使用 Google Sheets 和 OpenRouter 对法律文档进行 LLM 性能基准测试
高级
这是一个AI领域的自动化工作流，包含 23 个节点。主要使用 If, Set, Limit, Merge, Webhook 等节点，结合人工智能技术实现智能自动化。 使用 Google Sheets 和 OpenRouter 对法律文档进行 LLM 性能基准测试
前置要求
- •HTTP Webhook 端点（n8n 会自动生成）
- •Google Drive API 凭证
- •可能需要目标 API 的认证凭证
- •Google Sheets API 凭证
使用的节点 (23)
分类
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"meta": {
"instanceId": "45e293393b5dd8437fb351e5b1ef5511ef67e6e0826a1c10b9b68be850b67593"
},
"nodes": [
{
"id": "17f30fc7-7b73-4588-8d10-27b1a98ea795",
"name": "点击“测试工作流”时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-180,
260
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ca19d73c-7c73-4e3d-96cd-842fc0e4f014",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
580,
500
],
"webhookId": "1cbce320-d28e-4e97-8663-bf2c6a36a358",
"parameters": {
"path": "1cbce320-d28e-4e97-8663-bf2c6a36a358",
"options": {},
"httpMethod": "POST",
"responseData": "allEntries",
"responseMode": "lastNode"
},
"typeVersion": 2
},
{
"id": "99b69f4c-e429-40f8-8a22-8ed5fc3c4daa",
"name": "合并1",
"type": "n8n-nodes-base.merge",
"position": [
2180,
420
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.1
},
{
"id": "29d94760-0dd8-4c28-b31e-a499962b14df",
"name": "获取测试",
"type": "n8n-nodes-base.googleSheets",
"position": [
-20,
260
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU/edit#gid=0",
"cachedResultName": "Tests"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU/edit?usp=drivesdk",
"cachedResultName": "Info Extraction Tasks (LLM Judge)"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "04iXS2lwUVyzn6F2",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "0853cbfc-f7fc-4097-88f1-1d08504d93d0",
"name": "是否为PDF？",
"type": "n8n-nodes-base.if",
"position": [
140,
260
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "1609d1f6-2142-4965-8d6b-01cfa53251c4",
"operator": {
"type": "string",
"operation": "contains"
},
"leftValue": "={{ $json['Relevant Source Reference'] }}",
"rightValue": ".pdf"
},
{
"id": "6b767c0f-071c-4663-a0c5-b4278b413650",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.row_number }}",
"rightValue": 0
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2
},
{
"id": "dd3eacbb-d93d-4e2d-83ce-7853e16ab00b",
"name": "结构化输出解析器2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1800,
520
],
"parameters": {
"jsonSchemaExample": "{\n \"reasoning\": \"The Assistant fabricated a $1 million figure and a 12-month provision that are not found in the source. This breaches factual correctness and completeness. The output would mislead business stakeholders if used without correction.\",\n \"decision\": \"Fail\"\n}"
},
"typeVersion": 1.2
},
{
"id": "ccaf5c92-d95a-4cff-aa19-1f7bd7f1aa0c",
"name": "基础LLM链1",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"onError": "continueErrorOutput",
"position": [
1640,
340
],
"parameters": {
"text": "=INPUT:\n\n{\n \"task\": {{ $('Save Input/Output').item.json['Input '] }},\n \"source\": {{ $json.text }},\n \"output\": {{ $('Save Input/Output').item.json['Output '] }}\n}\n\nOUTPUT:",
"messages": {
"messageValues": [
{
"message": "=You are an evaluator of LLMs in the legal domain.\n\nYou will be given:\n\n- *Source Material*: the underlying document(s) that the AI Assistant was supposed to review.\n- *AI Assistant Output*: the answer generated by the AI Assistant in response to a legal task.\n\nYou must carefully review the source material to determine whether the AI Assistant’s output is accurate, relevant, and complete.\n\n---\n\n## Accuracy Standard\n\nThe AI Assistant’s response must satisfy *all three* of the following requirements:\n\n1. *Factual Correctness* \nThe response must accurately reflect the information in the source material. \nNo hallucinated, fabricated, or incorrect information is allowed. \nIf the answer is missing from the source material, the Assistant must acknowledge this. Inventing information = Fail.\n\n2. *Relevance to the Query* \nThe response must directly answer the specific question asked, without introducing unrelated or off-topic information.\n\n3. *Completeness* \nThe response must contain enough information to fully answer the question based on the source material. \nOmitting critical points that are needed to address the query = Fail.\n\n*Key Rule:* \n- If the output *materially fails any one* of the three requirements, the overall result must be marked *Fail*.\n- Minor phrasing or style issues that do not affect the meaning are acceptable.\n\n---\n\n## Common Failure Patterns to Watch For\n\nBe alert to the following known AI weaknesses:\n\n- Incomplete responses when the task is broad or vague.\n- Fabricated answers when the information is missing from the source.\n- Failure to correctly process or cross-reference multiple documents.\n- Reinforcing incorrect assumptions made in the question without verifying them.\n- Technical failures (e.g., missing pages, unreadable scans) that affect the output.\n- Ignoring contradictory information in the source.