mirror of
https://github.com/ruvnet/RuView
synced 2026-07-28 18:21:42 +00:00
ruv
582 lines
17 KiB
Rust
582 lines
17 KiB
Rust
//! REFRAG Store - Unified storage layer with hybrid search
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//!
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//! This module integrates the Compress, Sense, and Expand layers
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//! into a cohesive REFRAG-enabled vector store.
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use crate::compress::{BatchCompressor, CompressionStrategy, TensorCompressor};
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use crate::expand::{ExpandLayer, ProjectorRegistry};
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use crate::sense::{PolicyDecision, PolicyNetwork, RefragAction};
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use crate::types::{RefragConfig, RefragEntry, RefragSearchResult, RefragStats};
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use base64::{engine::general_purpose::STANDARD as BASE64, Engine};
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use ruvector_core::{SearchQuery, SearchResult, VectorEntry};
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use std::collections::HashMap;
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::sync::{Arc, RwLock};
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use std::time::Instant;
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use thiserror::Error;
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#[derive(Error, Debug)]
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pub enum StoreError {
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#[error("Entry not found: {0}")]
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NotFound(String),
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#[error("Dimension mismatch: expected {expected}, got {actual}")]
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DimensionMismatch { expected: usize, actual: usize },
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#[error("Compression error: {0}")]
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CompressionError(String),
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#[error("Policy error: {0}")]
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PolicyError(String),
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#[error("Projection error: {0}")]
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ProjectionError(String),
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#[error("Core error: {0}")]
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CoreError(String),
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}
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pub type Result<T> = std::result::Result<T, StoreError>;
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/// REFRAG-enabled vector store
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///
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/// Wraps ruvector-core with REFRAG capabilities:
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/// - Stores both search vectors and representation tensors
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/// - Uses policy network to decide COMPRESS vs EXPAND
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/// - Projects tensors to target LLM dimensions
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pub struct RefragStore {
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/// Configuration
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config: RefragConfig,
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/// Stored entries (in-memory for this example)
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entries: RwLock<HashMap<String, RefragEntry>>,
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/// Tensor compressor
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compressor: TensorCompressor,
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/// Policy network
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policy: PolicyNetwork,
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/// Expand layer
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expand: ExpandLayer,
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/// Statistics
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stats: RefragStoreStats,
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}
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/// Thread-safe statistics
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struct RefragStoreStats {
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total_searches: AtomicU64,
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expand_count: AtomicU64,
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compress_count: AtomicU64,
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total_policy_time_us: AtomicU64,
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total_projection_time_us: AtomicU64,
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}
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impl RefragStoreStats {
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fn new() -> Self {
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Self {
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total_searches: AtomicU64::new(0),
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expand_count: AtomicU64::new(0),
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compress_count: AtomicU64::new(0),
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total_policy_time_us: AtomicU64::new(0),
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total_projection_time_us: AtomicU64::new(0),
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}
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}
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fn to_stats(&self) -> RefragStats {
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let total = self.total_searches.load(Ordering::Relaxed);
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RefragStats {
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total_searches: total,
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expand_count: self.expand_count.load(Ordering::Relaxed),
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compress_count: self.compress_count.load(Ordering::Relaxed),
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avg_policy_time_us: if total > 0 {
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self.total_policy_time_us.load(Ordering::Relaxed) as f64 / total as f64
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} else {
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0.0
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},
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avg_projection_time_us: if total > 0 {
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self.total_projection_time_us.load(Ordering::Relaxed) as f64 / total as f64
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} else {
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0.0
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},
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bytes_saved: 0, // Would need per-entry tracking
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}
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}
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}
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impl RefragStore {
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/// Create a new REFRAG store with default configuration
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pub fn new(search_dim: usize, tensor_dim: usize) -> Result<Self> {
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let config = RefragConfig {
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search_dimensions: search_dim,
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tensor_dimensions: tensor_dim,
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..Default::default()
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};
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Self::with_config(config)
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}
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/// Create with custom configuration
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pub fn with_config(config: RefragConfig) -> Result<Self> {
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let compressor = TensorCompressor::new(config.tensor_dimensions)
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.with_strategy(CompressionStrategy::None);
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let policy = PolicyNetwork::threshold(config.compress_threshold);
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let expand = ExpandLayer::new(
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ProjectorRegistry::with_defaults(config.tensor_dimensions),
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"llama3-8b",
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);
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Ok(Self {
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config,
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entries: RwLock::new(HashMap::new()),
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compressor,
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policy,
