# Self-Learning Memory - v0.1.9+ Vision

**Target Date**: Q2-Q4 2026
**Status**: VISION
**Focus**: Advanced capabilities and future research integration

---

## Executive Summary

v0.1.9+ represents the future vision of Self-Learning Memory System, focusing on advanced capabilities beyond v0.1.8's research integration. This roadmap outlines ambitious but achievable goals for distributed systems, advanced observability, and continuous learning.

**Vision Goals**:
- Distributed memory synchronization across multiple instances
- Advanced observability with Prometheus metrics and distributed tracing
- Multi-tenancy with tenant isolation and RBAC
- Real-time pattern learning and refinement
- Custom embedding models and fine-tuning

---

## Vision Phase 1: Advanced Features (Months 6-9)

### 4.1 Distributed Memory Synchronization

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: LOW (requires stable single-instance first)
**Effort**: 6 weeks

**Planned Features**:

#### Multi-Instance Coordination

- Vector clocks for version tracking
- CRDT-based eventual consistency
- Peer-to-peer synchronization
- Conflict resolution strategies (last-write-wins, merge policies)

**Architecture**:
```rust
// New crate: memory-distributed/
memory-distributed/src/
├── lib.rs                    // Public API
├── coordinator.rs            // Cluster coordinator
├── vector_clock.rs           // Vector clock implementation
├── crdt/
│   ├── mod.rs               // CRDT trait
│   ├── episode_crdt.rs     // Episode CRDT
│   └── pattern_crdt.rs     // Pattern CRDT
├── sync/
│   ├── mod.rs               // Synchronization
│   ├── peer_sync.rs         // Peer-to-peer sync
│   └── conflict_resolution.rs // Conflict handling
└── gossip.rs               // Gossip protocol
```

**CRDT Implementation**:
```rust
pub trait CRDT<T> {
    fn merge(&self, other: &Self) -> Self;
    fn version(&self) -> VectorClock;
    fn is_conflict(&self, other: &Self) -> bool;
}

pub struct EpisodeCRDT {
    pub episode: Episode,
    pub vector_clock: VectorClock,
    pub deleted: bool,
}

impl CRDT<Episode> for EpisodeCRDT {
    fn merge(&self, other: &Self) -> Self {
        // Compare vector clocks
        // Select most recent version
        // Merge if concurrent
    }
}
```

#### Synchronization Strategy

- **Gossip Protocol**: Periodic peer discovery and state exchange
- **Vector Clocks**: Track causality across replicas
- **Conflict Resolution**: Last-write-wins for most common cases
- **Anti-entropy**: Periodic full state comparison

### 4.2 A/B Testing Framework

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: LOW (requires production usage data)
**Effort**: 3 weeks

**Planned Features**:

#### Experiment Framework

- Control/treatment group assignment
- Success metric tracking
- Statistical significance calculation
- Automated rollout decisions

**Architecture**:
```rust
// New crate: memory-experiments/
memory-experiments/src/
├── lib.rs                    // Public API
├── experiment.rs             // Experiment definition
├── assignment.rs            // Group assignment
├── metrics.rs               // Success metrics
├── statistics.rs            // Statistical analysis
└── rollout.rs              // Automated rollout
```

**Experiment Definition**:
```rust
pub struct Experiment {
    pub id: String,
    pub name: String,
    pub description: String,
    pub start_date: DateTime<Utc>,
    pub end_date: Option<DateTime<Utc>>,
    pub treatment_groups: Vec<TreatmentGroup>,
    pub success_metrics: Vec<SuccessMetric>,
    pub statistical_threshold: f64,  // p-value threshold
}

pub struct TreatmentGroup {
    pub id: String,
    pub name: String,
    pub weight: f64,  // Allocation percentage
    pub configuration: Config,
}

pub enum SuccessMetric {
    PatternAccuracy { baseline: f64, target: f64 },
    RetrievalLatency { baseline_ms: u64, target_ms: u64 },
    UserSatisfaction { baseline: f64, target: f64 },
}
```

---

## Vision Phase 2: Observability & Monitoring (Months 9-12)

### Advanced Observability

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: MEDIUM
**Effort**: 4 weeks

