> For the complete documentation index, see [llms.txt](https://polyglot.gitbook.io/polyglot/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://polyglot.gitbook.io/polyglot/deployment.md).

# 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

```bash
# 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

```javascript
// 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

```bash
# 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

```bash
# 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](https://github.com/ForestMars/Polyglot/blob/main/docs/deployment/deployment/client-deployment.md)

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

### [Server Deployment Configuration](https://github.com/ForestMars/Polyglot/blob/main/docs/deployment/deployment/server-deployment.md)

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

### [Docker Deployment](https://github.com/ForestMars/Polyglot/blob/main/docs/deployment/deployment/docker.md)

Containerized deployment for consistent research environments across infrastructure.

## Research Infrastructure Considerations

### Data Storage Planning

```yaml
# 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

```yaml
# 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

```yaml
# 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

```yaml
# 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

```javascript
// 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

```yaml
# 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"
```