{"id":"Yu2P4qvlbmpPT3mg","meta":{"instanceId":"b35269c8495db354f1459fb10ec8f343e34cd6c71fb24a004bb668ccda72b4e6","templateCredsSetupCompleted":true},"name":"[Template] - Dashboard Chat","tags":[],"nodes":[{"id":"85c930e9-cb8a-47ad-8082-5ae97c945b4b","name":"When chat message received","type":"@n8n/n8n-nodes-langchain.chatTrigger","position":[-320,1264],"webhookId":"09b95814-ef46-410f-adfb-7ef2683d499d","parameters":{"public":true,"options":{}},"typeVersion":1.3},{"id":"cbd8fb15-5cf0-4993-a5f5-c732496c3efe","name":"AI Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[-96,1264],"parameters":{"text":"=Réponds à ce message : \n{{ $json.chatInput }}\n","options":{},"promptType":"define"},"typeVersion":2.2},{"id":"e0decd9d-dc72-4283-815b-08f79b1b33b9","name":"Simple Memory","type":"@n8n/n8n-nodes-langchain.memoryBufferWindow","position":[-48,1456],"parameters":{},"typeVersion":1.3},{"id":"522553ba-e4eb-42d9-ae80-d6de88577792","name":"Sticky 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Pricing Table for n8n\n\n\n"},"typeVersion":1},{"id":"c89ef6c5-8b35-45d3-8e21-b54125323bee","name":"Get row(s)1","type":"n8n-nodes-base.dataTable","position":[80,992],"parameters":{"operation":"get","returnAll":true,"dataTableId":{"__rl":true,"mode":"list","value":"GyHAqQLTtmZbynYI","cachedResultUrl":"/projects/E58XbkoO8SgET2Sl/datatables/GyHAqQLTtmZbynYI","cachedResultName":"Template - data"}},"typeVersion":1},{"id":"9f4069e9-1d31-455c-9d4c-782ff0857ba0","name":"Edit Fields1","type":"n8n-nodes-base.set","position":[-80,2256],"parameters":{"options":{},"assignments":{"assignments":[{"id":"f52f1004-3bed-448b-9655-b6c61d238f57","name":"","type":"string","value":""}]}},"typeVersion":3.4},{"id":"c3c21917-4e60-4545-b524-2f5bb28789bf","name":"Loop Over Items","type":"n8n-nodes-base.splitInBatches","position":[128,1872],"parameters":{"options":{}},"typeVersion":3},{"id":"451c7483-b793-47d9-869b-695bea8771ab","name":"No Operation, do 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Price"}},"typeVersion":1},{"id":"0d493cb2-d367-4eb7-968e-1b05e8e0dee5","name":"Merge1","type":"n8n-nodes-base.merge","position":[944,1856],"parameters":{"mode":"combine","options":{},"advanced":true,"mergeByFields":{"values":[{"field1":"name","field2":"model_name"}]}},"typeVersion":3.2},{"id":"4f7b3e8e-8b1b-4ccc-af0f-2371ca846d95","name":"Code in JavaScript1","type":"n8n-nodes-base.code","position":[1152,1856],"parameters":{"jsCode":"// Parcours tous les items reçus\nreturn items.map(item => {\n // Récupération des valeurs depuis l'item\n const completionTokens = item.json.completionTokens || 0;\n const promptTokens = item.json.promptTokens || 0;\n const completionTokensPrice = item.json.completionTokensPrice || 0;\n const promptTokensPrice = item.json.promptTokensPrice || 0;\n\n // Calcul du coût global\n const globalCost = (completionTokens * completionTokensPrice) + (promptTokens * promptTokensPrice);\n\n // Retourner l'item avec globalCost ajouté\n return {\n json: {\n ...item.json,\n globalCost\n }\n };\n});\n"},"typeVersion":2},{"id":"a5c21eed-3dcf-4a38-a341-d8bc42aaaf22","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-416,1200],"parameters":{"color":4,"width":1104,"height":416,"content":"## Chat Example :\n\n\n"},"typeVersion":1},{"id":"07c09e80-88a2-443b-bd8a-59db83d8c0a1","name":"No Operation, do nothing1","type":"n8n-nodes-base.noOp","position":[272,1264],"parameters":{},"typeVersion":1},{"id":"10c02d77-5179-44a5-a219-213921b41377","name":"Code in JavaScript","type":"n8n-nodes-base.code","position":[288,992],"parameters":{"jsCode":"// === Calcul des KPI pour les conversations ===\nlet totalConversations = 0;\nlet totalCompletionTokens = 0;\nlet totalPromptTokens = 0;\nlet totalTokens = 0;\nlet totalCost = 0;\nlet sessions = new Set();\nlet modelUsage = {};\nlet conversationsParJour = [];\n\nfor (const item of items) {\n const data = item.json;\n\n totalConversations += 1;\n totalCompletionTokens += data.completionTokens || 0;\n totalPromptTokens += data.promptTokens || 0;\n