poc-memory v0.4.0: graph-structured memory with consolidation pipeline
Rust core: - Cap'n Proto append-only storage (nodes + relations) - Graph algorithms: clustering coefficient, community detection, schema fit, small-world metrics, interference detection - BM25 text similarity with Porter stemming - Spaced repetition replay queue - Commands: search, init, health, status, graph, categorize, link-add, link-impact, decay, consolidate-session, etc. Python scripts: - Episodic digest pipeline: daily/weekly/monthly-digest.py - retroactive-digest.py for backfilling - consolidation-agents.py: 3 parallel Sonnet agents - apply-consolidation.py: structured action extraction + apply - digest-link-parser.py: extract ~400 explicit links from digests - content-promotion-agent.py: promote episodic obs to semantic files - bulk-categorize.py: categorize all nodes via single Sonnet call - consolidation-loop.py: multi-round automated consolidation Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
This commit is contained in:
ProofOfConcept
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src/neuro.rs
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src/neuro.rs
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// Neuroscience-inspired memory algorithms
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//
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// Systematic replay (hippocampal replay), schema assimilation,
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// interference detection, emotional gating, consolidation priority
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// scoring, and the agent consolidation harness.
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use crate::capnp_store::Store;
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use crate::graph::{self, Graph};
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use crate::similarity;
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use std::time::{SystemTime, UNIX_EPOCH};
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fn now_epoch() -> f64 {
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SystemTime::now()
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.duration_since(UNIX_EPOCH)
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.unwrap()
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.as_secs_f64()
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}
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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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/// priority = (1 - schema_fit) × spaced_repetition_due × emotion × (1 + interference)
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pub fn consolidation_priority(store: &Store, key: &str, graph: &Graph) -> 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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// Schema fit: 0 = poorly integrated, 1 = well integrated
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let fit = graph::schema_fit(graph, key) as f64;
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let fit_factor = 1.0 - fit;
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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 > 0.0 {
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(now_epoch() - node.last_replayed).max(0.0)
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} else {
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// Never replayed — treat as very overdue
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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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fit_factor * 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 schema_fit: f32,
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}
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/// Generate the replay queue: nodes ordered by consolidation priority
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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 fits = graph::schema_fit_all(&graph);
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let mut items: Vec<ReplayItem> = store.nodes.iter()
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.map(|(key, node)| {
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let priority = consolidation_priority(store, key, &graph);
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let fit = fits.get(key).copied().unwrap_or(0.0);
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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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schema_fit: fit,
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}
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})
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.collect();
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items.sort_by(|a, b| b.priority.partial_cmp(&a.priority).unwrap());
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items.truncate(count);
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items
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}
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/// Detect interfering memory pairs: high text similarity but different communities
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pub fn detect_interference(
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store: &Store,
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graph: &Graph,
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threshold: f32,
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) -> Vec<(String, String, f32)> {
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let communities = graph.communities();
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// Only compare nodes within a reasonable set — take the most active ones
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let mut docs: Vec<(String, String)> = store.nodes.iter()
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.filter(|(_, n)| n.content.len() > 50) // skip tiny nodes
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.map(|(k, n)| (k.clone(), n.content.clone()))
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.collect();
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// For large stores, sample to keep pairwise comparison feasible
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if docs.len() > 200 {
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docs.sort_by(|a, b| b.1.len().cmp(&a.1.len()));
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docs.truncate(200);
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}
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let similar = similarity::pairwise_similar(&docs, threshold);
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// Filter to pairs in different communities
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similar.into_iter()
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.filter(|(a, b, _)| {
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let ca = communities.get(a);
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let cb = communities.get(b);
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match (ca, cb) {
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(Some(a), Some(b)) => a != b,
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_ => true, // if community unknown, flag it
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}
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})
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.collect()
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}
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/// Schema assimilation scoring for a new node.
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/// Returns how easily the node integrates into existing structure.
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///
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/// High fit (>0.5): auto-link, done
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/// Medium fit (0.2-0.5): agent reviews, proposes links
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/// Low fit (<0.2): deep examination needed — new schema seed, bridge, or noise?
