> 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/overview.md). # Architecture Polyglot's architecture is built around a central principle: **controlled memory management for AI research workflows**. Every component is designed to preserve, enhance, and leverage conversational memory across different AI models and research sessions. ## Core Architecture Philosophy ### Memory-Centric Design Traditional AI chat interfaces treat conversations as isolated sessions. Polyglot treats them as **accumulated research memory** that grows more valuable over time: * **Persistent Context**: Conversations maintain full context across browser sessions and model switches * **Memory Evolution**: Research insights accumulate and evolve through multiple interactions * **Knowledge Integration**: RAG documents and MCP tools become part of persistent memory * **Cross-Model Continuity**: Memory context transfers seamlessly between different AI models ### Research-First Architecture Every architectural decision prioritizes research workflows over generic chat functionality: * **Comparative Studies**: Architecture supports running identical prompts across multiple models with consistent context * **Long-term Projects**: System design accommodates research spanning weeks or months with growing knowledge bases * **Knowledge Synthesis**: Built-in support for integrating external documents and tools into AI conversations * **Privacy Control**: Research data remains local-first with optional synchronization ## System Architecture Layers ``` ┌─────────────────────────────────────────────────────────────┐ │ Research Interface Layer │ │ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ │ │ Conversation │ │ Comparative │ │ Knowledge │ │ │ │ Management │ │ Analysis │ │ Integration │ │ │ └─────────────────┘ └─────────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────┐ │ Memory Management Layer │ │ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ │ │ Memory Context │ │ Research State │ │ Cross-Model │ │ │ │ Preservation │ │ Tracking │ │ Context │ │ │ └─────────────────┘ └─────────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────┐ │ Knowledge Integration Layer │ │ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ │ │ RAG Document │ │ MCP Tool │ │ Semantic │ │ │ │ Processing │ │ Integration │ │ Search │ │ │ └─────────────────┘ └─────────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────┐ │ AI Provider Integration │ │ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ │ │ Multi-Provider │ │ Context Transfer│ │ Performance │ │ │ │ API Layer │ │ & Adaptation │ │ Monitoring │ │ │ └─────────────────┘ └─────────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────┐ │ Storage & Persistence │ │ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ │ │ Local Storage │ │ Memory Context │ │ Optional │ │ │ │ (IndexedDB) │ │ Management │ │ Sync │ │ │ └─────────────────┘ └─────────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ``` ## Key Architectural Components ### Memory Management System **Purpose**: Preserve and transfer conversation context across models and sessions **Components**: * **Context Snapshots**: Full conversation state with memory markers and research insights * **Memory Markers**: Extracted insights, decisions, and research findings that persist * **Cross-Model Translation**: Adapting memory context for different AI model capabilities * **Research State Tracking**: Long-term project context and hypothesis evolution **Research Benefits**: * Switch from GPT-4 to Claude mid-conversation without losing context * Build on previous research sessions with accumulated knowledge * Track insight evolution over weeks or months of research * Compare model performance with identical memory context ### Knowledge Integration Framework **Purpose**: Integrate external documents and tools into AI conversations **Components**: * **RAG Processing**: Document chunking, embedding, and semantic search * **MCP Integration**: Tool access and external data source connections * **Context Weaving**: Intelligent integration of knowledge into conversation flow * **Citation Tracking**: Maintain provenance of information sources **Research Benefits**: * Ground AI responses in your specific research documents and data * Access real-time information through connected tools and APIs * Maintain citation trails for research reproducibility * Build comprehensive knowledge bases that enhance AI capabilities ### Multi-Model Research Platform **Purpose**: Enable seamless switching between AI models for comparative research **Components**: * **Provider Abstraction**: Unified interface for OpenAI, Anthropic, Google, and local models * **Context Adaptation**: Intelligent context transfer between different model architectures * **Performance Monitoring**: Consistent metrics across all AI providers * **Comparative Analytics**: Side-by-side analysis of model responses **Research Benefits**: * Run identical experiments across multiple AI models * Identify model-specific strengths and biases in controlled conditions * Build model-agnostic research workflows * Generate comparative analyses with consistent methodology ### Local-First Storage Architecture **Purpose**: Maintain complete data control while enabling optional collaboration **Components**: * **IndexedDB Persistence**: Browser-based storage for all research data * **Encryption Layer**: Client-side encryption for sensitive research content * **Sync Protocol**: Optional server synchronization with privacy controls * **Conflict Resolution**: Intelligent merging of research data across devices **Research Benefits**: * Complete ownership of research data and conversations * Offline-capable research environment * Optional collaboration while maintaining privacy control * Multi-device access to research projects ## Research Workflow Architecture ### Comparative Study Workflow ``` Research Question ↓ ┌─────────────────┐ │ Baseline Setup │ ← Identical context and prompts └─────────────────┘ ↓ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Model A Test │ │ Model B Test │ │ Model C Test │ │ (with memory │ │ (with memory │ │ (with memory │ │ context) │ │ context) │ │ context) │ └─────────────────┘ └─────────────────┘ └─────────────────┘ ↓ ↓ ↓ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Response A + │ │ Response B + │ │ Response C + │ │ Updated Memory │ │ Updated Memory │ │ Updated Memory │ └─────────────────┘ └─────────────────┘ └─────────────────┘ ↓ ↓ ↓ └───────────────────────┼───────────────────────┘ ↓ ┌─────────────────────────┐ │ Comparative Analysis │ │ + Memory Integration │ │ + Research Synthesis │ └─────────────────────────┘ ``` ### Long-Term Research Workflow ``` Project Initiation ↓ ┌─────────────────┐ │ Knowledge Base │ ← RAG documents, MCP tools │ Integration │ └─────────────────┘ ↓ ┌─────────────────┐ │ Session 1: │ ← Initial conversations with memory markers │ Foundation │ └─────────────────┘ ↓ ┌─────────────────┐ │ Session 2: │ ← Builds on Session 1 memory + new insights │ Development │ └─────────────────┘ ↓ ┌─────────────────┐ │ Session N: │ ← Accumulated memory + evolved understanding │ Synthesis │ └─────────────────┘ ↓ ┌─────────────────┐ │ Research │ ← Export findings with full provenance │ Export │ └─────────────────┘ ``` ## Data Flow Architecture ### Memory Context Flow ``` User Input → Context Assembly → Model Processing → Response + Memory Update ↑ ↓ ↓ ↓ └──────── Memory Markers ←── Response Analysis ←── Context Integration ↓ Research State Update ↓ Cross-Session Persistence ``` **Context Assembly Process**: 1. **Current Conversation**: Recent message history and immediate context 2. **Memory Markers**: Persistent insights and research findings from previous sessions 3. **Knowledge Base**: Relevant RAG documents and MCP tool results 4. **Research Context**: Project goals, methodology, and accumulated understanding 5. **Model History**: Previous model interactions and comparative context ### Knowledge Integration Flow ``` External Knowledge → Processing → Integration → Context Enhancement ↓ ↓ ↓ ↓ RAG Documents Chunking + Semantic Enhanced AI MCP Tools Embedding Search Responses External APIs Indexing Ranking Research Context ``` **Integration Process**: 1. **Document Processing**: Semantic chunking and embedding generation 2. **Tool Integration**: MCP server connections and capability mapping 3. **Context Weaving**: Intelligent integration into conversation flow 4. **Citation Management**: Maintaining provenance and research integrity ## Performance and Scalability ### Memory Management Performance **Optimization Strategies**: * **Context Caching**: Pre-loaded memory contexts for instant model switching * **Semantic Indexing**: Fast retrieval of relevant memory markers and knowledge * **Incremental Updates**: Efficient memory marker updates without full context reload * **Compression**: Lossless compression of conversation data with integrity preservation **Scalability Targets**: * **Context Retrieval**: Sub-100ms for any conversation or memory marker * **Model Switching**: Sub-second model changes with full context preservation * **Knowledge Search**: Real-time semantic search across large document collections * **Research Projects**: Support for projects spanning months with gigabytes of data ### Storage Architecture Scaling **Local Storage Management**: * **Intelligent Archiving**: Automatic compression of older conversations with research preservation * **Priority-Based Retention**: Critical research data preserved, temporary data cleaned * **Memory Optimization**: Efficient storage patterns for long-term research projects * **Backup Integration**: Seamless export/import for data migration and backup **Optional Cloud Scaling**: * **Selective Sync**: Granular control over what data synchronizes across devices * **Privacy-Preserving Sync**: End-to-end encryption with zero-knowledge architecture * **Collaborative Research**: Multi-researcher coordination with individual privacy preservation * **Infrastructure Agnostic**: Deploy sync server on any infrastructure ## Security and Privacy Architecture ### Local-First Security Model **Data Sovereignty**: * **Local Processing**: All sensitive operations happen on user devices * **Optional Sync**: Cloud synchronization is opt-in with granular controls * **Encryption**: End-to-end encryption for any data that leaves the device * **Zero-Knowledge**: Sync servers cannot access conversation content or research data **Research Data Protection**: * **Compartmentalization**: Research projects can be isolated for sensitive work * **Access Controls**: Fine-grained permissions for collaborative research * **Audit Trails**: Complete logging of data access and modifications * **Data Retention**: User-controlled retention policies for research compliance ### Privacy-Preserving Collaboration **Multi-Researcher Support**: * **Individual Privacy**: Personal conversations remain private in collaborative projects * **Shared Insights**: Aggregated research findings can be shared while preserving individual data * **Anonymization**: Contribution tracking with identity protection * **Selective Sharing**: Granular control over what research components are shared This architecture enables Polyglot to function as a comprehensive AI research environment while maintaining the simplicity and performance of local-first design.