totalTokens += data.totalTokens || 0;\n\n // Calcul du coût global si absent\n let globalCost = data.globalCost;\n if (globalCost === null || globalCost === undefined) {\n const completionTokensPrice = data.completionTokensPrice || 0;\n const promptTokensPrice = data.promptTokensPrice || 0;\n globalCost = (data.completionTokens || 0) * completionTokensPrice + (data.promptTokens || 0) * promptTokensPrice;\n }\n totalCost += globalCost;\n\n // Comptage des sessions uniques\n if (data.sessionId) sessions.add(data.sessionId);\n\n // Comptage messages par modèle\n const model = data.modelName || \"unknown\";\n if (!modelUsage[model]) modelUsage[model] = 0;\n modelUsage[model] += 1;\n\n // Tableau journalier\n if (data.createdAt) {\n const day = data.createdAt.split(\"T\")[0];\n let dayEntry = conversationsParJour.find(d => d.date === day);\n if (!dayEntry) {\n dayEntry = { date: day, count: 0, totalCost: 0, promptTokens: 0, completionTokens: 0 };\n conversationsParJour.push(dayEntry);\n }\n dayEntry.count += 1;\n dayEntry.totalCost += globalCost;\n dayEntry.promptTokens += data.promptTokens || 0;\n dayEntry.completionTokens += data.completionTokens || 0;\n }\n}\n\n// Tri par date\nconversationsParJour = conversationsParJour.sort((a, b) => a.date.localeCompare(b.date));\n\n// Moyennes\nconst avgTokensPerConversation = totalConversations > 0 ? (totalTokens / totalConversations).toFixed(2) : 0;\nconst avgCostPerConversation = totalConversations > 0 ? (totalCost / totalConversations).toFixed(6) : 0;\n\n// === Génération HTML ===\nconst html = `\n\n\n\n\n\nDashboard Conversations\n\n\n\n\n\n
\n
\n

Dashboard Conversations

\n
\n 🌙\n
\n ☀️\n
\n
\n\n
Statistiques Clés
\n
\n
\n
${totalConversations.toLocaleString('fr-FR')}
\n
Total Messages
\n
\n
\n
${sessions.size.toLocaleString('fr-FR')}
\n
Sessions uniques
\n
\n
\n
${totalTokens.toLocaleString('fr-FR')}
\n
Total Tokens
\n
\n
\n
${avgTokensPerConversation}
\n
Tokens moyens par message
\n
\n
\n
${totalCost.toFixed(6)} €
\n
Coût total
\n
\n
\n
${avgCostPerConversation} €
\n
Coût moyen par message
\n
\n
\n\n
Historique Journalier
\n
\n
\n
\n
\n
\n\n\n\n\n\n`;\n\n// Retourner le HTML en binaire pour la node n8n\nreturn [{ binary: { data: Buffer.from(html, 'utf8') } }];\n"},"typeVersion":2},{"id":"63aae3c1-6479-477f-a26b-85f3d589a6bc","name":"Respond to Webhook","type":"n8n-nodes-base.respondToWebhook","position":[496,992],"parameters":{"options":{},"respondWith":"binary"},"typeVersion":1.4},{"id":"b2f02fbb-2604-4f60-9449-6a8fa76ee670","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-976,912],"parameters":{"width":496,"height":352,"content":"## 🤖 n8n AI Workflow Dashboard\n\n### This template helps you collect and visualize data from your AI workflows in a simple and interactive way.\n\n- Track messages, sessions, tokens, and costs for each model.\n- Interactive HTML dashboard with KPIs: messages, sessions, tokens, and costs.\n- Compatible with any AI Agent or RAG workflow in n8n.\n\n### Use this dashboard to monitor AI activity and usage metrics at a glance, and easily identify trends or anomalies in your workflows."},"typeVersion":1},{"id":"19d31dbb-e89e-40c6-853d-f898ebbe1122","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-976,1296],"parameters":{"width":496,"height":464,"content":"## ⚙️ Setup & Run\n\n### Follow these steps to get your workflow up and running in n8n:\n\n- Import the JSON workflow into your n8n instance.\n- Create the Model price and Messages tables.\n- Import token cost data for your LLM models (LLM Pricing).\n- Configure the “chat message” node according to your input channels.\n- Once messages are collected, the Token Tracking sub-workflow calculates token usage and costs.\n- Visualize the dashboard using the HTML response returned by the webhook.\n\n### After setup, your workflow will automatically track AI activity, compute costs, and provide a live dashboard to monitor all your KPIs."},"typeVersion":1}],"active":true,"pinData":{"Edit 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