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pub fn schema_assimilation(store: &Store, key: &str) -> (f32, &'static str) {
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let graph = store.build_graph();
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let fit = graph::schema_fit(&graph, key);
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let recommendation = if fit > 0.5 {
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"auto-integrate"
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} else if fit > 0.2 {
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"agent-review"
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} else if graph.degree(key) > 0 {
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"deep-examine-bridge"
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} else {
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"deep-examine-orphan"
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};
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(fit, recommendation)
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}
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/// Prompt template directory
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fn prompts_dir() -> std::path::PathBuf {
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// Check for prompts relative to binary, then fall back to ~/poc/memory/prompts/
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let home = std::env::var("HOME").unwrap_or_default();
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std::path::PathBuf::from(home).join("poc/memory/prompts")
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}
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/// Load a prompt template, replacing {{PLACEHOLDER}} with data
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fn load_prompt(name: &str, replacements: &[(&str, &str)]) -> Result<String, String> {
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let path = prompts_dir().join(format!("{}.md", name));
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let mut content = std::fs::read_to_string(&path)
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.map_err(|e| format!("load prompt {}: {}", path.display(), e))?;
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for (placeholder, data) in replacements {
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content = content.replace(placeholder, data);
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}
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Ok(content)
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}
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/// Format topology header for agent prompts — current graph health metrics
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fn format_topology_header(graph: &Graph) -> String {
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let sigma = graph.small_world_sigma();
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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 n = graph.nodes().len();
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let e = graph.edge_count();
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format!(
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"## Current graph topology\n\
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Nodes: {} Edges: {} Communities: {}\n\
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Small-world σ: {:.1} Power-law α: {:.2} Degree Gini: {:.3}\n\
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Avg clustering coefficient: {:.4}\n\n\
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Each node below shows its hub-link ratio (fraction of edges to top-5% degree nodes).\n\
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Use `poc-memory link-impact SOURCE TARGET` to evaluate proposed links.\n\n",
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n, e, graph.community_count(), sigma, alpha, gini, avg_cc)
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}
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/// Compute the hub degree threshold (top 5% by degree)
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fn hub_threshold(graph: &Graph) -> usize {
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let mut degrees: Vec<usize> = graph.nodes().iter()
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.map(|k| graph.degree(k))
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.collect();
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degrees.sort_unstable();
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if degrees.len() >= 20 {
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degrees[degrees.len() * 95 / 100]
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} else {
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usize::MAX
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}
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}
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/// Format node data section for prompt templates
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fn format_nodes_section(store: &Store, items: &[ReplayItem], graph: &Graph) -> String {
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let hub_thresh = hub_threshold(graph);
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let mut out = String::new();
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for item in items {
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let node = match store.nodes.get(&item.key) {
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Some(n) => n,
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None => continue,
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};
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out.push_str(&format!("## {} \n", item.key));
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out.push_str(&format!("Priority: {:.3} Schema fit: {:.3} Emotion: {:.1} ",
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item.priority, item.schema_fit, item.emotion));
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out.push_str(&format!("Category: {} Interval: {}d\n",
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node.category.label(), node.spaced_repetition_interval));
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if let Some(community) = node.community_id {
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out.push_str(&format!("Community: {} ", community));
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}
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let deg = graph.degree(&item.key);
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let cc = graph.clustering_coefficient(&item.key);
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// Hub-link ratio: what fraction of this node's edges go to hubs?
