使用GPT-4和PDF Vector生成多格式研究论文摘要
中级
这是一个AI Summarization, Multimodal AI领域的自动化工作流,包含 9 个节点。主要使用 Code, OpenAi, Webhook, PdfVector, RespondToWebhook 等节点。 使用GPT-4和PDF Vector生成多格式研究论文摘要
前置要求
- •OpenAI API Key
- •HTTP Webhook 端点(n8n 会自动生成)
工作流预览
可视化展示节点连接关系,支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入,即可使用此工作流
{
"meta": {
"instanceId": "placeholder"
},
"nodes": [
{
"id": "info-note",
"name": "摘要类型",
"type": "n8n-nodes-base.stickyNote",
"position": [
250,
150
],
"parameters": {
"content": "## 论文摘要生成器"
},
"typeVersion": 1
},
{
"id": "webhook-trigger",
"name": "Webhook - 论文 URL",
"type": "n8n-nodes-base.webhook",
"position": [
450,
300
],
"parameters": {
"path": "summarize",
"options": {},
"responseMode": "onReceived"
},
"typeVersion": 1
},
{
"id": "pdfvector-parse",
"name": "PDF 向量 - 解析论文",
"type": "n8n-nodes-pdfvector.pdfVector",
"position": [
650,
300
],
"parameters": {
"useLlm": "always",
"resource": "document",
"operation": "parse",
"documentUrl": "={{ $json.paperUrl }}"
},
"typeVersion": 1
},
{
"id": "exec-summary",
"name": "执行摘要",
"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": "技术摘要",
"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": "通俗摘要",
"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": "推文摘要",
"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": "合并所有摘要",
"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": "返回摘要",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
1250,
400
],
"parameters": {
"options": {}
},
"typeVersion": 1
}
],
"connections": {
"Lay Summary": {
"main": [
[
{
"node": "Combine All Summaries",
"type": "main",
"index": 0
}
]
]
},
"Tweet Summary": {
"main": [
[
{
"node": "Combine All Summaries",
"type": "main",
"index": 0
}
]
]
},
"Executive Summary": {
"main": [
[
{
"node": "Combine All Summaries",
"type": "main",
"index": 0
}
]
]
},
"Technical Summary": {
"main": [
[
{
"node": "Combine All Summaries",
"type": "main",
"index": 0
}
]
]
},
"Webhook - Paper URL": {
"main": [
[
{
"node": "PDF Vector - Parse Paper",
"type": "main",
"index": 0
}
]
]
},
"Combine All Summaries": {
"main": [
[
{
"node": "Return Summaries",
"type": "main",
"index": 0
}
]
]
},
"PDF Vector - Parse Paper": {
"main": [
[
{
"node": "Executive Summary",
"type": "main",
"index": 0
},
{
"node": "Technical Summary",
"type": "main",
"index": 0
},
{
"node": "Lay Summary",
"type": "main",
"index": 0
},
{
"node": "Tweet Summary",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流?
复制上方的 JSON 配置代码,在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」,粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景?
中级 - AI 摘要总结, 多模态 AI
需要付费吗?
本工作流完全免费,您可以直接导入使用。但请注意,工作流中使用的第三方服务(如 OpenAI API)可能需要您自行付费。
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人力资源
工作流信息
难度等级
中级
节点数量9
分类2
节点类型6
作者
PDF Vector
@pdfvectorA fully featured PDF APIs for developers - Parse any PDF or Word document, extract structured data, and access millions of academic papers - all through simple APIs.
外部链接
在 n8n.io 查看 →
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