Umfassende Literaturübersicht mit GPT-4 und Mehrdatenbankensuche erstellen

Fortgeschritten

Dies ist ein Document Extraction, Multimodal AI-Bereich Automatisierungsworkflow mit 8 Nodes. Hauptsächlich werden Code, OpenAi, PdfVector, WriteBinaryFile und andere Nodes verwendet. Umfassende Literaturrecherche mit GPT-4 und Mehrdatenbankensuche

Voraussetzungen
  • OpenAI API Key
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
  "meta": {
    "instanceId": "placeholder"
  },
  "nodes": [
    {
      "id": "start-node",
      "name": "Start",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        250,
        250
      ],
      "parameters": {
        "content": "## Literature Review Parameters\n\nTopic: {{ $json.topic }}\nYear Range: {{ $json.startYear }}-{{ $json.endYear }}\nMax Papers: {{ $json.maxPapers }}"
      },
      "typeVersion": 1
    },
    {
      "id": "pdfvector-search",
      "name": "PDF Vector - Search Papers",
      "type": "n8n-nodes-pdfvector.pdfVector",
      "notes": "Search across multiple academic databases",
      "position": [
        450,
        300
      ],
      "parameters": {
        "limit": 50,
        "query": "={{ $json.topic }}",
        "fields": [
          "title",
          "abstract",
          "authors",
          "year",
          "doi",
          "pdfUrl",
          "totalCitations"
        ],
        "yearTo": "={{ $json.endYear }}",
        "resource": "academic",
        "yearFrom": "={{ $json.startYear }}",
        "operation": "search",
        "providers": [
          "pubmed",
          "semantic_scholar",
          "arxiv",
          "google_scholar"
        ]
      },
      "typeVersion": 1
    },
    {
      "id": "sort-papers",
      "name": "Sortieren by Citations",
      "type": "n8n-nodes-base.code",
      "position": [
        650,
        300
      ],
      "parameters": {
        "functionCode": "// Sort papers by citations in descending order\nreturn items.sort((a, b) => (b.json.totalCitations || 0) - (a.json.totalCitations || 0));"
      },
      "typeVersion": 1
    },
    {
      "id": "limit-papers",
      "name": "Select Top Papers",
      "type": "n8n-nodes-base.code",
      "position": [
        850,
        300
      ],
      "parameters": {
        "functionCode": "// Limit to top N papers\nconst maxPapers = $node['Start'].json.maxPapers || 10;\nreturn items.slice(0, maxPapers);"
      },
      "typeVersion": 1
    },
    {
      "id": "pdfvector-parse",
      "name": "PDF Vector - Parse Papers",
      "type": "n8n-nodes-pdfvector.pdfVector",
      "notes": "Parse each paper's PDF",
      "position": [
        1050,
        300
      ],
      "parameters": {
        "useLlm": "auto",
        "resource": "document",
        "operation": "parse",
        "documentUrl": "={{ $json.pdfUrl }}"
      },
      "typeVersion": 1
    },
    {
      "id": "synthesize",
      "name": "Synthesize Review",
      "type": "n8n-nodes-base.openAi",
      "position": [
        1250,
        300
      ],
      "parameters": {
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "content": "Create a literature review section for this paper:\n\nTitle: {{ $json.title }}\nAuthors: {{ $json.authors }}\nYear: {{ $json.year }}\n\nContent: {{ $json.content }}\n\nGenerate:\n1. Key contribution summary (2-3 sentences)\n2. Methodology overview\n3. Main findings\n4. Relevance to topic: {{ $node['Start'].json.topic }}"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "combine-sections",
      "name": "Combine Sections",
      "type": "n8n-nodes-base.code",
      "position": [
        1450,
        300
      ],
      "parameters": {
        "functionCode": "// Combine all review sections into a single document\nconst reviewSections = items.map(item => item.json.reviewSection || item.json.content || '').filter(section => section);\nreturn [{ json: { reviewSections: reviewSections.join('\\n\\n') } }];"
      },
      "typeVersion": 1
    },
    {
      "id": "export-review",
      "name": "Export Review",
      "type": "n8n-nodes-base.writeBinaryFile",
      "position": [
        1650,
        300
      ],
      "parameters": {
        "fileName": "literature_review_{{ $now.format('yyyy-MM-dd') }}.md",
        "fileContent": "# Literature Review: {{ $node['Start'].json.topic }}\n\n{{ $json.reviewSections }}"
      },
      "typeVersion": 1
    }
  ],
  "connections": {
    "start-node": {
      "main": [
        [
          {
            "node": "pdfvector-search",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "combine-sections": {
      "main": [
        [
          {
            "node": "export-review",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "limit-papers": {
      "main": [
        [
          {
            "node": "pdfvector-parse",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Sort by Citations": {
      "main": [
        [
          {
            "node": "limit-papers",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "synthesize": {
      "main": [
        [
          {
            "node": "combine-sections",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "pdfvector-parse": {
      "main": [
        [
          {
            "node": "synthesize",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "pdfvector-search": {
      "main": [
        [
          {
            "node": "Sort by Citations",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
Häufig gestellte Fragen

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 - Dokumentenextraktion, 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.

Workflow-Informationen
Schwierigkeitsgrad
Fortgeschritten
Anzahl der Nodes8
Kategorie2
Node-Typen5
Schwierigkeitsbeschreibung

Für erfahrene Benutzer, mittelkomplexe Workflows mit 6-15 Nodes

Autor
PDF Vector

PDF Vector

@pdfvector

A 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.

Externe Links
Auf n8n.io ansehen

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Kategorien

Kategorien: 34