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let neighbors = graph.neighbors(&item.key);
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let hub_links = neighbors.iter()
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.filter(|(n, _)| graph.degree(n) >= hub_thresh)
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.count();
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let hub_ratio = if deg > 0 { hub_links as f32 / deg as f32 } else { 0.0 };
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let is_hub = deg >= hub_thresh;
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out.push_str(&format!("Degree: {} CC: {:.3} Hub-link ratio: {:.0}% ({}/{})",
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deg, cc, hub_ratio * 100.0, hub_links, deg));
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if is_hub {
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out.push_str(" ← THIS IS A HUB");
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} else if hub_ratio > 0.6 {
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out.push_str(" ← mostly hub-connected, needs lateral links");
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}
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out.push('\n');
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// Content (truncated for large nodes)
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let content = &node.content;
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if content.len() > 1500 {
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let end = content.floor_char_boundary(1500);
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out.push_str(&format!("\nContent ({} chars, truncated):\n{}\n[...]\n\n",
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content.len(), &content[..end]));
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} else {
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out.push_str(&format!("\nContent:\n{}\n\n", content));
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}
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// Neighbors
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let neighbors = graph.neighbors(&item.key);
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if !neighbors.is_empty() {
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out.push_str("Neighbors:\n");
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for (n, strength) in neighbors.iter().take(15) {
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let n_cc = graph.clustering_coefficient(n);
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let n_community = store.nodes.get(n.as_str())
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.and_then(|n| n.community_id);
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out.push_str(&format!(" - {} (str={:.2}, cc={:.3}",
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n, strength, n_cc));
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if let Some(c) = n_community {
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out.push_str(&format!(", c{}", c));
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}
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out.push_str(")\n");
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}
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}
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out.push_str("\n---\n\n");
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}
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out
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}
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/// Format health data for the health agent prompt
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fn format_health_section(store: &Store, graph: &Graph) -> String {
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let health = graph::health_report(graph, store);
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let mut out = health;
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out.push_str("\n\n## Weight distribution\n");
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// Weight histogram
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let mut buckets = [0u32; 10]; // 0.0-0.1, 0.1-0.2, ..., 0.9-1.0
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for node in store.nodes.values() {
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let bucket = ((node.weight * 10.0) as usize).min(9);
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buckets[bucket] += 1;
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}
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for (i, &count) in buckets.iter().enumerate() {
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let lo = i as f32 / 10.0;
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let hi = (i + 1) as f32 / 10.0;
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let bar: String = std::iter::repeat('█').take((count as usize) / 10).collect();
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out.push_str(&format!(" {:.1}-{:.1}: {:4} {}\n", lo, hi, count, bar));
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}
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// Near-prune nodes
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let near_prune: Vec<_> = store.nodes.iter()
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.filter(|(_, n)| n.weight < 0.15)
