Struct HmsCore 

pub struct HmsCore { /* private fields */ }

Lock ordering: ShardSet (read) -> Shard.vectors -> Shard.ivf -> Shard.nsg. Arena lock is independent (managed internally by PersistentArena).

Implementations

impl HmsCore

pub fn synthesize_concepts(&self) -> Vec<ConceptCandidate>

Cluster similar vectors and return concept candidates with centroids and coherence scores.

impl HmsCore

pub fn memorize_triplet( &self, id: String, h: String, r: String, t: String, ) -> Result<()>

Encode a (head, relation, tail) triplet as h XOR r XOR t and memorize it.

pub fn memorize_sequence(&self, id: String, sequence: &[String]) -> Result<()>

Encode an ordered sequence using position-based permutation and memorize it.

pub fn query_triplet( &self, h: String, r: String, k: u32, ) -> Result<Vec<RetrievalResult>>

Query for tails matching head XOR relation.

pub fn find_analogy( &self, a: &str, b: &str, c: &str, k: u32, ) -> Vec<RetrievalResult>

Find analogy: A is to B as C is to ? Returns k closest matches.

pub fn query_sequence( &self, partial: &[String], k: u32, ) -> Result<Vec<RetrievalResult>>

Query for sequences matching a partial sequence prefix.

impl HmsCore

pub fn query(&self, query_vec: &EntangledHVec, k: u32) -> Vec<RetrievalResult>

Query the memory system for the k most similar vectors.

pub fn query_batch( &self, queries: &[EntangledHVec], k: u32, ) -> Vec<Vec<RetrievalResult>>

Process multiple queries in parallel using rayon.

pub fn analyze_components(&self, vector: &EntangledHVec) -> Vec<RetrievalResult>

Analyze components of a vector by finding its nearest neighbors. Filters by similarity threshold from QueryConfig (default 0.05).

impl HmsCore

pub fn new( dimensions: u32, storage_path: Option<String>, config: Option<HmsConfig>, ) -> Result<Self>

Create a new HMS instance. If storage_path is None, uses the current directory.

pub fn bundle<V: Borrow<EntangledHVec>>(&self, vectors: &[V]) -> EntangledHVec

Bundle vectors respecting the PrivacyConfig. When dp_enabled, uses Laplace noise with the configured epsilon.

pub fn dimensions(&self) -> usize

Returns the dimensionality of the hypervector space.

pub fn encode_text(&self, text: &str) -> EntangledHVec

Encode text into a sparse hypervector using character trigrams.

pub fn analyze_text(&self, text: &str) -> TextMetrics

Compute word, sentence, syllable, and character-class counts for text.

pub fn calculate_readability(&self, metrics: &TextMetrics) -> f64

Compute Flesch Reading Ease score from text metrics.

pub fn delete(&self, id: &str) -> Result<bool>

Delete a vector by ID. Returns true if it existed. Crash-safe: tombstone is persisted first.

pub fn memorize_meaning(&self, id: &str, text: &str) -> Result<()>

pub fn compact(&self) -> Result<()>

Compact the arena log by rewriting only live vectors. Blocks all writes during compaction.

pub fn memorize(&self, id: String, vector: EntangledHVec) -> Result<()>

Store a vector with the given ID. Persists to the arena log and updates all indices.

pub fn memorize_vector(&self, id: String, dense: &[f32]) -> Result<()>

Convert a dense f32 vector to sparse and memorize it.

pub fn memorize_scalar( &self, id: String, value: f64, min: f64, max: f64, ) -> Result<()>

Encode a bounded scalar value as a hypervector and memorize it.

pub fn vector_count(&self) -> u64

Returns the total number of stored vectors across all shards.

pub fn add_relation(&self, rel: &Relation) -> Result<()>

pub fn remove_relation( &self, source_id: &str, relation_type: &str, target_id: &str, ) -> bool

pub fn declare_relation_type(&self, rel_type: RelationType)

pub fn traverse( &self, start_id: &str, relation_type: Option<&str>, max_depth: u32, at_time: f64, ) -> Vec<GraphPath>

pub fn outgoing_relations( &self, source_id: &str, relation_type: Option<&str>, at_time: f64, ) -> Vec<Relation>

pub fn incoming_relations( &self, target_id: &str, relation_type: Option<&str>, at_time: f64, ) -> Vec<Relation>

pub fn relation_count(&self) -> usize

pub fn federated_query( &self, peer_paths: &[String], query_vec: &EntangledHVec, k: u32, ) -> Result<Vec<RetrievalResult>>

pub fn structural_query( &self, known: &[(&str, &EntangledHVec)], target_role: &str, ) -> Vec<StructuralResult>

pub fn multi_hop(&self, start: &str, relations: &[&str]) -> Vec<MultiHopResult>

pub fn meaning_cleanup(&self, noisy: &EntangledHVec) -> Option<(String, f64)>

pub fn declare_rule( &self, name: &str, input_relations: Vec<String>, output_relation: String, )

pub fn meaning_enabled(&self) -> bool

pub fn meaning_atom_count(&self) -> usize

pub fn meaning_composite_count(&self) -> usize

pub fn meaning_triple_count(&self) -> usize

pub fn meaning_rule_count(&self) -> usize

pub fn register_role(&mut self, name: &str, shift: usize) -> Result<()>

pub fn start_cognition(&self) -> Result<()>

pub fn stop_cognition(&self)

pub fn cognition_running(&self) -> bool

pub fn cognition_cycle_count(&self) -> u64

pub fn take_insights(&self) -> Vec<Insight>

pub fn cognition_insight_count(&self) -> usize

pub fn run_cognition_once(&self) -> Vec<Insight>

pub fn govern_memory(&self) -> GovernanceReport

pub fn cognition_enabled(&self) -> bool

pub fn ivf_trained(&self) -> bool

Returns true if the IVF index has been trained.

pub fn train_ivf(&self) -> Result<()>

Train the IVF index on current vectors. Persists the index to disk.

pub fn nsg_trained(&self) -> bool

Returns true if the NSG graph index has been trained.

pub fn train_nsg(&self) -> Result<()>

Train the NSG graph index on current vectors. Persists the index to disk.

pub fn audit_since(&self, timestamp_ms: u64) -> Result<Vec<AuditEntry>>

Query the audit log for entries since timestamp_ms. Returns an empty vec if audit logging is disabled.

pub fn factorize_diffusion( &self, product: &EntangledHVec, domain_codebooks: &[Vec<EntangledHVec>], max_iter: usize, ) -> Vec<Option<EntangledHVec>>

Decompose a product vector into factors from domain codebooks using diffusion.