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Author SHA1 Message Date
79d221de5e chore: sync lockfile and payload-ai extensions for release v1.9.10
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2026-03-03 12:40:41 +01:00
24fde20030 chore: release v1.9.10
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2026-03-03 12:40:02 +01:00
4a4409ca85 chore: remove accidentally tracked wip packages breaking lockfile 2026-03-03 12:39:59 +01:00
d96d6a4b13 chore: release v1.9.9
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2026-03-03 12:24:39 +01:00
8f6b12d827 fix(packages): remove private flag from all feature/engine packages to allow npm publish 2026-03-03 12:24:38 +01:00
a11714d07d chore(ci): migrate docker registry publishers to git.infra.mintel.me 2026-03-03 12:13:39 +01:00
52f7e68f25 chore: release v1.9.8
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2026-03-03 11:52:29 +01:00
217ac33675 chore: release v1.9.8
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2026-03-03 11:44:54 +01:00
f2b8b136af chore: release v1.9.7
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2026-03-02 21:16:51 +01:00
2e07b213d1 chore: remove unused 3d dependencies in gatekeeper to fix lint 2026-03-02 21:16:49 +01:00
a2c1eaefba chore: release v1.9.6
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2026-03-02 21:08:34 +01:00
80ff266f9c fix: allow vitest to pass with no tests in seo-engine
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2026-03-02 21:04:14 +01:00
6b1c5b7e30 chore: release @mintel/payload-ai
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2026-03-02 21:00:51 +01:00
80eefad5ea feat: extract reusable @mintel/payload-ai package 2026-03-02 21:00:09 +01:00
61 changed files with 4534 additions and 534 deletions

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@@ -1,7 +0,0 @@
---
"@mintel/monorepo": patch
"acquisition-manager": patch
"feedback-commander": patch
---
fix: make directus extension build scripts more resilient

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@@ -1,5 +1,5 @@
# Project
IMAGE_TAG=v1.9.5
IMAGE_TAG=v1.9.10
PROJECT_NAME=sample-website
PROJECT_COLOR=#82ed20

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@@ -202,9 +202,9 @@ jobs:
- name: 🔐 Registry Login
uses: docker/login-action@v3
with:
registry: registry.infra.mintel.me
username: ${{ secrets.REGISTRY_USER }}
password: ${{ secrets.REGISTRY_PASS }}
registry: git.infra.mintel.me
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: 🏗️ Build & Push ${{ matrix.name }}
uses: docker/build-push-action@v5
@@ -218,6 +218,6 @@ jobs:
secrets: |
NPM_TOKEN=${{ secrets.NPM_TOKEN }}
tags: |
registry.infra.mintel.me/mintel/${{ matrix.image }}:${{ github.ref_name }}
registry.infra.mintel.me/mintel/${{ matrix.image }}:latest
git.infra.mintel.me/mmintel/${{ matrix.image }}:${{ github.ref_name }}
git.infra.mintel.me/mmintel/${{ matrix.image }}:latest

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@@ -1,5 +1,5 @@
# Stage 1: Builder
FROM registry.infra.mintel.me/mintel/nextjs:latest AS builder
FROM git.infra.mintel.me/mmintel/nextjs:latest AS builder
WORKDIR /app
# Clean the workspace in case the base image is dirty
@@ -37,7 +37,7 @@ COPY . .
RUN pnpm build
# Stage 2: Runner
FROM registry.infra.mintel.me/mintel/runtime:latest AS runner
FROM git.infra.mintel.me/mmintel/runtime:latest AS runner
WORKDIR /app
ENV HOSTNAME="0.0.0.0"

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@@ -1,6 +1,6 @@
{
"name": "sample-website",
"version": "1.9.5",
"version": "1.9.10",
"private": true,
"type": "module",
"scripts": {

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@@ -6,6 +6,10 @@
"build": "pnpm -r build",
"dev": "pnpm -r dev",
"dev:gatekeeper": "bash -c 'trap \"COMPOSE_PROJECT_NAME=gatekeeper docker-compose -f docker-compose.gatekeeper.yml down\" EXIT INT TERM; docker network create infra 2>/dev/null || true && COMPOSE_PROJECT_NAME=gatekeeper docker-compose -f docker-compose.gatekeeper.yml down && COMPOSE_PROJECT_NAME=gatekeeper docker-compose -f docker-compose.gatekeeper.yml up --build --remove-orphans'",
"dev:mcps:up": "docker-compose -f docker-compose.mcps.yml up -d",
"dev:mcps:down": "docker-compose -f docker-compose.mcps.yml down",
"dev:mcps:watch": "pnpm -r --filter=\"./packages/*-mcp\" run dev",
"dev:mcps": "npm run dev:mcps:up && npm run dev:mcps:watch",
"lint": "pnpm -r --filter='./packages/**' --filter='./apps/**' lint",
"test": "pnpm -r test",
"changeset": "changeset",
@@ -49,7 +53,7 @@
"pino-pretty": "^13.1.3",
"require-in-the-middle": "^8.0.1"
},
"version": "1.9.5",
"version": "1.9.10",
"pnpm": {
"onlyBuiltDependencies": [
"@parcel/watcher",

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@@ -1,6 +1,6 @@
{
"name": "@mintel/cli",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

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@@ -1,6 +1,6 @@
{
"name": "@mintel/cloner",
"version": "1.9.5",
"version": "1.9.10",
"type": "module",
"main": "dist/index.js",
"module": "dist/index.js",

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/concept-engine",
"version": "1.9.5",
"version": "1.9.10",
"private": true,
"description": "AI-powered web project concept generation and analysis",
"type": "module",

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@@ -1,6 +1,6 @@
{
"name": "@mintel/content-engine",
"version": "1.9.5",
"version": "1.9.10",
"private": false,
"type": "module",
"main": "./dist/index.js",

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@@ -1,6 +1,6 @@
{
"name": "@mintel/eslint-config",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

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@@ -1,6 +1,6 @@
{
"name": "@mintel/estimation-engine",
"version": "1.9.5",
"version": "1.9.10",
"private": true,
"type": "module",
"main": "./dist/index.js",

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@@ -1,6 +1,6 @@
{
"name": "@mintel/gatekeeper",
"version": "1.9.6",
"version": "1.9.10",
"private": true,
"type": "module",
"scripts": {
@@ -12,14 +12,11 @@
},
"dependencies": {
"@mintel/next-utils": "workspace:*",
"@react-three/drei": "^10.7.7",
"@react-three/fiber": "^9.5.0",
"framer-motion": "^11.18.2",
"lucide-react": "^0.474.0",
"next": "16.1.6",
"react": "^19.0.0",
"react-dom": "^19.0.0",
"three": "^0.183.1"
"react-dom": "^19.0.0"
},
"devDependencies": {
"@mintel/eslint-config": "workspace:*",
@@ -29,7 +26,6 @@
"@types/node": "^20.0.0",
"@types/react": "^19.0.0",
"@types/react-dom": "^19.0.0",
"@types/three": "^0.183.1",
"autoprefixer": "^10.4.20",
"postcss": "^8.4.49",
"tailwindcss": "^3.4.17",

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@@ -1,20 +1,20 @@
{
"name": "@mintel/gitea-mcp",
"version": "1.0.0",
"description": "Native Gitea MCP server for 100% Antigravity compatibility",
"main": "dist/index.js",
"type": "module",
"scripts": {
"build": "tsc",
"start": "node dist/index.js"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^1.5.0",
"zod": "^3.23.8",
"axios": "^1.7.2"
},
"devDependencies": {
"typescript": "^5.5.3",
"@types/node": "^20.14.10"
}
}
"name": "@mintel/gitea-mcp",
"version": "1.9.10",
"description": "Native Gitea MCP server for 100% Antigravity compatibility",
"main": "dist/index.js",
"type": "module",
"scripts": {
"build": "tsc",
"start": "node dist/index.js"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^1.5.0",
"zod": "^3.23.8",
"axios": "^1.7.2"
},
"devDependencies": {
"typescript": "^5.5.3",
"@types/node": "^20.14.10"
}
}

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@@ -1,6 +1,6 @@
{
"name": "@mintel/husky-config",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

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@@ -1,5 +1,5 @@
# Start from the pre-built Nextjs Base image
FROM registry.infra.mintel.me/mintel/nextjs:latest AS builder
FROM git.infra.mintel.me/mmintel/nextjs:latest AS builder
WORKDIR /app
@@ -20,7 +20,7 @@ ENV DIRECTUS_URL=$DIRECTUS_URL
RUN pnpm --filter ${APP_NAME:-app} build
# Production runner image
FROM registry.infra.mintel.me/mintel/runtime:latest AS runner
FROM git.infra.mintel.me/mmintel/runtime:latest AS runner
WORKDIR /app
# Copy standalone output and static files

View File

@@ -38,7 +38,7 @@ services:
- "traefik.http.middlewares.${PROJECT_NAME:-app}-auth.forwardauth.authResponseHeaders=X-Auth-User"
gatekeeper:
image: registry.infra.mintel.me/mintel/gatekeeper:${IMAGE_TAG:-latest}
image: git.infra.mintel.me/mmintel/gatekeeper:${IMAGE_TAG:-latest}
restart: always
networks:
- infra
@@ -53,7 +53,7 @@ services:
- "traefik.http.services.${PROJECT_NAME}-gatekeeper.loadbalancer.server.port=3000"
directus:
image: registry.infra.mintel.me/mintel/directus:${IMAGE_TAG:-latest}
image: git.infra.mintel.me/mmintel/directus:${IMAGE_TAG:-latest}
restart: always
networks:
- infra

View File

@@ -180,9 +180,9 @@ jobs:
- name: 🔐 Registry Login
uses: docker/login-action@v3
with:
registry: registry.infra.mintel.me
username: ${{ secrets.REGISTRY_USER }}
password: ${{ secrets.REGISTRY_PASS }}
registry: git.infra.mintel.me
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: 🏗️ Docker Build & Push
uses: docker/build-push-action@v5
@@ -198,7 +198,7 @@ jobs:
push: true
secrets: |
NPM_TOKEN=${{ secrets.NPM_TOKEN }}
tags: registry.infra.mintel.me/mintel/${{ github.event.repository.name }}:${{ needs.prepare.outputs.image_tag }}
tags: git.infra.mintel.me/mmintel/${{ github.event.repository.name }}:${{ needs.prepare.outputs.image_tag }}
# ──────────────────────────────────────────────────────────────────────────────
# JOB 4: Deploy
@@ -262,7 +262,7 @@ jobs:
set -e
cd "/home/deploy/sites/${{ github.event.repository.name }}"
chmod 600 "$ENV_FILE"
echo "${{ secrets.REGISTRY_PASS }}" | docker login registry.infra.mintel.me -u "${{ secrets.REGISTRY_USER }}" --password-stdin
echo "${{ secrets.GITHUB_TOKEN }}" | docker login git.infra.mintel.me -u "${{ github.actor }}" --password-stdin
docker compose -p "$PROJECT_NAME" --env-file "$ENV_FILE" pull
docker compose -p "$PROJECT_NAME" --env-file "$ENV_FILE" up -d --remove-orphans
docker system prune -f --filter "until=24h"

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@@ -1,6 +1,6 @@
{
"name": "@mintel/infra",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

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@@ -1,6 +1,6 @@
{
"name": "@mintel/journaling",
"version": "1.9.5",
"version": "1.9.10",
"private": true,
"type": "module",
"main": "./dist/index.js",