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.map(|(k, n)| (k.clone(), n.weight))
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.collect();
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if !near_prune.is_empty() {
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out.push_str(&format!("\n## Near-prune nodes ({} total)\n", near_prune.len()));
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for (k, w) in near_prune.iter().take(20) {
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out.push_str(&format!(" [{:.3}] {}\n", w, k));
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}
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}
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// Community sizes
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let communities = graph.communities();
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let mut comm_sizes: std::collections::HashMap<u32, Vec<String>> = std::collections::HashMap::new();
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for (key, &label) in communities {
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comm_sizes.entry(label).or_default().push(key.clone());
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}
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let mut sizes: Vec<_> = comm_sizes.iter()
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.map(|(id, members)| (*id, members.len(), members.clone()))
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.collect();
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sizes.sort_by(|a, b| b.1.cmp(&a.1));
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out.push_str("\n## Largest communities\n");
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for (id, size, members) in sizes.iter().take(10) {
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out.push_str(&format!(" Community {} ({} nodes): ", id, size));
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let sample: Vec<_> = members.iter().take(5).map(|s| s.as_str()).collect();
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out.push_str(&sample.join(", "));
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if *size > 5 { out.push_str(", ..."); }
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out.push('\n');
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}
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out
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}
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/// Format interference pairs for the separator agent prompt
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fn format_pairs_section(
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pairs: &[(String, String, f32)],
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store: &Store,
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graph: &Graph,
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) -> String {
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let mut out = String::new();
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let communities = graph.communities();
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for (a, b, sim) in pairs {
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out.push_str(&format!("## Pair: similarity={:.3}\n", sim));
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let ca = communities.get(a).map(|c| format!("c{}", c)).unwrap_or_else(|| "?".into());
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let cb = communities.get(b).map(|c| format!("c{}", c)).unwrap_or_else(|| "?".into());
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// Node A
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out.push_str(&format!("\n### {} ({})\n", a, ca));
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if let Some(node) = store.nodes.get(a) {
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let content = if node.content.len() > 500 {
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let end = node.content.floor_char_boundary(500);
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format!("{}...", &node.content[..end])
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} else {
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node.content.clone()
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};
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out.push_str(&format!("Category: {} Weight: {:.2}\n{}\n",
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node.category.label(), node.weight, content));
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}
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// Node B
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out.push_str(&format!("\n### {} ({})\n", b, cb));
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if let Some(node) = store.nodes.get(b) {
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let content = if node.content.len() > 500 {
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let end = node.content.floor_char_boundary(500);
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format!("{}...", &node.content[..end])
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} else {
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node.content.clone()
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};
