GPT-4とPDF Vectorを使用したマルチフォーマット研究論文要約の生成

• •OpenAI API Key
• •HTTP Webhookエンドポイント（n8nが自動生成）

{
  "meta": {
    "instanceId": "placeholder"
  },
  "nodes": [
    {
      "id": "info-note",
      "name": "要約の種類",
      "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 - 論文URL",
      "type": "n8n-nodes-base.webhook",
      "position": [
        450,
        300
      ],
      "parameters": {
        "path": "summarize",
        "options": {},
        "responseMode": "onReceived"
      },
      "typeVersion": 1
    },
    {
      "id": "pdfvector-parse",
      "name": "PDF Vector - 論文解析",
      "type": "n8n-nodes-pdfvector.pdfVector",
      "position": [
        650,
        300
      ],
      "parameters": {
        "useLlm": "always",
        "resource": "document",
        "operation": "parse",
        "documentUrl": "={{ $json.paperUrl }}"
      },
      "typeVersion": 1
    },
    {
      "id": "exec-summary",
      "name": "エグゼクティブサマリー",
      "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": "技術要約",
      "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": "一般向け要約",
      "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": "ツイート要約",
      "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": "全要約の結合",
      "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": "要約を返す",
      "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
          }
        ]
      ]
    }
  }
}