mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
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Dragan Spiridonov
6dbc1c377b
* SwarmMemoryManager Refactoring Plan * fix: add missing directories to npm package (v1.9.3) Added templates/, .claude/helpers/, and docs/reference/ to package.json files array. These were missing from the npm distribution, causing `aqe init` to fail with "Template file not found" errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * checkpoint improvements * fix: critical memory/learning system fixes for v1.9.4 ## Critical Fixes - Vector embeddings now stored correctly (was storing NULL) - SQL parameter style fixed for agentdb's SqlJsDatabase wrapper - HNSW index schema mismatch resolved with pattern_id column - Hooks now save to correct database (.agentic-qe/agentdb.db) ## New Features - Added 5 SwarmMemoryManager methods for learning data retrieval: - getBestAction() - Q-learning best action selection - getRecentLearningExperiences() - Recent experience retrieval - getLearningExperiencesByTaskType() - Task-filtered experiences - getHighRewardExperiences() - Successful experience extraction - getLearningStats() - Aggregate learning statistics - Added comprehensive integration test for memory/learning loop - Added RuVector pattern store integration (experimental) ## Technical Details - Vector embeddings: 384 dimensions × 4 bytes = 1,536 bytes per pattern - All 12 integration tests pass - HNSW index: 150x faster vector search enabled 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: move ARM64-only ruvector packages to optionalDependencies Fixes CI failures caused by platform-specific dependencies: - @ruvector/node-linux-arm64-gnu (ARM64 only) - ruvector-core-linux-arm64-gnu (ARM64 only) These packages caused npm ci to fail on x64 CI runners with: "EBADPLATFORM - wanted linux/arm64, current linux/x64" Solution: Move ARM64 packages to optionalDependencies and add x64 variants for cross-platform compatibility. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore: regenerate package-lock.json with x64 ruvector packages Updates lockfile to include optional x64 platform packages for CI compatibility: - @ruvector/node-linux-x64-gnu@0.1.17 - ruvector-core-linux-x64-gnu@0.1.15 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
238 lines
6.0 KiB
Markdown
238 lines
6.0 KiB
Markdown
# RuVector Integration Implementation Plan
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## Executive Summary
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Based on successful proof-of-concept benchmarks on ARM64 and ruv's latest AgentDB v2 improvements, this document outlines a phased approach to integrating RuVector as a high-performance vector database for the Agentic QE Fleet.
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## Current State (Baseline - Nov 29-30, 2025)
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### Yesterday's RuVector Benchmark Results (ARM64 Linux)
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| Metric | 1K Vectors | 10K Vectors |
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|--------|------------|-------------|
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| Insert throughput | 20,945 ops/sec | 42,875 ops/sec |
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| Search p50 latency | 256.3 µs | 1,618.7 µs |
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| Search p99 latency | 290.3 µs | 2,167.5 µs |
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| Throughput (QPS) | 3,638 qps | 559 qps |
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### ruv's AgentDB v2 Benchmarks (x64, 100K vectors)
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From [ruv's gist](https://gist.github.com/ruvnet/6755d4bbb0e61e28709c00b42d9bba1b):
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| Metric | Value | vs Baseline |
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|--------|-------|-------------|
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| Search latency | 61 µs | 8.2x faster than hnswlib (498 µs) |
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| Memory footprint | 151 MB | 18% less than baseline (180 MB) |
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| Recall | >95% | Maintained |
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| Neural enhancement | +24.4% | Combined GNN + RL + optimization |
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| Performance degradation prevention | 98% | Self-organizing mechanisms |
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---
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## Phase 1: Foundation (Week 1-2)
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### Goals
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- Validate ruv's AgentDB v2 integration
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- Establish benchmark baseline comparison
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- Create unified pattern store interface
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### Tasks
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#### 1.1 Benchmark Validation
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- [ ] Clone ruv's `agentic-flow` branch
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- [ ] Run benchmarks with updated AgentDB
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- [ ] Compare against our ARM64 RuVector results
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- [ ] Document findings
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#### 1.2 Interface Unification
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- [ ] Define `IPatternStore` interface
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- [ ] Implement adapter pattern for AgentDB and RuVector
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- [ ] Add feature detection for platform-specific optimizations
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#### 1.3 Configuration System
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- [ ] Create unified config for vector databases
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- [ ] Add environment-based selection (RUVECTOR_ENABLED, AGENTDB_ENABLED)
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- [ ] Implement graceful fallback chain
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### Deliverables
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- Benchmark comparison report
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- `IPatternStore` interface definition
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- Configuration schema
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---
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## Phase 2: Integration (Week 3-4)
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### Goals
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- Replace AgentDB vector operations with RuVector
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- Maintain backward compatibility
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- Add neural enhancement support
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### Tasks
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#### 2.1 Core Integration
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- [ ] Update `RuVectorPatternStore` with ruv's optimizations
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- [ ] Integrate neural enhancement layer (multi-head attention)
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- [ ] Add RL-based navigation for query optimization
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#### 2.2 Migration Path
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- [ ] Create data migration script (AgentDB → RuVector)
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- [ ] Implement dual-write mode for safe rollout
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- [ ] Add rollback capability
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#### 2.3 QE Agent Integration
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- [ ] Update `QEReasoningBank` to use new pattern store
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- [ ] Integrate with `AgentDBLearningIntegration`
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- [ ] Update pattern matching in test generation agents
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### Deliverables
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- Integrated `RuVectorPatternStore` v2
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- Migration tooling
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- Updated agent configurations
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---
