended generation tasks.", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "DeepSeek", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000025", + "completion": "0.00000085", + "input_cache_read": "0.000000125" + }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": 163840, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "morph/morph-v3-large", + "canonical_slug": "morph/morph-v3-large", + "hugging_face_id": "", + "name": "Morph: Morph V3 Large", + "created": 1751910858, + "description": "Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations.\n\nThe model requires the prompt to be in the following format: \n{instruction}\n{initial_code}\n{edit_snippet}\n\nZero Data Retention is enabled for Morph. Learn more about this model in their [documentation](https://docs.morphllm.com/quickstart)", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000009", "completion": "0.0000019" }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "stop", "temperature"], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "morph/morph-v3-fast", + "canonical_slug": "morph/morph-v3-fast", + "hugging_face_id": "", + "name": "Morph: Morph V3 Fast", + "created": 1751910002, + "description": "Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations.\n\nThe model requires the prompt to be in the following format: \n{instruction}\n{initial_code}\n{edit_snippet}\n\nZero Data Retention is enabled for Morph. Learn more about this model in their [documentation](https://docs.morphllm.com/quickstart)", + "context_length": 81920, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000008", "completion": "0.0000012" }, + "top_provider": { + "context_length": 81920, + "max_completion_tokens": 38000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "stop", "temperature"], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "baidu/ernie-4.5-vl-424b-a47b", + "canonical_slug": "baidu/ernie-4.5-vl-424b-a47b", + "hugging_face_id": "baidu/ERNIE-4.5-VL-424B-A47B-PT", + "name": "Baidu: ERNIE 4.5 VL 424B A47B ", + "created": 1751300903, + "description": "ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. 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Suitable for general-purpose LLM applications with high reasoning and throughput demands.", + "context_length": 123000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000028", "completion": "0.0000011" }, + "top_provider": { + "context_length": 123000, + "max_completion_tokens": 12000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "inception/mercury", + "canonical_slug": "inception/mercury", + "hugging_face_id": "", + "name": "Inception: Mercury", + "created": 1750973026, + "description": "Mercury is the first diffusion large language model (dLLM). 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It's their flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. 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Sonnet 4 is optimized for practical everyday use, providing advanced reasoning capabilities while maintaining efficiency and responsiveness in diverse internal and external scenarios.\n\nRead more at the [blog post here](https://www.anthropic.com/news/claude-4)", + "context_length": 1000000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "web_search": "0.01", + "input_cache_read": "0.0000003", + "input_cache_write": "0.00000375" + }, + "top_provider": { + "context_length": 1000000, + "max_completion_tokens": 64000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "reasoning", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "google/gemma-3n-e4b-it:free", + "canonical_slug": "google/gemma-3n-e4b-it", + "hugging_face_id": "google/gemma-3n-E4B-it", + "name": "Google: Gemma 3n 4B (free)", + "created": 1747776824, + "description": "Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. 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[Read more in the blog post](https://developers.googleblog.com/en/introducing-gemma-3n/)", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "google/gemma-3n-e4b-it", + "canonical_slug": "google/gemma-3n-e4b-it", + "hugging_face_id": "google/gemma-3n-E4B-it", + "name": "Google: Gemma 3n 4B", + "created": 1747776824, + "description": "Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. 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[Read more in the blog post](https://developers.googleblog.com/en/introducing-gemma-3n/)", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000002", "completion": "0.00000004" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mistral-medium-3", + "canonical_slug": "mistralai/mistral-medium-3", + "hugging_face_id": "", + "name": "Mistral: Mistral Medium 3", + "created": 1746627341, + "description": "Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. 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Mistral Medium 3 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000004", "completion": "0.000002" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "google/gemini-2.5-pro-preview-05-06", + "canonical_slug": "google/gemini-2.5-pro-preview-03-25", + "hugging_face_id": "", + "name": "Google: Gemini 2.5 Pro Preview 05-06", + "created": 1746578513, + "description": "Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. 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It offers a 32 k‑token context window, enabling rich multimodal conversations that combine lengthy documents with one or more images. Training emphasized fast inference on consumer GPUs while retaining strong captioning, visual‐question‑answering, and diagram‑analysis accuracy. As a result, Spotlight slots neatly into agent workflows where screenshots, charts or UI mock‑ups need to be interpreted on the fly. Early benchmarks show it matching or out‑scoring larger VLMs such as LLaVA‑1.6 13 B on popular VQA and POPE alignment tests. 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Unlike many 70 B peers, it retains the 128 k context inherited from Qwen 2.5, letting it ingest books, codebases or financial filings wholesale. Training blended DeepSeek R1 distillation, multi‑epoch supervised fine‑tuning and a final DPO/RLHF alignment stage, yielding strong performance on BIG‑Bench‑Hard, GSM‑8K and long‑context Needle‑In‑Haystack tests. Enterprises use Virtuoso‑Large as the \"fallback\" brain in Conductor pipelines when other SLMs flag low confidence. Despite its size, aggressive KV‑cache optimizations keep first‑token latency in the low‑second range on 8× H100 nodes, making it a practical production‑grade powerhouse.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000075", "completion": "0.0000012" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 64000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "arcee-ai/coder-large", + "canonical_slug": "arcee-ai/coder-large", + "hugging_face_id": "", + "name": "Arcee AI: Coder Large", + "created": 1746478663, + "description": "Coder‑Large is a 32 B‑parameter offspring of Qwen 2.5‑Instruct that has been further trained on permissively‑licensed GitHub, CodeSearchNet and synthetic bug‑fix corpora. It supports a 32k context window, enabling multi‑file refactoring or long diff review in a single call, and understands 30‑plus programming languages with special attention to TypeScript, Go and Terraform. Internal benchmarks show 5–8 pt gains over CodeLlama‑34 B‑Python on HumanEval and competitive BugFix scores thanks to a reinforcement pass that rewards compilable output. The model emits structured explanations alongside code blocks by default, making it suitable for educational tooling as well as production copilot scenarios. Cost‑wise, Together AI prices it well below proprietary incumbents, so teams can scale interactive coding without runaway spend. 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Read more in the [blog post here](https://www.inceptionlabs.ai/blog/introducing-mercury).", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000025", "completion": "0.000001" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-4b:free", + "canonical_slug": "qwen/qwen3-4b-04-28", + "hugging_face_id": "Qwen/Qwen3-4B", + "name": "Qwen: Qwen3 4B (free)", + "created": 1746031104, + "description": "Qwen3-4B is a 4 billion parameter dense language model from the Qwen3 series, designed to support both general-purpose and reasoning-intensive tasks. It introduces a dual-mode architecture—thinking and non-thinking—allowing dynamic switching between high-precision logical reasoning and efficient dialogue generation. This makes it well-suited for multi-turn chat, instruction following, and complex agent workflows.", + "context_length": 40960, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 40960, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-guard-4-12b", + "canonical_slug": "meta-llama/llama-guard-4-12b", + "hugging_face_id": "meta-llama/Llama-Guard-4-12B", + "name": "Meta: Llama Guard 4 12B", + "created": 1745975193, + "description": "Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.\n\nLlama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.", + "context_length": 163840, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["image", "text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000018", "completion": "0.00000018" }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-30b-a3b", + "canonical_slug": "qwen/qwen3-30b-a3b-04-28", + "hugging_face_id": "Qwen/Qwen3-30B-A3B", + "name": "Qwen: Qwen3 30B A3B", + "created": 1745878604, + "description": "Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. 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The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.", + "context_length": 40960, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { "prompt": "0.00000008", "completion": "0.00000028" }, + "top_provider": { + "context_length": 40960, + "max_completion_tokens": 40960, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-8b", + "canonical_slug": "qwen/qwen3-8b-04-28", + "hugging_face_id": "Qwen/Qwen3-8B", + "name": "Qwen: Qwen3 8B", + "created": 1745876632, + "description": "Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between \"thinking\" mode for math, coding, and logical inference, and \"non-thinking\" mode for general conversation. The model is fine-tuned for instruction-following, agent integration, creative writing, and multilingual use across 100+ languages and dialects. It natively supports a 32K token context window and can extend to 131K tokens with YaRN scaling.", + "context_length": 32000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { + "prompt": "0.00000005", + "completion": "0.0000004", + "input_cache_read": "0.00000005" + }, + "top_provider": { + "context_length": 32000, + "max_completion_tokens": 8192, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "qwen/qwen3-14b", + "canonical_slug": "qwen/qwen3-14b-04-28", + "hugging_face_id": "Qwen/Qwen3-14B", + "name": "Qwen: Qwen3 14B", + "created": 1745876478, + "description": "Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, programming, and logical inference, and a \"non-thinking\" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.", + "context_length": 40960, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { "prompt": "0.00000006", "completion": "0.00000024" }, + "top_provider": { + "context_length": 40960, + "max_completion_tokens": 40960, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-32b", + "canonical_slug": "qwen/qwen3-32b-04-28", + "hugging_face_id": "Qwen/Qwen3-32B", + "name": "Qwen: Qwen3 32B", + "created": 1745875945, + "description": "Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, coding, and logical inference, and a \"non-thinking\" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling. ", + "context_length": 40960, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { + "prompt": "0.00000008", + "completion": "0.00000024", + "input_cache_read": "0.00000004" + }, + "top_provider": { + "context_length": 40960, + "max_completion_tokens": 40960, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen3-235b-a22b", + "canonical_slug": "qwen/qwen3-235b-a22b-04-28", + "hugging_face_id": "Qwen/Qwen3-235B-A22B", + "name": "Qwen: Qwen3 235B A22B", + "created": 1745875757, + "description": "Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. 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It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.\n\nDespite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. 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It is tuned for precise code diffs, agent reliability, and high recall in large document contexts, making it ideal for agents, IDE tooling, and enterprise knowledge retrieval.", + "context_length": 1047576, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000002", + "completion": "0.000008", + "web_search": "0.01", + "input_cache_read": "0.0000005" + }, + "top_provider": { + "context_length": 1047576, + "max_completion_tokens": 32768, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4.1-mini", + "canonical_slug": "openai/gpt-4.1-mini-2025-04-14", + "hugging_face_id": "", + "name": "OpenAI: GPT-4.1 Mini", + "created": 1744651381, + "description": "GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. 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Mini also shows strong coding ability (e.g., 31.6% on Aider’s polyglot diff benchmark) and vision understanding, making it suitable for interactive applications with tight performance constraints.", + "context_length": 1047576, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000004", + "completion": "0.0000016", + "web_search": "0.01", + "input_cache_read": "0.0000001" + }, + "top_provider": { + "context_length": 1047576, + "max_completion_tokens": 32768, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4.1-nano", + "canonical_slug": "openai/gpt-4.1-nano-2025-04-14", + "hugging_face_id": "", + "name": "OpenAI: GPT-4.1 Nano", + "created": 1744651369, + "description": "For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. 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It’s ideal for tasks like classification or autocompletion.", + "context_length": 1047576, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["image", "text", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000004", + "web_search": "0.01", + "input_cache_read": "0.000000025" + }, + "top_provider": { + "context_length": 1047576, + "max_completion_tokens": 32768, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "eleutherai/llemma_7b", + "canonical_slug": "eleutherai/llemma_7b", + "hugging_face_id": "EleutherAI/llemma_7b", + "name": "EleutherAI: Llemma 7b", + "created": 1744643225, + "description": "Llemma 7B is a language model for mathematics. It was initialized with Code Llama 7B weights, and trained on the Proof-Pile-2 for 200B tokens. Llemma models are particularly strong at chain-of-thought mathematical reasoning and using computational tools for mathematics, such as Python and formal theorem provers.", + "context_length": 4096, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": "code-llama" + }, + "pricing": { "prompt": "0.0000008", "completion": "0.0000012" }, + "top_provider": { + "context_length": 4096, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "alfredpros/codellama-7b-instruct-solidity", + "canonical_slug": "alfredpros/codellama-7b-instruct-solidity", + "hugging_face_id": "AlfredPros/CodeLlama-7b-Instruct-Solidity", + "name": "AlfredPros: CodeLLaMa 7B Instruct Solidity", + "created": 1744641874, + "description": "A finetuned 7 billion parameters Code LLaMA - Instruct model to generate Solidity smart contract using 4-bit QLoRA finetuning provided by PEFT library.", + "context_length": 4096, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": "alpaca" + }, + "pricing": { "prompt": "0.0000008", "completion": "0.0000012" }, + "top_provider": { + "context_length": 4096, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "x-ai/grok-3-mini-beta", + "canonical_slug": "x-ai/grok-3-mini-beta", + "hugging_face_id": "", + "name": "xAI: Grok 3 Mini Beta", + "created": 1744240195, + "description": "Grok 3 Mini is a lightweight, smaller thinking model. 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If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. \n", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Grok", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000003", + "completion": "0.0000005", + "web_search": "0.005", + "input_cache_read": "0.000000075" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "include_reasoning", + "logprobs", + "max_tokens", + "reasoning", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "x-ai/grok-3-beta", + "canonical_slug": "x-ai/grok-3-beta", + "hugging_face_id": "", + "name": "xAI: Grok 3 Beta", + "created": 1744240068, + "description": "Grok 3 is the latest model from xAI. 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If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. \n", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Grok", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "web_search": "0.005", + "input_cache_read": "0.00000075" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "nvidia/llama-3.1-nemotron-ultra-253b-v1", + "canonical_slug": "nvidia/llama-3.1-nemotron-ultra-253b-v1", + "hugging_face_id": "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", + "name": "NVIDIA: Llama 3.1 Nemotron Ultra 253B v1", + "created": 1744115059, + "description": "Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) optimized for advanced reasoning, human-interactive chat, retrieval-augmented generation (RAG), and tool-calling tasks. 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Please see [Usage Recommendations](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1#quick-start-and-usage-recommendations) for more.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000006", "completion": "0.0000018" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "repetition_penalty", + "response_format", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-4-maverick", + "canonical_slug": "meta-llama/llama-4-maverick-17b-128e-instruct", + "hugging_face_id": "meta-llama/Llama-4-Maverick-17B-128E-Instruct", + "name": "Meta: Llama 4 Maverick", + "created": 1743881822, + "description": "Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). 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Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.", + "context_length": 1048576, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Llama4", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000015", "completion": "0.0000006" }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-4-scout", + "canonical_slug": "meta-llama/llama-4-scout-17b-16e-instruct", + "hugging_face_id": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "name": "Meta: Llama 4 Scout", + "created": 1743881519, + "description": "Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. 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Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.", + "context_length": 327680, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Llama4", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000008", "completion": "0.0000003" }, + "top_provider": { + "context_length": 327680, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen2.5-vl-32b-instruct", + "canonical_slug": "qwen/qwen2.5-vl-32b-instruct", + "hugging_face_id": "Qwen/Qwen2.5-VL-32B-Instruct", + "name": "Qwen: Qwen2.5 VL 32B Instruct", + "created": 1742839838, + "description": "Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. 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Gemma 3 27B is Google's latest open source model, successor to [Gemma 2](google/gemma-2-27b-it)", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": "gemma" + }, + "pricing": { + "prompt": "0.00000004", + "completion": "0.00000015", + "input_cache_read": "0.00000002" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "thedrummer/skyfall-36b-v2", + "canonical_slug": "thedrummer/skyfall-36b-v2", + "hugging_face_id": "TheDrummer/Skyfall-36B-v2", + "name": "TheDrummer: Skyfall 36B V2", + "created": 1741636566, + "description": "Skyfall 36B v2 is an enhanced iteration of Mistral Small 2501, specifically fine-tuned for improved creativity, nuanced writing, role-playing, and coherent storytelling.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000055", "completion": "0.0000008" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "perplexity/sonar-reasoning-pro", + "canonical_slug": "perplexity/sonar-reasoning-pro", + "hugging_face_id": "", + "name": "Perplexity: Sonar Reasoning Pro", + "created": 1741313308, + "description": "Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro)\n\nSonar Reasoning Pro is a premier reasoning model powered by DeepSeek R1 with Chain of Thought (CoT). Designed for advanced use cases, it supports in-depth, multi-step queries with a larger context window and can surface more citations per search, enabling more comprehensive and extensible responses.", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": "deepseek-r1" + }, + "pricing": { "prompt": "0.000002", "completion": "0.000008", "web_search": "0.005" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "max_tokens", + "presence_penalty", + "reasoning", + "temperature", + "top_k", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "perplexity/sonar-pro", + "canonical_slug": "perplexity/sonar-pro", + "hugging_face_id": "", + "name": "Perplexity: Sonar Pro", + "created": 1741312423, + "description": "Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro)\n\nFor enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries with added extensibility, like double the number of citations per search as Sonar on average. Plus, with a larger context window, it can handle longer and more nuanced searches and follow-up questions. ", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.000003", "completion": "0.000015", "web_search": "0.005" }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "temperature", + "top_k", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "perplexity/sonar-deep-research", + "canonical_slug": "perplexity/sonar-deep-research", + "hugging_face_id": "", + "name": "Perplexity: Sonar Deep Research", + "created": 1741311246, + "description": "Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. 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Learn more in the [launch announcement](https://openai.com/o1).\n", + "context_length": 200000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000015", "completion": "0.00006", "input_cache_read": "0.0000075" }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 100000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "response_format", + "seed", + "structured_outputs", + "tool_choice", + "tools" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "cohere/command-r7b-12-2024", + "canonical_slug": "cohere/command-r7b-12-2024", + "hugging_face_id": "", + "name": "Cohere: Command R7B (12-2024)", + "created": 1734158152, + "description": "Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. 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With a context length of 128K tokens and optimized for speed and cost, Amazon Nova Micro excels at tasks such as text summarization, translation, content classification, interactive chat, and brainstorming. 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GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000025", + "completion": "0.00001", + "input_cache_read": "0.00000125" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mistral-large-2411", + "canonical_slug": "mistralai/mistral-large-2411", + "hugging_face_id": "", + "name": "Mistral Large 2411", + "created": 1731978685, + "description": "Mistral Large 2 2411 is an update of [Mistral Large 2](/mistralai/mistral-large) released together with [Pixtral Large 2411](/mistralai/pixtral-large-2411)\n\nIt provides a significant upgrade on the previous [Mistral Large 24.07](/mistralai/mistral-large-2407), with notable improvements in long context understanding, a new system prompt, and more accurate function calling.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.000002", "completion": "0.000006" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "mistralai/mistral-large-2407", + "canonical_slug": "mistralai/mistral-large-2407", + "hugging_face_id": "", + "name": "Mistral Large 2407", + "created": 1731978415, + "description": "This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/).\n\nIt supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.\n", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.000002", "completion": "0.000006" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "mistralai/pixtral-large-2411", + "canonical_slug": "mistralai/pixtral-large-2411", + "hugging_face_id": "", + "name": "Mistral: Pixtral Large 2411", + "created": 1731977388, + "description": "Pixtral Large is a 124B parameter, open-weight, multimodal model built on top of [Mistral Large 2](/mistralai/mistral-large-2411). The model is able to understand documents, charts and natural images.\n\nThe model is available under the Mistral Research License (MRL) for research and educational use, and the Mistral Commercial License for experimentation, testing, and production for commercial purposes.\n\n", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.000002", "completion": "0.000006" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "qwen/qwen-2.5-coder-32b-instruct", + "canonical_slug": "qwen/qwen-2.5-coder-32b-instruct", + "hugging_face_id": "Qwen/Qwen2.5-Coder-32B-Instruct", + "name": "Qwen2.5 Coder 32B Instruct", + "created": 1731368400, + "description": "Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:\n\n- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. \n- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.\n\nTo read more about its evaluation results, check out [Qwen 2.5 Coder's blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.0000002", "completion": "0.0000002" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 8192, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "raifle/sorcererlm-8x22b", + "canonical_slug": "raifle/sorcererlm-8x22b", + "hugging_face_id": "rAIfle/SorcererLM-8x22b-bf16", + "name": "SorcererLM 8x22B", + "created": 1731105083, + "description": "SorcererLM is an advanced RP and storytelling model, built as a Low-rank 16-bit LoRA fine-tuned on [WizardLM-2 8x22B](/microsoft/wizardlm-2-8x22b).\n\n- Advanced reasoning and emotional intelligence for engaging and immersive interactions\n- Vivid writing capabilities enriched with spatial and contextual awareness\n- Enhanced narrative depth, promoting creative and dynamic storytelling", + "context_length": 16000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "vicuna" + }, + "pricing": { "prompt": "0.0000045", "completion": "0.0000045" }, + "top_provider": { + "context_length": 16000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "thedrummer/unslopnemo-12b", + "canonical_slug": "thedrummer/unslopnemo-12b", + "hugging_face_id": "TheDrummer/UnslopNemo-12B-v4.1", + "name": "TheDrummer: UnslopNemo 12B", + "created": 1731103448, + "description": "UnslopNemo v4.1 is the latest addition from the creator of Rocinante, designed for adventure writing and role-play scenarios.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "mistral" + }, + "pricing": { "prompt": "0.0000004", "completion": "0.0000004" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "anthropic/claude-3.5-haiku", + "canonical_slug": "anthropic/claude-3-5-haiku", + "hugging_face_id": null, + "name": "Anthropic: Claude 3.5 Haiku", + "created": 1730678400, + "description": "Claude 3.5 Haiku features offers enhanced capabilities in speed, coding accuracy, and tool use. Engineered to excel in real-time applications, it delivers quick response times that are essential for dynamic tasks such as chat interactions and immediate coding suggestions.