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out.push_str(&format!("Category: {} Weight: {:.2}\n{}\n",
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node.category.label(), node.weight, content));
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}
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out.push_str("\n---\n\n");
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}
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out
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}
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/// Run agent consolidation on top-priority nodes
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pub fn consolidation_batch(store: &Store, count: usize, auto: bool) -> Result<(), String> {
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let graph = store.build_graph();
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let items = replay_queue(store, count);
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if items.is_empty() {
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println!("No nodes to consolidate.");
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return Ok(());
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}
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let nodes_section = format_nodes_section(store, &items, &graph);
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if auto {
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// Generate the replay agent prompt with data filled in
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let prompt = load_prompt("replay", &[("{{NODES}}", &nodes_section)])?;
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println!("{}", prompt);
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} else {
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// Interactive: show what needs attention and available agent types
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println!("Consolidation batch ({} nodes):\n", items.len());
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for item in &items {
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let node_type = store.nodes.get(&item.key)
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.map(|n| if n.key.contains("journal") { "episodic" } else { "semantic" })
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.unwrap_or("?");
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println!(" [{:.3}] {} (fit={:.3}, interval={}d, type={})",
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item.priority, item.key, item.schema_fit, item.interval_days, node_type);
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}
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// Also show interference pairs
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let pairs = detect_interference(store, &graph, 0.6);
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if !pairs.is_empty() {
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println!("\nInterfering pairs ({}):", pairs.len());
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for (a, b, sim) in pairs.iter().take(5) {
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println!(" [{:.3}] {} ↔ {}", sim, a, b);
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}
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}
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println!("\nAgent prompts:");
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println!(" --auto Generate replay agent prompt");
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println!(" --agent replay Replay agent (schema assimilation)");
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println!(" --agent linker Linker agent (relational binding)");
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println!(" --agent separator Separator agent (pattern separation)");
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println!(" --agent transfer Transfer agent (CLS episodic→semantic)");
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println!(" --agent health Health agent (synaptic homeostasis)");
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}
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Ok(())
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}
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/// Generate a specific agent prompt with filled-in data
|
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pub fn agent_prompt(store: &Store, agent: &str, count: usize) -> Result<String, String> {
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let graph = store.build_graph();
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let topology = format_topology_header(&graph);
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|
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match agent {
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"replay" => {
|
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let items = replay_queue(store, count);
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let nodes_section = format_nodes_section(store, &items, &graph);
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load_prompt("replay", &[("{{TOPOLOGY}}", &topology), ("{{NODES}}", &nodes_section)])
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}
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"linker" => {
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// Filter to episodic entries
