David Roberts This workflow corresponds to n8n.io template #4273 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "86d6a658-f056-4434-9720-dfd3ca514cb3",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
420,
20
],
"parameters": {
"color": 7,
"width": 300,
"height": 180,
"content": "Check whether the documents returned a relevant to the question"
},
"typeVersion": 1
},
{
"id": "90aac144-164f-4961-89f8-4b8402177202",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1220,
-40
],
"parameters": {
"width": 200,
"height": 500,
"content": "## How it works\nThis template shows how to calculate a workflow evaluation metric: **retrieved document relevance** (i.e. whether the information retrieved from the vector store is relevant to the question).\n\nThe workflow takes a question and checks whether the information retrieved to answer it is relevant.\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": "f3970d8c-b205-4348-b375-d919d2f56f94",
"name": "Sticky Note4",
"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=630157309#gid=630157309) of questions"
},
"typeVersion": 1
},
{
"id": "677b90c8-aa6f-4cc5-88df-d8e635b0bde2",
"name": "When fetching a dataset row",
"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=630157309#gid=630157309"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.6
},
{
"id": "c381a3c3-12b6-44fe-a2ed-6adf6f997534",
"name": "Evaluating?",
"type": "n8n-nodes-base.evaluation",
"position": [
-60,
200
],
"parameters": {
"operation": "checkIfEvaluating"
},
"typeVersion": 4.6
},
{
"id": "e1cf0122-c668-41d2-9316-a173d425d897",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-900,
100
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "b521aa40-c8da-4485-b834-50a129c27d3c",
"name": "Match chat format",
"type": "n8n-nodes-base.set",
"position": [
-680,
340
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "93f89095-7918-45ad-aa74-a0bbcf0d5788",
"name": "chatInput",
"type": "string",
"value": "={{ $json.question }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d0170cbd-ba01-45db-a405-aec943bf1608",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
100
],
"parameters": {
"color": 7,
"width": 150,
"height": 260,
"content": "Only calculate metrics if we're evaluating, to reduce costs"
},
"typeVersion": 1
},
{
"id": "a21f98f0-25eb-44cc-a04a-33181d7e5288",
"name": "Return chat response",
"type": "n8n-nodes-base.noOp",
"position": [
220,
340
],
"parameters": {},
"typeVersion": 1
},
{
"id": "fd8628a1-9f31-4f42-88df-b87eda7216ee",
"name": "Set metrics",
"type": "n8n-nodes-base.evaluation",
"position": [
820,
80
],
"parameters": {
"metrics": {
"assignments": [
{
"id": "230589eb-34c8-4d10-9296-4a78d673077a",
"name": "similarity",
"type": "number",
"value": "={{ $json.message.content.score }}"
}
]
},
"operation": "setMetrics"
},
"typeVersion": 4.6
},
{
"id": "eefe3a5a-722a-4cce-b84b-65c0520a225f",
"name": "Get dataset",
"type": "n8n-nodes-base.googleSheets",
"position": [
-680,
-620
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "url",
"value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.5
},
{
"id": "ea83d68c-f499-4864-918c-5ccaccf555a5",
"name": "Remove Duplicates",
"type": "n8n-nodes-base.removeDuplicates",
"position": [
-460,
-620
],
"parameters": {
"compare": "selectedFields",
"options": {},
"fieldsToCompare": "document_id"
},
"typeVersion": 2
},
{
"id": "6bf59dc8-fe59-49cf-971f-e22dac5d0500",
"name": "Simple Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"position": [
-240,
-620
],
"parameters": {
"mode": "insert",
"memoryKey": "rag_evaluation_demo",
"clearStore": true
},
"typeVersion": 1.1
},
{
"id": "66dd59b0-b2e4-43c9-b1f5-7f5a65568f5e",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
-300,
-400
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "213b230d-0f40-4e71-b0bf-4783274e2cdb",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
-100,
-380
],
"parameters": {
"options": {},
"jsonData": "={{ $json.document_text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "64ec7e8c-63f9-4198-9e8d-61345c564c13",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
-20,
-220
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "c561f2ce-ef05-4f42-9b6e-6fabd12d6d23",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-980,
-700
],
"parameters": {
"color": 7,
"width": 1180,
"height": 620,
"content": "### Setup: Populate vector DB\nRun this once before running the main workflow. It inserts the documents from the dataset in the vector store, so they can be queried by the agent below"
},
"typeVersion": 1
},
{
"id": "4a7de6bd-17ac-40df-9b27-2d8f6fdbeb07",
"name": "When clicking \u2018Execute workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-900,
-620
],
"parameters": {},
"typeVersion": 1
},
{
"id": "a0f6211f-6202-462c-8fa3-52e10ad89b5d",
"name": "Calculate doc relevance metric",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
440,
80
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "GPT-4O-MINI"
},
"options": {},
"messages": {
"values": [
{
"role": "system",