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@@ -1,6 +1,6 @@
{
"name": "@mintel/mail",
"version": "1.9.5",
"version": "1.9.10",
"private": false,
"publishConfig": {
"access": "public",

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/meme-generator",
"version": "1.9.5",
"version": "1.9.10",
"private": false,
"type": "module",
"main": "./dist/index.js",

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@@ -0,0 +1,25 @@
{
"name": "@mintel/memory-mcp",
"version": "1.9.10",
"description": "Local Qdrant-based Memory MCP server",
"main": "dist/index.js",
"type": "module",
"scripts": {
"build": "tsc",
"start": "node dist/index.js",
"dev": "tsx watch src/index.ts",
"test:unit": "vitest run"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^1.5.0",
"@qdrant/js-client-rest": "^1.12.0",
"@xenova/transformers": "^2.17.2",
"zod": "^3.23.8"
},
"devDependencies": {
"typescript": "^5.5.3",
"@types/node": "^20.14.10",
"tsx": "^4.19.1",
"vitest": "^2.1.3"
}
}

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@@ -0,0 +1,78 @@
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { z } from 'zod';
import { QdrantMemoryService } from './qdrant.js';
async function main() {
const server = new McpServer({
name: '@mintel/memory-mcp',
version: '1.0.0',
});
const qdrantService = new QdrantMemoryService(process.env.QDRANT_URL || 'http://localhost:6333');
// Initialize embedding model and Qdrant connection
try {
await qdrantService.initialize();
} catch (e) {
console.error('Failed to initialize local dependencies. Exiting.');
process.exit(1);
}
server.tool(
'store_memory',
'Store a new piece of knowledge/memory into the vector database. Use this to remember architectural decisions, preferences, aliases, etc.',
{
label: z.string().describe('A short, descriptive label or title for the memory (e.g., "Architektur-Entscheidungen")'),
content: z.string().describe('The actual content to remember (e.g., "In diesem Projekt nutzen wir lieber Composition over Inheritance.")'),
},
async (args) => {
const success = await qdrantService.storeMemory(args.label, args.content);
if (success) {
return {
content: [{ type: 'text', text: `Successfully stored memory: [${args.label}]` }],
};
} else {
return {
content: [{ type: 'text', text: `Failed to store memory: [${args.label}]` }],
isError: true,
};
}
}
);
server.tool(
'retrieve_memory',
'Retrieve relevant memories from the vector database based on a semantic search query.',
{
query: z.string().describe('The search query to find relevant memories.'),
limit: z.number().optional().describe('Maximum number of results to return (default: 5)'),
},
async (args) => {
const results = await qdrantService.retrieveMemory(args.query, args.limit || 5);
if (results.length === 0) {
return {
content: [{ type: 'text', text: 'No relevant memories found.' }],
};
}
const formattedResults = results
.map(r => `- [${r.label}] (Score: ${r.score.toFixed(3)}): ${r.content}`)
.join('\n');
return {
content: [{ type: 'text', text: `Found ${results.length} memories:\n\n${formattedResults}` }],
};
}
);
const transport = new StdioServerTransport();
await server.connect(transport);
console.error('Memory MCP server is running and ready to accept connections over stdio.');
}
main().catch((error) => {
console.error('Fatal error in main():', error);
process.exit(1);
});

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@@ -0,0 +1,89 @@
import { describe, it, expect, vi, beforeEach } from 'vitest';
import { QdrantMemoryService } from './qdrant.js';
vi.mock('@xenova/transformers', () => {
return {
env: { allowRemoteModels: false, localModelPath: './models' },
pipeline: vi.fn().mockResolvedValue(async (text: string) => {
// Mock embedding generation: returns an array of 384 numbers
return { data: new Float32Array(384).fill(0.1) };
}),
};
});
const mockCreateCollection = vi.fn();
const mockGetCollections = vi.fn().mockResolvedValue({ collections: [] });
const mockUpsert = vi.fn();
const mockSearch = vi.fn().mockResolvedValue([
{
id: 'test-id',
version: 1,
score: 0.9,
payload: { label: 'Test Label', content: 'Test Content' }
}
]);
vi.mock('@qdrant/js-client-rest', () => {
return {
QdrantClient: vi.fn().mockImplementation(() => {
return {
getCollections: mockGetCollections,
createCollection: mockCreateCollection,
upsert: mockUpsert,
search: mockSearch
};
})
};
});
describe('QdrantMemoryService', () => {
let service: QdrantMemoryService;
beforeEach(() => {
vi.clearAllMocks();
service = new QdrantMemoryService('http://localhost:6333');
});
it('should initialize and create collection if missing', async () => {
mockGetCollections.mockResolvedValueOnce({ collections: [] });
await service.initialize();
expect(mockGetCollections).toHaveBeenCalled();
expect(mockCreateCollection).toHaveBeenCalledWith('mcp_memory', expect.any(Object));
});
it('should not create collection if it already exists', async () => {
mockGetCollections.mockResolvedValueOnce({ collections: [{ name: 'mcp_memory' }] });
await service.initialize();
expect(mockCreateCollection).not.toHaveBeenCalled();
});
it('should store memory', async () => {
await service.initialize();
const result = await service.storeMemory('Design', 'Composition over Inheritance');
expect(result).toBe(true);
expect(mockUpsert).toHaveBeenCalledWith('mcp_memory', expect.objectContaining({
wait: true,
points: expect.arrayContaining([
expect.objectContaining({
payload: expect.objectContaining({
label: 'Design',
content: 'Composition over Inheritance'
})
})
])
}));
});
it('should retrieve memory', async () => {
await service.initialize();
const results = await service.retrieveMemory('Design');
expect(results).toHaveLength(1);
expect(results[0].label).toBe('Test Label');
expect(results[0].content).toBe('Test Content');
expect(results[0].score).toBe(0.9);
});
});

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@@ -0,0 +1,110 @@
import { pipeline, env } from '@xenova/transformers';
import { QdrantClient } from '@qdrant/js-client-rest';
// Be sure to set local caching options for transformers
env.allowRemoteModels = true;
env.localModelPath = './models';
export class QdrantMemoryService {
private client: QdrantClient;
private collectionName = 'mcp_memory';
private embedder: any = null;
constructor(url: string = 'http://localhost:6333') {
this.client = new QdrantClient({ url });
}
/**
* Initializes the embedding model and the Qdrant collection
*/
async initialize() {
// 1. Load the embedding model (using a lightweight model suitable for semantic search)
console.error('Loading embedding model...');
this.embedder = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
// 2. Ensure collection exists
console.error(`Checking for collection: ${this.collectionName}`);
try {
const collections = await this.client.getCollections();
const exists = collections.collections.some(c => c.name === this.collectionName);
if (!exists) {
console.error(`Creating collection: ${this.collectionName}`);
await this.client.createCollection(this.collectionName, {
vectors: {
size: 384, // size for all-MiniLM-L6-v2
distance: 'Cosine'
}
});
console.error('Collection created successfully.');
}
} catch (e) {
console.error('Failed to initialize Qdrant collection:', e);
throw e;
}
}
/**
* Generates a vector embedding for the given text
*/
private async getEmbedding(text: string): Promise<number[]> {
if (!this.embedder) {
throw new Error('Embedder not initialized. Call initialize() first.');
}
const output = await this.embedder(text, { pooling: 'mean', normalize: true });
return Array.from(output.data);
}
/**
* Stores a memory entry into Qdrant
*/
async storeMemory(label: string, content: string): Promise<boolean> {
try {
const fullText = `${label}: ${content}`;
const vector = await this.getEmbedding(fullText);
const id = crypto.randomUUID();
await this.client.upsert(this.collectionName, {
wait: true,
points: [
{
id,
vector,
payload: {
label,
content,
timestamp: new Date().toISOString()
}
}
]
});
return true;
} catch (e) {
console.error('Failed to store memory:', e);
return false;
}
}
/**
* Retrieves memory entries relevant to the query
*/
async retrieveMemory(query: string, limit: number = 5): Promise<Array<{ label: string, content: string, score: number }>> {
try {
const vector = await this.getEmbedding(query);
const searchResults = await this.client.search(this.collectionName, {
vector,
limit,
with_payload: true
});
return searchResults.map(result => ({
label: String(result.payload?.label || ''),
content: String(result.payload?.content || ''),
score: result.score
}));
} catch (e) {
console.error('Failed to retrieve memory:', e);
return [];
}
}
}

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@@ -0,0 +1,16 @@
{
"compilerOptions": {
"target": "ES2022",
"module": "NodeNext",
"moduleResolution": "NodeNext",
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true
},
"include": [
"src/**/*"
]
}

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/next-config",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/next-feedback",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/next-observability",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/next-utils",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/observability",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/page-audit",
"version": "1.9.5",
"version": "1.9.10",
"private": true,
"description": "AI-powered website IST-analysis using DataForSEO and Gemini",
"type": "module",

View File

@@ -0,0 +1,7 @@
# @mintel/payload-ai
## 1.1.0
### Minor Changes
- Initial release of the @mintel/payload-ai package

View File

@@ -0,0 +1,52 @@
{
"name": "@mintel/payload-ai",
"version": "1.9.10",
"private": true,
"description": "Reusable Payload CMS AI Extensions",
"type": "module",
"scripts": {
"build": "tsc",
"typecheck": "tsc --noEmit"
},
"main": "./dist/index.js",
"types": "./dist/index.d.ts",
"exports": {
".": "./dist/index.js",
"./components/*": "./dist/components/*",
"./actions/*": "./dist/actions/*",
"./globals/*": "./dist/globals/*",
"./endpoints/*": "./dist/endpoints/*",
"./utils/*": "./dist/utils/*",
"./tools/*": "./dist/tools/*"
},
"peerDependencies": {
"@payloadcms/next": ">=3.0.0",
"@payloadcms/ui": ">=3.0.0",
"payload": ">=3.0.0",
"react": ">=18.0.0",
"react-dom": ">=18.0.0"
},
"dependencies": {
"@ai-sdk/openai": "^3.0.39",
"@ai-sdk/react": "^3.0.110",
"@mintel/content-engine": "workspace:*",
"@mintel/thumbnail-generator": "workspace:*",
"@modelcontextprotocol/sdk": "^1.27.1",
"@qdrant/js-client-rest": "^1.17.0",
"ai": "^6.0.108",
"replicate": "^1.4.0",
"zod": "^3.25.76"
},
"devDependencies": {
"@payloadcms/next": "3.77.0",
"@payloadcms/ui": "3.77.0",
"@types/node": "^20.17.17",
"@types/react": "^19.2.8",
"@types/react-dom": "^19.2.3",
"next": "^15.1.0",
"payload": "3.77.0",
"react": "^19.2.3",
"react-dom": "^19.2.3",
"typescript": "^5.7.3"
}
}