\n\nThis makes it highly suitable for environments that demand both speed and precision, such as software development, customer service bots, and data management systems.\n\nThis model is currently pointing to [Claude 3.5 Haiku (2024-10-22)](/anthropic/claude-3-5-haiku-20241022).", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000008", + "completion": "0.000004", + "web_search": "0.01", + "input_cache_read": "0.00000008", + "input_cache_write": "0.000001" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 8192, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "anthracite-org/magnum-v4-72b", + "canonical_slug": "anthracite-org/magnum-v4-72b", + "hugging_face_id": "anthracite-org/magnum-v4-72b", + "name": "Magnum v4 72B", + "created": 1729555200, + "description": "This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus).\n\nThe model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-2.5-72b-instruct).", + "context_length": 16384, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.000003", "completion": "0.000005" }, + "top_provider": { + "context_length": 16384, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_a", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "anthropic/claude-3.5-sonnet", + "canonical_slug": "anthropic/claude-3.5-sonnet", + "hugging_face_id": null, + "name": "Anthropic: Claude 3.5 Sonnet", + "created": 1729555200, + "description": "New Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Scores ~49% on SWE-Bench Verified, higher than the last best score, and without any fancy prompt scaffolding\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal", + "context_length": 200000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000006", + "completion": "0.00003", + "input_cache_read": "0.0000006", + "input_cache_write": "0.0000075" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 8192, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen-2.5-7b-instruct", + "canonical_slug": "qwen/qwen-2.5-7b-instruct", + "hugging_face_id": "Qwen/Qwen2.5-7B-Instruct", + "name": "Qwen: Qwen2.5 7B Instruct", + "created": 1729036800, + "description": "Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.00000004", "completion": "0.0000001" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "nvidia/llama-3.1-nemotron-70b-instruct", + "canonical_slug": "nvidia/llama-3.1-nemotron-70b-instruct", + "hugging_face_id": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", + "name": "NVIDIA: Llama 3.1 Nemotron 70B Instruct", + "created": 1728950400, + "description": "NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging [Llama 3.1 70B](/models/meta-llama/llama-3.1-70b-instruct) architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.0000012", "completion": "0.0000012" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "inflection/inflection-3-pi", + "canonical_slug": "inflection/inflection-3-pi", + "hugging_face_id": null, + "name": "Inflection: Inflection 3 Pi", + "created": 1728604800, + "description": "Inflection 3 Pi powers Inflection's [Pi](https://pi.ai) chatbot, including backstory, emotional intelligence, productivity, and safety. It has access to recent news, and excels in scenarios like customer support and roleplay.\n\nPi has been trained to mirror your tone and style, if you use more emojis, so will Pi! Try experimenting with various prompts and conversation styles.", + "context_length": 8000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000025", "completion": "0.00001" }, + "top_provider": { + "context_length": 8000, + "max_completion_tokens": 1024, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "stop", "temperature", "top_p"], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "inflection/inflection-3-productivity", + "canonical_slug": "inflection/inflection-3-productivity", + "hugging_face_id": null, + "name": "Inflection: Inflection 3 Productivity", + "created": 1728604800, + "description": "Inflection 3 Productivity is optimized for following instructions. It is better for tasks requiring JSON output or precise adherence to provided guidelines. It has access to recent news.\n\nFor emotional intelligence similar to Pi, see [Inflect 3 Pi](/inflection/inflection-3-pi)\n\nSee [Inflection's announcement](https://inflection.ai/blog/enterprise) for more details.", + "context_length": 8000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000025", "completion": "0.00001" }, + "top_provider": { + "context_length": 8000, + "max_completion_tokens": 1024, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": ["max_tokens", "stop", "temperature", "top_p"], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "thedrummer/rocinante-12b", + "canonical_slug": "thedrummer/rocinante-12b", + "hugging_face_id": "TheDrummer/Rocinante-12B-v1.1", + "name": "TheDrummer: Rocinante 12B", + "created": 1727654400, + "description": "Rocinante 12B is designed for engaging storytelling and rich prose.\n\nEarly testers have reported:\n- Expanded vocabulary with unique and expressive word choices\n- Enhanced creativity for vivid narratives\n- Adventure-filled and captivating stories", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.00000017", "completion": "0.00000043" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-3.2-3b-instruct:free", + "canonical_slug": "meta-llama/llama-3.2-3b-instruct", + "hugging_face_id": "meta-llama/Llama-3.2-3B-Instruct", + "name": "Meta: Llama 3.2 3B Instruct (free)", + "created": 1727222400, + "description": "Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0", "completion": "0" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-3.2-3b-instruct", + "canonical_slug": "meta-llama/llama-3.2-3b-instruct", + "hugging_face_id": "meta-llama/Llama-3.2-3B-Instruct", + "name": "Meta: Llama 3.2 3B Instruct", + "created": 1727222400, + "description": "Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000002", "completion": "0.00000002" }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-3.2-1b-instruct", + "canonical_slug": "meta-llama/llama-3.2-1b-instruct", + "hugging_face_id": "meta-llama/Llama-3.2-1B-Instruct", + "name": "Meta: Llama 3.2 1B Instruct", + "created": 1727222400, + "description": "Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. 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Sloppy chats output were purged.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000009", "completion": "0.0000006" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "cohere/command-r-08-2024", + "canonical_slug": "cohere/command-r-08-2024", + "hugging_face_id": null, + "name": "Cohere: Command R (08-2024)", + "created": 1724976000, + "description": "command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Cohere", + "instruct_type": null + }, + "pricing": { "prompt": "0.00000015", "completion": "0.0000006" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "cohere/command-r-plus-08-2024", + "canonical_slug": "cohere/command-r-plus-08-2024", + "hugging_face_id": null, + "name": "Cohere: Command R+ (08-2024)", + "created": 1724976000, + "description": "command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Cohere", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000025", "completion": "0.00001" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "sao10k/l3.1-euryale-70b", + "canonical_slug": "sao10k/l3.1-euryale-70b", + "hugging_face_id": "Sao10K/L3.1-70B-Euryale-v2.2", + "name": "Sao10K: Llama 3.1 Euryale 70B v2.2", + "created": 1724803200, + "description": "Euryale L3.1 70B v2.2 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.1](/models/sao10k/l3-euryale-70b).", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000065", "completion": "0.00000075" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 32768, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "qwen/qwen-2.5-vl-7b-instruct", + "canonical_slug": "qwen/qwen-2-vl-7b-instruct", + "hugging_face_id": "Qwen/Qwen2.5-VL-7B-Instruct", + "name": "Qwen: Qwen2.5-VL 7B Instruct", + 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Vietnamese, etc.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub repo](https://github.com/QwenLM/Qwen2-VL).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).", + "context_length": 32768, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Qwen", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000002", "completion": "0.0000002" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": 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improved code generation skills.", + "context_length": 65536, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.0000003", "completion": "0.0000003" }, + "top_provider": { + "context_length": 65536, + "max_completion_tokens": 65536, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "nousresearch/hermes-3-llama-3.1-405b:free", + "canonical_slug": "nousresearch/hermes-3-llama-3.1-405b", + "hugging_face_id": "NousResearch/Hermes-3-Llama-3.1-405B", + "name": "Nous: Hermes 3 405B Instruct (free)", + "created": 1723766400, + "description": 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It's a strategic merge of multiple models, designed to balance creativity with improved logic and general knowledge.\n\nCreated by [Sao10k](https://huggingface.co/Sao10k), this model aims to offer an improved experience over Stheno v3.2, with enhanced creativity and logical reasoning.\n\nFor best results, use with Llama 3 Instruct context template, temperature 1.4, and min_p 0.1.", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000004", "completion": "0.00000005" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4o-2024-08-06", + "canonical_slug": "openai/gpt-4o-2024-08-06", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o (2024-08-06)", + "created": 1722902400, + "description": "The 2024-08-06 version of GPT-4o offers improved performance in structured outputs, with the ability to supply a JSON schema in the respone_format. Read more [here](https://openai.com/index/introducing-structured-outputs-in-the-api/).\n\nGPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. 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It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000015", + "completion": "0.0000006", + "input_cache_read": "0.000000075" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "google/gemma-2-27b-it", + "canonical_slug": "google/gemma-2-27b-it", + "hugging_face_id": "google/gemma-2-27b-it", + "name": "Google: Gemma 2 27B", + "created": 1720828800, + "description": "Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini).\n\nGemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. 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Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Gemini", + "instruct_type": "gemma" + }, + "pricing": { "prompt": "0.00000003", "completion": "0.00000009" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "sao10k/l3-euryale-70b", + "canonical_slug": "sao10k/l3-euryale-70b", + "hugging_face_id": "Sao10K/L3-70B-Euryale-v2.1", + "name": "Sao10k: Llama 3 Euryale 70B v2.1", + "created": 1718668800, + "description": "Euryale 70B v2.1 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k).\n\n- Better prompt adherence.\n- Better anatomy / spatial awareness.\n- Adapts much better to unique and custom formatting / reply formats.\n- Very creative, lots of unique swipes.\n- Is not restrictive during roleplays.", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000148", "completion": "0.00000148" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 8192, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "nousresearch/hermes-2-pro-llama-3-8b", + "canonical_slug": "nousresearch/hermes-2-pro-llama-3-8b", + "hugging_face_id": "NousResearch/Hermes-2-Pro-Llama-3-8B", + "name": "NousResearch: Hermes 2 Pro - 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Just like is predecessor, [LlamaGuard 1](https://huggingface.co/meta-llama/LlamaGuard-7b), it can do both prompt and response classification.\n\nLlamaGuard 2 acts as a normal LLM would, generating text that indicates whether the given input/output is safe/unsafe. If deemed unsafe, it will also share the content categories violated.\n\nFor best results, please use raw prompt input or the `/completions` endpoint, instead of the chat API.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "none" + }, + "pricing": { "prompt": "0.0000002", "completion": "0.0000002" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": "2026-02-25" + }, + { + "id": "openai/gpt-4o-2024-05-13", + "canonical_slug": "openai/gpt-4o-2024-05-13", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o (2024-05-13)", + "created": 1715558400, + "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000005", "completion": "0.000015" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4o", + "canonical_slug": "openai/gpt-4o", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o", + "created": 1715558400, + "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000025", + "completion": "0.00001", + "input_cache_read": "0.00000125" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4o:extended", + "canonical_slug": "openai/gpt-4o", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o (extended)", + "created": 1715558400, + "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image+file->text", + "input_modalities": ["text", "image", "file"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000006", "completion": "0.000018" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 64000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-3-70b-instruct", + "canonical_slug": "meta-llama/llama-3-70b-instruct", + "hugging_face_id": "meta-llama/Meta-Llama-3-70B-Instruct", + "name": "Meta: Llama 3 70B Instruct", + "created": 1713398400, + "description": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000051", "completion": "0.00000074" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "meta-llama/llama-3-8b-instruct", + "canonical_slug": "meta-llama/llama-3-8b-instruct", + "hugging_face_id": "meta-llama/Meta-Llama-3-8B-Instruct", + "name": "Meta: Llama 3 8B Instruct", + "created": 1713398400, + "description": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { "prompt": "0.00000003", "completion": "0.00000004" }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mixtral-8x22b-instruct", + "canonical_slug": "mistralai/mixtral-8x22b-instruct", + "hugging_face_id": "mistralai/Mixtral-8x22B-Instruct-v0.1", + "name": "Mistral: Mixtral 8x22B Instruct", + "created": 1713312000, + "description": "Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). 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Its strengths include:\n- strong math, coding, and reasoning\n- large context length (64k)\n- fluency in English, French, Italian, German, and Spanish\n\nSee benchmarks on the launch announcement [here](https://mistral.ai/news/mixtral-8x22b/).\n#moe", + "context_length": 65536, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "mistral" + }, + "pricing": { "prompt": "0.000002", "completion": "0.000006" }, + "top_provider": { + "context_length": 65536, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "microsoft/wizardlm-2-8x22b", + "canonical_slug": "microsoft/wizardlm-2-8x22b", + "hugging_face_id": "microsoft/WizardLM-2-8x22B", + "name": "WizardLM-2 8x22B", + "created": 1713225600, + "description": "WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.\n\nIt is an instruct finetune of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b).\n\nTo read more about the model release, [click here](https://wizardlm.github.io/WizardLM2/).\n\n#moe", + "context_length": 65535, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "vicuna" + }, + "pricing": { "prompt": "0.00000062", "completion": "0.00000062" }, + "top_provider": { + "context_length": 65535, + "max_completion_tokens": 8000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4-turbo", + "canonical_slug": "openai/gpt-4-turbo", + "hugging_face_id": null, + "name": "OpenAI: GPT-4 Turbo", + "created": 1712620800, + "description": "The latest GPT-4 Turbo model with vision capabilities. 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Quick and accurate targeted performance.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku)\n\n#multimodal", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": ["text", "image"], + "output_modalities": ["text"], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000025", + "completion": "0.00000125", + "input_cache_read": "0.00000003", + "input_cache_write": "0.0000003" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mistral-large", + "canonical_slug": "mistralai/mistral-large", + "hugging_face_id": null, + "name": "Mistral Large", + "created": 1708905600, + "description": "This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/).\n\nIt supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { "prompt": "0.000002", "completion": "0.000006" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "openai/gpt-3.5-turbo-0613", + "canonical_slug": "openai/gpt-3.5-turbo-0613", + "hugging_face_id": null, + "name": "OpenAI: GPT-3.5 Turbo (older v0613)", + "created": 1706140800, + "description": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.", + "context_length": 4095, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000001", "completion": "0.000002" }, + "top_provider": { + "context_length": 4095, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4-turbo-preview", + "canonical_slug": "openai/gpt-4-turbo-preview", + "hugging_face_id": null, + "name": "OpenAI: GPT-4 Turbo Preview", + "created": 1706140800, + "description": "The preview GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Dec 2023.\n\n**Note:** heavily rate limited by OpenAI while in preview.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.00001", "completion": "0.00003" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mistral-7b-instruct-v0.2", + "canonical_slug": "mistralai/mistral-7b-instruct-v0.2", + "hugging_face_id": "mistralai/Mistral-7B-Instruct-v0.2", + "name": "Mistral: Mistral 7B Instruct v0.2", + "created": 1703721600, + "description": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\nAn improved version of [Mistral 7B Instruct](/modelsmistralai/mistral-7b-instruct-v0.1), with the following changes:\n\n- 32k context window (vs 8k context in v0.1)\n- Rope-theta = 1e6\n- No Sliding-Window Attention", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "mistral" + }, + "pricing": { "prompt": "0.0000002", "completion": "0.0000002" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "stop", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "mistralai/mixtral-8x7b-instruct", + "canonical_slug": "mistralai/mixtral-8x7b-instruct", + "hugging_face_id": "mistralai/Mixtral-8x7B-Instruct-v0.1", + "name": "Mistral: Mixtral 8x7B Instruct", + "created": 1702166400, + "description": "Mixtral 8x7B Instruct is a pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.\n\nInstruct model fine-tuned by Mistral. #moe", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "mistral" + }, + "pricing": { "prompt": "0.00000054", "completion": "0.00000054" }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "neversleep/noromaid-20b", + "canonical_slug": "neversleep/noromaid-20b", + "hugging_face_id": "NeverSleep/Noromaid-20b-v0.1.1", + "name": "Noromaid 20B", + "created": 1700956800, + "description": "A collab between IkariDev and Undi. This merge is suitable for RP, ERP, and general knowledge.\n\n#merge #uncensored", + "context_length": 4096, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama2", + "instruct_type": "alpaca" + }, + "pricing": { "prompt": "0.000001", "completion": "0.00000175" }, + "top_provider": { + "context_length": 4096, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "response_format", + "stop", + "structured_outputs", + "temperature", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "alpindale/goliath-120b", + "canonical_slug": "alpindale/goliath-120b", + "hugging_face_id": "alpindale/goliath-120b", + "name": "Goliath 120B", + "created": 1699574400, + "description": "A large LLM created by combining two fine-tuned Llama 70B models into one 120B model. Combines Xwin and Euryale.\n\nCredits to\n- [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).\n- [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.\n\n#merge", + "context_length": 6144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama2", + "instruct_type": "airoboros" + }, + "pricing": { "prompt": "0.00000375", "completion": "0.0000075" }, + "top_provider": { + "context_length": 6144, + "max_completion_tokens": 1024, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_a", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openrouter/auto", + "canonical_slug": "openrouter/auto", + "hugging_face_id": null, + "name": "Auto Router", + "created": 1699401600, + "description": "Your prompt will be processed by a meta-model and routed to one of dozens of models (see below), optimizing for the best possible output.\n\nTo see which model was used, visit [Activity](/activity), or read the `model` attribute of the response. Your response will be priced at the same rate as the routed model.\n\nLearn more, including how to customize the models for routing, in our [docs](/docs/guides/routing/routers/auto-router).\n\nRequests will be routed to the following models:\n- [anthropic/claude-haiku-4.5](/anthropic/claude-haiku-4.5)\n- [anthropic/claude-opus-4.6](/anthropic/claude-opus-4.6)\n- [anthropic/claude-sonnet-4.5](/anthropic/claude-sonnet-4.5)\n- [deepseek/deepseek-r1](/deepseek/deepseek-r1)\n- [google/gemini-2.5-flash-lite](/google/gemini-2.5-flash-lite)\n- [google/gemini-3-flash-preview](/google/gemini-3-flash-preview)\n- [google/gemini-3-pro-preview](/google/gemini-3-pro-preview)\n- [meta-llama/llama-3.3-70b-instruct](/meta-llama/llama-3.3-70b-instruct)\n- [mistralai/codestral-2508](/mistralai/codestral-2508)\n- [mistralai/mistral-large](/mistralai/mistral-large)\n- [mistralai/mistral-medium-3.1](/mistralai/mistral-medium-3.1)\n- [mistralai/mistral-small-3.2-24b-instruct-2506](/mistralai/mistral-small-3.2-24b-instruct-2506)\n- [moonshotai/kimi-k2-thinking](/moonshotai/kimi-k2-thinking)\n- [moonshotai/kimi-k2.5](/moonshotai/kimi-k2.5)\n- [openai/gpt-5](/openai/gpt-5)\n- [openai/gpt-5-mini](/openai/gpt-5-mini)\n- [openai/gpt-5-nano](/openai/gpt-5-nano)\n- [openai/gpt-5.1](/openai/gpt-5.1)\n- [openai/gpt-5.2](/openai/gpt-5.2)\n- [openai/gpt-5.2-pro](/openai/gpt-5.2-pro)\n- [openai/gpt-oss-120b](/openai/gpt-oss-120b)\n- [perplexity/sonar](/perplexity/sonar)\n- [qwen/qwen3-235b-a22b](/qwen/qwen3-235b-a22b)\n- [x-ai/grok-3](/x-ai/grok-3)\n- [x-ai/grok-3-mini](/x-ai/grok-3-mini)\n- [x-ai/grok-4](/x-ai/grok-4)", + "context_length": 2000000, + "architecture": { + "modality": "text+image+file+audio+video->text+image", + "input_modalities": ["text", "image", "audio", "file", "video"], + "output_modalities": ["text", "image"], + "tokenizer": "Router", + "instruct_type": null + }, + "pricing": { "prompt": "-1", "completion": "-1" }, + "top_provider": { + "context_length": null, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "include_reasoning", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "reasoning", + "reasoning_effort", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_k", + "top_logprobs", + "top_p", + "web_search_options" + ], + "default_parameters": { "temperature": null, "top_p": null, "frequency_penalty": null }, + "expiration_date": null + }, + { + "id": "openai/gpt-4-1106-preview", + "canonical_slug": "openai/gpt-4-1106-preview", + "hugging_face_id": null, + "name": "OpenAI: GPT-4 Turbo (older v1106)", + "created": 1699228800, + "description": "The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.\n\nTraining data: up to April 2023.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.00001", "completion": "0.00003" }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-3.5-turbo-instruct", + "canonical_slug": "openai/gpt-3.5-turbo-instruct", + "hugging_face_id": null, + "name": "OpenAI: GPT-3.5 Turbo Instruct", + "created": 1695859200, + "description": "This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.", + "context_length": 4095, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": "chatml" + }, + "pricing": { "prompt": "0.0000015", "completion": "0.000002" }, + "top_provider": { + "context_length": 4095, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mistralai/mistral-7b-instruct-v0.1", + "canonical_slug": "mistralai/mistral-7b-instruct-v0.1", + "hugging_face_id": "mistralai/Mistral-7B-Instruct-v0.1", + "name": "Mistral: Mistral 7B Instruct v0.1", + "created": 1695859200, + "description": "A 7.3B parameter model that outperforms Llama 2 13B on all benchmarks, with optimizations for speed and context length.", + "context_length": 2824, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Mistral", + "instruct_type": "mistral" + }, + "pricing": { "prompt": "0.00000011", "completion": "0.00000019" }, + "top_provider": { + "context_length": 2824, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "max_tokens", + "presence_penalty", + "repetition_penalty", + "seed", + "temperature", + "top_k", + "top_p" + ], + "default_parameters": { "temperature": 0.3 }, + "expiration_date": null + }, + { + "id": "openai/gpt-3.5-turbo-16k", + "canonical_slug": "openai/gpt-3.5-turbo-16k", + "hugging_face_id": null, + "name": "OpenAI: GPT-3.5 Turbo 16k", + "created": 1693180800, + "description": "This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up to Sep 2021.", + "context_length": 16385, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.000003", "completion": "0.000004" }, + "top_provider": { + "context_length": 16385, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "mancer/weaver", + "canonical_slug": "mancer/weaver", + "hugging_face_id": null, + "name": "Mancer: Weaver (alpha)", + "created": 1690934400, + "description": "An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.", + "context_length": 8000, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama2", + "instruct_type": "alpaca" + }, + "pricing": { "prompt": "0.00000075", "completion": "0.000001" }, + "top_provider": { + "context_length": 8000, + "max_completion_tokens": 2000, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "temperature", + "top_a", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "undi95/remm-slerp-l2-13b", + "canonical_slug": "undi95/remm-slerp-l2-13b", + "hugging_face_id": "Undi95/ReMM-SLERP-L2-13B", + "name": "ReMM SLERP 13B", + "created": 1689984000, + "description": "A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge", + "context_length": 6144, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama2", + "instruct_type": "alpaca" + }, + "pricing": { "prompt": "0.00000045", "completion": "0.00000065" }, + "top_provider": { + "context_length": 6144, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_a", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "gryphe/mythomax-l2-13b", + "canonical_slug": "gryphe/mythomax-l2-13b", + "hugging_face_id": "Gryphe/MythoMax-L2-13b", + "name": "MythoMax 13B", + "created": 1688256000, + "description": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge", + "context_length": 4096, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "Llama2", + "instruct_type": "alpaca" + }, + "pricing": { "prompt": "0.00000006", "completion": "0.00000006" }, + "top_provider": { + "context_length": 4096, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "presence_penalty", + "repetition_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "top_a", + "top_k", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4-0314", + "canonical_slug": "openai/gpt-4-0314", + "hugging_face_id": null, + "name": "OpenAI: GPT-4 (older v0314)", + "created": 1685232000, + "description": "GPT-4-0314 is the first version of GPT-4 released, with a context length of 8,192 tokens, and was supported until June 14. Training data: up to Sep 2021.", + "context_length": 8191, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.00003", "completion": "0.00006" }, + "top_provider": { + "context_length": 8191, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-4", + "canonical_slug": "openai/gpt-4", + "hugging_face_id": null, + "name": "OpenAI: GPT-4", + "created": 1685232000, + "description": "OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning capabilities. Training data: up to Sep 2021.", + "context_length": 8191, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.00003", "completion": "0.00006" }, + "top_provider": { + "context_length": 8191, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + }, + { + "id": "openai/gpt-3.5-turbo", + "canonical_slug": "openai/gpt-3.5-turbo", + "hugging_face_id": null, + "name": "OpenAI: GPT-3.5 Turbo", + "created": 1685232000, + "description": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.", + "context_length": 16385, + "architecture": { + "modality": "text->text", + "input_modalities": ["text"], + "output_modalities": ["text"], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { "prompt": "0.0000005", "completion": "0.0000015" }, + "top_provider": { + "context_length": 16385, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "presence_penalty", + "response_format", + "seed", + "stop", + "structured_outputs", + "temperature", + "tool_choice", + "tools", + "top_logprobs", + "top_p" + ], + "default_parameters": {}, + "expiration_date": null + } + ] +} diff --git a/package.json b/package.json index 31d59fb8..798d4bd0 100644 --- a/package.json +++ b/package.json @@ -4,11 +4,15 @@ "private": true, "packageManager": "pnpm@10.18.3", "dependencies": { - "@directus/sdk": "^21.0.0", "@mintel/mail": "1.8.3", "@mintel/next-config": "1.8.3", "@mintel/next-feedback": "1.8.10", "@mintel/next-utils": "^1.7.15", + "@payloadcms/db-postgres": "^3.77.0", + "@payloadcms/email-nodemailer": "^3.77.0", + "@payloadcms/next": "^3.77.0", + "@payloadcms/richtext-lexical": "^3.77.0", + "@payloadcms/ui": "^3.77.0", "@react-email/components": "^1.0.7", "@react-pdf/renderer": "^4.3.2", "@sentry/nextjs": "^10.39.0", @@ -16,6 +20,7 @@ "axios": "^1.13.5", "clsx": "^2.1.1", "framer-motion": "^12.34.0", + "graphql": "^16.12.0", "gray-matter": "^4.0.3", "i18next": "^25.7.3", "import-in-the-middle": "^1.11.0", @@ -26,6 +31,7 @@ "next-intl": "^4.8.2", "next-mdx-remote": "^5.0.0", "nodemailer": "^7.0.12", + "payload": "^3.77.0", "pdf-lib": "^1.17.1", "pino": "^10.3.0", "pino-pretty": "^13.1.3", @@ -85,9 +91,8 @@ "vitest": "^4.0.16" }, "scripts": { - "dev": "docker network create infra 2>/dev/null || true && echo '\\n🚀 Development Environment Starting...