Mehrformatige Forschungs-Zusammenfassungen mit GPT-4 und PDF Vector generieren
Dies ist ein AI Summarization, Multimodal AI-Bereich Automatisierungsworkflow mit 9 Nodes. Hauptsächlich werden Code, OpenAi, Webhook, PdfVector, RespondToWebhook und andere Nodes verwendet. Multiformat-Forschungs-Zusammenfassungen mit GPT-4 und PDF Vector generieren
- •OpenAI API Key
- •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
Verwendete Nodes (9)
Kategorie
{
"meta": {
"instanceId": "placeholder"
},
"nodes": [
{
"id": "info-note",
"name": "Zusammenfassungstypen",
"type": "n8n-nodes-base.stickyNote",
"position": [
250,
150
],
"parameters": {
"content": "## Paper Summarizer\n\nGenerates multiple summary types:\n- Executive (1 page)\n- Technical (detailed)\n- Lay (plain language)\n- Social (tweet-sized)"
},
"typeVersion": 1
},
{
"id": "webhook-trigger",
"name": "Webhook - Paper-URL",
"type": "n8n-nodes-base.webhook",
"position": [
450,
300
],
"parameters": {
"path": "summarize",
"options": {},
"responseMode": "onReceived"
},
"typeVersion": 1
},
{
"id": "pdfvector-parse",
"name": "PDF Vector - Paper analysieren",
"type": "n8n-nodes-pdfvector.pdfVector",
"position": [
650,
300
],
"parameters": {
"useLlm": "always",
"resource": "document",
"operation": "parse",
"documentUrl": "={{ $json.paperUrl }}"
},
"typeVersion": 1
},
{
"id": "exec-summary",
"name": "Management-Zusammenfassung",
"type": "n8n-nodes-base.openAi",
"position": [
850,
250
],
"parameters": {
"model": "gpt-4",
"messages": {
"values": [
{
"content": "Create an executive summary (max 500 words) of this research paper:\n\n{{ $json.content }}\n\nInclude:\n1. Research question and motivation\n2. Methodology overview\n3. Key findings (3-5 points)\n4. Practical implications\n5. Limitations and future work"
}
]
}
},
"typeVersion": 1
},
{
"id": "tech-summary",
"name": "Technische Zusammenfassung",
"type": "n8n-nodes-base.openAi",
"position": [
850,
350
],
"parameters": {
"model": "gpt-4",
"messages": {
"values": [
{
"content": "Create a detailed technical summary of this research paper:\n\n{{ $json.content }}\n\nInclude:\n1. Research objectives and hypotheses\n2. Detailed methodology\n3. Data analysis approach\n4. Complete results with statistics\n5. Technical contributions\n6. Comparison with prior work\n7. Future research directions"
}
]
}
},
"typeVersion": 1
},
{
"id": "lay-summary",
"name": "Laien-Zusammenfassung",
"type": "n8n-nodes-base.openAi",
"position": [
850,
450
],
"parameters": {
"model": "gpt-3.5-turbo",
"messages": {
"values": [
{
"content": "Explain this research paper in simple terms that anyone can understand (max 300 words):\n\n{{ $json.content }}\n\nAvoid jargon and technical terms. Use analogies where helpful."
}
]
}
},
"typeVersion": 1
},
{
"id": "tweet-summary",
"name": "Tweet-Zusammenfassung",
"type": "n8n-nodes-base.openAi",
"position": [
850,
550
],
"parameters": {
"model": "gpt-3.5-turbo",
"messages": {
"values": [
{
"content": "Create a tweet (max 280 characters) summarizing the key finding of this paper:\n\n{{ $json.content }}\n\nMake it engaging and include relevant hashtags."
}
]
}
},
"typeVersion": 1
},
{
"id": "combine-summaries",
"name": "Alle Zusammenfassungen kombinieren",
"type": "n8n-nodes-base.code",
"position": [
1050,
400
],
"parameters": {
"functionCode": "return {\n paperUrl: $node['Webhook - Paper URL'].json.paperUrl,\n summaries: {\n executive: $node['Executive Summary'].json.content,\n technical: $node['Technical Summary'].json.content,\n lay: $node['Lay Summary'].json.content,\n tweet: $node['Tweet Summary'].json.content\n },\n generatedAt: new Date().toISOString()\n};"
},
"typeVersion": 1
},
{
"id": "respond-webhook",
"name": "Zusammenfassungen zurückgeben",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
1250,
400
],
"parameters": {
"options": {}
},
"typeVersion": 1
}
],
"connections": {
"lay-summary": {
"main": [
[
{
"node": "combine-summaries",
"type": "main",
"index": 0
}
]
]
},
"tweet-summary": {
"main": [
[
{
"node": "combine-summaries",
"type": "main",
"index": 0
}
]
]
},
"exec-summary": {
"main": [
[
{
"node": "combine-summaries",
"type": "main",
"index": 0
}
]
]
},
"tech-summary": {
"main": [
[
{
"node": "combine-summaries",
"type": "main",
"index": 0
}
]
]
},
"webhook-trigger": {
"main": [
[
{
"node": "pdfvector-parse",
"type": "main",
"index": 0
}
]
]
},
"combine-summaries": {
"main": [
[
{
"node": "respond-webhook",
"type": "main",
"index": 0
}
]
]
},
"pdfvector-parse": {
"main": [
[
{
"node": "exec-summary",
"type": "main",
"index": 0
},
{
"node": "tech-summary",
"type": "main",
"index": 0
},
{
"node": "lay-summary",
"type": "main",
"index": 0
},
{
"node": "tweet-summary",
"type": "main",
"index": 0
}
]
]
}
}
}Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Fortgeschritten - KI-Zusammenfassung, Multimodales KI
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
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