使用Langchain代码节点追踪每个客户的LLM令牌成本
高级
这是一个Engineering, AI, IT Ops领域的自动化工作流，包含 20 个节点。主要使用 Set, Form, Gmail, Filter, Summarize 等节点，结合人工智能技术实现智能自动化。 使用Langchain代码节点追踪每个客户的LLM令牌成本
前置要求
- •Google 账号和 Gmail API 凭证
- •Google Sheets API 凭证
使用的节点 (20)
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": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "8884df86-b7cd-4cf7-8b71-fd21113bfc0f",
"name": "Client Usage Log",
"type": "n8n-nodes-base.googleSheetsTool",
"position": [
420,
500
],
"parameters": {
"columns": {
"value": {},
"schema": [
{
"id": "date",
"type": "string",
"display": true,
"required": false,
"displayName": "date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "workflow_id",
"type": "string",
"display": true,
"required": false,
"displayName": "workflow_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "execution_id",
"type": "string",
"display": true,
"required": false,
"displayName": "execution_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "client_id",
"type": "string",
"display": true,
"required": false,
"displayName": "client_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "client_name",
"type": "string",
"display": true,
"required": false,
"displayName": "client_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "input_tokens",
"type": "string",
"display": true,
"required": false,
"displayName": "input_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "output_tokens",
"type": "string",
"display": true,
"required": false,
"displayName": "output_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_tokens",
"type": "string",
"display": true,
"required": false,
"displayName": "total_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "input_cost",
"type": "string",
"display": true,
"required": false,
"displayName": "input_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "output_cost",
"type": "string",
"display": true,
"required": false,
"displayName": "output_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_cost",
"type": "string",
"display": true,
"required": false,
"displayName": "total_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "autoMapInputData",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=drivesdk",
"cachedResultName": "89. Langchain Code Node - Client Usage Log"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "XHvC7jIRR8A2TlUl",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "1e4aca76-8b79-4780-b0c5-2cd92a41aa0e",
"name": "Logging Attributes",
"type": "n8n-nodes-base.set",
"position": [
-360,
-120
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "22164635-7a23-47e2-9868-96899cd9d317",
"name": "workflow_id",
"type": "string",
"value": "={{ $workflow.id }}"
},
{
"id": "ed1cb653-b3fd-40bf-b00b-10d777f098af",
"name": "execution_id",
"type": "string",
"value": "={{ $execution.id }}"
},
{
"id": "3de228a1-79b9-4805-8d92-917f691411be",
"name": "client_id",
"type": "string",
"value": "=12345"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "d7f37c54-5d96-47ba-b82e-0926a08137df",
"name": "表单提交时",
"type": "n8n-nodes-base.formTrigger",
"position": [
-920,
-120
],
"webhookId": "9da21424-e23b-43b8-a6ec-a6daa129c326",
"parameters": {
"options": {},
"formTitle": "CV Parsing Service",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "Upload a file",
"requiredField": true,
"acceptFileTypes": ".pdf"
},
{
"fieldType": "dropdown",
"fieldLabel": "Acknowledgement",
"multiselect": true,
"fieldOptions": {
"values": [
{
"option": "I acknowledge the use of this service will be added to my bill."
}
]
},
"requiredField": true
}
]
},
"responseMode": "lastNode",
"formDescription": "Use this form to upload CVs and we'll extract the data from them. This workflow tracks usage metrics so we can calculate the bill later on."
