Beispiel für Bewertungsmetriken: String-Ähnlichkeit

• •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •OpenAI API Key

{
  "meta": {
    "instanceId": "bf40384a063e00f3b983f4f9bada22b57a8231a04c0fb48d363e26d7b0f2b7e7",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "b2a1a367-119f-4e2d-a982-ff675debf658",
      "name": "Notizzettel1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        220,
        -40
      ],
      "parameters": {
        "color": 7,
        "width": 180,
        "height": 260,
        "content": "Check how far apart the actual code is from the expected code (a score of 1 is a perfect match)"
      },
      "typeVersion": 1
    },
    {
      "id": "f5413855-20de-4b77-ba90-18610a9d9b4d",
      "name": "Notizzettel3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1300,
        40
      ],
      "parameters": {
        "width": 300,
        "height": 500,
        "content": "## How it works\nThis template shows how to calculate a workflow evaluation metric: **text similarity, measured character-by-character**.\n\nThe workflow takes images of hand-written codes, extracts the code and compares it with the expected answer from the dataset.\n\nThe images look like this:\n![image](https://storage.googleapis.com/n8n_template_data/handwriting_scans/doc20250302_08223946_001.jpg)\n\nYou can find more information on workflow evaluation [here](https://docs.n8n.io/advanced-ai/evaluations/overview), and other metric examples [here](https://docs.n8n.io/advanced-ai/evaluations/metric-based-evaluations/#2-calculate-metrics)."
      },
      "typeVersion": 1
    },
    {
      "id": "8921a4c4-cee1-44e7-8dce-55219db519d7",
      "name": "Notizzettel4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -960,
        280
      ],
      "parameters": {
        "color": 7,
        "width": 220,
        "height": 220,
        "content": "Read in [this test dataset](https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=1786963566#gid=1786963566) of images"
      },
      "typeVersion": 1
    },
    {
      "id": "fbf8337b-eb46-443a-8507-58a14b817be0",
      "name": "Match webhook format",
      "type": "n8n-nodes-base.set",
      "position": [
        -680,
        340
      ],
      "parameters": {
        "mode": "raw",
        "options": {},
        "jsonOutput": "=  {\n    \"headers\": {\n    },\n    \"params\": {},\n    \"query\": {\n      \"url\": {{ $json.file_url.toJsonString() }}\n    },\n    \"body\": {},\n    \"executionMode\": \"test\"\n  }"
      },
      "typeVersion": 3.4
    },
    {
      "id": "a03c9b79-d45d-4842-9325-df1af37697eb",
      "name": "Webhook-Trigger",
      "type": "n8n-nodes-base.webhook",
      "position": [
        -900,
        40
      ],
      "webhookId": "7ceb775c-b961-44f0-acfe-682a67612332",
      "parameters": {
        "path": "7ceb775c-b961-44f0-acfe-682a67612332",
        "options": {}
      },
      "typeVersion": 2
    },
    {
      "id": "85bd63e2-3039-4f0e-8721-bc2b843461c9",
      "name": "When fetching a dataset row",
      "type": "n8n-nodes-base.evaluationTrigger",
      "position": [
        -900,
        340
      ],
      "parameters": {
        "sheetName": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=1786963566#gid=1786963566"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=1786963566#gid=1786963566"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "bpr2LoSELMlxpwnN",
          "name": "Google Sheets account David"
        }
      },
      "typeVersion": 4.6
    },
    {
      "id": "4ed0b460-70af-4f1d-a7f3-97293f9b4ce0",
      "name": "Respond to Webhook-Trigger",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        260,
        320
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.3
    },
    {
      "id": "f1642aa1-94c5-4002-a7aa-533566dd20eb",
      "name": "Evaluating?",
      "type": "n8n-nodes-base.evaluation",
      "position": [
        -20,
        200
      ],
      "parameters": {
        "operation": "checkIfEvaluating"
      },
      "typeVersion": 4.6
    },
    {
      "id": "15115588-b9ca-4e24-b7d8-f0aa0974b5dd",
      "name": "Setzen metrics",
      "type": "n8n-nodes-base.evaluation",
      "position": [
        480,
        80
      ],
      "parameters": {
        "metrics": {
          "assignments": [
            {
              "id": "0e507b06-e6d5-4ace-aa22-f06c6db5b883",
              "name": "score",
              "type": "number",
              "value": "={{ $json.score }}"
            }