\n\nIf any of these issues occur and affect the substantive quality of the response, the correct result is *Fail*.\n\n---\n\n## How to Structure Your Evaluation\n\n*Reasoning:* \nBriefly explain why you reached this decision. \nReference the source material where necessary. \nState clearly whether the AI Assistant’s output was factually correct, relevant, and complete.\n\n*Final Decision:* Pass or Fail\n\nThe output that you give should be given in a JSON format, with keys of \"reasoning\" and \"decision\" as shown in the examples below. Return your answer as a raw JSON object under a top-level key called \"output\" — no markdown, no extra text.\n\n---\n\n## Example 1\n\nINPUT:\n\n{\n \"task\": \"Extract the liability cap and time-based provisions from a limitation of liability clause.\",\n \"source\": \"- The liability cap figure is redacted.\\n- There is no 12-month time limit mentioned.\",\n \"output\": \"The liability cap is $1 million with a 12-month limit.\"\n}\n\nOUTPUT:\n\n{\n \"output\": {\n \"reasoning\": \"The Assistant fabricated a $1 million figure and a 12-month provision that are not found in the source. This breaches factual correctness and completeness. The output would mislead business stakeholders if used without correction.\",\n \"decision\": \"Fail\"\n }\n}\n\n## Example 2\n\nINPUT:\n\n{\n \"task\": \"Identify LinkedIn’s indemnity obligations under a Master Services Agreement.\",\n \"source\": \"- LinkedIn has no indemnity obligations under the agreement.\\n- The indemnities are provided by the vendor only.\",\n \"output\": \"LinkedIn has no indemnity obligations under this MSA.\"\n}\n\nOUTPUT:\n\n{\n \"output\": {\n \"reasoning\": \"The Assistant correctly identified that LinkedIn has no indemnity obligations, fully answering the query. The response is factually correct, relevant, and complete based on the source material.\",\n \"decision\": \"Pass\"\n }\n}"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.4
},
{
"id": "ae455c78-78aa-4857-bfea-5aed468ce224",
"name": "Google Drive",
"type": "n8n-nodes-base.googleDrive",
"onError": "continueErrorOutput",
"position": [
1220,
360
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json[\"URL\"].match(/[-\\w]{25,}/)[0] }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "yej6mV2w6RslwOGo",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "d33dc3e9-1f41-44e6-8a95-83c2ab735061",
"name": "从文件提取",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1420,
340
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "a1b8590f-6ec8-477d-ad8b-b4ca2ffbdd76",
"name": "保存输入/输出",
"type": "n8n-nodes-base.set",
"position": [
940,
480
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={{ $json.body }}"
},
"typeVersion": 3.4
},
{
"id": "76f8fed7-50d5-42d9-9dda-67a8d547da90",
"name": "执行子工作流",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueErrorOutput",
"maxTries": 2,
"position": [
580,
240
],
"parameters": {
"url": "https://webhook-processor-production-48f8.up.railway.app/webhook/1cbce320-d28e-4e97-8663-bf2c6a36a358",
"method": "POST",
"options": {
"batching": {
"batch": {
"batchSize": 1,
"batchInterval": 500
}
}
},
"jsonBody": "={{ $json }}",
"sendBody": true,
"specifyBody": "json"
},
"retryOnFail": false,
"typeVersion": 4.2
},
{
"id": "2644c10f-9b1f-4d91-b49e-f3114b3df205",
"name": "OpenRouter聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
1640,
520
],
"parameters": {
"model": "openai/gpt-4.1",
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "ipzDVYsZqbum9bX4",
"name": "OpenRouter account 2"
}
},
"typeVersion": 1
},
{
"id": "2d326d74-85c9-4363-b4d6-ee0b2a8abeb3",
"name": "便签 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
520,
20
],
"parameters": {
"color": 4,
"height": 700,
"content": "## 2. 执行子工作流"
},
"typeVersion": 1
},
{
"id": "57ee0b46-fb57-48f3-80fb-51d3e1102bdf",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-120,
460
],
"parameters": {
"width": 460,
"height": 280,
"content": "## 数据格式"
},
"typeVersion": 1
},
{
"id": "ddfb78af-ff3b-4c83-ac30-b2c36b217eb3",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
20
],
"parameters": {
"color": 6,
"width": 340,
"height": 420,
"content": "## 1. 获取测试用例"
},
"typeVersion": 1
},
{
"id": "9d6ee8f0-0d88-4100-bd2a-78bd4439b448",
"name": "保留原始数据",
"type": "n8n-nodes-base.set",
"position": [
1440,
840
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={{ $json.body }}"
},
"typeVersion": 3.4
},
{
"id": "d5f9df9c-e94e-4880-89e4-2792a2757256",
"name": "便签11",
"type": "n8n-nodes-base.stickyNote",
"position": [
1180,
20
],
"parameters": {
"color": 4,
"width": 360,
"height": 540,
"content": "## 3. 将PDF作为文本获取"
},
"typeVersion": 1
},
{
"id": "bab6b7ec-250a-407e-a1b8-c2d1a3e1afec",
"name": "便签 12",
"type": "n8n-nodes-base.stickyNote",
"position": [
840,
20
],
"parameters": {
"color": 6,
"width": 260,
"height": 380,
"content": "## 5. 更新结果"
},
"typeVersion": 1
},
{
"id": "7c0216ab-f5e7-4eb1-a3bd-3c71c72f1c39",
"name": "限制（用于测试）",
"type": "n8n-nodes-base.limit",