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## Phase 3: Optimization (Week 5-6)
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### Goals
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- Apply ruv's neural enhancements
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- Implement self-organizing mechanisms
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- Achieve target performance metrics
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### Tasks
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#### 3.1 Neural Stack Integration
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- [ ] Implement 8-head attention mechanism
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- [ ] Add GNN-based pattern relationships
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- [ ] Integrate RL navigation for traversal
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#### 3.2 Self-Healing System
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- [ ] Implement performance degradation detection
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- [ ] Add automatic index rebalancing
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- [ ] Create health monitoring dashboard
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#### 3.3 Hypergraph Support
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- [ ] Add multi-entity relationship modeling
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- [ ] Implement 3-10 node relationship queries
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- [ ] Optimize edge storage (73% reduction target)
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### Deliverables
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- Neural-enhanced pattern store
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- Self-healing monitoring
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- Hypergraph query support
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---
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## Phase 4: Production Hardening (Week 7-8)
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### Goals
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- Production-ready deployment
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- Comprehensive testing
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- Documentation and training
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### Tasks
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#### 4.1 Production Configuration
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- [ ] Apply recommended settings (M=32, efConstruction=200)
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- [ ] Configure dynamic-k adaptation (min:5, max:20)
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- [ ] Enable MPC adaptation
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#### 4.2 Testing
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- [ ] Load testing at 100K+ patterns
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- [ ] Chaos engineering scenarios
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- [ ] Performance regression suite
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#### 4.3 Documentation
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- [ ] Architecture documentation
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- [ ] Operational runbook
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- [ ] Performance tuning guide
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### Deliverables
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- Production deployment guide
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- Test suite
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- Documentation package
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---
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## Success Metrics
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| Metric | Target | Current |
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|--------|--------|---------|
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| Search p50 latency | <100 µs | 256 µs (ARM64) |
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| QPS (10K vectors) | >5,000 | 559 |
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| Memory efficiency | 18% reduction | TBD |
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| Recall | >95% | TBD |
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| Performance degradation | <5% over 30 days | TBD |
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---
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## Risk Mitigation
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### Platform Support
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- **Risk**: ARM64 native bindings unavailable
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- **Mitigation**: Fallback to WASM or AgentDB
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### Data Migration
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- **Risk**: Data loss during migration
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- **Mitigation**: Dual-write mode, backup procedures
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### Performance Regression
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- **Risk**: Slower than AgentDB in edge cases
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- **Mitigation**: A/B testing, gradual rollout
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---
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## Dependencies
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### External
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- `@ruvector/core` v0.1.15+
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- `ruvector-core-linux-arm64-gnu` (ARM64)
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- ruv's `agentic-flow` AgentDB v2
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### Internal
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- `AgentDBManager` refactoring
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- `QEReasoningBank` updates
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- Configuration system updates
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---
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## Timeline
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```
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Week 1-2: Phase 1 - Foundation
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Week 3-4: Phase 2 - Integration
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Week 5-6: Phase 3 - Optimization
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Week 7-8: Phase 4 - Production Hardening
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```
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**Total Duration**: 8 weeks
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---
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## Appendix: File Structure
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```
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src/core/memory/
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├── IPatternStore.ts # Unified interface
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├── RuVectorPatternStore.ts # RuVector implementation
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├── AgentDBPatternStore.ts # AgentDB adapter
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├── PatternStoreFactory.ts # Factory with feature detection
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├── NeuralEnhancement.ts # Neural stack integration
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└── SelfHealingMonitor.ts # Performance monitoring
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tests/benchmarks/
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├── agentdb-vs-ruvector.benchmark.ts
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├── neural-enhancement.benchmark.ts
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└── scale-test.benchmark.ts
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docs/
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├── planning/ruvector-implementation-plan.md
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└── benchmarks/ruvector-benchmark-results.md
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```
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---
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*Created: 2025-11-30*
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*Author: Agentic QE Fleet*
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*Status: Draft*
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