**Planned Features**:

#### Prometheus Metrics Exporter

**Architecture**:
```rust
// New crate: memory-telemetry/
memory-telemetry/src/
├── lib.rs
├── prometheus.rs
├── metrics/
│   ├── episode.rs      // Episode metrics
│   ├── pattern.rs       // Pattern metrics
│   ├── storage.rs       // Storage metrics
│   └── performance.rs  // Performance metrics
└── registry.rs         // Metrics registry
```

**Metrics**:
- Episode metrics (created, completed, duration)
- Pattern metrics (extracted, stored, effectiveness)
- Performance metrics (latency histograms, throughput)
- Error metrics (failures by type, error rates)

**Example**:
```rust
pub struct EpisodeMetrics {
    pub created_total: IntCounter,
    pub completed_total: IntCounter,
    pub duration_seconds: Histogram,
}

impl EpisodeMetrics {
    pub fn new() -> Self {
        Self {
            created_total: IntCounter::new("episodes_created_total", "Total episodes created").unwrap(),
            completed_total: IntCounter::new("episodes_completed_total", "Total episodes completed").unwrap(),
            duration_seconds: Histogram::new("episode_duration_seconds", "Episode duration in seconds").unwrap(),
        }
    }
}
```

#### Distributed Tracing

**Integration**: OpenTelemetry + Jaeger/Zipkin

**Spans**:
- Episode lifecycle (start, step_log, complete)
- Pattern extraction (extraction, validation, storage)
- Retrieval operations (query, filter, rank)
- Storage operations (read, write, sync)

**Example**:
```rust
use opentelemetry::{trace::TraceContextExt, Context};

pub async fn complete_episode(&self, episode_id: Uuid, ...) -> Result<()> {
    let span = tracing::span!(target: "memory", "complete_episode", episode_id = %episode_id);
    let _enter = span.enter();

    // Episode completion logic
    self.extract_patterns(episode_id).await?;

    Ok(())
}
```

---

## Vision Phase 3: Multi-Tenancy & Security (Months 12-15)

### Multi-Tenancy Support

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: MEDIUM
**Effort**: 6 weeks

**Planned Features**:

#### Tenant Isolation

- Tenant-aware storage (separate schemas/collections)
- Resource quotas per tenant
- Tenant-level metrics and monitoring

**Architecture**:
```rust
pub struct TenantContext {
    pub tenant_id: String,
    pub user_id: Option<String>,
    pub quotas: TenantQuotas,
}

pub struct TenantQuotas {
    pub max_episodes: usize,
    pub max_storage_mb: usize,
    pub max_patterns: usize,
}

impl SelfLearningMemory {
    pub async fn create_episode_with_tenant(
        &self,
        tenant: &TenantContext,
        task: String,
        context: TaskContext,
        task_type: TaskType
    ) -> Result<Uuid> {
        // Enforce tenant quotas
        self.check_tenant_quota(tenant).await?;

        // Create episode with tenant context
        let episode_id = self.create_episode_internal(task, context, task_type).await?;

        self.tenant_storage.associate_episode(&tenant.tenant_id, episode_id).await?;

        Ok(episode_id)
    }
}
```

#### Role-Based Access Control (RBAC)

- User roles (admin, user, readonly)
- Permission system (read, write, delete, admin)
- API-level authorization

**Permission System**:
```rust
pub enum Permission {
    ReadEpisodes,
    WriteEpisodes,
    DeleteEpisodes,
    ReadPatterns,
    WritePatterns,
    DeletePatterns,
    AdministerTenant,
}

pub struct Role {
    pub name: String,
    pub permissions: HashSet<Permission>,
}

pub struct User {
    pub id: String,
    pub tenant_id: String,
    pub roles: Vec<Role>,
}

impl User {
    pub fn has_permission(&self, permission: Permission) -> bool {
        self.roles.iter()
            .any(|role| role.permissions.contains(&permission))
    }
}
```

---

## Vision Phase 4: Advanced Learning (Months 15-18)

### Real-Time Pattern Learning

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: MEDIUM
**Effort**: 8 weeks

**Planned Features**:

#### Continuous Pattern Refinement

- Online pattern learning (update patterns as new data arrives)
- Adaptive confidence scoring
- Real-time pattern effectiveness tracking

