# RuvLLM: Integration and Deployment

## SPARC Phase 5: Completion

---

## 1. Integration Strategy

### 1.1 Crate Structure

```
ruvector/
├── crates/
│   ├── ruvector-core/           # Existing: Vector DB
│   ├── ruvector-gnn/            # Existing: GNN + EWC + Replay
│   ├── ruvector-attention/      # Existing: Attention mechanisms
│   ├── ruvector-graph/          # Existing: Graph storage
│   └── ruvector-router-core/    # Existing: Routing primitives
│
└── examples/
    └── ruvLLM/                  # NEW: Self-learning LLM
        ├── src/
        │   ├── lib.rs           # Main library entry
        │   ├── orchestrator.rs  # Request orchestration
        │   ├── embedding.rs     # LFM2 embedding service
        │   ├── router.rs        # FastGRNN router
        │   ├── memory.rs        # Ruvector memory layer
        │   ├── attention.rs     # Graph attention wrapper
        │   ├── inference.rs     # LFM2 model pool
        │   ├── learning.rs      # Self-learning service
        │   ├── compression.rs   # Concept abstraction
        │   ├── config.rs        # Configuration
        │   ├── types.rs         # Core types
        │   └── error.rs         # Error handling
        ├── tests/
        │   ├── unit/
        │   └── integration/
        ├── benches/
        ├── config/
        └── docs/                # SPARC documentation
```

### 1.2 Dependency Integration

```toml
# examples/ruvLLM/Cargo.toml
[package]
name = "ruvllm"
version = "0.1.0"
edition = "2021"
description = "Self-learning LLM with LFM2 and Ruvector integration"

[dependencies]
# Internal dependencies (path-based for development)
ruvector-core = { path = "../../crates/ruvector-core" }
ruvector-gnn = { path = "../../crates/ruvector-gnn" }
ruvector-attention = { path = "../../crates/ruvector-attention" }
ruvector-graph = { path = "../../crates/ruvector-graph" }
ruvector-router-core = { path = "../../crates/ruvector-router-core" }

# LLM inference
llama-cpp-rs = "0.3"           # CPU inference via llama.cpp
tokenizers = "0.15"            # Fast tokenization

# Async runtime
tokio = { version = "1.41", features = ["full"] }
futures = "0.3"

# Serialization
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
bincode = "2.0.0-rc.3"

# Numerics
ndarray = { version = "0.16", features = ["serde"] }
rand = "0.8"

# Utilities
uuid = { version = "1.11", features = ["v4", "serde"] }
chrono = { version = "0.4", features = ["serde"] }
thiserror = "2.0"
anyhow = "1.0"
tracing = "0.1"

# Performance
dashmap = "6.1"
parking_lot = "0.12"
lru = "0.12"

# Metrics
prometheus = "0.13"

[dev-dependencies]
criterion = { version = "0.5", features = ["html_reports"] }
proptest = "1.5"
tokio-test = "0.4"
tempfile = "3.13"
tracing-subscriber = "0.3"

[features]
default = ["cpu"]
cpu = []                       # llama.cpp CPU inference
gpu = ["vllm"]                 # vLLM GPU inference (optional)
vllm = []

[[bench]]
name = "pipeline"
harness = false

[[bench]]
name = "router"
harness = false

[[bench]]
name = "memory"
harness = false
```

### 1.3 API Surface

```rust
//! # RuvLLM - Self-Learning LLM
//!
//! A self-learning language model system integrating LFM2 with Ruvector.
//!
//! ## Architecture
//!
//! - **LFM2**: Frozen reasoning engine (350M-2.6B parameters)
//! - **Ruvector**: Living memory that adapts continuously
//! - **FastGRNN**: Control circuit for intelligent routing
//!
//! ## Quick Start
//!
//! ```rust,ignore
//! use ruvllm::{RuvLLM, Config};
//!
//! #[tokio::main]
//! async fn main() -> Result<()> {
//!     // Initialize system
//!     let config = Config::builder()
//!         .db_path("./memory.db")
//!         .model_path_350m("./models/lfm2-350m-q4.gguf")
//!         .model_path_700m("./models/lfm2-700m-q4.gguf")
//!         .build()?;
//!
//!     let llm = RuvLLM::new(config).await?;
//!
//!     // Process query
//!     let response = llm.query("What is machine learning?").await?;
//!     println!("Response: {}", response.text);
//!     println!("Confidence: {:.2}", response.confidence);
//!
//!     Ok(())
//! }
//! ```
//!
//! ## Self-Learning Loops
//!
//! The system learns through three feedback loops:
//!
//! 1. **Memory Growth**: Every interaction strengthens/weakens graph edges
//! 2. **Router Learning**: FastGRNN learns optimal model selection
//! 3. **Compression**: Periodic summarization creates concept hierarchies

pub mod attention;
pub mod compression;
pub mod config;
pub mod embedding;
pub mod error;
pub mod inference;
pub mod learning;
pub mod memory;
pub mod orchestrator;
pub mod router;
pub mod types;

// Re-exports for convenience
pub use config::{Config, ConfigBuilder};
pub use error::{Error, Result};
pub use orchestrator::RuvLLM;
pub use types::{Request, Response, Session};

/// Library version
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
```