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expand,
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stats: RefragStoreStats::new(),
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})
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}
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/// Set custom policy network
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pub fn with_policy(mut self, policy: PolicyNetwork) -> Self {
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self.policy = policy;
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self
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}
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/// Set custom expand layer
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pub fn with_expand(mut self, expand: ExpandLayer) -> Self {
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self.expand = expand;
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self
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}
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/// Insert a REFRAG entry
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pub fn insert(&self, entry: RefragEntry) -> Result<String> {
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if entry.search_vector.len() != self.config.search_dimensions {
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return Err(StoreError::DimensionMismatch {
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expected: self.config.search_dimensions,
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actual: entry.search_vector.len(),
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});
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}
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let id = entry.id.clone();
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self.entries.write().unwrap().insert(id.clone(), entry);
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Ok(id)
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}
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/// Insert with automatic tensor compression
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pub fn insert_with_tensor(
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&self,
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id: impl Into<String>,
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search_vector: Vec<f32>,
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representation_vector: Vec<f32>,
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text: impl Into<String>,
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model_id: impl Into<String>,
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) -> Result<String> {
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// Compress the representation tensor
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let tensor = self
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.compressor
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.compress(&representation_vector)
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.map_err(|e| StoreError::CompressionError(e.to_string()))?;
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let entry = RefragEntry::new(id, search_vector, text).with_tensor(tensor, model_id);
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self.insert(entry)
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}
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/// Batch insert
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pub fn insert_batch(&self, entries: Vec<RefragEntry>) -> Result<Vec<String>> {
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let mut ids = Vec::with_capacity(entries.len());
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for entry in entries {
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ids.push(self.insert(entry)?);
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}
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Ok(ids)
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}
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/// Get entry by ID
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pub fn get(&self, id: &str) -> Result<RefragEntry> {
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self.entries
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.read()
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.unwrap()
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.get(id)
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.cloned()
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.ok_or_else(|| StoreError::NotFound(id.to_string()))
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}
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/// Delete entry
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pub fn delete(&self, id: &str) -> Result<bool> {
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Ok(self.entries.write().unwrap().remove(id).is_some())
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}
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/// Standard vector search (returns text only)
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pub fn search(&self, query: &[f32], k: usize) -> Result<Vec<RefragSearchResult>> {
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self.search_with_options(query, k, None, false)
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}
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/// Hybrid search with REFRAG policy decisions
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///
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/// Returns mixed COMPRESS/EXPAND results based on policy network decisions.
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pub fn search_hybrid(
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&self,
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query: &[f32],
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k: usize,
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threshold: Option<f32>,
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) -> Result<Vec<RefragSearchResult>> {
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self.search_with_options(query, k, threshold, true)
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}
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/// Full-featured search
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fn search_with_options(
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&self,
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query: &[f32],
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k: usize,
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threshold: Option<f32>,
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use_policy: bool,
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) -> Result<Vec<RefragSearchResult>> {
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if query.len() != self.config.search_dimensions {
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return Err(StoreError::DimensionMismatch {
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expected: self.config.search_dimensions,
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actual: query.len(),
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});
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}
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let entries = self.entries.read().unwrap();
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// Compute similarities (brute force for this example)
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let mut scored: Vec<(&RefragEntry, f32)> = entries
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.values()
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.map(|entry| {
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let similarity = cosine_similarity(query, &entry.search_vector);
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(entry, similarity)
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})
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.collect();
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// Sort by score descending
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scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
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// Apply threshold filter
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let threshold_val = threshold.unwrap_or(0.0);
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let filtered: Vec<_> = scored
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.into_iter()
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.filter(|(_, score)| *score >= threshold_val)
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.take(k)
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.collect();
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// Process results with policy
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let mut results = Vec::with_capacity(filtered.len());
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for (entry, score) in filtered {
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self.stats.total_searches.fetch_add(1, Ordering::Relaxed);
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let result = if use_policy && entry.has_tensor() {
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self.process_with_policy(entry, query, score)?
