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CodeSight Development Roadmap

Last Updated: March 1, 2026
Current Version: 0.1.0
Current Phase: Phase 7.1 Complete ✅

Phase Overview

Phase Status Completion Description
Phase 1-3 ✅ Complete 100% Core MCP implementation
Phase 4 ✅ Complete 100% AI-powered tools
Phase 5 ✅ Complete 100% Testing & validation
Phase 6 ✅ Complete 100% Production deployment
Phase 7.1 ✅ Complete 100% RAG service core
Phase 7.2 ⏳ Pending 0% pgvector & embeddings
Phase 8 ⏳ Pending 0% Performance optimization
Phase 9 ⏳ Pending 0% Enterprise features

Phase 7.2: Advanced AI Features (Q2 2026)

Task 1: pgvector Integration

Priority: 🔴 High
Estimated Effort: 3 days
Dependencies: None

  • Add pgvector dependency
  • Create PostgreSQL migration scripts
  • Implement VectorStoreManager for pgvector
  • Add vector index (HNSW/IVFFlat)
  • Write pgvector-specific queries
  • Performance benchmarks
  • Documentation

Acceptance Criteria:

  • pgvector backend working
  • 10x faster vector search vs SQLite
  • Support for 1M+ vectors
  • HNSW index configured

Task 2: Embedding Models

Priority: 🔴 High
Estimated Effort: 5 days
Dependencies: None

  • Integrate Ollama embeddings (nomic-embed-text)
  • Add sentence-transformers support
  • Implement BGE/BAAI models
  • Create embedding pipeline
  • Add embedding cache
  • Model configuration UI

Acceptance Criteria:

  • Real embeddings (384-1536 dim)
  • < 100ms embedding generation
  • Support 3+ embedding models
  • Automatic model fallback

Task 3: RAG MCP Tools

Priority: 🔴 High
Estimated Effort: 4 days
Dependencies: Task 1, Task 2

  • Create rag_search MCP tool
  • Create rag_explain MCP tool
  • Create rag_code_review MCP tool
  • Add context retrieval
  • Implement citation system
  • Add source attribution

Acceptance Criteria:

  • 3 new MCP tools
  • RAG-powered responses
  • Source citations included
  • < 2s response time

Task 4: Hybrid Search

Priority: 🟡 Medium
Estimated Effort: 3 days
Dependencies: Task 1, Task 2

  • Implement BM25 keyword search
  • Combine with vector search
  • Add reranking (cross-encoder)
  • Configurable weighting
  • Search result fusion

Acceptance Criteria:

  • Hybrid search (keyword + vector)
  • Better relevance scores
  • Configurable parameters
  • 20% improvement in search quality

Task 5: Embedding Cache

Priority: 🟡 Medium
Estimated Effort: 2 days
Dependencies: Task 2

  • Redis cache for embeddings
  • Cache invalidation strategy
  • TTL configuration
  • Cache statistics
  • Warm-up strategy

Acceptance Criteria:

  • 80% cache hit rate
  • < 10ms cache lookup
  • Automatic cache refresh
  • Memory-efficient storage

Phase 8: Performance Optimization (Q3 2026)

Task 1: Database Optimization

Priority: 🔴 High
Estimated Effort: 5 days

  • Query optimization
  • Add missing indexes
  • Connection pooling
  • Query result caching
  • Database sharding strategy

Task 2: Memory Optimization

Priority: 🔴 High
Estimated Effort: 4 days

  • Memory profiling
  • Reduce memory footprint
  • Stream large file processing
  • Lazy loading
  • Memory leak detection

Task 3: Parallel Processing

Priority: 🟡 Medium
Estimated Effort: 3 days

  • Multi-threaded indexing
  • Worker pool implementation
  • Parallel search queries
  • Batch processing optimization

Task 4: CDN & Edge

Priority: 🟡 Medium
Estimated Effort: 4 days

  • Static asset CDN
  • Edge caching
  • Global distribution
  • Reduced latency

Phase 9: Enterprise Features (Q4 2026)