---

## 2. Implementation Checklist

### 2.1 Core Components

```
Phase 1: Foundation
━━━━━━━━━━━━━━━━━━━━
[x] Project structure setup
[x] Cargo.toml with dependencies
[ ] Error types definition
[ ] Configuration system
[ ] Core types (Request, Response, Session)

Phase 2: Services
━━━━━━━━━━━━━━━━━━
[ ] EmbeddingService
    [ ] LFM2 encoder wrapper
    [ ] Dimension projection
    [ ] Tokenization
    [ ] Batch processing

[ ] MemoryService
    [ ] VectorDB initialization
    [ ] GraphStore integration
    [ ] HNSW search wrapper
    [ ] Graph expansion
    [ ] Writeback queue

[ ] FastGRNNRouter
    [ ] Cell implementation
    [ ] Sparse matrix operations
    [ ] Low-rank matrices
    [ ] Output heads
    [ ] Training loop

[ ] GraphAttentionEngine
    [ ] Attention layer wrapper
    [ ] Edge feature encoding
    [ ] Multi-head aggregation
    [ ] Context ranking

[ ] InferencePool
    [ ] Model loading
    [ ] Lazy initialization
    [ ] KV cache management
    [ ] LRU eviction

[ ] LearningService
    [ ] Quality judge
    [ ] Replay buffer
    [ ] EWC integration
    [ ] Background training
    [ ] Compression jobs

Phase 3: Orchestration
━━━━━━━━━━━━━━━━━━━━━━
[ ] Orchestrator
    [ ] Request routing
    [ ] Session management
    [ ] Pipeline coordination
    [ ] Metrics collection
    [ ] Error handling

Phase 4: Integration
━━━━━━━━━━━━━━━━━━━━
[ ] Integration tests
[ ] Benchmark suite
[ ] Example applications
[ ] Documentation
```

### 2.2 Test Coverage Requirements

| Component | Unit Tests | Integration | Benchmark |
|-----------|------------|-------------|-----------|
| Embedding | 15+ | 3+ | 2 |
| Memory | 20+ | 5+ | 3 |
| Router | 25+ | 5+ | 2 |
| Attention | 15+ | 3+ | 2 |
| Inference | 10+ | 3+ | 2 |
| Learning | 20+ | 5+ | 1 |
| Orchestrator | 10+ | 5+ | 2 |
| **Total** | **115+** | **29+** | **14** |

---

## 3. Deployment Configurations

### 3.1 Edge Deployment (Raspberry Pi / Mobile)

```toml
# config/edge.toml
[system]
device_class = "edge"
max_memory_mb = 2048
max_concurrent_requests = 2

[embedding]
model = "onnx"  # ONNX for portability
dimension = 384
batch_size = 1

[memory]
hnsw_m = 16
hnsw_ef_construction = 100
hnsw_ef_search = 32
max_nodes = 100_000

[router]
hidden_dim = 32
sparsity = 0.95
confidence_threshold = 0.6

[inference]
models = ["350m"]
quantization = "q4_k"
max_context = 1024
max_loaded_models = 1

[learning]
enabled = true
quality_threshold = 0.8
replay_capacity = 1000
training_interval_ms = 300_000  # 5 minutes
```

