# Akosha Complete Development Roadmap

**Project**: Akosha - Universal Memory Aggregation System
**Timeline**: 12 weeks (3 months) to full production
**Target Scale**: 100 → 100,000 systems

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## Quick Reference

| Phase | Duration | Focus | Status | Documentation |
|-------|----------|-------|--------|--------------|
| **Phase 1** | Weeks 1-4 | Foundation | ✅ Complete | [IMPLEMENTATION_GUIDE.md](IMPLEMENTATION_GUIDE.md) |
| **Phase 2** | Weeks 5-8 | Advanced Features | 📋 Ready | [PHASE_2_ADVANCED_FEATURES.md](PHASE_2_ADVANCED_FEATURES.md) |
| **Phase 3** | Weeks 9-10 | Production Hardening | 📋 Planned | [PHASE_3_PRODUCTION_HARDENING.md](PHASE_3_PRODUCTION_HARDENING.md) |
| **Phase 4** | Weeks 11-12 | Scale Preparation | 📋 Planned | [PHASE_4_SCALE_PREPARATION.md](PHASE_4_SCALE_PREPARATION.md) |

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## Architecture Evolution

```
Phase 1: Foundation (100 systems)
┌─────────────────────────────┐
│  Hot: DuckDB in-memory       │
│  Warm: DuckDB on-disk         │
│  Cold: Parquet + CF R2         │
│  Graph: DuckDB + Redis         │
└─────────────────────────────┘

Phase 2: Advanced Features (1,000 systems)
┌─────────────────────────────┐
│  Hot: DuckDB + HNSW            │
│  Warm: DuckDB on-disk         │
│  Cold: Parquet + CF R2         │
│  Graph: DuckDB + Redis         │
│  Embeddings: all-MiniLM        │
│  Analytics: Time-series       │
└─────────────────────────────┘

Phase 3: Production (10,000 systems)
┌─────────────────────────────┐
│  Circuit Breakers              │
│  Retry Logic                   │
│  Distributed Tracing           │
│  Prometheus Metrics            │
│  Kubernetes Deployment        │
└─────────────────────────────┘

Phase 4: Hyperscale (100,000 systems)
┌─────────────────────────────┐
│  Hot: DuckDB                   │
│  Warm: Milvus Cluster         │
│  Cold: Parquet + CF R2         │
│  Graph: Neo4j                   │
│  Analytics: TimescaleDB        │
│  Multi-Region DR               │
└─────────────────────────────┘
```

______________________________________________________________________

## Feature Matrix by Phase

| Feature | Phase 1 | Phase 2 | Phase 3 | Phase 4 |
|---------|---------|---------|---------|---------|
| **Storage Tiers** |
| Hot Store (DuckDB) | ✅ | ✅ | ✅ | ✅ |
| Warm Store (DuckDB) | ✅ | ✅ | ✅ | ✅ |
| Cold Store (Parquet/R2) | 📋 | ✅ | ✅ | ✅ |
| Milvus Warm Store | ❌ | ❌ | ❌ | ✅ |
| **Search** |
| Vector Similarity | ✅\* | ✅ | ✅ | ✅ |
| Hybrid Search | ❌ | ❌ | ❌ | ✅ |
| **Graph** |
| Basic Builder | ✅ | ✅ | ✅ | ✅ |
| Community Detection | ❌ | ✅ | ✅ | ✅ |
| Centrality Metrics | ❌ | ✅ | ✅ | ✅ |
| Path Finding (BFS) | ✅ | ✅ | ✅ | ✅ |
| **Analytics** |
| Metrics Recording | 📋 | ✅ | ✅ | ✅ |
| Trend Detection | ❌ | ✅ | ✅ | ✅ |
| Anomaly Detection | ❌ | ✅ | ✅ | ✅ |
| Cross-System Correlation | ❌ | ✅ | ✅ | ✅ |
| **Operations** |
| Ingestion Worker | ✅ | ✅ | ✅ | ✅ |
| Event-Driven Ingestion | ❌ | 📋 | ✅ | ✅ |
| Circuit Breakers | ❌ | ❌ | ✅ | ✅ |
| Distributed Tracing | ❌ | ❌ | ✅ | ✅ |
| **Infrastructure** |
| MCP Server | ✅ | ✅ | ✅ | ✅ |
| Kubernetes | ❌ | ❌ | ✅ | ✅ |
| Multi-Region DR | ❌ | ❌ | ❌ | ✅ |

- = Fallback cosine similarity (HNSW may not be available in all DuckDB versions)

______________________________________________________________________

## Technology Stack Evolution

### Databases

| Component | Phase 1 | Phase 2 | Phase 3 | Phase 4 |
|-----------|---------|---------|---------|---------|
| **Vectors** | DuckDB | DuckDB | DuckDB | DuckDB + Milvus |
| **Time-Series** | DuckDB | DuckDB | DuckDB | TimescaleDB |
| **Graph** | DuckDB + Redis | DuckDB + Redis | DuckDB + Redis | Neo4j |
| **Conversations** | Parquet | Parquet | Parquet | Parquet |

### Infrastructure

| Component | Phase 1 | Phase 2 | Phase 3 | Phase 4 |
|-----------|---------|---------|---------|---------|
| **Compute** | Single Pod | 3 Pods | HPA (3-50) | HPA (3-100) |
| **Storage** | Local SSD | Local SSD + R2 | Fast SSD + R2 | Distributed SSD + R2 |
| **Monitoring** | Logs | Logs + Metrics | Logs + Metrics + Tracing | Full Observability |
| **Resilience** | Basic retry | Retry + Backoff | Circuit Breaker + DR | Full DR Multi-Region |