"content": "=You are a teacher evaluating the relevance of student responses to questions.\n\nYou will be given a QUESTION and one or more FACTS provided by a student. Your task is to determine whether these FACTS are relevant to answering the QUESTION.\n\nGrading criteria:\n(1) Evaluate whether the provided FACTS contain information that is relevant to answering the QUESTION\n(2) A FACT is relevant if it contains keywords or concepts semantically related to the QUESTION\n(3) A FACT is relevant if it provides context, examples, or explanations that address any aspect of the QUESTION\n(4) A FACT may contain some unrelated information and still be considered relevant if it also contains information that satisfies criteria (2) or (3)\n\nScoring:\n- Score 1: At least one of the provided FACTS is relevant to the QUESTION\n- Score 0: All provided FACTS are completely unrelated to the QUESTION\n\nEvaluation process:\n1. Identify the key concepts and requirements in the QUESTION\n2. Examine each provided FACT for keywords or concepts related to these key elements\n3. Consider semantic relationships, not just exact keyword matches\n4. Determine if any FACT contributes useful information toward answering the QUESTION\n5. Provide your reasoning in a detailed, step-by-step manner first\n6. Then create a one-sentence summary of your reasoning\n7. Avoid stating your conclusion at the beginning of your extended reasoning\n\nOutput format:\n{\n \"extended_reasoning\": \"<detailed step-by-step analysis of how you evaluated the relevance>\",\n \"reasoning_summary\": \"<one sentence summary of your conclusion>\",\n \"score\": <number: either 0 or 1>\n}\n\nExamples:\n\nQUESTION: \"What factors contributed to the fall of the Roman Empire?\"\nFACTS: \"The Roman Empire fell due to economic problems, military failures, and political corruption. Germanic tribes invaded from the north while the empire was weakened.\"\nEVALUATION: This would receive a score of 1 because the facts directly address causes of the Roman Empire's fall.\n\nQUESTION: \"What factors contributed to the fall of the Roman Empire?\"\nFACTS: \"The Pythagorean theorem states that a\u00b2 + b\u00b2 = c\u00b2 in a right triangle. Jupiter is the largest planet in our solar system.\"\nEVALUATION: This would receive a score of 0 because neither fact relates to the Roman Empire or its fall."
},
{
"content": "=QUESTION: {{ $('When fetching a dataset row').item.json.question }}\n\nFACTS: \n{{ $json.documents }}"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.8
},
{
"id": "b01d3427-4907-4715-8c15-669500012f89",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-420,
200
],
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant. Answer the user's questions using information from your vector knowledge base only.",
"returnIntermediateSteps": true
}
},
"typeVersion": 1.9
},
{
"id": "97e2db38-0114-449f-bb41-0ab490fc7327",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-480,
440
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "5b3679ab-b0a6-4461-9276-89eddb16f699",
"name": "Extract documents",
"type": "n8n-nodes-base.set",
"position": [
220,
80
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2d771c52-6637-4af0-9ff7-c87368041bfd",
"name": "documents",
"type": "string",
"value": "={{ $json.intermediateSteps.filter(x => x.action.tool == 'vector_knowledge_base')[0].observation }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0e4c4512-7b9f-4881-8ac7-4c7c24a4a446",
"name": "Simple Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"position": [
-260,
440
],
"parameters": {
"mode": "retrieve-as-tool",
"toolName": "vector_knowledge_base",
"memoryKey": "rag_evaluation_demo",
"toolDescription": "Source of all knowledge to work with"
},
"typeVersion": 1.1
},
{
"id": "edde6c75-dea5-46cc-937e-68e4a925559b",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
-220,
620
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "e23289e7-a769-4aae-82e6-b7f9e6d903f0",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-980,
-40
],
"parameters": {
"color": 7,
"width": 2000,
"height": 820,
"content": "### Main workflow"
},
"typeVersion": 1
},
{
"id": "c90baefc-22bd-4782-a8eb-236bfb1d2751",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
120
],
"parameters": {
"color": 7,
"width": 300,
"height": 200,
"content": "Make sure to enable 'Return intermediate steps' in the agent, to get the list of executed tools"
},
"typeVersion": 1
}
],
"connections": {
"AI Agent": {
"main": [
[
{
"node": "Evaluating?",
"type": "main",
"index": 0
}
]
]
},
"Evaluating?": {
"main": [
[
{
"node": "Extract documents",
"type": "main",
"index": 0
}
],
[
{
"node": "Return chat response",
"type": "main",
"index": 0
}
]
]
},
"Get dataset": {
"main": [
[
{
"node": "Remove Duplicates",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Simple Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract documents": {
"main": [
[
{
"node": "Calculate doc relevance metric",
"type": "main",
"index": 0
}
]
]
},
"Match chat format": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Remove Duplicates": {
"main": [
[
{
"node": "Simple Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Simple Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Simple Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Simple Vector Store1": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"When fetching a dataset row": {
"main": [
[
{
"node": "Match chat format",
"type": "main",
"index": 0
}
]
]
},
"Calculate doc relevance metric": {
"main": [
[
{
"node": "Set metrics",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Get dataset",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googleSheetsOAuth2ApiopenAiApi
Pro
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About this workflow
This is a template for n8n's evaluation feature.
Source: https://n8n.io/workflows/4273/ — original creator credit. Request a take-down →
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