Recommandateur de contacts LinkedIn piloté par l'IA

• •Peut nécessiter les informations d'identification d'authentification de l'API cible

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  "name": "AI-Powered LinkedIn Connection Recommender",
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        "jsCode": "// Process search results and extract LinkedIn profiles\nconst results = $json.organic_results || [];\n\n// Get user profile from the previous node\nconst userProfile = $input.first().json || {};\n\n// Filter and process LinkedIn profiles\nconst linkedinProfiles = results\n  .filter(result => {\n    const link = result.link || '';\n    return link.includes('linkedin.com/in/') && \n           !link.includes('/posts/') && \n           !link.includes('/activity/');\n  })\n  .map(result => {\n    let name = result.title || 'Unknown';\n    // Clean up the title to extract just the name\n    name = name.replace(/\\s*[-|]\\s*LinkedIn.*$/i, '').trim();\n    \n    return {\n      name: name,\n      headline: result.snippet || result.title || '',\n      link: result.link,\n      description: result.snippet || '',\n      source: 'Google Search via SerpAPI'\n    };\n  })\n  .slice(0, 15); // Limit to top 15 results\n\n// Safely extract skills - handle undefined/null values\nconst getFirstSkill = (skills) => {\n  if (!skills || typeof skills !== 'string') {\n    return '';\n  }\n  const skillsArray = skills.split(',');\n  return skillsArray.length > 0 ? skillsArray[0].trim() : '';\n};\n\n// Build search query with safe property access\nconst buildSearchQuery = () => {\n  const position = userProfile.Position || '';\n  const location = userProfile.Location || '';\n  const firstSkill = getFirstSkill(userProfile.Skills);\n  \n  return `site:linkedin.com/in \"${position}\" \"${location}\" ${firstSkill}`.trim();\n};\n\n// Return structured data for AI analysis\nreturn {\n  json: {\n    userProfile: {\n      name: $('Your Profile Information').first().json.Name || '',\n      current_position: $('Your Profile Information').first().json.Position || '',\n      industry: $('Your Profile Information').first().json.Industry || '',\n      location:  $('Your Profile Information').first().json.Location|| '',\n      skills: $('Your Profile Information').first().json.Skills|| '',\n      interests: $('Your Profile Information').first().json.Interests || '',\n      target_roles: $('Your Profile Information').first().json['Target Roles']|| '',\n      company_types: $('Your Profile Information').first().json['Company Types'] || ''\n    },\n    foundProfiles: linkedinProfiles,\n    totalFound: linkedinProfiles.length,\n    searchQuery: buildSearchQuery()\n  }\n};"
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        "jsCode": "// Process AI recommendations and create final output\nconst inputData = $input.first();\nconst inputText = inputData.json?.text || inputData.json || inputData;\n\nlet aiRecommendations = {};\ntry {\n  // Extract JSON from the text content\n  const content = typeof inputText === 'string' ? inputText : JSON.stringify(inputText);\n  \n  // Try to parse JSON from the AI response\n  const jsonMatch = content.match(/```json\\n([\\s\\S]*?)\\n```/) || content.match(/\\{[\\s\\S]*\\}/);\n  if (jsonMatch) {\n    const jsonString = jsonMatch[1] || jsonMatch[0];\n    aiRecommendations = JSON.parse(jsonString);\n  } else {\n    throw new Error('No JSON found in response');\n  }\n} catch (error) {\n  console.log('Could not parse AI response:', error.message);\n  console.log('Input data:', inputData);\n  \n  // Create fallback recommendations if parsing fails\n  aiRecommendations = {\n    scored_profiles: [],\n    top_connections: [],\n    connection_strategies: [],\n    networking_insights: 'Focus on building meaningful professional relationships in your industry.'\n  };\n}\n\n// Helper function to safely split and get first element\nfunction safeGetFirst(str, delimiter = ',') {\n  if (!str || typeof str !== 'string') return '';\n  const parts = str.split(delimiter);\n  return parts.length > 0 ? parts[0].trim() : '';\n}\n\n// Helper function to safely split string\nfunction safeSplit(str, delimiter = ',') {\n  if (!str || typeof str !== 'string') return [];\n  return str.split(delimiter).map(item => item.trim()).filter(item => item);\n}\n\n// Helper function to generate connection message\nfunction generateConnectionMessage(profile, index) {\n  if (!profile || !profile.name) return 'Hi! I\\'d love to connect and share professional insights.';\n  \n  const firstName = safeGetFirst(profile.name, ' ');\n  const description = profile.description || '';\n  \n  // Create personalized messages based on profile\n  if (description.toLowerCase().includes('devops')) {\n    return `Hi ${firstName}, I'm interested in DevOps and cloud technologies. I'd love to connect and learn from your experience!`;\n  } else if (description.toLowerCase().includes('aws') || description.toLowerCase().includes('cloud')) {\n    return `Hi ${firstName}, I noticed your expertise in cloud computing. Would love to connect and share insights about the industry!`;\n  } else {\n    return `Hi ${firstName}, I'd love to connect with a fellow professional and learn from your experience in the industry.