think-bigger/docs/plans/project-phases/phase-1-foundation.md

Phase 1: Foundation and Core Infrastructure

Timeline: Weeks 1-4 Objective: Establish the technical foundation and core system architecture Success Criteria: Functional backend API with all core services operational

Overview

Phase 1 establishes the dual manifold cognitive architecture foundation - the revolutionary core that differentiates this system from traditional PKM tools. We implement the three-layer memory hierarchy (episodic, semantic, persona) and begin construction of both individual and collective manifolds. This phase creates the mathematical primitives for intelligence that transcends simple information retrieval.

Critical Dependencies

• Blocking for Phase 2: File system integration, API endpoints, basic data services
• Dana Runtime: Must be functional for agent development in later phases
• Database Setup: Required for knowledge representation throughout the system

Detailed Implementation Plan

Week 1: Dual Manifold Mathematical Foundation

Day 1-2: Manifold Primitives and Configuration

• Implement mathematical primitives for manifold operations
• Set up dual manifold configuration system
• Create vector space management for individual/collective manifolds
• Initialize geometric consistency validation
• Set up development environment with manifold libraries

Day 3-4: Episodic Memory Layer - Hybrid Indexing

• Implement FAISS dense vector indexing for conceptual similarity
• Build BM25 sparse indexing for exact technical term matching
• Create reciprocal rank fusion for hybrid search results
• Develop document chunking with temporal metadata preservation
• Test hybrid retrieval accuracy and performance

Day 5: Semantic Memory Layer - Temporal Distillation

• Implement LLM-powered concept extraction from chunks
• Build temporal trajectory analysis for cognitive evolution
• Create time-series modeling of concept strength and trends
• Develop focus shift detection algorithms
• Validate semantic distillation accuracy

Week 2: Persona Layer and Graph Construction

Day 1-3: Knowledge Graph Construction

• Implement NetworkX-based knowledge graph builder
• Create weighted edges based on co-occurrence analysis
• Develop centrality measure calculations (PageRank, betweenness)
• Build graph persistence and loading mechanisms
• Test graph construction from temporal concept data

Day 4-5: Gravity Well Manifold Representation

• Implement kernel density estimation for gravity wells
• Create manifold distance calculations (1 - cosine similarity)
• Build mass calculation based on graph centrality
• Develop geometric consistency validation
• Test manifold representation stability

Week 3: Collective Manifold Construction

Day 1-2: OpenAlex Integration

• Implement OpenAlex API client for scientific publications
• Create community knowledge graph construction
• Build citation network analysis
• Develop domain-specific publication filtering
• Test API reliability and rate limiting

Day 3-4: Wireframe Manifold Estimation

• Implement wireframe grid construction for collective manifold
• Create estimation points for manifold approximation
• Build interpolation algorithms for smooth surfaces
• Develop manifold boundary detection
• Validate wireframe geometric properties

Day 5: Cross-Manifold Validation

• Implement manifold intersection calculations
• Create consistency checks between individual/collective manifolds
• Build geometric validation metrics
• Develop manifold alignment algorithms
• Test cross-manifold operations

Week 4: Braiding Engine Implementation

Day 1-2: Individual Resonance (Alpha) Scoring

• Implement alpha calculation using gravity well distance
• Create graph centrality weighting for concept importance
• Build temporal relevance scoring
• Develop confidence interval calculations
• Test alpha scoring accuracy

Day 3-4: Collective Feasibility (Beta) Scoring

• Implement beta calculation using random walk probabilities
• Create wireframe support estimation
• Build citation network validation
• Develop community consensus metrics
• Test beta scoring reliability

Day 5: Structural Gate and Final Integration

• Implement structural gate function with hallucination filtering
• Create braiding parameter optimization
• Build final S_braid calculation pipeline
• Develop API endpoints for manifold operations
• Comprehensive testing of braiding engine

Deliverables

Code Deliverables

• Episodic Memory Layer: Hybrid indexing (dense vectors + BM25) with reciprocal rank fusion
• Semantic Memory Layer: Temporal distillation pipeline with cognitive trajectory analysis
• Persona Memory Layer: Knowledge graph construction with centrality measures
• Individual Manifold: Basic gravity well representation and novelty repulsion
• Collective Manifold: OpenAlex integration for community knowledge
• Braiding Engine: Structural gate implementation with alpha/beta scoring
• Comprehensive test suite (>80% coverage) for manifold operations

Documentation Deliverables

• API documentation with examples
• Architecture diagrams and data flow documentation
• Database schema documentation
• Deployment and configuration guides
• Integration testing procedures

Infrastructure Deliverables

• Docker containerization setup
• Development environment configuration
• CI/CD pipeline foundation
• Monitoring and logging setup
• Database backup and recovery procedures

Success Metrics

• Manifold Construction: Both individual and collective manifolds initialize correctly
• Hybrid Indexing: Episodic layer achieves >95% retrieval accuracy with <100ms query time
• Cognitive Distillation: Semantic layer processes temporal trajectories with >90% concept extraction accuracy
• Graph Construction: Persona layer builds knowledge graphs with proper centrality measures
• Braiding Validation: Structural gates correctly filter hallucinations (>95% accuracy)
• Mathematical Primitives: All manifold operations maintain geometric consistency
• API Endpoints: Manifold operations respond within 500ms

Risk Mitigation

Technical Risks

• Dana Runtime Maturity: If Dana integration proves difficult, implement fallback agent system
• Database Performance: Monitor query performance and optimize as needed
• File System Compatibility: Test on multiple platforms early

Timeline Risks

• Complex Integration: Allocate buffer time for unexpected integration challenges
• Dependency Issues: Use pinned versions and test thoroughly
• Learning Curve: Schedule architecture reviews and pair programming

Testing Strategy

Unit Testing

• Test all core services in isolation
• Mock external dependencies (APIs, databases)
• Test error conditions and edge cases
• Validate configuration loading

Integration Testing

• Test service-to-service communication
• Validate data flow through entire pipelines
• Test concurrent operations
• Verify resource cleanup

Performance Testing

• Load test API endpoints
• Test document processing at scale
• Validate memory usage patterns
• Monitor database query performance

Parallel Development Opportunities

While Phase 1 is primarily backend-focused, the following can be started in parallel:

• Frontend Architecture: Set up basic React/Next.js structure
• UI Design System: Begin implementing design tokens and components
• API Contract Definition: Define detailed API specifications
• Testing Infrastructure: Set up testing frameworks and CI/CD

Phase Gate Criteria

Phase 1 is complete when:

• Dual Manifold Architecture: Both individual and collective manifolds construct and validate correctly
• Three-Layer Memory: Episodic, semantic, and persona layers operate with >90% accuracy
• Braiding Engine: Structural gates filter hallucinations with >95% accuracy
• Mathematical Consistency: All manifold operations maintain geometric properties
• API Contracts: Manifold operations are documented and stable
• Demonstration: Team can show cognitive trajectory analysis and optimal suggestion generation

Next Steps

After Phase 1 completion:

1. Conduct architecture review with full team
2. Begin Phase 2 UI development with confidence
3. Schedule regular integration points between frontend/backend
4. Plan Phase 3 content processing based on Phase 1 learnings docs/plans/project-phases/phase-1-foundation.md