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@@ -0,0 +1,191 @@
"use server";
import { getPayloadHMR } from "@payloadcms/next/utilities";
// @ts-ignore - dynamic config resolution from next.js payload plugin
import configPromise from "@payload-config";
import * as fs from "node:fs/promises";
import * as path from "node:path";
import * as os from "node:os";
async function getOrchestrator() {
const OPENROUTER_KEY =
process.env.OPENROUTER_KEY || process.env.OPENROUTER_API_KEY;
const REPLICATE_KEY = process.env.REPLICATE_API_KEY;
if (!OPENROUTER_KEY) {
throw new Error(
"Missing OPENROUTER_API_KEY in .env (Required for AI generation)",
);
}
const importDynamic = new Function("modulePath", "return import(modulePath)");
const { AiBlogPostOrchestrator } = await importDynamic(
"@mintel/content-engine",
);
return new AiBlogPostOrchestrator({
apiKey: OPENROUTER_KEY,
replicateApiKey: REPLICATE_KEY,
model: "google/gemini-3-flash-preview",
});
}
export async function generateSlugAction(
title: string,
draftContent: string,
oldSlug?: string,
instructions?: string,
) {
try {
const orchestrator = await getOrchestrator();
const newSlug = await orchestrator.generateSlug(
draftContent,
title,
instructions,
);
if (oldSlug && oldSlug !== newSlug) {
const payload = await getPayloadHMR({ config: configPromise as any });
await payload.create({
collection: "redirects",
data: {
from: oldSlug,
to: newSlug,
},
});
}
return { success: true, slug: newSlug };
} catch (e: any) {
return { success: false, error: e.message };
}
}
export async function generateThumbnailAction(
draftContent: string,
title?: string,
instructions?: string,
) {
try {
const payload = await getPayloadHMR({ config: configPromise as any });
const OPENROUTER_KEY =
process.env.OPENROUTER_KEY || process.env.OPENROUTER_API_KEY;
const REPLICATE_KEY = process.env.REPLICATE_API_KEY;
if (!OPENROUTER_KEY) {
throw new Error("Missing OPENROUTER_API_KEY in .env");
}
if (!REPLICATE_KEY) {
throw new Error(
"Missing REPLICATE_API_KEY in .env (Required for Thumbnails)",
);
}
const importDynamic = new Function(
"modulePath",
"return import(modulePath)",
);
const { AiBlogPostOrchestrator } = await importDynamic(
"@mintel/content-engine",
);
const { ThumbnailGenerator } = await importDynamic(
"@mintel/thumbnail-generator",
);
const orchestrator = new AiBlogPostOrchestrator({
apiKey: OPENROUTER_KEY,
replicateApiKey: REPLICATE_KEY,
model: "google/gemini-3-flash-preview",
});
const tg = new ThumbnailGenerator({ replicateApiKey: REPLICATE_KEY });
const prompt = await orchestrator.generateVisualPrompt(
draftContent || title || "Technology",
instructions,
);
const tmpPath = path.join(os.tmpdir(), `mintel-thumb-${Date.now()}.png`);
await tg.generateImage(prompt, tmpPath);
const fileData = await fs.readFile(tmpPath);
const stat = await fs.stat(tmpPath);
const fileName = path.basename(tmpPath);
const newMedia = await payload.create({
collection: "media",
data: {
alt: title ? `Thumbnail for ${title}` : "AI Generated Thumbnail",
},
file: {
data: fileData,
name: fileName,
mimetype: "image/png",
size: stat.size,
},
});
// Cleanup temp file
await fs.unlink(tmpPath).catch(() => { });
return { success: true, mediaId: newMedia.id };
} catch (e: any) {
return { success: false, error: e.message };
}
}
export async function generateSingleFieldAction(
documentTitle: string,
documentContent: string,
fieldName: string,
fieldDescription: string,
instructions?: string,
) {
try {
const OPENROUTER_KEY =
process.env.OPENROUTER_KEY || process.env.OPENROUTER_API_KEY;
if (!OPENROUTER_KEY) throw new Error("Missing OPENROUTER_API_KEY");
const payload = await getPayloadHMR({ config: configPromise as any });
// Fetch context documents from DB
const contextDocsData = await payload.find({
collection: "context-files",
limit: 100,
});
const projectContext = contextDocsData.docs
.map((doc: any) => `--- ${doc.filename} ---\n${doc.content}`)
.join("\n\n");
const prompt = `You are an expert AI assistant perfectly trained for generating exact data values for CMS components.
PROJECT STRATEGY & CONTEXT:
${projectContext}
DOCUMENT TITLE: ${documentTitle}
DOCUMENT DRAFT:\n${documentContent}\n
YOUR TASK: Generate the exact value for a specific field named "${fieldName}".
${fieldDescription ? `FIELD DESCRIPTION / CONSTRAINTS: ${fieldDescription}\n` : ""}
${instructions ? `EDITOR INSTRUCTIONS for this field: ${instructions}\n` : ""}
CRITICAL RULES:
1. Respond ONLY with the requested content value.
2. NO markdown wrapping blocks (like \`\`\`mermaid or \`\`\`html) around the output! Just the raw code or text.
3. If the field implies a diagram or flow, output RAW Mermaid.js code.
4. If it's standard text, write professional B2B German. No quotes, no conversational filler.`;
const res = await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${OPENROUTER_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "google/gemini-3-flash-preview",
messages: [{ role: "user", content: prompt }],
}),
});
const data = await res.json();
const text = data.choices?.[0]?.message?.content?.trim() || "";
return { success: true, text };
} catch (e: any) {
return { success: false, error: e.message };
}
}

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"use server";
import { parseMarkdownToLexical } from "../utils/lexicalParser";
import { getPayloadHMR } from "@payloadcms/next/utilities";
// @ts-ignore - dynamic config resolution from next.js payload plugin
import configPromise from "@payload-config";
export async function optimizePostText(
draftContent: string,
instructions?: string,
) {
try {
const payload = await getPayloadHMR({ config: configPromise as any });
const globalAiSettings = (await payload.findGlobal({ slug: "ai-settings" })) as any;
const customSources =
globalAiSettings?.customSources?.map((s: any) => s.sourceName) || [];
const OPENROUTER_KEY =
process.env.OPENROUTER_KEY || process.env.OPENROUTER_API_KEY;
const REPLICATE_KEY = process.env.REPLICATE_API_KEY;
if (!OPENROUTER_KEY) {
throw new Error(
"OPENROUTER_KEY or OPENROUTER_API_KEY not found in environment.",
);
}
const importDynamic = new Function(
"modulePath",
"return import(modulePath)",
);
const { AiBlogPostOrchestrator } = await importDynamic(
"@mintel/content-engine",
);
const orchestrator = new AiBlogPostOrchestrator({
apiKey: OPENROUTER_KEY,
replicateApiKey: REPLICATE_KEY,
model: "google/gemini-3-flash-preview",
});
// Fetch context documents purely from DB
const contextDocsData = await payload.find({
collection: "context-files",
limit: 100,
});
const projectContext = contextDocsData.docs.map((doc: any) => doc.content);
const optimizedMarkdown = await orchestrator.optimizeDocument({
content: draftContent,
projectContext,
availableComponents: [], // Removed hardcoded config.components dependency
instructions,
internalLinks: [],
customSources,
});
if (!optimizedMarkdown || typeof optimizedMarkdown !== "string") {
throw new Error("AI returned invalid markup.");
}
const blocks = parseMarkdownToLexical(optimizedMarkdown);
return {
success: true,
lexicalAST: {
root: {
type: "root",
format: "",
indent: 0,
version: 1,
children: blocks,
direction: "ltr",
},
},
};
} catch (error: any) {
console.error("Failed to optimize post:", error);
return {
success: false,
error: error.message || "An unknown error occurred during optimization.",
};
}
}

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import type { Config, Plugin } from 'payload'
import { AIChatPermissionsCollection } from './collections/AIChatPermissions.js'
import type { PayloadChatPluginConfig } from './types.js'
export const payloadChatPlugin =
(pluginOptions: PayloadChatPluginConfig): Plugin =>
(incomingConfig) => {
let config = { ...incomingConfig }
// If disabled, return config untouched
if (pluginOptions.enabled === false) {
return config
}
// 1. Inject the Permissions Collection into the Schema
const existingCollections = config.collections || []
const mcpServers = pluginOptions.mcpServers || []
// Dynamically populate the select options for Collections and MCP Servers
const permissionCollection = { ...AIChatPermissionsCollection }
const collectionField = permissionCollection.fields.find(f => 'name' in f && f.name === 'allowedCollections') as any
if (collectionField) {
collectionField.options = existingCollections.map(c => ({
label: c.labels?.singular || c.slug,
value: c.slug
}))
}
const mcpField = permissionCollection.fields.find(f => 'name' in f && f.name === 'allowedMcpServers') as any
if (mcpField) {
mcpField.options = mcpServers.map(s => ({
label: s.name,
value: s.name
}))
}
config.collections = [...existingCollections, permissionCollection]
// 2. Register Custom API Endpoint for the AI Chat
config.endpoints = [
...(config.endpoints || []),
{
path: '/api/mcp-chat',
method: 'post',
handler: async (req) => {
// Fallback simple handler while developing endpoint logic
return Response.json({ message: "Chat endpoint active" })
},
},
]
// 3. Inject Chat React Component into Admin UI
if (pluginOptions.renderChatBubble !== false) {
config.admin = {
...(config.admin || {}),
components: {
...(config.admin?.components || {}),
providers: [
...(config.admin?.components?.providers || []),
'@mintel/payload-ai/components/ChatWindow#ChatWindowProvider',
],
},
}
}
return config
}

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@@ -0,0 +1,69 @@
import type { CollectionConfig } from 'payload'
/**
* A central collection to manage which AI Tools/MCPs a User or Role is allowed to use.
*/
export const AIChatPermissionsCollection: CollectionConfig = {
slug: 'ai-chat-permissions',
labels: {
singular: 'AI Chat Permission',
plural: 'AI Chat Permissions',
},
admin: {
useAsTitle: 'description',
group: 'AI & Tools',
},
fields: [
{
name: 'description',
type: 'text',
required: true,
admin: {
description: 'E.g. "Editors default AI permissions"',
},
},
{
type: 'row',
fields: [
{
name: 'targetUser',
type: 'relationship',
relationTo: 'users',
hasMany: false,
admin: {
description: 'Apply these permissions to a specific user (optional).',
},
},
{
name: 'targetRole',
type: 'select',
options: [
{ label: 'Admin', value: 'admin' },
{ label: 'Editor', value: 'editor' },
], // Ideally this is dynamically populated in a real scenario, but we hardcode standard roles for now
admin: {
description: 'Apply these permissions to all users with this role.',
},
},
],
},
{
name: 'allowedCollections',
type: 'select',
hasMany: true,
options: [], // Will be populated dynamically in the plugin init based on actual collections
admin: {
description: 'Which Payload collections is the AI allowed to read/write on behalf of this user?',
},
},
{
name: 'allowedMcpServers',
type: 'select',
hasMany: true,
options: [], // Will be populated dynamically based on plugin config
admin: {
description: 'Which external MCP Servers is the AI allowed to execute tools from?',
},
}
],
}

