d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
887 lines
21 KiB
Markdown
887 lines
21 KiB
Markdown
# RuvLLM: Integration and Deployment
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## SPARC Phase 5: Completion
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---
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## 1. Integration Strategy
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### 1.1 Crate Structure
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```
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ruvector/
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├── crates/
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│ ├── ruvector-core/ # Existing: Vector DB
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│ ├── ruvector-gnn/ # Existing: GNN + EWC + Replay
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│ ├── ruvector-attention/ # Existing: Attention mechanisms
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│ ├── ruvector-graph/ # Existing: Graph storage
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│ └── ruvector-router-core/ # Existing: Routing primitives
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│
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└── examples/
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└── ruvLLM/ # NEW: Self-learning LLM
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├── src/
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│ ├── lib.rs # Main library entry
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│ ├── orchestrator.rs # Request orchestration
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│ ├── embedding.rs # LFM2 embedding service
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│ ├── router.rs # FastGRNN router
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│ ├── memory.rs # Ruvector memory layer
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│ ├── attention.rs # Graph attention wrapper
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│ ├── inference.rs # LFM2 model pool
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│ ├── learning.rs # Self-learning service
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│ ├── compression.rs # Concept abstraction
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│ ├── config.rs # Configuration
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│ ├── types.rs # Core types
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│ └── error.rs # Error handling
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├── tests/
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│ ├── unit/
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│ └── integration/
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├── benches/
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├── config/
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└── docs/ # SPARC documentation
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```
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### 1.2 Dependency Integration
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```toml
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# examples/ruvLLM/Cargo.toml
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[package]
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name = "ruvllm"
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version = "0.1.0"
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edition = "2021"
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description = "Self-learning LLM with LFM2 and Ruvector integration"
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[dependencies]
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# Internal dependencies (path-based for development)
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ruvector-core = { path = "../../crates/ruvector-core" }
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ruvector-gnn = { path = "../../crates/ruvector-gnn" }
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ruvector-attention = { path = "../../crates/ruvector-attention" }
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ruvector-graph = { path = "../../crates/ruvector-graph" }
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ruvector-router-core = { path = "../../crates/ruvector-router-core" }
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# LLM inference
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llama-cpp-rs = "0.3" # CPU inference via llama.cpp
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tokenizers = "0.15" # Fast tokenization
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# Async runtime
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tokio = { version = "1.41", features = ["full"] }
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futures = "0.3"
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# Serialization
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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bincode = "2.0.0-rc.3"
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# Numerics
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ndarray = { version = "0.16", features = ["serde"] }
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rand = "0.8"
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# Utilities
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uuid = { version = "1.11", features = ["v4", "serde"] }
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chrono = { version = "0.4", features = ["serde"] }
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thiserror = "2.0"
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anyhow = "1.0"
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tracing = "0.1"
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# Performance
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dashmap = "6.1"
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parking_lot = "0.12"
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lru = "0.12"
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# Metrics
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prometheus = "0.13"
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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proptest = "1.5"
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tokio-test = "0.4"
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tempfile = "3.13"
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tracing-subscriber = "0.3"
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[features]
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default = ["cpu"]
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cpu = [] # llama.cpp CPU inference
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gpu = ["vllm"] # vLLM GPU inference (optional)
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vllm = []
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[[bench]]
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name = "pipeline"
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harness = false
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[[bench]]
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name = "router"
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harness = false
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[[bench]]
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name = "memory"
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harness = false
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```
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### 1.3 API Surface
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```rust
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//! # RuvLLM - Self-Learning LLM
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//!
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//! A self-learning language model system integrating LFM2 with Ruvector.
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//!
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//! ## Architecture
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//!
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//! - **LFM2**: Frozen reasoning engine (350M-2.6B parameters)
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//! - **Ruvector**: Living memory that adapts continuously
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//! - **FastGRNN**: Control circuit for intelligent routing
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//!
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//! ## Quick Start
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//!