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let mut items = replay_queue(store, count * 2);
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items.retain(|item| {
|
||||
store.nodes.get(&item.key)
|
||||
.map(|n| matches!(n.node_type, crate::capnp_store::NodeType::EpisodicSession))
|
||||
.unwrap_or(false)
|
||||
|| item.key.contains("journal")
|
||||
|| item.key.contains("session")
|
||||
});
|
||||
items.truncate(count);
|
||||
let nodes_section = format_nodes_section(store, &items, &graph);
|
||||
load_prompt("linker", &[("{{TOPOLOGY}}", &topology), ("{{NODES}}", &nodes_section)])
|
||||
}
|
||||
"separator" => {
|
||||
let pairs = detect_interference(store, &graph, 0.5);
|
||||
let pairs_section = format_pairs_section(&pairs, store, &graph);
|
||||
load_prompt("separator", &[("{{TOPOLOGY}}", &topology), ("{{PAIRS}}", &pairs_section)])
|
||||
}
|
||||
"transfer" => {
|
||||
// Recent episodic entries
|
||||
let mut episodes: Vec<_> = store.nodes.iter()
|
||||
.filter(|(k, _)| k.contains("journal") || k.contains("session"))
|
||||
.map(|(k, n)| (k.clone(), n.timestamp))
|
||||
.collect();
|
||||
episodes.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
|
||||
episodes.truncate(count);
|
||||
|
||||
let episode_keys: Vec<_> = episodes.iter().map(|(k, _)| k.clone()).collect();
|
||||
let items: Vec<ReplayItem> = episode_keys.iter()
|
||||
.filter_map(|k| {
|
||||
let node = store.nodes.get(k)?;
|
||||
let fit = graph::schema_fit(&graph, k);
|
||||
Some(ReplayItem {
|
||||
key: k.clone(),
|
||||
priority: consolidation_priority(store, k, &graph),
|
||||
interval_days: node.spaced_repetition_interval,
|
||||
emotion: node.emotion,
|
||||
schema_fit: fit,
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
let episodes_section = format_nodes_section(store, &items, &graph);
|
||||
load_prompt("transfer", &[("{{TOPOLOGY}}", &topology), ("{{EPISODES}}", &episodes_section)])
|
||||
}
|
||||
"health" => {
|
||||
let health_section = format_health_section(store, &graph);
|
||||
load_prompt("health", &[("{{TOPOLOGY}}", &topology), ("{{HEALTH}}", &health_section)])
|
||||
}
|
||||
_ => Err(format!("Unknown agent: {}. Use: replay, linker, separator, transfer, health", agent)),
|
||||
}
|
||||
}
|
||||
|
||||
/// Agent allocation from the control loop
|
||||
pub struct ConsolidationPlan {
|
||||
pub replay_count: usize,
|
||||
pub linker_count: usize,
|
||||
pub separator_count: usize,
|
||||
pub transfer_count: usize,
|
||||
pub run_health: bool,
|
||||
pub rationale: Vec<String>,
|
||||
}
|
||||
|
||||
/// Analyze metrics and decide how much each agent needs to run.
|
||||
///
|
||||
/// This is the control loop: metrics → error signal → agent allocation.
|
||||
/// Target values are based on healthy small-world networks.
|
||||
pub fn consolidation_plan(store: &Store) -> ConsolidationPlan {
|
||||
let graph = store.build_graph();
|
||||
let alpha = graph.degree_power_law_exponent();
|
||||
let gini = graph.degree_gini();
|
||||
let avg_fit = {
|
||||
let fits = graph::schema_fit_all(&graph);
|
||||
if fits.is_empty() { 0.0 } else {
|
||||
fits.values().sum::<f32>() / fits.len() as f32
|
||||
}
|
||||
};
|
||||
let interference_pairs = detect_interference(store, &graph, 0.5);
|
||||
let interference_count = interference_pairs.len();
|
||||
|
||||
// Count episodic vs semantic nodes
|
||||
let episodic_count = store.nodes.iter()
|
||||
.filter(|(k, _)| k.contains("journal") || k.contains("session"))
|
||||
.count();
|
||||
let semantic_count = store.nodes.len() - episodic_count;
|
||||
let episodic_ratio = if store.nodes.is_empty() { 0.0 }
|
||||
else { episodic_count as f32 / store.nodes.len() as f32 };
|
||||
|
||||
let mut plan = ConsolidationPlan {
|
||||
replay_count: 0,
|
||||
linker_count: 0,
|
||||
separator_count: 0,
|
||||
transfer_count: 0,
|
||||
run_health: true, // always run health first
|
||||
rationale: Vec::new(),
|
||||
};
|
||||
|
||||
// Target: α ≥ 2.5 (healthy scale-free)
|
||||
// Current distance determines replay + linker allocation
|
||||
if alpha < 2.0 {
|
||||
plan.replay_count += 10;
|
||||
plan.linker_count += 5;
|
||||
plan.rationale.push(format!(
|
||||
"α={:.2} (target ≥2.5): extreme hub dominance → 10 replay + 5 linker for lateral links",
|
||||
alpha));
|
||||
} else if alpha < 2.5 {
|
||||
plan.replay_count += 5;
|
||||
plan.linker_count += 3;
|
||||
plan.rationale.push(format!(
|
||||
"α={:.2} (target ≥2.5): moderate hub dominance → 5 replay + 3 linker",
|
||||
alpha));
|
||||
} else {
|
||||
plan.replay_count += 3;
|
||||
plan.rationale.push(format!(
|
||||
"α={:.2}: healthy — 3 replay for maintenance", alpha));
|
||||
}
|
||||
|
||||
// Target: Gini ≤ 0.4
|
||||
if gini > 0.5 {
|
||||
plan.replay_count += 3;
|
||||
plan.rationale.push(format!(
|
||||
"Gini={:.3} (target ≤0.4): high inequality → +3 replay (lateral focus)",
|
||||
gini));
|
||||
}
|
||||
|
||||
// Target: avg schema fit ≥ 0.2
|
||||
if avg_fit < 0.1 {
|
||||
plan.replay_count += 5;
|
||||
plan.rationale.push(format!(
|
||||
"Schema fit={:.3} (target ≥0.2): very poor integration → +5 replay",
|
||||
avg_fit));
|
||||
} else if avg_fit < 0.2 {
|
||||
plan.replay_count += 2;
|
||||
plan.rationale.push(format!(
|
||||
"Schema fit={:.3} (target ≥0.2): low integration → +2 replay",
|
||||
avg_fit));
|
||||
}
|
||||
|
||||
// Interference: >100 pairs is a lot, <10 is clean
|
||||