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@@ -0,0 +1,163 @@
"use client";
import React, { useState } from "react";
import { useDocumentInfo, toast } from "@payloadcms/ui";
type Action = "upscale" | "recover";
interface ActionState {
loading: boolean;
resultId?: string | number;
}
export const AiMediaButtons: React.FC = () => {
const { id } = useDocumentInfo();
const [upscale, setUpscale] = useState<ActionState>({ loading: false });
const [recover, setRecover] = useState<ActionState>({ loading: false });
if (!id) return null; // Only show on existing documents
const runAction = async (action: Action) => {
const setter = action === "upscale" ? setUpscale : setRecover;
setter({ loading: true });
const label = action === "upscale" ? "AI Upscale" : "AI Recover";
toast.info(
`${label} started this can take 3090 seconds, please wait…`,
);
try {
// The API path is hardcoded here and assuming that's where the host app registers the endpoint.
const response = await fetch(`/api/media/${id}/ai-process`, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ action }),
});
const result = await response.json();
if (!response.ok) {
throw new Error(result.error || `${label} failed`);
}
setter({ loading: false, resultId: result.mediaId });
toast.success(
`${label} erfolgreich! Neues Bild (ID: ${result.mediaId}) wurde gespeichert.`,
);
} catch (err: any) {
console.error(`[AiMediaButtons] ${action} error:`, err);
toast.error(
err instanceof Error ? err.message : `${label} fehlgeschlagen`,
);
setter({ loading: false });
}
};
const buttonStyle: React.CSSProperties = {
background: "var(--theme-elevation-150)",
border: "1px solid var(--theme-elevation-200)",
color: "var(--theme-text)",
padding: "8px 14px",
borderRadius: "4px",
fontSize: "13px",
fontWeight: 500,
display: "inline-flex",
alignItems: "center",
gap: "6px",
transition: "opacity 0.15s ease",
};
const disabledStyle: React.CSSProperties = {
opacity: 0.55,
cursor: "not-allowed",
};
return (
<div
style={{
display: "flex",
flexWrap: "wrap",
gap: "10px",
marginBottom: "1.5rem",
marginTop: "0.5rem",
}}
>
{/* AI Upscale */}
<div style={{ display: "flex", flexDirection: "column", gap: "6px" }}>
<button
type="button"
disabled={upscale.loading || recover.loading}
onClick={() => runAction("upscale")}
style={{
...buttonStyle,
...(upscale.loading || recover.loading ? disabledStyle : { cursor: "pointer" }),
}}
>
{upscale.loading ? "⏳ AI Upscale läuft…" : "✨ AI Upscale"}
</button>
{upscale.resultId && (
<a
href={`/admin/collections/media/${upscale.resultId}`}
target="_blank"
rel="noopener noreferrer"
style={{
fontSize: "12px",
color: "var(--theme-elevation-500)",
textDecoration: "underline",
}}
>
Neues Bild öffnen (ID: {upscale.resultId})
</a>
)}
</div>
{/* AI Recover */}
<div style={{ display: "flex", flexDirection: "column", gap: "6px" }}>
<button
type="button"
disabled={upscale.loading || recover.loading}
onClick={() => runAction("recover")}
style={{
...buttonStyle,
...(upscale.loading || recover.loading ? disabledStyle : { cursor: "pointer" }),
}}
>
{recover.loading ? "⏳ AI Recover läuft…" : "🔄 AI Recover"}
</button>
{recover.resultId && (
<a
href={`/admin/collections/media/${recover.resultId}`}
target="_blank"
rel="noopener noreferrer"
style={{
fontSize: "12px",
color: "var(--theme-elevation-500)",
textDecoration: "underline",
}}
>
Neues Bild öffnen (ID: {recover.resultId})
</a>
)}
</div>
<p
style={{
width: "100%",
fontSize: "0.8rem",
color: "var(--theme-elevation-500)",
margin: 0,
lineHeight: 1.4,
}}
>
<strong>AI Upscale</strong> verbessert die Auflösung via{" "}
<code>google/upscaler</code>. <strong>AI Recover</strong> restauriert
alte/beschädigte Fotos via{" "}
<code>microsoft/bringing-old-photos-back-to-life</code>. Das
Ergebnis wird als neues Medium gespeichert.
</p>
</div>
);
};

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'use client'
import React, { useState } from 'react'
import { useChat } from '@ai-sdk/react'
import './ChatWindow.scss'
export const ChatWindowProvider: React.FC<{ children: React.ReactNode }> = ({ children }) => {
return (
<>
{children}
<ChatWindow />
</>
)
}
const ChatWindow: React.FC = () => {
const [isOpen, setIsOpen] = useState(false)
// @ts-ignore - AI hook version mismatch between core and react packages
const { messages, input, handleInputChange, handleSubmit, setMessages } = useChat({
api: '/api/mcp-chat',
initialMessages: []
} as any)
// Basic implementation to toggle chat window and submit messages
return (
<div className="payload-mcp-chat-container">
<button
className="payload-mcp-chat-toggle"
onClick={() => setIsOpen(!isOpen)}
style={{
position: 'fixed',
bottom: '20px',
right: '20px',
zIndex: 9999,
padding: '12px 24px',
backgroundColor: '#000',
color: '#fff',
borderRadius: '8px',
border: 'none',
cursor: 'pointer',
fontWeight: 'bold'
}}
>
{isOpen ? 'Close AI Chat' : 'Ask AI'}
</button>
{isOpen && (
<div
className="payload-mcp-chat-window"
style={{
position: 'fixed',
bottom: '80px',
right: '20px',
width: '400px',
height: '600px',
backgroundColor: '#fff',
border: '1px solid #eaeaea',
borderRadius: '12px',
zIndex: 9999,
display: 'flex',
flexDirection: 'column',
boxShadow: '0 10px 40px rgba(0,0,0,0.1)'
}}
>
<div className="chat-header" style={{ padding: '16px', borderBottom: '1px solid #eaeaea', backgroundColor: '#f9f9f9', borderTopLeftRadius: '12px', borderTopRightRadius: '12px' }}>
<h3 style={{ margin: 0, fontSize: '16px' }}>Payload MCP Chat</h3>
</div>
<div className="chat-messages" style={{ flex: 1, padding: '16px', overflowY: 'auto' }}>
{messages.map((m: any) => (
<div key={m.id} style={{
marginBottom: '12px',
textAlign: m.role === 'user' ? 'right' : 'left'
}}>
<div style={{
display: 'inline-block',
padding: '8px 12px',
borderRadius: '8px',
backgroundColor: m.role === 'user' ? '#000' : '#f0f0f0',
color: m.role === 'user' ? '#fff' : '#000',
maxWidth: '80%'
}}>
{m.role === 'user' ? 'G: ' : 'AI: '}
{m.content}
</div>
</div>
))}
</div>
<form onSubmit={handleSubmit} style={{ padding: '16px', borderTop: '1px solid #eaeaea' }}>
<input
value={input}
placeholder="Ask me anything or use /commands..."
onChange={handleInputChange}
style={{
width: '100%',
padding: '12px',
borderRadius: '8px',
border: '1px solid #eaeaea',
boxSizing: 'border-box'
}}
/>
</form>
</div>
)}
</div>
)
}

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"use client";
import React, { useState } from "react";
import { useField, useDocumentInfo, useForm } from "@payloadcms/ui";
import { generateSingleFieldAction } from "../../actions/generateField.js";
export function AiFieldButton({ path, field }: { path: string; field: any }) {
const [isGenerating, setIsGenerating] = useState(false);
const [instructions, setInstructions] = useState("");
const [showInstructions, setShowInstructions] = useState(false);
// Payload hooks
const { value, setValue } = useField<string>({ path });
const { title } = useDocumentInfo();
const { fields } = useForm();
const extractText = (lexicalRoot: any): string => {
if (!lexicalRoot) return "";
let text = "";
const iterate = (node: any) => {
if (node.text) text += node.text + " ";
if (node.children) node.children.forEach(iterate);
};
iterate(lexicalRoot);
return text;
};
const handleGenerate = async (e: React.MouseEvent) => {
e.preventDefault();
const lexicalValue = fields?.content?.value as any;
const legacyValue = fields?.legacyMdx?.value as string;
let draftContent = legacyValue || "";
if (!draftContent && lexicalValue?.root) {
draftContent = extractText(lexicalValue.root);
}
setIsGenerating(true);
try {
// Field name is passed as a label usually, fallback to path
const fieldName = typeof field?.label === "string" ? field.label : path;
const fieldDescription =
typeof field?.admin?.description === "string"
? field.admin.description
: "";
const res = await generateSingleFieldAction(
(title as string) || "",
draftContent,
fieldName,
fieldDescription,
instructions,
);
if (res.success && res.text) {
setValue(res.text);
} else {
alert("Fehler: " + res.error);
}
} catch (e) {
alert("Fehler bei der Generierung.");
} finally {
setIsGenerating(false);
setShowInstructions(false);
}
};
return (
<div
style={{
marginTop: "8px",
marginBottom: "8px",
display: "flex",
flexDirection: "column",
gap: "8px",
}}
>
<div style={{ display: "flex", gap: "8px", alignItems: "center" }}>
<button
type="button"
onClick={handleGenerate}
disabled={isGenerating}
style={{
background: "var(--theme-elevation-150)",
border: "1px solid var(--theme-elevation-200)",
color: "var(--theme-text)",
padding: "4px 12px",
borderRadius: "4px",
fontSize: "12px",
cursor: isGenerating ? "not-allowed" : "pointer",
display: "flex",
alignItems: "center",
gap: "6px",
opacity: isGenerating ? 0.6 : 1,
}}
>
{isGenerating ? "✨ AI arbeitet..." : "✨ AI Ausfüllen"}
</button>
<button
type="button"
onClick={(e) => {
e.preventDefault();
setShowInstructions(!showInstructions);
}}
style={{
background: "transparent",
border: "none",
color: "var(--theme-elevation-500)",
fontSize: "12px",
cursor: "pointer",
textDecoration: "underline",
}}
>
{showInstructions ? "Prompt verbergen" : "Mit Prompt..."}
</button>
</div>
{showInstructions && (
<textarea
value={instructions}
onChange={(e) => setInstructions(e.target.value)}
placeholder="Eigene Anweisung an AI (z.B. 'als catchy slogan')"
disabled={isGenerating}
style={{
width: "100%",
padding: "6px 8px",
fontSize: "12px",
borderRadius: "4px",
border: "1px solid var(--theme-elevation-200)",
background: "var(--theme-elevation-50)",
color: "var(--theme-text)",
}}
rows={2}
/>
)}
</div>
);
}

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"use client";
import React, { useState, useEffect } from "react";
import { useForm, useField } from "@payloadcms/ui";
import { generateSlugAction } from "../../actions/generateField.js";
export function GenerateSlugButton({ path }: { path: string }) {
const [isGenerating, setIsGenerating] = useState(false);
const [instructions, setInstructions] = useState("");
useEffect(() => {
if (!isGenerating) return;
const handleBeforeUnload = (e: BeforeUnloadEvent) => {
e.preventDefault();
e.returnValue =
"Slug-Generierung läuft noch. Wenn Sie neu laden, bricht der Vorgang ab!";
};
window.addEventListener("beforeunload", handleBeforeUnload);
return () => window.removeEventListener("beforeunload", handleBeforeUnload);
}, [isGenerating]);
const { fields, replaceState } = useForm();
const { value, initialValue, setValue } = useField({ path });
const extractText = (lexicalRoot: any): string => {
if (!lexicalRoot) return "";
let text = "";
const iterate = (node: any) => {
if (node.text) text += node.text + " ";
if (node.children) node.children.forEach(iterate);
};
iterate(lexicalRoot);
return text;
};
const handleGenerate = async () => {
const title = (fields?.title?.value as string) || "";
const lexicalValue = fields?.content?.value as any;
const legacyValue = fields?.legacyMdx?.value as string;
let draftContent = legacyValue || "";
if (!draftContent && lexicalValue?.root) {
draftContent = extractText(lexicalValue.root);
}
setIsGenerating(true);
try {
const res = await generateSlugAction(
title,
draftContent,
initialValue as string,
instructions,
);
if (res.success && res.slug) {
setValue(res.slug);
} else {
alert("Fehler: " + res.error);
}
} catch (e) {
console.error(e);
alert("Unerwarteter Fehler.");
} finally {
setIsGenerating(false);
}
};
return (
<div className="flex gap-2 items-center mb-4">
<textarea
value={instructions}
onChange={(e) => setInstructions(e.target.value)}
placeholder="Optionale AI Anweisung für den Slug..."
disabled={isGenerating}
style={{
width: "100%",
minHeight: "40px",
padding: "8px 12px",
fontSize: "14px",
borderRadius: "4px",
border: "1px solid var(--theme-elevation-200)",
background: "var(--theme-elevation-50)",
color: "var(--theme-text)",
marginBottom: "8px",
}}
/>
<button
type="button"
onClick={handleGenerate}
disabled={isGenerating}
className="btn btn--icon-style-none btn--size-medium ml-auto"
style={{
background: "var(--theme-elevation-150)",
border: "1px solid var(--theme-elevation-200)",
color: "var(--theme-text)",
boxShadow: "0 2px 4px rgba(0,0,0,0.05)",
transition: "all 0.2s ease",
opacity: isGenerating ? 0.6 : 1,
cursor: isGenerating ? "not-allowed" : "pointer",
}}
>
<span className="btn__content">
{isGenerating ? "✨ Generiere (ca 10s)..." : "✨ AI Slug Generieren"}
</span>
</button>
</div>
);
}

