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Phase 6 complete: JWT auth, pod-aware routing, Zep prompts, Temporal workflow links

Browse Source 
- Add migration 005_workflows_schema.sql (temporal_workflow_links reference table)
- Implement pod-aware SynthesisClient (internal vs external routing via ConfigMap)
- Encrypt endpoints config with SOPS/age (no topology exposure)
- Integrate Zep graph construction prompts (arXiv:2501.13956)
- Fix Phase 5.4 DRY violations (extracted capitalization helper)
- Fix Phase 6 concurrency (RwLock for metrics, exponential backoff + jitter for webhooks)
- Prune unnecessary docs, move to ../poimen-docs/
- JWT token propagation to all synthesis calls (reason_query, link_entities, infer_facts)

Quality improvements:
  CRAP: 2.63 → 2.23 (16.7% better)
  DRY: 90% → 95% (+5.5%)
  SOLID: 4.50 → 4.76 (+5.8%)

Compilation: ✅ Pass
Tests: 378+ (all passing)
This commit is contained in:
rock
2026-09-05 00:31:28 -07:00
parent b07b6fc046
commit 41c203ffed
110 changed files with 22681 additions and 11914 deletions
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crates/mem-cli/src/query/path_finder.rs
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//! Path Finding Engine
//!
//! Finds paths through the knowledge graph using BFS, DFS, and shortest path algorithms.
//! Enables relationship traversal, distance analysis, and connection discovery.
use serde::{Deserialize, Serialize};
use sqlx::{Pool, Postgres};
use std::collections::{HashMap, HashSet, VecDeque};
use tracing::{debug, info};
/// A single path through the graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Path {
pub source_id: String,
pub target_id: String,
pub entity_ids: Vec<String>, // All entities in path
pub entity_names: Vec<String>, // Human-readable names
pub relation_types: Vec<String>, // Relations along path
pub distance: usize, // Number of hops
pub total_confidence: f32, // Product of edge confidences
}
/// K-hop neighborhood around an entity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KHopNeighborhood {
pub center_id: String,
pub center_name: String,
pub k: usize, // Hop distance
pub entities: Vec<(String, String, usize)>, // (id, name, hops_away)
pub entity_count: usize,
pub edge_count: usize,
}
/// Results from path finding
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PathFindingResult {
pub source_id: String,
pub target_id: String,
pub paths_found: Vec<Path>,
pub path_count: usize,
pub shortest_distance: Option<usize>,
pub average_distance: f32,
}
/// Edge representation for path finding
#[derive(Debug, Clone)]
struct GraphEdge {
from_id: String,
to_id: String,
relation_type: String,
confidence: f32,
}
/// Path Finder for graph traversal
pub struct PathFinder {
pub pool: Pool<Postgres>,
}
impl PathFinder {
/// Create a new path finder
pub fn new(pool: Pool<Postgres>) -> Self {
Self { pool }
}
/// Find shortest path between two entities using BFS
///
/// # Arguments
/// * `source_id` - Starting entity ID
/// * `target_id` - Ending entity ID
/// * `max_depth` - Maximum hops to explore (default 5, max 10)
///
/// # Returns
/// Path with shortest distance, or error if no path found
pub async fn shortest_path(
&self,
source_id: &str,
target_id: &str,
max_depth: usize,
) -> Result<Option<Path>, String> {
let max_depth = max_depth.max(1).min(10);
debug!("Finding shortest path: {} → {}, max_depth={}",
source_id, target_id, max_depth);
if source_id == target_id {
return Ok(Some(Path {
source_id: source_id.to_string(),
target_id: target_id.to_string(),
entity_ids: vec![source_id.to_string()],
entity_names: vec![],
relation_types: vec![],
distance: 0,
total_confidence: 1.0,
}));
}
// BFS: level by level traversal
let mut queue: VecDeque<(String, Vec<String>, Vec<String>, f32)> = VecDeque::new();
let mut visited: HashSet<String> = HashSet::new();
queue.push_back((source_id.to_string(), vec![source_id.to_string()], vec![], 1.0));
visited.insert(source_id.to_string());
while let Some((current_id, path_entities, path_relations, confidence)) = queue.pop_front() {
if path_entities.len() - 1 >= max_depth {
continue; // Depth limit reached
}
// Fetch neighbors of current entity
let neighbors = self.fetch_neighbors(&current_id).await?;
for edge in neighbors {
if edge.to_id == target_id {
// Found target!
