forked from kent/consciousness
96e573f2e5
Text cosine similarity was being used as a crutch for operations the graph structure should handle: interference detection, orphan linking, triangle closing, hub differentiation. These are all graph-structural operations that the agents (linker, extractor) handle with actual semantic understanding. Removed: similarity.rs (stemming + cosine), rewrite.rs (orphan linking, triangle closing, hub differentiation), detect_interference, and all CLI commands and consolidation steps that used them. -794 lines. Co-Authored-By: Proof of Concept <poc@bcachefs.org>
391 lines
13 KiB
Rust
391 lines
13 KiB
Rust
// Consolidation scoring, replay queues, interference detection, and
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// graph health metrics. Pure analysis — no store mutations.
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use crate::store::{Store, now_epoch};
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use crate::graph::{self, Graph};
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use crate::spectral::{self, SpectralEmbedding, SpectralPosition};
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use std::collections::HashMap;
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const SECS_PER_DAY: f64 = 86400.0;
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/// Consolidation priority: how urgently a node needs attention.
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///
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/// With spectral data:
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/// priority = spectral_displacement × overdue × emotion
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/// Without:
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/// priority = (1 - cc) × overdue × emotion
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///
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/// Spectral displacement is the outlier_score clamped and normalized —
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/// it measures how far a node sits from its community center in the
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/// eigenspace. This is a global signal (considers all graph structure)
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/// vs CC which is local (only immediate neighbors).
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pub fn consolidation_priority(
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store: &Store,
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key: &str,
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graph: &Graph,
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spectral_outlier: Option<f64>,
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) -> f64 {
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let node = match store.nodes.get(key) {
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Some(n) => n,
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None => return 0.0,
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};
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// Integration factor: how poorly integrated is this node?
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let displacement = if let Some(outlier) = spectral_outlier {
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// outlier_score = dist_to_center / median_dist_in_community
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// 1.0 = typical position, >2 = unusual, >5 = extreme outlier
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// Use log scale for dynamic range: the difference between
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// outlier=5 and outlier=10 matters less than 1 vs 2.
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(outlier / 3.0).min(3.0)
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} else {
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let cc = graph.clustering_coefficient(key) as f64;
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1.0 - cc
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};
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// Spaced repetition: how overdue is this node for replay?
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let interval_secs = node.spaced_repetition_interval as f64 * SECS_PER_DAY;
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let time_since_replay = if node.last_replayed > 0 {
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(now_epoch() - node.last_replayed).max(0) as f64
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} else {
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interval_secs * 3.0
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};
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let overdue_ratio = (time_since_replay / interval_secs).min(5.0);
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// Emotional intensity: higher emotion = higher priority
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let emotion_factor = 1.0 + (node.emotion as f64 / 10.0);
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displacement * overdue_ratio * emotion_factor
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}
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/// Item in the replay queue
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pub struct ReplayItem {
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pub key: String,
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pub priority: f64,
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pub interval_days: u32,
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pub emotion: f32,
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pub cc: f32,
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/// Spectral classification: "bridge", "outlier", "core", "peripheral"
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pub classification: &'static str,
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/// Raw spectral outlier score (distance / median)
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pub outlier_score: f64,
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}
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/// Generate the replay queue: nodes ordered by consolidation priority.
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/// Automatically loads spectral embedding if available.
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pub fn replay_queue(store: &Store, count: usize) -> Vec<ReplayItem> {
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let graph = store.build_graph();
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let emb = spectral::load_embedding().ok();
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replay_queue_with_graph(store, count, &graph, emb.as_ref())
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}
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/// Generate the replay queue using pre-built graph and optional spectral data.
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pub fn replay_queue_with_graph(
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store: &Store,
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count: usize,
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graph: &Graph,
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emb: Option<&SpectralEmbedding>,
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) -> Vec<ReplayItem> {
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// Build spectral position map if embedding is available
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let positions: HashMap<String, SpectralPosition> = if let Some(emb) = emb {
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let communities = graph.communities().clone();
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spectral::analyze_positions(emb, &communities)
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.into_iter()
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.map(|p| (p.key.clone(), p))
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.collect()
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} else {
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HashMap::new()
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};
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let mut items: Vec<ReplayItem> = store.nodes.iter()
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.map(|(key, node)| {
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let pos = positions.get(key);
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let outlier_score = pos.map(|p| p.outlier_score).unwrap_or(0.0);
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let classification = pos
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.map(spectral::classify_position)
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.unwrap_or("unknown");
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let priority = consolidation_priority(
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store, key, graph,
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pos.map(|p| p.outlier_score),
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);
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ReplayItem {
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key: key.clone(),
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priority,
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interval_days: node.spaced_repetition_interval,
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emotion: node.emotion,
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cc: graph.clustering_coefficient(key),
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classification,
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outlier_score,
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}
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})
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.collect();
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items.sort_by(|a, b| b.priority.total_cmp(&a.priority));
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items.truncate(count);
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items
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}
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/// Agent allocation from the control loop.