\\n\\n📱 App (Next.js): http://localhost:3000\\n📱 App (Traefik): http://klz.localhost\\n🗄️ CMS: http://cms.klz.localhost/admin\\n🚦 Traefik: http://localhost:8080\\n\\n(Press Ctrl+C to stop)\\n' && docker-compose down --remove-orphans && docker-compose up --build klz-app klz-cms klz-db klz-gatekeeper", - "dev:infra": "docker network create infra 2>/dev/null || true && docker-compose up -d klz-cms klz-db klz-gatekeeper", - "dev:local": "next dev", + "dev": "docker network create infra 2>/dev/null || true && echo \"\\n🚀 Dockerized Environment Starting...\\n\" && COMPOSE_PROJECT_NAME=klz-2026 docker-compose -f docker-compose.dev.yml up klz-app klz-db", + "dev:infra": "docker-compose up klz-db klz-gatekeeper", "build": "next build", "start": "next start", "lint": "eslint .", diff --git a/payload-types.ts b/payload-types.ts new file mode 100644 index 00000000..3b35e246 --- /dev/null +++ b/payload-types.ts @@ -0,0 +1,702 @@ +/* tslint:disable */ +/* eslint-disable */ +/** + * This file was automatically generated by Payload. + * DO NOT MODIFY IT BY HAND. Instead, modify your source Payload config, + * and re-run `payload generate:types` to regenerate this file. + */ + +/** + * Supported timezones in IANA format. + * + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "supportedTimezones". + */ +export type SupportedTimezones = + | 'Pacific/Midway' + | 'Pacific/Niue' + | 'Pacific/Honolulu' + | 'Pacific/Rarotonga' + | 'America/Anchorage' + | 'Pacific/Gambier' + | 'America/Los_Angeles' + | 'America/Tijuana' + | 'America/Denver' + | 'America/Phoenix' + | 'America/Chicago' + | 'America/Guatemala' + | 'America/New_York' + | 'America/Bogota' + | 'America/Caracas' + | 'America/Santiago' + | 'America/Buenos_Aires' + | 'America/Sao_Paulo' + | 'Atlantic/South_Georgia' + | 'Atlantic/Azores' + | 'Atlantic/Cape_Verde' + | 'Europe/London' + | 'Europe/Berlin' + | 'Africa/Lagos' + | 'Europe/Athens' + | 'Africa/Cairo' + | 'Europe/Moscow' + | 'Asia/Riyadh' + | 'Asia/Dubai' + | 'Asia/Baku' + | 'Asia/Karachi' + | 'Asia/Tashkent' + | 'Asia/Calcutta' + | 'Asia/Dhaka' + | 'Asia/Almaty' + | 'Asia/Jakarta' + | 'Asia/Bangkok' + | 'Asia/Shanghai' + | 'Asia/Singapore' + | 'Asia/Tokyo' + | 'Asia/Seoul' + | 'Australia/Brisbane' + | 'Australia/Sydney' + | 'Pacific/Guam' + | 'Pacific/Noumea' + | 'Pacific/Auckland' + | 'Pacific/Fiji'; + +export interface Config { + auth: { + users: UserAuthOperations; + }; + blocks: {}; + collections: { + users: User; + media: Media; + posts: Post; + 'form-submissions': FormSubmission; + products: Product; + pages: Page; + 'payload-kv': PayloadKv; + 'payload-locked-documents': PayloadLockedDocument; + 'payload-preferences': PayloadPreference; + 'payload-migrations': PayloadMigration; + }; + collectionsJoins: {}; + collectionsSelect: { + users: UsersSelect | UsersSelect; + media: MediaSelect | MediaSelect; + posts: PostsSelect | PostsSelect; + 'form-submissions': FormSubmissionsSelect | FormSubmissionsSelect; + products: ProductsSelect | ProductsSelect; + pages: PagesSelect | PagesSelect; + 'payload-kv': PayloadKvSelect | PayloadKvSelect; + 'payload-locked-documents': + | PayloadLockedDocumentsSelect + | PayloadLockedDocumentsSelect; + 'payload-preferences': PayloadPreferencesSelect | PayloadPreferencesSelect; + 'payload-migrations': PayloadMigrationsSelect | PayloadMigrationsSelect; + }; + db: { + defaultIDType: number; + }; + fallbackLocale: null; + globals: {}; + globalsSelect: {}; + locale: null; + user: User; + jobs: { + tasks: unknown; + workflows: unknown; + }; +} +export interface UserAuthOperations { + forgotPassword: { + email: string; + password: string; + }; + login: { + email: string; + password: string; + }; + registerFirstUser: { + email: string; + password: string; + }; + unlock: { + email: string; + password: string; + }; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "users". + */ +export interface User { + id: number; + updatedAt: string; + createdAt: string; + email: string; + resetPasswordToken?: string | null; + resetPasswordExpiration?: string | null; + salt?: string | null; + hash?: string | null; + loginAttempts?: number | null; + lockUntil?: string | null; + sessions?: + | { + id: string; + createdAt?: string | null; + expiresAt: string; + }[] + | null; + password?: string | null; + collection: 'users'; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "media". + */ +export interface Media { + id: number; + alt: string; + caption?: string | null; + updatedAt: string; + createdAt: string; + url?: string | null; + thumbnailURL?: string | null; + filename?: string | null; + mimeType?: string | null; + filesize?: number | null; + width?: number | null; + height?: number | null; + focalX?: number | null; + focalY?: number | null; + sizes?: { + thumbnail?: { + url?: string | null; + width?: number | null; + height?: number | null; + mimeType?: string | null; + filesize?: number | null; + filename?: string | null; + }; + card?: { + url?: string | null; + width?: number | null; + height?: number | null; + mimeType?: string | null; + filesize?: number | null; + filename?: string | null; + }; + tablet?: { + url?: string | null; + width?: number | null; + height?: number | null; + mimeType?: string | null; + filesize?: number | null; + filename?: string | null; + }; + }; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "posts". + */ +export interface Post { + id: number; + title: string; + slug: string; + /** + * A short summary for blog feed cards and SEO. + */ + excerpt?: string | null; + /** + * Future dates will schedule the post to publish automatically. + */ + date: string; + /** + * The primary Hero image used for headers and OpenGraph previews. + */ + featuredImage?: (number | null) | Media; + locale: 'en' | 'de'; + /** + * Used for tag bucketing (e.g. "Kabel Technologie"). + */ + category?: string | null; + content?: { + root: { + type: string; + children: { + type: any; + version: number; + [k: string]: unknown; + }[]; + direction: ('ltr' | 'rtl') | null; + format: 'left' | 'start' | 'center' | 'right' | 'end' | 'justify' | ''; + indent: number; + version: number; + }; + [k: string]: unknown; + } | null; + updatedAt: string; + createdAt: string; + _status?: ('draft' | 'published') | null; +} +/** + * Captured leads from Contact and Product Quote forms. + * + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "form-submissions". + */ +export interface FormSubmission { + id: number; + name: string; + email: string; + type: 'contact' | 'product_quote'; + /** + * The specific KLZ product the user requested a quote for. + */ + productName?: string | null; + message: string; + updatedAt: string; + createdAt: string; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "products". + */ +export interface Product { + id: number; + title: string; + sku: string; + slug: string; + description: string; + locale: 'en' | 'de'; + categories: { + category?: string | null; + id?: string | null; + }[]; + /** + * The primary thumbnail used in list views. + */ + featuredImage?: (number | null) | Media; + images?: (number | Media)[] | null; + application?: { + root: { + type: string; + children: { + type: any; + version: number; + [k: string]: unknown; + }[]; + direction: ('ltr' | 'rtl') | null; + format: 'left' | 'start' | 'center' | 'right' | 'end' | 'justify' | ''; + indent: number; + version: number; + }; + [k: string]: unknown; + } | null; + content?: { + root: { + type: string; + children: { + type: any; + version: number; + [k: string]: unknown; + }[]; + direction: ('ltr' | 'rtl') | null; + format: 'left' | 'start' | 'center' | 'right' | 'end' | 'justify' | ''; + indent: number; + version: number; + }; + [k: string]: unknown; + } | null; + updatedAt: string; + createdAt: string; + _status?: ('draft' | 'published') | null; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "pages". + */ +export interface Page { + id: number; + title: string; + slug: string; + locale: 'en' | 'de'; + excerpt?: string | null; + featuredImage?: (number | null) | Media; + content: { + root: { + type: string; + children: { + type: any; + version: number; + [k: string]: unknown; + }[]; + direction: ('ltr' | 'rtl') | null; + format: 'left' | 'start' | 'center' | 'right' | 'end' | 'justify' | ''; + indent: number; + version: number; + }; + [k: string]: unknown; + }; + updatedAt: string; + createdAt: string; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-kv". + */ +export interface PayloadKv { + id: number; + key: string; + data: + | { + [k: string]: unknown; + } + | unknown[] + | string + | number + | boolean + | null; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-locked-documents". + */ +export interface PayloadLockedDocument { + id: number; + document?: + | ({ + relationTo: 'users'; + value: number | User; + } | null) + | ({ + relationTo: 'media'; + value: number | Media; + } | null) + | ({ + relationTo: 'posts'; + value: number | Post; + } | null) + | ({ + relationTo: 'form-submissions'; + value: number | FormSubmission; + } | null) + | ({ + relationTo: 'products'; + value: number | Product; + } | null) + | ({ + relationTo: 'pages'; + value: number | Page; + } | null); + globalSlug?: string | null; + user: { + relationTo: 'users'; + value: number | User; + }; + updatedAt: string; + createdAt: string; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-preferences". + */ +export interface PayloadPreference { + id: number; + user: { + relationTo: 'users'; + value: number | User; + }; + key?: string | null; + value?: + | { + [k: string]: unknown; + } + | unknown[] + | string + | number + | boolean + | null; + updatedAt: string; + createdAt: string; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-migrations". + */ +export interface PayloadMigration { + id: number; + name?: string | null; + batch?: number | null; + updatedAt: string; + createdAt: string; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "users_select". + */ +export interface UsersSelect { + updatedAt?: T; + createdAt?: T; + email?: T; + resetPasswordToken?: T; + resetPasswordExpiration?: T; + salt?: T; + hash?: T; + loginAttempts?: T; + lockUntil?: T; + sessions?: + | T + | { + id?: T; + createdAt?: T; + expiresAt?: T; + }; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "media_select". + */ +export interface MediaSelect { + alt?: T; + caption?: T; + updatedAt?: T; + createdAt?: T; + url?: T; + thumbnailURL?: T; + filename?: T; + mimeType?: T; + filesize?: T; + width?: T; + height?: T; + focalX?: T; + focalY?: T; + sizes?: + | T + | { + thumbnail?: + | T + | { + url?: T; + width?: T; + height?: T; + mimeType?: T; + filesize?: T; + filename?: T; + }; + card?: + | T + | { + url?: T; + width?: T; + height?: T; + mimeType?: T; + filesize?: T; + filename?: T; + }; + tablet?: + | T + | { + url?: T; + width?: T; + height?: T; + mimeType?: T; + filesize?: T; + filename?: T; + }; + }; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "posts_select". + */ +export interface PostsSelect { + title?: T; + slug?: T; + excerpt?: T; + date?: T; + featuredImage?: T; + locale?: T; + category?: T; + content?: T; + updatedAt?: T; + createdAt?: T; + _status?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "form-submissions_select". + */ +export interface FormSubmissionsSelect { + name?: T; + email?: T; + type?: T; + productName?: T; + message?: T; + updatedAt?: T; + createdAt?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "products_select". + */ +export interface ProductsSelect { + title?: T; + sku?: T; + slug?: T; + description?: T; + locale?: T; + categories?: + | T + | { + category?: T; + id?: T; + }; + featuredImage?: T; + images?: T; + application?: T; + content?: T; + updatedAt?: T; + createdAt?: T; + _status?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "pages_select". + */ +export interface PagesSelect { + title?: T; + slug?: T; + locale?: T; + excerpt?: T; + featuredImage?: T; + content?: T; + updatedAt?: T; + createdAt?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-kv_select". + */ +export interface PayloadKvSelect { + key?: T; + data?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-locked-documents_select". + */ +export interface PayloadLockedDocumentsSelect { + document?: T; + globalSlug?: T; + user?: T; + updatedAt?: T; + createdAt?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-preferences_select". + */ +export interface PayloadPreferencesSelect { + user?: T; + key?: T; + value?: T; + updatedAt?: T; + createdAt?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "payload-migrations_select". + */ +export interface PayloadMigrationsSelect { + name?: T; + batch?: T; + updatedAt?: T; + createdAt?: T; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "StatsBlock". + */ +export interface StatsBlock { + stats: { + value: string; + label: string; + id?: string | null; + }[]; + id?: string | null; + blockName?: string | null; + blockType: 'stats'; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "SplitHeadingBlock". + */ +export interface SplitHeadingBlock { + title: string; + id?: string | null; + level?: ('h1' | 'h2' | 'h3' | 'h4' | 'h5' | 'h6') | null; + blockName?: string | null; + blockType: 'splitHeading'; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "ProductTabsBlock". + */ +export interface ProductTabsBlock { + technicalItems?: + | { + label: string; + value: string; + unit?: string | null; + id?: string | null; + }[] + | null; + voltageTables?: + | { + voltageLabel: string; + metaItems?: + | { + label: string; + value: string; + unit?: string | null; + id?: string | null; + }[] + | null; + columns?: + | { + key: string; + label: string; + id?: string | null; + }[] + | null; + rows?: + | { + configuration: string; + cells?: + | { + value?: string | null; + id?: string | null; + }[] + | null; + id?: string | null; + }[] + | null; + id?: string | null; + }[] + | null; + id?: string | null; + blockName?: string | null; + blockType: 'productTabs'; +} +/** + * This interface was referenced by `Config`'s JSON-Schema + * via the `definition` "auth". + */ +export interface Auth { + [k: string]: unknown; +} + +declare module 'payload' { + export interface GeneratedTypes extends Config {} +} diff --git a/payload.config.ts b/payload.config.ts new file mode 100644 index 00000000..eba84639 --- /dev/null +++ b/payload.config.ts @@ -0,0 +1,66 @@ +import { buildConfig } from 'payload'; +import { postgresAdapter } from '@payloadcms/db-postgres'; +import { lexicalEditor } from '@payloadcms/richtext-lexical'; +import sharp from 'sharp'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { nodemailerAdapter } from '@payloadcms/email-nodemailer'; +import { BlocksFeature } from '@payloadcms/richtext-lexical'; +import { payloadBlocks } from './src/payload/blocks/allBlocks'; + +// Disable sharp cache to prevent memory leaks in Docker +sharp.cache(false); + +import { Users } from './src/payload/collections/Users'; +import { Media } from './src/payload/collections/Media'; +import { Posts } from './src/payload/collections/Posts'; +import { FormSubmissions } from './src/payload/collections/FormSubmissions'; +import { Products } from './src/payload/collections/Products'; +import { Pages } from './src/payload/collections/Pages'; + +const filename = fileURLToPath(import.meta.url); +const dirname = path.dirname(filename); + +export default buildConfig({ + admin: { + user: Users.slug, + importMap: { + baseDir: path.resolve(dirname), + }, + }, + collections: [Users, Media, Posts, FormSubmissions, Products, Pages], + editor: lexicalEditor({ + features: ({ defaultFeatures }) => [ + ...defaultFeatures, + BlocksFeature({ + blocks: payloadBlocks, + }), + ], + }), + secret: process.env.PAYLOAD_SECRET || 'fallback-secret-for-dev', + typescript: { + outputFile: path.resolve(dirname, 'payload-types.ts'), + }, + db: postgresAdapter({ + pool: { + connectionString: + process.env.DATABASE_URI || + process.env.POSTGRES_URI || + 'postgresql://payload:120in09oenaoinsd9iaidon@localhost:5432/payload', + }, + }), + email: nodemailerAdapter({ + defaultFromAddress: process.env.MAIL_FROM?.replace(/.*<|>.*/g, '') || 'postmaster@mg.mintel.me', + defaultFromName: process.env.MAIL_FROM?.split('<')[0]?.trim() || 'KLZ Cables', + transportOptions: { + host: process.env.MAIL_HOST || 'smtp.eu.mailgun.org', + port: Number(process.env.MAIL_PORT) || 587, + auth: { + user: process.env.MAIL_USERNAME, + pass: process.env.MAIL_PASSWORD, + }, + }, + }), + sharp, + plugins: [], +}); diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index d9b76910..af47eb93 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -12,21 +12,33 @@ importers: .: dependencies: - '@directus/sdk': - specifier: ^21.0.0 - version: 21.1.0 '@mintel/mail': specifier: 1.8.3 version: 1.8.3(react-dom@19.2.4(react@19.2.4))(react@19.2.4) '@mintel/next-config': specifier: 1.8.3 - version: 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3.77.0(@faceless-ui/modal@3.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(@faceless-ui/scroll-info@2.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(@payloadcms/next@3.77.0(@types/react@19.2.13)(graphql@16.12.0)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3))(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3)(yjs@13.6.29) + '@payloadcms/ui': + specifier: ^3.77.0 + version: 3.77.0(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3) '@react-email/components': specifier: ^1.0.7 version: 1.0.7(react-dom@19.2.4(react@19.2.4))(react@19.2.4) @@ -35,7 +47,7 @@ importers: version: 4.3.2(react@19.2.4) '@sentry/nextjs': specifier: ^10.39.0 - version: 10.39.0(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(webpack@5.105.0) + version: 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'@esbuild/win32-arm64@0.25.12': optional: true '@esbuild/win32-arm64@0.27.3': optional: true + '@esbuild/win32-ia32@0.18.20': + optional: true + '@esbuild/win32-ia32@0.25.12': optional: true '@esbuild/win32-ia32@0.27.3': optional: true + '@esbuild/win32-x64@0.18.20': + optional: true + '@esbuild/win32-x64@0.25.12': optional: true @@ -8440,6 +9588,49 @@ snapshots: '@exodus/bytes@1.12.0': {} + '@faceless-ui/modal@3.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + body-scroll-lock: 4.0.0-beta.0 + focus-trap: 7.5.4 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + react-transition-group: 4.4.5(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + + '@faceless-ui/scroll-info@2.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + + '@faceless-ui/window-info@3.0.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + + '@floating-ui/core@1.7.4': + dependencies: + '@floating-ui/utils': 0.2.10 + + '@floating-ui/dom@1.7.5': + dependencies: + '@floating-ui/core': 1.7.4 + '@floating-ui/utils': 0.2.10 + + '@floating-ui/react-dom@2.1.7(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + '@floating-ui/dom': 1.7.5 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + + '@floating-ui/react@0.27.18(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + '@floating-ui/react-dom': 2.1.7(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@floating-ui/utils': 0.2.10 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + tabbable: 6.4.0 + + '@floating-ui/utils@0.2.10': {} + '@formatjs/ecma402-abstract@2.3.6': dependencies: '@formatjs/fast-memoize': 2.2.7 @@ -8659,6 +9850,158 @@ snapshots: '@jridgewell/resolve-uri': 3.1.2 '@jridgewell/sourcemap-codec': 1.5.5 + '@jsdevtools/ono@7.1.3': {} + + '@lexical/clipboard@0.35.0': + dependencies: + '@lexical/html': 0.35.0 + '@lexical/list': 0.35.0 + '@lexical/selection': 0.35.0 + 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'@mintel/next-config@1.8.3(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(@swc/helpers@0.5.18)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3)(typescript@5.9.3)(webpack@5.105.0(esbuild@0.25.12))': dependencies: - '@sentry/nextjs': 10.39.0(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(webpack@5.105.0) + '@sentry/nextjs': 10.39.0(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(webpack@5.105.0(esbuild@0.25.12)) next: 16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3) next-intl: 4.8.2(@swc/helpers@0.5.18)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(typescript@5.9.3) transitivePeerDependencies: @@ -8826,6 +10169,17 @@ snapshots: '@mintel/tsconfig@1.8.3': {} + '@monaco-editor/loader@1.7.0': + dependencies: + state-local: 1.0.7 + + '@monaco-editor/react@4.7.0(monaco-editor@0.55.1)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)': + dependencies: + '@monaco-editor/loader': 1.7.0 + monaco-editor: 0.55.1 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + '@napi-rs/wasm-runtime@0.2.12': dependencies: '@emnapi/core': 1.8.1 @@ -8840,6 +10194,8 @@ snapshots: - bufferutil - utf-8-validate + '@next/env@15.5.12': {} + '@next/env@16.1.6': {} '@next/eslint-plugin-next@16.1.6': @@ -9208,6 +10564,217 @@ snapshots: legacy-javascript: 0.0.1 third-party-web: 0.29.0 + '@payloadcms/db-postgres@3.77.0(@opentelemetry/api@1.9.0)(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))': + dependencies: + '@payloadcms/drizzle': 3.77.0(@opentelemetry/api@1.9.0)(@types/pg@8.10.2)(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(pg@8.16.3) + '@types/pg': 8.10.2 + console-table-printer: 2.12.1 + drizzle-kit: 0.31.7 + drizzle-orm: 0.44.7(@opentelemetry/api@1.9.0)(@types/pg@8.10.2)(pg@8.16.3) + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + pg: 8.16.3 + prompts: 2.4.2 + to-snake-case: 1.0.0 + uuid: 10.0.0 + transitivePeerDependencies: + - '@aws-sdk/client-rds-data' + - '@cloudflare/workers-types' + - '@electric-sql/pglite' + - '@libsql/client' + - '@libsql/client-wasm' + - '@neondatabase/serverless' + - '@op-engineering/op-sqlite' + - '@opentelemetry/api' + - '@planetscale/database' + - '@prisma/client' + - '@tidbcloud/serverless' + - '@types/better-sqlite3' + - '@types/sql.js' + - '@upstash/redis' + - '@vercel/postgres' + - '@xata.io/client' + - better-sqlite3 + - bun-types + - expo-sqlite + - gel + - knex + - kysely + - mysql2 + - pg-native + - postgres + - prisma + - sql.js + - sqlite3 + - supports-color + + '@payloadcms/drizzle@3.77.0(@opentelemetry/api@1.9.0)(@types/pg@8.10.2)(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(pg@8.16.3)': + dependencies: + console-table-printer: 2.12.1 + dequal: 2.0.3 + drizzle-orm: 0.44.7(@opentelemetry/api@1.9.0)(@types/pg@8.10.2)(pg@8.16.3) + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + prompts: 2.4.2 + to-snake-case: 1.0.0 + uuid: 9.0.0 + transitivePeerDependencies: + - '@aws-sdk/client-rds-data' + - '@cloudflare/workers-types' + - '@electric-sql/pglite' + - '@libsql/client' + - '@libsql/client-wasm' + - '@neondatabase/serverless' + - '@op-engineering/op-sqlite' + - '@opentelemetry/api' + - '@planetscale/database' + - '@prisma/client' + - '@tidbcloud/serverless' + - '@types/better-sqlite3' + - '@types/pg' + - '@types/sql.js' + - '@upstash/redis' + - '@vercel/postgres' + - '@xata.io/client' + - better-sqlite3 + - bun-types + - expo-sqlite + - gel + - knex + - kysely + - mysql2 + - pg + - postgres + - prisma + - sql.js + - sqlite3 + + '@payloadcms/email-nodemailer@3.77.0(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))': + dependencies: + nodemailer: 7.0.12 + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + + '@payloadcms/graphql@3.77.0(graphql@16.12.0)(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(typescript@5.9.3)': + dependencies: + graphql: 16.12.0 + graphql-scalars: 1.22.2(graphql@16.12.0) + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + pluralize: 8.0.0 + ts-essentials: 10.0.3(typescript@5.9.3) + tsx: 4.21.0 + transitivePeerDependencies: + - typescript + + '@payloadcms/next@3.77.0(@types/react@19.2.13)(graphql@16.12.0)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3)': + dependencies: + '@dnd-kit/core': 6.3.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@dnd-kit/modifiers': 9.0.0(@dnd-kit/core@6.3.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(react@19.2.4) + '@dnd-kit/sortable': 10.0.0(@dnd-kit/core@6.3.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(react@19.2.4) + '@payloadcms/graphql': 3.77.0(graphql@16.12.0)(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(typescript@5.9.3) + '@payloadcms/translations': 3.77.0 + '@payloadcms/ui': 3.77.0(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3) + busboy: 1.6.0 + dequal: 2.0.3 + file-type: 19.3.0 + graphql: 16.12.0 + graphql-http: 1.22.4(graphql@16.12.0) + graphql-playground-html: 1.6.30 + http-status: 2.1.0 + next: 16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3) + path-to-regexp: 6.3.0 + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + qs-esm: 7.0.2 + sass: 1.77.4 + uuid: 10.0.0 + transitivePeerDependencies: + - '@types/react' + - monaco-editor + - react + - react-dom + - supports-color + - typescript + + '@payloadcms/richtext-lexical@3.77.0(@faceless-ui/modal@3.