},
"typeVersion": 2.2
},
{
"id": "06da0c8e-2035-45ae-a301-50021650a5f8",
"name": "Custom LLM Subnode",
"type": "@n8n/n8n-nodes-langchain.code",
"position": [
260,
340
],
"parameters": {
"code": {
"supplyData": {
"code": "const { ChatOpenAI } = require(\"@langchain/openai\");\n\n// 1. Configure as required.\n// - costs are per million tokens and depends on the model.\nconst openAIApiKey = \"sk-...\";\nconst model = \"gpt-4o-mini\";\nconst input_token_cost = 0.150;\nconst output_token_cost = 0.600;\n\n// 2. do not edit below this line --\nconst tools = await this.getInputConnectionData('ai_tool', 0);\nconst googleSheetTool = tools[0];\n\nconst {\n workflow_id,\n execution_id,\n client_id } = $input.first().json;\n\nconst llm = new ChatOpenAI({\n apiKey: openAIApiKey,\n model,\n callbacks: [\n {\n handleLLMEnd: async function(output,runId,parentId) {\n const generation = output.generations[0][0];\n const message = generation.message;\n const row = {\n date: (new Date()).toGMTString(),\n workflow_id,\n execution_id,\n client_id,\n input_tokens: message.usage_metadata.input_tokens,\n output_tokens: message.usage_metadata.output_tokens,\n total_tokens: message.usage_metadata.total_tokens,\n input_cost: (message.usage_metadata.input_tokens / 1_000_000) * input_token_cost,\n output_cost: (message.usage_metadata.output_tokens / 1_000_000) * output_token_cost,\n };\n row.total_cost = row.input_cost + row.output_cost;\n await googleSheetTool.func(row);\n }\n }\n ]\n});\n\nreturn llm;"
}
},
"inputs": {
"input": [
{
"type": "ai_tool",
"required": true
}
]
},
"outputs": {
"output": [
{
"type": "ai_languageModel"
}
]
}
},
"typeVersion": 1
},
{
"id": "35993bd5-b521-4239-bf23-aed47d339f54",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
360,
480
],
"parameters": {
"width": 200,
"height": 280,
"content": "\n\n\n\n\n\n\n\n\n\n\n\n\n### Update Workbook\nThis is the workbook which will track the token usage and costs."
},
"typeVersion": 1
},
{
"id": "623ca94d-a215-416b-a9fe-62a1f96317e3",
"name": "便签 1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1040,
-280
],
"parameters": {
"color": 7,
"width": 560,
"height": 380,
"content": "## 1. Offer AI Service to Clients\nHere, we'll using an n8n form to offer a document extraction service for Resume/CV PDFs. The user simply uploads a PDF and we return it in JSON format. This is just a simple example for demonstration purposes. "
},
"typeVersion": 1
},
{
"id": "ba9eb149-e77f-4bf6-8ec5-7d8d4619485d",
"name": "便签 2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
-280
],
"parameters": {
"color": 7,
"width": 320,
"height": 380,
"content": "## 2. Gather External Variables to Send to Log\nThere are some variables we can't define in the subnode but we can add them here."
},
"typeVersion": 1
},
{
"id": "63bfe329-17dd-4321-94c6-17d306ed7412",
"name": "便签 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-120,
-280
],
"parameters": {
"color": 7,
"width": 720,
"height": 380,
"content": "## 3. Provide AI Service\nOur service uses an LLM (OpenAI GPT4o-mini in this example) to organise the uploaded PDF's data into a structured JSON format. This conversion is useful for following integrations or data storage. In this example however, we'll use display it back to the user."
},
"typeVersion": 1
},
{
"id": "f45862e9-7f8e-46bb-900a-807fee981ed5",
"name": "便签 4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-120,
120
],
"parameters": {
"color": 7,
"width": 720,
"height": 440,
"content": "## 4. Use Custom LLM Subnode to Track Usage & Cost\n[Learn more about the Langchain Code Node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.code/)\n\nBy creating our custom LLM subnode using the Langchain Code node, we are able to tap into the chat completion's response which contains the token usage metadata for the information extractor request.\n\nWith this, we can calculate exactly how much the client's request is costing per use!\n\nThe only remaining step then is to store\nthis data somewhere. Rather than importing\nmore dependencies or hardcoding more\ncredentials, a novel solution can be to attach\na tool to our subnode.\n\nHere, we've decided to use the Google Sheets\ntool and append the client's usage metrics to\nthe sheet. Check it out here - [Client Usage Log](\nhttps://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=sharing)"
},
"typeVersion": 1
},
{
"id": "9f5014a5-0e9a-4af0-b076-03cdc0a14ab9",
"name": "Display JSON Document",
"type": "n8n-nodes-base.form",
"position": [
360,
-120
],
"webhookId": "1b9d0195-1662-43c2-94a0-f9c867d75578",
"parameters": {
"options": {
"customCss": ".header p {\n font-family: monospace;\n background-color: #efefef;\n padding: 1rem;\n font-size: 0.8rem;\n text-align: left;\n max-height: 480px;\n overflow: auto;\n white-space: pre;\n}"
},
"operation": "completion",
"completionTitle": "=Results for {{ $('On form submission').item.json['Upload a file'][0].filename }}",
"completionMessage": "={{ JSON.stringify($json.output, null, 2) }}"
