A/B セルフテスト
上級
これはContent Creation, Multimodal AI分野の自動化ワークフローで、16個のノードを含みます。主にIf, Set, Supabase, Agent, ChatTriggerなどのノードを使用。 Supabase、Langchain Agent、OpenAI GPT-4o を使った AI プロンプト A/B テスト
前提条件
- •Supabase URL と API Key
- •OpenAI API Key
- •PostgreSQLデータベース接続情報
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
{
"id": "TEA7K9MSVQGCWKe6",
"meta": {
"instanceId": "ac63467607103d9c95dd644384984672b90b1cb03e07edbaf18fe72b2a6c45bb",
"templateCredsSetupCompleted": true
},
"name": "A/B Split Testing",
"tags": [],
"nodes": [
{
"id": "e8404493-4297-4169-a72f-89e668ae5fbc",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-1460,
-140
],
"webhookId": "334e3a8d-73d2-4d3c-9927-158c1169ef5e",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "582e1c1b-12ff-42ff-8130-48f94eebd706",
"name": "AIエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
220,
-160
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"options": {
"systemMessage": "={{ $json.prompt }}"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "39ca5c70-11d4-4f86-bde5-0f9827297be9",
"name": "セッション存在確認",
"type": "n8n-nodes-base.supabase",
"position": [
-960,
-140
],
"parameters": {
"filters": {
"conditions": [
{
"keyName": "session_id",
"keyValue": "={{ $('When chat message received').item.json.sessionId }}"
}
]
},
"tableId": "split_test_sessions",
"operation": "get"
},
"credentials": {
"supabaseApi": {
"id": "1iEg1EzFrF29iqp2",
"name": "Supabase (bsde.ai)"
}
},
"executeOnce": false,
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "35f2c270-9571-41ba-ab7c-47a6742d7d90",
"name": "セッションが存在する場合",
"type": "n8n-nodes-base.if",
"position": [
-720,
-140
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "4270c464-6874-45d2-aa3b-606f45544c3d",
"operator": {
"type": "number",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.id }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "ec00ad92-96e9-4936-a547-2a2715ff5c32",
"name": "セッションへのパス割り当て",
"type": "n8n-nodes-base.supabase",
"position": [
-400,
-20
],
"parameters": {
"tableId": "split_test_sessions",
"fieldsUi": {
"fieldValues": [
{
"fieldId": "show_alternative",
"fieldValue": "={{ Math.random() < 0.5 }}"
},
{
"fieldId": "session_id",
"fieldValue": "={{ $('When chat message received').item.json.sessionId }}"
}
]
}
},
"credentials": {
"supabaseApi": {
"id": "1iEg1EzFrF29iqp2",
"name": "Supabase (bsde.ai)"
}
},
"typeVersion": 1
},
{
"id": "92ee7145-30ae-41e9-bc04-eef03b84485e",
"name": "パス値の定義",
"type": "n8n-nodes-base.set",
"position": [
-1200,
-140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9581a184-a120-4b1f-8408-cfe97520d107",
"name": "baseline_value",
"type": "string",
"value": "The dog's name is Ben"
},
{
"id": "1752f2c4-4ce4-4893-b8db-1c59131c298a",
"name": "alternative_value",
"type": "string",
"value": "The dog's name is Tom"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "23c1b4e2-2ba2-4237-bb4b-b92da127d201",
"name": "OpenAIチャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
300,
60
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "1OMpAMAKR9l3eUDI",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "be3f14b9-68c7-457d-b5bf-a6abbadf5b67",
"name": "Postgresチャットメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
480,
60
],
"parameters": {
"tableName": "n8n_split_test_chat_histories",
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"credentials": {
"postgres": {
"id": "tzLXHvhykxvYghPC",
"name": "bsde.ai Supabase (Session Pooler)"
}
},
"typeVersion": 1.3
},
{
"id": "1c20d274-2482-4551-a4ea-64860eb35276",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1520,
-260
],
"parameters": {
"color": 7,
"width": 220,
"height": 300,
"content": "## 1. Receive Message\n\n"
},
"typeVersion": 1
},
{
"id": "ee22d3b1-d447-4e35-8ac4-d093edf6deee",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1280,
-340
],
"parameters": {
"color": 7,
"width": 1340,
"height": 500,
"content": "## 2. Determine Prompt for LLM\n"
},
"typeVersion": 1
},
{
"id": "7d90ec00-5fca-4b0d-bc1f-e8b8c179b960",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
80,
-240
],
"parameters": {
"color": 7,
"width": 520,
"height": 440,
"content": "## 3. AI Agent"
},
"typeVersion": 1
},
{
"id": "b9b9e0e8-53c1-4d6a-bbdc-c2a13d740dfb",
"name": "正しいプロンプトの取得",
"type": "n8n-nodes-base.set",
"position": [
-80,