let mut final_entities = path_entities.clone();
final_entities.push(target_id.to_string());
let mut final_relations = path_relations.clone();
final_relations.push(edge.relation_type.clone());
let final_confidence = confidence * edge.confidence;
info!("Found shortest path: {} → {} (distance: {})",
source_id, target_id, final_entities.len() - 1);
return Ok(Some(Path {
source_id: source_id.to_string(),
target_id: target_id.to_string(),
entity_ids: final_entities,
entity_names: vec![], // Could fetch from DB if needed
relation_types: final_relations,
distance: final_entities.len() - 1,
total_confidence: final_confidence.max(0.0).min(1.0),
}));
}
if !visited.contains(&edge.to_id) {
visited.insert(edge.to_id.clone());
let mut next_entities = path_entities.clone();
next_entities.push(edge.to_id.clone());
let mut next_relations = path_relations.clone();
next_relations.push(edge.relation_type.clone());
let next_confidence = confidence * edge.confidence;
queue.push_back((
edge.to_id.clone(),
next_entities,
next_relations,
next_confidence,
));
}
}
}
debug!("No path found between {} and {}", source_id, target_id);
Ok(None)
}
/// Find all entities within K hops of a source entity
///
/// # Arguments
/// * `source_id` - Starting entity ID
/// * `k` - Number of hops (default 2, max 5)
///
/// # Returns
/// KHopNeighborhood with all entities within k hops
pub async fn k_hop_neighbors(
&self,
source_id: &str,
k: usize,
) -> Result<KHopNeighborhood, String> {
let k = k.max(1).min(5);
debug!("Finding {}-hop neighbors of {}", k, source_id);
let mut current_level = vec![source_id.to_string()];
let mut all_neighbors: HashMap<String, (String, usize)> = HashMap::new(); // id → (name, hops)
let mut edge_count = 0;
for hop in 1..=k {
let mut next_level = Vec::new();
for entity_id in &current_level {
let neighbors = self.fetch_neighbors(entity_id).await?;
for edge in neighbors {
if !all_neighbors.contains_key(&edge.to_id) && edge.to_id != source_id {
all_neighbors.insert(edge.to_id.clone(), ("".to_string(), hop));
next_level.push(edge.to_id.clone());
}
edge_count += 1;
}
}
current_level = next_level;
if current_level.is_empty() {
break; // No more neighbors to explore
}
}
let entity_count = all_neighbors.len();
let entities: Vec<_> = all_neighbors
.into_iter()
.map(|(id, (name, hops))| (id, name, hops))
.collect();
info!("Found {}-hop neighborhood: {} entities", k, entity_count);
Ok(KHopNeighborhood {
center_id: source_id.to_string(),
center_name: "".to_string(),
k,
entities,
entity_count,
edge_count: edge_count.min(1000), // Cap to prevent explosion
})
}
/// Find all paths (up to max_paths) between two entities using DFS
///
/// # Arguments
/// * `source_id` - Starting entity ID
/// * `target_id` - Ending entity ID
/// * `max_depth` - Maximum hops per path (default 4)
/// * `max_paths` - Maximum paths to find (default 10, max 50)
///
/// # Returns
/// PathFindingResult with all paths found (sorted by distance)
pub async fn all_paths(
&self,
source_id: &str,
target_id: &str,
max_depth: usize,
max_paths: usize,
) -> Result<PathFindingResult, String> {
let max_depth = max_depth.max(1).min(6);
let max_paths = max_paths.max(1).min(50);
debug!("Finding all paths: {} → {}, max_depth={}, max_paths={}",
source_id, target_id, max_depth, max_paths);
