Deployment

Deploying Polyglot for AI research environments requires consideration of research workflows, data privacy, collaboration needs, and performance requirements for memory-intensive operations.

Deployment Scenarios

Individual Research Environment

Use Case: Solo AI researcher needing persistent memory and model comparison capabilities

Architecture: Client-only deployment with local storage

Storage: Browser IndexedDB for conversation memory and knowledge base
AI Providers: Direct API connections to OpenAI, Anthropic, Google, and/or local Ollama
Memory Management: Local memory context preservation and research continuity
Knowledge Integration: Local RAG document processing and MCP tool connections

Benefits:

Complete data privacy and control
No server dependencies or maintenance
Instant deployment and setup
Offline research capability with local models

Research Team Environment

Use Case: Small research team (2-10 researchers) collaborating on AI studies

Architecture: Client + optional sync server for team coordination

Client: Full local research environment for each researcher
Sync Server: Lightweight coordination server for shared research components
Privacy: Individual conversations remain private, shared insights anonymized
Collaboration: Synchronized methodology, shared knowledge base, comparative analyses

Benefits:

Individual privacy with team collaboration
Shared research infrastructure and knowledge bases
Coordinated comparative studies across researchers
Collective insight aggregation while preserving individual contributions

Research Organization Environment

Use Case: Large research organization with multiple projects and compliance requirements

Architecture: Full deployment with enterprise features

Multi-Project Management: Isolated research environments per project
Compliance Integration: Data retention, audit trails, access controls
Infrastructure Integration: SSO, enterprise storage, backup systems
Collaboration Controls: Fine-grained permissions and data sharing policies

Benefits:

Enterprise-grade research infrastructure
Compliance with institutional research policies
Scalable to hundreds of researchers and projects
Integration with existing research IT infrastructure

Client Deployment

Basic Client Setup

# Production build for research environment
npm run build

# Deploy to static hosting with research-optimized configuration
# Recommended: Configure for research workflow performance

Research-Optimized Client Configuration

// polyglot.config.js - Research Environment Configuration
export default {
  // Memory management optimized for research workflows
  memoryManagement: {
    contextCaching: 'aggressive',        // Fast model switching
    researchDataRetention: 'permanent',  // Never auto-delete research data
    memoryMarkerIndexing: 'full',        // Complete searchability
    knowledgeBaseSize: '10GB'            // Large document collections
  },

  // Research workflow optimizations
  researchWorkflows: {
    comparativeAnalysis: true,           // Enable cross-model comparison
    longTermProjects: true,              // Support multi-month projects
    collaborativeFeatures: false,       // Disable for individual deployment
    knowledgeIntegration: {
      ragDocuments: true,
      mcpConnections: true,
      semanticSearch: 'enhanced'
    }
  },

  // Privacy and security for research data
  privacy: {
    dataEncryption: 'client-side',       // Encrypt sensitive research data
    apiKeyStorage: 'secure-local',       // Secure API key management
    researchDataIsolation: true,        // Isolate research projects
    auditLogging: 'research-activities'  // Log for research integrity
  },

  // Performance tuning for research workloads
  performance: {
    indexedDbQuota: '5GB',               // Large local storage quota
    memoryContextCache: '500MB',         // Cache for instant context switching
    knowledgeSearchIndex: 'comprehensive', // Full-text + semantic search
    backgroundProcessing: 'research-prioritized'
  }
};

Static Hosting for Research Teams

# Vercel deployment with research optimizations
vercel --prod --env VITE_RESEARCH_MODE=true

# Netlify deployment with large storage allocation
netlify deploy --prod --dir=dist --functions=research-functions

# AWS S3 + CloudFront for research organization
aws s3 sync dist/ s3://research-ai-workspace --delete
aws cloudfront create-invalidation --distribution-id RESEARCH_DIST_ID --paths "/*"

Custom Domain for Research Environment

# Configure custom domain for research team
# Example: ai-research.university.edu

# SSL certificate for research data security
certbot --nginx -d ai-research.university.edu

# Security headers for research environment
# nginx configuration
add_header X-Frame-Options "SAMEORIGIN" always;
add_header X-Content-Type-Options "nosniff" always;
add_header Referrer-Policy "strict-origin-when-cross-origin" always;
add_header Content-Security-Policy "default-src 'self'; script-src 'self' 'unsafe-inline' cdnjs.cloudflare.com; style-src 'self' 'unsafe-inline';" always;

Server Deployment

Client Deployment Details

Complete guide for deploying the research client interface with various hosting options.

Server Deployment Configuration

Setup instructions for the optional sync server for collaborative research environments.

Docker Deployment

Containerized deployment for consistent research environments across infrastructure.