-160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "08de68ec-0f12-43ee-98ab-59d8a414f114",
"name": "prompt",
"type": "string",
"value": "={{ $json.show_alternative ? $('Define Path Values').item.json.alternative_value : $('Define Path Values').item.json.baseline_value }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2b78ce9b-e6b4-4744-8ddf-00f8ae990fc8",
"name": "付箋6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1260,
-240
],
"parameters": {
"color": 5,
"width": 220,
"height": 260,
"content": "### Modification\nSet the values of the baseline and alternative prompts"
},
"typeVersion": 1
},
{
"id": "0646f176-407a-41ee-b602-34bd681fc421",
"name": "付箋8",
"type": "n8n-nodes-base.stickyNote",
"position": [
100,
40
],
"parameters": {
"color": 5,
"width": 340,
"height": 140,
"content": "### Modification\nReplace this sub-node \nto use a different language\n model"
},
"typeVersion": 1
},
{
"id": "a391018c-5d28-4384-89e1-0435758a6945",
"name": "付箋5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1480,
-600
],
"parameters": {
"width": 520,
"height": 240,
"content": "### Setup\n1. Create a table in Supabase called **split_test_sessions**. It needs to have the following columns: **session_id** (`text`) and **show_alternative** (`bool`)\n2. Add your **Supabase**, **OpenAI**, and **PostgreSQL** credentials\n3. Modify the **Define Path Values** node to set the baseline and alternative prompt values.\n4. Activate the workflow and test by sending messages through n8n's inbuilt chat\n5. Experiment with different chat sessions to test see both prompts in action"
},
"typeVersion": 1
},
{
"id": "2382d146-f0c1-4de6-9e90-b17c304df692",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2120,
-360
],
"parameters": {
"width": 560,
"height": 480,
"content": "\n## Split Test Different Agent Prompts with Supabase and OpenAI\n### Use Case\nOftentimes, it's useful to test different settings for a large language model in production against various metrics. Split testing is a good method for doing this.\n### What it Does\nThis workflow randomly assigns chat sessions to one of two prompts, the baseline and the alternative. The agent will use the same prompt for all interactions in that chat session.\n### How it Works\n1. When messages arrive, a table containing information regarding session ID and which prompt to use is checked to see if the chat already exists\n2. If it does not, the session ID is added to the table and a prompt is randomly assigned\n3. These values are then used to generate a response\n### Next Steps\n- Modify the workflow to test different LLM settings such as temperature\n- Add a method to measure the efficacy of the two alternative prompts"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "339c6f2f-e4d1-4922-9442-2c1a78e96067",
"connections": {
"23c1b4e2-2ba2-4237-bb4b-b92da127d201": {
"ai_languageModel": [
[
{
"node": "582e1c1b-12ff-42ff-8130-48f94eebd706",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"92ee7145-30ae-41e9-bc04-eef03b84485e": {
"main": [
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"type": "main",
"index": 0
}
]
]
},
"b9b9e0e8-53c1-4d6a-bbdc-c2a13d740dfb": {
"main": [
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{
"node": "582e1c1b-12ff-42ff-8130-48f94eebd706",
"type": "main",
"index": 0
}
]
]
},
"be3f14b9-68c7-457d-b5bf-a6abbadf5b67": {
"ai_memory": [
[
{
"node": "582e1c1b-12ff-42ff-8130-48f94eebd706",
"type": "ai_memory",
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},
"35f2c270-9571-41ba-ab7c-47a6742d7d90": {
"main": [
[
{
"node": "b9b9e0e8-53c1-4d6a-bbdc-c2a13d740dfb",
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[
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},
"ec00ad92-96e9-4936-a547-2a2715ff5c32": {
"main": [
[
{
"node": "b9b9e0e8-53c1-4d6a-bbdc-c2a13d740dfb",
"type": "main",
"index": 0
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},
"39ca5c70-11d4-4f86-bde5-0f9827297be9": {
"main": [
[
{
"node": "35f2c270-9571-41ba-ab7c-47a6742d7d90",
"type": "main",
"index": 0
}
]
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},
"e8404493-4297-4169-a72f-89e668ae5fbc": {
"main": [
[
{
"node": "92ee7145-30ae-41e9-bc04-eef03b84485e",
"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？
上級 - コンテンツ作成, マルチモーダルAI
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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