forked from kent/consciousness
b4e674806d
Each agent is a .agent file: JSON config on the first line, blank line, then the raw prompt markdown. Fully self-contained, fully readable. No separate template files needed. Agents dir: checked into repo at poc-memory/agents/. Code looks there first (via CARGO_MANIFEST_DIR), falls back to ~/.claude/memory/agents/. Three agents migrated: replay, linker, transfer. Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
552 lines
22 KiB
Rust
552 lines
22 KiB
Rust
// Agent prompt generation and formatting. Presentation logic —
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// builds text prompts from store data for consolidation agents.
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use crate::store::Store;
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use crate::graph::Graph;
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use crate::similarity;
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use crate::spectral;
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use crate::neuro::{
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ReplayItem, consolidation_priority,
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replay_queue, replay_queue_with_graph, detect_interference,
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};
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/// Result of building an agent prompt — includes both the prompt text
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/// and the keys of nodes selected for processing, so the caller can
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/// record visits after successful completion.
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pub struct AgentBatch {
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pub prompt: String,
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pub node_keys: Vec<String>,
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}
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/// Load a prompt template, replacing {{PLACEHOLDER}} with data
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pub fn load_prompt(name: &str, replacements: &[(&str, &str)]) -> Result<String, String> {
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let path = crate::config::get().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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/// Public alias for use from defs.rs (config-driven agents).
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pub fn format_topology_header_pub(graph: &Graph) -> String {
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format_topology_header(graph)
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}
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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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// Identify saturated hubs — nodes with degree well above threshold
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let threshold = graph.hub_threshold();
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let mut hubs: Vec<_> = graph.nodes().iter()
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.map(|k| (k.clone(), graph.degree(k)))
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.filter(|(_, d)| *d >= threshold)
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.collect();
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hubs.sort_by(|a, b| b.1.cmp(&a.1));
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hubs.truncate(15);
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let hub_list = if hubs.is_empty() {
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String::new()
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} else {
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let lines: Vec<String> = hubs.iter()
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.map(|(k, d)| format!(" - {} (degree {})", k, d))
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.collect();
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format!(
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"### SATURATED HUBS — DO NOT LINK TO THESE\n\
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The following nodes are already over-connected. Adding more links\n\
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to them makes the graph worse (star topology). Find lateral\n\
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connections between peripheral nodes instead.\n\n{}\n\n\
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Only link to a hub if it is genuinely the ONLY reasonable target.\n\n",
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lines.join("\n"))
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};
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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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{}\
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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, hub_list)
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}
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/// Public alias for use from defs.rs (config-driven agents).
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pub fn format_nodes_section_pub(store: &Store, items: &[ReplayItem], graph: &Graph) -> String {
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format_nodes_section(store, items, graph)
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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 = graph.hub_threshold();
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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} CC: {:.3} Emotion: {:.1} ",
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item.priority, item.cc, item.emotion));
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out.push_str(&format!("Interval: {}d\n",
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node.spaced_repetition_interval));
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if item.outlier_score > 0.0 {
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out.push_str(&format!("Spectral: {} (outlier={:.1})\n",
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item.classification, item.outlier_score));
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}
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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 truncated = crate::util::truncate(content, 1500, "\n[...]");
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out.push_str(&format!("\nContent ({} chars, truncated):\n{}\n\n",
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content.len(), truncated));
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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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// Suggested link targets: text-similar semantic nodes not already neighbors
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let neighbor_keys: std::collections::HashSet<&str> = neighbors.iter()
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.map(|(k, _)| k.as_str()).collect();
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let mut candidates: Vec<(&str, f32)> = store.nodes.iter()
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.filter(|(k, _)| {
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*k != &item.key
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&& !neighbor_keys.contains(k.as_str())
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})
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.map(|(k, n)| {
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let sim = similarity::cosine_similarity(content, &n.content);
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(k.as_str(), sim)
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})
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.filter(|(_, sim)| *sim > 0.1)
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.collect();
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candidates.sort_by(|a, b| b.1.total_cmp(&a.1));
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candidates.truncate(8);
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if !candidates.is_empty() {
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out.push_str("\nSuggested link targets (by text similarity, not yet linked):\n");
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for (k, sim) in &candidates {
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let is_hub = graph.degree(k) >= hub_thresh;
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out.push_str(&format!(" - {} (sim={:.3}{})\n",
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k, sim, if is_hub { ", HUB" } else { "" }));
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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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use crate::graph;
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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 = "█".repeat((count as usize) / 10);
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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 = crate::util::truncate(&node.content, 500, "...");
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out.push_str(&format!("Weight: {:.2}\n{}\n",
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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 = crate::util::truncate(&node.content, 500, "...");
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out.push_str(&format!("Weight: {:.2}\n{}\n",
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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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/// Format rename candidates, returning both keys and formatted section
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fn format_rename_candidates_with_keys(store: &Store, count: usize) -> (Vec<String>, String) {
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let mut candidates: Vec<(&str, &crate::store::Node)> = store.nodes.iter()
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.filter(|(key, _)| {