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(@faceless-ui/scroll-info@2.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(@payloadcms/next@3.77.0(@types/react@19.2.13)(graphql@16.12.0)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3))(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3)(yjs@13.6.29)': + dependencies: + '@faceless-ui/modal': 3.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@faceless-ui/scroll-info': 2.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@lexical/clipboard': 0.35.0 + '@lexical/headless': 0.35.0 + '@lexical/html': 0.35.0 + '@lexical/link': 0.35.0 + '@lexical/list': 0.35.0 + '@lexical/mark': 0.35.0 + '@lexical/react': 0.35.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(yjs@13.6.29) + '@lexical/rich-text': 0.35.0 + '@lexical/selection': 0.35.0 + '@lexical/table': 0.35.0 + '@lexical/utils': 0.35.0 + '@payloadcms/next': 3.77.0(@types/react@19.2.13)(graphql@16.12.0)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3) + '@payloadcms/translations': 3.77.0 + '@payloadcms/ui': 3.77.0(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3) + '@types/uuid': 10.0.0 + acorn: 8.12.1 + bson-objectid: 2.0.4 + csstype: 3.1.3 + dequal: 2.0.3 + escape-html: 1.0.3 + jsox: 1.2.121 + lexical: 0.35.0 + mdast-util-from-markdown: 2.0.2 + mdast-util-mdx-jsx: 3.1.3 + micromark-extension-mdx-jsx: 3.0.1 + payload: 3.77.0(graphql@16.12.0)(typescript@5.9.3) + qs-esm: 7.0.2 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + react-error-boundary: 4.1.2(react@19.2.4) + ts-essentials: 10.0.3(typescript@5.9.3) + uuid: 10.0.0 + transitivePeerDependencies: + - '@types/react' + - monaco-editor + - next + - supports-color + - typescript + - yjs + + '@payloadcms/translations@3.77.0': + dependencies: + date-fns: 4.1.0 + + '@payloadcms/ui@3.77.0(@types/react@19.2.13)(monaco-editor@0.55.1)(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(payload@3.77.0(graphql@16.12.0)(typescript@5.9.3))(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(typescript@5.9.3)': + dependencies: + '@date-fns/tz': 1.2.0 + '@dnd-kit/core': 6.3.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@dnd-kit/sortable': 10.0.0(@dnd-kit/core@6.3.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4))(react@19.2.4) + '@dnd-kit/utilities': 3.2.2(react@19.2.4) + '@faceless-ui/modal': 3.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@faceless-ui/scroll-info': 2.0.0(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@faceless-ui/window-info': 3.0.1(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@monaco-editor/react': 4.7.0(monaco-editor@0.55.1)(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + '@payloadcms/translations': 3.77.0 + bson-objectid: 2.0.4 + date-fns: 4.1.0 + dequal: 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'@sentry/nextjs@10.39.0(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(webpack@5.105.0)': + '@sentry/nextjs@10.39.0(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(next@16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3))(react@19.2.4)(webpack@5.105.0(esbuild@0.25.12))': dependencies: '@opentelemetry/api': 1.9.0 '@opentelemetry/semantic-conventions': 1.39.0 @@ -9922,7 +11489,7 @@ snapshots: '@sentry/opentelemetry': 10.39.0(@opentelemetry/api@1.9.0)(@opentelemetry/context-async-hooks@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.5.1(@opentelemetry/api@1.9.0))(@opentelemetry/semantic-conventions@1.39.0) '@sentry/react': 10.39.0(react@19.2.4) '@sentry/vercel-edge': 10.39.0 - '@sentry/webpack-plugin': 4.9.1(webpack@5.105.0) + '@sentry/webpack-plugin': 4.9.1(webpack@5.105.0(esbuild@0.25.12)) next: 16.1.6(@babel/core@7.29.0)(@opentelemetry/api@1.9.0)(react-dom@19.2.4(react@19.2.4))(react@19.2.4)(sass@1.97.3) rollup: 4.58.0 stacktrace-parser: 0.1.11 @@ -10027,12 +11594,12 @@ snapshots: '@opentelemetry/resources': 2.5.1(@opentelemetry/api@1.9.0) '@sentry/core': 10.39.0 - '@sentry/webpack-plugin@4.9.1(webpack@5.105.0)': + '@sentry/webpack-plugin@4.9.1(webpack@5.105.0(esbuild@0.25.12))': dependencies: '@sentry/bundler-plugin-core': 4.9.1 unplugin: 1.0.1 uuid: 9.0.1 - webpack: 5.105.0 + webpack: 5.105.0(esbuild@0.25.12) transitivePeerDependencies: - encoding - supports-color @@ -10188,6 +11755,8 @@ snapshots: postcss: 8.5.6 tailwindcss: 4.1.18 + '@tokenizer/token@0.3.0': {} + '@tootallnate/quickjs-emscripten@0.23.0': {} '@tybys/wasm-util@0.10.1': @@ -10195,6 +11764,10 @@ snapshots: tslib: 2.8.1 optional: true + '@types/acorn@4.0.6': + dependencies: + '@types/estree': 1.0.8 + '@types/babel__core@7.20.5': dependencies: '@babel/parser': 7.29.0 @@ -10216,6 +11789,10 @@ snapshots: dependencies: '@babel/types': 7.29.0 + '@types/busboy@1.5.4': + dependencies: + '@types/node': 22.19.11 + '@types/chai@5.2.3': dependencies: '@types/deep-eql': 4.0.2 @@ -10294,6 +11871,8 @@ snapshots: dependencies: '@types/geojson': 7946.0.16 + '@types/lodash@4.17.24': {} + '@types/mdast@4.0.4': dependencies: '@types/unist': 3.0.3 @@ -10318,13 +11897,21 @@ snapshots: dependencies: '@types/node': 22.19.10 + '@types/parse-json@4.0.2': {} + '@types/pg-pool@2.0.7': dependencies: '@types/pg': 8.15.6 + '@types/pg@8.10.2': + dependencies: + '@types/node': 22.19.11 + pg-protocol: 1.11.0 + pg-types: 4.1.0 + '@types/pg@8.15.6': dependencies: - '@types/node': 22.19.10 + '@types/node': 22.19.11 pg-protocol: 1.11.0 pg-types: 2.2.0 @@ -10332,6 +11919,10 @@ snapshots: dependencies: '@types/react': 19.2.13 + '@types/react-transition-group@4.4.12(@types/react@19.2.13)': + dependencies: + '@types/react': 19.2.13 + '@types/react@19.2.13': dependencies: csstype: 3.2.3 @@ -10354,12 +11945,17 @@ snapshots: dependencies: '@types/node': 22.19.10 + '@types/trusted-types@2.0.7': + optional: true + '@types/unist@2.0.11': {} '@types/unist@3.0.3': {} '@types/use-sync-external-store@0.0.6': {} + '@types/uuid@10.0.0': {} + '@types/whatwg-mimetype@3.0.2': {} '@types/ws@8.18.1': @@ -10368,7 +11964,7 @@ snapshots: '@types/yauzl@2.10.3': dependencies: - '@types/node': 22.19.10 + '@types/node': 22.19.11 optional: true '@typescript-eslint/eslint-plugin@8.55.0(@typescript-eslint/parser@8.55.0(eslint@9.39.2(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.2(jiti@2.6.1))(typescript@5.9.3)': @@ -10690,10 +12286,16 @@ snapshots: dependencies: acorn: 8.15.0 + acorn-jsx@5.3.2(acorn@8.16.0): + dependencies: + acorn: 8.16.0 + acorn-walk@8.3.5: dependencies: acorn: 8.15.0 + acorn@8.12.1: {} + acorn@8.15.0: {} acorn@8.16.0: {} @@ -10910,6 +12512,12 @@ snapshots: b4a@1.7.3: {} + babel-plugin-macros@3.1.0: + dependencies: + '@babel/runtime': 7.28.6 + cosmiconfig: 7.1.0 + resolve: 1.22.11 + bail@2.0.2: {} balanced-match@4.0.3: {} @@ -10994,6 +12602,8 @@ snapshots: transitivePeerDependencies: - supports-color + body-scroll-lock@4.0.0-beta.0: {} + boolbase@1.0.0: {} brace-expansion@5.0.2: @@ -11020,10 +12630,16 @@ snapshots: node-releases: 2.0.27 update-browserslist-db: 1.2.3(browserslist@4.28.1) + bson-objectid@2.0.4: {} + buffer-crc32@0.2.13: {} buffer-from@1.1.2: {} + busboy@1.6.0: + dependencies: + streamsearch: 1.1.0 + bytes@3.1.2: {} call-bind-apply-helpers@1.0.2: @@ -11080,6 +12696,8 @@ snapshots: chardet@0.7.0: {} + charenc@0.0.2: {} + check-more-types@2.24.0: {} check-types@11.2.3: {} @@ -11165,6 +12783,8 @@ snapshots: mitt: 3.0.1 zod: 3.25.76 + ci-info@4.4.0: {} + citty@0.1.6: dependencies: consola: 3.4.2 @@ -11309,6 +12929,10 @@ snapshots: consola@3.4.2: {} + console-table-printer@2.12.1: + dependencies: + simple-wcswidth: 1.1.2 + content-disposition@0.5.4: dependencies: safe-buffer: 5.2.1 @@ -11327,6 +12951,8 @@ snapshots: dependencies: meow: 13.2.0 + convert-source-map@1.9.0: {} + convert-source-map@2.0.0: {} cookie-signature@1.0.7: {} @@ -11349,6 +12975,14 @@ snapshots: jiti: 2.6.1 typescript: 5.9.3 + cosmiconfig@7.1.0: + dependencies: + '@types/parse-json': 4.0.2 + import-fresh: 3.3.1 + parse-json: 5.2.0 + path-type: 4.0.0 + yaml: 1.10.2 + cosmiconfig@9.0.0(typescript@5.9.3): dependencies: env-paths: 2.2.1 @@ -11368,12 +13002,16 @@ snapshots: postcss: 8.5.6 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'@types/trusted-types': 2.0.7 + domutils@3.2.2: dependencies: dom-serializer: 2.0.0 @@ -11674,6 +13331,21 @@ snapshots: dotenv@16.6.1: {} + drizzle-kit@0.31.7: + dependencies: + '@drizzle-team/brocli': 0.10.2 + '@esbuild-kit/esm-loader': 2.6.5 + esbuild: 0.25.12 + esbuild-register: 3.6.0(esbuild@0.25.12) + transitivePeerDependencies: + - supports-color + + drizzle-orm@0.44.7(@opentelemetry/api@1.9.0)(@types/pg@8.10.2)(pg@8.16.3): + optionalDependencies: + '@opentelemetry/api': 1.9.0 + '@types/pg': 8.10.2 + pg: 8.16.3 + dunder-proto@1.0.1: dependencies: call-bind-apply-helpers: 1.0.2 @@ -11881,10 +13553,42 @@ snapshots: esast-util-from-js@2.0.1: dependencies: '@types/estree-jsx': 1.0.5 - acorn: 8.15.0 + acorn: 8.16.0 esast-util-from-estree: 2.0.0 vfile-message: 4.0.3 + esbuild-register@3.6.0(esbuild@0.25.12): + dependencies: + debug: 4.4.3 + esbuild: 0.25.12 + transitivePeerDependencies: + - supports-color + + esbuild@0.18.20: + optionalDependencies: + '@esbuild/android-arm': 0.18.20 + 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+ file-type@19.3.0: + dependencies: + strtok3: 8.1.0 + token-types: 6.1.2 + uint8array-extras: 1.5.0 + file-url@3.0.0: {} fill-range@7.1.1: @@ -12368,6 +14080,8 @@ snapshots: transitivePeerDependencies: - supports-color + find-root@1.1.0: {} + find-up@4.1.0: dependencies: locate-path: 5.0.0 @@ -12385,6 +14099,10 @@ snapshots: flatted@3.3.3: {} + focus-trap@7.5.4: + dependencies: + tabbable: 6.4.0 + follow-redirects@1.15.11(debug@4.4.3): optionalDependencies: debug: 4.4.3 @@ -12508,6 +14226,10 @@ snapshots: dependencies: resolve-pkg-maps: 1.0.0 + get-tsconfig@4.8.1: + dependencies: + resolve-pkg-maps: 1.0.0 + get-uri@6.0.5: dependencies: basic-ftp: 5.1.0 @@ -12590,6 +14312,21 @@ snapshots: graceful-fs@4.2.11: {} + graphql-http@1.22.4(graphql@16.12.0): + dependencies: + graphql: 16.12.0 + + graphql-playground-html@1.6.30: + dependencies: + xss: 1.0.15 + + graphql-scalars@1.22.2(graphql@16.12.0): + dependencies: + graphql: 16.12.0 + tslib: 2.8.1 + + graphql@16.12.0: {} + 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-13490,6 +15265,12 @@ snapshots: marked: 7.0.4 react: 19.2.4 + md5@2.3.0: + dependencies: + charenc: 0.0.2 + crypt: 0.0.2 + is-buffer: 1.1.6 + mdast-util-from-markdown@2.0.2: dependencies: '@types/mdast': 4.0.4 @@ -13518,6 +15299,23 @@ snapshots: transitivePeerDependencies: - supports-color + mdast-util-mdx-jsx@3.1.3: + dependencies: + '@types/estree-jsx': 1.0.5 + '@types/hast': 3.0.4 + '@types/mdast': 4.0.4 + '@types/unist': 3.0.3 + ccount: 2.0.1 + devlop: 1.1.0 + mdast-util-from-markdown: 2.0.2 + mdast-util-to-markdown: 2.1.2 + parse-entities: 4.0.2 + stringify-entities: 4.0.4 + unist-util-stringify-position: 4.0.0 + vfile-message: 4.0.3 + transitivePeerDependencies: + - supports-color + mdast-util-mdx-jsx@3.2.0: dependencies: '@types/estree-jsx': 1.0.5 @@ -13595,6 +15393,8 @@ snapshots: media-typer@0.3.0: {} + memoize-one@6.0.0: {} + meow@12.1.1: {} meow@13.2.0: {} @@ -13639,6 +15439,20 @@ snapshots: micromark-util-symbol: 2.0.1 micromark-util-types: 2.0.2 + 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react-leaflet@4.2.1(leaflet@1.9.4)(react-dom@19.2.4(react@19.2.4))(react@19.2.4): @@ -14621,6 +16598,32 @@ snapshots: react-refresh@0.18.0: {} + react-select@5.9.0(@types/react@19.2.13)(react-dom@19.2.4(react@19.2.4))(react@19.2.4): + dependencies: + '@babel/runtime': 7.28.6 + '@emotion/cache': 11.14.0 + '@emotion/react': 11.14.0(@types/react@19.2.13)(react@19.2.4) + '@floating-ui/dom': 1.7.5 + '@types/react-transition-group': 4.4.12(@types/react@19.2.13) + memoize-one: 6.0.0 + prop-types: 15.8.1 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + react-transition-group: 4.4.5(react-dom@19.2.4(react@19.2.4))(react@19.2.4) + use-isomorphic-layout-effect: 1.2.1(@types/react@19.2.13)(react@19.2.4) + transitivePeerDependencies: + - '@types/react' + - supports-color + + react-transition-group@4.4.5(react-dom@19.2.4(react@19.2.4))(react@19.2.4): + dependencies: + '@babel/runtime': 7.28.6 + dom-helpers: 5.2.1 + loose-envify: 1.4.0 + prop-types: 15.8.1 + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + react@19.2.4: {} readdirp@3.6.0: @@ -14905,6 +16908,16 @@ snapshots: safer-buffer@2.1.2: {} + sanitize-filename@1.6.3: + dependencies: + truncate-utf8-bytes: 1.0.2 + + sass@1.77.4: + dependencies: + chokidar: 3.6.0 + immutable: 4.3.7 + source-map-js: 1.2.1 + sass@1.97.3: dependencies: chokidar: 4.0.3 @@ -14917,6 +16930,8 @@ snapshots: dependencies: xmlchars: 2.2.0 + scheduler@0.25.0: {} + scheduler@0.25.0-rc-603e6108-20241029: {} scheduler@0.27.0: {} @@ -15079,6 +17094,8 @@ snapshots: dependencies: is-arrayish: 0.3.4 + simple-wcswidth@1.1.2: {} + sirv@2.0.4: dependencies: '@polka/url': 1.0.0-next.29 @@ -15149,6 +17166,11 @@ snapshots: dependencies: atomic-sleep: 1.0.0 + sonner@1.7.4(react-dom@19.2.4(react@19.2.4))(react@19.2.4): + dependencies: + react: 19.2.4 + react-dom: 19.2.4(react@19.2.4) + source-map-js@1.2.1: {} source-map-support@0.5.21: @@ -15156,6 +17178,8 @@ snapshots: buffer-from: 1.1.2 source-map: 0.6.1 + source-map@0.5.7: {} + source-map@0.6.1: {} source-map@0.7.6: {} @@ -15197,6 +17221,8 @@ snapshots: transitivePeerDependencies: - supports-color + state-local@1.0.7: {} + statuses@2.0.2: {} std-env@3.10.0: {} @@ -15212,6 +17238,8 @@ snapshots: dependencies: duplexer: 0.1.2 + streamsearch@1.1.0: {} + streamx@2.23.0: dependencies: events-universal: 1.0.1 @@ -15336,6 +17364,11 @@ snapshots: strip-json-comments@5.0.3: {} + strtok3@8.1.0: + dependencies: + '@tokenizer/token': 0.3.0 + peek-readable: 5.4.2 + stubborn-fs@2.0.0: dependencies: stubborn-utils: 1.0.2 @@ -15357,6 +17390,8 @@ snapshots: optionalDependencies: '@babel/core': 7.29.0 + stylis@4.2.0: {} + supports-color@5.5.0: dependencies: has-flag: 3.0.0 @@ -15379,6 +17414,8 @@ snapshots: symbol-tree@3.2.4: {} + tabbable@6.4.0: {} + tagged-tag@1.0.0: {} tailwind-merge@2.6.1: {} @@ -15410,14 +17447,16 @@ snapshots: - bare-abort-controller - react-native-b4a - terser-webpack-plugin@5.3.16(webpack@5.105.0): + terser-webpack-plugin@5.3.16(esbuild@0.25.12)(webpack@5.105.0(esbuild@0.25.12)): dependencies: '@jridgewell/trace-mapping': 0.3.31 jest-worker: 27.5.1 schema-utils: 4.3.3 serialize-javascript: 6.0.2 terser: 5.46.0 - webpack: 5.105.0 + webpack: 5.105.0(esbuild@0.25.12) + optionalDependencies: + esbuild: 0.25.12 terser@5.46.0: dependencies: @@ -15440,6 +17479,10 @@ snapshots: third-party-web@0.29.0: {} + thread-stream@3.1.0: + dependencies: + real-require: 0.2.0 + thread-stream@4.0.0: dependencies: real-require: 0.2.0 @@ -15481,12 +17524,28 @@ snapshots: dependencies: rimraf: 2.7.1 + to-no-case@1.0.2: {} + to-regex-range@5.0.1: dependencies: is-number: 7.0.0 + to-snake-case@1.0.0: + dependencies: + to-space-case: 1.0.0 + + to-space-case@1.0.0: + dependencies: + to-no-case: 1.0.2 + toidentifier@1.0.1: {} + token-types@6.1.2: + dependencies: + '@borewit/text-codec': 0.2.1 + '@tokenizer/token': 0.3.0 + ieee754: 1.2.1 + totalist@3.0.1: {} tough-cookie@6.0.0: @@ -15505,12 +17564,20 @@ snapshots: trough@2.2.0: {} + truncate-utf8-bytes@1.0.2: + dependencies: + utf8-byte-length: 1.0.5 + tryer@1.0.1: {} ts-api-utils@2.4.0(typescript@5.9.3): dependencies: typescript: 5.9.3 + ts-essentials@10.0.3(typescript@5.9.3): + optionalDependencies: + typescript: 5.9.3 + tsconfig-paths@3.15.0: dependencies: '@types/json5': 0.0.29 @@ -15642,6 +17709,8 @@ snapshots: undici@6.23.0: {} + undici@7.18.2: {} + undici@7.22.0: {} unicode-properties@1.4.1: @@ -15753,6 +17822,11 @@ snapshots: dependencies: punycode: 2.3.1 + use-context-selector@2.0.0(react@19.2.4)(scheduler@0.25.0): + dependencies: + react: 19.2.4 + scheduler: 0.25.0 + use-intl@4.8.2(react@19.2.4): dependencies: '@formatjs/fast-memoize': 3.1.0 @@ -15761,10 +17835,18 @@ snapshots: intl-messageformat: 11.1.2 react: 19.2.4 + use-isomorphic-layout-effect@1.2.1(@types/react@19.2.13)(react@19.2.4): + dependencies: + react: 19.2.4 + optionalDependencies: + '@types/react': 19.2.13 + use-sync-external-store@1.6.0(react@19.2.4): dependencies: react: 19.2.4 + utf8-byte-length@1.0.5: {} + util-deprecate@1.0.2: {} utils-merge@1.0.1: {} @@ -15773,8 +17855,12 @@ snapshots: dependencies: base64-arraybuffer: 1.0.2 + uuid@10.0.0: {} + uuid@8.3.2: {} + uuid@9.0.0: {} + uuid@9.0.1: {} vary@1.1.2: {} @@ -15930,7 +18016,7 @@ snapshots: webpack-virtual-modules@0.5.0: {} - webpack@5.105.0: + webpack@5.105.0(esbuild@0.25.12): dependencies: '@types/eslint-scope': 3.7.7 '@types/estree': 1.0.8 @@ -15954,7 +18040,7 @@ snapshots: neo-async: 2.6.2 schema-utils: 4.3.3 tapable: 2.3.0 - terser-webpack-plugin: 5.3.16(webpack@5.105.0) + terser-webpack-plugin: 5.3.16(esbuild@0.25.12)(webpack@5.105.0(esbuild@0.25.12)) watchpack: 2.5.1 webpack-sources: 3.3.4 transitivePeerDependencies: @@ -16089,6 +18175,11 @@ snapshots: xmlchars@2.2.0: {} + xss@1.0.15: + dependencies: + commander: 2.20.3 + cssfilter: 0.0.10 + xtend@4.0.2: {} y18n@4.0.3: {} @@ -16097,6 +18188,8 @@ snapshots: yallist@3.1.1: {} + yaml@1.10.2: {} + yaml@2.8.2: {} yargs-parser@13.1.2: @@ -16140,6 +18233,10 @@ snapshots: buffer-crc32: 0.2.13 fd-slicer: 1.1.0 + yjs@13.6.29: + dependencies: + lib0: 0.2.117 + yocto-queue@0.1.0: {} yoctocolors@2.1.2: {} diff --git a/scripts/cms-apply.sh b/scripts/cms-apply.sh deleted file mode 100755 index 0d7f5e12..00000000 --- a/scripts/cms-apply.sh +++ /dev/null @@ -1,65 +0,0 @@ -#!/bin/bash - -ENV=$1 -REMOTE_HOST="root@alpha.mintel.me" -REMOTE_DIR="/home/deploy/sites/klz-cables.com" - -if [ -z "$ENV" ]; then - echo "Usage: ./scripts/cms-apply.sh [local|testing|staging|production]" - exit 1 -fi - -PRJ_ID=$(jq -r .name package.json | sed 's/@mintel\///' | sed 's/\.com$//' | sed 's/-nextjs$//' | sed 's/-cables$//') - -case $ENV in - local) - CONTAINER=$(docker compose ps -q klz-cms) - if [ -z "$CONTAINER" ]; then - echo "❌ Local directus container not found." - exit 1 - fi - echo "🚀 Applying schema locally..." - docker exec "$CONTAINER" npx directus schema apply /directus/schema/snapshot.yaml --yes - ;; - testing|staging|production) - case $ENV in - testing) PROJECT_NAME="${PRJ_ID}-testing" ;; - staging) PROJECT_NAME="${PRJ_ID}-staging" ;; - production) - PROJECT_NAME="${PRJ_ID}-production" - OLD_PROJECT_NAME="${PRJ_ID}-prod" # Fallback for previous convention - ;; - esac - - echo "📤 Uploading snapshot to $ENV..." - scp ./directus/schema/snapshot.yaml "$REMOTE_HOST:$REMOTE_DIR/directus/schema/snapshot.yaml" - - echo "🔍 Detecting remote container..." - REMOTE_CONTAINER=$(ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $PROJECT_NAME ps -q directus") - - if [ -z "$REMOTE_CONTAINER" ] && [ -n "$OLD_PROJECT_NAME" ]; then - echo "⚠️ $PROJECT_NAME not found, trying fallback $OLD_PROJECT_NAME..." - REMOTE_CONTAINER=$(ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $OLD_PROJECT_NAME ps -q directus") - if [ -n "$REMOTE_CONTAINER" ]; then - PROJECT_NAME=$OLD_PROJECT_NAME - fi - fi - - if [ -z "$REMOTE_CONTAINER" ]; then - echo "❌ Remote container for $ENV not found (checked $PROJECT_NAME)." - exit 1 - fi - - echo "🚀 Applying schema to $ENV..." - ssh "$REMOTE_HOST" "docker exec $REMOTE_CONTAINER npx directus schema apply /directus/schema/snapshot.yaml --yes" - - echo "🔄 Restarting Directus to clear cache..." - ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $PROJECT_NAME restart directus" - ;; - *) - echo "❌ Invalid environment." - exit 1 - ;; -esac - -echo "✨ Schema apply complete!" diff --git a/scripts/cms-snapshot.sh b/scripts/cms-snapshot.sh deleted file mode 100755 index e3d45adf..00000000 --- a/scripts/cms-snapshot.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Detect local container -LOCAL_CONTAINER=$(docker compose ps -q directus) - -if [ -z "$LOCAL_CONTAINER" ]; then - echo "❌ Local directus container not found. Is it running?" - exit 1 -fi - -echo "📸 Creating schema snapshot..." -# Note: we save it to the mounted volume path inside the container -docker exec "$LOCAL_CONTAINER" npx directus schema snapshot /directus/schema/snapshot.yaml - -echo "✅ Snapshot saved to ./directus/schema/snapshot.yaml" diff --git a/scripts/migrate-mdx.ts b/scripts/migrate-mdx.ts new file mode 100644 index 00000000..961c9cc6 --- /dev/null +++ b/scripts/migrate-mdx.ts @@ -0,0 +1,140 @@ +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; +import fs from 'fs'; +import path from 'path'; +import matter from 'gray-matter'; +import { parseMarkdownToLexical } from '../src/payload/utils/lexicalParser'; + +async function mapImageToMediaId(payload: any, imagePath: string): Promise { + if (!imagePath) return null; + const filename = path.basename(imagePath); + + const media = await payload.find({ + collection: 'media', + where: { + filename: { + equals: filename, + }, + }, + limit: 1, + }); + + if (media.docs.length > 0) { + return media.docs[0].id; + } + + // Auto-ingest missing images from legacy public/ directory + const cleanPath = imagePath.startsWith('/') ? imagePath.slice(1) : imagePath; + const fullPath = path.join(process.cwd(), 'public', cleanPath); + + if (fs.existsSync(fullPath)) { + try { + console.log(`[Blog Migration] 📤 Ingesting missing Media into Payload: ${filename}`); + const newMedia = await payload.create({ + collection: 'media', + data: { + alt: filename.replace(/[-_]/g, ' ').replace(/\.[^/.]+$/, ''), // create a human readable alt text + }, + filePath: fullPath, + }); + return newMedia.id; + } catch (err: any) { + console.error(`[Blog Migration] ❌ Failed to ingest ${filename}:`, err); + } + } else { + console.warn(`[Blog Migration] ⚠️ Missing image entirely from disk: ${fullPath}`); + } + return null; +} + +async function migrateBlogPosts() { + const payload = await getPayload({ config: configPromise }); + + const locales = ['en', 'de']; + for (const locale of locales) { + const postsDir = path.join(process.cwd(), 'data', 'blog', locale); + if (!fs.existsSync(postsDir)) continue; + + const files = fs.readdirSync(postsDir); + for (const file of files) { + if (!file.endsWith('.mdx')) continue; + + const slug = file.replace(/\.mdx$/, ''); + const filePath = path.join(postsDir, file); + const fileContent = fs.readFileSync(filePath, 'utf8'); + const { data, content } = matter(fileContent); + + console.log(`Migrating ${locale}/${slug}...`); + + const lexicalBlocks = parseMarkdownToLexical(content); + const lexicalAST = { + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + children: lexicalBlocks, + direction: 'ltr', + }, + }; + + const publishDate = data.date ? new Date(data.date).toISOString() : new Date().toISOString(); + const status = data.public === false ? 'draft' : 'published'; + + let featuredImageId = null; + if (data.featuredImage || data.image) { + featuredImageId = await mapImageToMediaId(payload, data.featuredImage || data.image); + } + + try { + // Find existing post + const existing = await payload.find({ + collection: 'posts', + where: { slug: { equals: slug }, locale: { equals: locale } as any }, + }); + + if (slug.includes('welcome-to-the-future')) { + console.log(`\n--- AST for ${slug} ---`); + console.log(JSON.stringify(lexicalAST, null, 2)); + console.log(`-----------------------\n`); + } + + if (existing.docs.length > 0) { + await payload.update({ + collection: 'posts', + id: existing.docs[0].id, + data: { + content: lexicalAST as any, + _status: status as any, + ...(featuredImageId ? { featuredImage: featuredImageId } : {}), + }, + }); + console.log(`✅ AST Components & Image RE-INJECTED for ${slug}`); + } else { + await payload.create({ + collection: 'posts', + data: { + title: data.title, + slug: slug, + locale: locale, + date: publishDate, + category: data.category || '', + excerpt: data.excerpt || '', + content: lexicalAST as any, + _status: status as any, + ...(featuredImageId ? { featuredImage: featuredImageId } : {}), + }, + }); + console.log(`✅ Created ${slug}`); + } + } catch (err: any) { + console.error(`❌ Failed ${slug}`, err.message); + } + } + } + + console.log('Migration completed.'); + process.exit(0); +} + +migrateBlogPosts().catch(console.error); diff --git a/scripts/migrate-pages.ts b/scripts/migrate-pages.ts new file mode 100644 index 00000000..c6ed8e7f --- /dev/null +++ b/scripts/migrate-pages.ts @@ -0,0 +1,135 @@ +import { getPayload } from 'payload'; +import configPromise from '@payload-config'; +import fs from 'fs'; +import path from 'path'; +import matter from 'gray-matter'; +import { parseMarkdownToLexical } from '../src/payload/utils/lexicalParser'; + +async function mapImageToMediaId(payload: any, imagePath: string): Promise { + if (!imagePath) return null; + const filename = path.basename(imagePath); + + const media = await payload.find({ + collection: 'media', + where: { + filename: { + equals: filename, + }, + }, + limit: 1, + }); + + if (media.docs.length > 0) { + return media.docs[0].id; + } + + // Auto-ingest missing images from legacy public/ directory + const cleanPath = imagePath.startsWith('/') ? imagePath.slice(1) : imagePath; + const fullPath = path.join(process.cwd(), 'public', cleanPath); + + if (fs.existsSync(fullPath)) { + try { + console.log(`[Blog Migration] 📤 Ingesting missing Media into Payload: ${filename}`); + const newMedia = await payload.create({ + collection: 'media', + data: { + alt: filename.replace(/[-_]/g, ' ').replace(/\.[^/.]+$/, ''), // create a human readable alt text + }, + filePath: fullPath, + }); + return newMedia.id; + } catch (err: any) { + console.error(`[Blog Migration] ❌ Failed to ingest ${filename}:`, err); + } + } else { + console.warn(`[Blog Migration] ⚠️ Missing image entirely from disk: ${fullPath}`); + } + return null; +} + +async function migrateBlogPages() { + const payload = await getPayload({ config: configPromise }); + + const locales = ['en', 'de']; + for (const locale of locales) { + const pagesDir = path.join(process.cwd(), 'data', 'pages', locale); + if (!fs.existsSync(pagesDir)) continue; + + const files = fs.readdirSync(pagesDir); + for (const file of files) { + if (!file.endsWith('.mdx')) continue; + + const slug = file.replace(/\.mdx$/, ''); + const filePath = path.join(pagesDir, file); + const fileContent = fs.readFileSync(filePath, 'utf8'); + const { data, content } = matter(fileContent); + + console.log(`Migrating ${locale}/${slug}...`); + + const lexicalBlocks = parseMarkdownToLexical(content); + const lexicalAST = { + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + children: lexicalBlocks, + direction: 'ltr', + }, + }; + + const status = data.public === false ? 'draft' : 'published'; + + let featuredImageId = null; + if (data.featuredImage || data.image) { + featuredImageId = await mapImageToMediaId(payload, data.featuredImage || data.image); + } + + try { + // Find existing post + const existing = await payload.find({ + collection: 'pages', + where: { slug: { equals: slug }, locale: { equals: locale } as any }, + }); + + if (slug.includes('welcome-to-the-future')) { + console.log(`\n--- AST for ${slug} ---`); + console.log(JSON.stringify(lexicalAST, null, 2)); + console.log(`-----------------------\n`); + } + + if (existing.docs.length > 0) { + await payload.update({ + collection: 'pages', + id: existing.docs[0].id, + data: { + content: lexicalAST as any, + ...(featuredImageId ? { featuredImage: featuredImageId } : {}), + }, + }); + console.log(`✅ AST Components & Image RE-INJECTED for ${slug}`); + } else { + await payload.create({ + collection: 'pages', + data: { + title: data.title, + slug: slug, + locale: locale, + excerpt: data.excerpt || '', + content: lexicalAST as any, + ...