Client Storage
The client storage layer is designed specifically for AI research workflows, providing persistent memory management, knowledge integration, and research continuity across sessions and model switches.
Storage Architecture
Memory-First Design
All storage operations prioritize research memory preservation and retrieval:
IndexedDB Storage Structure
├── ConversationMemory
│ ├── Messages (full conversation history)
│ ├── MemoryMarkers (research insights, decisions)
│ ├── ModelHistory (model switches with context)
│ └── ResearchContext (project links, methodology)
├── KnowledgeBase
│ ├── RAGDocuments (processed document chunks)
│ ├── Embeddings (semantic search vectors)
│ ├── MCPIntegrations (tool usage and results)
│ └── CrossReferences (links to conversations)
├── ResearchProjects
│ ├── ProjectMetadata (goals, methodology, timeline)
│ ├── ConversationLinks (related discussions)
│ ├── InsightEvolution (knowledge growth over time)
│ └── ComparativeData (cross-model performance)
└── SyncData
├── DeviceState (multi-device coordination)
├── ConflictResolution (merge strategies)
└── PrivacyControls (sync permissions)Research-Optimized Storage Patterns
Memory Context Retrieval: Optimized indexing for instant access to conversation memory when switching between models or resuming research sessions.
Knowledge Base Integration: Semantic indexing of RAG documents with conversation cross-referencing for enhanced research context.
Comparative Study Storage: Specialized storage for side-by-side model comparisons with identical input contexts and performance metrics.
Long-term Research Persistence: Efficient storage patterns for research projects spanning weeks or months with accumulated knowledge.
Memory Management
Conversation Memory Storage
// Store conversation with research memory context
const conversationMemory = {
id: 'research-session-1',
projectId: 'ai-comparison-study-2024',
title: 'GPT-4 Baseline Analysis',
messages: [
{
id: 'msg-1',
role: 'user',
content: 'Analyze the methodology in this research paper...',
timestamp: new Date(),
memoryMarkers: ['methodology-analysis-request'],
knowledgeReferences: ['research-paper-doc-1']
},
{
id: 'msg-2',
role: 'assistant',
content: 'The methodology follows a comparative approach...',
timestamp: new Date(),
model: 'gpt-4o',
memoryMarkers: ['methodology-insight-1', 'comparative-approach-identified'],
contextQuality: 'high'
}
],
memoryMarkers: [
{
id: 'methodology-insight-1',
type: 'research-finding',
content: 'Comparative approach with controlled variables',
confidence: 0.9,
messageIds: ['msg-2'],
researchRelevance: 'critical'
}
],
modelHistory: [
{
model: 'gpt-4o',
provider: 'openai',
messageRange: [0, 10],
contextPreservation: 'full',
performanceMetrics: {
responseTime: '2.3s',
contextRetention: 'excellent',
insightQuality: 'high'
}
}
],
knowledgeIntegration: {
ragDocuments: ['research-paper-doc-1'],
mcpTools: ['file-analyzer'],
externalData: []
},
researchMetadata: {
studyPhase: 'baseline-establishment',
hypotheses: ['hypothesis-1'],
methodology: 'comparative-analysis'
}
};
await clientStorage.saveConversationMemory(conversationMemory);Memory Context Preservation
// Preserve memory context for model switching
const memoryContext = {
conversationId: 'research-session-1',
contextSnapshot: {
fullHistory: messages,
criticalInsights: extractedMemoryMarkers,
researchState: currentHypotheses,
knowledgeState: activeRAGDocuments,
modelPerformance: performanceMetrics
},
preservationLevel: 'full', // maintains complete research context
timestamp: new Date(),
integrityHash: computeContextHash(contextSnapshot)
};
await clientStorage.saveMemoryContext(memoryContext);
// Restore context after model switch
const restoredContext = await clientStorage.loadMemoryContext({
conversationId: 'research-session-1',
targetModel: 'claude-sonnet-4',
preservationLevel: 'full'
});Knowledge Base Storage
RAG Document Integration
// Store processed RAG documents with research context
const ragDocument = {
id: 'methodology-paper-2024',
title: 'Advanced AI Model Comparison Methodologies',
metadata: {
authors: ['Dr. Smith', 'Dr. Johnson'],
publicationDate: '2024-01-15',
researchDomain: 'ai-evaluation',
documentType: 'research-paper'
},
content: {
rawText: fullDocumentText,
processedChunks: [
{
id: 'chunk-1',
text: 'Comparative methodology requires controlled variables...',
semanticSummary: 'Methodology requirements for fair comparison',
embedding: vectorEmbedding,
researchRelevance: 0.95,
keyTerms: ['comparative-methodology', 'controlled-variables']
}
]
},
researchIntegration: {
projectIds: ['ai-comparison-study-2024'],
