d14150da75
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.
58 lines
1.1 KiB
TOML
58 lines
1.1 KiB
TOML
[taskserv]
|
|
name = "vapora-mcp-gateway"
|
|
type = "service"
|
|
version = "0.2.0"
|
|
description = "MCP (Model Context Protocol) Gateway - Plugin System"
|
|
|
|
[source]
|
|
repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"
|
|
branch = "main"
|
|
path = "vapora-mcp-gateway/"
|
|
|
|
[build]
|
|
runtime = "rust"
|
|
build_command = "cargo build --release -p vapora-mcp-gateway"
|
|
|
|
[deployment]
|
|
namespace = "vapora-system"
|
|
replicas = 2
|
|
image = "vapora/mcp-gateway"
|
|
image_tag = "${version}"
|
|
|
|
[ports]
|
|
http = 8888
|
|
metrics = 9090
|
|
|
|
[resources]
|
|
requests = { cpu = "1000m", memory = "2Gi" }
|
|
limits = { cpu = "2000m", memory = "4Gi" }
|
|
|
|
[health_check]
|
|
type = "http"
|
|
path = "/health"
|
|
interval_secs = 10
|
|
|
|
[environment]
|
|
NATS_URL = "nats://nats-0.vapora-system:4222"
|
|
DATABASE_URL = "surrealdb://surrealdb-0.vapora-system:8000"
|
|
MCP_PLUGINS_PATH = "/plugins"
|
|
RUST_LOG = "debug,vapora_mcp=trace"
|
|
|
|
[dependencies]
|
|
required = ["nats", "surrealdb"]
|
|
optional = []
|
|
|
|
[scaling]
|
|
min_replicas = 2
|
|
max_replicas = 5
|
|
target_cpu = 70
|
|
|
|
[persistence]
|
|
enabled = true
|
|
size = "10Gi"
|
|
mount_path = "/plugins"
|
|
storage_class = "ssd"
|
|
|
|
[monitoring]
|
|
prometheus_metrics = true
|