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"use client";
import React, { useState, useEffect } from "react";
import { useForm, useField } from "@payloadcms/ui";
import { generateThumbnailAction } from "../../actions/generateField.js";
export function GenerateThumbnailButton({ path }: { path: string }) {
const [isGenerating, setIsGenerating] = useState(false);
const [instructions, setInstructions] = useState("");
useEffect(() => {
if (!isGenerating) return;
const handleBeforeUnload = (e: BeforeUnloadEvent) => {
e.preventDefault();
e.returnValue =
"Bild-Generierung läuft noch (dies dauert bis zu 2 Minuten). Wenn Sie neu laden, bricht der Vorgang ab!";
};
window.addEventListener("beforeunload", handleBeforeUnload);
return () => window.removeEventListener("beforeunload", handleBeforeUnload);
}, [isGenerating]);
const { fields } = useForm();
const { value, setValue } = useField({ path });
const extractText = (lexicalRoot: any): string => {
if (!lexicalRoot) return "";
let text = "";
const iterate = (node: any) => {
if (node.text) text += node.text + " ";
if (node.children) node.children.forEach(iterate);
};
iterate(lexicalRoot);
return text;
};
const handleGenerate = async () => {
const title = (fields?.title?.value as string) || "";
const lexicalValue = fields?.content?.value as any;
const legacyValue = fields?.legacyMdx?.value as string;
let draftContent = legacyValue || "";
if (!draftContent && lexicalValue?.root) {
draftContent = extractText(lexicalValue.root);
}
setIsGenerating(true);
try {
const res = await generateThumbnailAction(
draftContent,
title,
instructions,
);
if (res.success && res.mediaId) {
setValue(res.mediaId);
} else {
alert("Fehler: " + res.error);
}
} catch (e) {
console.error(e);
alert("Unerwarteter Fehler.");
} finally {
setIsGenerating(false);
}
};
return (
<div className="flex gap-2 items-center mt-2 mb-4">
<textarea
value={instructions}
onChange={(e) => setInstructions(e.target.value)}
placeholder="Optionale Thumbnail-Detailanweisung (Farben, Stimmung, etc.)..."
disabled={isGenerating}
style={{
width: "100%",
minHeight: "40px",
padding: "8px 12px",
fontSize: "14px",
borderRadius: "4px",
border: "1px solid var(--theme-elevation-200)",
background: "var(--theme-elevation-50)",
color: "var(--theme-text)",
marginBottom: "8px",
}}
/>
<button
type="button"
onClick={handleGenerate}
disabled={isGenerating}
className="btn btn--icon-style-none btn--size-medium"
style={{
background: "var(--theme-elevation-150)",
border: "1px solid var(--theme-elevation-200)",
color: "var(--theme-text)",
boxShadow: "0 2px 4px rgba(0,0,0,0.05)",
transition: "all 0.2s ease",
opacity: isGenerating ? 0.6 : 1,
cursor: isGenerating ? "not-allowed" : "pointer",
}}
>
<span className="btn__content">
{isGenerating
? "✨ AI arbeitet (dauert ca. 1-2 Min)..."
: "✨ AI Thumbnail Generieren"}
</span>
</button>
</div>
);
}

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"use client";
import React, { useState, useEffect } from "react";
import { useForm, useDocumentInfo } from "@payloadcms/ui";
import { optimizePostText } from "../actions/optimizePost.js";
import { Button } from "@payloadcms/ui";
export function OptimizeButton() {
const [isOptimizing, setIsOptimizing] = useState(false);
const [instructions, setInstructions] = useState("");
useEffect(() => {
if (!isOptimizing) return;
const handleBeforeUnload = (e: BeforeUnloadEvent) => {
e.preventDefault();
e.returnValue =
"Lexical Block-Optimierung läuft noch (dies dauert bis zu 45 Sekunden). Wenn Sie neu laden, bricht der Vorgang ab!";
};
window.addEventListener("beforeunload", handleBeforeUnload);
return () => window.removeEventListener("beforeunload", handleBeforeUnload);
}, [isOptimizing]);
const { fields, setModified, replaceState } = useForm();
const { title } = useDocumentInfo();
const handleOptimize = async () => {
// ... gathering draftContent logic
const lexicalValue = fields?.content?.value as any;
const legacyValue = fields?.legacyMdx?.value as string;
let draftContent = legacyValue || "";
const extractText = (lexicalRoot: any): string => {
if (!lexicalRoot) return "";
let text = "";
const iterate = (node: any) => {
if (node.text) text += node.text + " ";
if (node.children) node.children.forEach(iterate);
};
iterate(lexicalRoot);
return text;
};
if (!draftContent && lexicalValue?.root) {
draftContent = extractText(lexicalValue.root);
}
if (!draftContent || draftContent.trim().length < 50) {
alert(
"Der Entwurf ist zu kurz. Bitte tippe zuerst ein paar Stichpunkte oder einen Rohling ein.",
);
return;
}
setIsOptimizing(true);
try {
// 2. We inject the title so the AI knows what it's writing about
const payloadText = `---\ntitle: "${title}"\n---\n\n${draftContent}`;
const response = await optimizePostText(payloadText, instructions);
if (response.success && response.lexicalAST) {
// 3. Inject the new Lexical AST directly into the field form state
// We use Payload's useForm hook replacing the value of the 'content' field.
replaceState({
...fields,
content: {
...fields.content,
value: response.lexicalAST,
initialValue: response.lexicalAST,
},
});
setModified(true);
alert(
"🎉 Artikel wurde erfolgreich von der AI optimiert und mit Lexical Components angereichert.",
);
} else {
alert("❌ Fehler: " + response.error);
}
} catch (error) {
console.error("Optimization failed:", error);
alert("Ein unerwarteter Fehler ist aufgetreten.");
} finally {
setIsOptimizing(false);
}
};
return (
<div className="mb-8 p-4 bg-slate-50 border border-slate-200 rounded-md">
<h3 className="text-sm font-semibold mb-2">AI Post Optimizer</h3>
<p className="text-xs text-slate-500 mb-4">
Lass Mintel AI deinen Text-Rohentwurf analysieren und automatisch in
einen voll formatierten Lexical Artikel mit passenden B2B Komponenten
(MemeCards, Mermaids) umwandeln.
</p>
<textarea
value={instructions}
onChange={(e) => setInstructions(e.target.value)}
placeholder="Optionale Anweisungen an die AI (z.B. 'schreibe etwas lockerer' oder 'fokussiere dich auf SEO')..."
disabled={isOptimizing}
style={{
width: "100%",
minHeight: "60px",
padding: "8px 12px",
fontSize: "14px",
borderRadius: "4px",
border: "1px solid var(--theme-elevation-200)",
background: "var(--theme-elevation-50)",
color: "var(--theme-text)",
marginBottom: "16px",
}}
/>
<button
type="button"
onClick={handleOptimize}
disabled={isOptimizing}
className="btn btn--icon-style-none btn--size-medium mt-4"
style={{
background: "var(--theme-elevation-150)",
border: "1px solid var(--theme-elevation-200)",
color: "var(--theme-text)",
boxShadow: "0 2px 4px rgba(0,0,0,0.05)",
transition: "all 0.2s ease",
opacity: isOptimizing ? 0.7 : 1,
cursor: isOptimizing ? "not-allowed" : "pointer",
}}
>
<span className="btn__content" style={{ fontWeight: 600 }}>
{isOptimizing ? "✨ AI arbeitet (ca 30s)..." : "✨ Jetzt optimieren"}
</span>
</button>
</div>
);
}

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import { streamText } from 'ai'
import { createOpenAI } from '@ai-sdk/openai'
import { generatePayloadLocalTools } from '../tools/payloadLocal.js'
import { createMcpTools } from '../tools/mcpAdapter.js'
import { generateMemoryTools } from '../tools/memoryDb.js'
import type { PayloadRequest } from 'payload'
const openrouter = createOpenAI({
baseURL: 'https://openrouter.ai/api/v1',
apiKey: process.env.OPENROUTER_API_KEY || 'dummy_key',
})
export const handleMcpChat = async (req: PayloadRequest) => {
if (!req.user) {
return Response.json({ error: 'Unauthorized. You must be logged in to use AI Chat.' }, { status: 401 })
}
const { messages } = (await req.json?.() || { messages: [] }) as { messages: any[] }
// 1. Check AI Permissions for req.user
// In a real implementation this looks up the global or collection for permissions
const allowedCollections = ['users'] // Stub
let activeTools: Record<string, any> = {}
// 2. Generate Payload Local Tools
if (allowedCollections.length > 0) {
const payloadTools = generatePayloadLocalTools(req.payload, req, allowedCollections)
activeTools = { ...activeTools, ...payloadTools }
}
// 3. Connect External MCPs
const allowedMcpServers: string[] = [] // Stub
if (allowedMcpServers.includes('gitea')) {
try {
const { tools: giteaTools } = await createMcpTools({
name: 'gitea',
command: 'npx',
args: ['-y', '@modelcontextprotocol/server-gitea', '--url', 'https://git.mintel.int', '--token', process.env.GITEA_TOKEN || '']
})
activeTools = { ...activeTools, ...giteaTools }
} catch (e) {
console.error('Failed to connect to Gitea MCP', e)
}
}
// 4. Inject Memory Database Tools
// We provide the user ID so memory is partitioned per user
const memoryTools = generateMemoryTools(req.user.id)
activeTools = { ...activeTools, ...memoryTools }
// 5. Build prompt to ensure it asks before saving
const memorySystemPrompt = `
You have access to a long-term vector memory database (Qdrant).
If the user says "speicher das", "merk dir das", "vergiss das nicht" etc., you MUST use the save_memory tool.
If the user shares important context but doesn't explicitly ask you to remember it, you should ask "Soll ich mir das für die Zukunft merken?" before saving it. Do not ask for trivial things.
`
try {
const result = streamText({
// @ts-ignore - AI SDK type mismatch
model: openrouter('google/gemini-3.0-flash'),
messages,
tools: activeTools,
system: `You are a helpful Payload CMS MCP Assistant orchestrating the local Mintel ecosystem.
You only have access to tools explicitly granted by the Admin.
You cannot do anything outside these tools. Always explain what you are doing.
${memorySystemPrompt}`
})
return result.toTextStreamResponse()
} catch (error) {
console.error("AI Error:", error)
return Response.json({ error: 'Failed to process AI request' }, { status: 500 })
}
}

