{ "edges": [ { "to": "process-doc-data", "from": "get-doc-content", "toPort": "0", "fromPort": "0" }, { "to": "process-doc-data", "from": "get-doc-metadata", "toPort": "0", "fromPort": "0" }, { "to": "aggregate-docs", "from": "process-doc-data", "toPort": "0", "fromPort": "0" }, { "to": "get-doc-content", "from": "prepare-docs-list", "toPort": "0", "fromPort": "0" }, { "to": "get-doc-metadata", "from": "prepare-docs-list", "toPort": "0", "fromPort": "0" }, { "to": "prepare-email-content", "from": "generate-ai-summary", "toPort": "0", "fromPort": "0" }, { "to": "send-summary-email", "from": "prepare-email-content", "toPort": "0", "fromPort": "0" }, { "to": "generate-ai-summary", "from": "aggregate-docs", "toPort": "0", "fromPort": "0" }, { "to": "prepare-docs-list", "from": "weekly-trigger", "toPort": "0", "fromPort": "0" } ], "nodes": [ { "id": "weekly-trigger", "name": "Weekly Monday 9AM Trigger", "type": "trigger", "params": { "kind": "n8n-nodes-base.cron", "rule": { "interval": [ { "field": "cronExpression", "expression": "0 9 * * 1" } ] } }, "position": [ 200, 340 ] }, { "id": "prepare-docs-list", "name": "Prepare Docs List", "type": "code", "params": { "jsCode": "// Define Google Docs to monitor\nconst docsToMonitor = [\n {\n id: 'DOC_ID_1_HERE',\n name: 'Project Status Doc',\n category: 'Projects'\n },\n {\n id: 'DOC_ID_2_HERE', \n name: 'Meeting Notes',\n category: 'Meetings'\n },\n {\n id: 'DOC_ID_3_HERE',\n name: 'Team Updates',\n category: 'Updates'\n }\n // Add more docs as needed\n];\n\n// Calculate date range for last week\nconst now = new Date();\nconst lastWeek = new Date(now.getTime() - (7 * 24 * 60 * 60 * 1000));\n\nconsole.log(`Checking docs for updates between ${lastWeek.toISOString()} and ${now.toISOString()}`);\n\nconst docsList = {\n docs: docsToMonitor,\n check_date_start: lastWeek.toISOString(),\n check_date_end: now.toISOString(),\n total_docs: docsToMonitor.length\n};\n\nreturn docsToMonitor.map(doc => ({ json: doc }));" }, "position": [ 400, 340 ] }, { "id": "get-doc-content", "name": "Get Doc Content", "type": "integration", "params": { "fileId": "={{ $json.id }}", "service": "googleDocs", "operation": "get", "authentication": "oAuth2" }, "position": [ 600, 340 ] }, { "id": "get-doc-metadata", "name": "Get Doc Metadata", "type": "integration", "params": { "fileId": "={{ $json.id }}", "options": { "fields": "modifiedTime,lastModifyingUser,version" }, "service": "googleDrive", "operation": "get", "authentication": "oAuth2" }, "position": [ 600, 480 ] }, { "id": "process-doc-data", "name": "Process Doc Data", "type": "code", "params": { "jsCode": "// Combine doc content with metadata and check if updated\nconst docContent = $('Get Doc Content').item.json;\nconst docMetadata = $('Get Doc Metadata').item.json;\nconst docInfo = $input.first().json;\n\n// Check if doc was modified in the last week\nconst lastModified = new Date(docMetadata.modifiedTime);\nconst oneWeekAgo = new Date(Date.now() - (7 * 24 * 60 * 60 * 1000));\nconst wasUpdated = lastModified > oneWeekAgo;\n\nif (!wasUpdated) {\n console.log(`Document ${docInfo.name} not updated in last week, skipping`);\n return null;\n}\n\n// Extract text content from Google Docs structure\nlet textContent = '';\nif (docContent.body && docContent.body.content) {\n docContent.body.content.forEach(element => {\n if (element.paragraph && element.paragraph.elements) {\n element.paragraph.elements.forEach(textElement => {\n if (textElement.textRun && textElement.textRun.content) {\n textContent += textElement.textRun.content;\n }\n });\n }\n });\n}\n\n// Clean up text content\ntextContent = textContent.replace(/\\s+/g, ' ').trim();\n\nconst processedDoc = {\n id: docInfo.id,\n name: docInfo.name,\n category: docInfo.category,\n content: textContent,\n last_modified: docMetadata.modifiedTime,\n modified_by: docMetadata.lastModifyingUser?.displayName || 'Unknown',\n version: docMetadata.version,\n word_count: textContent.split(' ').length,\n was_updated: wasUpdated,\n content_preview: textContent.substring(0, 200) + '...'\n};\n\nconsole.log(`Processed doc: ${processedDoc.name} (${processedDoc.word_count} words, updated: ${processedDoc.was_updated})`);\n\nreturn {\n json: processedDoc\n};" }, "position": [ 800, 340 ] }, { "id": "aggregate-docs", "name": "Aggregate Updated Docs", "type": "code", "params": { "jsCode": "// Aggregate all processed docs for AI summarization\nconst allDocs = $input.all();\nconst updatedDocs = allDocs.filter(item => item.json.was_updated);\n\nif (updatedDocs.length === 0) {\n console.log('No documents were updated this week');\n return {\n json: {\n has_updates: false,\n message: 'No documents were updated this week.',\n summary: 'No weekly summary needed - all documents remain unchanged.'