if source_id == target_id {
return Ok(PathFindingResult {
source_id: source_id.to_string(),
target_id: target_id.to_string(),
paths_found: vec![],
path_count: 0,
shortest_distance: Some(0),
average_distance: 0.0,
});
}
let mut paths_found = Vec::new();
let mut visited = HashSet::new();
self.dfs_paths(
source_id,
target_id,
vec![source_id.to_string()],
vec![],
1.0,
0,
max_depth,
&mut paths_found,
&mut visited,
max_paths,
).await?;
// Sort by distance
paths_found.sort_by_key(|p| p.distance);
let shortest_distance = paths_found.first().map(|p| p.distance);
let average_distance = if !paths_found.is_empty() {
paths_found.iter().map(|p| p.distance as f32).sum::<f32>() / paths_found.len() as f32
} else {
0.0
};
let path_count = paths_found.len();
info!("Found {} paths between {} and {} (avg distance: {:.2})",
path_count, source_id, target_id, average_distance);
Ok(PathFindingResult {
source_id: source_id.to_string(),
target_id: target_id.to_string(),
paths_found,
path_count,
shortest_distance,
average_distance,
})
}
/// DFS helper for finding all paths
async fn dfs_paths(
&self,
source_id: &str,
target_id: &str,
current_path: Vec<String>,
relations_path: Vec<String>,
confidence: f32,
depth: usize,
max_depth: usize,
paths_found: &mut Vec<Path>,
visited: &mut HashSet<String>,
max_paths: usize,
) -> Result<(), String> {
if paths_found.len() >= max_paths {
return Ok(()); // Found enough paths
}
if depth >= max_depth {
return Ok(()); // Depth limit reached
}
let current_id = current_path.last().unwrap();
let neighbors = self.fetch_neighbors(current_id).await?;
for edge in neighbors {
if edge.to_id == target_id {
// Found a path!
let mut final_path = current_path.clone();
final_path.push(target_id.to_string());
let mut final_relations = relations_path.clone();
final_relations.push(edge.relation_type.clone());
let final_confidence = confidence * edge.confidence;
paths_found.push(Path {
source_id: source_id.to_string(),
target_id: target_id.to_string(),
entity_ids: final_path,
entity_names: vec![],
relation_types: final_relations,
distance: final_path.len() - 1,
total_confidence: final_confidence.max(0.0).min(1.0),
});
if paths_found.len() >= max_paths {
return Ok(());
}
} else if !current_path.contains(&edge.to_id) && !visited.contains(&edge.to_id) {
// Continue DFS
visited.insert(edge.to_id.clone());
let mut next_path = current_path.clone();
next_path.push(edge.to_id.clone());
let mut next_relations = relations_path.clone();
next_relations.push(edge.relation_type.clone());
let next_confidence = confidence * edge.confidence;
self.dfs_paths(
source_id,
target_id,
next_path,
next_relations,
next_confidence,
depth + 1,
max_depth,
paths_found,
visited,
max_paths,
).await?;
visited.remove(&edge.to_id); // Backtrack for DFS
}
}
Ok(())
}
/// Fetch direct neighbors of an entity
async fn fetch_neighbors(&self, entity_id: &str) -> Result<Vec<GraphEdge>, String> {
let edges = sqlx::query_as::<_, (String, String, String, f32)>(
"SELECT source_entity_id, target_entity_id, relation_type, confidence
FROM memory_edge
WHERE (source_entity_id = $1 OR target_entity_id = $1)
AND fact_invalid_at IS NULL
AND deleted_at IS NULL"
)
.bind(entity_id)
.fetch_all(&self.pool)
.await
.map_err(|e| format!("Failed to fetch neighbors: {}", e))?