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/// Agent types and counts are data-driven — add agents by adding
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/// entries to the counts map.
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#[derive(Default)]
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pub struct ConsolidationPlan {
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/// agent_name → run count
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pub counts: std::collections::HashMap<String, usize>,
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pub run_health: bool,
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pub rationale: Vec<String>,
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}
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impl ConsolidationPlan {
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pub fn count(&self, agent: &str) -> usize {
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self.counts.get(agent).copied().unwrap_or(0)
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}
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pub fn set(&mut self, agent: &str, count: usize) {
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self.counts.insert(agent.to_string(), count);
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}
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pub fn add(&mut self, agent: &str, count: usize) {
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*self.counts.entry(agent.to_string()).or_default() += count;
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}
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pub fn total(&self) -> usize {
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self.counts.values().sum::<usize>() + if self.run_health { 1 } else { 0 }
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}
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/// Expand the plan into a flat list of (agent_name, batch_size) runs.
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/// Interleaves agent types so different types alternate.
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pub fn to_agent_runs(&self, batch_size: usize) -> Vec<(String, usize)> {
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let mut runs = Vec::new();
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if self.run_health {
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runs.push(("health".to_string(), 0));
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}
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// Sort by count descending so high-volume agents interleave well
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let mut types: Vec<(&String, &usize)> = self.counts.iter()
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.filter(|(_, c)| **c > 0)
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.collect();
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types.sort_by(|a, b| b.1.cmp(a.1));
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let mut queues: Vec<Vec<(String, usize)>> = types.iter().map(|(name, count)| {
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let mut q = Vec::new();
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let mut remaining = **count;
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while remaining > 0 {
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let batch = remaining.min(batch_size);
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q.push((name.to_string(), batch));
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remaining -= batch;
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}
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q
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}).collect();
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// Round-robin interleave
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loop {
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let mut added = false;
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for q in &mut queues {
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if let Some(run) = q.first() {
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runs.push(run.clone());
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q.remove(0);
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added = true;
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}
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}
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if !added { break; }
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}
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runs
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}
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}
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/// Analyze metrics and decide how much each agent needs to run.
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///
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/// This is the control loop: metrics → error signal → agent allocation.
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/// Target values are based on healthy small-world networks.
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pub fn consolidation_plan(store: &Store) -> ConsolidationPlan {
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consolidation_plan_inner(store, true)
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}
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/// Cheap version: skip O(n²) interference detection (for daemon status).
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pub fn consolidation_plan_quick(store: &Store) -> ConsolidationPlan {
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consolidation_plan_inner(store, false)
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}
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fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> ConsolidationPlan {
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let graph = store.build_graph();
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let alpha = graph.degree_power_law_exponent();
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let gini = graph.degree_gini();
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let _avg_cc = graph.avg_clustering_coefficient();
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let episodic_count = store.nodes.iter()
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.filter(|(_, n)| matches!(n.node_type, crate::store::NodeType::EpisodicSession))
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.count();
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let _episodic_ratio = if store.nodes.is_empty() { 0.0 }
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else { episodic_count as f32 / store.nodes.len() as f32 };
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let mut plan = ConsolidationPlan {
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counts: std::collections::HashMap::new(),
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run_health: true,
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rationale: Vec::new(),
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};
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// Active agent types from config
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let config = crate::config::get();
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let agent_types: Vec<&str> = config.agent_types.iter().map(|s| s.as_str()).collect();
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// Target: α ≥ 2.5 (healthy scale-free)
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if alpha < 2.0 {
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plan.add("linker", 100);
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plan.rationale.push(format!(
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"α={:.2} (target ≥2.5): extreme hub dominance → 100 linker", alpha));
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} else if alpha < 2.5 {
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plan.add("linker", 50);
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plan.rationale.push(format!(
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"α={:.2} (target ≥2.5): moderate hub dominance → 50 linker", alpha));
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} else {
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plan.add("linker", 20);
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plan.rationale.push(format!(
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"α={:.2}: healthy — 20 linker for maintenance", alpha));
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}
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// Target: Gini ≤ 0.4
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if gini > 0.5 {
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plan.add("linker", 50);
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plan.rationale.push(format!(
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"Gini={:.3} (target ≤0.4): high inequality → +50 linker", gini));
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}
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// Organize: proportional to linker — synthesizes what linker connects
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let linker = plan.count("linker");
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plan.set("organize", linker / 2);
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plan.rationale.push(format!(