**Architecture**:
```rust
pub struct OnlinePatternLearner {
    pub pattern_registry: Arc<RwLock<PatternRegistry>>,
    pub confidence_tracker: ConfidenceTracker,
    pub update_queue: mpsc::Sender<PatternUpdate>,
}

pub enum PatternUpdate {
    NewPattern { pattern: Pattern },
    PatternUsed { pattern_id: String, success: bool },
    PatternFeedback { pattern_id: String, feedback: f32 },
}

impl OnlinePatternLearner {
    pub async fn start(&self) {
        while let Some(update) = self.update_queue.recv().await {
            match update {
                PatternUpdate::NewPattern { pattern } => {
                    self.pattern_registry.write().await.add(pattern).await?;
                }
                PatternUpdate::PatternUsed { pattern_id, success } => {
                    self.confidence_tracker.update(&pattern_id, success).await?;
                }
                _ => {}
            }
        }
    }
}
```

#### Adaptive Confidence Scoring

- Bayesian updating of pattern confidence
- Context-aware confidence (domain, task type)
- Temporal decay (older patterns less relevant)

**Example**:
```rust
pub struct BayesianConfidence {
    pub prior: f64,           // Initial confidence
    pub successes: i32,       // Success count
    pub failures: i32,        // Failure count
}

impl BayesianConfidence {
    pub fn update(&mut self, success: bool) {
        if success {
            self.successes += 1;
        } else {
            self.failures += 1;
        }

        // Posterior = Beta(alpha + successes, beta + failures)
        self.prior = (self.successes as f64 + 1.0) /
                     ((self.successes + self.failures) as f64 + 2.0);
    }

    pub fn confidence(&self) -> f32 {
        self.prior as f32
    }
}
```

---

## Vision Phase 5: Custom Embedding Models (Months 18-24)

### Custom Embedding Model Support

**Status**: 🔵 FUTURE - Conceptual Design
**Priority**: LOW
**Effort**: 10 weeks

**Planned Features**:

#### Custom Model Loading

- Support for custom sentence-transformer models
- Model fine-tuning for domain-specific tasks
- A/B testing of different embedding models

**Architecture**:
```rust
pub enum EmbeddingModel {
    Pretrained {
        name: String,  // "gte-small", "all-MiniLM-L6-v2", etc.
    },
    Custom {
        model_path: PathBuf,
        tokenizer_path: PathBuf,
    },
    FineTuned {
        base_model: String,
        adapter_path: PathBuf,
    },
}

pub struct DynamicEmbeddingProvider {
    pub models: HashMap<String, EmbeddingModel>,
    pub default_model: String,
}

impl DynamicEmbeddingProvider {
    pub async fn embed(&self, text: &str, model_id: Option<&str>) -> Result<Vec<f32>> {
        let model_id = model_id.unwrap_or(&self.default_model);
        let model = self.models.get(model_id)
            .ok_or_else(|| anyhow!("Model not found: {}", model_id))?;

        // Load and use model
        self.load_and_embed(model, text).await
    }
}
```

#### Model Fine-Tuning

- Fine-tune pre-trained models on domain-specific data
- Transfer learning from general to specialized domains
- Model versioning and rollback

**Fine-Tuning Workflow**:
```rust
pub async fn fine_tune_model(
    &self,
    base_model: &str,
    training_data: &[TrainingExample],
    config: FineTuningConfig
) -> Result<PathBuf> {
    // Load base model
    let model = self.load_model(base_model).await?;

    // Prepare training data
    let dataset = self.prepare_dataset(training_data)?;

    // Fine-tune with specified epochs, learning rate
    let fine_tuned = model.fine_tune(&dataset, config)?;

    // Save adapter weights
    let adapter_path = self.save_adapter(&fine_tuned, config.output_dir).await?;

    Ok(adapter_path)
}
```

---

## Cross-References

- **Version History**: See [../../CHANGELOG.md](../../CHANGELOG.md)
- **Current Status**: See [ROADMAP_ACTIVE.md](ROADMAP_ACTIVE.md)
- **Architecture**: See [../ARCHITECTURE/ARCHITECTURE_CORE.md](../ARCHITECTURE/ARCHITECTURE_CORE.md)

---

*Status: Vision Phase*
*Timeline: Q2-Q4 2026*
*Focus: Advanced capabilities and future research*