`;\n  }\n}\n\n// Helper function to extract tags from description\nfunction extractTags(description) {\n  if (!description) return ['Professional Contact'];\n  \n  const tags = [];\n  const descText = description.toLowerCase();\n  \n  // Add technology tags\n  const techs = ['aws', 'azure', 'gcp', 'devops', 'terraform', 'ansible', 'jenkins', 'kubernetes', 'docker'];\n  techs.forEach(tech => {\n    if (descText.includes(tech)) {\n      tags.push(tech.toUpperCase());\n    }\n  });\n  \n  // Add role tags\n  if (descText.includes('senior') || descText.includes('lead')) {\n    tags.push('Senior Level');\n  }\n  \n  if (descText.includes('mentor') || descText.includes('enthusiast')) {\n    tags.push('Mentor');\n  }\n  \n  return tags.length > 0 ? tags : ['Professional Contact'];\n}\n\n// Process the connections from AI recommendations\nconst connections = aiRecommendations.top_connections || [];\nconst scoredProfiles = aiRecommendations.scored_profiles || [];\n\nconst enhancedConnections = connections.map((profile, index) => {\n  // Find matching score data\n  const scoreData = scoredProfiles.find(p => \n    p.name && profile.name && \n    p.name.toLowerCase().trim() === profile.name.toLowerCase().trim()\n  );\n  \n  const score = scoreData?.score || (9.5 - index * 0.3); // Fallback scoring\n  \n  return {\n    name: profile.name || 'Unknown',\n    description: profile.description || '',\n    connections: profile.connections || 0,\n    mutual_connections: profile.mutual_connections || 0,\n    priority: score >= 8.5 ? 'High' : score >= 7.5 ? 'Medium' : 'Low',\n    aiScore: score,\n    connectionReason: scoreData?.reasons?.join(', ') || 'Professional networking opportunity',\n    suggestedMessage: generateConnectionMessage(profile, index),\n    tags: extractTags(profile.description),\n    estimatedResponseRate: score >= 8.5 ? 'High (70-90%)' : score >= 7.5 ? 'Medium (40-70%)' : 'Low (20-40%)',\n    link: profile.link || '#'\n  };\n}).sort((a, b) => b.aiScore - a.aiScore); // Sort by AI score\n\n// Create individual connection strategies\nconst connectionStrategies = enhancedConnections.map(conn => {\n  const firstName = safeGetFirst(conn.name, ' ');\n  return `${firstName}: ${conn.suggestedMessage.replace(`Hi ${firstName}, `, '')}`;\n});\n\nreturn {\n  json: {\n    summary: {\n      total_profiles_found: scoredProfiles.length,\n      analyzed_connections: enhancedConnections.length,\n      high_priority: enhancedConnections.filter(c => c.priority === 'High').length,\n      medium_priority: enhancedConnections.filter(c => c.priority === 'Medium').length,\n      low_priority: enhancedConnections.filter(c => c.priority === 'Low').length,\n      average_score: enhancedConnections.length > 0 ? \n        (enhancedConnections.reduce((sum, c) => sum + c.aiScore, 0) / enhancedConnections.length).toFixed(1) : 0\n    },\n    user_profile: {\n      name: \"Vrushti Sukhadiya\", // Extracted from the analysis text\n      interests: \"DevOps, Cloud Computing, Automation\",\n      target_industry: \"IT, Tech Startups, Cloud Companies\"\n    },\n    connection_recommendations: enhancedConnections,\n    connection_strategies: connectionStrategies,\n    ai_insights: aiRecommendations.networking_insights || 'Focus on building meaningful professional relationships in your industry.',\n    networking_strategy: {\n      weekly_goal: '5-10 new connections',\n      best_days: ['Tuesday', 'Wednesday', 'Thursday'],\n      best_times: ['9-11 AM', '2-4 PM'],\n      follow_up_schedule: 'Within 48 hours of connection acceptance',\n      focus_areas: ['DevOps professionals', 'Cloud Computing experts', 'AI/ML enthusiasts']\n    },\n    next_steps: [\n      'Start with highest-scored profiles (Mihir Suthar - 9.5, Muneeswaran M - 9.2)',\n      'Check for mutual connections before reaching out',\n      'Engage with their recent posts about DevOps/Cloud topics',\n      'Send personalized connection requests highlighting common interests',\n      'Follow up with thoughtful messages about industry trends',\n      'Consider attending DevOps/Cloud computing events where they might be present'\n    ],\n    top_recommendations: enhancedConnections.slice(0, 3).map(conn => ({\n      name: conn.name,\n      why_connect: conn.connectionReason,\n      action_item: `Connect with ${safeGetFirst(conn.name, ' ')} focusing on ${conn.tags.slice(0, 2).join(' and ')} expertise`\n    }))\n  }\n};"
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        "content": "## System Architecture\n- **Profile Analysis Pipeline**:\n  - **Get User Data from Email**: Initiates with manual user input.\n  - **Your Profile Information**: Provides initial data.\n  - **Search LinkedIn Profiles**: Fetches profile data via API.\n  - **Process LinkedIn Search Results**: Extracts relevant information.\n- **AI Recommendation Flow**:\n  - **AI Profile Analysis**: Analyzes data with AI.\n  - **Create Recommendations**: Generates initial connection list.\n  - **Create Final Recommendations**: Refines the list.\n- **Delivery Flow**:\n  - **Create Email**: Prepares the email content.\n  - **Send Email**: Sends the curated list to the user."
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