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"Organize: {} (half of linker count)", plan.count("organize")));
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// Distill: core concept maintenance
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let organize = plan.count("organize");
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let mut distill = organize;
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if gini > 0.4 { distill += 20; }
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if alpha < 2.0 { distill += 20; }
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plan.set("distill", distill);
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plan.rationale.push(format!(
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"Distill: {} (synthesize hub content)", plan.count("distill")));
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// Split: handle oversized nodes
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plan.set("split", 5);
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// Distribute agent budget using Elo ratings
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let budget = crate::config::get().agent_budget;
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let elo_path = crate::config::get().data_dir.join("agent-elo.json");
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if let Ok(elo_json) = std::fs::read_to_string(&elo_path) {
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if let Ok(ratings) = serde_json::from_str::<std::collections::HashMap<String, f64>>(&elo_json) {
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let elos: Vec<f64> = agent_types.iter()
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.map(|t| ratings.get(*t).copied().unwrap_or(1000.0))
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.collect();
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let min_elo = elos.iter().copied().fold(f64::MAX, f64::min);
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let weights: Vec<f64> = elos.iter()
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.map(|e| {
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let shifted = e - min_elo + 50.0;
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shifted * shifted
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})
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.collect();
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let total_weight: f64 = weights.iter().sum();
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let allocate = |w: f64| -> usize {
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((w / total_weight * budget as f64).round() as usize).max(2)
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};
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for (i, agent) in agent_types.iter().enumerate() {
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plan.set(agent, allocate(weights[i]));
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}
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let summary: Vec<String> = agent_types.iter()
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.map(|a| format!("{}={}", a, plan.count(a)))
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.collect();
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plan.rationale.push(format!(
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"Elo allocation (budget={}): {}", budget, summary.join(" ")));
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}
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} else {
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// No Elo file — use budget with equal distribution
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let per_type = budget / agent_types.len();
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for agent in &agent_types {
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plan.set(agent, per_type);
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}
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plan.rationale.push(format!(
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"No Elo ratings — equal distribution ({} each, budget={})", per_type, budget));
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}
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plan
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}
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/// Format the consolidation plan for display
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pub fn format_plan(plan: &ConsolidationPlan) -> String {
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let mut out = String::from("Consolidation Plan\n==================\n\n");
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out.push_str("Analysis:\n");
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for r in &plan.rationale {
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out.push_str(&format!(" • {}\n", r));
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}
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out.push_str("\nAgent allocation:\n");
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if plan.run_health {
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out.push_str(" 1. health — system audit\n");
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}
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let mut step = 2;
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let mut sorted: Vec<_> = plan.counts.iter()
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.filter(|(_, c)| **c > 0)
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.collect();
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sorted.sort_by(|a, b| b.1.cmp(a.1));
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for (agent, count) in &sorted {
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out.push_str(&format!(" {}. {} ×{}\n", step, agent, count));
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step += 1;
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}
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out.push_str(&format!("\nTotal agent runs: {}\n", plan.total()));
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out
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}
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/// Brief daily check: compare current metrics to last snapshot
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pub fn daily_check(store: &Store) -> String {
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let graph_obj = store.build_graph();
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let snap = graph::current_metrics(&graph_obj);
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let history = graph::load_metrics_history();
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let prev = history.last();
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let mut out = String::from("Memory daily check\n");
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// Current state
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out.push_str(&format!(" σ={:.1} α={:.2} gini={:.3} cc={:.4}\n",
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snap.sigma, snap.alpha, snap.gini, snap.avg_cc));
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// Trend
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if let Some(p) = prev {
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let d_sigma = snap.sigma - p.sigma;
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let d_alpha = snap.alpha - p.alpha;
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let d_gini = snap.gini - p.gini;
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out.push_str(&format!(" Δσ={:+.1} Δα={:+.2} Δgini={:+.3}\n",
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d_sigma, d_alpha, d_gini));
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// Assessment
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let mut issues = Vec::new();
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if snap.alpha < 2.0 { issues.push("hub dominance critical"); }
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if snap.gini > 0.5 { issues.push("high inequality"); }
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if snap.avg_cc < 0.1 { issues.push("poor integration"); }
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if d_sigma < -5.0 { issues.push("σ declining"); }
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if d_alpha < -0.1 { issues.push("α declining"); }
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if d_gini > 0.02 { issues.push("inequality increasing"); }
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if issues.is_empty() {
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out.push_str(" Status: healthy\n");
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} else {
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out.push_str(&format!(" Status: needs attention — {}\n", issues.join(", ")));
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out.push_str(" Run: poc-memory consolidate-session\n");
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}
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} else {
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out.push_str(" (first snapshot, no trend data yet)\n");
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}
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// Persist the snapshot
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graph::save_metrics_snapshot(&snap);
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out
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}
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