Research Infrastructure Considerations

Data Storage Planning

# Research data storage requirements
storage_planning:
  individual_researcher:
    conversations: "100MB - 1GB per researcher per year"
    memory_contexts: "50MB - 500MB per researcher per year"
    knowledge_base: "500MB - 10GB per researcher (depends on document collection)"
    total_estimate: "650MB - 11.5GB per researcher per year"

  research_team_5_members:
    individual_data: "3.25GB - 57.5GB total"
    shared_components: "1GB - 5GB (methodology, shared documents)"
    collaborative_analytics: "100MB - 1GB"
    total_estimate: "4.35GB - 63.5GB per year"

  research_organization_100_researchers:
    individual_data: "65GB - 1.15TB"
    shared_infrastructure: "10GB - 50GB"
    collaborative_projects: "5GB - 25GB"
    compliance_and_audit: "2GB - 10GB"
    total_estimate: "82GB - 1.235TB per year"

Performance Requirements

# Research workflow performance targets
performance_targets:
  memory_context_operations:
    context_retrieval: "< 100ms"
    model_switching: "< 1 second"
    cross_conversation_search: "< 200ms"
    memory_marker_updates: "< 50ms"

  knowledge_base_operations:
    document_upload_processing: "< 30 seconds per 10MB"
    semantic_search: "< 500ms across GB datasets"
    rag_integration: "< 200ms per query"
    mcp_tool_execution: "< 2 seconds per operation"

  collaborative_operations:
    sync_completion: "< 5 seconds for active projects"
    conflict_resolution: "< 10 seconds for complex conflicts"
    team_coordination: "< 1 second for presence updates"
    privacy_preserving_aggregation: "< 30 seconds for team insights"

Security Considerations for Research Data

# Research data security requirements
security_requirements:
  data_protection:
    encryption_at_rest: "AES-256 for sensitive research data"
    encryption_in_transit: "TLS 1.3 for all communications"
    key_management: "User-controlled keys for research data"
    access_controls: "Role-based access for collaborative projects"

  privacy_preservation:
    individual_research_privacy: "Complete isolation by default"
    collaborative_anonymization: "Cryptographic anonymization for shared insights"
    data_sovereignty: "Researcher control over data location and sharing"
    audit_capabilities: "Complete audit trail for research integrity"

  compliance_support:
    gdpr_compliance: "Right to export, delete, and data portability"
    research_ethics: "Institutional review board compliance support"
    data_retention: "Configurable retention policies per project"
    international_regulations: "Flexible deployment for regulatory requirements"

Monitoring and Analytics for Research

# Research environment monitoring
monitoring_strategy:
  research_productivity:
    conversation_growth: "Track research progress over time"
    insight_generation: "Measure memory marker creation rate"
    knowledge_utilization: "Monitor RAG and MCP usage patterns"
    model_performance: "Comparative analysis across AI providers"

  system_performance:
    memory_context_performance: "Monitor context retrieval and preservation"
    storage_utilization: "Track research data growth and optimization"
    collaboration_efficiency: "Measure team coordination effectiveness"
    infrastructure_health: "System reliability and availability metrics"

  privacy_and_security:
    data_access_patterns: "Monitor for unusual access or potential breaches"
    encryption_status: "Verify encryption coverage for sensitive data"
    compliance_reporting: "Generate reports for research governance"
    audit_trail_integrity: "Ensure complete audit capability"

Research Environment Optimization

Large-Scale Knowledge Base Deployment

// Configuration for research environments with extensive document collections
const knowledgeBaseOptimization = {
  // Document processing optimization
  documentProcessing: {
    chunkingStrategy: 'research-optimized',     // Preserve academic paper structure
    embeddingModel: 'domain-specific',          // Use research domain embeddings
    batchProcessing: true,                      // Handle large document uploads
    parallelProcessing: 8                       // CPU cores for document processing
  },

  // Search and retrieval optimization
  searchOptimization: {
    indexingStrategy: 'hybrid',                 // Full-text + semantic search
    cacheSize: '2GB',                          // Large search result cache
    precomputedQueries: 'research-common',      // Cache common research queries
    crossReferenceIndex: true                   // Enable citation network search
  },

  // Storage optimization for research scale
  storageOptimization: {
    compressionLevel: 'research-preserving',    // Compress without losing fidelity
    archivalStrategy: 'importance-based',       // Archive by research relevance
    redundancy: 'research-critical',            // Backup critical research data
    cleanup: 'never-delete-research-data'       // Preserve all research content
  }
};

Multi-Model Deployment Strategy

# AI provider configuration for research environments
ai_provider_strategy:
  cloud_providers:
    openai:
      models: ["gpt-4o", "gpt-4", "gpt-3.5-turbo"]
      rate_limits: "research-tier"
      cost_optimization: "research-budget-aware"

    anthropic:
      models: ["claude-3-opus", "claude-3-sonnet", "claude-3-haiku"]
      rate_limits: "professional-tier"
      research_features: "citation-support"

    google:
      models: ["gemini-pro", "gemini-pro-vision"]
      integration: "research-workspace"

  local_deployment:
    ollama_integration:
      models: ["llama2", "mistral", "codellama", "research-tuned-models"]
      hardware_requirements: "GPU-accelerated for large models"
      offline_capability: "full-research-workflow"

    custom_models:
      domain_specific: "Support for research-domain fine-tuned models"
      privacy_models: "Completely private model deployment"
      research_optimization: "Models optimized for research tasks"

deployment_redundancy:
  model_availability: "Multiple providers for research continuity"
  fallback_strategy: "Graceful degradation to available models"
  offline_mode: "Local models for internet-independent research"
  cost_management: "Intelligent routing based on research budget"

PreviousCRDT NextClient Deployment

Last updated 14 days ago