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Implement intelligent agent learning from Knowledge Graph execution history with per-task-type expertise tracking, recency bias, and learning curves. ## Phase 5.3 Implementation ### Learning Infrastructure (✅ Complete) - LearningProfileService with per-task-type expertise metrics - TaskTypeExpertise model tracking success_rate, confidence, learning curves - Recency bias weighting: recent 7 days weighted 3x higher (exponential decay) - Confidence scoring prevents overfitting: min(1.0, executions / 20) - Learning curves computed from daily execution windows ### Agent Scoring Service (✅ Complete) - Unified AgentScore combining SwarmCoordinator + learning profiles - Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence - Rank agents by combined score for intelligent assignment - Support for recency-biased scoring (recent_success_rate) - Methods: rank_agents, select_best, rank_agents_with_recency ### KG Integration (✅ Complete) - KGPersistence::get_executions_for_task_type() - query by agent + task type - KGPersistence::get_agent_executions() - all executions for agent - Coordinator::load_learning_profile_from_kg() - core KG→Learning integration - Coordinator::load_all_learning_profiles() - batch load for multiple agents - Convert PersistedExecution → ExecutionData for learning calculations ### Agent Assignment Integration (✅ Complete) - AgentCoordinator uses learning profiles for task assignment - extract_task_type() infers task type from title/description - assign_task() scores candidates using AgentScoringService - Fallback to load-based selection if no learning data available - Learning profiles stored in coordinator.learning_profiles RwLock ### Profile Adapter Enhancements (✅ Complete) - create_learning_profile() - initialize empty profiles - add_task_type_expertise() - set task-type expertise - update_profile_with_learning() - update swarm profiles from learning ## Files Modified ### vapora-knowledge-graph/src/persistence.rs (+30 lines) - get_executions_for_task_type(agent_id, task_type, limit) - get_agent_executions(agent_id, limit) ### vapora-agents/src/coordinator.rs (+100 lines) - load_learning_profile_from_kg() - core KG integration method - load_all_learning_profiles() - batch loading for agents - assign_task() already uses learning-based scoring via AgentScoringService ### Existing Complete Implementation - vapora-knowledge-graph/src/learning.rs - calculation functions - vapora-agents/src/learning_profile.rs - data structures and expertise - vapora-agents/src/scoring.rs - unified scoring service - vapora-agents/src/profile_adapter.rs - adapter methods ## Tests Passing - learning_profile: 7 tests ✅ - scoring: 5 tests ✅ - profile_adapter: 6 tests ✅ - coordinator: learning-specific tests ✅ ## Data Flow 1. Task arrives → AgentCoordinator::assign_task() 2. Extract task_type from description 3. Query KG for task-type executions (load_learning_profile_from_kg) 4. Calculate expertise with recency bias 5. Score candidates (SwarmCoordinator + learning) 6. Assign to top-scored agent 7. Execution result → KG → Update learning profiles ## Key Design Decisions ✅ Recency bias: 7-day half-life with 3x weight for recent performance ✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting ✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence ✅ KG query limit: 100 recent executions per task-type for performance ✅ Async loading: load_learning_profile_from_kg supports concurrent loads ## Next: Phase 5.4 - Cost Optimization Ready to implement budget enforcement and cost-aware provider selection.
62 lines
2.0 KiB
Markdown
62 lines
2.0 KiB
Markdown
# VAPORA Documentation
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Complete user-facing documentation for VAPORA, an intelligent development orchestration platform.
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## Quick Navigation
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- **[Getting Started](getting-started.md)** — Start here
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- **[Quickstart](quickstart.md)** — Quick setup guide
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- **[Setup & Deployment](setup/)** — Installation, configuration, deployment
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- **[Features](features/)** — Capabilities and overview
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- **[Architecture](architecture/)** — Design, planning, and system overview
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- **[Integrations](integrations/)** — Integration guides and APIs
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- **[Branding](branding.md)** — Brand assets and guidelines
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- **[Executive Summary](executive/)** — Executive-level summaries
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## Documentation Structure
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```
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docs/
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├── README.md (this file - directory index)
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├── getting-started.md (entry point)
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├── quickstart.md (quick setup)
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├── branding.md (brand guidelines)
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├── setup/ (installation & deployment)
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│ ├── README.md
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│ ├── setup-guide.md
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│ ├── deployment.md
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│ ├── tracking-setup.md
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│ └── ...
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├── features/ (product capabilities)
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│ ├── README.md
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│ └── overview.md
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├── architecture/ (design & planning)
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│ ├── README.md
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│ ├── project-plan.md
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│ ├── phase1-integration.md
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│ ├── completion-report.md
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│ └── ...
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├── integrations/ (integration guides)
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│ ├── README.md
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│ ├── doc-lifecycle.md
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│ └── ...
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└── executive/ (executive summaries)
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├── README.md
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├── executive-summary.md
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└── resumen-ejecutivo.md
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```
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## For mdBook
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This documentation is compatible with mdBook. Generate the book with:
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```bash
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mdbook build
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mdbook serve
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```
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Ensure all documents follow:
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- Lowercase filenames (except README.md)
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- Kebab-case for multi-word files
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- Each subdirectory has README.md
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