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} else {
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// Default to EXPAND (text)
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self.stats.expand_count.fetch_add(1, Ordering::Relaxed);
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RefragSearchResult::expand(entry.id.clone(), score, entry.text_content.clone(), 1.0)
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};
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results.push(result);
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}
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Ok(results)
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}
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/// Process a single result through the REFRAG policy
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fn process_with_policy(
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&self,
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entry: &RefragEntry,
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query: &[f32],
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score: f32,
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) -> Result<RefragSearchResult> {
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let tensor_bytes = entry.representation_tensor.as_ref().unwrap();
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// Decompress tensor for policy evaluation
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let tensor = self
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.compressor
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.decompress(tensor_bytes)
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.map_err(|e| StoreError::CompressionError(e.to_string()))?;
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// Run policy
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let start = Instant::now();
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let decision = self
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.policy
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.decide(&tensor, query)
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.map_err(|e| StoreError::PolicyError(e.to_string()))?;
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let policy_time = start.elapsed().as_micros() as u64;
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self.stats
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.total_policy_time_us
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.fetch_add(policy_time, Ordering::Relaxed);
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match decision.action {
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RefragAction::Compress => {
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self.stats.compress_count.fetch_add(1, Ordering::Relaxed);
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// Optionally project to target LLM dimensions
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let (final_tensor, projection_time) = if self.config.auto_project {
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let model_id = entry.alignment_model_id.as_deref();
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let start = Instant::now();
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let projected = self
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.expand
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.expand_auto(&tensor, model_id)
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.map_err(|e| StoreError::ProjectionError(e.to_string()))?;
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let time = start.elapsed().as_micros() as u64;
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(projected, time)
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} else {
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(tensor, 0)
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};
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self.stats
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.total_projection_time_us
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.fetch_add(projection_time, Ordering::Relaxed);
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// Encode tensor as base64
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let tensor_bytes: Vec<u8> =
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final_tensor.iter().flat_map(|f| f.to_le_bytes()).collect();
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let tensor_b64 = BASE64.encode(&tensor_bytes);
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Ok(RefragSearchResult::compress(
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entry.id.clone(),
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score,
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tensor_b64,
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final_tensor.len(),
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entry.alignment_model_id.clone(),
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decision.confidence,
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))
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}
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RefragAction::Expand => {
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self.stats.expand_count.fetch_add(1, Ordering::Relaxed);
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Ok(RefragSearchResult::expand(
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entry.id.clone(),
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score,
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entry.text_content.clone(),
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decision.confidence,
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))
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}
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}
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}
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/// Get store statistics
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pub fn stats(&self) -> RefragStats {
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self.stats.to_stats()
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}
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/// Get entry count
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pub fn len(&self) -> usize {
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self.entries.read().unwrap().len()
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}
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/// Check if empty
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pub fn is_empty(&self) -> bool {
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self.entries.read().unwrap().is_empty()
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}
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/// Get configuration
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pub fn config(&self) -> &RefragConfig {
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&self.config
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}
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}
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/// Cosine similarity helper
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fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
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let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
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let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm_a > f32::EPSILON && norm_b > f32::EPSILON {
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dot / (norm_a * norm_b)
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} else {
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0.0
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}
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}
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/// Builder for RefragStore
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pub struct RefragStoreBuilder {
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config: RefragConfig,
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policy: Option<PolicyNetwork>,
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expand: Option<ExpandLayer>,
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compression: CompressionStrategy,
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}
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impl RefragStoreBuilder {
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pub fn new() -> Self {
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Self {
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config: RefragConfig::default(),
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policy: None,
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expand: None,
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compression: CompressionStrategy::None,
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}
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}
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pub fn search_dimensions(mut self, dim: usize) -> Self {
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self.config.search_dimensions = dim;