(featuredImageId ? { featuredImage: featuredImageId } : {}), + }, + }); + console.log(`✅ Created ${slug}`); + } + } catch (err: any) { + console.error(`❌ Failed ${slug}`, err.message); + } + } + } + + console.log('Migration completed.'); + process.exit(0); +} + +migrateBlogPages().catch(console.error); diff --git a/scripts/migrate-products.ts b/scripts/migrate-products.ts new file mode 100644 index 00000000..9c064b59 --- /dev/null +++ b/scripts/migrate-products.ts @@ -0,0 +1,153 @@ +import { getPayload } from 'payload'; +import configPromise from '../payload.config'; +import fs from 'fs'; +import path from 'path'; +import matter from 'gray-matter'; +import { parseMarkdownToLexical } from '../src/payload/utils/lexicalParser'; + +async function mapImageToMediaId(payload: any, imagePath: string): Promise { + if (!imagePath) return null; + const filename = path.basename(imagePath); + + // Exact match instead of substring to avoid matching "cable-black.jpg" with "cable.jpg" + const media = await payload.find({ + collection: 'media', + where: { + filename: { + equals: filename, + }, + }, + limit: 1, + }); + + if (media.docs.length > 0) { + return media.docs[0].id; + } + + const cleanPath = imagePath.startsWith('/') ? imagePath.slice(1) : imagePath; + const fullPath = path.join(process.cwd(), 'public', cleanPath); + + if (fs.existsSync(fullPath)) { + try { + console.log(`[Products Migration] 📤 Ingesting missing Media into Payload: ${filename}`); + const newMedia = await payload.create({ + collection: 'media', + data: { + alt: filename.replace(/[-_]/g, ' ').replace(/\.[^/.]+$/, ''), + }, + filePath: fullPath, + }); + return newMedia.id; + } catch (err: any) { + console.error(`[Products Migration] ❌ Failed to ingest ${filename}:`, err); + } + } else { + console.warn(`[Products Migration] ⚠️ Missing image entirely from disk: ${fullPath}`); + } + return null; +} + +export async function migrateProducts() { + const payload = await getPayload({ config: configPromise }); + const productLocales = ['en', 'de']; + + for (const locale of productLocales) { + const productsDir = path.join(process.cwd(), 'data', 'products', locale); + if (!fs.existsSync(productsDir)) continue; + + // Recursive file finder + const mdFiles: string[] = []; + const walk = (dir: string) => { + const files = fs.readdirSync(dir); + for (const file of files) { + const fullPath = path.join(dir, file); + const stat = fs.statSync(fullPath); + if (stat.isDirectory()) { + walk(fullPath); + } else if (file.endsWith('.mdx')) { + mdFiles.push(fullPath); + } + } + }; + walk(productsDir); + + for (const filePath of mdFiles) { + const fileContent = fs.readFileSync(filePath, 'utf8'); + const { data, content } = matter(fileContent); + + console.log(`Processing Product: [${locale.toUpperCase()}] ${data.title}`); + + // 1. Process Images + const mediaIds = []; + if (data.images && Array.isArray(data.images)) { + for (const imgPath of data.images) { + const id = await mapImageToMediaId(payload, imgPath); + if (id) mediaIds.push(id); + } + } + + // 2. Map Lexical AST for deeply nested components (like ProductTabs + Technical data) + const lexicalContent = parseMarkdownToLexical(content); + + const wrapLexical = (blocks: any[]) => ({ + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + children: blocks, + direction: 'ltr', + }, + }); + + // Payload expects category objects via the 'category' key + const formattedCategories = Array.isArray(data.categories) + ? data.categories.map((c: string) => ({ category: c })) + : []; + + const productData = { + title: data.title, + sku: data.sku || path.basename(filePath, '.mdx'), + slug: path.basename(filePath, '.mdx'), + locale: locale as 'en' | 'de', + categories: formattedCategories, + description: data.description || '', + featuredImage: mediaIds.length > 0 ? mediaIds[0] : undefined, + images: mediaIds.length > 0 ? mediaIds : undefined, + content: wrapLexical(lexicalContent) as any, + application: data.application + ? (wrapLexical(parseMarkdownToLexical(data.application)) as any) + : undefined, + _status: 'published' as any, + }; + + // Check if product exists (by sku combined with locale, since slug may differ by language) + const existing = await payload.find({ + collection: 'products', + where: { + and: [{ slug: { equals: productData.slug } }, { locale: { equals: locale } }], + }, + }); + + if (existing.docs.length > 0) { + console.log(`Updating existing product ${productData.slug} (${locale})`); + await payload.update({ + collection: 'products', + id: existing.docs[0].id, + data: productData, + }); + } else { + console.log(`Creating new product ${productData.slug} (${locale})`); + await payload.create({ + collection: 'products', + data: productData, + }); + } + } + } + + console.log(`\n✅ Products Migration Complete!`); + process.exit(0); +} + +migrateProducts().catch(console.error); diff --git a/scripts/setup-directus-branding.ts b/scripts/setup-directus-branding.ts deleted file mode 100644 index 68385b24..00000000 --- a/scripts/setup-directus-branding.ts +++ /dev/null @@ -1,217 +0,0 @@ -import client, { ensureAuthenticated } from '../lib/directus'; -import { updateSettings, uploadFiles } from '@directus/sdk'; -import fs from 'fs'; -import path from 'path'; -import { fileURLToPath } from 'url'; - -// Helper for ESM __dirname -const __filename = fileURLToPath(import.meta.url); -const __dirname = path.dirname(__filename); - -async function setupBranding() { - console.log('🎨 Refining Directus Branding for Premium Website Look...'); - - // 1. Authenticate - await ensureAuthenticated(); - - try { - // 2. Upload Assets (MIME FIXED) - console.log('📤 Re-uploading assets for clean IDs...'); - - const getMimeType = (filePath: string) => { - const ext = path.extname(filePath).toLowerCase(); - switch (ext) { - case '.svg': - return 'image/svg+xml'; - case '.png': - return 'image/png'; - case '.jpg': - case '.jpeg': - return 'image/jpeg'; - case '.ico': - return 'image/x-icon'; - default: - return 'application/octet-stream'; - } - }; - - const uploadAsset = async (filePath: string, title: string) => { - if (!fs.existsSync(filePath)) { - console.warn(`⚠️ File not found: ${filePath}`); - return null; - } - const mimeType = getMimeType(filePath); - const form = new FormData(); - const fileBuffer = fs.readFileSync(filePath); - const blob = new Blob([fileBuffer], { type: mimeType }); - form.append('file', blob, path.basename(filePath)); - form.append('title', title); - const res = await client.request(uploadFiles(form)); - return res.id; - }; - - const logoWhiteId = await uploadAsset( - path.resolve(__dirname, '../public/logo-white.svg'), - 'Logo White', - ); - const logoBlueId = await uploadAsset( - path.resolve(__dirname, '../public/logo-blue.svg'), - 'Logo Blue', - ); - const faviconId = await uploadAsset( - path.resolve(__dirname, '../public/favicon.ico'), - 'Favicon', - ); - - // Smoother Background SVG - const bgSvgPath = path.resolve(__dirname, '../public/login-bg.svg'); - fs.writeFileSync( - bgSvgPath, - ` - - - - - - - - -`, - ); - const backgroundId = await uploadAsset(bgSvgPath, 'Login Bg'); - if (fs.existsSync(bgSvgPath)) fs.unlinkSync(bgSvgPath); - - // 3. Update Settings with "Premium Web" Theme - console.log('⚙️ Updating Directus settings...'); - - const COLOR_PRIMARY = '#001a4d'; // Deep Blue - const COLOR_ACCENT = '#82ed20'; // Sustainability Green - const COLOR_SECONDARY = '#003d82'; - - const customCss = ` - @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); - - /* Global Login Styles */ - body, .v-app { - font-family: 'Inter', sans-serif !important; - -webkit-font-smoothing: antialiased; - } - - /* Glassmorphism Effect for Login Card */ - .public-view .v-card { - background: rgba(255, 255, 255, 0.95) !important; - backdrop-filter: blur(20px); - border: 1px solid rgba(255, 255, 255, 0.3) !important; - border-radius: 32px !important; - box-shadow: 0 50px 100px -20px rgba(0, 0, 0, 0.4) !important; - padding: 40px !important; - } - - .public-view .v-button { - border-radius: 9999px !important; - height: 56px !important; - font-weight: 600 !important; - letter-spacing: -0.01em !important; - transition: all 0.4s cubic-bezier(0.16, 1, 0.3, 1) !important; - } - - .public-view .v-button:hover { - transform: translateY(-2px); - box-shadow: 0 15px 30px rgba(130, 237, 32, 0.2) !important; - } - - .public-view .v-input { - --v-input-border-radius: 12px !important; - --v-input-background-color: #f8f9fa !important; - } - - /* Inject Headline via CSS to avoid raw HTML display in public_note */ - .public-view .form::before { - content: 'Sustainable Energy. Industrial Reliability.'; - display: block; - text-align: center; - font-size: 18px; - font-weight: 700; - color: #ffffff; - margin-bottom: 8px; - } - - .public-view .form::after { - content: 'KLZ INFRASTRUCTURE ENGINE'; - display: block; - text-align: center; - font-size: 11px; - font-weight: 600; - letter-spacing: 0.1em; - color: rgba(255, 255, 255, 0.5); - margin-top: 24px; - } - `; - - const publicNote = ''; - - await client.request( - updateSettings({ - project_name: 'KLZ Cables', - project_url: 'https://klz-cables.com', - project_color: COLOR_ACCENT, - project_descriptor: 'Sustainable Energy Infrastructure', - - // FIXED: Use WHITE logo for the Blue Sidebar - project_logo: logoWhiteId as any, - - public_foreground: logoWhiteId as any, - public_background: backgroundId as any, - public_note: publicNote, - public_favicon: faviconId as any, - custom_css: customCss, - - // DEEP PREMIUM THEME - theme_light_overrides: { - // Brands - primary: COLOR_ACCENT, // Buttons/Actions are GREEN like the website - secondary: COLOR_SECONDARY, - - // Content Area - background: '#f1f3f7', - backgroundNormal: '#ffffff', - backgroundAccent: '#eef2ff', - - // Sidebar Branding - navigationBackground: COLOR_PRIMARY, - navigationForeground: '#ffffff', - navigationBackgroundHover: 'rgba(255,255,255,0.05)', - navigationForegroundHover: '#ffffff', - navigationBackgroundActive: 'rgba(130, 237, 32, 0.15)', // Subtle Green highlight - navigationForegroundActive: COLOR_ACCENT, // Active item is GREEN - - // Module Bar (Thin far left) - moduleBarBackground: '#000d26', - moduleBarForeground: '#ffffff', - moduleBarForegroundActive: COLOR_ACCENT, - - // UI Standards - borderRadius: '16px', // Larger radius for modern feel - borderWidth: '1px', - borderColor: '#e2e8f0', - formFieldHeight: '48px', // Touch-target height - } as any, - - theme_dark_overrides: { - primary: COLOR_ACCENT, - background: '#0a0a0a', - navigationBackground: '#000000', - moduleBarBackground: COLOR_PRIMARY, - borderRadius: '16px', - formFieldHeight: '48px', - } as any, - }), - ); - - console.log('✨ Premium Theme applied successfully!'); - } catch (error: any) { - console.error('❌ Error:', JSON.stringify(error, null, 2)); - } -} - -setupBranding(); diff --git a/scripts/sync-directus.sh b/scripts/sync-directus.sh deleted file mode 100755 index 946455fd..00000000 --- a/scripts/sync-directus.sh +++ /dev/null @@ -1,132 +0,0 @@ -#!/bin/bash - -# Configuration -REMOTE_HOST="root@alpha.mintel.me" -REMOTE_DIR="/home/deploy/sites/klz-cables.com" - -# DB Details (matching docker-compose defaults) -LOCAL_DB_USER="klz_db_user" -REMOTE_DB_USER="directus" -DB_NAME="directus" - -ACTION=$1 -ENV=$2 - -# Help -if [ -z "$ACTION" ] || [ -z "$ENV" ]; then - echo "Usage: ./scripts/sync-directus.sh [push|pull] [testing|staging|production]" - echo "" - echo "Commands:" - echo " push Sync LOCAL data -> REMOTE" - echo " pull Sync REMOTE data -> LOCAL" - echo "" - echo "Environments:" - echo " testing, staging, production" - exit 1 -fi - -# Map Environment to Project Name -case $ENV in - production|staging|testing) - PROJECT_NAME="klz-cablescom" - ENV_FILE=".env" - ;; - *) - echo "❌ Invalid environment: $ENV. Use testing, staging, or production." - exit 1 - ;; -esac - -# Detect local container -echo "🔍 Detecting local database..." -# Use a more robust way to find the container if multiple projects exist locally -LOCAL_DB_CONTAINER=$(docker compose ps -q klz-db) -if [ -z "$LOCAL_DB_CONTAINER" ]; then - echo "❌ Local klz-directus-db container not found. Is it running? (npm run dev)" - exit 1 -fi - -if [ "$ACTION" == "push" ]; then - echo "🚀 Pushing Local Data to $ENV ($PROJECT_NAME)..." - - # 1. DB Dump - echo "📦 Dumping local database..." - # Note: we use --no-owner --no-privileges to ensure restore works on remote with different user setup - docker exec "$LOCAL_DB_CONTAINER" pg_dump -U "$DB_USER" --clean --if-exists --no-owner --no-privileges "$DB_NAME" > dump.sql - - # 2. Upload Dump - echo "📤 Uploading dump to remote server..." - scp dump.sql "$REMOTE_HOST:$REMOTE_DIR/dump.sql" - - # 3. Restore on Remote - echo "🔄 Restoring dump on $ENV..." - REMOTE_DB_CONTAINER=$(ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $PROJECT_NAME ps -q directus-db") - if [ -z "$REMOTE_DB_CONTAINER" ] && [ -n "$OLD_PROJECT_NAME" ]; then - echo "⚠️ $PROJECT_NAME not found, trying fallback $OLD_PROJECT_NAME..." - REMOTE_DB_CONTAINER=$(ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $OLD_PROJECT_NAME ps -q directus-db") - fi - - if [ -z "$REMOTE_DB_CONTAINER" ]; then - echo "❌ Remote $ENV-db container not found!" - exit 1 - fi - - # Wipe remote DB clean before restore to avoid constraint errors - echo "🧹 Wiping remote database schema..." - ssh "$REMOTE_HOST" "docker exec $REMOTE_DB_CONTAINER psql -U $REMOTE_DB_USER $DB_NAME -c 'DROP SCHEMA public CASCADE; CREATE SCHEMA public;'" - - echo "⚡ Restoring database..." - ssh "$REMOTE_HOST" "docker exec -i $REMOTE_DB_CONTAINER psql -U $REMOTE_DB_USER $DB_NAME < $REMOTE_DIR/dump.sql" - - # 4. Sync Uploads - echo "📁 Syncing uploads (Local -> $ENV)..." - # Note: If environments share the same directory, this might overwrite others' files if not careful. - # But since they share the same host directory currently, rsync will update the shared folder. - rsync -avz --progress ./directus/uploads/ "$REMOTE_HOST:$REMOTE_DIR/directus/uploads/" - - # Clean up - rm dump.sql - ssh "$REMOTE_HOST" "rm $REMOTE_DIR/dump.sql" - - # 5. Restart Directus to trigger migrations and refresh schema cache - echo "🔄 Restarting remote Directus to apply migrations..." - ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $PROJECT_NAME restart directus" - - echo "✨ Push to $ENV complete!" - -elif [ "$ACTION" == "pull" ]; then - echo "📥 Pulling $ENV Data to Local..." - - # The remote service name is 'klz-db' according to docker compose config - REMOTE_DB_CONTAINER=$(ssh "$REMOTE_HOST" "cd $REMOTE_DIR && docker compose -p $PROJECT_NAME ps -q klz-db") - - if [ -z "$REMOTE_DB_CONTAINER" ]; then - echo "❌ Remote $ENV-db container not found!" - exit 1 - fi - ssh "$REMOTE_HOST" "docker exec $REMOTE_DB_CONTAINER pg_dump -U $REMOTE_DB_USER --clean --if-exists --no-owner --no-privileges $DB_NAME > $REMOTE_DIR/dump.sql" - - # 2. Download Dump - echo "📥 Downloading dump..." - scp "$REMOTE_HOST:$REMOTE_DIR/dump.sql" dump.sql - - # Wipe local DB clean before restore to avoid constraint errors - echo "🧹 Wiping local database schema..." - docker exec "$LOCAL_DB_CONTAINER" psql -U "$LOCAL_DB_USER" "$DB_NAME" -c 'DROP SCHEMA public CASCADE; CREATE SCHEMA public;' - - echo "⚡ Restoring database locally..." - docker exec -i "$LOCAL_DB_CONTAINER" psql -U "$LOCAL_DB_USER" "$DB_NAME" < dump.sql - - # 4. Sync Uploads - echo "📁 Syncing uploads ($ENV -> Local)..." - rsync -avz --progress "$REMOTE_HOST:$REMOTE_DIR/directus/uploads/" ./directus/uploads/ - - # Clean up - rm dump.sql - ssh "$REMOTE_HOST" "rm $REMOTE_DIR/dump.sql" - - echo "✨ Pull to Local complete!" -else - echo "Invalid action: $ACTION. Use push or pull." - exit 1 -fi diff --git a/src/migrations/20260223_195005_products_collection.json b/src/migrations/20260223_195005_products_collection.json new file mode 100644 index 00000000..262be6d1 --- /dev/null +++ b/src/migrations/20260223_195005_products_collection.json @@ -0,0 +1,2621 @@ +{ + "version": "7", + "dialect": "postgresql", + "tables": { + "public.users_sessions": { + "name": "users_sessions", + "schema": "", + "columns": { + "_order": { + "name": "_order", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "_parent_id": { + "name": "_parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "id": { + "name": "id", + "type": "varchar", + "primaryKey": true, + "notNull": true + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": false + }, + "expires_at": { + "name": "expires_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true + } + }, + "indexes": { + "users_sessions_order_idx": { + "name": "users_sessions_order_idx", + "columns": [ + { + "expression": "_order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "users_sessions_parent_id_idx": { + "name": "users_sessions_parent_id_idx", + "columns": [ + { + "expression": "_parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "users_sessions_parent_id_fk": { + "name": "users_sessions_parent_id_fk", + "tableFrom": "users_sessions", + "tableTo": "users", + "columnsFrom": ["_parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.users": { + "name": "users", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "email": { + "name": "email", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "reset_password_token": { + "name": "reset_password_token", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "reset_password_expiration": { + "name": "reset_password_expiration", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": false + }, + "salt": { + "name": "salt", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "hash": { + "name": "hash", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "login_attempts": { + "name": "login_attempts", + "type": "numeric", + "primaryKey": false, + "notNull": false, + "default": 0 + }, + "lock_until": { + "name": "lock_until", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "users_updated_at_idx": { + "name": "users_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "users_created_at_idx": { + "name": "users_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "users_email_idx": { + "name": "users_email_idx", + "columns": [ + { + "expression": "email", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.media": { + "name": "media", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "alt": { + "name": "alt", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "caption": { + "name": "caption", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "url": { + "name": "url", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "thumbnail_u_r_l": { + "name": "thumbnail_u_r_l", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "filename": { + "name": "filename", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "mime_type": { + "name": "mime_type", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "filesize": { + "name": "filesize", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "width": { + "name": "width", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "height": { + "name": "height", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "focal_x": { + "name": "focal_x", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "focal_y": { + "name": "focal_y", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_url": { + "name": "sizes_thumbnail_url", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_width": { + "name": "sizes_thumbnail_width", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_height": { + "name": "sizes_thumbnail_height", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_mime_type": { + "name": "sizes_thumbnail_mime_type", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_filesize": { + "name": "sizes_thumbnail_filesize", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_thumbnail_filename": { + "name": "sizes_thumbnail_filename", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_card_url": { + "name": "sizes_card_url", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_card_width": { + "name": "sizes_card_width", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_card_height": { + "name": "sizes_card_height", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_card_mime_type": { + "name": "sizes_card_mime_type", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_card_filesize": { + "name": "sizes_card_filesize", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_card_filename": { + "name": "sizes_card_filename", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_url": { + "name": "sizes_hero_url", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_width": { + "name": "sizes_hero_width", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_height": { + "name": "sizes_hero_height", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mime_type": { + "name": "sizes_hero_mime_type", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_filesize": { + "name": "sizes_hero_filesize", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_filename": { + "name": "sizes_hero_filename", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_url": { + "name": "sizes_hero_mobile_url", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_width": { + "name": "sizes_hero_mobile_width", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_height": { + "name": "sizes_hero_mobile_height", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_mime_type": { + "name": "sizes_hero_mobile_mime_type", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_filesize": { + "name": "sizes_hero_mobile_filesize", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "sizes_hero_mobile_filename": { + "name": "sizes_hero_mobile_filename", + "type": "varchar", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "media_updated_at_idx": { + "name": "media_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_created_at_idx": { + "name": "media_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_filename_idx": { + "name": "media_filename_idx", + "columns": [ + { + "expression": "filename", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_sizes_thumbnail_sizes_thumbnail_filename_idx": { + "name": "media_sizes_thumbnail_sizes_thumbnail_filename_idx", + "columns": [ + { + "expression": "sizes_thumbnail_filename", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_sizes_card_sizes_card_filename_idx": { + "name": "media_sizes_card_sizes_card_filename_idx", + "columns": [ + { + "expression": "sizes_card_filename", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_sizes_hero_sizes_hero_filename_idx": { + "name": "media_sizes_hero_sizes_hero_filename_idx", + "columns": [ + { + "expression": "sizes_hero_filename", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "media_sizes_hero_mobile_sizes_hero_mobile_filename_idx": { + "name": "media_sizes_hero_mobile_sizes_hero_mobile_filename_idx", + "columns": [ + { + "expression": "sizes_hero_mobile_filename", + 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+ "name": "_products_v_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "_products_v_latest_idx": { + "name": "_products_v_latest_idx", + "columns": [ + { + "expression": "latest", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "_products_v_parent_id_products_id_fk": { + "name": "_products_v_parent_id_products_id_fk", + "tableFrom": "_products_v", + "tableTo": "products", + "columnsFrom": ["parent_id"], + "columnsTo": ["id"], + "onDelete": "set null", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public._products_v_rels": { + "name": "_products_v_rels", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "order": { + "name": "order", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "parent_id": { + "name": "parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "path": { + "name": "path", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "media_id": { + "name": "media_id", + "type": "integer", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "_products_v_rels_order_idx": { + "name": "_products_v_rels_order_idx", + "columns": [ + { + "expression": "order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "_products_v_rels_parent_idx": { + "name": "_products_v_rels_parent_idx", + "columns": [ + { + "expression": "parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "_products_v_rels_path_idx": { + "name": "_products_v_rels_path_idx", + "columns": [ + { + "expression": "path", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "_products_v_rels_media_id_idx": { + "name": "_products_v_rels_media_id_idx", + "columns": [ + { + "expression": "media_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "_products_v_rels_parent_fk": { + "name": "_products_v_rels_parent_fk", + "tableFrom": "_products_v_rels", + "tableTo": "_products_v", + "columnsFrom": ["parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "_products_v_rels_media_fk": { + "name": "_products_v_rels_media_fk", + "tableFrom": "_products_v_rels", + "tableTo": "media", + "columnsFrom": ["media_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_kv": { + "name": "payload_kv", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "key": { + "name": "key", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "data": { + "name": "data", + "type": "jsonb", + "primaryKey": false, + "notNull": true + } + }, + "indexes": { + "payload_kv_key_idx": { + "name": "payload_kv_key_idx", + "columns": [ + { + "expression": "key", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_locked_documents": { + "name": "payload_locked_documents", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "global_slug": { + "name": "global_slug", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "payload_locked_documents_global_slug_idx": { + "name": "payload_locked_documents_global_slug_idx", + "columns": [ + { + "expression": "global_slug", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_updated_at_idx": { + "name": "payload_locked_documents_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_created_at_idx": { + "name": "payload_locked_documents_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_locked_documents_rels": { + "name": "payload_locked_documents_rels", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "order": { + "name": "order", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "parent_id": { + "name": "parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "path": { + "name": "path", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "users_id": { + "name": "users_id", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "media_id": { + "name": "media_id", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "posts_id": { + "name": "posts_id", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "form_submissions_id": { + "name": "form_submissions_id", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "products_id": { + "name": "products_id", + "type": "integer", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "payload_locked_documents_rels_order_idx": { + "name": "payload_locked_documents_rels_order_idx", + "columns": [ + { + "expression": "order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_parent_idx": { + "name": "payload_locked_documents_rels_parent_idx", + "columns": [ + { + "expression": "parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_path_idx": { + "name": "payload_locked_documents_rels_path_idx", + "columns": [ + { + "expression": "path", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_users_id_idx": { + "name": "payload_locked_documents_rels_users_id_idx", + "columns": [ + { + "expression": "users_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_media_id_idx": { + "name": "payload_locked_documents_rels_media_id_idx", + "columns": [ + { + "expression": "media_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_posts_id_idx": { + "name": "payload_locked_documents_rels_posts_id_idx", + "columns": [ + { + "expression": "posts_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_form_submissions_id_idx": { + "name": "payload_locked_documents_rels_form_submissions_id_idx", + "columns": [ + { + "expression": "form_submissions_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_locked_documents_rels_products_id_idx": { + "name": "payload_locked_documents_rels_products_id_idx", + "columns": [ + { + "expression": "products_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "payload_locked_documents_rels_parent_fk": { + "name": "payload_locked_documents_rels_parent_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "payload_locked_documents", + "columnsFrom": ["parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_locked_documents_rels_users_fk": { + "name": "payload_locked_documents_rels_users_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "users", + "columnsFrom": ["users_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_locked_documents_rels_media_fk": { + "name": "payload_locked_documents_rels_media_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "media", + "columnsFrom": ["media_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_locked_documents_rels_posts_fk": { + "name": "payload_locked_documents_rels_posts_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "posts", + "columnsFrom": ["posts_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_locked_documents_rels_form_submissions_fk": { + "name": "payload_locked_documents_rels_form_submissions_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "form_submissions", + "columnsFrom": ["form_submissions_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_locked_documents_rels_products_fk": { + "name": "payload_locked_documents_rels_products_fk", + "tableFrom": "payload_locked_documents_rels", + "tableTo": "products", + "columnsFrom": ["products_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_preferences": { + "name": "payload_preferences", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "key": { + "name": "key", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "value": { + "name": "value", + "type": "jsonb", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "payload_preferences_key_idx": { + "name": "payload_preferences_key_idx", + "columns": [ + { + "expression": "key", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_updated_at_idx": { + "name": "payload_preferences_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_created_at_idx": { + "name": "payload_preferences_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_preferences_rels": { + "name": "payload_preferences_rels", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "order": { + "name": "order", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "parent_id": { + "name": "parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "path": { + "name": "path", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "users_id": { + "name": "users_id", + "type": "integer", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "payload_preferences_rels_order_idx": { + "name": "payload_preferences_rels_order_idx", + "columns": [ + { + "expression": "order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_parent_idx": { + "name": "payload_preferences_rels_parent_idx", + "columns": [ + { + "expression": "parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_path_idx": { + "name": "payload_preferences_rels_path_idx", + "columns": [ + { + "expression": "path", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_users_id_idx": { + "name": "payload_preferences_rels_users_id_idx", + "columns": [ + { + "expression": "users_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "payload_preferences_rels_parent_fk": { + "name": "payload_preferences_rels_parent_fk", + "tableFrom": "payload_preferences_rels", + "tableTo": "payload_preferences", + "columnsFrom": ["parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_preferences_rels_users_fk": { + "name": "payload_preferences_rels_users_fk", + "tableFrom": "payload_preferences_rels", + "tableTo": "users", + "columnsFrom": ["users_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_migrations": { + "name": "payload_migrations", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "name": { + "name": "name", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "batch": { + "name": "batch", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "payload_migrations_updated_at_idx": { + "name": "payload_migrations_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_migrations_created_at_idx": { + "name": "payload_migrations_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + } + }, + "enums": { + "public.enum_posts_locale": { + "name": "enum_posts_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum_posts_status": { + "name": "enum_posts_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum__posts_v_version_locale": { + "name": "enum__posts_v_version_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum__posts_v_version_status": { + "name": "enum__posts_v_version_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum_form_submissions_type": { + "name": "enum_form_submissions_type", + "schema": "public", + "values": ["contact", "product_quote"] + }, + "public.enum_products_locale": { + "name": "enum_products_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum_products_status": { + "name": "enum_products_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum__products_v_version_locale": { + "name": "enum__products_v_version_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum__products_v_version_status": { + "name": "enum__products_v_version_status", + "schema": "public", + "values": ["draft", "published"] + } + }, + "schemas": {}, + "sequences": {}, + "roles": {}, + "policies": {}, + "views": {}, + "_meta": { + "schemas": {}, + "tables": {}, + "columns": {} + }, + "id": "3fade2d9-eea5-494b-9b6d-602d48969eef", + "prevId": "00000000-0000-0000-0000-000000000000" +} diff --git a/src/migrations/20260223_195005_products_collection.ts b/src/migrations/20260223_195005_products_collection.ts new file mode 100644 index 00000000..bb88c23c --- /dev/null +++ b/src/migrations/20260223_195005_products_collection.ts @@ -0,0 +1,356 @@ +import { MigrateUpArgs, MigrateDownArgs, sql } from '@payloadcms/db-postgres'; + +export async function up({ db }: MigrateUpArgs): Promise { + await db.execute(sql` + CREATE TYPE "public"."enum_posts_locale" AS ENUM('en', 'de'); + CREATE TYPE "public"."enum_posts_status" AS ENUM('draft', 'published'); + CREATE TYPE "public"."enum__posts_v_version_locale" AS ENUM('en', 'de'); + CREATE TYPE "public"."enum__posts_v_version_status" AS ENUM('draft', 'published'); + CREATE TYPE "public"."enum_form_submissions_type" AS ENUM('contact', 'product_quote'); + CREATE TYPE "public"."enum_products_locale" AS ENUM('en', 'de'); + CREATE TYPE "public"."enum_products_status" AS ENUM('draft', 'published'); + CREATE TYPE "public"."enum__products_v_version_locale" AS ENUM('en', 'de'); + CREATE TYPE "public"."enum__products_v_version_status" AS ENUM('draft', 'published'); + CREATE TABLE "users_sessions" ( + "_order" integer NOT NULL, + "_parent_id" integer NOT NULL, + "id" varchar PRIMARY KEY NOT NULL, + "created_at" timestamp(3) with time zone, + "expires_at" timestamp(3) with time zone NOT NULL + ); + + CREATE TABLE "users" ( + "id" serial PRIMARY KEY NOT NULL, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "email" varchar NOT NULL, + "reset_password_token" varchar, + "reset_password_expiration" timestamp(3) with time zone, + "salt" varchar, + "hash" varchar, + "login_attempts" numeric DEFAULT 0, + "lock_until" timestamp(3) with time zone + ); + + CREATE TABLE "media" ( + "id" serial PRIMARY KEY NOT NULL, + "alt" varchar NOT NULL, + "caption" varchar, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "url" varchar, + "thumbnail_u_r_l" varchar, + "filename" varchar, + "mime_type" varchar, + "filesize" numeric, + "width" numeric, + "height" numeric, + "focal_x" numeric, + "focal_y" numeric, + "sizes_thumbnail_url" varchar, + "sizes_thumbnail_width" numeric, + "sizes_thumbnail_height" numeric, + "sizes_thumbnail_mime_type" varchar, + "sizes_thumbnail_filesize" numeric, + "sizes_thumbnail_filename" varchar, + "sizes_card_url" varchar, + "sizes_card_width" numeric, + "sizes_card_height" numeric, + "sizes_card_mime_type" varchar, + "sizes_card_filesize" numeric, + "sizes_card_filename" varchar, + "sizes_hero_url" varchar, + "sizes_hero_width" numeric, + "sizes_hero_height" numeric, + "sizes_hero_mime_type" varchar, + "sizes_hero_filesize" numeric, + "sizes_hero_filename" varchar, + "sizes_hero_mobile_url" varchar, + "sizes_hero_mobile_width" numeric, + "sizes_hero_mobile_height" numeric, + "sizes_hero_mobile_mime_type" varchar, + "sizes_hero_mobile_filesize" numeric, + "sizes_hero_mobile_filename" varchar + ); + + CREATE TABLE "posts" ( + "id" serial PRIMARY KEY NOT NULL, + "title" varchar, + "slug" varchar, + "excerpt" varchar, + "date" timestamp(3) with time zone, + "featured_image_id" integer, + "locale" "enum_posts_locale" DEFAULT 'en', + "category" varchar, + "content" jsonb, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "_status" "enum_posts_status" DEFAULT 'draft' + ); + + CREATE TABLE "_posts_v" ( + "id" serial PRIMARY KEY NOT NULL, + "parent_id" integer, + "version_title" varchar, + "version_slug" varchar, + "version_excerpt" varchar, + "version_date" timestamp(3) with time zone, + "version_featured_image_id" integer, + "version_locale" "enum__posts_v_version_locale" DEFAULT 'en', + "version_category" varchar, + "version_content" jsonb, + "version_updated_at" timestamp(3) with time zone, + "version_created_at" timestamp(3) with time zone, + "version__status" "enum__posts_v_version_status" DEFAULT 'draft', + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "latest" boolean + ); + + CREATE TABLE "form_submissions" ( + "id" serial PRIMARY KEY NOT NULL, + "name" varchar NOT NULL, + "email" varchar NOT NULL, + "type" "enum_form_submissions_type" NOT NULL, + "product_name" varchar, + "message" varchar NOT NULL, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL + ); + + CREATE TABLE "products_categories" ( + "_order" integer NOT NULL, + "_parent_id" integer NOT NULL, + "id" varchar PRIMARY KEY NOT NULL, + "category" varchar + ); + + CREATE TABLE "products" ( + "id" serial PRIMARY KEY NOT NULL, + "title" varchar, + "sku" varchar, + "slug" varchar, + "description" varchar, + "locale" "enum_products_locale" DEFAULT 'de', + "application" jsonb, + "content" jsonb, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "_status" "enum_products_status" DEFAULT 'draft' + ); + + CREATE TABLE "products_rels" ( + "id" serial PRIMARY KEY NOT NULL, + "order" integer, + "parent_id" integer NOT NULL, + "path" varchar NOT NULL, + "media_id" integer + ); + + CREATE TABLE "_products_v_version_categories" ( + "_order" integer NOT NULL, + "_parent_id" integer NOT NULL, + "id" serial PRIMARY KEY NOT NULL, + "category" varchar, + "_uuid" varchar + ); + + CREATE TABLE "_products_v" ( + "id" serial PRIMARY KEY NOT NULL, + "parent_id" integer, + "version_title" varchar, + "version_sku" varchar, + "version_slug" varchar, + "version_description" varchar, + "version_locale" "enum__products_v_version_locale" DEFAULT 'de', + "version_application" jsonb, + "version_content" jsonb, + "version_updated_at" timestamp(3) with time zone, + "version_created_at" timestamp(3) with time zone, + "version__status" "enum__products_v_version_status" DEFAULT 'draft', + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "latest" boolean + ); + + CREATE TABLE "_products_v_rels" ( + "id" serial PRIMARY KEY NOT NULL, + "order" integer, + "parent_id" integer NOT NULL, + "path" varchar NOT NULL, + "media_id" integer + ); + + CREATE TABLE "payload_kv" ( + "id" serial PRIMARY KEY NOT NULL, + "key" varchar NOT NULL, + "data" jsonb NOT NULL + ); + + CREATE TABLE "payload_locked_documents" ( + "id" serial PRIMARY KEY NOT NULL, + "global_slug" varchar, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL + ); + + CREATE TABLE "payload_locked_documents_rels" ( + "id" serial PRIMARY KEY NOT NULL, + "order" integer, + "parent_id" integer NOT NULL, + "path" varchar NOT NULL, + "users_id" integer, + "media_id" integer, + "posts_id" integer, + "form_submissions_id" integer, + "products_id" integer + ); + + CREATE TABLE "payload_preferences" ( + "id" serial PRIMARY KEY NOT NULL, + "key" varchar, + "value" jsonb, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL + ); + + CREATE TABLE "payload_preferences_rels" ( + "id" serial PRIMARY KEY NOT NULL, + "order" integer, + "parent_id" integer NOT NULL, + "path" varchar NOT NULL, + "users_id" integer + ); + + CREATE TABLE "payload_migrations" ( + "id" serial PRIMARY KEY NOT NULL, + "name" varchar, + "batch" numeric, + "updated_at" timestamp(3) with time zone DEFAULT now() NOT NULL, + "created_at" timestamp(3) with time zone DEFAULT now() NOT NULL + ); + + ALTER TABLE "users_sessions" ADD CONSTRAINT "users_sessions_parent_id_fk" FOREIGN KEY ("_parent_id") REFERENCES "public"."users"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "posts" ADD CONSTRAINT "posts_featured_image_id_media_id_fk" FOREIGN KEY ("featured_image_id") REFERENCES "public"."media"("id") ON DELETE set null ON UPDATE no action; + ALTER TABLE "_posts_v" ADD CONSTRAINT "_posts_v_parent_id_posts_id_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."posts"("id") ON DELETE set null ON UPDATE no action; + ALTER TABLE "_posts_v" ADD CONSTRAINT "_posts_v_version_featured_image_id_media_id_fk" FOREIGN KEY ("version_featured_image_id") REFERENCES "public"."media"("id") ON DELETE set null ON UPDATE no action; + ALTER TABLE "products_categories" ADD CONSTRAINT "products_categories_parent_id_fk" FOREIGN KEY ("_parent_id") REFERENCES "public"."products"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "products_rels" ADD CONSTRAINT "products_rels_parent_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."products"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "products_rels" ADD CONSTRAINT "products_rels_media_fk" FOREIGN KEY ("media_id") REFERENCES "public"."media"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "_products_v_version_categories" ADD CONSTRAINT "_products_v_version_categories_parent_id_fk" FOREIGN KEY ("_parent_id") REFERENCES "public"."_products_v"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "_products_v" ADD CONSTRAINT "_products_v_parent_id_products_id_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."products"("id") ON DELETE set null ON UPDATE no action; + ALTER TABLE "_products_v_rels" ADD CONSTRAINT "_products_v_rels_parent_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."_products_v"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "_products_v_rels" ADD CONSTRAINT "_products_v_rels_media_fk" FOREIGN KEY ("media_id") REFERENCES "public"."media"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_parent_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."payload_locked_documents"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_users_fk" FOREIGN KEY ("users_id") REFERENCES "public"."users"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_media_fk" FOREIGN KEY ("media_id") REFERENCES "public"."media"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_posts_fk" FOREIGN KEY ("posts_id") REFERENCES "public"."posts"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_form_submissions_fk" FOREIGN KEY ("form_submissions_id") REFERENCES "public"."form_submissions"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_locked_documents_rels" ADD CONSTRAINT "payload_locked_documents_rels_products_fk" FOREIGN KEY ("products_id") REFERENCES "public"."products"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_preferences_rels" ADD CONSTRAINT "payload_preferences_rels_parent_fk" FOREIGN KEY ("parent_id") REFERENCES "public"."payload_preferences"("id") ON DELETE cascade ON UPDATE no action; + ALTER TABLE "payload_preferences_rels" ADD CONSTRAINT "payload_preferences_rels_users_fk" FOREIGN KEY ("users_id") REFERENCES "public"."users"("id") ON DELETE cascade ON UPDATE no action; + CREATE INDEX "users_sessions_order_idx" ON "users_sessions" USING btree ("_order"); + CREATE INDEX "users_sessions_parent_id_idx" ON "users_sessions" USING btree ("_parent_id"); + CREATE INDEX "users_updated_at_idx" ON "users" USING btree ("updated_at"); + CREATE INDEX "users_created_at_idx" ON "users" USING btree ("created_at"); + CREATE UNIQUE INDEX "users_email_idx" ON "users" USING btree ("email"); + CREATE INDEX "media_updated_at_idx" ON "media" USING btree ("updated_at"); + CREATE INDEX "media_created_at_idx" ON "media" USING btree ("created_at"); + CREATE UNIQUE INDEX "media_filename_idx" ON "media" USING btree ("filename"); + CREATE INDEX "media_sizes_thumbnail_sizes_thumbnail_filename_idx" ON "media" USING btree ("sizes_thumbnail_filename"); + CREATE INDEX "media_sizes_card_sizes_card_filename_idx" ON "media" USING btree ("sizes_card_filename"); + CREATE INDEX "media_sizes_hero_sizes_hero_filename_idx" ON "media" USING btree ("sizes_hero_filename"); + CREATE INDEX "media_sizes_hero_mobile_sizes_hero_mobile_filename_idx" ON "media" USING btree ("sizes_hero_mobile_filename"); + CREATE UNIQUE INDEX "posts_slug_idx" ON "posts" USING btree ("slug"); + CREATE INDEX "posts_featured_image_idx" ON "posts" USING btree ("featured_image_id"); + CREATE INDEX "posts_updated_at_idx" ON "posts" USING btree ("updated_at"); + CREATE INDEX "posts_created_at_idx" ON "posts" USING btree ("created_at"); + CREATE INDEX "posts__status_idx" ON "posts" USING btree ("_status"); + CREATE INDEX "_posts_v_parent_idx" ON "_posts_v" USING btree ("parent_id"); + CREATE INDEX "_posts_v_version_version_slug_idx" ON "_posts_v" USING btree ("version_slug"); + CREATE INDEX "_posts_v_version_version_featured_image_idx" ON "_posts_v" USING btree ("version_featured_image_id"); + CREATE INDEX "_posts_v_version_version_updated_at_idx" ON "_posts_v" USING btree ("version_updated_at"); + CREATE INDEX "_posts_v_version_version_created_at_idx" ON "_posts_v" USING btree ("version_created_at"); + CREATE INDEX "_posts_v_version_version__status_idx" ON "_posts_v" USING btree ("version__status"); + CREATE INDEX "_posts_v_created_at_idx" ON "_posts_v" USING btree ("created_at"); + CREATE INDEX "_posts_v_updated_at_idx" ON "_posts_v" USING btree ("updated_at"); + CREATE INDEX "_posts_v_latest_idx" ON "_posts_v" USING btree ("latest"); + CREATE INDEX "form_submissions_updated_at_idx" ON "form_submissions" USING btree ("updated_at"); + CREATE INDEX "form_submissions_created_at_idx" ON "form_submissions" USING btree ("created_at"); + CREATE INDEX "products_categories_order_idx" ON "products_categories" USING btree ("_order"); + CREATE INDEX "products_categories_parent_id_idx" ON "products_categories" USING btree ("_parent_id"); + CREATE UNIQUE INDEX "products_sku_idx" ON "products" USING btree ("sku"); + CREATE INDEX "products_updated_at_idx" ON "products" USING btree ("updated_at"); + CREATE INDEX "products_created_at_idx" ON "products" USING btree ("created_at"); + CREATE INDEX "products__status_idx" ON "products" USING btree ("_status"); + CREATE INDEX "products_rels_order_idx" ON "products_rels" USING btree ("order"); + CREATE INDEX "products_rels_parent_idx" ON "products_rels" USING btree ("parent_id"); + CREATE INDEX "products_rels_path_idx" ON "products_rels" USING btree ("path"); + CREATE INDEX "products_rels_media_id_idx" ON "products_rels" USING btree ("media_id"); + CREATE INDEX "_products_v_version_categories_order_idx" ON "_products_v_version_categories" USING btree ("_order"); + CREATE INDEX "_products_v_version_categories_parent_id_idx" ON "_products_v_version_categories" USING btree ("_parent_id"); + CREATE INDEX "_products_v_parent_idx" ON "_products_v" USING btree ("parent_id"); + CREATE INDEX "_products_v_version_version_sku_idx" ON "_products_v" USING btree ("version_sku"); + CREATE INDEX "_products_v_version_version_updated_at_idx" ON "_products_v" USING btree ("version_updated_at"); + CREATE INDEX "_products_v_version_version_created_at_idx" ON "_products_v" USING btree ("version_created_at"); + CREATE INDEX "_products_v_version_version__status_idx" ON "_products_v" USING btree ("version__status"); + CREATE INDEX "_products_v_created_at_idx" ON "_products_v" USING btree ("created_at"); + CREATE INDEX "_products_v_updated_at_idx" ON "_products_v" USING btree ("updated_at"); + CREATE INDEX "_products_v_latest_idx" ON "_products_v" USING btree ("latest"); + CREATE INDEX "_products_v_rels_order_idx" ON "_products_v_rels" USING btree ("order"); + CREATE INDEX "_products_v_rels_parent_idx" ON "_products_v_rels" USING btree ("parent_id"); + CREATE INDEX "_products_v_rels_path_idx" ON "_products_v_rels" USING btree ("path"); + CREATE INDEX "_products_v_rels_media_id_idx" ON "_products_v_rels" USING btree ("media_id"); + CREATE UNIQUE INDEX "payload_kv_key_idx" ON "payload_kv" USING btree ("key"); + CREATE INDEX "payload_locked_documents_global_slug_idx" ON "payload_locked_documents" USING btree ("global_slug"); + CREATE INDEX "payload_locked_documents_updated_at_idx" ON "payload_locked_documents" USING btree ("updated_at"); + CREATE INDEX "payload_locked_documents_created_at_idx" ON "payload_locked_documents" USING btree ("created_at"); + CREATE INDEX "payload_locked_documents_rels_order_idx" ON "payload_locked_documents_rels" USING btree ("order"); + CREATE INDEX "payload_locked_documents_rels_parent_idx" ON "payload_locked_documents_rels" USING btree ("parent_id"); + CREATE INDEX "payload_locked_documents_rels_path_idx" ON "payload_locked_documents_rels" USING btree ("path"); + CREATE INDEX "payload_locked_documents_rels_users_id_idx" ON "payload_locked_documents_rels" USING btree ("users_id"); + CREATE INDEX "payload_locked_documents_rels_media_id_idx" ON "payload_locked_documents_rels" USING btree ("media_id"); + CREATE INDEX "payload_locked_documents_rels_posts_id_idx" ON "payload_locked_documents_rels" USING btree ("posts_id"); + CREATE INDEX "payload_locked_documents_rels_form_submissions_id_idx" ON "payload_locked_documents_rels" USING btree ("form_submissions_id"); + CREATE INDEX "payload_locked_documents_rels_products_id_idx" ON "payload_locked_documents_rels" USING btree ("products_id"); + CREATE INDEX "payload_preferences_key_idx" ON "payload_preferences" USING btree ("key"); + CREATE INDEX "payload_preferences_updated_at_idx" ON "payload_preferences" USING btree ("updated_at"); + CREATE INDEX "payload_preferences_created_at_idx" ON "payload_preferences" USING btree ("created_at"); + CREATE INDEX "payload_preferences_rels_order_idx" ON "payload_preferences_rels" USING btree ("order"); + CREATE INDEX "payload_preferences_rels_parent_idx" ON "payload_preferences_rels" USING btree ("parent_id"); + CREATE INDEX "payload_preferences_rels_path_idx" ON "payload_preferences_rels" USING btree ("path"); + CREATE INDEX "payload_preferences_rels_users_id_idx" ON "payload_preferences_rels" USING btree ("users_id"); + CREATE INDEX "payload_migrations_updated_at_idx" ON "payload_migrations" USING btree ("updated_at"); + CREATE INDEX "payload_migrations_created_at_idx" ON "payload_migrations" USING btree ("created_at");`); +} + +export async function down({ db }: MigrateDownArgs): Promise { + await db.execute(sql` + DROP TABLE "users_sessions" CASCADE; + DROP TABLE "users" CASCADE; + DROP TABLE "media" CASCADE; + DROP TABLE "posts" CASCADE; + DROP TABLE "_posts_v" CASCADE; + DROP TABLE "form_submissions" CASCADE; + DROP TABLE "products_categories" CASCADE; + DROP TABLE "products" CASCADE; + DROP TABLE "products_rels" CASCADE; + DROP TABLE "_products_v_version_categories" CASCADE; + DROP TABLE "_products_v" CASCADE; + DROP TABLE "_products_v_rels" CASCADE; + DROP TABLE "payload_kv" CASCADE; + DROP TABLE "payload_locked_documents" CASCADE; + DROP TABLE "payload_locked_documents_rels" CASCADE; + DROP TABLE "payload_preferences" CASCADE; + DROP TABLE "payload_preferences_rels" CASCADE; + DROP TABLE "payload_migrations" CASCADE; + DROP TYPE "public"."enum_posts_locale"; + DROP TYPE "public"."enum_posts_status"; + DROP TYPE "public"."enum__posts_v_version_locale"; + DROP TYPE "public"."enum__posts_v_version_status"; + DROP TYPE "public"."enum_form_submissions_type"; + DROP TYPE "public"."enum_products_locale"; + DROP TYPE "public"."enum_products_status"; + DROP TYPE "public"."enum__products_v_version_locale"; + DROP TYPE "public"."enum__products_v_version_status";`); +} diff --git a/src/migrations/20260223_195151_remove_sku_unique.json b/src/migrations/20260223_195151_remove_sku_unique.json new file mode 100644 index 00000000..3697b364 --- /dev/null +++ b/src/migrations/20260223_195151_remove_sku_unique.json @@ -0,0 +1,2591 @@ +{ + "version": "7", + "dialect": "postgresql", + "tables": { + "public.users_sessions": { + "name": "users_sessions", + "schema": "", + "columns": { + "_order": { + "name": "_order", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "_parent_id": { + "name": "_parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "id": { + "name": "id", + "type": "varchar", + "primaryKey": true, + "notNull": true + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": false + }, + "expires_at": { + "name": "expires_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true + } + }, + "indexes": { + "users_sessions_order_idx": { + "name": "users_sessions_order_idx", + "columns": [ + { + "expression": "_order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "users_sessions_parent_id_idx": { + "name": "users_sessions_parent_id_idx", + "columns": [ + { + "expression": "_parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "users_sessions_parent_id_fk": { + "name": "users_sessions_parent_id_fk", + "tableFrom": "users_sessions", + "tableTo": "users", + "columnsFrom": ["_parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.users": { + "name": "users", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "email": { + "name": "email", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "reset_password_token": { + "name": "reset_password_token", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + 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"isRLSEnabled": false + }, + "public.payload_preferences": { + "name": "payload_preferences", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "key": { + "name": "key", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "value": { + "name": "value", + "type": "jsonb", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "payload_preferences_key_idx": { + "name": "payload_preferences_key_idx", + "columns": [ + { + "expression": "key", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_updated_at_idx": { + "name": "payload_preferences_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_created_at_idx": { + "name": "payload_preferences_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_preferences_rels": { + "name": "payload_preferences_rels", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "order": { + "name": "order", + "type": "integer", + "primaryKey": false, + "notNull": false + }, + "parent_id": { + "name": "parent_id", + "type": "integer", + "primaryKey": false, + "notNull": true + }, + "path": { + "name": "path", + "type": "varchar", + "primaryKey": false, + "notNull": true + }, + "users_id": { + "name": "users_id", + "type": "integer", + "primaryKey": false, + "notNull": false + } + }, + "indexes": { + "payload_preferences_rels_order_idx": { + "name": "payload_preferences_rels_order_idx", + "columns": [ + { + "expression": "order", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_parent_idx": { + "name": "payload_preferences_rels_parent_idx", + "columns": [ + { + "expression": "parent_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_path_idx": { + "name": "payload_preferences_rels_path_idx", + "columns": [ + { + "expression": "path", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_preferences_rels_users_id_idx": { + "name": "payload_preferences_rels_users_id_idx", + "columns": [ + { + "expression": "users_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "payload_preferences_rels_parent_fk": { + "name": "payload_preferences_rels_parent_fk", + "tableFrom": "payload_preferences_rels", + "tableTo": "payload_preferences", + "columnsFrom": ["parent_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "payload_preferences_rels_users_fk": { + "name": "payload_preferences_rels_users_fk", + "tableFrom": "payload_preferences_rels", + "tableTo": "users", + "columnsFrom": ["users_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.payload_migrations": { + "name": "payload_migrations", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "serial", + "primaryKey": true, + "notNull": true + }, + "name": { + "name": "name", + "type": "varchar", + "primaryKey": false, + "notNull": false + }, + "batch": { + "name": "batch", + "type": "numeric", + "primaryKey": false, + "notNull": false + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "created_at": { + "name": "created_at", + "type": "timestamp(3) with time zone", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "payload_migrations_updated_at_idx": { + "name": "payload_migrations_updated_at_idx", + "columns": [ + { + "expression": "updated_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "payload_migrations_created_at_idx": { + "name": "payload_migrations_created_at_idx", + "columns": [ + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": {}, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + } + }, + "enums": { + "public.enum_posts_locale": { + "name": "enum_posts_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum_posts_status": { + "name": "enum_posts_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum__posts_v_version_locale": { + "name": "enum__posts_v_version_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum__posts_v_version_status": { + "name": "enum__posts_v_version_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum_form_submissions_type": { + "name": "enum_form_submissions_type", + "schema": "public", + "values": ["contact", "product_quote"] + }, + "public.enum_products_locale": { + "name": "enum_products_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum_products_status": { + "name": "enum_products_status", + "schema": "public", + "values": ["draft", "published"] + }, + "public.enum__products_v_version_locale": { + "name": "enum__products_v_version_locale", + "schema": "public", + "values": ["en", "de"] + }, + "public.enum__products_v_version_status": { + "name": "enum__products_v_version_status", + "schema": "public", + "values": ["draft", "published"] + } + }, + "schemas": {}, + "sequences": {}, + "roles": {}, + "policies": {}, + "views": {}, + "_meta": { + "schemas": {}, + "tables": {}, + "columns": {} + }, + "id": "023150d4-4505-46d5-8683-a3ea443b4d72", + "prevId": "00000000-0000-0000-0000-000000000000" +} diff --git a/src/migrations/20260223_195151_remove_sku_unique.ts b/src/migrations/20260223_195151_remove_sku_unique.ts new file mode 100644 index 00000000..0e8fef41 --- /dev/null +++ b/src/migrations/20260223_195151_remove_sku_unique.ts @@ -0,0 +1,13 @@ +import { MigrateUpArgs, MigrateDownArgs, sql } from '@payloadcms/db-postgres'; + +export async function up({ db }: MigrateUpArgs): Promise { + await db.execute(sql` + DROP INDEX "products_sku_idx"; + DROP INDEX "_products_v_version_version_sku_idx";`); +} + +export async function down({ db }: MigrateDownArgs): Promise { + await db.execute(sql` + CREATE UNIQUE INDEX "products_sku_idx" ON "products" USING btree ("sku"); + CREATE INDEX "_products_v_version_version_sku_idx" ON "_products_v" USING btree ("version_sku");`); +} diff --git a/src/migrations/index.ts b/src/migrations/index.ts new file mode 100644 index 00000000..0880a26d --- /dev/null +++ b/src/migrations/index.ts @@ -0,0 +1,15 @@ +import * as migration_20260223_195005_products_collection from './20260223_195005_products_collection'; +import * as migration_20260223_195151_remove_sku_unique from './20260223_195151_remove_sku_unique'; + +export const migrations = [ + { + up: migration_20260223_195005_products_collection.up, + down: migration_20260223_195005_products_collection.down, + name: '20260223_195005_products_collection', + }, + { + up: migration_20260223_195151_remove_sku_unique.up, + down: migration_20260223_195151_remove_sku_unique.down, + name: '20260223_195151_remove_sku_unique', + }, +]; diff --git a/src/payload-generated-schema.ts b/src/payload-generated-schema.ts new file mode 100644 index 00000000..1fe22d26 --- /dev/null +++ b/src/payload-generated-schema.ts @@ -0,0 +1,793 @@ +/* tslint:disable */ +/* eslint-disable */ +/** + * This file was automatically generated by Payload. + * DO NOT MODIFY IT BY HAND. Instead, modify your source Payload config, + * and re-run `payload generate:db-schema` to regenerate this file. + */ + +import type {} from '@payloadcms/db-postgres'; +import { + pgTable, + index, + uniqueIndex, + foreignKey, + integer, + varchar, + timestamp, + serial, + numeric, + jsonb, + boolean, + pgEnum, +} from '@payloadcms/db-postgres/drizzle/pg-core'; +import { sql, relations } from '@payloadcms/db-postgres/drizzle'; +export const enum_posts_locale = pgEnum('enum_posts_locale', ['en', 'de']); +export const enum_posts_status = pgEnum('enum_posts_status', ['draft', 'published']); +export const enum__posts_v_version_locale = pgEnum('enum__posts_v_version_locale', ['en', 'de']); +export const enum__posts_v_version_status = pgEnum('enum__posts_v_version_status', [ + 'draft', + 'published', +]); +export const enum_form_submissions_type = pgEnum('enum_form_submissions_type', [ + 'contact', + 'product_quote', +]); +export const enum_products_locale = pgEnum('enum_products_locale', ['en', 'de']); +export const enum_products_status = pgEnum('enum_products_status', ['draft', 'published']); +export const enum__products_v_version_locale = pgEnum('enum__products_v_version_locale', [ + 'en', + 'de', +]); +export const enum__products_v_version_status = pgEnum('enum__products_v_version_status', [ + 'draft', + 'published', +]); + +export const users_sessions = pgTable( + 'users_sessions', + { + _order: integer('_order').notNull(), + _parentID: integer('_parent_id').notNull(), + id: varchar('id').primaryKey(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }), + expiresAt: timestamp('expires_at', { + mode: 'string', + withTimezone: true, + precision: 3, + }).notNull(), + }, + (columns) => [ + index('users_sessions_order_idx').on(columns._order), + index('users_sessions_parent_id_idx').on(columns._parentID), + foreignKey({ + columns: [columns['_parentID']], + foreignColumns: [users.id], + name: 'users_sessions_parent_id_fk', + }).onDelete('cascade'), + ], +); + +export const users = pgTable( + 'users', + { + id: serial('id').primaryKey(), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + email: varchar('email').notNull(), + resetPasswordToken: varchar('reset_password_token'), + resetPasswordExpiration: timestamp('reset_password_expiration', { + mode: 'string', + withTimezone: true, + precision: 3, + }), + salt: varchar('salt'), + hash: varchar('hash'), + loginAttempts: numeric('login_attempts', { mode: 'number' }).default(0), + lockUntil: timestamp('lock_until', { mode: 'string', withTimezone: true, precision: 3 }), + }, + (columns) => [ + index('users_updated_at_idx').on(columns.updatedAt), + index('users_created_at_idx').on(columns.createdAt), + uniqueIndex('users_email_idx').on(columns.email), + ], +); + +export const media = pgTable( + 'media', + { + id: serial('id').primaryKey(), + alt: varchar('alt').notNull(), + caption: varchar('caption'), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + url: varchar('url'), + thumbnailURL: varchar('thumbnail_u_r_l'), + filename: varchar('filename'), + mimeType: varchar('mime_type'), + filesize: numeric('filesize', { mode: 'number' }), + width: numeric('width', { mode: 'number' }), + height: numeric('height', { mode: 'number' }), + focalX: numeric('focal_x', { mode: 'number' }), + focalY: numeric('focal_y', { mode: 'number' }), + sizes_thumbnail_url: varchar('sizes_thumbnail_url'), + sizes_thumbnail_width: numeric('sizes_thumbnail_width', { mode: 'number' }), + sizes_thumbnail_height: numeric('sizes_thumbnail_height', { mode: 'number' }), + sizes_thumbnail_mimeType: varchar('sizes_thumbnail_mime_type'), + sizes_thumbnail_filesize: numeric('sizes_thumbnail_filesize', { mode: 'number' }), + sizes_thumbnail_filename: varchar('sizes_thumbnail_filename'), + sizes_card_url: varchar('sizes_card_url'), + sizes_card_width: numeric('sizes_card_width', { mode: 'number' }), + sizes_card_height: numeric('sizes_card_height', { mode: 'number' }), + sizes_card_mimeType: varchar('sizes_card_mime_type'), + sizes_card_filesize: numeric('sizes_card_filesize', { mode: 'number' }), + sizes_card_filename: varchar('sizes_card_filename'), + sizes_hero_url: varchar('sizes_hero_url'), + sizes_hero_width: numeric('sizes_hero_width', { mode: 'number' }), + sizes_hero_height: numeric('sizes_hero_height', { mode: 'number' }), + sizes_hero_mimeType: varchar('sizes_hero_mime_type'), + sizes_hero_filesize: numeric('sizes_hero_filesize', { mode: 'number' }), + sizes_hero_filename: varchar('sizes_hero_filename'), + sizes_hero_mobile_url: varchar('sizes_hero_mobile_url'), + sizes_hero_mobile_width: numeric('sizes_hero_mobile_width', { mode: 'number' }), + sizes_hero_mobile_height: numeric('sizes_hero_mobile_height', { mode: 'number' }), + sizes_hero_mobile_mimeType: varchar('sizes_hero_mobile_mime_type'), + sizes_hero_mobile_filesize: numeric('sizes_hero_mobile_filesize', { mode: 'number' }), + sizes_hero_mobile_filename: varchar('sizes_hero_mobile_filename'), + }, + (columns) => [ + index('media_updated_at_idx').on(columns.updatedAt), + index('media_created_at_idx').on(columns.createdAt), + uniqueIndex('media_filename_idx').on(columns.filename), + index('media_sizes_thumbnail_sizes_thumbnail_filename_idx').on( + columns.sizes_thumbnail_filename, + ), + index('media_sizes_card_sizes_card_filename_idx').on(columns.sizes_card_filename), + index('media_sizes_hero_sizes_hero_filename_idx').on(columns.sizes_hero_filename), + index('media_sizes_hero_mobile_sizes_hero_mobile_filename_idx').on( + columns.sizes_hero_mobile_filename, + ), + ], +); + +export const posts = pgTable( + 'posts', + { + id: serial('id').primaryKey(), + title: varchar('title'), + slug: varchar('slug'), + excerpt: varchar('excerpt'), + date: timestamp('date', { mode: 'string', withTimezone: true, precision: 3 }), + featuredImage: integer('featured_image_id').references(() => media.id, { + onDelete: 'set null', + }), + locale: enum_posts_locale('locale').default('en'), + category: varchar('category'), + content: jsonb('content'), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + _status: enum_posts_status('_status').default('draft'), + }, + (columns) => [ + uniqueIndex('posts_slug_idx').on(columns.slug), + index('posts_featured_image_idx').on(columns.featuredImage), + index('posts_updated_at_idx').on(columns.updatedAt), + index('posts_created_at_idx').on(columns.createdAt), + index('posts__status_idx').on(columns._status), + ], +); + +export const _posts_v = pgTable( + '_posts_v', + { + id: serial('id').primaryKey(), + parent: integer('parent_id').references(() => posts.id, { + onDelete: 'set null', + }), + version_title: varchar('version_title'), + version_slug: varchar('version_slug'), + version_excerpt: varchar('version_excerpt'), + version_date: timestamp('version_date', { mode: 'string', withTimezone: true, precision: 3 }), + version_featuredImage: integer('version_featured_image_id').references(() => media.id, { + onDelete: 'set null', + }), + version_locale: enum__posts_v_version_locale('version_locale').default('en'), + version_category: varchar('version_category'), + version_content: jsonb('version_content'), + version_updatedAt: timestamp('version_updated_at', { + mode: 'string', + withTimezone: true, + precision: 3, + }), + version_createdAt: timestamp('version_created_at', { + mode: 'string', + withTimezone: true, + precision: 3, + }), + version__status: enum__posts_v_version_status('version__status').default('draft'), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + latest: boolean('latest'), + }, + (columns) => [ + index('_posts_v_parent_idx').on(columns.parent), + index('_posts_v_version_version_slug_idx').on(columns.version_slug), + index('_posts_v_version_version_featured_image_idx').on(columns.version_featuredImage), + index('_posts_v_version_version_updated_at_idx').on(columns.version_updatedAt), + index('_posts_v_version_version_created_at_idx').on(columns.version_createdAt), + index('_posts_v_version_version__status_idx').on(columns.version__status), + index('_posts_v_created_at_idx').on(columns.createdAt), + index('_posts_v_updated_at_idx').on(columns.updatedAt), + index('_posts_v_latest_idx').on(columns.latest), + ], +); + +export const form_submissions = pgTable( + 'form_submissions', + { + id: serial('id').primaryKey(), + name: varchar('name').notNull(), + email: varchar('email').notNull(), + type: enum_form_submissions_type('type').notNull(), + productName: varchar('product_name'), + message: varchar('message').notNull(), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + }, + (columns) => [ + index('form_submissions_updated_at_idx').on(columns.updatedAt), + index('form_submissions_created_at_idx').on(columns.createdAt), + ], +); + +export const products_categories = pgTable( + 'products_categories', + { + _order: integer('_order').notNull(), + _parentID: integer('_parent_id').notNull(), + id: varchar('id').primaryKey(), + category: varchar('category'), + }, + (columns) => [ + index('products_categories_order_idx').on(columns._order), + index('products_categories_parent_id_idx').on(columns._parentID), + foreignKey({ + columns: [columns['_parentID']], + foreignColumns: [products.id], + name: 'products_categories_parent_id_fk', + }).onDelete('cascade'), + ], +); + +export const products = pgTable( + 'products', + { + id: serial('id').primaryKey(), + title: varchar('title'), + sku: varchar('sku'), + slug: varchar('slug'), + description: varchar('description'), + locale: enum_products_locale('locale').default('de'), + application: jsonb('application'), + content: jsonb('content'), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + _status: enum_products_status('_status').default('draft'), + }, + (columns) => [ + uniqueIndex('products_sku_idx').on(columns.sku), + index('products_updated_at_idx').on(columns.updatedAt), + index('products_created_at_idx').on(columns.createdAt), + index('products__status_idx').on(columns._status), + ], +); + +export const products_rels = pgTable( + 'products_rels', + { + id: serial('id').primaryKey(), + order: integer('order'), + parent: integer('parent_id').notNull(), + path: varchar('path').notNull(), + mediaID: integer('media_id'), + }, + (columns) => [ + index('products_rels_order_idx').on(columns.order), + index('products_rels_parent_idx').on(columns.parent), + index('products_rels_path_idx').on(columns.path), + index('products_rels_media_id_idx').on(columns.mediaID), + foreignKey({ + columns: [columns['parent']], + foreignColumns: [products.id], + name: 'products_rels_parent_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['mediaID']], + foreignColumns: [media.id], + name: 'products_rels_media_fk', + }).onDelete('cascade'), + ], +); + +export const _products_v_version_categories = pgTable( + '_products_v_version_categories', + { + _order: integer('_order').notNull(), + _parentID: integer('_parent_id').notNull(), + id: serial('id').primaryKey(), + category: varchar('category'), + _uuid: varchar('_uuid'), + }, + (columns) => [ + index('_products_v_version_categories_order_idx').on(columns._order), + index('_products_v_version_categories_parent_id_idx').on(columns._parentID), + foreignKey({ + columns: [columns['_parentID']], + foreignColumns: [_products_v.id], + name: '_products_v_version_categories_parent_id_fk', + }).onDelete('cascade'), + ], +); + +export const _products_v = pgTable( + '_products_v', + { + id: serial('id').primaryKey(), + parent: integer('parent_id').references(() => products.id, { + onDelete: 'set null', + }), + version_title: varchar('version_title'), + version_sku: varchar('version_sku'), + version_slug: varchar('version_slug'), + version_description: varchar('version_description'), + version_locale: enum__products_v_version_locale('version_locale').default('de'), + version_application: jsonb('version_application'), + version_content: jsonb('version_content'), + version_updatedAt: timestamp('version_updated_at', { + mode: 'string', + withTimezone: true, + precision: 3, + }), + version_createdAt: timestamp('version_created_at', { + mode: 'string', + withTimezone: true, + precision: 3, + }), + version__status: enum__products_v_version_status('version__status').default('draft'), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + latest: boolean('latest'), + }, + (columns) => [ + index('_products_v_parent_idx').on(columns.parent), + index('_products_v_version_version_sku_idx').on(columns.version_sku), + index('_products_v_version_version_updated_at_idx').on(columns.version_updatedAt), + index('_products_v_version_version_created_at_idx').on(columns.version_createdAt), + index('_products_v_version_version__status_idx').on(columns.version__status), + index('_products_v_created_at_idx').on(columns.createdAt), + index('_products_v_updated_at_idx').on(columns.updatedAt), + index('_products_v_latest_idx').on(columns.latest), + ], +); + +export const _products_v_rels = pgTable( + '_products_v_rels', + { + id: serial('id').primaryKey(), + order: integer('order'), + parent: integer('parent_id').notNull(), + path: varchar('path').notNull(), + mediaID: integer('media_id'), + }, + (columns) => [ + index('_products_v_rels_order_idx').on(columns.order), + index('_products_v_rels_parent_idx').on(columns.parent), + index('_products_v_rels_path_idx').on(columns.path), + index('_products_v_rels_media_id_idx').on(columns.mediaID), + foreignKey({ + columns: [columns['parent']], + foreignColumns: [_products_v.id], + name: '_products_v_rels_parent_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['mediaID']], + foreignColumns: [media.id], + name: '_products_v_rels_media_fk', + }).onDelete('cascade'), + ], +); + +export const payload_kv = pgTable( + 'payload_kv', + { + id: serial('id').primaryKey(), + key: varchar('key').notNull(), + data: jsonb('data').notNull(), + }, + (columns) => [uniqueIndex('payload_kv_key_idx').on(columns.key)], +); + +export const payload_locked_documents = pgTable( + 'payload_locked_documents', + { + id: serial('id').primaryKey(), + globalSlug: varchar('global_slug'), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + }, + (columns) => [ + index('payload_locked_documents_global_slug_idx').on(columns.globalSlug), + index('payload_locked_documents_updated_at_idx').on(columns.updatedAt), + index('payload_locked_documents_created_at_idx').on(columns.createdAt), + ], +); + +export const payload_locked_documents_rels = pgTable( + 'payload_locked_documents_rels', + { + id: serial('id').primaryKey(), + order: integer('order'), + parent: integer('parent_id').notNull(), + path: varchar('path').notNull(), + usersID: integer('users_id'), + mediaID: integer('media_id'), + postsID: integer('posts_id'), + 'form-submissionsID': integer('form_submissions_id'), + productsID: integer('products_id'), + }, + (columns) => [ + index('payload_locked_documents_rels_order_idx').on(columns.order), + index('payload_locked_documents_rels_parent_idx').on(columns.parent), + index('payload_locked_documents_rels_path_idx').on(columns.path), + index('payload_locked_documents_rels_users_id_idx').on(columns.usersID), + index('payload_locked_documents_rels_media_id_idx').on(columns.mediaID), + index('payload_locked_documents_rels_posts_id_idx').on(columns.postsID), + index('payload_locked_documents_rels_form_submissions_id_idx').on( + columns['form-submissionsID'], + ), + index('payload_locked_documents_rels_products_id_idx').on(columns.productsID), + foreignKey({ + columns: [columns['parent']], + foreignColumns: [payload_locked_documents.id], + name: 'payload_locked_documents_rels_parent_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['usersID']], + foreignColumns: [users.id], + name: 'payload_locked_documents_rels_users_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['mediaID']], + foreignColumns: [media.id], + name: 'payload_locked_documents_rels_media_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['postsID']], + foreignColumns: [posts.id], + name: 'payload_locked_documents_rels_posts_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['form-submissionsID']], + foreignColumns: [form_submissions.id], + name: 'payload_locked_documents_rels_form_submissions_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['productsID']], + foreignColumns: [products.id], + name: 'payload_locked_documents_rels_products_fk', + }).onDelete('cascade'), + ], +); + +export const payload_preferences = pgTable( + 'payload_preferences', + { + id: serial('id').primaryKey(), + key: varchar('key'), + value: jsonb('value'), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + }, + (columns) => [ + index('payload_preferences_key_idx').on(columns.key), + index('payload_preferences_updated_at_idx').on(columns.updatedAt), + index('payload_preferences_created_at_idx').on(columns.createdAt), + ], +); + +export const payload_preferences_rels = pgTable( + 'payload_preferences_rels', + { + id: serial('id').primaryKey(), + order: integer('order'), + parent: integer('parent_id').notNull(), + path: varchar('path').notNull(), + usersID: integer('users_id'), + }, + (columns) => [ + index('payload_preferences_rels_order_idx').on(columns.order), + index('payload_preferences_rels_parent_idx').on(columns.parent), + index('payload_preferences_rels_path_idx').on(columns.path), + index('payload_preferences_rels_users_id_idx').on(columns.usersID), + foreignKey({ + columns: [columns['parent']], + foreignColumns: [payload_preferences.id], + name: 'payload_preferences_rels_parent_fk', + }).onDelete('cascade'), + foreignKey({ + columns: [columns['usersID']], + foreignColumns: [users.id], + name: 'payload_preferences_rels_users_fk', + }).onDelete('cascade'), + ], +); + +export const payload_migrations = pgTable( + 'payload_migrations', + { + id: serial('id').primaryKey(), + name: varchar('name'), + batch: numeric('batch', { mode: 'number' }), + updatedAt: timestamp('updated_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + createdAt: timestamp('created_at', { mode: 'string', withTimezone: true, precision: 3 }) + .defaultNow() + .notNull(), + }, + (columns) => [ + index('payload_migrations_updated_at_idx').on(columns.updatedAt), + index('payload_migrations_created_at_idx').on(columns.createdAt), + ], +); + +export const relations_users_sessions = relations(users_sessions, ({ one }) => ({ + _parentID: one(users, { + fields: [users_sessions._parentID], + references: [users.id], + relationName: 'sessions', + }), +})); +export const relations_users = relations(users, ({ many }) => ({ + sessions: many(users_sessions, { + relationName: 'sessions', + }), +})); +export const relations_media = relations(media, () => ({})); +export const relations_posts = relations(posts, ({ one }) => ({ + featuredImage: one(media, { + fields: [posts.featuredImage], + references: [media.id], + relationName: 'featuredImage', + }), +})); +export const relations__posts_v = relations(_posts_v, ({ one }) => ({ + parent: one(posts, { + fields: [_posts_v.parent], + references: [posts.id], + relationName: 'parent', + }), + version_featuredImage: one(media, { + fields: [_posts_v.version_featuredImage], + references: [media.id], + relationName: 'version_featuredImage', + }), +})); +export const relations_form_submissions = relations(form_submissions, () => ({})); +export const relations_products_categories = relations(products_categories, ({ one }) => ({ + _parentID: one(products, { + fields: [products_categories._parentID], + references: [products.id], + relationName: 'categories', + }), +})); +export const relations_products_rels = relations(products_rels, ({ one }) => ({ + parent: one(products, { + fields: [products_rels.parent], + references: [products.id], + relationName: '_rels', + }), + mediaID: one(media, { + fields: [products_rels.mediaID], + references: [media.id], + relationName: 'media', + }), +})); +export const relations_products = relations(products, ({ many }) => ({ + categories: many(products_categories, { + relationName: 'categories', + }), + _rels: many(products_rels, { + relationName: '_rels', + }), +})); +export const relations__products_v_version_categories = relations( + _products_v_version_categories, + ({ one }) => ({ + _parentID: one(_products_v, { + fields: [_products_v_version_categories._parentID], + references: [_products_v.id], + relationName: 'version_categories', + }), + }), +); +export const relations__products_v_rels = relations(_products_v_rels, ({ one }) => ({ + parent: one(_products_v, { + fields: [_products_v_rels.parent], + references: [_products_v.id], + relationName: '_rels', + }), + mediaID: one(media, { + fields: [_products_v_rels.mediaID], + references: [media.id], + relationName: 'media', + }), +})); +export const relations__products_v = relations(_products_v, ({ one, many }) => ({ + parent: one(products, { + fields: [_products_v.parent], + references: [products.id], + relationName: 'parent', + }), + version_categories: many(_products_v_version_categories, { + relationName: 'version_categories', + }), + _rels: many(_products_v_rels, { + relationName: '_rels', + }), +})); +export const relations_payload_kv = relations(payload_kv, () => ({})); +export const relations_payload_locked_documents_rels = relations( + payload_locked_documents_rels, + ({ one }) => ({ + parent: one(payload_locked_documents, { + fields: [payload_locked_documents_rels.parent], + references: [payload_locked_documents.id], + relationName: '_rels', + }), + usersID: one(users, { + fields: [payload_locked_documents_rels.usersID], + references: [users.id], + relationName: 'users', + }), + mediaID: one(media, { + fields: [payload_locked_documents_rels.mediaID], + references: [media.id], + relationName: 'media', + }), + postsID: one(posts, { + fields: [payload_locked_documents_rels.postsID], + references: [posts.id], + relationName: 'posts', + }), + 'form-submissionsID': one(form_submissions, { + fields: [payload_locked_documents_rels['form-submissionsID']], + references: [form_submissions.id], + relationName: 'form-submissions', + }), + productsID: one(products, { + fields: [payload_locked_documents_rels.productsID], + references: [products.id], + relationName: 'products', + }), + }), +); +export const relations_payload_locked_documents = relations( + payload_locked_documents, + ({ many }) => ({ + _rels: many(payload_locked_documents_rels, { + relationName: '_rels', + }), + }), +); +export const relations_payload_preferences_rels = relations( + payload_preferences_rels, + ({ one }) => ({ + parent: one(payload_preferences, { + fields: [payload_preferences_rels.parent], + references: [payload_preferences.id], + relationName: '_rels', + }), + usersID: one(users, { + fields: [payload_preferences_rels.usersID], + references: [users.id], + relationName: 'users', + }), + }), +); +export const relations_payload_preferences = relations(payload_preferences, ({ many }) => ({ + _rels: many(payload_preferences_rels, { + relationName: '_rels', + }), +})); +export const relations_payload_migrations = relations(payload_migrations, () => ({})); + +type DatabaseSchema = { + enum_posts_locale: typeof