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if key.len() < 60 { return false; }
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if key.starts_with("journal#j-") { return true; }
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if key.starts_with("_mined-transcripts#f-") { return true; }
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false
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})
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.map(|(k, n)| (k.as_str(), n))
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.collect();
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candidates.sort_by(|a, b| b.1.timestamp.cmp(&a.1.timestamp));
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candidates.truncate(count);
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let keys: Vec<String> = candidates.iter().map(|(k, _)| k.to_string()).collect();
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let mut out = String::new();
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out.push_str(&format!("## Nodes to rename ({} of {} candidates)\n\n",
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candidates.len(),
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store.nodes.keys().filter(|k| k.len() >= 60 &&
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(k.starts_with("journal#j-") || k.starts_with("_mined-transcripts#f-"))).count()));
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for (key, node) in &candidates {
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out.push_str(&format!("### {}\n", key));
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let created = if node.timestamp > 0 {
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crate::store::format_datetime(node.timestamp)
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} else {
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"unknown".to_string()
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};
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out.push_str(&format!("Created: {}\n", created));
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let content = &node.content;
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if content.len() > 800 {
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let truncated = crate::util::truncate(content, 800, "\n[...]");
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out.push_str(&format!("\nContent ({} chars, truncated):\n{}\n\n",
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content.len(), truncated));
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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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out.push_str("---\n\n");
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}
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(keys, out)
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}
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/// Get split candidates sorted by size (largest first)
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pub fn split_candidates(store: &Store) -> Vec<String> {
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let mut candidates: Vec<(&str, usize)> = store.nodes.iter()
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.filter(|(key, node)| {
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!key.starts_with('_')
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&& !node.deleted
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&& matches!(node.node_type, crate::store::NodeType::Semantic)
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})
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.map(|(k, n)| (k.as_str(), n.content.len()))
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.collect();
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candidates.sort_by(|a, b| b.1.cmp(&a.1));
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candidates.into_iter().map(|(k, _)| k.to_string()).collect()
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}
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/// Format a single node for split-plan prompt (phase 1)
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fn format_split_plan_node(store: &Store, graph: &Graph, key: &str) -> String {
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let communities = graph.communities();
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let node = match store.nodes.get(key) {
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Some(n) => n,
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None => return format!("Node '{}' not found\n", key),
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};
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let mut out = String::new();
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out.push_str(&format!("### {} ({} chars)\n", key, node.content.len()));
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// Show neighbors grouped by community
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let neighbors = graph.neighbors(key);
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if !neighbors.is_empty() {
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let mut by_community: std::collections::BTreeMap<String, Vec<(&str, f32)>> =
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std::collections::BTreeMap::new();
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for (nkey, strength) in &neighbors {
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let comm = communities.get(nkey.as_str())
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.map(|c| format!("c{}", c))
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.unwrap_or_else(|| "unclustered".into());
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by_community.entry(comm)
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.or_default()
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.push((nkey.as_str(), *strength));
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}
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out.push_str("\nNeighbors by community:\n");
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for (comm, members) in &by_community {
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out.push_str(&format!(" {} ({}):", comm, members.len()));
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for (nkey, strength) in members.iter().take(5) {
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out.push_str(&format!(" {}({:.2})", nkey, strength));
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}
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if members.len() > 5 {
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out.push_str(&format!(" +{} more", members.len() - 5));
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}
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out.push('\n');
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}
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}
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// Full content
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out.push_str(&format!("\nContent:\n{}\n\n", node.content));
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out.push_str("---\n\n");
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out
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}
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/// Build split-plan prompt for a single node (phase 1)
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pub fn split_plan_prompt(store: &Store, key: &str) -> 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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let node_section = format_split_plan_node(store, &graph, key);
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load_prompt("split-plan", &[("{{TOPOLOGY}}", &topology), ("{{NODE}}", &node_section)])
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}
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/// Build split-extract prompt for one child (phase 2)
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pub fn split_extract_prompt(store: &Store, parent_key: &str, child_key: &str, child_desc: &str, child_sections: &str) -> Result<String, String> {
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let parent_content = store.nodes.get(parent_key)
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.map(|n| n.content.as_str())
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.ok_or_else(|| format!("No node '{}'", parent_key))?;
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load_prompt("split-extract", &[
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("{{CHILD_KEY}}", child_key),
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("{{CHILD_DESC}}", child_desc),
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("{{CHILD_SECTIONS}}", child_sections),
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("{{PARENT_CONTENT}}", parent_content),
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])
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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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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 matches!(n.node_type, crate::store::NodeType::EpisodicSession) { "episodic" } else { "semantic" })
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.unwrap_or("?");
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println!(" [{:.3}] {} (cc={:.3}, interval={}d, type={})",
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item.priority, item.key, item.cc, 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:");
|
||
println!(" --auto Generate replay agent prompt");
|
||
println!(" --agent replay Replay agent (schema assimilation)");
|
||
println!(" --agent linker Linker agent (relational binding)");
|
||
println!(" --agent separator Separator agent (pattern separation)");
|
||
println!(" --agent transfer Transfer agent (CLS episodic→semantic)");
|
||
println!(" --agent health Health agent (synaptic homeostasis)");
|
||
}
|
||
|
||
Ok(())
|
||
}
|
||
|
||
/// Generate a specific agent prompt with filled-in data.