\n }\n };\n}\n\n// Prepare content for AI summarization\nlet combinedContent = 'WEEKLY DOCUMENT UPDATES SUMMARY REQUEST:\\n\\n';\n\nupdatedDocs.forEach((doc, index) => {\n combinedContent += `DOCUMENT ${index + 1}: ${doc.json.name} (${doc.json.category})\\n`;\n combinedContent += `Last Modified: ${new Date(doc.json.last_modified).toLocaleDateString()} by ${doc.json.modified_by}\\n`;\n combinedContent += `Word Count: ${doc.json.word_count}\\n`;\n combinedContent += `Content: ${doc.json.content}\\n\\n---\\n\\n`;\n});\n\nconst aggregatedData = {\n has_updates: true,\n total_updated_docs: updatedDocs.length,\n docs_summary: updatedDocs.map(doc => ({\n name: doc.json.name,\n category: doc.json.category,\n modified_by: doc.json.modified_by,\n last_modified: doc.json.last_modified\n })),\n combined_content: combinedContent,\n week_range: `${new Date(Date.now() - (7 * 24 * 60 * 60 * 1000)).toLocaleDateString()} - ${new Date().toLocaleDateString()}`\n};\n\nconsole.log(`Aggregated ${aggregatedData.total_updated_docs} updated documents for AI summary`);\n\nreturn {\n json: aggregatedData\n};" }, "position": [ 1000, 340 ] }, { "id": "generate-ai-summary", "name": "Generate AI Summary", "type": "llm", "params": { "provider": "openai", "resource": "chat", "operation": "create", "requestBody": { "model": "gpt-4", "messages": [ { "role": "system", "content": "You are an executive assistant creating weekly document update summaries. Create a professional, concise summary highlighting key changes, updates, and action items from the provided documents. Format as a business email body with clear sections and bullet points." }, { "role": "user", "content": "{{ $json.combined_content }}\n\nPlease create a professional weekly summary email covering:\n1. Executive Summary\n2. Key Updates by Document\n3. Important Changes\n4. Action Items (if any)\n5. Next Week's Focus\n\nKeep it concise but informative for team leadership." } ], "max_tokens": 1500, "temperature": 0.3 } }, "position": [ 1200, 340 ] }, { "id": "prepare-email-content", "name": "Prepare Email Content", "type": "code", "params": { "jsCode": "// Prepare final email content\nconst summaryData = $('Aggregate Updated Docs').item.json;\nconst aiResponse = $input.first().json;\n\nif (!summaryData.has_updates) {\n return {\n json: {\n send_email: false,\n subject: 'Weekly Document Updates - No Changes',\n body: summaryData.message\n }\n };\n}\n\nconst aiSummary = aiResponse.choices[0].message.content;\n\n// Create professional email\nconst emailContent = {\n send_email: true,\n subject: `📄 Weekly Document Updates Summary - ${summaryData.week_range}`,\n body: `Dear Team,\\n\\nHere's your weekly document updates summary for ${summaryData.week_range}:\\n\\n${aiSummary}\\n\\n📊 UPDATED DOCUMENTS (${summaryData.total_updated_docs}):\\n${summaryData.docs_summary.map(doc => `• ${doc.name} (${doc.category}) - Updated by ${doc.modified_by} on ${new Date(doc.last_modified).toLocaleDateString()}`).join('\\n')}\\n\\n---\\nThis summary was automatically generated by your n8n workflow.\\nTo modify monitored documents, update the workflow configuration.\\n\\nBest regards,\\nYour Automation Assistant`,\n html_body: `\n

📄 Weekly Document Updates Summary

Period: ${summaryData.week_range}

\n \n

\n ${aiSummary.replace(/\\n/g, '
')}\n

\n \n

📊 Updated Documents (${summaryData.total_updated_docs})

${doc.name} (${doc.category})
Updated by ${doc.modified_by} on ${new Date(doc.last_modified).toLocaleDateString()}

\n \n

This summary was automatically generated by your n8n workflow.

\n `\n};\n\nconsole.log('Prepared email summary:', {\n subject: emailContent.subject,\n docs_count: summaryData.total_updated_docs\n});\n\nreturn {\n json: emailContent\n};" }, "position": [ 1400, 340 ] }, { "id": "send-summary-email", "name": "Send Summary Email", "type": "integration", "params": { "email": "user@example.com,user@example.com", "message": "={{ $json.body }}", "options": { "htmlMessage": "={{ $json.html_body }}" }, "service": "gmail", "subject": "={{ $json.subject }}", "operation": "send" }, "position": [ 1600, 340 ] } ] }

Generate weekly document digests from Google Docs with GPT-4 and email delivery