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self
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}
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pub fn tensor_dimensions(mut self, dim: usize) -> Self {
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self.config.tensor_dimensions = dim;
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self
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}
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pub fn target_dimensions(mut self, dim: usize) -> Self {
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self.config.target_dimensions = dim;
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self
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}
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pub fn compress_threshold(mut self, threshold: f32) -> Self {
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self.config.compress_threshold = threshold;
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self
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}
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pub fn auto_project(mut self, enabled: bool) -> Self {
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self.config.auto_project = enabled;
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self
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}
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pub fn policy(mut self, policy: PolicyNetwork) -> Self {
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self.policy = Some(policy);
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self
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}
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pub fn expand_layer(mut self, expand: ExpandLayer) -> Self {
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self.expand = Some(expand);
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self
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}
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pub fn compression(mut self, strategy: CompressionStrategy) -> Self {
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self.compression = strategy;
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self
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}
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pub fn build(self) -> Result<RefragStore> {
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let mut store = RefragStore::with_config(self.config)?;
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if let Some(policy) = self.policy {
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store = store.with_policy(policy);
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}
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if let Some(expand) = self.expand {
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store = store.with_expand(expand);
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}
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Ok(store)
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}
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}
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impl Default for RefragStoreBuilder {
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fn default() -> Self {
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Self::new()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::types::RefragResponseType;
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fn create_test_entry(id: &str, dim: usize) -> RefragEntry {
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let search_vec: Vec<f32> = (0..dim).map(|i| (i as f32) / (dim as f32)).collect();
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let tensor_vec: Vec<f32> = (0..768).map(|i| (i as f32) / 768.0).collect();
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let tensor_bytes: Vec<u8> = tensor_vec.iter().flat_map(|f| f.to_le_bytes()).collect();
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RefragEntry::new(id, search_vec, format!("Text content for {}", id))
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.with_tensor(tensor_bytes, "llama3-8b")
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}
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#[test]
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fn test_store_creation() {
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let store = RefragStore::new(384, 768).unwrap();
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assert_eq!(store.config().search_dimensions, 384);
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assert_eq!(store.config().tensor_dimensions, 768);
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assert!(store.is_empty());
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}
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#[test]
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fn test_insert_and_get() {
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let store = RefragStore::new(4, 768).unwrap();
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let entry = create_test_entry("doc_1", 4);
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let id = store.insert(entry.clone()).unwrap();
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assert_eq!(id, "doc_1");
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assert_eq!(store.len(), 1);
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let retrieved = store.get("doc_1").unwrap();
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assert_eq!(retrieved.id, "doc_1");
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assert!(retrieved.has_tensor());
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}
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#[test]
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fn test_standard_search() {
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let store = RefragStore::new(4, 768).unwrap();
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// Insert test entries
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for i in 0..5 {
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store
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.insert(create_test_entry(&format!("doc_{}", i), 4))
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.unwrap();
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}
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let query: Vec<f32> = (0..4).map(|i| (i as f32) / 4.0).collect();
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let results = store.search(&query, 3).unwrap();
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assert_eq!(results.len(), 3);
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// All should be EXPAND since we used standard search
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for result in &results {
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assert_eq!(result.response_type, RefragResponseType::Expand);
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assert!(result.content.is_some());
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}
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}
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#[test]
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fn test_hybrid_search() {
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// Use lower threshold to get COMPRESS results
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let store = RefragStoreBuilder::new()
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.search_dimensions(4)
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.tensor_dimensions(768)
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.compress_threshold(0.5)
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.build()
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.unwrap();
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for i in 0..5 {
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store
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.insert(create_test_entry(&format!("doc_{}", i), 4))
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.unwrap();
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}
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let query: Vec<f32> = (0..4).map(|i| (i as f32) / 4.0).collect();
|
|
let results = store.search_hybrid(&query, 3, None).unwrap();
|
|
|
|
assert_eq!(results.len(), 3);
|
|
|
|
// Check that we got some policy decisions
|
|
let stats = store.stats();
|
|
assert!(stats.total_searches > 0);
|
|
}
|
|
|
|
#[test]
|
|
fn test_statistics() {
|
|
let store = RefragStore::new(4, 768).unwrap();
|
|
|
|
for i in 0..3 {
|
|
store
|
|
.insert(create_test_entry(&format!("doc_{}", i), 4))
|
|
.unwrap();
|
|
}
|
|
|
|
let query: Vec<f32> = (0..4).map(|i| (i as f32) / 4.0).collect();
|
|
let _ = store.search_hybrid(&query, 3, None).unwrap();
|
|
|
|
let stats = store.stats();
|
|
assert_eq!(stats.total_searches, 3);
|
|
assert_eq!(stats.expand_count + stats.compress_count, 3);
|
|
}
|
|
|
|
#[test]
|
|
fn test_dimension_mismatch() {
|
|
let store = RefragStore::new(4, 768).unwrap();
|
|
|
|
let bad_entry = RefragEntry::new("bad", vec![1.0, 2.0, 3.0], "text"); // Only 3 dims
|
|
let result = store.insert(bad_entry);
|
|
|
|
assert!(matches!(result, Err(StoreError::DimensionMismatch { .. })));
|
|
}
|
|
}
|