Task 1: Multi-tenancy

Priority: 🔴 High
Estimated Effort: 10 days

  • Tenant isolation
  • Per-tenant databases
  • Resource quotas
  • Tenant management UI
  • Billing integration

Task 2: Authentication & Authorization

Priority: 🔴 High
Estimated Effort: 7 days

  • SSO (SAML, OAuth2)
  • RBAC implementation
  • API key management
  • Audit logging
  • SSO integration guides

Task 3: Team Collaboration

Priority: 🟡 Medium
Estimated Effort: 8 days

  • Shared workspaces
  • Comments and annotations
  • Real-time collaboration
  • Team dashboards
  • Notifications

Task 4: Compliance

Priority: 🟡 Medium
Estimated Effort: 6 days

  • SOC2 compliance
  • GDPR compliance
  • Data retention policies
  • Export capabilities
  • Compliance documentation

Backlog (Future Consideration)

Phase 10: Developer Experience

  • VS Code extension improvements
  • JetBrains plugin
  • Vim/Neovim integration
  • CLI enhancements
  • Interactive tutorials

Phase 11: AI Enhancements

  • Fine-tuning support
  • Custom AI models
  • Multi-model ensemble
  • AI response streaming
  • Code execution sandbox

Phase 12: Analytics

  • Usage analytics
  • Code quality trends
  • Team productivity metrics
  • Custom dashboards
  • Export reports

Sprint Planning

Sprint 1 (Week 1-2)

  • Task 7.2.1: pgvector Integration
  • Task 7.2.2: Embedding Models

Sprint 2 (Week 3-4)

  • Task 7.2.3: RAG MCP Tools
  • Task 7.2.4: Hybrid Search

Sprint 3 (Week 5-6)

  • Task 7.2.5: Embedding Cache
  • Phase 7.2 Testing & Documentation

Resource Requirements

Development Team

  • 2-3 Backend Engineers
  • 1 ML Engineer
  • 1 DevOps Engineer
  • 1 Frontend Engineer (Phase 9+)

Infrastructure

  • Development: Current setup sufficient
  • Staging: Kubernetes cluster
  • Production: Multi-region deployment
  • ML: GPU instances for embeddings

Budget Estimate

Category Q2 2026 Q3 2026 Q4 2026
Infrastructure $500/mo $1,000/mo $2,500/mo
AI/ML APIs $200/mo $500/mo $1,000/mo
Development 160 hrs 160 hrs 240 hrs

Success Metrics

Phase 7.2 KPIs

  • Search accuracy > 90%
  • Response time < 2s
  • Embedding latency < 100ms
  • Cache hit rate > 80%

Phase 8 KPIs

  • Indexing speed > 200 files/s
  • Memory usage < 500MB
  • Search latency < 50ms
  • 99.9% uptime

Phase 9 KPIs

  • 10+ enterprise customers
  • SOC2 certification
  • < 1hr onboarding time
  • 95% customer satisfaction

Risk Assessment

Risk Probability Impact Mitigation
pgvector performance issues Low High Benchmark early, fallback to SQLite
Embedding model costs Medium Medium Use local models, caching
Enterprise adoption slow Medium High Free tier, community edition
Competition High Medium Focus on differentiation

Review Cadence

  • Daily: Standup meetings
  • Weekly: Sprint reviews
  • Bi-weekly: Sprint planning
  • Monthly: Roadmap review
  • Quarterly: Strategic planning

Getting Started (Next Steps)

Immediate (This Week)

  1. Review and prioritize Phase 7.2 tasks
  2. Set up pgvector development environment
  3. Research embedding models
  4. Create GitHub issues for tasks

Short-term (Next 2 Weeks)

  1. Start pgvector integration
  2. Test Ollama embeddings
  3. Design RAG MCP tool interfaces

Medium-term (Next Month)

  1. Complete Phase 7.2
  2. User testing for RAG features
  3. Performance benchmarks

Document Owner: Development Team
Review Date: Weekly
Status Updates: Every sprint