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import type { PayloadRequest, PayloadHandler } from "payload";
import Replicate from "replicate";
type Action = "upscale" | "recover";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_KEY,
});
/**
* Downloads a remote URL and returns a Buffer.
*/
async function downloadImage(url: string): Promise<Buffer> {
const res = await fetch(url);
if (!res.ok) throw new Error(`Failed to download image: ${res.status} ${res.statusText}`);
const arrayBuffer = await res.arrayBuffer();
return Buffer.from(arrayBuffer);
}
/**
* Resolves the public URL for a media document.
* Handles both S3 and local static files.
*/
function resolveMediaUrl(doc: any): string | null {
// S3 storage sets `url` directly
if (doc.url) return doc.url;
// Local static files: build from NEXT_PUBLIC_BASE_URL + /media/<filename>
const base = process.env.NEXT_PUBLIC_BASE_URL || "http://localhost:3000";
if (doc.filename) return `${base}/media/${doc.filename}`;
return null;
}
export const replicateMediaHandler: PayloadHandler = async (
req: PayloadRequest,
) => {
const { id } = req.routeParams as { id: string };
const payload = req.payload;
// Parse action from request body
let action: Action;
try {
const body = await req.json?.();
action = body?.action as Action;
} catch {
return Response.json({ error: "Invalid request body" }, { status: 400 });
}
if (action !== "upscale" && action !== "recover") {
return Response.json(
{ error: "Invalid action. Must be 'upscale' or 'recover'." },
{ status: 400 },
);
}
// Fetch the media document
let mediaDoc: any;
try {
mediaDoc = await payload.findByID({ collection: "media", id });
} catch {
return Response.json({ error: "Media not found" }, { status: 404 });
}
if (!mediaDoc) {
return Response.json({ error: "Media not found" }, { status: 404 });
}
// Check that it's an image
const mimeType: string = mediaDoc.mimeType || "";
if (!mimeType.startsWith("image/")) {
return Response.json(
{ error: "This media file is not an image and cannot be AI-processed." },
{ status: 422 },
);
}
const imageUrl = resolveMediaUrl(mediaDoc);
if (!imageUrl) {
return Response.json(
{ error: "Could not resolve a public URL for this media file." },
{ status: 422 },
);
}
// --- Run Replicate ---
let outputUrl: string;
try {
if (action === "upscale") {
console.log(`[AI Media] Starting upscale for media ${id} ${imageUrl}`);
const output = await replicate.run("google/upscaler", {
input: {
image: imageUrl,
},
});
// google/upscaler returns a string URL
outputUrl = typeof output === "string" ? output : (output as any)?.url ?? String(output);
} else {
// recover
console.log(`[AI Media] Starting photo recovery for media ${id} ${imageUrl}`);
const output = await replicate.run(
"microsoft/bringing-old-photos-back-to-life",
{
input: {
image: imageUrl,
HR: true,
},
},
);
// returns a FileOutput or URL string
outputUrl = typeof output === "string" ? output : (output as any)?.url ?? String(output);
}
} catch (err: any) {
console.error("[AI Media] Replicate error:", err);
return Response.json(
{ error: err?.message ?? "Replicate API call failed" },
{ status: 500 },
);
}
// --- Download and re-upload as new media document ---
let imageBuffer: Buffer;
try {
imageBuffer = await downloadImage(outputUrl);
} catch (err: any) {
console.error("[AI Media] Download error:", err);
return Response.json(
{ error: `Failed to download result: ${err?.message}` },
{ status: 500 },
);
}
const suffix = action === "upscale" ? "_upscaled" : "_recovered";
const originalName: string = mediaDoc.filename || "image.jpg";
const ext = originalName.includes(".") ? `.${originalName.split(".").pop()}` : ".jpg";
const baseName = originalName.includes(".")
? originalName.slice(0, originalName.lastIndexOf("."))
: originalName;
const newFilename = `${baseName}${suffix}${ext}`;
const originalAlt: string = mediaDoc.alt || originalName;
let newMedia: any;
try {
newMedia = await payload.create({
collection: "media",
data: {
alt: `${originalAlt}${suffix}`,
},
file: {
data: imageBuffer,
mimetype: mimeType,
name: newFilename,
size: imageBuffer.byteLength,
},
});
} catch (err: any) {
console.error("[AI Media] Upload error:", err);
return Response.json(
{ error: `Failed to save result: ${err?.message}` },
{ status: 500 },
);
}
console.log(
`[AI Media] ${action} complete new media ID: ${newMedia.id}`,
);
return Response.json(
{
message: `AI ${action} successful. New media document created.`,
mediaId: newMedia.id,
url: resolveMediaUrl(newMedia),
},
{ status: 200 },
);
};

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import type { GlobalConfig } from "payload";
export const AiSettings: GlobalConfig = {
slug: "ai-settings",
label: "AI Settings",
access: {
read: () => true, // Needed if the Next.js frontend or server actions need to fetch it
},
admin: {
group: "Configuration",
},
fields: [
{
name: "customSources",
type: "array",
label: "Custom Trusted Sources",
admin: {
description:
"List of trusted B2B/Tech sources (e.g. 'Vercel Blog', 'Fireship', 'Theo - t3.gg') the AI should prioritize when researching facts or videos. This overrides the hardcoded defaults.",
},
fields: [
{
name: "sourceName",
type: "text",
required: true,
label: "Channel or Publication Name",
},
],
},
],
};

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/**
* @mintel/payload-ai
* Primary entry point for reusing Mintel AI extensions in Payload CMS.
*/
export * from './globals/AiSettings';
export * from './actions/generateField';
export * from './actions/optimizePost';
export * from './components/FieldGenerators/AiFieldButton';
export * from './components/AiMediaButtons';
export * from './components/OptimizeButton';
export * from './components/FieldGenerators/GenerateThumbnailButton';
export * from './components/FieldGenerators/GenerateSlugButton';
export * from './utils/lexicalParser';
export * from './endpoints/replicateMediaEndpoint';
export * from './chatPlugin.js';
export * from './types.js';
export * from './endpoints/chatEndpoint.js';
export * from './tools/mcpAdapter.js';
export * from './tools/memoryDb.js';
export * from './tools/payloadLocal.js';

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import { Client } from '@modelcontextprotocol/sdk/client/index.js'
import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js'
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'
import { tool } from 'ai'
import { z } from 'zod'
/**
* Connects to an external MCP Server and maps its tools to Vercel AI SDK Tools.
*/
export async function createMcpTools(mcpConfig: { name: string, url?: string, command?: string, args?: string[] }) {
let transport
// Support both HTTP/SSE and STDIO transports
if (mcpConfig.url) {
transport = new SSEClientTransport(new URL(mcpConfig.url))
} else if (mcpConfig.command) {
transport = new StdioClientTransport({
command: mcpConfig.command,
args: mcpConfig.args || [],
})
} else {
throw new Error('Invalid MCP config: Must provide either URL or Command.')
}
const client = new Client(
{ name: `payload-ai-client-${mcpConfig.name}`, version: '1.0.0' },
{ capabilities: {} }
)
await client.connect(transport)
// Fetch available tools from the external MCP server
const toolListResult = await client.listTools()
const externalTools = toolListResult.tools || []
const aiSdkTools: Record<string, any> = {}
// Map each external tool to a Vercel AI SDK Tool
for (const extTool of externalTools) {
// Basic conversion of JSON Schema to Zod for the AI SDK
// Note: For a production ready adapter, you might need a more robust jsonSchemaToZod converter
// or use AI SDK's new experimental generateSchema feature if available.
// Here we use a generic `z.any()` as a fallback since AI SDK requires a Zod schema.
const toolSchema = extTool.inputSchema as Record<string, any>
// We create a simplified parameter parser.
// An ideal approach uses `jsonSchemaToZod` library or native AI SDK JSON schema support
// (introduced recently in `ai` package).
aiSdkTools[`${mcpConfig.name}_${extTool.name}`] = tool({
description: `[From ${mcpConfig.name}] ${extTool.description || extTool.name}`,
parameters: z.any().describe('JSON matching the original MCP input_schema'), // Simplify for prototype
// @ts-ignore - AI strict mode overload bug with implicit zod inferences
execute: async (args: any) => {
const result = await client.callTool({
name: extTool.name,
arguments: args
})
return result
}
})
}
return { tools: aiSdkTools, client }
}

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@@ -0,0 +1,115 @@
import { tool } from 'ai'
import { z } from 'zod'
import { QdrantClient } from '@qdrant/js-client-rest'
// Qdrant initialization
// This requires the user to have Qdrant running and QDRANT_URL/QDRANT_API_KEY environment variables set
const qdrantClient = new QdrantClient({
url: process.env.QDRANT_URL || 'http://localhost:6333',
apiKey: process.env.QDRANT_API_KEY,
})
const MEMORY_COLLECTION = 'mintel_ai_memory'
// Ensure collection exists on load
async function initQdrant() {
try {
const res = await qdrantClient.getCollections()
const exists = res.collections.find((c: any) => c.name === MEMORY_COLLECTION)
if (!exists) {
await qdrantClient.createCollection(MEMORY_COLLECTION, {
vectors: {
size: 1536, // typical embedding size, adjust based on the embedding model used
distance: 'Cosine',
},
})
console.log(`Qdrant collection '${MEMORY_COLLECTION}' created.`)
}
} catch (error) {
console.error('Failed to initialize Qdrant memory collection:', error)
}
}
// Call init, but don't block
initQdrant()
/**
* Returns memory tools for the AI SDK.
* Note: A real implementation would require an embedding step before inserting into Qdrant.
* For this implementation, we use a placeholder or assume the embeddings are handled
* by a utility function, or we use Qdrant's FastEmbed (if running their specialized container).
*/
export const generateMemoryTools = (userId: string | number) => {
return {
save_memory: tool({
description: 'Save an important preference, fact, or instruction about the user to long-term memory. Only use this when explicitly asked or when it is clearly a long-term preference.',
parameters: z.object({
fact: z.string().describe('The fact or instruction to remember.'),
category: z.string().optional().describe('An optional category like "preference", "rule", or "project_detail".'),
}),
// @ts-ignore - AI SDK strict mode bug
execute: async ({ fact, category }: { fact: string; category?: string }) => {
// In a real scenario, you MUST generate embeddings for the 'fact' string here
// using OpenAI or another embedding provider before inserting into Qdrant.
// const embedding = await generateEmbedding(fact)
try {
// Mock embedding payload for demonstration
const mockEmbedding = new Array(1536).fill(0).map(() => Math.random())
await qdrantClient.upsert(MEMORY_COLLECTION, {
wait: true,
points: [
{
id: crypto.randomUUID(),
vector: mockEmbedding,
payload: {
userId: String(userId), // Partition memory by user
fact,
category,
createdAt: new Date().toISOString(),
},
},
],
})
return { success: true, message: `Successfully remembered: "${fact}"` }
} catch (error) {
console.error("Qdrant save error:", error)
return { success: false, error: 'Failed to save to memory database.' }
}
},
}),
search_memory: tool({
description: 'Search the user\'s long-term memory for past factual context, preferences, or rules.',
parameters: z.object({
query: z.string().describe('The search string to find in memory.'),
}),
// @ts-ignore - AI SDK strict mode bug
execute: async ({ query }: { query: string }) => {
// Generate embedding for query
const mockQueryEmbedding = new Array(1536).fill(0).map(() => Math.random())
try {
const results = await qdrantClient.search(MEMORY_COLLECTION, {
vector: mockQueryEmbedding,
limit: 5,
filter: {
must: [
{
key: 'userId',
match: { value: String(userId) }
}
]
}
})
return results.map((r: any) => r.payload?.fact || '')
} catch (error) {
console.error("Qdrant search error:", error)
return []
}
}
})
}
}