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//! ```rust,ignore
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//! use ruvllm::{RuvLLM, Config};
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//!
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//! #[tokio::main]
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//! async fn main() -> Result<()> {
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//! // Initialize system
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//! let config = Config::builder()
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//! .db_path("./memory.db")
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//! .model_path_350m("./models/lfm2-350m-q4.gguf")
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//! .model_path_700m("./models/lfm2-700m-q4.gguf")
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//! .build()?;
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//!
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//! let llm = RuvLLM::new(config).await?;
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//!
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//! // Process query
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//! let response = llm.query("What is machine learning?").await?;
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//! println!("Response: {}", response.text);
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//! println!("Confidence: {:.2}", response.confidence);
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//!
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//! Ok(())
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//! }
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//! ```
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//!
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//! ## Self-Learning Loops
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//!
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//! The system learns through three feedback loops:
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//!
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//! 1. **Memory Growth**: Every interaction strengthens/weakens graph edges
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//! 2. **Router Learning**: FastGRNN learns optimal model selection
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//! 3. **Compression**: Periodic summarization creates concept hierarchies
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pub mod attention;
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pub mod compression;
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pub mod config;
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pub mod embedding;
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pub mod error;
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pub mod inference;
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pub mod learning;
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pub mod memory;
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pub mod orchestrator;
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pub mod router;
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pub mod types;
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// Re-exports for convenience
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pub use config::{Config, ConfigBuilder};
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pub use error::{Error, Result};
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pub use orchestrator::RuvLLM;
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pub use types::{Request, Response, Session};
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/// Library version
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pub const VERSION: &str = env!("CARGO_PKG_VERSION");
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```
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---
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## 2. Implementation Checklist
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### 2.1 Core Components
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```
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Phase 1: Foundation
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━━━━━━━━━━━━━━━━━━━━
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[x] Project structure setup
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[x] Cargo.toml with dependencies
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[ ] Error types definition
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[ ] Configuration system
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[ ] Core types (Request, Response, Session)
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Phase 2: Services
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━━━━━━━━━━━━━━━━━━
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[ ] EmbeddingService
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[ ] LFM2 encoder wrapper
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[ ] Dimension projection
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[ ] Tokenization
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[ ] Batch processing
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[ ] MemoryService
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[ ] VectorDB initialization
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[ ] GraphStore integration
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[ ] HNSW search wrapper
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[ ] Graph expansion
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[ ] Writeback queue
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[ ] FastGRNNRouter
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[ ] Cell implementation
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[ ] Sparse matrix operations
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[ ] Low-rank matrices
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[ ] Output heads
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[ ] Training loop
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[ ] GraphAttentionEngine
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[ ] Attention layer wrapper
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[ ] Edge feature encoding
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[ ] Multi-head aggregation
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[ ] Context ranking
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[ ] InferencePool
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[ ] Model loading
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[ ] Lazy initialization
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[ ] KV cache management
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[ ] LRU eviction
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[ ] LearningService
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[ ] Quality judge
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[ ] Replay buffer
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[ ] EWC integration
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[ ] Background training
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[ ] Compression jobs
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Phase 3: Orchestration
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━━━━━━━━━━━━━━━━━━━━━━
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[ ] Orchestrator
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[ ] Request routing
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[ ] Session management
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[ ] Pipeline coordination
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[ ] Metrics collection
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[ ] Error handling
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Phase 4: Integration
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━━━━━━━━━━━━━━━━━━━━
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[ ] Integration tests
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[ ] Benchmark suite
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[ ] Example applications
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[ ] Documentation
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```
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### 2.2 Test Coverage Requirements
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| Component | Unit Tests | Integration | Benchmark |
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|-----------|------------|-------------|-----------|
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| Embedding | 15+ | 3+ | 2 |
