Monitoreo automatizado de artículos académicos, con vectores PDF, GPT-3.5 y notificaciones de Slack
Este es unPersonal Productivity, Multimodal AIflujo de automatización del dominio deautomatización que contiene 10 nodos.Utiliza principalmente nodos como Set, Code, Slack, OpenAi, EmailSend. Monitoreo automatizado de artículos académicos, con vectores PDF, GPT-3.5 y alertas de Slack
- •Bot Token de Slack o URL de Webhook
- •Clave de API de OpenAI
Nodos utilizados (10)
Categoría
{
"meta": {
"instanceId": "placeholder"
},
"nodes": [
{
"id": "config-note",
"name": "Configuración del Bot",
"type": "n8n-nodes-base.stickyNote",
"position": [
250,
150
],
"parameters": {
"content": "## Paper Monitoring Bot\n\nMonitors these topics:\n- Machine Learning\n- Neural Networks\n- Computer Vision\n\nRuns: Daily at 9 AM"
},
"typeVersion": 1
},
{
"id": "schedule-trigger",
"name": "Programación Diaria",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
450,
300
],
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 24,
"triggerAtHour": 9
}
]
}
},
"typeVersion": 1
},
{
"id": "set-params",
"name": "Establecer Parámetros de Búsqueda",
"type": "n8n-nodes-base.set",
"position": [
650,
300
],
"parameters": {
"values": {
"number": [
{
"name": "daysBack",
"value": 1
}
],
"string": [
{
"name": "searchQueries",
"value": "machine learning,neural networks,computer vision,deep learning"
}
]
}
},
"typeVersion": 1
},
{
"id": "split-queries",
"name": "Dividir Consultas",
"type": "n8n-nodes-base.code",
"position": [
850,
300
],
"parameters": {
"functionCode": "const queries = $json.searchQueries.split(',').map(q => q.trim());\nreturn queries.map(query => ({ query }));"
},
"typeVersion": 1
},
{
"id": "pdfvector-search",
"name": "PDF Vector - Buscar Nuevos Artículos",
"type": "n8n-nodes-pdfvector.pdfVector",
"position": [
1050,
300
],
"parameters": {
"limit": 10,
"query": "={{ $json.query }}",
"fields": [
"title",
"authors",
"abstract",
"date",
"doi",
"pdfUrl",
"totalCitations"
],
"resource": "academic",
"yearFrom": "={{ new Date().getFullYear() }}",
"operation": "search",
"providers": [
"arxiv",
"pubmed",
"semantic_scholar"
]
},
"typeVersion": 1
},
{
"id": "filter-recent",
"name": "Filtrar Artículos Recientes",
"type": "n8n-nodes-base.code",
"position": [
1250,
300
],
"parameters": {
"functionCode": "// Filter papers from last N days\nconst daysBack = $node['Set Search Parameters'].json.daysBack;\nconst cutoffDate = new Date();\ncutoffDate.setDate(cutoffDate.getDate() - daysBack);\n\nconst recentPapers = $json.filter(paper => {\n const paperDate = new Date(paper.date);\n return paperDate >= cutoffDate;\n});\n\nreturn recentPapers.length > 0 ? recentPapers : [];"
},
"typeVersion": 1
},
{
"id": "summarize-paper",
"name": "Generar Resumen",
"type": "n8n-nodes-base.openAi",
"position": [
1450,
300
],
"parameters": {
"model": "gpt-3.5-turbo",
"messages": {
"values": [
{
"content": "Summarize this research paper in 2-3 sentences:\n\nTitle: {{ $json.title }}\nAuthors: {{ $json.authors.join(', ') }}\nAbstract: {{ $json.abstract }}\n\nFocus on the main contribution and findings."
}
]
}
},
"typeVersion": 1
},
{
"id": "format-digest",
"name": "Formatear Resumen",
"type": "n8n-nodes-base.code",
"position": [
1650,
300
],
"parameters": {
"functionCode": "// Format papers for notification\nconst papers = $items().map(item => {\n const paper = item.json;\n return {\n title: paper.title,\n authors: paper.authors.slice(0, 3).join(', ') + (paper.authors.length > 3 ? ' et al.' : ''),\n summary: paper.summary,\n link: paper.doi ? `https://doi.org/${paper.doi}` : paper.url,\n citations: paper.totalCitations || 0,\n query: paper.originalQuery\n };\n});\n\n// Group by query\nconst grouped = papers.reduce((acc, paper) => {\n if (!acc[paper.query]) acc[paper.query] = [];\n acc[paper.query].push(paper);\n return acc;\n}, {});\n\nreturn { papers: grouped, totalCount: papers.length, date: new Date().toISOString() };"
},
"typeVersion": 1
},
{
"id": "slack-notify",
"name": "Enviar Alerta Slack",
"type": "n8n-nodes-base.slack",
"position": [
1850,
300
],
"parameters": {
"channel": "#research-alerts",
"message": "=📚 *Daily Research Digest* - {{ $now.format('MMM DD, YYYY') }}\n\nFound {{ $json.totalCount }} new papers:\n\n{{ Object.entries($json.papers).map(([query, papers]) => `*${query}:*\\n${papers.map(p => `• ${p.title}\\n _${p.authors}_\\n ${p.summary}\\n 🔗 ${p.link}`).join('\\n\\n')}`).join('\\n\\n---\\n\\n') }}",
"attachments": []
},
"typeVersion": 1
},
{
"id": "email-digest",
"name": "Resumen por Correo Electrónico",
"type": "n8n-nodes-base.emailSend",
"position": [
1850,
450
],
"parameters": {
"html": "=<h2>Daily Research Digest</h2>\n<p>Found {{ $json.totalCount }} new papers</p>\n\n{{ Object.entries($json.papers).map(([query, papers]) => \n `<h3>${query}</h3>\n ${papers.map(p => \n `<div style=\"margin-bottom: 20px;\">\n <h4>${p.title}</h4>\n <p><em>${p.authors}</em></p>\n <p>${p.summary}</p>\n <p><a href=\"${p.link}\">Read Paper</a> | Citations: ${p.citations}</p>\n </div>`\n ).join('')}`\n).join('\\n') }}",
"subject": "=Daily Research Digest - {{ $now.format('MMM DD, YYYY') }}",
"toEmail": "research-team@company.com"
},
"typeVersion": 1
}
],
"connections": {
"format-digest": {
"main": [
[
{
"node": "slack-notify",
"type": "main",
"index": 0
},
{
"node": "email-digest",
"type": "main",
"index": 0
}
]
]
},
"split-queries": {
"main": [
[
{
"node": "pdfvector-search",
"type": "main",
"index": 0
}
]
]
},
"schedule-trigger": {
"main": [
[
{
"node": "set-params",
"type": "main",
"index": 0
}
]
]
},
"summarize-paper": {
"main": [
[
{
"node": "format-digest",
"type": "main",
"index": 0
}
]
]
},
"filter-recent": {
"main": [
[
{
"node": "summarize-paper",
"type": "main",
"index": 0
}
]
]
},
"set-params": {
"main": [
[
{
"node": "split-queries",
"type": "main",
"index": 0
}
]
]
},
"pdfvector-search": {
"main": [
[
{
"node": "filter-recent",
"type": "main",
"index": 0
}
]
]
}
}
}¿Cómo usar este flujo de trabajo?
Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.
¿En qué escenarios es adecuado este flujo de trabajo?
Intermedio - Productividad personal, IA Multimodal
¿Es de pago?
Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.
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