______________________________________________________________________

## Key Milestones

### ✅ Phase 1 Complete (Week 4)

- [x] Hot/Warm/Cold storage architecture
- [x] Basic ingestion worker
- [x] Deduplication service
- [x] Knowledge graph builder
- [x] Akosha MCP server (6 tools)
- [x] Cloudflare R2 integration ready

### 📋 Phase 2 Targets (Week 8)

- [ ] ONNX embedding generation
- [ ] Semantic search with real embeddings
- [ ] Time-series aggregator
- [ ] Trend and anomaly detection
- [ ] Community detection
- [ ] Centrality metrics
- [ ] Event-driven ingestion

### 📋 Phase 3 Targets (Week 10)

- [ ] Circuit breakers for all external calls
- [ ] Retry with exponential backoff
- [ ] OpenTelemetry tracing
- [ ] Prometheus metrics
- [ ] Kubernetes deployment
- [ ] Load testing (1000 req/min)
- [ ] Zero-downtime deployments

### 📋 Phase 4 Targets (Week 12)

- [ ] Milvus cluster deployment
- \] Hybrid vector search (DuckDB + Milvus)
- \] TimescaleDB with continuous aggregates
- \] Neo4j for complex graph queries
- [ ] Multi-region disaster recovery
- [ ] Test at 10,000 systems scale

______________________________________________________________________

## Estimated Resource Requirements

| Phase | Systems | Embeddings | Storage | RAM | CPU | Monthly Cost (est.) |
|-------|---------|-----------|---------|-----|-----|-------------------|
| **Phase 1** | 100 | 1M | 100 GB | 4 GB | 2 cores | $200 |
| **Phase 2** | 1,000 | 10M | 1 TB | 16 GB | 4 cores | $500 |
| **Phase 3** | 10,000 | 100M | 10 TB | 64 GB | 16 cores | $2,000 |
| **Phase 4** | 100,000 | 1B | 100 TB | 256 GB | 64 cores | $8,000 |

*Costs assume Cloudflare R2 for storage + Google Cloud Platform compute*

______________________________________________________________________

## Development Team

**Recommended Team Size**: 2-3 engineers

- **Weeks 1-4**: 1 engineer (foundation)
- **Weeks 5-8**: 2 engineers (features + testing)
- **Weeks 9-12**: 2-3 engineers (hardening + scale)

______________________________________________________________________

## Risk Mitigation

| Risk | Mitigation Strategy |
|------|---------------------|
| **DuckDB HNSW unavailable** | Cosine similarity fallback (Phase 1) |
| **Embedding generation slow** | ONNX optimization + async processing |
| **Cloudflare R2 delays** | Event-driven ingestion + buffering |
| **Milvus complexity** | Start with DuckDB, migrate when 100M+ embeddings |
| **Multi-region latency** | Async replication + eventual consistency |
| **Cost overruns** | Tiered auto-aging + lifecycle policies |

______________________________________________________________________

## Getting Started

**For Developers**:

```bash
# Clone and setup
git clone <repository>
cd akosha
uv sync --group dev

# Run tests
pytest -m "not slow"

# Start local server
uv run python -m akosha.main

# Start MCP server
uv run python -m akosha.mcp
```

**For Operations**:

```bash
# Deploy to Kubernetes
kubectl apply -f k8s/

# Check status
kubectl get pods -n akosha
kubectl logs -f deployment/akosha-ingestion -n akosha

# Scale up
kubectl scale deployment/akosha-ingestion --replicas=10 -n akosha
```

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## Success Metrics

Phase is successful when:

**Phase 1**:

- ✅ Storage layers initialized
- ✅ Basic search working
- ✅ Knowledge graph building entities
- ✅ MCP tools responding

**Phase 2**:

- ✅ Embeddings generated < 50ms
- ✅ Trends detected with \<10% false positive rate
- ✅ Communities detected in \<5 seconds
- ✅ Search precision >0.8 (semantic similarity)

**Phase 3**:

- ✅ P99 latency < 500ms
- ✅ 99.9% uptime (SLA)
- ✅ MTTR < 15 minutes
- ✅ Deployments < 5 minutes downtime

**Phase 4**:

- ✅ Handles 100M+ embeddings
- ✅ Search latency \<100ms (hot), \<500ms (warm)
- ✅ RPO < 5 minutes, RTO < 1 hour
- ✅ Cost per query < $0.001

______________________________________________________________________

## Documentation Index

- [ADR-001](ADR_001_ARCHITECTURE_DECISIONS.md) - Complete architecture decisions
- [Implementation Guide](IMPLEMENTATION_GUIDE.md) - Phase 1 detailed guide
- [PROJECT_STRUCTURE](PROJECT_STRUCTURE.md) - Directory reference
- [Phase 2: Advanced Features](PHASE_2_ADVANCED_FEATURES.md) - Embeddings, trends, graph
- [Phase 3: Production Hardening](PHASE_3_PRODUCTION_HARDENING.md) - Resilience, observability
- [Phase 4: Scale Preparation](PHASE_4_SCALE_PREPARATION.md) - Milvus, TimescaleDB, Neo4j

______________________________________________________________________

**Last Updated**: 2025-01-27
**Status**: Phase 1 Complete, Phases 2-4 Ready to Implement
**Next**: Start Phase 2 implementation with embeddings and time-series analytics