### 3.2 Server Deployment (CPU)

```toml
# config/server-cpu.toml
[system]
device_class = "server"
max_memory_mb = 16384
max_concurrent_requests = 20

[embedding]
model = "lfm2-encoder"
dimension = 768
batch_size = 8

[memory]
hnsw_m = 32
hnsw_ef_construction = 200
hnsw_ef_search = 64
max_nodes = 10_000_000

[router]
hidden_dim = 64
sparsity = 0.9
confidence_threshold = 0.7

[inference]
models = ["700m", "1.2b", "2.6b"]
quantization = "q5_k"
max_context = 4096
max_loaded_models = 2

[learning]
enabled = true
quality_threshold = 0.75
replay_capacity = 100_000
training_interval_ms = 60_000  # 1 minute
```

### 3.3 Server Deployment (GPU)

```toml
# config/server-gpu.toml
[system]
device_class = "gpu"
max_memory_mb = 32768
max_concurrent_requests = 100

[embedding]
model = "lfm2-encoder"
dimension = 1024
batch_size = 32

[memory]
hnsw_m = 48
hnsw_ef_construction = 300
hnsw_ef_search = 128
max_nodes = 100_000_000

[router]
hidden_dim = 64
sparsity = 0.85
confidence_threshold = 0.75

[inference]
models = ["1.2b", "2.6b"]
quantization = "fp16"
max_context = 8192
max_loaded_models = 2
use_vllm = true
tensor_parallel = 1

[learning]
enabled = true
quality_threshold = 0.7
replay_capacity = 1_000_000
training_interval_ms = 30_000  # 30 seconds
```

---

## 4. Operational Runbook

### 4.1 Startup Sequence

```bash
#!/bin/bash
# scripts/start.sh

set -e

CONFIG=${1:-"config/server-cpu.toml"}
LOG_LEVEL=${LOG_LEVEL:-"info"}

echo "Starting RuvLLM with config: $CONFIG"

# 1. Validate configuration
cargo run --release --bin ruvllm-validate -- --config "$CONFIG"

# 2. Initialize database if needed
if [ ! -f "data/memory.db" ]; then
    echo "Initializing database..."
    cargo run --release --bin ruvllm-init -- --config "$CONFIG"
fi

# 3. Download models if needed
cargo run --release --bin ruvllm-models -- --config "$CONFIG" --check-or-download

# 4. Start server
RUST_LOG=$LOG_LEVEL cargo run --release --bin ruvllm-server -- \
    --config "$CONFIG" \
    --metrics-port 9090 \
    --http-port 8080
```

### 4.2 Health Checks

```rust
/// Health check endpoint implementation
pub struct HealthCheck {
    memory: Arc<RuvectorMemory>,
    router: Arc<FastGRNNRouter>,
    inference: Arc<InferencePool>,
}

impl HealthCheck {
    pub async fn check(&self) -> HealthStatus {
        let mut status = HealthStatus::default();

        // Check memory service
        status.memory = match self.memory.ping().await {
            Ok(latency) => ComponentHealth::Healthy { latency_ms: latency },
            Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
        };

        // Check router
        status.router = match self.router.ping() {
            Ok(latency) => ComponentHealth::Healthy { latency_ms: latency },
            Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
        };

        // Check inference (at least one model loadable)
        status.inference = match self.inference.health_check().await {
            Ok(info) => ComponentHealth::Healthy {
                latency_ms: info.latency,
                details: json!({
                    "loaded_models": info.loaded_models,
                    "available_memory": info.available_memory,
                }),
            },
            Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
        };

        status.overall = if status.all_healthy() {
            OverallHealth::Healthy
        } else if status.any_critical() {
            OverallHealth::Critical
        } else {
            OverallHealth::Degraded
        };

        status
    }
}
```

### 4.3 Monitoring Dashboards

```yaml
# Prometheus alerting rules
groups:
  - name: ruvllm
    rules:
      - alert: HighLatency
        expr: histogram_quantile(0.95, ruvllm_request_latency_seconds_bucket) > 1.0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "RuvLLM P95 latency above 1s"

      - alert: LowQualityScore
        expr: avg(ruvllm_quality_score) < 0.7
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Average quality score dropped below 0.7"

      - alert: MemoryPressure
        expr: ruvllm_memory_usage_bytes / ruvllm_memory_limit_bytes > 0.9
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Memory usage above 90%"

      - alert: RouterLowConfidence
        expr: avg(ruvllm_router_confidence) < 0.5
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Router confidence consistently low"

      - alert: HighErrorRate
        expr: rate(ruvllm_errors_total[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 10%"
```

### 4.4 Backup and Recovery

```bash
#!/bin/bash
# scripts/backup.sh

BACKUP_DIR="/backups/ruvllm/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$BACKUP_DIR"

echo "Creating backup in $BACKUP_DIR"

# 1. Backup memory database
cp -r data/memory.db "$BACKUP_DIR/memory.db"

# 2. Backup router weights
cp -r data/router_weights.bin "$BACKUP_DIR/router_weights.bin"

# 3. Backup EWC state
cp -r data/ewc_state.bin "$BACKUP_DIR/ewc_state.bin"

# 4. Backup replay buffer
cp -r data/replay_buffer.bin "$BACKUP_DIR/replay_buffer.bin"

# 5. Backup configuration
cp -r config/ "$BACKUP_DIR/config/"

# 6. Create manifest
cat > "$BACKUP_DIR/manifest.json" << EOF
{
  "timestamp": "$(date -Iseconds)",
  "version": "$(cargo run --release --bin ruvllm-version)",
  "components": {
    "memory_db": "memory.db",
    "router_weights": "router_weights.bin",
    "ewc_state": "ewc_state.bin",
    "replay_buffer": "replay_buffer.bin",
    "config": "config/"
  }
}
EOF

echo "Backup complete: $BACKUP_DIR"

# 7. Upload to S3 if configured
if [ -n "$S3_BACKUP_BUCKET" ]; then
    aws s3 sync "$BACKUP_DIR" "s3://$S3_BACKUP_BUCKET/$(basename $BACKUP_DIR)/"
    echo "Uploaded to S3: $S3_BACKUP_BUCKET"
fi
```

---

## 5. Production Checklist

### 5.1 Pre-Launch

```
Security
━━━━━━━━
[ ] Input validation and sanitization
[ ] Rate limiting configured
[ ] TLS/HTTPS enabled
[ ] API authentication (if public)
[ ] Secrets in environment variables
[ ] Model integrity verification

Performance
━━━━━━━━━━━
[ ] Load tested to expected traffic
[ ] Memory profiled (no leaks)
[ ] Latency targets met
[ ] Caching configured
[ ] Connection pooling

Reliability
━━━━━━━━━━━
[ ] Health checks implemented
[ ] Graceful shutdown
[ ] Automatic restarts (systemd/k8s)
[ ] Backup procedures tested
[ ] Recovery procedures documented

Observability
━━━━━━━━━━━━━
[ ] Structured logging
[ ] Metrics exported
[ ] Distributed tracing
[ ] Alerting rules configured
[ ] Dashboards created
```

### 5.2 Post-Launch

```
Daily
━━━━━
[ ] Check error rates
[ ] Review quality scores
[ ] Monitor latency trends
[ ] Verify backup success

Weekly
━━━━━━
[ ] Review router decisions distribution
[ ] Analyze forgetting metrics
[ ] Check memory growth rate
[ ] Run compression job
[ ] Update router weights

Monthly
━━━━━━━
[ ] Full system backup
[ ] Performance benchmark
[ ] Security audit
[ ] Dependency updates
[ ] Evaluate student model candidates
```

---

## 6. API Reference

### 6.1 HTTP API

```yaml
openapi: "3.0.0"
info:
  title: RuvLLM API
  version: "0.1.0"
  description: Self-learning LLM with LFM2 and Ruvector

paths:
  /v1/query:
    post:
      summary: Process a query
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - query
              properties:
                query:
                  type: string
                  description: The user query
                session_id:
                  type: string
                  description: Optional session for multi-turn
                constraints:
                  type: object
                  properties:
                    max_latency_ms:
                      type: integer
                    max_tokens:
                      type: integer
                    temperature:
                      type: number
      responses:
        "200":
          description: Successful response
          content:
            application/json:
              schema:
                type: object
                properties:
                  text:
                    type: string
                  confidence:
                    type: number
                  sources:
                    type: array
                    items:
                      type: object
                  routing_info:
                    type: object

  /v1/feedback:
    post:
      summary: Provide feedback on a response
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - request_id
              properties:
                request_id:
                  type: string
                rating:
                  type: integer
                  minimum: 1
                  maximum: 5
                correction:
                  type: string
      responses:
        "200":
          description: Feedback recorded

  /v1/health:
    get:
      summary: Health check
      responses:
        "200":
          description: System healthy
        "503":
          description: System unhealthy

  /v1/metrics:
    get:
      summary: Prometheus metrics
      responses:
        "200":
          description: Metrics in Prometheus format
```

### 6.2 Rust SDK

```rust
use ruvllm::{RuvLLM, Config, Request, Response};

/// Simple query
async fn simple_query(llm: &RuvLLM) -> Result<Response> {
    llm.query("What is Rust?").await
}

/// Query with options
async fn query_with_options(llm: &RuvLLM) -> Result<Response> {
    llm.query_with(Request {
        query: "Explain backpropagation".into(),
        session_id: Some("user-123".into()),
        constraints: Constraints {
            max_latency_ms: Some(500),
            max_tokens: Some(500),
            temperature: Some(0.7),
            ..Default::default()
        },
    }).await
}

/// Multi-turn conversation
async fn conversation(llm: &RuvLLM) -> Result<()> {
    let session = llm.new_session();

    let r1 = llm.query_session(&session, "What is a neural network?").await?;
    println!("Turn 1: {}", r1.text);

    let r2 = llm.query_session(&session, "How do you train one?").await?;
    println!("Turn 2: {}", r2.text);

    let r3 = llm.query_session(&session, "What about overfitting?").await?;
    println!("Turn 3: {}", r3.text);

    Ok(())
}

/// Provide feedback
async fn with_feedback(llm: &RuvLLM) -> Result<()> {
    let response = llm.query("What is 2+2?").await?;

    llm.feedback(Feedback {
        request_id: response.request_id,
        rating: 5,
        correction: None,
    }).await?;

    Ok(())
}

/// Stream response
async fn streaming(llm: &RuvLLM) -> Result<()> {
    let mut stream = llm.query_stream("Tell me a story").await?;

    while let Some(chunk) = stream.next().await {
        print!("{}", chunk?);
    }

    Ok(())
}
```

---

## 7. Future Roadmap

### 7.1 Short-Term (1-3 months)

- [ ] LFM2-VL integration (vision-language)
- [ ] Multi-GPU inference with tensor parallelism
- [ ] Retrieval-augmented fine-tuning pipeline
- [ ] Improved compression algorithms
- [ ] WebAssembly deployment target

### 7.2 Medium-Term (3-6 months)

- [ ] Federated learning across edge nodes
- [ ] LFM2-Audio integration (speech)
- [ ] Custom domain fine-tuning toolkit
- [ ] Advanced curriculum learning
- [ ] Hyperbolic embeddings for hierarchies

### 7.3 Long-Term (6-12 months)

- [ ] Multi-agent collaboration
- [ ] Neuro-symbolic reasoning integration
- [ ] Continuous pre-training pipeline
- [ ] Hardware-specific optimizations (NPU, TPU)
- [ ] Enterprise multi-tenancy

---

## 8. Success Criteria

### 8.1 Technical Metrics

| Metric | Target | Current |
|--------|--------|---------|
| Latency P50 | <500ms | - |
| Latency P99 | <2s | - |
| Quality Score | >0.8 | - |
| Router Accuracy | >90% | - |
| Memory Efficiency | <4GB (edge) | - |
| Throughput | 20 QPS (edge) | - |
| Forgetting Rate | <5%/10K | - |
| Test Coverage | >80% | - |

### 8.2 Business Metrics

| Metric | Target | Notes |
|--------|--------|-------|
| User Satisfaction | >4.0/5.0 | Survey scores |
| Response Relevance | >85% | Human eval |
| Knowledge Retention | >90% | Multi-turn coherence |
| Cost Reduction | >50% | vs. always-big baseline |

---

## 9. Conclusion

RuvLLM represents a paradigm shift from static LLMs to adaptive, self-learning systems. By treating:

- **LFM2 as the stable cortex** (reasoning)
- **Ruvector as the living synaptic mesh** (memory)
- **FastGRNN as the control circuit** (routing)

We create intelligence that emerges from the loop, not just the model.

The three learning loops—memory growth, router optimization, and concept compression—enable continuous adaptation without the risks of in-place weight modification.

**The intelligence is not in one model anymore. It is in the loop.**

---

*Document Version: 1.0*
*Last Updated: 2025-12-02*
*Author: RuvLLM Architecture Team*