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| Memory | 20+ | 5+ | 3 |
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| Router | 25+ | 5+ | 2 |
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| Attention | 15+ | 3+ | 2 |
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| Inference | 10+ | 3+ | 2 |
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| Learning | 20+ | 5+ | 1 |
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| Orchestrator | 10+ | 5+ | 2 |
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| **Total** | **115+** | **29+** | **14** |
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---
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## 3. Deployment Configurations
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### 3.1 Edge Deployment (Raspberry Pi / Mobile)
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```toml
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# config/edge.toml
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[system]
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device_class = "edge"
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max_memory_mb = 2048
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max_concurrent_requests = 2
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[embedding]
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model = "onnx" # ONNX for portability
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dimension = 384
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batch_size = 1
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[memory]
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hnsw_m = 16
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hnsw_ef_construction = 100
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hnsw_ef_search = 32
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max_nodes = 100_000
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[router]
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hidden_dim = 32
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sparsity = 0.95
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confidence_threshold = 0.6
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[inference]
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models = ["350m"]
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quantization = "q4_k"
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max_context = 1024
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max_loaded_models = 1
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[learning]
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enabled = true
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quality_threshold = 0.8
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replay_capacity = 1000
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training_interval_ms = 300_000 # 5 minutes
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```
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### 3.2 Server Deployment (CPU)
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```toml
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# config/server-cpu.toml
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[system]
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device_class = "server"
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max_memory_mb = 16384
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max_concurrent_requests = 20
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[embedding]
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model = "lfm2-encoder"
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dimension = 768
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batch_size = 8
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[memory]
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hnsw_m = 32
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hnsw_ef_construction = 200
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hnsw_ef_search = 64
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max_nodes = 10_000_000
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[router]
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hidden_dim = 64
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sparsity = 0.9
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confidence_threshold = 0.7
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[inference]
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models = ["700m", "1.2b", "2.6b"]
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quantization = "q5_k"
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max_context = 4096
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max_loaded_models = 2
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[learning]
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enabled = true
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quality_threshold = 0.75
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replay_capacity = 100_000
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training_interval_ms = 60_000 # 1 minute
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```
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### 3.3 Server Deployment (GPU)
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```toml
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# config/server-gpu.toml
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[system]
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device_class = "gpu"
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max_memory_mb = 32768
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max_concurrent_requests = 100
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[embedding]
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model = "lfm2-encoder"
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dimension = 1024
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batch_size = 32
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[memory]
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hnsw_m = 48
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hnsw_ef_construction = 300
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hnsw_ef_search = 128
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max_nodes = 100_000_000
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[router]
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hidden_dim = 64
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sparsity = 0.85
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confidence_threshold = 0.75
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[inference]
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models = ["1.2b", "2.6b"]
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quantization = "fp16"
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max_context = 8192
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max_loaded_models = 2
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use_vllm = true
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tensor_parallel = 1
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[learning]
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enabled = true
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quality_threshold = 0.7
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replay_capacity = 1_000_000
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training_interval_ms = 30_000 # 30 seconds
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```
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---
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## 4. Operational Runbook
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### 4.1 Startup Sequence
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```bash
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#!/bin/bash
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# scripts/start.sh
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set -e
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CONFIG=${1:-"config/server-cpu.toml"}
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LOG_LEVEL=${LOG_LEVEL:-"info"}
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echo "Starting RuvLLM with config: $CONFIG"
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# 1. Validate configuration
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cargo run --release --bin ruvllm-validate -- --config "$CONFIG"
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# 2. Initialize database if needed
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if [ ! -f "data/memory.db" ]; then
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echo "Initializing database..."