          ]
        },
        "operation": "setMetrics"
      },
      "typeVersion": 4.6
    },
    {
      "id": "af028132-c866-487d-be85-e3af049bc793",
      "name": "Extract code from image",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
        -240,
        200
      ],
      "parameters": {
        "text": "=Extract ONLY the handwritten code in the top-right corner of this image.\n\nThe code MUST follow this EXACT format:\nBT/ED/[1-3 capital letters]/[1-3 capital letters]/[1-3 capital letters]/[1-3 capital letters or empty]/[single letter + number (2-4 chars total)]\n\nExamples of correct format:\nBT/ED/ABC/DE/F/G/H1\nBT/ED/A/BC/DEF/GH/I23\nBT/ED/AB/CD/EF/GH/I234\n\nDO NOT include any explanations, notes, or other text.\nDO NOT return anything if the code doesn't match the required format.\nVERIFY the extracted code matches the format before returning it.\nReturn ONLY the extracted code - nothing else.",
        "modelId": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o",
          "cachedResultName": "GPT-4O"
        },
        "options": {},
        "resource": "image",
        "inputType": "base64",
        "operation": "analyze"
      },
      "credentials": {
        "openAiApi": {
          "id": "Ag9qPAsY7lpIGkvC",
          "name": "JPs n8n openAI key"
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "50a26635-078f-40a7-8944-2e43ed8cd482",
      "name": "Calc string distance",
      "type": "n8n-nodes-base.code",
      "position": [
        260,
        80
      ],
      "parameters": {
        "mode": "runOnceForEachItem",
        "jsCode": "const expected_code = $('When fetching a dataset row').item.json.expected_output\nconst actual_code = $json.content\n\nfunction levenshteinDistance(str1, str2) {\n  const m = str1.length;\n  const n = str2.length;\n  const dp = Array(m + 1).fill().map(() => Array(n + 1).fill(0));\n\n  for (let i = 0; i <= m; i++) {\n    dp[i][0] = i;\n  }\n  \n  for (let j = 0; j <= n; j++) {\n    dp[0][j] = j;\n  }\n\n  for (let i = 1; i <= m; i++) {\n    for (let j = 1; j <= n; j++) {\n      if (str1[i - 1] === str2[j - 1]) {\n        dp[i][j] = dp[i - 1][j - 1];\n      } else {\n        dp[i][j] = 1 + Math.min(\n          dp[i - 1][j],     // deletion\n          dp[i][j - 1],     // insertion\n          dp[i - 1][j - 1]  // substitution\n        );\n      }\n    }\n  }\n\n  return dp[m][n];\n}\n\nconst dist = levenshteinDistance(\n  expected_code, \n  actual_code\n)\n\nconst max_dist = Math.max(\n  expected_code.length,\n  actual_code.length\n)\n\nconsole.log('truth', expected_code)\nconsole.log('effort', actual_code)\nconsole.log('dist', dist)\nconsole.log('max_dist', max_dist)\n\n$input.item.json.score = 1 - (dist / max_dist)\n\nreturn $input.item;"
      },
      "typeVersion": 2
    },
    {
      "id": "383db4b0-9665-4608-bbf9-3dca88508bff",
      "name": "Download image",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -460,
        200
      ],
      "parameters": {
        "url": "={{ $json.query.url }}",
        "options": {}
      },
      "typeVersion": 4.2
    }
  ],
  "pinData": {},
  "connections": {
    "Webhook": {
      "main": [
        [
          {
            "node": "383db4b0-9665-4608-bbf9-3dca88508bff",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "f1642aa1-94c5-4002-a7aa-533566dd20eb": {
      "main": [
        [
          {
            "node": "50a26635-078f-40a7-8944-2e43ed8cd482",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Respond to Webhook",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "383db4b0-9665-4608-bbf9-3dca88508bff": {
      "main": [
        [
          {
            "node": "af028132-c866-487d-be85-e3af049bc793",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "50a26635-078f-40a7-8944-2e43ed8cd482": {
      "main": [
        [
          {
            "node": "Set metrics",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "fbf8337b-eb46-443a-8507-58a14b817be0": {
      "main": [
        [
          {
            "node": "383db4b0-9665-4608-bbf9-3dca88508bff",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "af028132-c866-487d-be85-e3af049bc793": {
      "main": [
        [
          {
            "node": "f1642aa1-94c5-4002-a7aa-533566dd20eb",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "85bd63e2-3039-4f0e-8721-bc2b843461c9": {
      "main": [
        [
          {
            "node": "fbf8337b-eb46-443a-8507-58a14b817be0",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}