Struct VectorStore 

pub struct VectorStore { /* private fields */ }

Enhanced vector store with embedding management and advanced features

Implementations

impl VectorStore

pub fn new() -> Self

Create a new vector store with default memory index

pub fn with_embedding_strategy(strategy: EmbeddingStrategy) -> Result<Self>

Create vector store with specific embedding strategy

pub fn with_index(index: Box<dyn VectorIndex>) -> Self

Create vector store with custom index

pub fn with_index_and_embeddings( index: Box<dyn VectorIndex>, strategy: EmbeddingStrategy, ) -> Result<Self>

Create vector store with custom index and embedding strategy

pub fn with_config(self, config: VectorStoreConfig) -> Self

Set vector store configuration

pub fn index_resource(&mut self, uri: String, content: &str) -> Result<()>

Index a resource with automatic embedding generation

pub fn index_rdf_resource( &mut self, uri: String, label: Option<String>, description: Option<String>, properties: HashMap<String, Vec<String>>, ) -> Result<()>

Index an RDF resource with structured content

pub fn index_vector(&mut self, uri: String, vector: Vector) -> Result<()>

Index a pre-computed vector

pub fn similarity_search( &self, query: &str, limit: usize, ) -> Result<Vec<(String, f32)>>

Search for similar resources using text query

pub fn similarity_search_vector( &self, query: &Vector, limit: usize, ) -> Result<Vec<(String, f32)>>

Search for similar resources using a vector query

pub fn threshold_search( &self, query: &str, threshold: f32, ) -> Result<Vec<(String, f32)>>

Find resources within similarity threshold

pub fn advanced_search( &self, options: SearchOptions, ) -> Result<Vec<(String, f32)>>

Advanced search with multiple options

pub fn embedding_stats(&self) -> Option<(usize, usize)>

Get embedding manager statistics

pub fn build_vocabulary(&mut self, documents: &[String]) -> Result<()>

Build vocabulary for TF-IDF embeddings

pub fn calculate_similarity(&self, uri1: &str, uri2: &str) -> Result<f32>

Calculate similarity between two resources by their URIs

pub fn get_vector(&self, id: &str) -> Option<&Vector>

Get a vector by its ID (delegates to VectorIndex)

pub fn iter_vectors(&self) -> Vec<(String, Vector)>

Iterate all (id, vector) pairs stored in the underlying index.

Only index types that override VectorIndex::iter_vectors (e.g. MemoryVectorIndex) return a non-empty list; other implementations return an empty Vec by default.

pub fn index_vector_with_metadata( &mut self, uri: String, vector: Vector, _metadata: HashMap<String, String>, ) -> Result<()>

Index a vector with metadata (stub)

pub fn index_resource_with_metadata( &mut self, uri: String, content: &str, _metadata: HashMap<String, String>, ) -> Result<()>

Index a resource with metadata (stub)

pub fn similarity_search_with_params( &self, query: &str, limit: usize, _params: HashMap<String, String>, ) -> Result<Vec<(String, f32)>>

Search with additional parameters (stub)

pub fn vector_search_with_params( &self, query: &Vector, limit: usize, _params: HashMap<String, String>, ) -> Result<Vec<(String, f32)>>

Vector search with additional parameters (stub)

pub fn get_vector_ids(&self) -> Result<Vec<String>>

Get all vector IDs (stub)

pub fn remove_vector(&mut self, uri: &str) -> Result<()>

Remove a vector by its URI (stub)

pub fn get_statistics(&self) -> Result<HashMap<String, String>>

Get store statistics (stub)

pub fn save_to_disk(&self, path: &str) -> Result<()>

Save store to disk.

Creates {path}/metadata.json (config + vector count) and {path}/vectors.json (all (id, Vector) pairs). The embedding manager (in-memory cache only) is not persisted; call with_embedding_strategy again after loading if needed.

Only index types that report VectorIndex::supports_enumeration as true (e.g. MemoryVectorIndex, HnswIndex, IvfIndex, PQIndex) can be saved this way. Index types that cannot enumerate their vectors cause this to return an error rather than silently persisting an empty (and misleading) snapshot.

pub fn load_from_disk(path: &str) -> Result<Self>

Load a store that was previously saved with VectorStore::save_to_disk.

Reconstructs a VectorStore backed by a fresh MemoryVectorIndex and re-inserts all vectors from the saved snapshot. The embedding manager is not restored; create a new one with with_embedding_strategy if needed.

pub fn optimize_index(&mut self) -> Result<()>

Optimize the underlying index (stub)

Trait Implementations

impl Default for VectorStore

fn default() -> Self

Returns the “default value” for a type.

impl VectorStoreTrait for VectorStore

fn insert_vector(&mut self, id: VectorId, vector: Vector) -> Result<()>

Insert a vector with metadata

fn add_vector(&mut self, vector: Vector) -> Result<VectorId>

Add a vector and return its ID

fn get_vector(&self, id: &VectorId) -> Result<Option<Vector>>

Get a vector by its ID

fn get_all_vector_ids(&self) -> Result<Vec<VectorId>>

Get all vector IDs

fn search_similar( &self, query: &Vector, k: usize, ) -> Result<Vec<(VectorId, f32)>>

Search for similar vectors

fn remove_vector(&mut self, id: &VectorId) -> Result<bool>

Remove a vector by ID

fn len(&self) -> usize

Get the number of vectors stored

fn is_empty(&self) -> bool

Check if the store is empty