Struct VectorStore 

pub struct VectorStore {
    pub name_to_id: Arc<DashMap<String, EntityId>>,
    pub id_to_name: Arc<DashMap<EntityId, String>>,
    pub dims: u32,
    pub count: AtomicUsize,
    pub db_path: PathBuf,
    /* private fields */
}

Fields

name_to_id: Arc<DashMap<String, EntityId>>

id_to_name: Arc<DashMap<EntityId, String>>

dims: u32

count: AtomicUsize

db_path: PathBuf

Implementations

impl VectorStore

pub fn new(db_path: &Path, dims: u32) -> Result<Self>

Open a store with the default HNSW configuration for dims.

pub fn with_config(db_path: &Path, cfg: &VectorConfig) -> Result<Self>

Open a store with an explicit HNSW configuration.

pub fn upsert_embedding( &self, entity_name: &str, embedding: &[f32], model: &str, ) -> Result<()>

pub fn delete_embedding(&self, entity_name: &str) -> Result<bool>

pub fn search_embeddings( &self, query: &[f32], top_k: usize, ) -> Result<Vec<(EntityId, f32)>>

pub fn search_entities_json( &self, query: &[f32], top_k: usize, entity_type_filter: Option<&str>, ) -> Result<String>

pub fn build_search_response_json(&self, results: &[(EntityId, f32)]) -> String

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

pub fn graph_node_count(&self) -> usize

pub fn graph_edge_count(&self) -> usize

pub fn get_entity_type(&self, entity_id: EntityId) -> Result<Option<String>>

pub fn count(&self) -> usize

pub const fn dims(&self) -> u32

pub fn index_memory_bytes(&self) -> usize

Approximate resident RAM used by the ANN index, in bytes.

pub fn index_memory_breakdown(&self) -> (usize, usize)

Breakdown of index RAM into (graph_bytes, vectors_bytes). IVF has no graph component, so its graph_bytes is 0.

pub fn index_capacity(&self) -> usize

Current allocated capacity of the index (number of vectors it can hold before the next reservation).

pub const fn index_kind(&self) -> IndexKind

The active ANN backend.

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

Rebuild the ANN index structure: retrains the IVF centroids over the current vectors (no-op for HNSW). Call after large batch ingestion to keep IVF recall high.

pub fn entity_id_of(&self, name: &str) -> Result<Option<EntityId>>

Resolve a live entity id by name (cache first, then the KG table).

pub fn get_embedding_by_id(&self, id: EntityId) -> Result<Option<Vec<f32>>>

Fetch the stored embedding for an entity id, if any.

pub fn get_embedding_by_name( &self, name: &str, ) -> Result<Option<(EntityId, Vec<f32>, String)>>

Fetch (entity_id, embedding, model) for an entity by name.

pub fn resolve_name_type(&self, id: EntityId) -> (String, String)

Resolve an entity id to (name, entityType), preferring the in-memory name cache and reading the type from the KG.

pub fn search_resolved( &self, query: &[f32], top_k: usize, entity_type: Option<&str>, exclude: &HashSet<EntityId>, ) -> Result<Vec<(EntityId, String, String, f32)>>

k-NN that returns resolved (id, name, entityType, distance), optionally filtered by entity_type and excluding exclude ids. Over-fetches to compensate for filtered-out rows.

pub fn invalidate_entity_cache(&self, names: &[String])

pub fn name_to_id(&self) -> &DashMap<String, EntityId>

pub fn id_to_name(&self) -> &DashMap<EntityId, String>