Exemple de métrique d'évaluation : Similitude de chaîne de caractères
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
- •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
- •Peut nécessiter les informations d'identification d'authentification de l'API cible
- •Clé API OpenAI
Nœuds utilisés (12)
Catégorie
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"content": "Check how far apart the actual code is from the expected code (a score of 1 is a perfect match)"
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"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\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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"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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{
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"name": "Évaluation en cours ?",
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"name": "Extraire le code de l'image",
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"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": {
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"mode": "list",
"value": "gpt-4o",
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"type": "n8n-nodes-base.code",
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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;"
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}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
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