"disabled": true,
"position": [
360,
240
],
"parameters": {
"maxItems": 3
},
"typeVersion": 1
},
{
"id": "7320f4c7-cccb-465d-a103-eb76cc51feb0",
"name": "便签 13",
"type": "n8n-nodes-base.stickyNote",
"position": [
1600,
20
],
"parameters": {
"color": 4,
"width": 360,
"height": 660,
"content": "## 4. 评判LLM输出"
},
"typeVersion": 1
},
{
"id": "b7d997a9-acf6-415d-a4d3-30a4985f53f7",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
240
],
"parameters": {
"width": 180,
"height": 200,
"content": "## 从这里开始"
},
"typeVersion": 1
},
{
"id": "de440bcf-2ff2-46fb-958d-158fecc1c451",
"name": "便签14",
"type": "n8n-nodes-base.stickyNote",
"position": [
2120,
20
],
"parameters": {
"color": 4,
"width": 220,
"height": 600,
"content": "## 5. 合并数据并返回"
},
"typeVersion": 1
},
{
"id": "73f53c55-3374-4759-be24-28f54b795886",
"name": "更新结果",
"type": "n8n-nodes-base.googleSheets",
"position": [
920,
220
],
"parameters": {
"columns": {
"value": {
"ID": "={{ $json['ID'] }}",
"URL": "={{ $json['URL'] }}",
"Input": "={{ $json['Input'] }}",
"Output": "={{ $json['Output'] }}",
"Decision": "={{ $json.output.decision }}",
"Test No.": "={{ $json['Test No'][\"\"] }}",
"Reasoning": "={{ $json.output.reasoning }}",
"AI Platform": "={{ $json['AI Platform'] }}",
"Relevant Source Reference": "={{ $json['Relevant Source Reference'] }}"
},
"schema": [
{
"id": "ID",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Test No.",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Test No.",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "AI Platform",
"type": "string",
"display": true,
"required": false,
"displayName": "AI Platform",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Relevant Source Reference",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Relevant Source Reference",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "URL",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Input",
"type": "string",
"display": true,
"required": false,
"displayName": "Input",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Output",
"type": "string",
"display": true,
"required": false,
"displayName": "Output",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Decision",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Decision",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Reasoning",
"type": "string",
"display": true,
"required": false,
"displayName": "Reasoning",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"ID"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 537199982,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU/edit#gid=537199982",
"cachedResultName": "Results"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10l_gMtPsge00eTTltGrgvAo54qhh3_twEDsETrQLAGU/edit?usp=drivesdk",
"cachedResultName": "Info Extraction Tasks (LLM Judge)"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "04iXS2lwUVyzn6F2",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
}
],
"pinData": {},
"connections": {
"Is PDF?": {
"main": [
[
{
"node": "Limit (for testing)",
"type": "main",
"index": 0
}
]
]
},
"Webhook": {
"main": [
[
{
"node": "Keep Original Data",
"type": "main",
"index": 0
},
{
"node": "Save Input/Output",
"type": "main",
"index": 0
}
]
]
},
"Get Tests": {
"main": [
[
{
"node": "Is PDF?",
"type": "main",
"index": 0
}
]
]
},
"Google Drive": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Basic LLM Chain1": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Basic LLM Chain1",
"type": "main",
"index": 0
}
]
]
},
"Save Input/Output": {
"main": [
[
{
"node": "Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Keep Original Data": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 1
}
]
]
},
"Execute Subworkflow": {
"main": [
[
{
"node": "Update Results",
"type": "main",
"index": 0
}
]
]
},
"Limit (for testing)": {
"main": [
[
{
"node": "Execute Subworkflow",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter Chat Model": {
"ai_languageModel": [
[
{
"node": "Basic LLM Chain1",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Structured Output Parser2": {
"ai_outputParser": [
[
{
"node": "Basic LLM Chain1",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Get Tests",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 人工智能
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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难度等级
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节点数量23
分类1
节点类型14
作者
Adam Janes
@adamjanesI am a product-minded technologist with hacker DNA building things in AI automation. I have a broad and varied background - having worked in Product, Design, and Sales - combined with deep technical experience as a Senior Developer and Fractional CTO. I am also a best-selling Udemy instructor (with 25K+ students), and founder of WOOFCODE - a free coding camp for fullstack developers. I practice non-violent communication, motivational interviewing, and Tibetan Buddhist meditation.
外部链接
在 n8n.io 查看 →
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