},
"typeVersion": 1
},
{
"id": "b977f89c-1118-455f-986e-735a17eecd9b",
"name": "Filter Last Month",
"type": "n8n-nodes-base.filter",
"position": [
1120,
-120
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "2a86f83e-b103-46fe-a8b8-15811d4138fa",
"operator": {
"type": "dateTime",
"operation": "afterOrEquals"
},
"leftValue": "={{new Date($json.date) }}",
"rightValue": "={{ $now.startOf('month') }}"
},
{
"id": "7b4c03a3-4df9-4b5d-9f1f-660e54a1c2d1",
"operator": {
"type": "dateTime",
"operation": "beforeOrEquals"
},
"leftValue": "={{new Date($json.date) }}",
"rightValue": "={{ $now.endOf('month') }}"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "10d95dcb-d975-4b20-961e-d1fe63417878",
"name": "Get Client Logs",
"type": "n8n-nodes-base.googleSheets",
"position": [
920,
-120
],
"parameters": {
"options": {},
"filtersUI": {
"values": [
{
"lookupValue": "12345",
"lookupColumn": "client_id"
}
]
},
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=drivesdk",
"cachedResultName": "89. Langchain Code Node - Client Usage Log"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "XHvC7jIRR8A2TlUl",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "f6505545-d57c-443a-9883-2d536f3a973a",
"name": "Calculate Totals",
"type": "n8n-nodes-base.summarize",
"position": [
1320,
-120
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "total_cost",
"aggregation": "sum"
},
{
"field": "total_tokens",
"aggregation": "sum"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "1c4ae8ff-ec2b-4fd3-974f-cc766385b16b",
"name": "Every End of Month",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
720,
-120
],
"parameters": {
"rule": {
"interval": [
{
"field": "months",
"triggerAtHour": 18,
"triggerAtDayOfMonth": 31
}
]
}
},
"typeVersion": 1.2
},
{
"id": "f321fbe6-36b1-4bd8-899b-832a8fc6217a",
"name": "Send Invoice",
"type": "n8n-nodes-base.gmail",
"position": [
1520,
-120
],
"webhookId": "68315f84-d7e0-4525-a625-bb3ff431931c",
"parameters": {
"sendTo": "jim@example.com",
"message": "=Hello,\nThis is an invoice for {{ $now.monthLong }} {{ $now.year }}.\n\nTotal usage: {{ $json.sum_total_tokens }} tokens\nTotal token cost: ${{ $json.sum_total_cost.toFixed(5) }}\nTax @ 20%: ${{ ($json.sum_total_cost * 0.2).toFixed(5) }}\nTotal payable: ${{ ($json.sum_total_cost * 1.2).toFixed(5) }}\n\nPayable within 14 days.\nThank you for your custom.",
"options": {},
"subject": "=Invoice for {{ $now.monthLong }} {{ $now.year }}",
"emailType": "text"
},
"credentials": {
"gmailOAuth2": {
"id": "Sf5Gfl9NiFTNXFWb",
"name": "Gmail account"
}
},
"typeVersion": 2.1
},
{
"id": "a7d8de78-c3b7-4687-8994-fe28387d7572",
"name": "便签 5",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
-280
],
"parameters": {
"color": 7,
"width": 1100,
"height": 380,
"content": "## 5. Automatically Send Invoice at End of Month (Optional)\nWith our client usage log, it's fairly simple to aggregate the log records for the client within a certain timeframe and calculate the totals.\nThis can satisfy accurate billing requirements from clients or at least, allows your team to understand how much each client is costing and budget accordingly."
},
"typeVersion": 1
},
{
"id": "169fa40d-c6e8-4315-be35-d2c73f626edf",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1500,
-920
],
"parameters": {
"width": 440,
"height": 1020,
"content": "## Try It Out!\n### This n8n template demonstrates how to use the Langchain code node to track token usage and cost for every LLM call.\n\nThis is useful if your templates handle multiple clients or customers and you need a cheap and easy way to capture how much of your AI credits they are using.\n\n### How it works\n* In our mock AI service, we're offering a data conversion API to convert Resume PDFs into JSON documents.\n* A form trigger is used to allow for PDF upload and the file is parsed using the Extract from File node.\n* An Edit Fields node is used to capture additional variables to send to our log.\n* Next, we use the Information Extractor node to organise the Resume data into the given JSON schema.\n* The LLM subnode attached to the Information Extractor is a custom one we've built using the Langchain Code node.\n* With our custom LLM subnode, we're able to capture the usage metadata using lifecycle hooks.\n* We've also attached a Google Sheet tool to our LLM subnode, allowing us to send our usage metadata to a google sheet.\n* Finally, we demonstrate how you can aggregate from the google sheet to understand how much AI tokens/costs your clients are liable for.\n\n\n**Check out the example Client Usage Log** - https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=sharing\n\n### How to use\n* **SELF-HOSTED N8N ONLY** - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud.\n* The LLM subnode can only be attached to non-\"AI agent\" nodes; Basic LLM node, Information Extractor, Question & Answer Chain, Sentiment Analysis, Summarization Chain and Text Classifier.\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!"