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import { tool } from 'ai'
import { z } from 'zod'
import type { Payload, PayloadRequest, User } from 'payload'
export const generatePayloadLocalTools = (
payload: Payload,
req: PayloadRequest,
allowedCollections: string[]
) => {
const tools: Record<string, any> = {}
for (const collectionSlug of allowedCollections) {
const slugKey = collectionSlug.replace(/-/g, '_')
// 1. Read (Find) Tool
tools[`read_${slugKey}`] = tool({
description: `Read/Find documents from the Payload CMS collection: ${collectionSlug}`,
parameters: z.object({
limit: z.number().optional().describe('Number of documents to return, max 100.'),
page: z.number().optional().describe('Page number for pagination.'),
// Simple string-based query for demo purposes. For a robust implementation,
// we'd map this to Payload's where query logic using a structured Zod schema.
query: z.string().optional().describe('Optional text to search within the collection.'),
}),
// @ts-ignore - AI SDK strict mode type inference bug
execute: async ({ limit = 10, page = 1, query }: { limit?: number; page?: number; query?: string }) => {
const where = query ? { id: { equals: query } } : undefined // Placeholder logic
return await payload.find({
collection: collectionSlug as any,
limit: Math.min(limit, 100),
page,
where,
req, // Crucial for passing the user context and respecting access control!
})
},
})
// 2. Read by ID Tool
tools[`read_${slugKey}_by_id`] = tool({
description: `Get a specific document by its ID from the ${collectionSlug} collection.`,
parameters: z.object({
id: z.union([z.string(), z.number()]).describe('The ID of the document.'),
}),
// @ts-ignore - AI SDK strict mode type inference bug
execute: async ({ id }: { id: string | number }) => {
return await payload.findByID({
collection: collectionSlug as any,
id,
req, // Enforce access control
})
},
})
// 3. Create Tool
tools[`create_${slugKey}`] = tool({
description: `Create a new document in the ${collectionSlug} collection.`,
parameters: z.object({
data: z.record(z.any()).describe('A JSON object containing the data to insert.'),
}),
// @ts-ignore - AI SDK strict mode type inference bug
execute: async ({ data }: { data: Record<string, any> }) => {
return await payload.create({
collection: collectionSlug as any,
data,
req, // Enforce access control
})
},
})
// 4. Update Tool
tools[`update_${slugKey}`] = tool({
description: `Update an existing document in the ${collectionSlug} collection.`,
parameters: z.object({
id: z.union([z.string(), z.number()]).describe('The ID of the document to update.'),
data: z.record(z.any()).describe('A JSON object containing the fields to update.'),
}),
// @ts-ignore - AI SDK strict mode type inference bug
execute: async ({ id, data }: { id: string | number; data: Record<string, any> }) => {
return await payload.update({
collection: collectionSlug as any,
id,
data,
req, // Enforce access control
})
},
})
// 5. Delete Tool
tools[`delete_${slugKey}`] = tool({
description: `Delete a document from the ${collectionSlug} collection by ID.`,
parameters: z.object({
id: z.union([z.string(), z.number()]).describe('The ID of the document to delete.'),
}),
// @ts-ignore - AI SDK strict mode type inference bug
execute: async ({ id }: { id: string | number }) => {
return await payload.delete({
collection: collectionSlug as any,
id,
req, // Enforce access control
})
},
})
}
return tools
}

5
packages/payload-ai/src/types.d.ts vendored Normal file
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@@ -0,0 +1,5 @@
declare module "@payload-config" {
import { Config } from "payload";
const configPromise: Promise<any>;
export default configPromise;
}

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@@ -0,0 +1,18 @@
import type { Plugin } from 'payload'
export interface PayloadChatPluginConfig {
enabled?: boolean
/**
* Defines whether to render the floating chat bubble in the admin panel automatically.
* Defaults to true.
*/
renderChatBubble?: boolean
/**
* Used to register external MCP servers that the AI can explicitly connect to if the admin permits it.
*/
mcpServers?: {
name: string
url?: string
// Command based STDIO later via configuration
}[]
}

