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.
167 lines
5.3 KiB
Markdown
167 lines
5.3 KiB
Markdown
# SecretumVault Integration
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VAPORA integrates with **SecretumVault**, a post-quantum ready secrets management system, for secure credential and API key management across all microservices.
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## Overview
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SecretumVault provides:
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- **Post-quantum cryptography** ready for future-proof security
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- **Multi-backend storage** (filesystem, SurrealDB, PostgreSQL, etcd)
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- **Fine-grained access control** with Cedar policy engine
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- **Secrets server** for centralized credential management
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- **CLI tools** for operations and development
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## Integration Points
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SecretumVault is integrated into these VAPORA services:
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| Service | Purpose | Features |
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|---------|---------|----------|
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| **vapora-backend** | REST API credentials, database secrets, JWT keys | Central secrets management |
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| **vapora-agents** | Agent authentication, service credentials | Secure agent-to-service auth |
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| **vapora-llm-router** | LLM provider API keys (Claude, OpenAI, Gemini, Ollama) | Cost tracking + credential rotation |
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ VAPORA Services │
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├─────────────┬──────────────────┬────────────────────────────┤
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│ Backend API │ Agent Orchestration │ LLM Router │
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└──────┬──────┴────────┬─────────┴──────────┬─────────────────┘
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│ │ │
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└───────────────┼────────────────────┘
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│
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▼
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┌─────────────────────────────┐
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│ SecretumVault Server │
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├─────────────────────────────┤
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│ • Credential storage │
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│ • Policy enforcement │
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│ • Audit logging │
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│ • Key rotation │
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└──────────┬──────────────────┘
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│
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┌───────────┴────────────┐
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▼ ▼
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Storage Layer Policy Engine
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(SurrealDB) (Cedar)
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```
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## Configuration
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### Environment Variables
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```bash
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# SecretumVault server connection
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SECRETUMVAULT_URL=http://secretumvault:3030
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SECRETUMVAULT_TOKEN=<identity-token>
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# Storage backend
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SECRETUMVAULT_STORAGE=surrealdb
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SURREAL_URL=ws://surrealdb:8000
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SURREAL_DB=secretumvault
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# Crypto backend
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SECRETUMVAULT_CRYPTO=openssl # or aws-lc for post-quantum
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```
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### Cargo Features
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SecretumVault is integrated with these features enabled:
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```toml
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secretumvault = { workspace = true }
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# Automatically uses: "server", "surrealdb-storage"
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```
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## Usage Examples
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### In vapora-backend
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```rust
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use secretumvault::SecretClient;
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// Initialize client
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let client = SecretClient::new(
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&env::var("SECRETUMVAULT_URL")?,
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&env::var("SECRETUMVAULT_TOKEN")?,
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).await?;
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// Retrieve API key
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let api_key = client.get_secret("llm/claude-api-key").await?;
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// Store credential securely
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client.store_secret(
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"database/postgres-password",
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&password,
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Some("postgres-creds"),
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).await?;
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```
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### In vapora-llm-router
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```rust
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use secretumvault::SecretClient;
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// Get LLM provider credentials
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let openai_key = client.get_secret("llm/openai-api-key").await?;
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let claude_key = client.get_secret("llm/claude-api-key").await?;
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let gemini_key = client.get_secret("llm/gemini-api-key").await?;
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// Fallback to Ollama (local, no key needed)
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```
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## Running SecretumVault
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### Local Development
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```bash
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# Terminal 1: Start SecretumVault server
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cd /Users/Akasha/Development/secretumvault
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cargo run --bin secretumvault-server --features server,surrealdb-storage
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# Terminal 2: Initialize with default policies
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cargo run --bin secretumvault-cli -- init-policies
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```
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### Production (Kubernetes)
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```bash
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# Will be added to kubernetes/
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kubectl apply -f kubernetes/secretumvault/
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```
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## Security Best Practices
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1. **Token Management**
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- Use identity-based tokens (not basic auth)
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- Rotate tokens regularly
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- Store token in `.env.local` (not in git)
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2. **Secret Storage**
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- Never commit credentials to git
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- Use SecretumVault for all sensitive data
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- Enable audit logging for compliance
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3. **Policy Enforcement**
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- Define Cedar policies per role/service
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- Restrict access by principle of least privilege
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- Review policies during security audits
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4. **Crypto Backend**
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- Use `aws-lc` for post-quantum readiness
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- Plan migration as quantum threats evolve
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## Related Documentation
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- [SecretumVault Project](../../../../secretumvault/)
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- [VAPORA Architecture](vapora-architecture.md)
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- [Security & RBAC](../architecture/roles-permissions-profiles.md)
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---
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**Integration Status**: ✅ Active
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**Services**: Backend, Agents, LLM Router
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**Features**: server, surrealdb-storage, cedar-policies
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