AIエージェントの応答関連性をOpenAIとコサイン類似度で評価
上級
これはEngineering, AI分野の自動化ワークフローで、20個のノードを含みます。主にSet, Code, Evaluation, HttpRequest, Agent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。 OpenAIとコサイン類似度を使用してAIエージェントの応答の関連性を評価する
前提条件
- •ターゲットAPIの認証情報が必要な場合あり
- •OpenAI API Key
使用ノード (20)
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": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9",
"templateCredsSetupCompleted": true
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"nodes": [
{
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"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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"parameters": {
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"__rl": true,
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"value": "gpt-4.1-mini",
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{
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"position": [
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"parameters": {
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"assignments": [
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"assignments": [
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"name": "question",
"type": "string",
"value": "={{ $('When fetching a dataset row').first().json.input }}"
},
{
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"name": "answer",
"type": "string",
"value": "={{ $json.output }}"
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{
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{
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"parameters": {
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},
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{
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"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
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"webhookId": "ba1fadeb-b566-469a-97b3-3159a99f1805",
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{
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"type": "n8n-nodes-base.evaluation",
"position": [
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],
"parameters": {
"outputs": {
"values": [
{
"outputName": "output",
"outputValue": "={{ $('Set Input Fields').first().json.answer }}"
},
{
"outputName": "score",
"outputValue": "={{ $json.score }}"
}
]
},
"sheetName": {
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}
},
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{
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"name": "メトリクスの更新",
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"parameters": {
"metrics": {
"assignments": [
{
"id": "1fd7759c-f4ef-4eda-87ad-9d9563b63e99",
"name": "score",
"type": "number",
"value": "={{ $json.score }}"
}
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},
"operation": "setMetrics"
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},
{
"id": "04f714cd-99ce-45a9-bc43-351fb6ea14d4",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini",
"cachedResultName": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
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},
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{
"id": "d821b0ec-93c9-46e1-ab03-280aeb9b87b1",
"name": "構造化出力パーサー",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1040,
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],
"parameters": {
"jsonSchemaExample": "{\n \"question\": \"Where was Albert Einstein born?\",\n \"noncommittal\": 0\n}"
},
"typeVersion": 1.2
},
{
"id": "be975ef4-c50b-4bf9-b871-cc8d479661eb",
"name": "回答関連性",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
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],
"parameters": {
"text": "={{ $json.answer }}",
"batching": {},
"messages": {
"messageValues": [
{
"message": "=Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, \"I don't know\" or \"I'm not sure\" are noncommittal answers\""
}
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},
{
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"name": "付箋1",
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"position": [
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],
"parameters": {
"color": 7,
"width": 840,
"height": 720,
"content": "## 1. Setup Your AI Workflow to Use Evaluations\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nThe Evaluations Trigger is a separate execution which does not affect your production workflow in any way. It is manually triggered and automatically pulled datasets from the assigned Google Sheet."
},
"typeVersion": 1
},
{
"id": "eeeac9d0-39c6-4d17-8d93-9c60b6c612f8",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 7,
"width": 1200,
"height": 720,
"content": "## 2. Answer Relevance: How relevant is the agent response to the user question?\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nFor this evaluation, we analyse the agent's answer and ask another AI to generate a question for it - kinda like the game show [Jeopardy!](https://en.wikipedia.org/wiki/Jeopardy!).\nWe do this in order to compare the generated question with the original question for the test. If the questions are similar, then relevancy is high."
},
"typeVersion": 1
},
{
"id": "c86f66e2-9997-4ca6-9ec6-cfa185d404f2",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-920,
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],
"parameters": {
"width": 420,
"height": 720,
"content": "## Try It Out!\n### This n8n template demonstrates how to calculate the evaluation metric \"Relevance\" which in this scenario, measures the relevance of the agent's response to the user's question.\n\nThe scoring approach is adapted from [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_relevance.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_relevance.py)\n\n### How it works\n* This evaluation works best for Q&A agents.\n* For our scoring, we analyse the agent's response and ask another AI to generate a question from it. This generated question is then compared to the original question using cosine similarity.\n* A high score indicates relevance and the agent's successful ability to answer the question whereas a low score means agent may have added too much irrelevant info, went off script or hallucinated.\n\n### Requirements\n* n8n version 1.94+\n* Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)\n\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
},
"typeVersion": 1
},
{
"id": "3bdd3254-7cdf-455a-ae63-9639069993bc",
"name": "埋め込みの取得",
"type": "n8n-nodes-base.httpRequest",
"position": [
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-140
],
"parameters": {
"url": "https://api.openai.com/v1/embeddings",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "input",
"value": "={{ $json.data }}"
},
{
"name": "model",
"value": "text-embedding-3-small"
},
{
"name": "encoding_format",
"value": "float"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 4.2
},
{
"id": "9e3bcdee-5f24-45e6-a85f-6dcfacb58d5d",
"name": "類似度スコアの計算",
"type": "n8n-nodes-base.code",
"position": [
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],
"parameters": {
"jsCode": "const [vectorsA, vectorsB] = $input.all().map(item => item.json.data[0].embedding);\nconst score = cosineSimilarity(vectorsA, vectorsB);\n\nreturn { json: { similarityScore: score } };\n\nfunction cosineSimilarity(a, b) { \n let dotProduct = normA = normB = 0;\n for (let i = 0; i < a.length; i++) {\n dotProduct += a[i] * b[i];\n normA += a[i] ** 2;\n normB += b[i] ** 2;\n }\n return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n}"
},
"typeVersion": 2
},
{
"id": "69c30814-9725-426d-9572-9f92bd81dea5",
"name": "質問から項目への変換",
"type": "n8n-nodes-base.code",
"position": [
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-140
],
"parameters": {
"jsCode": "return [\n { json: { data: $('Set Input Fields').first().json.question } },\n { json: { data: $input.first().json.output.question } }\n]"
},
"typeVersion": 2
},
{
"id": "83be23ff-6857-4265-b1d0-f4b682c66903",
"name": "関連性スコアの計算",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "c5abdf27-cb9e-4969-b669-25c6fbd135f1",
"name": "score",
"type": "number",
"value": "={{ $json.similarityScore * Math.abs(!$('Answer Relevance').first().json.output.noncommittal) }}"
}
]
}
},
"typeVersion": 3.4
}
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}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？
上級 - 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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ワークフロー情報
難易度
上級
ノード数20
カテゴリー2
ノードタイプ12
作成者
Jimleuk
@jimleukFreelance consultant based in the UK specialising in AI-powered automations. I work with select clients tackling their most challenging projects. For business enquiries, send me an email at hello@jimle.uk LinkedIn: https://www.linkedin.com/in/jimleuk/ X/Twitter: https://x.com/jimle_uk
外部リンク
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