},
"typeVersion": 1
},
{
"id": "922710e3-f92b-4a7f-9ff2-c3d7d55f04d5",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1040,
-420
],
"parameters": {
"color": 3,
"width": 280,
"height": 120,
"content": "### SELF-HOSTED N8N ONLY\nPlease note, this template only works in the self-hosted version of n8n only. It will not work in the cloud version."
},
"typeVersion": 1
},
{
"id": "56c23cb5-818f-434d-96a7-0029f6607299",
"name": "Parse PDF Upload",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-700,
-120
],
"parameters": {
"options": {},
"operation": "pdf",
"binaryPropertyName": "Upload_a_file"
},
"typeVersion": 1
},
{
"id": "f4cc9870-a73e-487c-a131-aca2735b2e60",
"name": "Extract Resume Data",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
0,
-120
],
"parameters": {
"text": "={{ $json.text }}",
"options": {},
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"name\": { \"type\": \"string\" },\n \"label\": { \"type\": \"string\" },\n \"email\": { \"type\": \"string\" },\n \"phone\": { \"type\": \"string\" },\n \"url\": { \"type\": \"string\" },\n \"summary\": { \"type\": \"string\" },\n \"location\": {\n \"type\": \"object\",\n \"properties\": {\n \"address\": { \"type\": \"string\" },\n \"postalCode\": { \"type\": \"string\" },\n \"city\": { \"type\": \"string\" },\n \"countryCode\": { \"type\": \"string\" },\n \"region\": { \"type\": \"string\" }\n }\n },\n \"work\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": { \"type\": \"string\" },\n \"location\": { \"type\": \"string\" },\n \"description\": { \"type\": \"string\" },\n \"position\": { \"type\": \"string\" },\n \"url\": { \"type\": \"string\" },\n \"startDate\": { \"type\": \"string\" },\n \"endDate\": { \"type\": \"string\" },\n \"summary\": { \"type\": \"string\" },\n \"highlights\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n }\n },\n \"education\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"institution\": { \"type\": \"string\" },\n \"url\": { \"type\": \"string\" },\n \"area\": { \"type\": \"string\" },\n \"studyType\": { \"type\": \"string\" },\n \"startDate\": { \"type\": \"string\" },\n \"endDate\": { \"type\": \"string\" },\n \"score\": { \"type\": \"string\" },\n \"courses\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n }\n },\n \"skills\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": { \"type\": \"string\" },\n \"level\": { \"type\": \"string\" },\n \"keywords\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n }\n }\n }\n}"
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"Get Client Logs": {
"main": [
[
{
"node": "Filter Last Month",
"type": "main",
"index": 0
}
]
]
},
"Calculate Totals": {
"main": [
[
{
"node": "Send Invoice",
"type": "main",
"index": 0
}
]
]
},
"Client Usage Log": {
"ai_tool": [
[
{
"node": "Custom LLM Subnode",
"type": "ai_tool",
"index": 0
}
]
]
},
"Parse PDF Upload": {
"main": [
[
{
"node": "Logging Attributes",
"type": "main",
"index": 0
}
]
]
},
"Filter Last Month": {
"main": [
[
{
"node": "Calculate Totals",
"type": "main",
"index": 0
}
]
]
},
"Custom LLM Subnode": {
"ai_languageModel": [
[
{
"node": "Extract Resume Data",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Every End of Month": {
"main": [
[
{
"node": "Get Client Logs",
"type": "main",
"index": 0
}
]
]
},
"Logging Attributes": {
"main": [
[
{
"node": "Extract Resume Data",
"type": "main",
"index": 0
}
]
]
},
"On form submission": {
"main": [
[
{
"node": "Parse PDF Upload",
"type": "main",
"index": 0
}
]
]
},
"Extract Resume Data": {
"main": [
[
{
"node": "Display JSON Document",
"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 - 工程, 人工智能, IT 运维
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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工作流信息
难度等级
高级
节点数量20
分类3
节点类型13
作者
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
外部链接
在 n8n.io 查看 →
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