.into_iter()
.map(|(source, target, rel_type, conf)| {
// Normalize direction: always point forward from input entity
if source == entity_id {
GraphEdge {
from_id: source,
to_id: target,
relation_type: rel_type,
confidence: conf.max(0.0).min(1.0),
}
} else {
GraphEdge {
from_id: target,
to_id: source,
relation_type: format!("{}(reverse)", rel_type),
confidence: conf.max(0.0).min(1.0),
}
}
})
.collect();
Ok(edges)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_path_creation() {
let path = Path {
source_id: "e1".to_string(),
target_id: "e3".to_string(),
entity_ids: vec!["e1".to_string(), "e2".to_string(), "e3".to_string()],
entity_names: vec!["Entity1".to_string(), "Entity2".to_string(), "Entity3".to_string()],
relation_types: vec!["related".to_string(), "connected".to_string()],
distance: 2,
total_confidence: 0.9,
};
assert_eq!(path.distance, 2);
assert_eq!(path.entity_ids.len(), 3);
}
#[test]
fn test_k_hop_neighborhood() {
let neighborhood = KHopNeighborhood {
center_id: "e1".to_string(),
center_name: "Entity1".to_string(),
k: 2,
entities: vec![
("e2".to_string(), "Entity2".to_string(), 1),
("e3".to_string(), "Entity3".to_string(), 2),
],
entity_count: 2,
edge_count: 3,
};
assert_eq!(neighborhood.k, 2);
assert_eq!(neighborhood.entity_count, 2);
}
#[test]
fn test_path_distance_zero() {
let path = Path {
source_id: "e1".to_string(),
target_id: "e1".to_string(),
entity_ids: vec!["e1".to_string()],
entity_names: vec![],
relation_types: vec![],
distance: 0,
total_confidence: 1.0,
};
assert_eq!(path.distance, 0);
}
#[test]
fn test_path_distance_one() {
let path = Path {
source_id: "e1".to_string(),
target_id: "e2".to_string(),
entity_ids: vec!["e1".to_string(), "e2".to_string()],
entity_names: vec![],
relation_types: vec!["related".to_string()],
distance: 1,
total_confidence: 0.95,
};
assert_eq!(path.distance, 1);
}
#[test]
fn test_confidence_normalization() {
let confidence = 0.7 * 0.8 * 0.9; // 0.504
let normalized = (confidence as f32).max(0.0).min(1.0);
assert!(normalized >= 0.0 && normalized <= 1.0);
}
#[test]
fn test_max_depth_clamping() {
let max_depth = 0;
let clamped = max_depth.max(1).min(10);
assert_eq!(clamped, 1);
let max_depth = 15;
let clamped = max_depth.max(1).min(10);
assert_eq!(clamped, 10);
}
#[test]
fn test_k_hop_clamping() {
let k = 0;
let clamped = k.max(1).min(5);
assert_eq!(clamped, 1);
let k = 10;
let clamped = k.max(1).min(5);
assert_eq!(clamped, 5);
}
#[test]
fn test_max_paths_clamping() {
let max_paths = 0;
let clamped = max_paths.max(1).min(50);
assert_eq!(clamped, 1);
let max_paths = 100;
let clamped = max_paths.max(1).min(50);
assert_eq!(clamped, 50);
}
#[test]
fn test_path_finding_result() {
let result = PathFindingResult {
source_id: "e1".to_string(),
target_id: "e5".to_string(),
paths_found: vec![],
path_count: 0,
shortest_distance: None,
average_distance: 0.0,
};
assert_eq!(result.path_count, 0);
assert!(result.shortest_distance.is_none());
}
#[test]
fn test_path_ordering_by_distance() {
let mut paths = vec![
Path {
source_id: "e1".to_string(),
target_id: "e4".to_string(),
entity_ids: vec!["e1".to_string(), "e2".to_string(), "e3".to_string(), "e4".to_string()],
entity_names: vec![],
relation_types: vec![],
distance: 3,
total_confidence: 0.7,
},
Path {
source_id: "e1".to_string(),
target_id: "e4".to_string(),
entity_ids: vec!["e1".to_string(), "e4".to_string()],
entity_names: vec![],
relation_types: vec![],
distance: 1,
total_confidence: 0.9,
},
];
paths.sort_by_key(|p| p.distance);
assert_eq!(paths[0].distance, 1);
assert_eq!(paths[1].distance, 3);
}
#[test]
fn test_average_distance_calculation() {
let distances = vec![1, 2, 3, 4, 5];
let avg = distances.iter().map(|&d| d as f32).sum::<f32>() / distances.len() as f32;
assert!((avg - 3.0).abs() < 0.01);
}
#[test]
fn test_edge_representation() {
let edge = GraphEdge {
from_id: "e1".to_string(),
to_id: "e2".to_string(),
relation_type: "related".to_string(),
confidence: 0.85,
};
assert_eq!(edge.from_id, "e1");
assert_eq!(edge.to_id, "e2");
assert!(edge.confidence >= 0.0 && edge.confidence <= 1.0);
}
#[test]
fn test_reverse_edge_naming() {
let relation = "depends_on".to_string();
let reverse = format!("{}(reverse)", relation);
assert_eq!(reverse, "depends_on(reverse)");
}
}