conversationReferences: ['research-session-1'],
insightGeneration: [
{
insight: 'controlled-variable-importance',
confidence: 0.9,
sourceChunk: 'chunk-1'
}
]
},
accessControl: {
scope: 'project-wide',
permissions: 'read-reference',
retention: 'permanent'
}
};
await clientStorage.storeRAGDocument(ragDocument);Semantic Search Integration
// Semantic search with research context awareness
const searchResults = await clientStorage.searchKnowledgeBase({
query: 'methodology for comparative AI analysis',
conversationContext: 'research-session-1',
projectContext: 'ai-comparison-study-2024',
searchParameters: {
semanticSimilarity: 0.8,
maxResults: 10,
includeContext: true,
prioritizeRecent: false, // prioritize relevance over recency
researchRelevance: 'high'
}
});
// Results include semantic matches with research context
const enhancedResults = searchResults.map(result => ({
...result,
researchContext: {
projectRelevance: calculateProjectRelevance(result, projectContext),
conversationRelevance: calculateConversationRelevance(result, conversationContext),
insightPotential: assessInsightPotential(result)
}
}));Research Project Storage
Project Organization
// Store research project with hierarchical organization
const researchProject = {
id: 'ai-comparison-study-2024',
title: 'Multi-Model AI Comparative Analysis',
metadata: {
startDate: new Date('2024-01-01'),
researchers: ['primary-researcher-id'],
researchDomain: 'ai-evaluation',
methodology: 'controlled-comparative-analysis'
},
structure: {
conversations: [
{
id: 'gpt4-baseline-session',
role: 'baseline-establishment',
model: 'gpt-4o',
phase: 'initial-testing'
},
{
id: 'claude-baseline-session',
role: 'baseline-establishment',
model: 'claude-sonnet-4',
phase: 'initial-testing'
},
{
id: 'cross-model-comparison',
role: 'comparative-analysis',
models: ['gpt-4o', 'claude-sonnet-4'],
phase: 'comparison'
}
],
knowledgeBase: [
{
id: 'methodology-paper',
type: 'research-foundation',
importance: 'critical'
},
{
id: 'previous-study-results',
type: 'comparative-data',
importance: 'high'
}
],
memoryArchitecture: {
crossConversationLinks: true,
insightEvolution: true,
comparativeTracking: true
}
},
researchProgress: {
currentPhase: 'baseline-complete',
completedMilestones: ['methodology-established', 'tools-configured'],
nextMilestones: ['comparative-analysis', 'results-synthesis'],
insightEvolution: [
{
timestamp: new Date('2024-01-15'),
insight: 'baseline-methodology-effective',
confidence: 0.9,
source: 'gpt4-baseline-session'
}
]
}
};
await clientStorage.saveResearchProject(researchProject);Cross-Conversation Linking
// Link related conversations for research continuity
await clientStorage.linkConversations({
projectId: 'ai-comparison-study-2024',
links: [
{
primary: 'gpt4-baseline-session',
related: 'claude-baseline-session',
relationship: 'comparative-pair',
linkMetadata: {
comparisonType: 'baseline-establishment',
sharedContext: 'identical-input-prompts',
analysisReadiness: true
}
},
{
primary: 'cross-model-comparison',
related: ['gpt4-baseline-session', 'claude-baseline-session'],
relationship: 'synthesis-source',
linkMetadata: {
synthesisType: 'comparative-analysis',
dataSource: 'baseline-results'
}
}
]
});Performance and Optimization
Memory-Optimized Storage
// Configure storage optimization for research workloads
await clientStorage.configureOptimization({
memoryManagement: {
contextCaching: 'aggressive', // fast model switching
insightIndexing: 'semantic', // research-aware search
compressionStrategy: 'lossless-research' // preserve research integrity
},
knowledgeBase: {
embeddingStorage: 'optimized', // fast semantic search
chunkRetrieval: 'context-aware', // research context prioritization
crossReferenceIndex: 'comprehensive' // full relationship mapping
},
researchWorkflow: {
projectLoadTime: 'sub-100ms', // instant project switching
conversationRetrieval: 'instant', // immediate memory access
comparativeDataAccess: 'prioritized' // fast cross-model analysis
}
});Storage Analytics
// Monitor storage performance for research efficiency
const storageAnalytics = await clientStorage.getAnalytics({
timeRange: '30-days',
metrics: [
'memory-context-retrieval-time',
'knowledge-base-search-performance',
'cross-conversation-link-efficiency',
'research-data-growth-rate'
]
});
console.log({
averageContextRetrievalTime: storageAnalytics.memoryContext.averageTime,
knowledgeSearchPerformance: storageAnalytics.knowledgeBase.searchTime,
researchDataGrowth: storageAnalytics.growth.monthlyIncrease,
storageEfficiency: storageAnalytics.optimization.compressionRatio
});Last updated