Exemple de métrique d'évaluation : Similitude de chaîne de caractères

Intermédiaire

Ceci est unEngineering, AIworkflow d'automatisation du domainecontenant 12 nœuds.Utilise principalement des nœuds comme Set, Code, Webhook, Evaluation, HttpRequest, combinant la technologie d'intelligence artificielle pour une automatisation intelligente. Exemple d'indicateur d'évaluation : Similarité des chaînes de caractères

Prérequis
  • Point de terminaison HTTP Webhook (généré automatiquement par n8n)
  • Peut nécessiter les informations d'identification d'authentification de l'API cible
  • Clé API OpenAI
Aperçu du workflow
Visualisation des connexions entre les nœuds, avec support du zoom et du déplacement
Exporter le workflow
Copiez la configuration JSON suivante dans n8n pour importer et utiliser ce workflow
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      "position": [
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      "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
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      "position": [
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      "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)."
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      "typeVersion": 1
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      "parameters": {
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        "content": "Read in [this test dataset](https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=1786963566#gid=1786963566) of images"
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        "jsonOutput": "=  {\n    \"headers\": {\n    },\n    \"params\": {},\n    \"query\": {\n      \"url\": {{ $json.file_url.toJsonString() }}\n    },\n    \"body\": {},\n    \"executionMode\": \"test\"\n  }"
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      "position": [
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      "parameters": {
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      "typeVersion": 1.3
    },
    {
      "id": "f1642aa1-94c5-4002-a7aa-533566dd20eb",
      "name": "Évaluation en cours ?",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "operation": "checkIfEvaluating"
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      "name": "Définir les métriques",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "metrics": {
          "assignments": [
            {
              "id": "0e507b06-e6d5-4ace-aa22-f06c6db5b883",
              "name": "score",
              "type": "number",
              "value": "={{ $json.score }}"
            }
          ]
        },
        "operation": "setMetrics"
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      "typeVersion": 4.6
    },
    {
      "id": "af028132-c866-487d-be85-e3af049bc793",
      "name": "Extraire le code de l'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": "Calculer la distance des chaînes",
      "type": "n8n-nodes-base.code",
      "position": [
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      ],
      "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": "Télécharger l'image",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
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        200
      ],
      "parameters": {
        "url": "={{ $json.query.url }}",
        "options": {}
      },
      "typeVersion": 4.2
    }
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}
Foire aux questions

Comment utiliser ce workflow ?

Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.

Dans quelles scénarios ce workflow est-il adapté ?

Intermédiaire - Ingénierie, Intelligence Artificielle

Est-ce payant ?

Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.

Informations sur le workflow
Niveau de difficulté
Intermédiaire
Nombre de nœuds12
Catégorie2
Types de nœuds9
Description de la difficulté

Adapté aux utilisateurs expérimentés, avec des workflows de complexité moyenne contenant 6-15 nœuds

Liens externes
Voir sur n8n.io

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Catégories

Catégories: 34