評価指標の例: 文字列の類似性
中級
これはEngineering, AI分野の自動化ワークフローで、12個のノードを含みます。主にSet, Code, Webhook, Evaluation, HttpRequestなどのノードを使用、AI技術を活用したスマート自動化を実現。 評価指標サンプル：文字列類似度
前提条件
- •HTTP Webhookエンドポイント（n8nが自動生成）
- •ターゲットAPIの認証情報が必要な場合あり
- •OpenAI API Key
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"meta": {
"instanceId": "bf40384a063e00f3b983f4f9bada22b57a8231a04c0fb48d363e26d7b0f2b7e7",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "b2a1a367-119f-4e2d-a982-ff675debf658",
"name": "付箋1",
"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": "付箋3",
"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\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": "付箋4",
"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": "webhook形式に一致",
"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",
"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": "データセット行を取得時",
"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": "Webhookに対応",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
260,
320
],
"parameters": {
"options": {}
},
"typeVersion": 1.3
},
{
"id": "f1642aa1-94c5-4002-a7aa-533566dd20eb",
"name": "評価中？",
"type": "n8n-nodes-base.evaluation",
"position": [
-20,
200
],
"parameters": {
"operation": "checkIfEvaluating"
},
"typeVersion": 4.6
},
{
"id": "15115588-b9ca-4e24-b7d8-f0aa0974b5dd",
"name": "指標を設定",
"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": "画像からコードを抽出",
"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": "文字列距離を計算",
"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": "画像をダウンロード",
"type": "n8n-nodes-base.httpRequest",
"position": [
-460,
200
],
"parameters": {
"url": "={{ $json.query.url }}",
"options": {}
},
"typeVersion": 4.2
}
],
"pinData": {},
"connections": {
"a03c9b79-d45d-4842-9325-df1af37697eb": {
"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": "4ed0b460-70af-4f1d-a7f3-97293f9b4ce0",
"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": "15115588-b9ca-4e24-b7d8-f0aa0974b5dd",
"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
}
]
]
}
}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
中級 - エンジニアリング, 人工知能
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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