enum_posts_locale; + enum_posts_status: typeof enum_posts_status; + enum__posts_v_version_locale: typeof enum__posts_v_version_locale; + enum__posts_v_version_status: typeof enum__posts_v_version_status; + enum_form_submissions_type: typeof enum_form_submissions_type; + enum_products_locale: typeof enum_products_locale; + enum_products_status: typeof enum_products_status; + enum__products_v_version_locale: typeof enum__products_v_version_locale; + enum__products_v_version_status: typeof enum__products_v_version_status; + users_sessions: typeof users_sessions; + users: typeof users; + media: typeof media; + posts: typeof posts; + _posts_v: typeof _posts_v; + form_submissions: typeof form_submissions; + products_categories: typeof products_categories; + products: typeof products; + products_rels: typeof products_rels; + _products_v_version_categories: typeof _products_v_version_categories; + _products_v: typeof _products_v; + _products_v_rels: typeof _products_v_rels; + payload_kv: typeof payload_kv; + payload_locked_documents: typeof payload_locked_documents; + payload_locked_documents_rels: typeof payload_locked_documents_rels; + payload_preferences: typeof payload_preferences; + payload_preferences_rels: typeof payload_preferences_rels; + payload_migrations: typeof payload_migrations; + relations_users_sessions: typeof relations_users_sessions; + relations_users: typeof relations_users; + relations_media: typeof relations_media; + relations_posts: typeof relations_posts; + relations__posts_v: typeof relations__posts_v; + relations_form_submissions: typeof relations_form_submissions; + relations_products_categories: typeof relations_products_categories; + relations_products_rels: typeof relations_products_rels; + relations_products: typeof relations_products; + relations__products_v_version_categories: typeof relations__products_v_version_categories; + relations__products_v_rels: typeof relations__products_v_rels; + relations__products_v: typeof relations__products_v; + relations_payload_kv: typeof relations_payload_kv; + relations_payload_locked_documents_rels: typeof relations_payload_locked_documents_rels; + relations_payload_locked_documents: typeof relations_payload_locked_documents; + relations_payload_preferences_rels: typeof relations_payload_preferences_rels; + relations_payload_preferences: typeof relations_payload_preferences; + relations_payload_migrations: typeof relations_payload_migrations; +}; + +declare module '@payloadcms/db-postgres' { + export interface GeneratedDatabaseSchema { + schema: DatabaseSchema; + } +} diff --git a/src/payload/blocks/AnimatedImage.ts b/src/payload/blocks/AnimatedImage.ts new file mode 100644 index 00000000..b8ade9e0 --- /dev/null +++ b/src/payload/blocks/AnimatedImage.ts @@ -0,0 +1,25 @@ +import { Block } from 'payload'; + +export const AnimatedImage: Block = { + slug: 'animatedImage', + fields: [ + { + name: 'src', + type: 'text', + required: true, + }, + { + name: 'alt', + type: 'text', + required: true, + }, + { + name: 'width', + type: 'number', + }, + { + name: 'height', + type: 'number', + }, + ], +}; diff --git a/src/payload/blocks/Callout.ts b/src/payload/blocks/Callout.ts new file mode 100644 index 00000000..c31f4db6 --- /dev/null +++ b/src/payload/blocks/Callout.ts @@ -0,0 +1,20 @@ +import { Block } from 'payload'; +import { lexicalEditor } from '@payloadcms/richtext-lexical'; + +export const Callout: Block = { + slug: 'callout', + fields: [ + { + name: 'type', + type: 'select', + options: ['info', 'warning', 'important', 'tip', 'caution'], + defaultValue: 'info', + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({}), + required: true, + }, + ], +}; diff --git a/src/payload/blocks/ChatBubble.ts b/src/payload/blocks/ChatBubble.ts new file mode 100644 index 00000000..3ec730f0 --- /dev/null +++ b/src/payload/blocks/ChatBubble.ts @@ -0,0 +1,34 @@ +import { Block } from 'payload'; +import { lexicalEditor } from '@payloadcms/richtext-lexical'; + +export const ChatBubble: Block = { + slug: 'chatBubble', + fields: [ + { + name: 'author', + type: 'text', + defaultValue: 'KLZ Team', + }, + { + name: 'avatar', + type: 'text', + }, + { + name: 'role', + type: 'text', + defaultValue: 'Assistant', + }, + { + name: 'align', + type: 'select', + options: ['left', 'right'], + defaultValue: 'left', + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({}), + required: true, + }, + ], +}; diff --git a/src/payload/blocks/ComparisonGrid.ts b/src/payload/blocks/ComparisonGrid.ts new file mode 100644 index 00000000..a9c127c2 --- /dev/null +++ b/src/payload/blocks/ComparisonGrid.ts @@ -0,0 +1,47 @@ +import { Block } from 'payload'; + +export const ComparisonGrid: Block = { + slug: 'comparisonGrid', + fields: [ + { + name: 'title', + label: 'Main Heading', + type: 'text', + required: true, + }, + { + name: 'leftLabel', + type: 'text', + required: true, + }, + { + name: 'rightLabel', + type: 'text', + required: true, + }, + { + name: 'items', + type: 'array', + required: true, + minRows: 1, + fields: [ + { + name: 'label', + label: 'Row Label', + type: 'text', + required: true, + }, + { + name: 'leftValue', + type: 'text', + required: true, + }, + { + name: 'rightValue', + type: 'text', + required: true, + }, + ], + }, + ], +}; diff --git a/src/payload/blocks/HighlightBox.ts b/src/payload/blocks/HighlightBox.ts new file mode 100644 index 00000000..9f5ef3ad --- /dev/null +++ b/src/payload/blocks/HighlightBox.ts @@ -0,0 +1,20 @@ +import { Block } from 'payload'; +import { lexicalEditor } from '@payloadcms/richtext-lexical'; + +export const HighlightBox: Block = { + slug: 'highlightBox', + fields: [ + { + name: 'type', + type: 'select', + options: ['info', 'warning', 'success', 'error', 'neutral'], + defaultValue: 'neutral', + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({}), + required: true, + }, + ], +}; diff --git a/src/payload/blocks/PowerCTA.ts b/src/payload/blocks/PowerCTA.ts new file mode 100644 index 00000000..d4d468a4 --- /dev/null +++ b/src/payload/blocks/PowerCTA.ts @@ -0,0 +1,12 @@ +import { Block } from 'payload'; + +export const PowerCTA: Block = { + slug: 'powerCTA', + fields: [ + { + name: 'locale', + type: 'text', + required: true, + }, + ], +}; diff --git a/src/payload/blocks/ProductTabs.ts b/src/payload/blocks/ProductTabs.ts new file mode 100644 index 00000000..822ae662 --- /dev/null +++ b/src/payload/blocks/ProductTabs.ts @@ -0,0 +1,96 @@ +import { Block } from 'payload'; + +export const ProductTabs: Block = { + slug: 'productTabs', + interfaceName: 'ProductTabsBlock', + fields: [ + { + name: 'technicalItems', + type: 'array', + fields: [ + { + name: 'label', + type: 'text', + required: true, + }, + { + name: 'value', + type: 'text', + required: true, + }, + { + name: 'unit', + type: 'text', + }, + ], + }, + { + name: 'voltageTables', + type: 'array', + fields: [ + { + name: 'voltageLabel', + type: 'text', + required: true, + }, + { + name: 'metaItems', + type: 'array', + fields: [ + { + name: 'label', + type: 'text', + required: true, + }, + { + name: 'value', + type: 'text', + required: true, + }, + { + name: 'unit', + type: 'text', + }, + ], + }, + { + name: 'columns', + type: 'array', + fields: [ + { + name: 'key', + type: 'text', + required: true, + }, + { + name: 'label', + type: 'text', + required: true, + }, + ], + }, + { + name: 'rows', + type: 'array', + fields: [ + { + name: 'configuration', + type: 'text', + required: true, + }, + { + name: 'cells', + type: 'array', + fields: [ + { + name: 'value', + type: 'text', + }, + ], + }, + ], + }, + ], + }, + ], +}; diff --git a/src/payload/blocks/SplitHeading.ts b/src/payload/blocks/SplitHeading.ts new file mode 100644 index 00000000..6c97fef4 --- /dev/null +++ b/src/payload/blocks/SplitHeading.ts @@ -0,0 +1,23 @@ +import { Block } from 'payload'; + +export const SplitHeading: Block = { + slug: 'splitHeading', + interfaceName: 'SplitHeadingBlock', + fields: [ + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'id', + type: 'text', + }, + { + name: 'level', + type: 'select', + options: ['h1', 'h2', 'h3', 'h4', 'h5', 'h6'], + defaultValue: 'h2', + }, + ], +}; diff --git a/src/payload/blocks/Stats.ts b/src/payload/blocks/Stats.ts new file mode 100644 index 00000000..ff3bc2be --- /dev/null +++ b/src/payload/blocks/Stats.ts @@ -0,0 +1,25 @@ +import { Block } from 'payload'; + +export const Stats: Block = { + slug: 'stats', + interfaceName: 'StatsBlock', + fields: [ + { + name: 'stats', + type: 'array', + required: true, + fields: [ + { + name: 'value', + type: 'text', + required: true, + }, + { + name: 'label', + type: 'text', + required: true, + }, + ], + }, + ], +}; diff --git a/src/payload/blocks/StickyNarrative.ts b/src/payload/blocks/StickyNarrative.ts new file mode 100644 index 00000000..8b32968d --- /dev/null +++ b/src/payload/blocks/StickyNarrative.ts @@ -0,0 +1,31 @@ +import { Block } from 'payload'; + +export const StickyNarrative: Block = { + slug: 'stickyNarrative', + fields: [ + { + name: 'title', + label: 'Main Heading', + type: 'text', + required: true, + }, + { + name: 'items', + type: 'array', + required: true, + minRows: 1, + fields: [ + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'content', + type: 'textarea', + required: true, + }, + ], + }, + ], +}; diff --git a/src/payload/blocks/TechnicalGrid.ts b/src/payload/blocks/TechnicalGrid.ts new file mode 100644 index 00000000..90b0ecb1 --- /dev/null +++ b/src/payload/blocks/TechnicalGrid.ts @@ -0,0 +1,30 @@ +import { Block } from 'payload'; + +export const TechnicalGrid: Block = { + slug: 'technicalGrid', + fields: [ + { + name: 'title', + label: 'Main Heading', + type: 'text', + }, + { + name: 'items', + type: 'array', + required: true, + minRows: 1, + fields: [ + { + name: 'label', + type: 'text', + required: true, + }, + { + name: 'value', + type: 'text', + required: true, + }, + ], + }, + ], +}; diff --git a/src/payload/blocks/VisualLinkPreview.ts b/src/payload/blocks/VisualLinkPreview.ts new file mode 100644 index 00000000..fe770dc2 --- /dev/null +++ b/src/payload/blocks/VisualLinkPreview.ts @@ -0,0 +1,30 @@ +import { Block } from 'payload'; + +export const VisualLinkPreview: Block = { + slug: 'visualLinkPreview', + fields: [ + { + name: 'url', + type: 'text', + required: true, + }, + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'summary', + type: 'textarea', + required: true, + }, + { + name: 'image', + type: 'text', + admin: { + description: 'Legacy HTTP string from the old hardcoded images.', + }, + required: false, + }, + ], +}; diff --git a/src/payload/blocks/allBlocks.ts b/src/payload/blocks/allBlocks.ts new file mode 100644 index 00000000..4872e3f6 --- /dev/null +++ b/src/payload/blocks/allBlocks.ts @@ -0,0 +1,27 @@ +import { AnimatedImage } from './AnimatedImage'; +import { Callout } from './Callout'; +import { ChatBubble } from './ChatBubble'; +import { ComparisonGrid } from './ComparisonGrid'; +import { HighlightBox } from './HighlightBox'; +import { PowerCTA } from './PowerCTA'; +import { ProductTabs } from './ProductTabs'; +import { SplitHeading } from './SplitHeading'; +import { Stats } from './Stats'; +import { StickyNarrative } from './StickyNarrative'; +import { TechnicalGrid } from './TechnicalGrid'; +import { VisualLinkPreview } from './VisualLinkPreview'; + +export const payloadBlocks = [ + AnimatedImage, + Callout, + ChatBubble, + ComparisonGrid, + HighlightBox, + PowerCTA, + ProductTabs, + SplitHeading, + Stats, + StickyNarrative, + TechnicalGrid, + VisualLinkPreview, +]; diff --git a/src/payload/collections/FormSubmissions.ts b/src/payload/collections/FormSubmissions.ts new file mode 100644 index 00000000..eee43c7f --- /dev/null +++ b/src/payload/collections/FormSubmissions.ts @@ -0,0 +1,67 @@ +import type { CollectionConfig } from 'payload'; + +export const FormSubmissions: CollectionConfig = { + slug: 'form-submissions', + admin: { + useAsTitle: 'name', + defaultColumns: ['name', 'email', 'type', 'createdAt'], + description: 'Captured leads from Contact and Product Quote forms.', + }, + access: { + // Only Admins can view and delete leads via dashboard. + read: ({ req: { user } }) => Boolean(user) || process.env.NODE_ENV === 'development', + update: ({ req: { user } }) => Boolean(user) || process.env.NODE_ENV === 'development', + delete: ({ req: { user } }) => Boolean(user) || process.env.NODE_ENV === 'development', + // Next.js server actions handle secure inserts natively. No public client create access. + create: () => false, + }, + fields: [ + { + name: 'name', + type: 'text', + required: true, + admin: { + readOnly: true, + }, + }, + { + name: 'email', + type: 'email', + required: true, + admin: { + readOnly: true, + }, + }, + { + name: 'type', + type: 'select', + options: [ + { label: 'General Contact', value: 'contact' }, + { label: 'Product Quote', value: 'product_quote' }, + ], + required: true, + admin: { + position: 'sidebar', + readOnly: true, + }, + }, + { + name: 'productName', + type: 'text', + admin: { + position: 'sidebar', + readOnly: true, + condition: (data) => data.type === 'product_quote', + description: 'The specific KLZ product the user requested a quote for.', + }, + }, + { + name: 'message', + type: 'textarea', + required: true, + admin: { + readOnly: true, + }, + }, + ], +}; diff --git a/src/payload/collections/Media.ts b/src/payload/collections/Media.ts new file mode 100644 index 00000000..927d5bda --- /dev/null +++ b/src/payload/collections/Media.ts @@ -0,0 +1,48 @@ +import type { CollectionConfig } from 'payload'; + +export const Media: CollectionConfig = { + slug: 'media', + access: { + read: () => true, + }, + admin: { + useAsTitle: 'alt', + defaultColumns: ['filename', 'alt', 'updatedAt'], + }, + upload: { + staticDir: 'public/media', + adminThumbnail: 'thumbnail', + imageSizes: [ + { + name: 'thumbnail', + width: 600, + // height: undefined allows wide 5:1 aspect ratios to be preserved without cropping + height: undefined, + position: 'centre', + }, + { + name: 'card', + width: 768, + height: undefined, + position: 'centre', + }, + { + name: 'tablet', + width: 1024, + height: undefined, + position: 'centre', + }, + ], + }, + fields: [ + { + name: 'alt', + type: 'text', + required: true, + }, + { + name: 'caption', + type: 'text', + }, + ], +}; diff --git a/src/payload/collections/Pages.ts b/src/payload/collections/Pages.ts new file mode 100644 index 00000000..8ba93b85 --- /dev/null +++ b/src/payload/collections/Pages.ts @@ -0,0 +1,61 @@ +import { CollectionConfig } from 'payload'; +import { lexicalEditor } from '@payloadcms/richtext-lexical'; + +export const Pages: CollectionConfig = { + slug: 'pages', + admin: { + useAsTitle: 'title', + defaultColumns: ['title', 'slug', 'locale', 'updatedAt'], + }, + access: { + read: () => true, + }, + fields: [ + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'slug', + type: 'text', + required: true, + admin: { + position: 'sidebar', + }, + }, + { + name: 'locale', + type: 'select', + options: [ + { label: 'English', value: 'en' }, + { label: 'German', value: 'de' }, + ], + required: true, + admin: { + position: 'sidebar', + }, + }, + { + name: 'excerpt', + type: 'textarea', + admin: { + position: 'sidebar', + }, + }, + { + name: 'featuredImage', + type: 'upload', + relationTo: 'media', + admin: { + position: 'sidebar', + }, + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({}), + required: true, + }, + ], +}; diff --git a/src/payload/collections/Posts.ts b/src/payload/collections/Posts.ts new file mode 100644 index 00000000..9fa2d622 --- /dev/null +++ b/src/payload/collections/Posts.ts @@ -0,0 +1,154 @@ +import type { CollectionConfig } from 'payload'; +import { lexicalEditor, BlocksFeature } from '@payloadcms/richtext-lexical'; + +import { StickyNarrative } from '../blocks/StickyNarrative'; +import { ComparisonGrid } from '../blocks/ComparisonGrid'; +import { VisualLinkPreview } from '../blocks/VisualLinkPreview'; +import { TechnicalGrid } from '../blocks/TechnicalGrid'; +import { HighlightBox } from '../blocks/HighlightBox'; +import { AnimatedImage } from '../blocks/AnimatedImage'; +import { ChatBubble } from '../blocks/ChatBubble'; +import { PowerCTA } from '../blocks/PowerCTA'; +import { Callout } from '../blocks/Callout'; +import { Stats } from '../blocks/Stats'; +import { SplitHeading } from '../blocks/SplitHeading'; + +export const Posts: CollectionConfig = { + slug: 'posts', + admin: { + useAsTitle: 'title', + defaultColumns: ['featuredImage', 'title', 'date', 'updatedAt', '_status'], + }, + versions: { + drafts: true, // Enables Draft/Published workflows + }, + access: { + read: ({ req: { user } }) => { + // In local development, always show everything (including Drafts and scheduled future posts) + if (process.env.NODE_ENV === 'development') { + return true; + } + + // If an Admin user is logged in, they can view everything + if (user) { + return true; + } + + // For public unauthenticated visitors in PROD/STAGING contexts: + // Only serve Posts where Status = "published" AND the publish Date is in the past! + return { + and: [ + { + _status: { + equals: 'published', + }, + }, + { + date: { + less_than_equal: new Date().toISOString(), + }, + }, + ], + }; + }, + }, + fields: [ + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'slug', + type: 'text', + required: true, + unique: true, + admin: { + position: 'sidebar', + }, + hooks: { + beforeValidate: [ + ({ value, data }) => { + // Auto-generate slug from title if left blank + if (value || !data?.title) return value; + return data.title + .toLowerCase() + .replace(/ /g, '-') + .replace(/[^\w-]+/g, ''); + }, + ], + }, + }, + { + name: 'excerpt', + type: 'text', + admin: { + description: 'A short summary for blog feed cards and SEO.', + }, + }, + { + name: 'date', + type: 'date', + required: true, + admin: { + position: 'sidebar', + description: 'Future dates will schedule the post to publish automatically.', + }, + defaultValue: () => new Date().toISOString(), + }, + { + name: 'featuredImage', + type: 'upload', + relationTo: 'media', + admin: { + position: 'sidebar', + description: 'The primary Hero image used for headers and OpenGraph previews.', + }, + }, + { + name: 'locale', + type: 'select', + required: true, + admin: { + position: 'sidebar', + }, + options: [ + { label: 'English', value: 'en' }, + { label: 'German', value: 'de' }, + ], + defaultValue: 'en', + }, + { + name: 'category', + type: 'text', + admin: { + position: 'sidebar', + description: 'Used for tag bucketing (e.g. "Kabel Technologie").', + }, + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({ + features: ({ defaultFeatures }) => [ + ...defaultFeatures, + BlocksFeature({ + blocks: [ + StickyNarrative, + ComparisonGrid, + VisualLinkPreview, + TechnicalGrid, + HighlightBox, + AnimatedImage, + ChatBubble, + PowerCTA, + Callout, + Stats, + SplitHeading, + ], + }), + ], + }), + }, + ], +}; diff --git a/src/payload/collections/Products.ts b/src/payload/collections/Products.ts new file mode 100644 index 00000000..6f7edc40 --- /dev/null +++ b/src/payload/collections/Products.ts @@ -0,0 +1,144 @@ +import type { CollectionConfig } from 'payload'; +import { lexicalEditor, BlocksFeature } from '@payloadcms/richtext-lexical'; + +import { StickyNarrative } from '../blocks/StickyNarrative'; +import { ComparisonGrid } from '../blocks/ComparisonGrid'; +import { VisualLinkPreview } from '../blocks/VisualLinkPreview'; +import { TechnicalGrid } from '../blocks/TechnicalGrid'; +import { HighlightBox } from '../blocks/HighlightBox'; +import { AnimatedImage } from '../blocks/AnimatedImage'; +import { ChatBubble } from '../blocks/ChatBubble'; +import { PowerCTA } from '../blocks/PowerCTA'; +import { Callout } from '../blocks/Callout'; +import { Stats } from '../blocks/Stats'; +import { SplitHeading } from '../blocks/SplitHeading'; +import { ProductTabs } from '../blocks/ProductTabs'; + +export const Products: CollectionConfig = { + slug: 'products', + admin: { + useAsTitle: 'title', + defaultColumns: ['featuredImage', 'title', 'sku', 'locale', 'updatedAt', '_status'], + }, + versions: { + drafts: true, + }, + access: { + read: ({ req: { user } }) => { + if (process.env.NODE_ENV === 'development') { + return true; + } + if (user) { + return true; + } + return { + _status: { + equals: 'published', + }, + }; + }, + }, + fields: [ + { + name: 'title', + type: 'text', + required: true, + }, + { + name: 'sku', + type: 'text', + required: true, + admin: { + position: 'sidebar', + }, + }, + { + name: 'slug', + type: 'text', + required: true, + admin: { + position: 'sidebar', + }, + }, + { + name: 'description', + type: 'textarea', + required: true, + }, + { + name: 'locale', + type: 'select', + required: true, + admin: { + position: 'sidebar', + }, + options: [ + { label: 'English', value: 'en' }, + { label: 'German', value: 'de' }, + ], + defaultValue: 'de', + }, + { + name: 'categories', + type: 'array', + required: true, + fields: [ + { + name: 'category', + type: 'text', + }, + ], + admin: { + position: 'sidebar', + }, + }, + { + name: 'featuredImage', + type: 'upload', + relationTo: 'media', + admin: { + position: 'sidebar', + description: 'The primary thumbnail used in list views.', + }, + }, + { + name: 'images', + type: 'upload', + relationTo: 'media', + hasMany: true, + admin: { + position: 'sidebar', + }, + }, + { + name: 'application', + type: 'richText', + editor: lexicalEditor({}), + }, + { + name: 'content', + type: 'richText', + editor: lexicalEditor({ + features: ({ defaultFeatures }) => [ + ...defaultFeatures, + BlocksFeature({ + blocks: [ + StickyNarrative, + ComparisonGrid, + VisualLinkPreview, + TechnicalGrid, + HighlightBox, + AnimatedImage, + ChatBubble, + PowerCTA, + Callout, + Stats, + SplitHeading, + ProductTabs, + ], + }), + ], + }), + }, + ], +}; diff --git a/src/payload/collections/Users.ts b/src/payload/collections/Users.ts new file mode 100644 index 00000000..a10c97ed --- /dev/null +++ b/src/payload/collections/Users.ts @@ -0,0 +1,12 @@ +import type { CollectionConfig } from 'payload'; + +export const Users: CollectionConfig = { + slug: 'users', + admin: { + useAsTitle: 'email', + }, + auth: true, + fields: [ + // Email added by default + ], +}; diff --git a/src/payload/utils/lexicalParser.ts b/src/payload/utils/lexicalParser.ts new file mode 100644 index 00000000..b3cf4aed --- /dev/null +++ b/src/payload/utils/lexicalParser.ts @@ -0,0 +1,296 @@ +/** + * Converts a Markdown+JSX string into a Lexical AST node array. + * Specifically adapted for klz-cables.com custom Component Blocks. + */ + +function propValue(chunk: string, prop: string): string { + // Match prop="value" or prop='value' or prop={value} + // and also multiline props like prop={\n [\n {...}\n ]\n} + // For arrays or complex objects passed as props, basic regex might fail, + // but the MDX in klz-cables usually uses simpler props or children. + const match = + chunk.match(new RegExp(`${prop}=["']([^"']+)["']`)) || + chunk.match(new RegExp(`${prop}=\\{([^}]+)\\}`)); + return match ? match[1] : ''; +} + +function extractItemsProp(chunk: string, startTag: string): any[] { + // Match items={ [ ... ] } robustly without stopping at inner object braces + const itemsMatch = chunk.match(/items=\{\s*(\[[\s\S]*?\])\s*\}/); + if (itemsMatch) { + try { + const arrayString = itemsMatch[1].trim(); + // Since klz-cables MDX passes pure JS object arrays like `items={[{title: 'A', content: 'B'}]}`, + // parsing it via Regex to JSON is extremely brittle due to unquoted keys and trailing commas. + // Using `new Function` safely evaluates the array AST directly in this Node script environment. + const fn = new Function(`return ${arrayString};`); + return fn(); + } catch (_e: any) { + console.warn(`Could not parse items array for block ${startTag}:`, _e.message); + return []; + } + } + return []; +} + +function blockNode(blockType: string, fields: Record) { + return { type: 'block', format: '', version: 2, fields: { blockType, ...fields } }; +} + +function ensureChildren(parsedNodes: any[]): any[] { + // Lexical root nodes require at least one child node, or validation fails + if (parsedNodes.length === 0) { + return [ + { + type: 'paragraph', + format: '', + indent: 0, + version: 1, + children: [{ mode: 'normal', type: 'text', text: ' ', version: 1 }], + }, + ]; + } + return parsedNodes; +} + +export function parseMarkdownToLexical(markdown: string): any[] { + const textNode = (text: string) => ({ + type: 'paragraph', + format: '', + indent: 0, + version: 1, + children: [{ mode: 'normal', type: 'text', text, version: 1 }], + }); + + const nodes: any[] = []; + let content = markdown; + + // Strip frontmatter + const fm = content.match(/^---\s*\n[\s\S]*?\n---/); + if (fm) content = content.replace(fm[0], '').trim(); + + // 1. EXTRACT MULTILINE WRAPPERS BEFORE CHUNKING + // This allows nested newlines inside components without breaking them. + const extractBlocks = [ + { + tag: 'HighlightBox', + regex: /]*)>([\s\S]*?)<\/HighlightBox>/g, + build: (props: string, inner: string) => + blockNode('highlightBox', { + title: propValue(``, 'title'), + color: propValue(``, 'color') || 'primary', + content: { + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + direction: 'ltr', + children: ensureChildren(parseMarkdownToLexical(inner.trim())), + }, + }, + }), + }, + { + tag: 'ChatBubble', + regex: /]*)>([\s\S]*?)<\/ChatBubble>/g, + build: (props: string, inner: string) => + blockNode('chatBubble', { + author: propValue(``, 'author') || 'KLZ Team', + avatar: propValue(``, 'avatar'), + role: propValue(``, 'role') || 'Assistant', + align: propValue(``, 'align') || 'left', + content: { + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + direction: 'ltr', + children: ensureChildren(parseMarkdownToLexical(inner.trim())), + }, + }, + }), + }, + { + tag: 'Callout', + regex: /]*)>([\s\S]*?)<\/Callout>/g, + build: (props: string, inner: string) => + blockNode('callout', { + type: propValue(``, 'type') || 'info', + title: propValue(``, 'title'), + content: { + root: { + type: 'root', + format: '', + indent: 0, + version: 1, + direction: 'ltr', + children: ensureChildren(parseMarkdownToLexical(inner.trim())), + }, + }, + }), + }, + ]; + + // Placeholder map to temporarily store extracted multi-line blocks + const placeholders = new Map(); + let placeholderIdx = 0; + + for (const block of extractBlocks) { + content = content.replace(block.regex, (match, propsMatch, innerMatch) => { + const id = `__BLOCK_PLACEHOLDER_${placeholderIdx++}__`; + placeholders.set(id, block.build(propsMatch, innerMatch)); + return `\n\n${id}\n\n`; // Pad with newlines so it becomes its own chunk + }); + } + + // 2. CHUNK THE REST (Paragraphs, Single-line Components) + const rawChunks = content.split(/\n\s*\n/); + + for (let chunk of rawChunks) { + chunk = chunk.trim(); + if (!chunk) continue; + + // Has Placeholder? + if (chunk.startsWith('__BLOCK_PLACEHOLDER_')) { + nodes.push(placeholders.get(chunk)); + continue; + } + + // --- Custom Component: ProductTabs --- + if (chunk.includes('/); + if (dataMatch) { + try { + const parsedData = JSON.parse(dataMatch[1]); + + // Normalize String Arrays to Payload Object Arrays { value: "string" } + if (parsedData.voltageTables) { + parsedData.voltageTables.forEach((vt: any) => { + if (vt.rows) { + vt.rows.forEach((row: any) => { + if (row.cells && Array.isArray(row.cells)) { + row.cells = row.cells.map((cell: any) => + typeof cell !== 'object' || cell === null ? { value: String(cell) } : cell, + ); + } + }); + } + }); + } + + nodes.push( + blockNode('productTabs', { + technicalItems: parsedData.technicalItems || [], + voltageTables: parsedData.voltageTables || [], + }), + ); + } catch (e: any) { + console.warn(`Could not parse JSON payload for ProductTabs:`, e.message); + } + } + continue; + } + + // --- Custom Component: StickyNarrative --- + if (chunk.includes('