|
||
/// Returns an AgentBatch with the prompt text and the keys of nodes
|
||
/// selected for processing (for visit tracking on success).
|
||
pub fn agent_prompt(store: &Store, agent: &str, count: usize) -> Result<AgentBatch, String> {
|
||
// Config-driven agents take priority over hardcoded ones
|
||
if let Some(def) = super::defs::get_def(agent) {
|
||
return super::defs::run_agent(store, &def, count);
|
||
}
|
||
|
||
let graph = store.build_graph();
|
||
let topology = format_topology_header(&graph);
|
||
|
||
let emb = spectral::load_embedding().ok();
|
||
|
||
match agent {
|
||
"replay" => {
|
||
let items = replay_queue_with_graph(store, count, &graph, emb.as_ref());
|
||
let keys: Vec<String> = items.iter().map(|i| i.key.clone()).collect();
|
||
let nodes_section = format_nodes_section(store, &items, &graph);
|
||
let prompt = load_prompt("replay", &[("{{TOPOLOGY}}", &topology), ("{{NODES}}", &nodes_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: keys })
|
||
}
|
||
"linker" => {
|
||
// Filter to episodic entries
|
||
let mut items = replay_queue_with_graph(store, count * 2, &graph, emb.as_ref());
|
||
items.retain(|item| {
|
||
store.nodes.get(&item.key)
|
||
.map(|n| matches!(n.node_type, crate::store::NodeType::EpisodicSession))
|
||
.unwrap_or(false)
|
||
});
|
||
items.truncate(count);
|
||
let keys: Vec<String> = items.iter().map(|i| i.key.clone()).collect();
|
||
let nodes_section = format_nodes_section(store, &items, &graph);
|
||
let prompt = load_prompt("linker", &[("{{TOPOLOGY}}", &topology), ("{{NODES}}", &nodes_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: keys })
|
||
}
|
||
"separator" => {
|
||
let mut pairs = detect_interference(store, &graph, 0.5);
|
||
pairs.truncate(count);
|
||
// Both nodes in each pair count as visited
|
||
let keys: Vec<String> = pairs.iter()
|
||
.flat_map(|(a, b, _)| vec![a.clone(), b.clone()])
|
||
.collect();
|
||
let pairs_section = format_pairs_section(&pairs, store, &graph);
|
||
let prompt = load_prompt("separator", &[("{{TOPOLOGY}}", &topology), ("{{PAIRS}}", &pairs_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: keys })
|
||
}
|
||
"transfer" => {
|
||
// Recent episodic entries
|
||
let mut episodes: Vec<_> = store.nodes.iter()
|
||
.filter(|(_, n)| matches!(n.node_type, crate::store::NodeType::EpisodicSession))
|
||
.map(|(k, n)| (k.clone(), n.timestamp))
|
||
.collect();
|
||
episodes.sort_by(|a, b| b.1.cmp(&a.1));
|
||
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)?;
|
||
Some(ReplayItem {
|
||
key: k.clone(),
|
||
priority: consolidation_priority(store, k, &graph, None),
|
||
interval_days: node.spaced_repetition_interval,
|
||
emotion: node.emotion,
|
||
cc: graph.clustering_coefficient(k),
|
||
classification: "unknown",
|
||
outlier_score: 0.0,
|
||
})
|
||
})
|
||
.collect();
|
||
let episodes_section = format_nodes_section(store, &items, &graph);
|
||
let prompt = load_prompt("transfer", &[("{{TOPOLOGY}}", &topology), ("{{EPISODES}}", &episodes_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: episode_keys })
|
||
}
|
||
"health" => {
|
||
// Health agent analyzes the whole graph, no specific nodes
|
||
let health_section = format_health_section(store, &graph);
|
||
let prompt = load_prompt("health", &[("{{TOPOLOGY}}", &topology), ("{{HEALTH}}", &health_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: vec![] })
|
||
}
|
||
"rename" => {
|
||
let (keys, nodes_section) = format_rename_candidates_with_keys(store, count);
|
||
let prompt = load_prompt("rename", &[("{{NODES}}", &nodes_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: keys })
|
||
}
|
||
"split" => {
|
||
// Phase 1: plan prompt for the largest candidate
|
||
let candidates = split_candidates(store);
|
||
if candidates.is_empty() {
|
||
return Err("No nodes large enough to split".to_string());
|
||
}
|
||
let key = candidates[0].clone();
|
||
let node_section = format_split_plan_node(store, &graph, &key);
|
||
let prompt = load_prompt("split-plan", &[("{{TOPOLOGY}}", &topology), ("{{NODE}}", &node_section)])?;
|
||
Ok(AgentBatch { prompt, node_keys: vec![key] })
|
||
}
|
||
_ => Err(format!("Unknown agent: {}. Use: replay, linker, separator, transfer, health, rename, split", agent)),
|
||
}
|
||
}
|