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cargo run --release --bin ruvllm-init -- --config "$CONFIG"
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fi
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# 3. Download models if needed
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cargo run --release --bin ruvllm-models -- --config "$CONFIG" --check-or-download
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# 4. Start server
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RUST_LOG=$LOG_LEVEL cargo run --release --bin ruvllm-server -- \
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--config "$CONFIG" \
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--metrics-port 9090 \
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--http-port 8080
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```
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### 4.2 Health Checks
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```rust
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/// Health check endpoint implementation
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pub struct HealthCheck {
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memory: Arc<RuvectorMemory>,
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router: Arc<FastGRNNRouter>,
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inference: Arc<InferencePool>,
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}
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impl HealthCheck {
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pub async fn check(&self) -> HealthStatus {
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let mut status = HealthStatus::default();
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// Check memory service
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status.memory = match self.memory.ping().await {
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Ok(latency) => ComponentHealth::Healthy { latency_ms: latency },
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Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
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};
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// Check router
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status.router = match self.router.ping() {
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Ok(latency) => ComponentHealth::Healthy { latency_ms: latency },
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Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
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};
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// Check inference (at least one model loadable)
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status.inference = match self.inference.health_check().await {
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Ok(info) => ComponentHealth::Healthy {
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latency_ms: info.latency,
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details: json!({
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"loaded_models": info.loaded_models,
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"available_memory": info.available_memory,
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}),
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},
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Err(e) => ComponentHealth::Unhealthy { error: e.to_string() },
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};
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status.overall = if status.all_healthy() {
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OverallHealth::Healthy
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} else if status.any_critical() {
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OverallHealth::Critical
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} else {
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OverallHealth::Degraded
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};
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status
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}
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}
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```
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### 4.3 Monitoring Dashboards
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```yaml
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# Prometheus alerting rules
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groups:
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- name: ruvllm
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rules:
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- alert: HighLatency
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expr: histogram_quantile(0.95, ruvllm_request_latency_seconds_bucket) > 1.0
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for: 5m
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labels:
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severity: warning
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annotations:
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summary: "RuvLLM P95 latency above 1s"
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- alert: LowQualityScore
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expr: avg(ruvllm_quality_score) < 0.7
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for: 10m
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labels:
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severity: warning
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annotations:
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summary: "Average quality score dropped below 0.7"
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- alert: MemoryPressure
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expr: ruvllm_memory_usage_bytes / ruvllm_memory_limit_bytes > 0.9
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for: 5m
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labels:
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severity: critical
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annotations:
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summary: "Memory usage above 90%"
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- alert: RouterLowConfidence
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expr: avg(ruvllm_router_confidence) < 0.5
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for: 15m
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labels:
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severity: warning
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annotations:
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summary: "Router confidence consistently low"
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- alert: HighErrorRate
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expr: rate(ruvllm_errors_total[5m]) > 0.1
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for: 5m
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labels:
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severity: critical
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annotations:
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summary: "Error rate above 10%"
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```
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### 4.4 Backup and Recovery
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```bash
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#!/bin/bash
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# scripts/backup.sh
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BACKUP_DIR="/backups/ruvllm/$(date +%Y%m%d_%H%M%S)"
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mkdir -p "$BACKUP_DIR"
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echo "Creating backup in $BACKUP_DIR"
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# 1. Backup memory database
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cp -r data/memory.db "$BACKUP_DIR/memory.db"
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# 2. Backup router weights
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cp -r data/router_weights.bin "$BACKUP_DIR/router_weights.bin"
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# 3. Backup EWC state
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cp -r data/ewc_state.bin "$BACKUP_DIR/ewc_state.bin"
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# 4. Backup replay buffer
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cp -r data/replay_buffer.bin "$BACKUP_DIR/replay_buffer.bin"
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# 5. Backup configuration
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cp -r config/ "$BACKUP_DIR/config/"
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# 6. Create manifest
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cat > "$BACKUP_DIR/manifest.json" << EOF
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{
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"timestamp": "$(date -Iseconds)",
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"version": "$(cargo run --release --bin ruvllm-version)",
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"components": {
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"memory_db": "memory.db",
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"router_weights": "router_weights.bin",
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"ewc_state": "ewc_state.bin",
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"replay_buffer": "replay_buffer.bin",
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"config": "config/"
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}
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}
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EOF
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echo "Backup complete: $BACKUP_DIR"
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# 7. Upload to S3 if configured
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if [ -n "$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*
|