if interference_count > 100 {
|
||||
plan.separator_count += 10;
|
||||
plan.rationale.push(format!(
|
||||
"Interference: {} pairs (target <50) → 10 separator",
|
||||
interference_count));
|
||||
} else if interference_count > 20 {
|
||||
plan.separator_count += 5;
|
||||
plan.rationale.push(format!(
|
||||
"Interference: {} pairs (target <50) → 5 separator",
|
||||
interference_count));
|
||||
} else if interference_count > 0 {
|
||||
plan.separator_count += interference_count.min(3);
|
||||
plan.rationale.push(format!(
|
||||
"Interference: {} pairs → {} separator",
|
||||
interference_count, plan.separator_count));
|
||||
}
|
||||
|
||||
// Episodic → semantic transfer
|
||||
// If >60% of nodes are episodic, knowledge isn't being extracted
|
||||
if episodic_ratio > 0.6 {
|
||||
plan.transfer_count += 10;
|
||||
plan.rationale.push(format!(
|
||||
"Episodic ratio: {:.0}% ({}/{}) → 10 transfer (knowledge extraction needed)",
|
||||
episodic_ratio * 100.0, episodic_count, store.nodes.len()));
|
||||
} else if episodic_ratio > 0.4 {
|
||||
plan.transfer_count += 5;
|
||||
plan.rationale.push(format!(
|
||||
"Episodic ratio: {:.0}% → 5 transfer",
|
||||
episodic_ratio * 100.0));
|
||||
}
|
||||
|
||||
plan
|
||||
}
|
||||
|
||||
/// Format the consolidation plan for display
|
||||
pub fn format_plan(plan: &ConsolidationPlan) -> String {
|
||||
let mut out = String::from("Consolidation Plan\n==================\n\n");
|
||||
|
||||
out.push_str("Analysis:\n");
|
||||
for r in &plan.rationale {
|
||||
out.push_str(&format!(" • {}\n", r));
|
||||
}
|
||||
|
||||
out.push_str("\nAgent allocation:\n");
|
||||
if plan.run_health {
|
||||
out.push_str(" 1. health — system audit\n");
|
||||
}
|
||||
let mut step = 2;
|
||||
if plan.replay_count > 0 {
|
||||
out.push_str(&format!(" {}. replay ×{:2} — schema assimilation + lateral linking\n",
|
||||
step, plan.replay_count));
|
||||
step += 1;
|
||||
}
|
||||
if plan.linker_count > 0 {
|
||||
out.push_str(&format!(" {}. linker ×{:2} — relational binding from episodes\n",
|
||||
step, plan.linker_count));
|
||||
step += 1;
|
||||
}
|
||||
if plan.separator_count > 0 {
|
||||
out.push_str(&format!(" {}. separator ×{} — pattern separation\n",
|
||||
step, plan.separator_count));
|
||||
step += 1;
|
||||
}
|
||||
if plan.transfer_count > 0 {
|
||||
out.push_str(&format!(" {}. transfer ×{:2} — episodic→semantic extraction\n",
|
||||
step, plan.transfer_count));
|
||||
}
|
||||
|
||||
let total = plan.replay_count + plan.linker_count
|
||||
+ plan.separator_count + plan.transfer_count
|
||||
+ if plan.run_health { 1 } else { 0 };
|
||||
out.push_str(&format!("\nTotal agent runs: {}\n", total));
|
||||
|
||||
out
|
||||
}
|
||||
|
||||
/// Brief daily check: compare current metrics to last snapshot
|
||||
pub fn daily_check(store: &Store) -> String {
|
||||
let graph = store.build_graph();
|
||||
let alpha = graph.degree_power_law_exponent();
|
||||
let gini = graph.degree_gini();
|
||||
let sigma = graph.small_world_sigma();
|
||||
let avg_cc = graph.avg_clustering_coefficient();
|
||||
let avg_fit = {
|
||||
let fits = graph::schema_fit_all(&graph);
|
||||
if fits.is_empty() { 0.0 } else {
|
||||
fits.values().sum::<f32>() / fits.len() as f32
|
||||
}
|
||||
};
|
||||
|
||||
let history = graph::load_metrics_history();
|
||||
let prev = history.last();
|
||||
|
||||
let mut out = String::from("Memory daily check\n");
|
||||
|
||||
// Current state
|
||||
out.push_str(&format!(" σ={:.1} α={:.2} gini={:.3} cc={:.4} fit={:.3}\n",
|
||||
sigma, alpha, gini, avg_cc, avg_fit));
|
||||
|
||||
// Trend
|
||||
if let Some(p) = prev {
|
||||
let d_sigma = sigma - p.sigma;
|
||||
let d_alpha = alpha - p.alpha;
|
||||
let d_gini = gini - p.gini;
|
||||
|
||||
out.push_str(&format!(" Δσ={:+.1} Δα={:+.2} Δgini={:+.3}\n",
|
||||
d_sigma, d_alpha, d_gini));
|
||||
|
||||
// Assessment
|
||||
let mut issues = Vec::new();
|
||||
if alpha < 2.0 { issues.push("hub dominance critical"); }
|
||||
if gini > 0.5 { issues.push("high inequality"); }
|
||||
if avg_fit < 0.1 { issues.push("poor integration"); }
|
||||
if d_sigma < -5.0 { issues.push("σ declining"); }
|
||||
if d_alpha < -0.1 { issues.push("α declining"); }
|
||||
if d_gini > 0.02 { issues.push("inequality increasing"); }
|
||||
|
||||
if issues.is_empty() {
|
||||
out.push_str(" Status: healthy\n");
|
||||
} else {
|
||||
out.push_str(&format!(" Status: needs attention — {}\n", issues.join(", ")));
|
||||
out.push_str(" Run: poc-memory consolidate-session\n");
|
||||
}
|
||||
} else {
|
||||
out.push_str(" (first snapshot, no trend data yet)\n");
|
||||
}
|
||||
|
||||
// Log this snapshot too
|
||||
let now = std::time::SystemTime::now()
|
||||
.duration_since(std::time::UNIX_EPOCH).unwrap().as_secs_f64();
|
||||
let date = {
|
||||
let o = std::process::Command::new("date").arg("+%Y-%m-%d %H:%M")
|
||||
.output().unwrap_or_else(|_| std::process::Command::new("echo").output().unwrap());
|
||||
String::from_utf8_lossy(&o.stdout).trim().to_string()
|
||||
};
|
||||
graph::save_metrics_snapshot(&graph::MetricsSnapshot {
|
||||
timestamp: now, date,
|
||||
nodes: graph.nodes().len(),
|
||||
edges: graph.edge_count(),
|
||||
communities: graph.community_count(),
|
||||
sigma, alpha, gini, avg_cc,
|
||||
avg_path_length: graph.avg_path_length(),
|
||||
avg_schema_fit: avg_fit,
|
||||
});
|
||||
|
||||
out
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue