Prüfungsfragen generieren

• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •Google Sheets API-Anmeldedaten
• •OpenAI API Key
• •Qdrant-Serververbindungsdaten
• •Google Gemini API Key

{
  "id": "7Qa2mH7PnDxy7Qat",
  "meta": {
    "instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462",
    "templateCredsSetupCompleted": true
  },
  "name": "Generate Exam Questions",
  "tags": [],
  "nodes": [
    {
      "id": "4e037d6e-93a9-4c1b-b84a-dbbcf77beaf5",
      "name": "Bei Klick auf 'Test-Workflow'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
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      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "febc8bb7-5de7-46d6-bc23-54673089cd3d",
      "name": "Qdrant Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
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        240
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "list",
          "value": "ai_article_test",
          "cachedResultName": "ai_article_test"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "iyQ6MQiVaF3VMBmt",
          "name": "QdrantApi account (Hetzner)"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "2d7e2673-6559-49b3-9ed0-29ca2c376f00",
      "name": "Collection erstellen",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -440,
        -20
      ],
      "parameters": {
        "url": "http://QDRANT_URL/collections/COLLECTIONS",
        "method": "PUT",
        "options": {},
        "jsonBody": "{\n  \"vectors\": {\n    \"size\": 1536,\n    \"distance\": \"Cosine\"  \n  },\n  \"shard_number\": 1,  \n  \"replication_factor\": 1,  \n  \"write_consistency_factor\": 1 \n}",
        "sendBody": true,
        "sendHeaders": true,
        "specifyBody": "json",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": "application/json"
            }
          ]
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "qhny6r5ql9wwotpn",
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      },
      "typeVersion": 4.2
    },
    {
      "id": "615f26b2-930c-4b74-a35c-00b83460a7c9",
      "name": "Collection aktualisieren",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -440,
        240
      ],
      "parameters": {
        "url": "http://QDRANT_URL/collections/COLLECTIONS/points/delete",
        "method": "POST",
        "options": {},
        "jsonBody": "{\n  \"filter\": {}\n}",
        "sendBody": true,
        "sendHeaders": true,
        "specifyBody": "json",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": "application/json"
            }
          ]
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "qhny6r5ql9wwotpn",
          "name": "Qdrant API (Hetzner)"
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      },
      "typeVersion": 4.2
    },
    {
      "id": "eb34b8dd-353b-41c4-8a02-6565c3f8a7d3",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        820,
        440
      ],
      "parameters": {
        "options": {
          "stripNewLines": false
        }
      },
      "credentials": {
        "openAiApi": {
          "id": "4zwP0MSr8zkNvvV9",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "fb639802-e099-4857-823b-5e6d89fb3e86",
      "name": "Standard-Datenlader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        1080,
        460
      ],
      "parameters": {
        "loader": "textLoader",
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "0af5028d-56a4-4bbc-8af0-f088e54f178b",
      "name": "Token-Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
      "position": [
        1040,
        640
      ],
      "parameters": {
        "chunkSize": 450,
        "chunkOverlap": 50
      },
      "typeVersion": 1
    },
    {
      "id": "6a10192e-4b2e-4705-865a-fa90328ba3c1",
      "name": "Notizzettel3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -240,
        -80
      ],
      "parameters": {
        "color": 6,
        "width": 880,
        "height": 220,
        "content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
      },
      "typeVersion": 1
    },
    {
      "id": "1ebefe44-e5c9-43fb-b9fa-fee47b08e2c2",
      "name": "Notizzettel4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -460,
        180
      ],
      "parameters": {
        "color": 4,
        "width": 620,
        "height": 400,
        "content": "# STEP 2\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
      },
      "typeVersion": 1
    },
    {
      "id": "88f816ae-4331-46e0-b1f9-636ec94e8bb3",
      "name": "Converto di MD",
      "type": "n8n-nodes-base.code",
      "position": [
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      ],
      "parameters": {
        "jsCode": "function convertToMarkdown(docContent) {\n  let markdown = '';\n\n  const headingMap = {\n    'HEADING_1': '#',\n    'HEADING_2': '##',\n    'HEADING_3': '###',\n    'HEADING_4': '####',\n    'HEADING_5': '#####',\n    'HEADING_6': '######',\n  };\n\n  for (const element of docContent.body.content) {\n    if (!element.paragraph) continue;\n\n    const para = element.paragraph;\n    let line = '';\n\n    // Tipo di paragrafo (normale o heading)\n    const style = para.paragraphStyle?.namedStyleType;\n    const prefix = headingMap[style] || '';\n\n    for (const el of para.elements) {\n      if (!el.textRun) continue;\n\n      let text = el.textRun.content || '';\n      const style = el.textRun.textStyle || {};\n\n      if (style.bold) text = `**${text.trim()}**`;\n      if (style.italic) text = `*${text.trim()}*`;\n      if (!style.bold && !style.italic) text = text.trim();\n\n      line += text;\n    }\n\n    if (prefix) {\n      markdown += `${prefix} ${line}\\n\\n`;\n    } else {\n      markdown += `${line}\\n\\n`;\n    }\n  }\n\n  return markdown.trim();\n}\n\n// Assumiamo che il JSON completo sia in items[0].json\nconst docJson = items[0].json;\nconst markdown = convertToMarkdown(docJson);\n\nreturn [\n  {\n    json: {\n      markdown,\n    },\n  },\n];"
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      "typeVersion": 2
    },
    {
      "id": "5c733b2d-3d0a-4260-af88-7907907e209f",
      "name": "Dokument abrufen",
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        240
      ],
      "parameters": {
        "simple": false,
        "operation": "get",
        "documentURL": "XXXXXXXXXXXXXXXX"
      },
      "credentials": {
        "googleDocsOAuth2Api": {
          "id": "LpmDV1ry0BPLvW8b",
          "name": "Google Docs account"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "5de82976-2376-4201-a5a4-dbdd6bfcb596",
      "name": "Vector Store Retriever",
      "type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
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      ],
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      "typeVersion": 1
    },
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      "id": "25bcb865-7b15-4272-81da-4ff41a4ccc60",
      "name": "Qdrant Vector Store1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        1440,
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      ],
      "parameters": {
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "list",
          "value": "ai_article_test",
          "cachedResultName": "ai_article_test"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "iyQ6MQiVaF3VMBmt",
          "name": "QdrantApi account (Hetzner)"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "7dacd3ac-2d25-4960-ba53-e44ae9722dca",
      "name": "In Datei konvertieren",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        560,
        240
      ],
      "parameters": {
        "options": {},
        "operation": "toText",
        "sourceProperty": "markdown"
      },
      "typeVersion": 1.1
    },
    {
      "id": "9d7561f0-5b01-4327-ab62-68a105364155",
      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        540,
        980
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "0p34rXqIqy8WuoPg",
          "name": "Google Gemini(PaLM) Api account"
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      "typeVersion": 1
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        "options": {}
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      "typeVersion": 3
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      "name": "Google Gemini Chat Model1",
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        "modelName": "models/gemini-2.0-pro-exp"
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      "name": "Google Gemini Chat Model2",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
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      ],
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        "modelName": "models/gemini-2.0-flash-exp"
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      "credentials": {
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          "name": "Google Gemini(PaLM) Api account"
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    },
    {
      "id": "2f4ca583-8005-4e26-88df-ffebdc2be2f6",
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      "type": "@n8n/n8n-nodes-langchain.outputParserItemList",
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        }
      },
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    },
    {
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      "name": "Über Elemente iterieren1",
      "type": "n8n-nodes-base.splitInBatches",
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      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "2de66223-475c-4fef-aa85-13e954a5c1cc",
      "name": "Google Gemini Chat Model3",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
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        1840
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "0p34rXqIqy8WuoPg",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "43058954-369c-477d-beee-ece1916aebb7",
      "name": "Qdrant Vector Store2",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        1380,
        2020
      ],
      "parameters": {
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "list",
          "value": "ai_article_test",
          "cachedResultName": "ai_article_test"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "iyQ6MQiVaF3VMBmt",
          "name": "QdrantApi account (Hetzner)"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "27dddcae-e20a-41a9-879e-ce8ae8a0347f",
      "name": "Embeddings OpenAI2",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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        2200
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "4zwP0MSr8zkNvvV9",
          "name": "OpenAi account"
        }
      },
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    },
    {
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      "name": "Strukturierter Ausgabeparser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1820,
        1820
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"correct\": {\n\t\t\t\"type\": \"string\"\n\t\t},\n\t\t\"answers\": {\n\t\t\t\"type\": \"array\",\n\t\t\t\"items\": {\n\t\t\t\t\"type\": \"string\"\n\t\t\t}\n\t\t}\n\t}\n}"
      },
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    },
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      "id": "42d627b5-c033-4b2e-8ea4-fe704601b3d6",
      "name": "RAG",
      "type": "@n8n/n8n-nodes-langchain.toolVectorStore",
      "position": [
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        1820
      ],
      "parameters": {
        "description": "In base alla domanda consulta il database vettoriale ed estrapola la risposta corretta. Elabora anche altre 3 risposte non corrette."
      },
      "typeVersion": 1.1
    },
    {
      "id": "ce763ef2-eb54-484b-8046-7bc008012ec5",
      "name": "Google Gemini Chat Model4",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1700,
        1980
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-pro-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "0p34rXqIqy8WuoPg",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "076994e8-0326-424e-a5c3-3d07958af0af",
      "name": "Offene Fragen",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
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      ],
      "parameters": {
        "text": "=Article:\n'''\n{{ $json.markdown }}\n'''",
        "messages": {
          "messageValues": [
            {
              "message": "=## Purpose\nYou are a specialized AI designed to analyze articles and create challenging questions that test comprehension and knowledge retention. Your task is to generate questions that encourage critical thinking about the article's content.\n\n## Input\nThe input will be a text article on any subject. This could be academic, news, technical, or general interest content.\n\n## Output Requirements\n- Create exactly 10 questions based on the article content\n- DO NOT number the questions\n- Questions should cover key facts, concepts, and implications from the article\n- Include a mix of question types:\n  - Factual recall questions\n  - Inference questions that require reading between the lines\n  - Application questions that ask how concepts might be applied\n  - Analysis questions that probe deeper understanding\n  - Questions about relationships between different parts of the article\n- Questions should vary in difficulty level\n- Avoid creating questions with simple yes/no answers\n- Ensure questions are clearly worded and unambiguous\n- Questions should test genuine understanding rather than trivial details\n\n## Output Format\n- Present each question as a separate paragraph\n- Do not include answers\n- Do not include numbering or bullet points\n- Do not include any introductory text\n- Do not include any explanatory notes\n\n## Behavior Guidelines\n- Focus on the most significant and meaningful content in the article\n- Ensure questions thoroughly cover the entire article, not just the beginning\n- If the article contains technical terms, create questions that test understanding of these terms\n- If the article presents contrasting viewpoints, create questions about both perspectives\n- Maintain neutrality - do not frame questions that suggest a particular stance\n- If the article is highly specialized, adjust question complexity accordingly\n- Do not create questions about information not contained in the article\n- If the article is in a language other than English, generate questions in the same language\n\n## Examples of Good Questions\n- How does the author's description of X relate to the concept of Y discussed later in the article?\n- What evidence does the article provide to support the claim that X leads to Y?\n- How might the framework described in the article be applied to solve similar problems in different contexts?\n- What underlying assumptions inform the author's perspective on this issue?\n- In what ways does the article suggest the relationship between X and Y has evolved over time?"
            }
          ]
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.6
    },
    {
      "id": "5df02a14-175f-4923-9a2f-ad4514f98c71",
      "name": "Geschlossene Fragen",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        560,
        1540
      ],
      "parameters": {
        "text": "=Article:\n'''\n{{ $json.markdown }}\n'''",
        "messages": {
          "messageValues": [
            {
              "message": "=## Purpose\nYou are a specialized AI designed to analyze articles and create high-quality multiple-choice questions that effectively test knowledge comprehension and retention. Your task is to generate questions with appropriate answer options that accurately assess understanding of the article's content.\n\n## Input\nThe input will be a text article on any subject. This could be academic, news, technical, or general interest content.\n\n## Output Requirements\n- Create exactly 10 multiple-choice questions based on the article content\n- DO NOT number the questions\n- Each question must include:\n  - A clear question stem\n  - Four answer options (labeled A, B, C, D)\n  - One correct answer and three plausible distractors\n- Questions should cover key facts, concepts, and implications from the article\n- Include a mix of question types:\n  - Factual recall questions\n  - Inference questions requiring deeper understanding\n  - Application questions testing practical knowledge\n  - Analysis questions examining relationships between concepts\n- Questions should vary in difficulty level\n- Ensure questions are clearly worded and unambiguous\n- Distractors should be plausible but clearly incorrect upon careful reading of the article\n\n## Output Format\n- Present each question as a separate paragraph\n- Format each question as:\n  [Question]\n  A. [Option A]\n  B. [Option B]\n  C. [Option C]\n  D. [Option D]\n- Do not indicate which answer is correct in the output\n- Do not include any introductory text\n- Do not include any explanatory notes\n- Do not include numbering for questions\n\n## Behavior Guidelines\n- Focus on the most significant and meaningful content in the article\n- Ensure questions thoroughly cover the entire article, not just the beginning\n- Make all answer options approximately the same length\n- Avoid using absolute terms like \"always\" or \"never\" in the options\n- Avoid grammatical clues that hint at the correct answer\n- Make distractors plausible by:\n  - Using common misconceptions\n  - Including partially correct information\n  - Using correct information from the wrong context\n- If the article contains technical terms, create questions that test understanding of these terms\n- If the article presents contrasting viewpoints, create questions about both perspectives\n- Maintain neutrality - do not frame questions that suggest a particular stance\n- If the article is in a language other than English, generate questions in the same language\n\n## Examples of Good Multiple-Choice Questions\n- What is the primary factor contributing to the phenomenon described in the article?\n  A. [Plausible but incorrect factor]\n  B. [Correct factor from article]\n  C. [Plausible but incorrect factor]\n  D. [Plausible but incorrect factor]\n\n- According to the article, how does [concept X] impact [concept Y]?\n  A. [Correct relationship described in article]\n  B. [Plausible but incorrect relationship]\n  C. [Plausible but incorrect relationship]\n  D. [Plausible but incorrect relationship]\n\n- Which application of the described technology would align with the principles outlined in the article?\n  A. [Plausible but incorrect application]\n  B. [Plausible but incorrect application]\n  C. [Correct application based on article]\n  D. [Plausible but incorrect application]"
            }
          ]
        },
        "promptType": "define",
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      },
      "typeVersion": 1.6
    },
    {
      "id": "53c89d9a-4a69-47f7-bbf1-f523e2763741",
      "name": "Fragen beantworten",
      "type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
      "position": [
        1400,
        800
      ],
      "parameters": {
        "text": "={{ $json.text }}",
        "options": {
          "systemPromptTemplate": "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.\n\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\nUse text plain (not markdown).\n----------------\nContext: {context}"
        },
        "promptType": "define"
      },
      "typeVersion": 1.5
    },
    {
      "id": "93d55b4f-2a93-474e-b431-6fd8ef868c45",
      "name": "Antworten und Optionen erstellen",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1420,
        1560
      ],
      "parameters": {
        "text": "={{ $json.text }}",
        "options": {
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      "position": [
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        "content": "# STEP 3\n\nThe chain analyzes the document and creates 10 \"open\" questions and another chain analyzes each single question and through the consultation of the vector database the optimal answer is obtained."
      },
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    },
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        "content": "# STEP 4\n\nThe chain analyzes the document and creates 10 questions with \"closed\" answers and another chain analyzes each single question and through the consultation of the vector database the correct answer and 3 other wrong answers are obtained to be used as a quiz."
      },
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      "position": [
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        "width": 1120,
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        "content": "## Auto-Generate Exam Questions from Google Docs with AI\n\nThis workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval\n\nThis workflow **saves educators hours of manual work** while ensuring high-quality, curriculum-aligned assessments. Let me know if you'd like help adapting it for specific subjects!\n"
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