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/**
* Converts a Markdown+JSX string into a Lexical AST node array.
* Handles all registered Payload blocks and standard markdown formatting.
*/
function propValue(chunk: string, prop: string): string {
// Match prop="value" or prop='value' or prop={value}
const match =
chunk.match(new RegExp(`${prop}=["']([^"']+)["']`)) ||
chunk.match(new RegExp(`${prop}=\\{([^}]+)\\}`));
return match ? match[1] : "";
}
function innerContent(chunk: string, tag: string): string {
const match = chunk.match(
new RegExp(`<${tag}(?:\\s[^>]*)?>([\\s\\S]*?)<\\/${tag}>`),
);
return match ? match[1].trim() : "";
}
function blockNode(blockType: string, fields: Record<string, any>) {
return {
type: "block",
format: "",
version: 2,
fields: { blockType, ...fields },
};
}
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[] = [];
// Strip frontmatter
let content = markdown;
const fm = content.match(/^---\s*\n[\s\S]*?\n---/);
if (fm) content = content.replace(fm[0], "").trim();
// Pre-process: reassemble multi-line JSX tags that got split by double-newline chunking.
// This handles tags like <IconList>\n\n<IconListItem ... />\n\n</IconList>
content = reassembleMultiLineJSX(content);
const rawChunks = content.split(/\n\s*\n/);
for (let chunk of rawChunks) {
chunk = chunk.trim();
if (!chunk) continue;
// === Self-closing tags (no children) ===
// ArticleMeme / MemeCard
if (chunk.includes("<ArticleMeme") || chunk.includes("<MemeCard")) {
nodes.push(
blockNode("memeCard", {
template: propValue(chunk, "template"),
captions: propValue(chunk, "captions"),
}),
);
continue;
}
// BoldNumber
if (chunk.includes("<BoldNumber")) {
nodes.push(
blockNode("boldNumber", {
value: propValue(chunk, "value"),
label: propValue(chunk, "label"),
source: propValue(chunk, "source"),
sourceUrl: propValue(chunk, "sourceUrl"),
}),
);
continue;
}
// WebVitalsScore
if (chunk.includes("<WebVitalsScore")) {
nodes.push(
blockNode("webVitalsScore", {
lcp: parseFloat(propValue(chunk, "lcp")) || 0,
inp: parseFloat(propValue(chunk, "inp")) || 0,
cls: parseFloat(propValue(chunk, "cls")) || 0,
description: propValue(chunk, "description"),
}),
);
continue;
}
// LeadMagnet
if (chunk.includes("<LeadMagnet")) {
nodes.push(
blockNode("leadMagnet", {
title: propValue(chunk, "title"),
description: propValue(chunk, "description"),
buttonText: propValue(chunk, "buttonText") || "Jetzt anfragen",
href: propValue(chunk, "href") || "/contact",
variant: propValue(chunk, "variant") || "standard",
}),
);
continue;
}
// ComparisonRow
if (chunk.includes("<ComparisonRow")) {
nodes.push(
blockNode("comparisonRow", {
description: propValue(chunk, "description"),
negativeLabel: propValue(chunk, "negativeLabel"),
negativeText: propValue(chunk, "negativeText"),
positiveLabel: propValue(chunk, "positiveLabel"),
positiveText: propValue(chunk, "positiveText"),
reverse: chunk.includes("reverse={true}"),
}),
);
continue;
}
// StatsDisplay
if (chunk.includes("<StatsDisplay")) {
nodes.push(
blockNode("statsDisplay", {
label: propValue(chunk, "label"),
value: propValue(chunk, "value"),
subtext: propValue(chunk, "subtext"),
}),
);
continue;
}
// MetricBar
if (chunk.includes("<MetricBar")) {
nodes.push(
blockNode("metricBar", {
label: propValue(chunk, "label"),
value: parseFloat(propValue(chunk, "value")) || 0,
max: parseFloat(propValue(chunk, "max")) || 100,
unit: propValue(chunk, "unit") || "%",
}),
);
continue;
}
// ExternalLink
if (chunk.includes("<ExternalLink")) {
nodes.push(
blockNode("externalLink", {
href: propValue(chunk, "href"),
label:
propValue(chunk, "label") || innerContent(chunk, "ExternalLink"),
}),
);
continue;
}
// TrackedLink
if (chunk.includes("<TrackedLink")) {
nodes.push(
blockNode("trackedLink", {
href: propValue(chunk, "href"),
label:
propValue(chunk, "label") || innerContent(chunk, "TrackedLink"),
eventName: propValue(chunk, "eventName"),
}),
);
continue;
}
// YouTube
if (chunk.includes("<YouTubeEmbed")) {
nodes.push(
blockNode("youTubeEmbed", {
videoId: propValue(chunk, "videoId"),
}),
);
continue;
}
// LinkedIn
if (chunk.includes("<LinkedInEmbed")) {
nodes.push(
blockNode("linkedInEmbed", {
url: propValue(chunk, "url"),
}),
);
continue;
}
// Twitter
if (chunk.includes("<TwitterEmbed")) {
nodes.push(
blockNode("twitterEmbed", {
url: propValue(chunk, "url"),
}),
);
continue;
}
// Interactive (self-closing, defaults only)
if (chunk.includes("<RevenueLossCalculator")) {
nodes.push(
blockNode("revenueLossCalculator", {
title: propValue(chunk, "title") || "Performance Revenue Simulator",
}),
);
continue;
}
if (chunk.includes("<PerformanceChart")) {
nodes.push(
blockNode("performanceChart", {
title: propValue(chunk, "title") || "Website Performance",
}),
);
continue;
}
if (chunk.includes("<PerformanceROICalculator")) {
nodes.push(
blockNode("performanceROICalculator", {
baseConversionRate:
parseFloat(propValue(chunk, "baseConversionRate")) || 2.5,
monthlyVisitors:
parseInt(propValue(chunk, "monthlyVisitors")) || 50000,
}),
);
continue;
}
if (chunk.includes("<LoadTimeSimulator")) {
nodes.push(
blockNode("loadTimeSimulator", {
initialLoadTime:
parseFloat(propValue(chunk, "initialLoadTime")) || 3.5,
}),
);
continue;
}
if (chunk.includes("<ArchitectureBuilder")) {
nodes.push(
blockNode("architectureBuilder", {
preset: propValue(chunk, "preset") || "standard",
}),
);
continue;
}
if (chunk.includes("<DigitalAssetVisualizer")) {
nodes.push(
blockNode("digitalAssetVisualizer", {
assetId: propValue(chunk, "assetId"),
}),
);
continue;
}
// === Tags with inner content ===
// TLDR
if (chunk.includes("<TLDR>")) {
const inner = innerContent(chunk, "TLDR");
if (inner) {
nodes.push(blockNode("mintelTldr", { content: inner }));
continue;
}
}
// Paragraph (handles <Paragraph>, <Paragraph ...attrs>)
if (/<Paragraph[\s>]/.test(chunk)) {
const inner = innerContent(chunk, "Paragraph");
if (inner) {
nodes.push(blockNode("mintelP", { text: inner }));
continue;
}
}
// H2 (handles <H2>, <H2 id="...">)
if (/<H2[\s>]/.test(chunk)) {
const inner = innerContent(chunk, "H2");
if (inner) {
nodes.push(
blockNode("mintelHeading", {
text: inner,
seoLevel: "h2",
displayLevel: "h2",
}),
);
continue;
}
}
// H3 (handles <H3>, <H3 id="...">)
if (/<H3[\s>]/.test(chunk)) {
const inner = innerContent(chunk, "H3");
if (inner) {
nodes.push(
blockNode("mintelHeading", {
text: inner,
seoLevel: "h3",
displayLevel: "h3",
}),
);
continue;
}
}
// Marker (inline highlight, usually inside Paragraph store as standalone block)
if (chunk.includes("<Marker>") && !chunk.includes("<Paragraph")) {
const inner = innerContent(chunk, "Marker");
if (inner) {
nodes.push(blockNode("marker", { text: inner }));
continue;
}
}
// LeadParagraph
if (chunk.includes("<LeadParagraph>")) {
const inner = innerContent(chunk, "LeadParagraph");
if (inner) {
nodes.push(blockNode("leadParagraph", { text: inner }));
continue;
}
}
// ArticleBlockquote
if (chunk.includes("<ArticleBlockquote")) {
nodes.push(
blockNode("articleBlockquote", {
quote: innerContent(chunk, "ArticleBlockquote"),
author: propValue(chunk, "author"),
role: propValue(chunk, "role"),
}),
);
continue;
}
// ArticleQuote
if (chunk.includes("<ArticleQuote")) {
nodes.push(
blockNode("articleQuote", {
quote:
innerContent(chunk, "ArticleQuote") || propValue(chunk, "quote"),
author: propValue(chunk, "author"),
role: propValue(chunk, "role"),
source: propValue(chunk, "source"),
}),
);
continue;
}
// Mermaid
if (chunk.includes("<Mermaid")) {
nodes.push(
blockNode("mermaid", {
id: propValue(chunk, "id") || `chart-${Date.now()}`,
title: propValue(chunk, "title"),
showShare: chunk.includes("showShare={true}"),
chartDefinition: innerContent(chunk, "Mermaid"),
}),
);
continue;
}
// Diagram variants (prefer inner definition, fall back to raw chunk text)
if (chunk.includes("<DiagramFlow")) {
nodes.push(
blockNode("diagramFlow", {
definition:
innerContent(chunk, "DiagramFlow") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
if (chunk.includes("<DiagramSequence")) {
nodes.push(
blockNode("diagramSequence", {
definition:
innerContent(chunk, "DiagramSequence") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
if (chunk.includes("<DiagramGantt")) {
nodes.push(
blockNode("diagramGantt", {
definition:
innerContent(chunk, "DiagramGantt") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
if (chunk.includes("<DiagramPie")) {
nodes.push(
blockNode("diagramPie", {
definition:
innerContent(chunk, "DiagramPie") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
if (chunk.includes("<DiagramState")) {
nodes.push(
blockNode("diagramState", {
definition:
innerContent(chunk, "DiagramState") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
if (chunk.includes("<DiagramTimeline")) {
nodes.push(
blockNode("diagramTimeline", {
definition:
innerContent(chunk, "DiagramTimeline") ||
propValue(chunk, "definition") ||
chunk,
}),
);
continue;
}
// Section (wrapping container unwrap and parse inner content as top-level blocks)
if (chunk.includes("<Section")) {
const inner = innerContent(chunk, "Section");
if (inner) {
const innerNodes = parseMarkdownToLexical(inner);
nodes.push(...innerNodes);
}
continue;
}
// FAQSection (wrapping container)
if (chunk.includes("<FAQSection")) {
// FAQSection contains nested H3/Paragraph pairs.
// We extract them as individual blocks instead.
const faqContent = innerContent(chunk, "FAQSection");
if (faqContent) {
// Parse nested content recursively
const innerNodes = parseMarkdownToLexical(faqContent);
nodes.push(...innerNodes);
}
continue;
}
// IconList with IconListItems
if (chunk.includes("<IconList")) {
const items: any[] = [];
// Self-closing: <IconListItem icon="Check" title="..." description="..." />
const itemMatches = chunk.matchAll(/<IconListItem\s+([^>]*?)\/>/g);
for (const m of itemMatches) {
const attrs = m[1];
const title = (attrs.match(/title=["']([^"']+)["']/) || [])[1] || "";
const desc =
(attrs.match(/description=["']([^"']+)["']/) || [])[1] || "";
items.push({
icon: (attrs.match(/icon=["']([^"']+)["']/) || [])[1] || "Check",
title: title || "•",
description: desc,
});
}
// Content-wrapped: <IconListItem check>HTML content</IconListItem>
const itemMatches2 = chunk.matchAll(
/<IconListItem([^>]*)>([\s\S]*?)<\/IconListItem>/g,
);
for (const m of itemMatches2) {
const attrs = m[1] || "";
const innerHtml = m[2].trim();
// Use title attr if present, otherwise use inner HTML (stripped of tags) as title
const titleAttr = (attrs.match(/title=["']([^"']+)["']/) || [])[1];
const strippedInner = innerHtml.replace(/<[^>]+>/g, "").trim();
items.push({
icon: (attrs.match(/icon=["']([^"']+)["']/) || [])[1] || "Check",
title: titleAttr || strippedInner || "•",
description: "",
});
}
if (items.length > 0) {
nodes.push(blockNode("iconList", { items }));
continue;
}
}
// StatsGrid
if (chunk.includes("<StatsGrid")) {
const stats: any[] = [];
const statMatches = chunk.matchAll(/<StatItem\s+([^>]*?)\/>/g);
for (const m of statMatches) {
const attrs = m[1];
stats.push({
label: (attrs.match(/label=["']([^"']+)["']/) || [])[1] || "",
value: (attrs.match(/value=["']([^"']+)["']/) || [])[1] || "",
});
}
// Also try inline props pattern
if (stats.length === 0) {
const innerStats = innerContent(chunk, "StatsGrid");
if (innerStats) {
// fallback: store the raw content
nodes.push(blockNode("statsGrid", { stats: [] }));
continue;
}
}
nodes.push(blockNode("statsGrid", { stats }));
continue;
}
// PremiumComparisonChart
if (chunk.includes("<PremiumComparisonChart")) {
nodes.push(
blockNode("premiumComparisonChart", {
title: propValue(chunk, "title"),
}),
);
continue;
}
// WaterfallChart
if (chunk.includes("<WaterfallChart")) {
nodes.push(
blockNode("waterfallChart", {
title: propValue(chunk, "title"),
}),
);
continue;
}
// Reveal (animation wrapper just pass through)
if (chunk.includes("<Reveal")) {
const inner = innerContent(chunk, "Reveal");
if (inner) {
// Parse inner content as regular nodes
const innerNodes = parseMarkdownToLexical(inner);
nodes.push(...innerNodes);
continue;
}
}
// Standalone IconListItem (outside IconList context)
if (chunk.includes("<IconListItem")) {
// Skip these should be inside an IconList
continue;
}
// Skip wrapper divs (like <div className="my-8">)
if (/^<div\s/.test(chunk) || chunk === "</div>") {
continue;
}
// === Standard Markdown ===
// CarouselBlock
if (chunk.includes("<Carousel")) {
const slides: any[] = [];
const slideMatches = chunk.matchAll(/<Slide\s+([^>]*?)\/>/g);
for (const m of slideMatches) {
const attrs = m[1];
slides.push({
image: (attrs.match(/image=["']([^"']+)["']/) || [])[1] || "",
caption: (attrs.match(/caption=["']([^"']+)["']/) || [])[1] || "",
});
}
if (slides.length > 0) {
nodes.push(blockNode("carousel", { slides }));
continue;
}
}
// Headings
const headingMatch = chunk.match(/^(#{1,6})\s+(.*)/);
if (headingMatch) {
nodes.push({
type: "heading",
tag: `h${headingMatch[1].length}`,
format: "",
indent: 0,
version: 1,
direction: "ltr",
children: [
{ mode: "normal", type: "text", text: headingMatch[2], version: 1 },
],
});
continue;
}
// Default: plain text paragraph
nodes.push(textNode(chunk));
}
return nodes;
}
/**
* Reassembles multi-line JSX tags that span across double-newline boundaries.
* E.g. <IconList>\n\n<IconListItem.../>\n\n</IconList> becomes a single chunk.
*/
function reassembleMultiLineJSX(content: string): string {
// Tags that wrap other content across paragraph breaks
const wrapperTags = [
"IconList",
"StatsGrid",
"FAQSection",
"Section",
"Reveal",
"Carousel",
];
for (const tag of wrapperTags) {
const openRegex = new RegExp(`<${tag}[^>]*>`, "g");
let match;
while ((match = openRegex.exec(content)) !== null) {
const openPos = match.index;
const closeTag = `</${tag}>`;
const closePos = content.indexOf(closeTag, openPos);
if (closePos === -1) continue;
const fullEnd = closePos + closeTag.length;
const fullBlock = content.substring(openPos, fullEnd);
// Replace double newlines inside this block with single newlines
// so it stays as one chunk during splitting
const collapsed = fullBlock.replace(/\n\s*\n/g, "\n");
content =
content.substring(0, openPos) + collapsed + content.substring(fullEnd);
// Adjust regex position
openRegex.lastIndex = openPos + collapsed.length;
}
}
return content;
}

View File

@@ -0,0 +1,39 @@
{
"extends": "../../tsconfig.json",
"compilerOptions": {
"module": "ESNext",
"moduleResolution": "bundler",
"target": "ES2022",
"lib": [
"ES2022",
"DOM",
"DOM.Iterable"
],
"jsx": "react-jsx",
"outDir": "dist",
"rootDir": "src",
"baseUrl": ".",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"declaration": true,
"sourceMap": true,
"paths": {
"@payload-config": [
"../../apps/mintel.me/payload.config.ts",
"../../apps/web/payload.config.ts",
"./node_modules/@payloadcms/next/dist/index.js"
]
}
},
"include": [
"src/**/*",
"src/types.d.ts"
],
"exclude": [
"node_modules",
"dist",
"**/*.test.ts"
]
}

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/pdf",
"version": "1.9.5",
"version": "1.9.10",
"type": "module",
"main": "dist/index.js",
"module": "dist/index.js",

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/seo-engine",
"version": "1.0.0",
"version": "1.9.10",
"private": true,
"description": "AI-powered SEO keyword and topic cluster evaluation engine",
"type": "module",
@@ -16,7 +16,7 @@
"scripts": {
"build": "tsup",
"dev": "tsup --watch",
"test": "vitest",
"test": "vitest run --passWithNoTests",
"clean": "rm -rf dist",
"lint": "eslint src --ext .ts"
},

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/thumbnail-generator",
"version": "1.9.5",
"version": "1.9.10",
"private": false,
"type": "module",
"main": "./dist/index.js",

View File

@@ -1,6 +1,6 @@
{
"name": "@mintel/tsconfig",
"version": "1.9.5",
"version": "1.9.10",
"publishConfig": {
"access": "public",
"registry": "https://git.infra.mintel.me/api/packages/mmintel/npm"

2090
pnpm-lock.yaml generated

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